diff --git a/.gitignore b/.gitignore index 02f6a92a..1296aebf 100644 --- a/.gitignore +++ b/.gitignore @@ -168,3 +168,5 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +*.pt \ No newline at end of file diff --git a/Non-code/documentation/README.md b/Non-code/documentation/README.md new file mode 100644 index 00000000..d61726a5 --- /dev/null +++ b/Non-code/documentation/README.md @@ -0,0 +1,235 @@ +# Configuration Documentation + +This documentation details the required inputs for configuring the model, trainer, and general settings. Note, experimental architectures are not included here, as they can be subject to frequent change + +# Model Configuration (`model`) + +Conceptually the model is split into three parts, the `core_model`[core_model.py], `embedder` and `lm_head`, where the `embedder` is responsible for converting strings to token-embeddings (i.e. includes both the tokenizer and the token embedder), the `core_model` does most of the 'thinking' (i.e. contains the actual transformer blocks), and the `lm_head` links the final hidden state back to the vocabulary. The model configuration should contain the following. + +## Core Model `core_model_type` + +The name of the core-model architecture to be used. Depending on the selected option, additional sub-configurations may be required. The following core-models are available: + +- **generic (link the code)**: A basic transformer model with multiple attn+ffn blocks. This can be used for almost all classic architectures. + - `num_layers`: The number of transformer blocks in the core model + - `ffn`: Configuration for the Feedforward Network (FFN). [Link to the sub-config] + - `attn`: Configuration for the Attention mechanism. [Link to the sub-config] + - `ffn_weight_tying`: Whether to tie the weights of the FFN layers across the transformer blocks. _[default: false]_ + - `c_proj_weight_tying`: Whether to tie the weights of the c*proj linear layer in the attention block across the transformer blocks. *[default: false]\_ +- **hf_core (link the code)**: This can be used to load huggingface models + - `freeze_core`: Whether to freeze the core model. _[default: true]_ + - `todo` + +## Embedding Model `embedding_model_type` + +The name of the embedding model to be used. The available options are: + +- **generic (link the code)**: A simple embedding model using torch.nn.Embedding to link the tokenizer indecies to learned embeddings. + - `embedding_weight_tying`: Whether to tie the weights of the embedding with those of the language modeling head. _[default: true]_ + - `embedding_dropout`: The dropout rate to be used in the embedding layer. _[default: 0.0]_ + +## Tokenizer `tokenizer_type` + +The name of the tokenizer to be used. Depending on the tokenizer selected, additional variables might be necessary. The options include: + +- **GPT-2 (link the code)**: The GPT-2 tokenizer via tiktoken. +- **o200k_base (link the code)**: The GPT-40 tokenizer via tiktoken. +- **cl100k_base (link the code)**: The GPT-4 tokenizer via tiktoken. +- **p50k_base (link the code)**: The davinci tokenizer via tiktoken. +- **llama_32k (link the code)**: The old llama tokenizer via huggingface. +- **opt_50k (link the code)**: The OPT tokenizer via huggingface. +- **mistral_32k (link the code)**: The mistral tokenizer via huggingface. +- **bpe (link the code)**: A custom byte-pair encoding tokenizer using the hf library. + - `vocab_size`: The size of the vocabulary + - `tokenizer_dataset_name`: The name of the dataset used to train the tokenizer + - `tokenizer_simplify`: Whether to simplify the tokenizer by removing foreign symbols and forcing digits to be tokenized at an individual level _[default: true]_ + +## Hidden Dimension Size `hidden_dim` + +The dimensionality of the hidden layers in the model. + +## Context Window `context_window` + +The size of the context window (sequence length). + +## Language Model Head `lm_head_type` + +The name of the language model head to be used. The available options are: + +- **generic (link the code)**: A simple linear layer to map the hidden state back to the vocabulary. + - `lm_head_normalization`: The name of the normalization function to be used. The options are listed here _[TODO: link to normalization]_ + - `lm_head_bias`: true/false whether the bias units in the linear layers should be used _[defaul: false]_ + - `lm_head_dropout`: The dropout rate to be used in the language modeling head. _[default: 0.0]_ + +## Model Shell `model_shell_type` + +The model shell combines the embedder, core_model and lm_head into a single model. The available options are: + +- **standard (link the code)**: A simple model shell that combines the embedder, core_model and lm_head into a single model. + +## Positional encoding `positional_encoding_type` + +The type of positional encoding to be used. The options include: + +- **learned**: A learned positional encoding applied right after token embedding in the embedder. +- **sincos**: A sinusoidal positional encoding applied right after token embedding in the embedder. +- **rope**: A rotary positional encoding applied in the attention blocks. +- **none**: No positional encoding is applied. + +## The initialization function `initialization_fn` + +The function to be used to initialize the model weights. Options include: _[default: kaiming]_ + +- **xavier**: Xavier initialization + - `gain`: The gain value for the initialization _[default: 1.0]_ +- **kaiming**: Kaiming initialization + - `mode`: The mode of the initialization _[default: fan_in]_ + - `nonlinearity`: The nonlinearity function to be used _[default: gelu]_ +- **none**: No initialization + +# TODO: implement the initialization parameters + +# Other sub-configs that might be necessary (as specified above) + +### Feed Forward Network `ffn` [feedforward.py] + +- **generic (link the code)**: A standard FFN block with two linear layers and an activation in-between. + + - `ffn_dim`: The feed-forward dimension (commonly 4x hidden-dim) + - `bias`: true/false whether the bias units in the linear layers should be used _[defaul: false]_ + - `ffn_dropout`: The dropout rate to be used at the beginning of the feed-forward network. _[default: 0.0]_ + - `activation`: The name of the activation function to be used. Options include: _[defaul: gelu]_ + - _gelu_: https://pytorch.org/docs/stable/generated/torch.nn.GELU.html + - _relu_: https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html + - _leakyrelu_: https://pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html + - _tanh_: https://pytorch.org/docs/stable/generated/torch.nn.Tanh.html + - _sigmoid_: https://pytorch.org/docs/stable/generated/torch.nn.Sigmoid.html + - _silu_: https://pytorch.org/docs/stable/generated/torch.nn.SiLU.html + - _none_: https://pytorch.org/docs/stable/generated/torch.nn.Identity.html + - `normalization`: The name of the normalization function to be used. The options are listed here _[TODO: link to normalization]_ + +- **silu_ffn (link the code)**: introduces a gating mechanism by applying the SiLU activation function to one linear transformation of the input, then multiplying it with another linear transformation. This allows for dynamic feature modulation, enhancing the network's expressiveness. + - `ffn_dim`: The feed-forward dimension + - `bias`: true/false whether the bias units in the linear layers should be used _[defaul: false]_ + - `normalization`: The name of the normalization function to be used. The options are listed here _[TODO: link to normalization]_ + +### Attention `attn` [attention.py] + +- **causal (link the code)**: A standard MHA block. + + - `num_kv_heads`: The number of K and V heads. + - `num_q_heads`: The number of Q heads. If GQA is not intended to be used, this can be skipped and will default to `num_kv_heads`. _[default: num_kv_heads]_ + - `bias`: true/false whether the bias units in the linear layers should be used _[defaul: false]_ + - `attn_dropout`: The dropout rate to be used at the beginning of the attention block. _[default: 0.0]_ + - `normalization`: The name of the normalization function to be used. The options are listed here _[TODO: link to normalization]_ + +- **bidirectional (link the code)**: A standard MHA block. + - `num_kv_heads`: The number of K and V heads. + - `num_q_heads`: The number of Q heads. If GQA is not intended to be used, this can be skipped and will default to `num_kv_heads`. _[default: num_kv_heads]_ + - `bias`: true/false whether the bias units in the linear layers should be used _[defaul: false]_ + +### Normalization `normalization` [normalization.py] + +The name of the normalization to be used. Options include: _[defaul: rms_norm]_ + +- **rms_norm**: (Root Mean Square Normalization): Normalizes the input based on its root mean square (RMS) to stabilize training by controlling the variance of the activations. +- **layer_norm**: (Layer Normalization): Normalizes the input across features in each layer to ensure consistent activation distributions and improve convergence. +- **none**: No normalization is applied. + +(TODO include an example .yaml file and link to it) + +# Trainer Configuration (`trainer`) + +The trainer configuration contains the settings for training the model. There are a number of main trainers to choose from, each with their own sub-configurations. The main trainers are: + +## Base Trainer + +The base trainer is a standard trainer that can be used for most standard language model training tasks (like autoregressive pre-training, MLM-based pre-training, fine-tuning, etc.). To use the base trainer, just set `trainer_type` to **base_trainer**. The following are the sub-configurations for the base trainer: + +- `dataset`: The name of the dataset to be used for training. +- `batch_size`: The batch size to be used for training. +- `gradient_accumulation_steps`: The number of gradient accumulation steps. +- `max_iters`: The maximum number of iterations. +- `decay_lr`: Whether the learning rate should be decayed during training _[default: true]_ +- `lr_decay_iters`: The number of iterations after which the learning rate decays _[default: max_iters]_ +- `warmup_iters`: The number of warmup iterations. +- `eval_interval`: The number of steps after which the model goes through the inter-training evaluation. _[default: 2000]_ +- `log_interval`: The number of steps after which the model logs the training progress. _[default: 10]_ +- `checkpoint_interval`: The number of steps after which the model saves a checkpoint. _[default: 10000]_ +- `lr_scheduler`: The learning rate scheduler to be used. Options include: + - **cosine**: Cosine learning rate decay. +- `dataloader`: The dataloader to be used. Options include: + - **standard**: Standard dataloader. +- `datasampling`: The data sampling strategy to be used. Options include: + - **standard**: Standard dataloader. +- `loss_fn`: The loss function to be used. Options include: + - **cross_entropy**: Cross-entropy loss. + - **next_token_mlm**: The masked language modeling next token loss. +- `eval`: The evaluation sub-config _[TODO: link to the sub-config]_ +- `optimizer`: The optimizer sub-config _[TODO: link to the sub-config]_ + +### Evaluation Sub-config (`eval`) + +It can be important to estimate model performance during training. To that end, you can specify the evaluation sub-config. + +- `mcq_benchmarks`: A list of benchmarks to evaluate on. The available benchmarks are: + - **winogrande**: + - **hellaswag**: + - **arc_easy**: + - **mmlu**: + - **blimp**: + - **truthful_qa**: + - **piqa**: + - **race_middle**: + - **race_high**: + - **boolq**: + - **openbook_qa_closed**: + - **openbook_qa_open**: + - **copa**: + - **commonsense_qa**: + - **stlm_eval_arc_easy**: + - **stlm_eval_hellaswag**: + - **stlm_eval_truthful_qa**: + - **stlm_eval_winogrande**: +- `num_samples`: The number of samples for evaluation. _[default: 1000]_ +- `dataset_loss_eval_iters`: The number of iterations after which the dataset loss is evaluated. _[default: 1000]_ +- `text_modeling_eval`: true/false whether to evaluate the text modeling capacity. _[default: true]_ + +### Optimizer Sub-config (`optimizer`) + +Each optimizer requires a number of different values to be specified. Here are the optimizer options: + +- **AdamW** (`optimizer_name`): The basic AdamW optimizer + - `lr`: The learning rate _[default: 6e-4]_ + - `min_lr`: The minimum learning rate _[default: 6e-6]_ + - `weight_decay`: The weight decay factor _[default: 0.01]_ + - `beta1`: The beta1 value for the Adam optimizer _[default: 0.9]_ + - `beta2`: The beta2 value for the Adam optimizer _[default: 0.999]_ + - `grad_clip`: The gradient clipping value _[default: 1.0]_ + +# General Configuration (`general`) + +## Logging (`logging`) + +Defines logging options. + +- **wandb_log**: Boolean flag to enable/disable logging to Weights & Biases. _[default: false]_ +- **wandb_project**: Name of the Weights & Biases project. _[default: SuperTinyLanguageModels]_ +- **wandb_run_name**: Custom name for the run. Highly encouraged. If not provided, will be as descriptive as possible. _[default: None]_ + +## Paths (`paths`) + +Defines paths for saving outputs, data, checkpoints, and evaluations. + +- **output_dir**: Directory for output files. +- **data_dir**: Directory for data files. +- **checkpoint_dir**: Directory for checkpoints. +- **eval_dir**: Directory for evaluation files. + +## Seed (`seed`) + +Defines the random seed used for reproducibility. + +## Device (`device`) + +Defines the device for training and evaluation _[default: cuda]_ diff --git a/pre_reports/dropout_prereport.pdf b/Non-code/pre_reports/dropout_prereport.pdf similarity index 100% rename from pre_reports/dropout_prereport.pdf rename to Non-code/pre_reports/dropout_prereport.pdf diff --git a/README.md b/README.md index 69fddb43..8536be99 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,14 @@ +# Work in Progress +## TODO +- change the generators to accept custom default values on initialization +- train LLama-3.2 is a value model +- standalone eval script for model (MATH) for easy search and prompting +- add start thought and end thought tokens to LLama +- fix the print_evaluation_results function to be pretty again (maybe return eval results as dicts, and process to wandb string afterwards) + # Super Tiny Language Models + [Model Interfaces](models/README.md) | [Full Configs](configs/full_configs/) | [Intro Paper](https://arxiv.org/abs/2405.14159) | [Discord](https://discord.gg/wwTruDPH) This GitHub repository presents our research on Super Tiny Language Models (STLMs), aimed at delivering high performance with significantly reduced parameter counts (90-95% smaller) compared to traditional large language models. We explore innovative techniques such as byte-level tokenization with pooling, weight tying, and efficient training strategies. The codebase covers various subproblems, including tokenizer-free models, self-play based training, and alternative training objectives, targeting models with 10M, 50M, and 100M parameters while maintaining competitive performance. diff --git a/check_size.py b/check_size.py new file mode 100644 index 00000000..a553944a --- /dev/null +++ b/check_size.py @@ -0,0 +1,26 @@ +""" +Check model parameter count +""" +import hydra +from models.build_models import build_model +from models.utils import print_model_stats + +@hydra.main(config_path="configs/train", config_name="baseline-10m") +def main(cfg): + if len(cfg) == 1: + cfg = cfg[list(cfg.keys())[0]] + if "full_configs" in cfg: + cfg = cfg["full_configs"] + + + model, _ = build_model(model_cfg=cfg["model"]) + + # print full parameter count + print_model_stats(model) + + + +if __name__ == "__main__": + # pylint: disable=no-value-for-parameter + main() + # pylint: enable=no-value-for-parameter \ No newline at end of file diff --git a/configs/full_configs/baseline.yaml b/configs/full_configs/baseline.yaml deleted file mode 100644 index 85b5e908..00000000 --- a/configs/full_configs/baseline.yaml +++ /dev/null @@ -1,85 +0,0 @@ -model: - core_model: - core_model_type: generic - num_layers: 8 - ffn: - ffn_type: swiglu - ffn_dim: 1320 - normalization: rms_norm - bias: false - attn: - attn_type: generic - num_heads: 16 - normalization: rms_norm - group_size: 4 - bias: false - is_causal: true - embedder: - tokenizer_type: gpt2 - embedding_model_type: generic - dataset_name: stlm - lm_head: - normalization: rms_norm - bias: false - lm_head_type: generic - hidden_dim: 512 - context_window: 512 - vocab_size: 50257 - model_shell_type: standard - embedding_weight_tying: true - positional_encoding_type: rope -trainer: - dropout_scheduler: - dropout_type: constant - dropout: 0.1 - dataset: openwebtext - training: - trainer_type: base_trainer - batch_size: 24 - gradient_accumulation_steps: 20 - max_iters: 30000 - lr_decay_iters: 30000 - warmup_iters: 5000 - eval_interval: 2000 - log_interval: 10 - eval_iters: 500 - checkpoint_interval: 1000000000.0 - run_profiler: false - eval: - - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - num_samples: 1000 - evaluator: "mcq" - - evaluator: "prog" - optimizer: - name: nanoGPTadamW - lr: 0.0006 - min_lr: 6.0e-05 - weight_decay: 0.1 - beta1: 0.9 - beta2: 0.95 - grad_clip: 1.0 - decay_lr: true - warmup_iters: 5000 - lr_scheduler: - name: cosine - dataloader: - name: standard - datasampling: - name: standard - loss_fn: - name: cross_entropy -general: - logging: - wandb_log: false - wandb_project: SuperTinyLanguageModels - paths: - output_dir: outputs - data_dir: data - checkpoint_dir: checkpoints - seed: 489 - device: cuda diff --git a/configs/full_configs/baseline_next_thought.yaml b/configs/full_configs/baseline_next_thought.yaml deleted file mode 100644 index 72631043..00000000 --- a/configs/full_configs/baseline_next_thought.yaml +++ /dev/null @@ -1,114 +0,0 @@ -model: - core_model: - core_model_type: next_thought_baseline - - embedder: - tokenizer_type: gpt2 - embedding_model_type: hierarchical - dataset_name: simple_en_wiki - pooling_layers: 5 - pooling_dims: [768, 1920, 1920, 1920, 4800] - pooling_pct_per_layer: [0.3, 0.5, 0.6, 0.6] - num_heads: 12 - context_window: 512 - - standard_ffn_block: - ffn_type: swiglu - ffn_dim: 1536 - normalization: rms_norm - bias: false - - standard_attn_block: - attn_type: generic - num_heads: 16 - normalization: rms_norm - group_size: 4 - bias: false - is_causal: false - - lm_head: - lm_head_type: latent_2_seq - latent_decoded_into: 16 - num_layers: 4 - - standard_ffn_block: - ffn_type: swiglu - ffn_dim: 1536 - normalization: rms_norm - bias: false - - standard_attn_block: - attn_type: generic - num_heads: 16 - normalization: rms_norm - group_size: 4 - bias: false - is_causal: true - - latent_dim: 4800 - embedding_dim: 768 - hidden_dim: 768 - - context_window: 512 - vocab_size: 50257 - model_shell_type: standard - embedding_weight_tying: false - positional_encoding_type: learned - -trainer: - dropout_scheduler: - dropout_type: constant - dropout: 0.1 - start_dropout_p: 0.0 - end_dropout_p: 0.1 - start_iter: 0 - end_iter: 10000 - dataset: openhermes-2.5 - training: - trainer_type: base_trainer - batch_size: 24 - gradient_accumulation_steps: 20 - max_iters: 25000 - lr_decay_iters: 25000 - warmup_iters: 5000 - eval_interval: 2000 - log_interval: 10 - eval_iters: 500 - checkpoint_interval: 1000000000.0 - run_profiler: false - optimizer: - name: nanoGPTadamW - lr: 0.0018 - min_lr: 6.0e-05 - weight_decay: 0.1 - beta1: 0.9 - beta2: 0.95 - grad_clip: 1.0 - decay_lr: true - warmup_iters: 5000 - lr_scheduler: - name: cosine - dataloader: - name: conversational - loss_fn: - name: cross_entropy - eval: - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - num_samples: 5000 - evaluator: "mcq" - -general: - logging: - wandb_log: true - wandb_project: SuperTinyLanguageModels - paths: - output_dir: outputs - data_dir: data - checkpoint_dir: checkpoints - seed: 489 - device: cuda diff --git a/configs/full_configs/byte_debug.yaml b/configs/full_configs/byte_debug.yaml deleted file mode 100644 index cdedfac6..00000000 --- a/configs/full_configs/byte_debug.yaml +++ /dev/null @@ -1,102 +0,0 @@ -model: - core_model: - core_model_type: generic - num_layers: 10 - ffn: - ffn_type: swiglu - ffn_dim: 1536 - normalization: rms_norm - bias: false - attn: - attn_type: generic - num_heads: 16 - normalization: rms_norm - group_size: 4 - bias: false - is_causal: true - embedder: - tokenizer_type: "gpt2" - byte_tokenizer_type: "bpe" - embedding_model_type: byte_level - byte_context_window: 12 - dataset_name: simple_en_wiki - lm_head: - normalization: rms_norm - bias: false - lm_head_type: byte_level - - hidden_dim: 768 - context_window: 512 - vocab_size: 50257 - byte_vocab_size: 258 - byte_context_window: 12 - byte_embedding_dim: 128 - model_shell_type: standard - embedding_weight_tying: false - positional_encoding_type: rope -trainer: - dropout_scheduler: - dropout_type: constant - dropout: 0.1 - dataset: simple_en_wiki - training: - trainer_type: base_trainer - batch_size: 4 - gradient_accumulation_steps: 5 - max_iters: 20 - lr_decay_iters: 20 - warmup_iters: 5000 - eval_interval: 10 - log_interval: 5 - eval_iters: 100 - checkpoint_interval: 1000000000.0 - run_profiler: false - - optimizer: - name: nanoGPTadamW - lr: 0.0006 - min_lr: 6.0e-05 - weight_decay: 0.1 - beta1: 0.9 - beta2: 0.95 - grad_clip: 1.0 - decay_lr: true - warmup_iters: 10 - lr_scheduler: - name: cosine - dataloader: - name: byte_pooling - loss_fn: - name: cross_entropy - eval: - - evaluator: "ft_qa" - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - max_train_samples: 1000 - max_eval_samples: 1000 - - evaluator: "glue" - - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - num_samples: 1000 - evaluator: "mcq" - - evaluator: "prog" - - -general: - logging: - wandb_log: true - wandb_project: SuperTinyLanguageModels - paths: - output_dir: outputs - data_dir: data - checkpoint_dir: checkpoints - seed: 489 - device: cuda diff --git a/configs/full_configs/byte_level.yaml b/configs/full_configs/byte_level.yaml deleted file mode 100644 index 41798751..00000000 --- a/configs/full_configs/byte_level.yaml +++ /dev/null @@ -1,104 +0,0 @@ -model: - core_model: - core_model_type: generic - num_layers: 10 - ffn: - ffn_type: swiglu - ffn_dim: 1536 - normalization: rms_norm - bias: false - attn: - attn_type: generic - num_heads: 16 - normalization: rms_norm - group_size: 4 - bias: false - is_causal: true - embedder: - tokenizer_type: "gpt2" - byte_tokenizer_type: "bpe" - embedding_model_type: byte_level - byte_context_window: 12 - dataset_name: simple_en_wiki - lm_head: - normalization: rms_norm - bias: false - lm_head_type: byte_level - - hidden_dim: 768 - context_window: 512 - vocab_size: 50257 - byte_vocab_size: 258 - byte_context_window: 12 - byte_embedding_dim: 128 - model_shell_type: byte_shell - embedding_weight_tying: false - positional_encoding_type: rope -trainer: - dropout_scheduler: - dropout_type: constant - dropout: 0.1 - start_dropout_p: 0.0 - end_dropout_p: 0.1 - start_iter: 0 - end_iter: 10000 - dataset: stlm - training: - trainer_type: base_trainer - batch_size: 16 - gradient_accumulation_steps: 30 - max_iters: 50000 - lr_decay_iters: 50000 - warmup_iters: 5000 - eval_interval: 500 - log_interval: 100 - eval_iters: 200 - checkpoint_interval: 1000000000.0 - run_profiler: false - optimizer: - name: nanoGPTadamW - lr: 0.0006 - min_lr: 6.0e-05 - weight_decay: 0.1 - beta1: 0.9 - beta2: 0.99 - grad_clip: 1.0 - decay_lr: true - warmup_iters: 5000 - lr_scheduler: - name: cosine - dataloader: - name: byte_pooling - loss_fn: - name: masked_cross_entropy - eval: - - evaluator: "ft_qa" - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - max_train_samples: 1000 - max_eval_samples: 1000 - - evaluator: "glue" - - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - num_samples: 1000 - evaluator: "mcq" - - evaluator: "prog" - -general: - logging: - wandb_log: true - wandb_project: SuperTinyLanguageModels - paths: - output_dir: outputs - data_dir: data - checkpoint_dir: checkpoints - seed: 489 - device: cuda diff --git a/configs/full_configs/byte_level_ffn_sharing.yaml b/configs/full_configs/byte_level_ffn_sharing.yaml deleted file mode 100644 index 7e285265..00000000 --- a/configs/full_configs/byte_level_ffn_sharing.yaml +++ /dev/null @@ -1,92 +0,0 @@ -model: - core_model: - core_model_type: generic_ffn_sharing - num_layers: 16 - ffn: - ffn_type: swiglu - ffn_dim: 1536 - normalization: rms_norm - bias: false - attn: - attn_type: generic - num_heads: 16 - normalization: rms_norm - group_size: 4 - bias: false - is_causal: true - embedder: - tokenizer_type: "gpt2" - byte_tokenizer_type: "bpe" - embedding_model_type: byte_level - byte_context_window: 12 - dataset_name: simple_en_wiki - lm_head: - normalization: rms_norm - bias: false - lm_head_type: byte_level - - hidden_dim: 512 - context_window: 512 - vocab_size: 50257 - byte_vocab_size: 258 - byte_context_window: 12 - byte_embedding_dim: 128 - model_shell_type: standard - embedding_weight_tying: false - positional_encoding_type: rope -trainer: - dropout_scheduler: - dropout_type: linear - start_dropout_p: 0.0 - end_dropout_p: 0.1 - start_iter: 0 - end_iter: 10000 - dataset: simple_en_wiki - training: - trainer_type: base_trainer - batch_size: 24 - gradient_accumulation_steps: 20 - max_iters: 25000 - lr_decay_iters: 25000 - warmup_iters: 5000 - eval_interval: 5000 - log_interval: 100 - eval_iters: 500 - checkpoint_interval: 1000000000.0 - run_profiler: false - optimizer: - name: nanoGPTadamW - lr: 0.0006 - min_lr: 6.0e-05 - weight_decay: 0.1 - beta1: 0.9 - beta2: 0.95 - grad_clip: 1.0 - decay_lr: true - warmup_iters: 5000 - lr_scheduler: - name: cosine - dataloader: - name: byte_pooling - loss_fn: - name: cross_entropy - eval: - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - num_samples: 5000 - evaluator: "mcq" - -general: - logging: - wandb_log: true - wandb_project: SuperTinyLanguageModels - paths: - output_dir: outputs - data_dir: data - checkpoint_dir: checkpoints - seed: 489 - device: cuda diff --git a/configs/full_configs/debug.yaml b/configs/full_configs/debug.yaml deleted file mode 100644 index 9bcde612..00000000 --- a/configs/full_configs/debug.yaml +++ /dev/null @@ -1,96 +0,0 @@ -model: - core_model: - core_model_type: generic - num_layers: 10 - ffn: - ffn_type: swiglu - ffn_dim: 1536 - normalization: rms_norm - bias: false - attn: - attn_type: generic - num_heads: 16 - normalization: rms_norm - group_size: 4 - bias: false - is_causal: true - embedder: - tokenizer_type: gpt2 - embedding_model_type: generic - dataset_name: stlm - lm_head: - normalization: rms_norm - bias: false - lm_head_type: generic - hidden_dim: 512 - context_window: 512 - vocab_size: 50257 - model_shell_type: standard - embedding_weight_tying: true - positional_encoding_type: rope -trainer: - dropout_scheduler: - dropout_type: constant - dropout: 0.1 - dataset: stlm - training: - trainer_type: base_trainer - batch_size: 4 - gradient_accumulation_steps: 4 - max_iters: 20 - lr_decay_iters: 20 - warmup_iters: 5000 - eval_interval: 10 - log_interval: 5 - eval_iters: 100 - checkpoint_interval: 1000000000.0 - run_profiler: false - - optimizer: - name: nanoGPTadamW - lr: 0.0006 - min_lr: 6.0e-05 - weight_decay: 0.1 - beta1: 0.9 - beta2: 0.95 - grad_clip: 1.0 - decay_lr: true - warmup_iters: 10 - lr_scheduler: - name: cosine - dataloader: - name: standard - loss_fn: - name: cross_entropy - - eval: - - evaluator: "ft_qa" - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - max_train_samples: 1000 - max_eval_samples: 1000 - - evaluator: "glue" - - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - num_samples: 1000 - evaluator: "mcq" - - evaluator: "prog" - -general: - logging: - wandb_log: false - wandb_project: SuperTinyLanguageModels - paths: - output_dir: outputs - data_dir: data - checkpoint_dir: checkpoints - seed: 489 - device: cuda diff --git a/configs/full_configs/ffn_sharing.yaml b/configs/full_configs/ffn_sharing.yaml deleted file mode 100644 index 2a8e1177..00000000 --- a/configs/full_configs/ffn_sharing.yaml +++ /dev/null @@ -1,82 +0,0 @@ -model: - core_model: - core_model_type: generic_ffn_sharing - num_layers: 10 - ffn: - ffn_type: swiglu - ffn_dim: 1536 - normalization: rms_norm - bias: false - attn: - attn_type: generic - num_heads: 16 - normalization: rms_norm - group_size: 4 - bias: false - is_causal: true - embedder: - tokenizer_type: gpt2 - embedding_model_type: generic - dataset_name: simple_en_wiki - lm_head: - normalization: rms_norm - bias: false - lm_head_type: generic - hidden_dim: 512 - context_window: 512 - vocab_size: 50257 - model_shell_type: standard - embedding_weight_tying: true - positional_encoding_type: rope -trainer: - dropout_scheduler: - dropout_type: constant - dropout: 0.1 - dataset: stlm - training: - trainer_type: base_trainer - batch_size: 24 - gradient_accumulation_steps: 20 - max_iters: 25000 - lr_decay_iters: 25000 - warmup_iters: 5000 - eval_interval: 2000 - log_interval: 10 - eval_iters: 500 - checkpoint_interval: 1000000000.0 - run_profiler: false - eval: - benchmarks: - - winograd - - hellaswag - - arc - - mmlu - - blimp - num_samples: 5000 - evaluator: mcq - optimizer: - name: nanoGPTadamW - lr: 0.0006 - min_lr: 6.0e-05 - weight_decay: 0.1 - beta1: 0.9 - beta2: 0.95 - grad_clip: 1.0 - decay_lr: true - warmup_iters: 5000 - lr_scheduler: - name: cosine - dataloader: - name: standard - loss_fn: - name: cross_entropy -general: - logging: - wandb_log: true - wandb_project: SuperTinyLanguageModels - paths: - output_dir: outputs - data_dir: data - checkpoint_dir: checkpoints - seed: 489 - device: cuda diff --git a/configs/full_configs/gpt2_next_token_mlm.yaml b/configs/full_configs/gpt2_next_token_mlm.yaml deleted file mode 100644 index 000415ad..00000000 --- a/configs/full_configs/gpt2_next_token_mlm.yaml +++ /dev/null @@ -1,86 +0,0 @@ -model: - core_model: - core_model_type: generic - num_layers: 10 - ffn: - ffn_type: swiglu - ffn_dim: 1536 - normalization: rms_norm - bias: false - attn: - attn_type: generic - num_heads: 16 - normalization: rms_norm - group_size: 4 - bias: false - is_causal: false - embedder: - tokenizer_type: gpt2 - embedding_model_type: generic - dataset_name: simple_en_wiki - lm_head: - normalization: rms_norm - bias: false - lm_head_type: generic - hidden_dim: 512 - context_window: 512 - vocab_size: 50257 - model_shell_type: standard - embedding_weight_tying: true - positional_encoding_type: rope -trainer: - dropout_scheduler: - dropout_type: linear - start_dropout_p: 0.0 - end_dropout_p: 0.1 - start_iter: 0 - end_iter: 10000 - dataset: simple_en_wiki - training: - trainer_type: base_trainer - batch_size: 24 - gradient_accumulation_steps: 20 - max_iters: 25000 - lr_decay_iters: 25000 - warmup_iters: 5000 - eval_interval: 10 - log_interval: 1 - eval_iters: 200 - checkpoint_interval: 1000000000.0 - run_profiler: false - optimizer: - name: nanoGPTadamW - lr: 0.0006 - min_lr: 6.0e-05 - weight_decay: 0.1 - beta1: 0.9 - beta2: 0.95 - grad_clip: 1.0 - decay_lr: true - warmup_iters: 5000 - lr_scheduler: - name: cosine - dataloader: - name: next_token_mlm - loss_fn: - name: next_token_mlm - eval: - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - num_samples: 5000 - evaluator: "mcq" - -general: - logging: - wandb_log: false - wandb_project: SuperTinyLanguageModels - paths: - output_dir: outputs - data_dir: data - checkpoint_dir: checkpoints - seed: 489 - device: cuda diff --git a/configs/full_configs/gpt2_qwen_training.yaml b/configs/full_configs/gpt2_qwen_training.yaml deleted file mode 100644 index 50971552..00000000 --- a/configs/full_configs/gpt2_qwen_training.yaml +++ /dev/null @@ -1,81 +0,0 @@ -model: - model_string: "Qwen/Qwen2-0.5B" - core_model: - core_model_type: hf_core - embedder: - tokenizer_type: gpt2 - embedding_model_type: generic - dataset_name: stlm - lm_head: - normalization: rms_norm - bias: false - lm_head_type: generic - hidden_dim: 896 - context_window: 512 - vocab_size: 50257 - model_shell_type: standard - embedding_weight_tying: true - positional_encoding_type: rope -trainer: - dropout_scheduler: - dropout_type: constant - dropout: 0.1 - dataset: openwebtext - training: - trainer_type: base_trainer - batch_size: 6 - gradient_accumulation_steps: 20 - max_iters: 50000 - lr_decay_iters: 50000 - warmup_iters: 5000 - eval_interval: 2000 - log_interval: 10 - eval_iters: 500 - checkpoint_interval: 1000000000.0 - run_profiler: false - eval: - - evaluator: "ft_qa" - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - max_train_samples: 1000 - max_eval_samples: 1000 - - evaluator: "glue" - - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - num_samples: 1000 - evaluator: "mcq" - - evaluator: "prog" - optimizer: - name: nanoGPTadamW - lr: 0.0006 - min_lr: 6.0e-05 - weight_decay: 0.1 - beta1: 0.9 - beta2: 0.95 - grad_clip: 1.0 - decay_lr: true - warmup_iters: 5000 - lr_scheduler: - name: cosine - dataloader: - name: standard - loss_fn: - name: cross_entropy -general: - logging: - wandb_log: False - wandb_project: SuperTinyLanguageModels - paths: - output_dir: outputs - data_dir: data - checkpoint_dir: checkpoints - seed: 489 - device: cuda diff --git a/configs/full_configs/hugging_face.yaml b/configs/full_configs/hugging_face.yaml deleted file mode 100644 index a4f88ea6..00000000 --- a/configs/full_configs/hugging_face.yaml +++ /dev/null @@ -1,63 +0,0 @@ -model: - model_string: "microsoft/Phi-3-mini-4k-instruct" - core_model: - core_model_type: hf_core - embedder: - embedding_model_type: hf_embedder - tokenizer_type: hf_tokenizer - dataset_name: simple_en_wiki - lm_head: - lm_head_type: hf_head - hidden_dim: 3072 - context_window: 512 - vocab_size: 32064 - model_shell_type: standard - embedding_weight_tying: false - positional_encoding_type: rope -trainer: - dropout_scheduler: - dropout_type: constant - dropout: 0.1 - dataset: simple_en_wiki - training: - trainer_type: mock_trainer - batch_size: 2 - gradient_accumulation_steps: 1 - max_iters: 3 - lr_decay_iters: 10 - warmup_iters: 0 - eval_interval: 2 - log_interval: 1 - eval_iters: 1 - checkpoint_interval: 1000000000.0 - run_profiler: false - - optimizer: - name: nanoGPTadamW - lr: 0.0006 - min_lr: 6.0e-05 - weight_decay: 0.1 - beta1: 0.9 - beta2: 0.95 - grad_clip: 1.0 - decay_lr: true - warmup_iters: 5000 - lr_scheduler: - name: cosine - dataloader: - name: standard - loss_fn: - name: cross_entropy - eval: - - evaluator: "glue" - -general: - logging: - wandb_log: true - wandb_project: SuperTinyLanguageModels - paths: - output_dir: outputs - data_dir: data - checkpoint_dir: checkpoints - seed: 489 - device: cuda diff --git a/configs/general/default.yaml b/configs/general/default.yaml deleted file mode 100644 index 9467d1c2..00000000 --- a/configs/general/default.yaml +++ /dev/null @@ -1,11 +0,0 @@ -logging: - wandb_log: True - wandb_project: "SuperTinyLanguageModels" - -paths: - output_dir: "outputs" - data_dir: "data" - checkpoint_dir: "checkpoints" - -seed: 489 -device: cuda diff --git a/configs/generate.yaml b/configs/generate.yaml index 1a9cbf40..90821ec5 100644 --- a/configs/generate.yaml +++ b/configs/generate.yaml @@ -1,4 +1,7 @@ defaults: - - generator: baseline + - generator: beam_search + - _self_ model_ckpt: "checkpoints/...pt" - +attention_type: standard_detailed +max_new_tokens: 200 +input_text: "Earth is the third planet from the Sun and the only astronomical object known to harbor life. This is enabled by Earth being an ocean world, the only one in the Solar System sustaining liquid surface water." diff --git a/configs/generator/adaptive_sampler.yaml b/configs/generator/adaptive_sampler.yaml new file mode 100644 index 00000000..2351791b --- /dev/null +++ b/configs/generator/adaptive_sampler.yaml @@ -0,0 +1,49 @@ +generator_type: adaptive_sampler +# temperature: 1.2 +# top_k: 200 +max_new_tokens: 200 +beam_width: 15 +use_sampling: true +repetition_penalty: 1.2 +repetition_window: 32 +input_text: "Let's consider a simple Python program that adds two integers." + +temperature: 0.666 +top_p: 0.90 +top_k: 27 +min_p: 0.03 # Turn this down to 0.01 to reduce the shoggoth + +low_ent_thresh: 0.1 +low_vent_thresh: 0.1 +med_ent_thresh: 3.0 +high_ent_thresh: 5.0 +high_vent_thresh: 5.0 + +# TODO this is a bit of a nasty mess, but also makes all the hyperparameters visible +helv_attn_ent_offset: 1.3 +helv_attn_ent_coef: 0.2 + +lehv_interaction_strength_offset: 1.2 +lehv_interaction_strength_coef: 0.3 + +hehv_attn_ent_coef: 0.2 +hehv_attn_vent_offset: 2.0 +hehv_attn_vent_coef: 0.5 + +# TODO not convinced this should +n_adaptive_samples: 5 + +# Adaptive sampling parameters +ada_temp_logits: 0.3 +ada_temp_attn: 0.2 +ada_temp_agree: 0.2 +ada_top_p: 0.1 +ada_top_k_int: 0.3 +ada_top_k_agree: 0.2 +ada_min_p: 0.5 +ada_score_logits_ent: 0.1 +ada_score_attn_ent: 0.2 +ada_score_logits_vent: 0.3 +ada_score_attn_vent: 0.4 +ada_score_agree: 0.5 +ada_score_int: 0.6 \ No newline at end of file diff --git a/configs/generator/baseline.yaml b/configs/generator/baseline.yaml deleted file mode 100644 index a62d72b7..00000000 --- a/configs/generator/baseline.yaml +++ /dev/null @@ -1,4 +0,0 @@ -temperature: 0.8 -top_k: 10 -max_new_tokens: 300 -input_text: "Earth is the third planet from the Sun and the only astronomical object known to harbor life. This is enabled by Earth being an ocean world, the only one in the Solar System sustaining liquid surface water." diff --git a/configs/generator/beam_search.yaml b/configs/generator/beam_search.yaml new file mode 100644 index 00000000..c4c24a8f --- /dev/null +++ b/configs/generator/beam_search.yaml @@ -0,0 +1,9 @@ +generator_type: beam_search +temperature: 1.2 +top_k: 200 +max_new_tokens: 100 +beam_width: 15 +use_sampling: true +repetition_penalty: 1.2 +repetition_window: 32 +input_text: "Let's consider a simple Python program that adds two integers." diff --git a/configs/generator/entropy_temperature.yaml b/configs/generator/entropy_temperature.yaml new file mode 100644 index 00000000..a606dd64 --- /dev/null +++ b/configs/generator/entropy_temperature.yaml @@ -0,0 +1,6 @@ +generator_type: entropy_temperature +temperature: 0.95 +temperature_scaling_factor: 0.1 +top_k: 200 +max_new_tokens: 100 +input_text: "Let's consider a simple Python program that adds two integers." diff --git a/configs/generator/standard.yaml b/configs/generator/standard.yaml new file mode 100644 index 00000000..5cdee491 --- /dev/null +++ b/configs/generator/standard.yaml @@ -0,0 +1,7 @@ +generator_type: standard +temperature: 1.2 +top_k: 200 +max_new_tokens: 100 +repetition_penalty: 1.2 +repetition_window: 32 +input_text: "Let's consider a simple Python program that adds two integers." diff --git a/configs/model/baseline.yaml b/configs/model/baseline.yaml deleted file mode 100644 index a4201ef4..00000000 --- a/configs/model/baseline.yaml +++ /dev/null @@ -1,11 +0,0 @@ -defaults: - - core_model: baseline - - embedder: baseline - - lm_head: baseline - -hidden_dim: 512 -context_window: 512 -vocab_size: 50257 -model_shell_type: "standard" -embedding_weight_tying: True -positional_encoding_type: "rope" diff --git a/configs/model/core_model/baseline.yaml b/configs/model/core_model/baseline.yaml deleted file mode 100644 index a16c2d3e..00000000 --- a/configs/model/core_model/baseline.yaml +++ /dev/null @@ -1,14 +0,0 @@ -core_model_type: "generic" -num_layers: 10 -ffn: - ffn_type: "swiglu" - ffn_dim: 1536 - normalization: "rms_norm" - bias: False -attn: - attn_type: "generic" - num_heads: 16 - normalization: "rms_norm" - group_size: 4 - bias: False - is_causal: True diff --git a/configs/model/embedder/baseline.yaml b/configs/model/embedder/baseline.yaml deleted file mode 100644 index ded849e0..00000000 --- a/configs/model/embedder/baseline.yaml +++ /dev/null @@ -1,3 +0,0 @@ -tokenizer_type: "gpt2" -embedding_model_type: "generic" -dataset_name: "simple_en_wiki" \ No newline at end of file diff --git a/configs/model/lm_head/baseline.yaml b/configs/model/lm_head/baseline.yaml deleted file mode 100644 index 0fb66768..00000000 --- a/configs/model/lm_head/baseline.yaml +++ /dev/null @@ -1,3 +0,0 @@ -normalization: "rms_norm" -bias: False -lm_head_type: "generic" \ No newline at end of file diff --git a/configs/model/lm_head/hf_lm.yaml b/configs/model/lm_head/hf_lm.yaml deleted file mode 100644 index c15bb88d..00000000 --- a/configs/model/lm_head/hf_lm.yaml +++ /dev/null @@ -1 +0,0 @@ -lm_head_type: "hf_lm" diff --git a/configs/process_generation.yaml b/configs/process_generation.yaml new file mode 100644 index 00000000..73c6fdce --- /dev/null +++ b/configs/process_generation.yaml @@ -0,0 +1,13 @@ +defaults: + - generator: beam_search + - process_strategy: standard + - _self_ +model_ckpt: "checkpoints/...pt" +value_model_ckpt: "checkpoints/...pt" +attention_type: standard_detailed +samples_per_input_text: 20 +output_path: "outputs/" +step_token: "[/step]" + +## input_text should be a filepath? +# input_text: "Earth is the third planet from the Sun and the only astronomical object known to harbor life. This is enabled by Earth being an ocean world, the only one in the Solar System sustaining liquid surface water." diff --git a/configs/process_strategy/mcts.yaml b/configs/process_strategy/mcts.yaml new file mode 100644 index 00000000..e9e0ec6a --- /dev/null +++ b/configs/process_strategy/mcts.yaml @@ -0,0 +1,6 @@ +type: mcts +max_process_tokens: 200 +max_tree_depth: 5 +max_child_nodes: 3 +value_threshold: 0.5 +expansion_prompt: "In one sentence, can you further explain the reasoning of '{reasoning}'?" \ No newline at end of file diff --git a/configs/process_strategy/standard.yaml b/configs/process_strategy/standard.yaml new file mode 100644 index 00000000..67a0be07 --- /dev/null +++ b/configs/process_strategy/standard.yaml @@ -0,0 +1,3 @@ +type: standard +max_process_tokens: 200 +value_threshold: 0.5 \ No newline at end of file diff --git a/configs/test.yaml b/configs/test.yaml deleted file mode 100644 index 1fb0f9c6..00000000 --- a/configs/test.yaml +++ /dev/null @@ -1,25 +0,0 @@ -defaults: - # - testing: "llm_harness" - - testing: "baseline" -model_ckpt: "checkpoints/..." -# model: -# model_string: "microsoft/Phi-3-mini-4k-instruct" -# flash_attention: false -# core_model: -# core_model_type: hf_core -# embedder: -# embedding_model_type: hf_embedder -# tokenizer_type: hf_tokenizer -# dataset_name: simple_en_wiki -# lm_head: -# lm_head_type: hf_head -# hidden_dim: 3072 -# context_window: 512 -# vocab_size: 32064 -# model_shell_type: standard -# embedding_weight_tying: false -# positional_encoding_type: rope -generate_config: - temperature: 0.8 - top_k: 10 -output_path: "results.json" \ No newline at end of file diff --git a/configs/test/test_hf.yaml b/configs/test/test_hf.yaml new file mode 100644 index 00000000..bd66f55f --- /dev/null +++ b/configs/test/test_hf.yaml @@ -0,0 +1,35 @@ +mcq_benchmarks: + - "winograd" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + +model: + model_string: "openai-community/gpt2" + flash_attention: false + core_model: + core_model_type: hf_core + embedder: + embedding_model_type: hf_embedder + tokenizer_type: hf_tokenizer + dataset_name: simple_en_wiki + lm_head: + lm_head_type: hf_head + hidden_dim: 3072 + context_window: 512 + vocab_size: 32064 + model_shell_type: standard + embedding_weight_tying: false + positional_encoding_type: rope +generate_config: + temperature: 0.8 + top_k: 10 +output_path: "results.json" diff --git a/configs/test/test_stlm.yaml b/configs/test/test_stlm.yaml new file mode 100644 index 00000000..88872fe0 --- /dev/null +++ b/configs/test/test_stlm.yaml @@ -0,0 +1,20 @@ +mcq_benchmarks: + - "winograd" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + + +model_ckpt: "checkpoints/..." +generate_config: + temperature: 0.8 + top_k: 10 +output_path: "results.json" diff --git a/configs/testing/baseline.yaml b/configs/testing/baseline.yaml deleted file mode 100644 index fa35e573..00000000 --- a/configs/testing/baseline.yaml +++ /dev/null @@ -1,7 +0,0 @@ -benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" -evaluator_name: "mcq" diff --git a/configs/testing/llm_harness.yaml b/configs/testing/llm_harness.yaml deleted file mode 100644 index 89d6c3de..00000000 --- a/configs/testing/llm_harness.yaml +++ /dev/null @@ -1,7 +0,0 @@ -benchmarks: - - "winogrande" - - "hellaswag" - - "arc_easy" - - "mmlu" - - "blimp" -evaluator_name: "llm_harness" diff --git a/configs/train.yaml b/configs/train.yaml deleted file mode 100644 index 15502d14..00000000 --- a/configs/train.yaml +++ /dev/null @@ -1,4 +0,0 @@ -defaults: -- model: baseline -- trainer: baseline -- general: default diff --git a/configs/train/b.yaml b/configs/train/b.yaml new file mode 100644 index 00000000..159c04d9 --- /dev/null +++ b/configs/train/b.yaml @@ -0,0 +1,107 @@ +model: + core_model_type: generic + num_layers: 1 + + ffn: + ffn_type: generic + ffn_dim: 3072 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 12 + num_q_heads: 4 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 4000 + + hidden_dim: 768 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: openwebtext + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 100 + + eval: + #mcq_benchmarks: + # - "winogrande" + # - "hellaswag" + # - "arc_easy" + # - "blimp" + # - "piqa" + # - "race_middle" + # - "race_high" + # - "boolq" + # - "openbook_qa_closed" + # - "openbook_qa_open" + # - "copa" + # - "commonsense_qa" + #mcq_num_samples: 500 + eval_byte_metrics: false + text_modeling_eval: false + text_generation_eval: false + + optimizer: + optimizer_name: adamW + lr: 6.0e-6 + min_lr: 6.0e-8 + weight_decay: 0.1 + beta1: 0.9 + beta2: 0.95 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 5000 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: base_group + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-100m.yaml b/configs/train/baseline-100m.yaml new file mode 100644 index 00000000..7dedcda8 --- /dev/null +++ b/configs/train/baseline-100m.yaml @@ -0,0 +1,109 @@ +model: + core_model_type: generic + num_layers: 27 + + ffn: + ffn_type: generic + ffn_dim: 4096 + activation: gelu + normalization: rms_norm + bias: true + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 16 + num_q_heads: 4 + normalization: rms_norm + bias: true + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_names: [en_wiki, MATH] # GSM8k + tokenizer_simplify_data: true + tokenizer_num_reserved_tokens: 20 + vocab_size: 20000 + + hidden_dim: 1024 + context_window: 4096 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: true + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: MATH #[fineweb_edu_10B, MATH] # GSM8k + batch_size: 24 + gradient_accumulation_steps: 20 + + max_iters: 80000 + eval_interval: 500000000 + log_interval: 10 + checkpoint_interval: 5000 + eval_iters: 100 + + eval: + benchmarks: + - ArcEasy + # mcq_benchmarks: + # - "winogrande" + # - "hellaswag" + # - "arc_easy" + # - "blimp" + # - "piqa" + # - "race_middle" + # - "race_high" + # - "boolq" + # - "openbook_qa_closed" + # - "openbook_qa_open" + # - "copa" + # - "commonsense_qa" + # mcq_num_samples: 500 + # eval_byte_metrics: true + # text_modeling_eval: true + # text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 6.0e-4 + min_lr: 6.0e-6 + weight_decay: 1 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 5000 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: base_group + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-10m.yaml b/configs/train/baseline-10m.yaml new file mode 100644 index 00000000..62f0f04a --- /dev/null +++ b/configs/train/baseline-10m.yaml @@ -0,0 +1,98 @@ +model: + core_model_type: generic + num_layers: 5 + + ffn: + ffn_type: generic + ffn_dim: 1536 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attention_type: standard + attn_type: causal + num_kv_heads: 8 + num_q_heads: 4 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_names: [simple_en_wiki] + tokenizer_simplify_data: true + vocab_size: 4000 + + hidden_dim: 384 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: [simple_en_wiki] + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + benchmarks: + - MATH-small + - STLM-Text-Modeling (full) + - ArcEasy + - ArcEasySubset + # - ArcEasyLinearSubset + val_loss_iters: 1000 + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.1 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: base_group + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-50m.yaml b/configs/train/baseline-50m.yaml new file mode 100644 index 00000000..e9197891 --- /dev/null +++ b/configs/train/baseline-50m.yaml @@ -0,0 +1,106 @@ +model: + core_model_type: generic + num_layers: 12 + + ffn: + ffn_type: generic + ffn_dim: 2048 + activation: gelu + normalization: rms_norm + bias: true + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 16 + num_q_heads: 4 + normalization: rms_norm + bias: true + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_names: en_wiki + tokenizer_simplify_data: true + vocab_size: 10000 + + hidden_dim: 512 + context_window: 1024 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: true + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: pints + batch_size: 24 + gradient_accumulation_steps: 20 + + max_iters: 80000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 100 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 500 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 6.0e-4 + min_lr: 6.0e-6 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.999 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 10000 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: base_group + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_128-vocab_3900.yaml b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_128-vocab_3900.yaml new file mode 100644 index 00000000..b69b1596 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_128-vocab_3900.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 48 + + ffn: + ffn_type: generic + ffn_dim: 515 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 3900 + + hidden_dim: 128 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 1M, Weight-Tying: No, Hidden: 128, Vocab-Size: 3900" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_256-vocab_1950.yaml b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_256-vocab_1950.yaml new file mode 100644 index 00000000..ce11eba9 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_256-vocab_1950.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 12 + + ffn: + ffn_type: generic + ffn_dim: 1032 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 1950 + + hidden_dim: 256 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_10B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 25000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 1000 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 1M, Weight-Tying: No, Hidden: 256, Vocab-Size: 1950" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_384-vocab_1300.yaml b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_384-vocab_1300.yaml new file mode 100644 index 00000000..9df04301 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_384-vocab_1300.yaml @@ -0,0 +1,107 @@ +model: + core_model_type: generic + num_layers: 5 + + ffn: + ffn_type: generic + ffn_dim: 1702 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 1300 + + hidden_dim: 384 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_512-vocab_980.yaml b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_512-vocab_980.yaml new file mode 100644 index 00000000..66ae6458 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_no-hidden_512-vocab_980.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 3 + + ffn: + ffn_type: generic + ffn_dim: 2064 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 980 + + hidden_dim: 512 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 1M, Weight-Tying: No, Hidden: 512, Vocab-Size: 980" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_128-vocab_7800.yaml b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_128-vocab_7800.yaml new file mode 100644 index 00000000..83be7c21 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_128-vocab_7800.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 48 + + ffn: + ffn_type: generic + ffn_dim: 515 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 7800 + + hidden_dim: 128 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: yes + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 1M, Weight-Tying: Yes, Hidden: 128, Vocab-Size: 7800" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_256-vocab_3900.yaml b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_256-vocab_3900.yaml new file mode 100644 index 00000000..cfda4cb6 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_256-vocab_3900.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 12 + + ffn: + ffn_type: generic + ffn_dim: 1032 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 3900 + + hidden_dim: 256 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: yes + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 1M, Weight-Tying: Yes, Hidden: 256, Vocab-Size: 3900" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_384-vocab_2600.yaml b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_384-vocab_2600.yaml new file mode 100644 index 00000000..d82615ae --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_384-vocab_2600.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 5 + + ffn: + ffn_type: generic + ffn_dim: 1702 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 2600 + + hidden_dim: 384 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 1M, Weight-Tying: Yes, Hidden: 384, Vocab-Size: 2600" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_512-vocab_1950.yaml b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_512-vocab_1950.yaml new file mode 100644 index 00000000..7a73a0d6 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_1m-weight_tying_yes-hidden_512-vocab_1950.yaml @@ -0,0 +1,109 @@ +model: + core_model_type: generic + num_layers: 3 + + ffn: + ffn_type: generic + ffn_dim: 2064 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 1950 + + hidden_dim: 512 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + attention_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_10B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 25000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 1000 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 1M, Weight-Tying: Yes, Hidden: 512, Vocab-Size: 1950" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_no-hidden_128-vocab_9900.yaml b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_no-hidden_128-vocab_9900.yaml new file mode 100644 index 00000000..d7e6a15c --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_no-hidden_128-vocab_9900.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 40 + + ffn: + ffn_type: generic + ffn_dim: 520 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 9900 + + hidden_dim: 128 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2.55M, Weight-Tying: No, Hidden: 128, Vocab-Size: 9900" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_no-hidden_256-vocab_5000.yaml b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_no-hidden_256-vocab_5000.yaml new file mode 100644 index 00000000..5fcd69c1 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_no-hidden_256-vocab_5000.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 10 + + ffn: + ffn_type: generic + ffn_dim: 1036 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 5000 + + hidden_dim: 256 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2.55M, Weight-Tying: No, Hidden: 256, Vocab-Size: 5000" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_no-hidden_512-vocab_2600.yaml b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_no-hidden_512-vocab_2600.yaml new file mode 100644 index 00000000..f9852c18 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_no-hidden_512-vocab_2600.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 3 + + ffn: + ffn_type: generic + ffn_dim: 1524 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 2600 + + hidden_dim: 512 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2.55M, Weight-Tying: No, Hidden: 512, Vocab-Size: 2600" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_yes-hidden_128-vocab_20000.yaml b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_yes-hidden_128-vocab_20000.yaml new file mode 100644 index 00000000..baa43d40 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_yes-hidden_128-vocab_20000.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 24 + + ffn: + ffn_type: generic + ffn_dim: 512 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 20000 + + hidden_dim: 128 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2.55M, Weight-Tying: Yes, Hidden: 128, Vocab-Size: 20000" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_yes-hidden_256-vocab_9900.yaml b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_yes-hidden_256-vocab_9900.yaml new file mode 100644 index 00000000..b17d8fd5 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_yes-hidden_256-vocab_9900.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 10 + + ffn: + ffn_type: generic + ffn_dim: 1040 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 9900 + + hidden_dim: 256 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2.55M, Weight-Tying: Yes, Hidden: 256, Vocab-Size: 9900" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_yes-hidden_512-vocab_5000.yaml b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_yes-hidden_512-vocab_5000.yaml new file mode 100644 index 00000000..78629314 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2_55_m-weight_tying_yes-hidden_512-vocab_5000.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 3 + + ffn: + ffn_type: generic + ffn_dim: 1556 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 5000 + + hidden_dim: 512 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2.55M, Weight-Tying: Yes, Hidden: 512, Vocab-Size: 5000" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_128-vocab_7800.yaml b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_128-vocab_7800.yaml new file mode 100644 index 00000000..b2eb3e23 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_128-vocab_7800.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 43 + + ffn: + ffn_type: generic + ffn_dim: 514 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 7800 + + hidden_dim: 128 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2M, Weight-Tying: No, Hidden: 128, Vocab-Size: 7800" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_256-vocab_3900.yaml b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_256-vocab_3900.yaml new file mode 100644 index 00000000..defbd7f8 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_256-vocab_3900.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 10 + + ffn: + ffn_type: generic + ffn_dim: 1146 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 3900 + + hidden_dim: 256 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2M, Weight-Tying: No, Hidden: 256, Vocab-Size: 3900" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_384-vocab_2600.yaml b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_384-vocab_2600.yaml new file mode 100644 index 00000000..3b7b8aec --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_384-vocab_2600.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 5 + + ffn: + ffn_type: generic + ffn_dim: 1442 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 2600 + + hidden_dim: 384 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2M, Weight-Tying: No, Hidden: 384, Vocab-Size: 2600" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_512-vocab_1950.yaml b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_512-vocab_1950.yaml new file mode 100644 index 00000000..b4598781 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_no-hidden_512-vocab_1950.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 3 + + ffn: + ffn_type: generic + ffn_dim: 1740 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 1950 + + hidden_dim: 512 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_10B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 25000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 1000 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2M, Weight-Tying: No, Hidden: 512, Vocab-Size: 1950" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes-hidden_256-vocab_7800.yaml b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes-hidden_256-vocab_7800.yaml new file mode 100644 index 00000000..c7820916 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes-hidden_256-vocab_7800.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 11 + + ffn: + ffn_type: generic + ffn_dim: 994 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 7800 + + hidden_dim: 256 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: yes + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2M, Weight-Tying: Yes, Hidden: 256, Vocab-Size: 7800" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes-hidden_384-vocab_5000.yaml b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes-hidden_384-vocab_5000.yaml new file mode 100644 index 00000000..85b080c7 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes-hidden_384-vocab_5000.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 5 + + ffn: + ffn_type: generic + ffn_dim: 1462 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 5000 + + hidden_dim: 384 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2M, Weight-Tying: Yes, Hidden: 384, Vocab-Size: 5000" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes-hidden_512-vocab_3900.yaml b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes-hidden_512-vocab_3900.yaml new file mode 100644 index 00000000..0953be7d --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes-hidden_512-vocab_3900.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 3 + + ffn: + ffn_type: generic + ffn_dim: 1740 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 3900 + + hidden_dim: 512 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: yes + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2M, Weight-Tying: Yes, Hidden: 512, Vocab-Size: 3900" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes_hidden_128-vocab_15625.yaml b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes_hidden_128-vocab_15625.yaml new file mode 100644 index 00000000..43cc0b75 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_2m-weight_tying_yes_hidden_128-vocab_15625.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 43 + + ffn: + ffn_type: generic + ffn_dim: 514 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 15625 + + hidden_dim: 128 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 2M, Weight-Tying: Yes, Hidden: 128, Vocab-Size: 15625" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_no-hidden_128-vocab_12000.yaml b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_no-hidden_128-vocab_12000.yaml new file mode 100644 index 00000000..92d04af0 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_no-hidden_128-vocab_12000.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 38 + + ffn: + ffn_type: generic + ffn_dim: 506 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 12000 + + hidden_dim: 128 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 3M, Weight-Tying: No, Hidden: 128, Vocab-Size: 12000" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_no-hidden_256-vocab_5900.yaml b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_no-hidden_256-vocab_5900.yaml new file mode 100644 index 00000000..5134d904 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_no-hidden_256-vocab_5900.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 10 + + ffn: + ffn_type: generic + ffn_dim: 945 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 5900 + + hidden_dim: 256 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 3M, Weight-Tying: No, Hidden: 256, Vocab-Size: 5900" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_no-hidden_384-vocab_3900.yaml b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_no-hidden_384-vocab_3900.yaml new file mode 100644 index 00000000..75c81758 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_no-hidden_384-vocab_3900.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 4 + + ffn: + ffn_type: generic + ffn_dim: 1670 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 3900 + + hidden_dim: 384 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 3M, Weight-Tying: No, Hidden: 384, Vocab-Size: 3900" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_yes-hidden_256-vocab_12000.yaml b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_yes-hidden_256-vocab_12000.yaml new file mode 100644 index 00000000..72b87496 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_yes-hidden_256-vocab_12000.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 10 + + ffn: + ffn_type: generic + ffn_dim: 936 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 12000 + + hidden_dim: 256 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 3M, Weight-Tying: Yes, Hidden: 256, Vocab-Size: 12000" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_yes-hidden_384-vocab_7800.yaml b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_yes-hidden_384-vocab_7800.yaml new file mode 100644 index 00000000..f4e0de04 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_yes-hidden_384-vocab_7800.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 4 + + ffn: + ffn_type: generic + ffn_dim: 1670 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 7800 + + hidden_dim: 384 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: yes + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 3M, Weight-Tying: Yes, Hidden: 384, Vocab-Size: 7800" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_yes-hidden_512-vocab_5900.yaml b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_yes-hidden_512-vocab_5900.yaml new file mode 100644 index 00000000..b75300b4 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_3m-weight_tying_yes-hidden_512-vocab_5900.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 2 + + ffn: + ffn_type: generic + ffn_dim: 2620 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 5900 + + hidden_dim: 512 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 3M, Weight-Tying: Yes, Hidden: 512, Vocab-Size: 5900" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no-hidden_256-vocab_7800.yaml b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no-hidden_256-vocab_7800.yaml new file mode 100644 index 00000000..d50b8e03 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no-hidden_256-vocab_7800.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 8 + + ffn: + ffn_type: generic + ffn_dim: 1070 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 7800 + + hidden_dim: 256 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 4M, Weight-Tying: No, Hidden: 256, Vocab-Size: 7800" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no-hidden_384-vocab_5000.yaml b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no-hidden_384-vocab_5000.yaml new file mode 100644 index 00000000..40944125 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no-hidden_384-vocab_5000.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 4 + + ffn: + ffn_type: generic + ffn_dim: 1394 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 5000 + + hidden_dim: 384 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 4M, Weight-Tying: No, Hidden: 384, Vocab-Size: 5000" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no-hidden_512-vocab_3900.yaml b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no-hidden_512-vocab_3900.yaml new file mode 100644 index 00000000..aeecd697 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no-hidden_512-vocab_3900.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 2 + + ffn: + ffn_type: generic + ffn_dim: 2148 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 3900 + + hidden_dim: 512 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 4M, Weight-Tying: No, Hidden: 512, Vocab-Size: 3900" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no_hidden_128-vocab_15625.yaml b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no_hidden_128-vocab_15625.yaml new file mode 100644 index 00000000..d97b6dda --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_no_hidden_128-vocab_15625.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 33 + + ffn: + ffn_type: generic + ffn_dim: 510 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 15625 + + hidden_dim: 128 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 4M, Weight-Tying: No, Hidden: 128, Vocab-Size: 15625" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_yes-hidden_512-vocab_7800.yaml b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_yes-hidden_512-vocab_7800.yaml new file mode 100644 index 00000000..54780212 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_yes-hidden_512-vocab_7800.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 2 + + ffn: + ffn_type: generic + ffn_dim: 2148 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 7800 + + hidden_dim: 512 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: yes + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 4M, Weight-Tying: Yes, Hidden: 512, Vocab-Size: 7800" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_yes_hidden_256-vocab_15625.yaml b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_yes_hidden_256-vocab_15625.yaml new file mode 100644 index 00000000..7fab3d95 --- /dev/null +++ b/configs/train/baseline-vocab-size/10m-nv_4m-weight_tying_yes_hidden_256-vocab_15625.yaml @@ -0,0 +1,108 @@ +model: + core_model_type: generic + num_layers: 8 + + ffn: + ffn_type: generic + ffn_dim: 1072 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 8 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_name: en_wiki + tokenizer_simplify_data: true + vocab_size: 15625 + + hidden_dim: 256 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: fineweb_edu_100B + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 1000 + + eval: + mcq_benchmarks: + - "winogrande" + - "hellaswag" + - "arc_easy" + - "blimp" + - "piqa" + - "race_middle" + - "race_high" + - "boolq" + - "openbook_qa_closed" + - "openbook_qa_open" + - "copa" + - "commonsense_qa" + mcq_num_samples: 1000 + eval_byte_metrics: true + text_modeling_eval: true + text_generation_eval: true + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 2500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: baseline-vocab + run_name: "NonVocab: 4M, Weight-Tying: Yes, Hidden: 256, Vocab-Size: 15625" + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/train/debugging.yaml b/configs/train/debugging.yaml new file mode 100644 index 00000000..1f1ec1de --- /dev/null +++ b/configs/train/debugging.yaml @@ -0,0 +1,98 @@ +model: + core_model_type: generic + num_layers: 1 + + ffn: + ffn_type: generic + ffn_dim: 1536 + activation: gelu + normalization: rms_norm + bias: false + dropout: 0.0 + + attn: + attn_type: causal + num_kv_heads: 8 + num_q_heads: 4 + normalization: rms_norm + bias: false + dropout: false + + embedding_model_type: generic + tokenizer_type: bpe + tokenizer_dataset_names: [simple_en_wiki] + tokenizer_simplify_data: true + tokenizer_num_reserved_tokens: 20 + vocab_size: 4000 + + hidden_dim: 384 + context_window: 2048 + + lm_head_type: generic + lm_head_normalization: rms_norm + lm_head_bias: false + lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: true + ffn_weight_tying: false + cproj_weight_tying: false + positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: [simple_en_wiki, simple_en_wiki] + batch_size: 24 + gradient_accumulation_steps: 10 + + max_iters: 1000 + eval_interval: 500 + log_interval: 10 + checkpoint_interval: 5000 + eval_iters: 1000 + + eval: + benchmarks: + - MATH-small + - STLM-Text-Modeling (full) + - ArcEasy + - ArcEasySubset + # - ArcEasyLinearSubset + val_loss_iters: 1000 + + optimizer: + optimizer_name: adamW + lr: 5.0e-4 + min_lr: 5.0e-5 + weight_decay: 0.01 + beta1: 0.9 + beta2: 0.98 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 500 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: false + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: base_group + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda:0 diff --git a/configs/train/llama.yaml b/configs/train/llama.yaml new file mode 100644 index 00000000..9842608a --- /dev/null +++ b/configs/train/llama.yaml @@ -0,0 +1,110 @@ +model: + core_model_type: hf_core + # num_layers: 1 + model_string: "meta-llama/Llama-3.2-1B" + use_lora: true + lora_targets: ["q_proj", "k_proj", "v_proj", "c_proj", "gate_proj", "up_proj", "down_proj"] + # freeze: true + # ffn: + # ffn_type: generic + # ffn_dim: 3072 + # activation: gelu + # normalization: rms_norm + # bias: false + # dropout: 0.0 + + # attn: + # attn_type: causal + # num_kv_heads: 12 + # num_q_heads: 4 + # normalization: rms_norm + # bias: false + # dropout: false + + embedding_model_type: hf_embedder + tokenizer_type: hf + # tokenizer_dataset_name: en_wiki + # tokenizer_simplify_data: true + vocab_size: 128255 + + # hidden_dim: 768 + context_window: 4096 + + lm_head_type: hf_head + # lm_head_normalization: rms_norm + # lm_head_bias: false + # lm_head_dropout: 0.0 + + model_shell_type: standard + embedding_weight_tying: false + ffn_weight_tying: false + cproj_weight_tying: false + # positional_encoding_type: rope + +trainer: + trainer_type: base_trainer + dataset: simple_en_wiki + batch_size: 1 + gradient_accumulation_steps: 2 + + max_iters: 50000 + eval_interval: 5000 + log_interval: 10 + checkpoint_interval: 10000 + eval_iters: 100 + + eval: + #mcq_benchmarks: + # - "winogrande" + # - "hellaswag" + # - "arc_easy" + # - "blimp" + # - "piqa" + # - "race_middle" + # - "race_high" + # - "boolq" + # - "openbook_qa_closed" + # - "openbook_qa_open" + # - "copa" + # - "commonsense_qa" + #mcq_num_samples: 500 + eval_byte_metrics: false + text_modeling_eval: false + text_generation_eval: false + + optimizer: + optimizer_name: adamW + lr: 6.0e-6 + min_lr: 6.0e-8 + weight_decay: 0.1 + beta1: 0.9 + beta2: 0.95 + grad_clip: 1.0 + + lr_scheduler: + name: cosine + warmup_iters: 5000 + + dataloader: + name: standard + + datasampling: + name: standard + + loss_fn: + name: cross_entropy + +general: + logging: + wandb_log: true + wandb_project: SuperTinyLanguageModels + wandb_run_name: Null + group_name: base_group + + paths: + output_dir: outputs + data_dir: data + checkpoint_dir: checkpoints + eval_dir: evals + seed: 489 + device: cuda diff --git a/configs/trainer/baseline.yaml b/configs/trainer/baseline.yaml deleted file mode 100644 index 1fe35634..00000000 --- a/configs/trainer/baseline.yaml +++ /dev/null @@ -1,47 +0,0 @@ -defaults: - - dropout_scheduler: constant - -dataset: "stlm" - -training: - trainer_type: "base_trainer" - batch_size: 24 - gradient_accumulation_steps: 20 - max_iters: 25000 - lr_decay_iters: 25000 - warmup_iters: 5000 - eval_interval: 2000 - log_interval: 10 - eval_iters: 200 - checkpoint_interval: 1e9 - run_profiler: false - -eval: - benchmarks: - - "winograd" - - "hellaswag" - - "arc" - - "mmlu" - - "blimp" - num_samples: 5000 - evaluator: "mcq" - -optimizer: - name: "nanoGPTadamW" - lr: 6e-4 - min_lr: 6e-5 - weight_decay: 1e-1 - beta1: 0.9 - beta2: 0.95 - grad_clip: 1.0 - decay_lr: True - warmup_iters: 5000 - -lr_scheduler: - name: "cosine" - -dataloader: - name: "standard" - -loss_fn: - name: "cross_entropy" diff --git a/configs/trainer/dropout_scheduler/constant.yaml b/configs/trainer/dropout_scheduler/constant.yaml deleted file mode 100644 index c609361b..00000000 --- a/configs/trainer/dropout_scheduler/constant.yaml +++ /dev/null @@ -1,2 +0,0 @@ -dropout_type: "constant" -dropout: 0.1 diff --git a/configs/trainer/dropout_scheduler/linear_up.yaml b/configs/trainer/dropout_scheduler/linear_up.yaml deleted file mode 100644 index a4d5dd19..00000000 --- a/configs/trainer/dropout_scheduler/linear_up.yaml +++ /dev/null @@ -1,5 +0,0 @@ -dropout_type: "linear" -start_dropout_p: 0.0 -end_dropout_p: 0.1 -start_iter: 0 -end_iter: 25000 diff --git a/configs/trainer/dropout_scheduler/triangle.yaml b/configs/trainer/dropout_scheduler/triangle.yaml deleted file mode 100644 index d55780fe..00000000 --- a/configs/trainer/dropout_scheduler/triangle.yaml +++ /dev/null @@ -1,4 +0,0 @@ -dropout_type: "triangle" -dropout_trough: 0.0 -dropout_peak: 0.1 -cycle_factor: 4.0 diff --git a/data_generation.py b/data_generation.py new file mode 100644 index 00000000..378254eb --- /dev/null +++ b/data_generation.py @@ -0,0 +1,78 @@ +""" +A collection of data generating strategies that are used by the model to generate +data responses for any given input, which the model can then use to train further. + +The data generation strategies will can range from simple data generation strategies +to monte carlo tree search strategies. +""" + +import hydra +import torch + +from models.build_models import build_model +from models.components.utils.generation_utils import get_generator_type + +@hydra.main(config_path="configs", config_name="process_generation") +def main(cfg): + # set the checkpoint path to absolute path + cfg["model_ckpt"] = hydra.utils.to_absolute_path(cfg["model_ckpt"]) + cfg["value_model_ckpt"] = hydra.utils.to_absolute_path(cfg["value_model_ckpt"]) + + device = "cpu" if not torch.cuda.is_available() else "cuda" + + ## load the base model + model, _ = build_model( + checkpoint_path=cfg["model_ckpt"], + device=device, + attention_type=cfg["attention_type"] + ) + model.eval() # ensure that is in eval mode + + ## load the value model + value_model, _ = build_model( + checkpoint_path=cfg["value_model_ckpt"], + device=device, + attention_type=cfg["attention_type"] + ) + value_model.eval() # ensure that is in eval mode + + ## get the generator type based on the strategy provided in the config + ## the strategy should have the + generator = get_generator_type( + model=model, + value_model=value_model, + generate_cfg=cfg["generator"], + strategy_cfg=cfg["process_strategy"], + device=device + ) + + ## load the input text which can be as a list or a dataset from huggingface. + input_text = cfg["input_text"] ## TODO - dataloader or just self inserted in .yaml + if isinstance(input_text, str): + input_text = [input_text] + + ## generate N responses for each input text + ## can this be made more efficient? + N = cfg["samples_per_input_text"] + input_text_data = [] + generated_data = [] + generated_values = [] + + for text in input_text: + for _ in range(N): + generated_text, value = generator.generate_data(text) ## value *= + if value > cfg["process_strategy"]["value_threshold"]: ## only store the responses that are above the value threshold + input_text_data.append(text) + generated_data.append("".join(generated_text)) + generated_values.append(value) + + ## save the generated data responses and the value of the responses ## TODO + with open(cfg["output_path"], "w") as f: + f.write("Input Text,Generated Text,Value\n") + for input_text, generated_text, value in zip(input_text_data, generated_data, generated_values): + f.write(f"{input_text},{generated_text},{value}\n") + +if __name__ == "__main__": + # pylint: disable=no-value-for-parameter + main() + # pylint: enable=no-value-for-parameter \ No newline at end of file diff --git a/debugging/__init__.py b/debugging/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/debugging/debugging_models/__init__.py b/debugging/debugging_models/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/debugging/debugging_models/debugging_components/__init__.py b/debugging/debugging_models/debugging_components/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/debugging/debugging_models/debugging_components/debugging_layers/__init__.py b/debugging/debugging_models/debugging_components/debugging_layers/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/debugging/debugging_models/debugging_components/debugging_layers/test_attention.py b/debugging/debugging_models/debugging_components/debugging_layers/test_attention.py deleted file mode 100644 index 63c0d77e..00000000 --- a/debugging/debugging_models/debugging_components/debugging_layers/test_attention.py +++ /dev/null @@ -1,123 +0,0 @@ -""" -Pytest functions for the individual components of the different attention. -""" - -import pytest -import torch - -from models.components.layers.attention import build_attention - - -def test_normal_causal_attention(): - """ - Just test that it runs and - return the correct shape - """ - attention = build_attention( - hidden_dim=64, - context_window=16, - use_rope=True, - attn_cfg={ - "attn_type": "generic", - "num_heads": 8, - "bias": False, - "is_causal": True, - "group_size": 1, - }, - ) - x = torch.randn(2, 16, 64) - y = attention(x) - - assert y.shape == (2, 16, 64) - - -def test_rope_causal_attention(): - """ - Just test that it runs and - return the correct shape - """ - attention = build_attention( - hidden_dim=64, - context_window=16, - use_rope=True, - attn_cfg={ - "attn_type": "generic", - "num_heads": 8, - "bias": False, - "is_causal": True, - "group_size": 1, - }, - ) - x = torch.randn(2, 16, 64) - y = attention(x) - - assert y.shape == (2, 16, 64) - - -def test_normal_bidirectional_attention(): - """ - Just test that it runs and - return the correct shape - """ - attention = build_attention( - hidden_dim=64, - context_window=16, - use_rope=True, - attn_cfg={ - "attn_type": "generic", - "num_heads": 8, - "bias": False, - "is_causal": False, - "group_size": 1, - }, - ) - x = torch.randn(2, 16, 64) - y = attention(x) - - assert y.shape == (2, 16, 64) - - -def test_grouped_bidirectional_attention(): - """ - Just test that it runs and - return the correct shape - """ - attention = build_attention( - hidden_dim=64, - context_window=16, - use_rope=True, - attn_cfg={ - "attn_type": "generic", - "num_heads": 8, - "bias": False, - "is_causal": False, - "group_size": 2, - }, - ) - x = torch.randn(2, 16, 64) - y = attention(x) - - assert y.shape == (2, 16, 64) - - -def test_grouped_roped_attention(): - """ - Just test that it runs and - return the correct shape - """ - attention = build_attention( - hidden_dim=64, - context_window=16, - use_rope=True, - attn_cfg={ - "attn_type": "generic", - "num_heads": 8, - "bias": False, - "is_causal": True, - "group_size": 2, - }, - ) - x = torch.randn(2, 16, 64) - y = attention(x) - - assert y.shape == (2, 16, 64) diff --git a/debugging/debugging_models/debugging_components/debugging_layers/test_feedforward.py b/debugging/debugging_models/debugging_components/debugging_layers/test_feedforward.py deleted file mode 100644 index 721f9956..00000000 --- a/debugging/debugging_models/debugging_components/debugging_layers/test_feedforward.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Tests the different feedforward layers. -""" - -import pytest -import torch - -from models.components.layers.feedforward import build_ffn - - -def test_generic(): - """ - Simple test to check that the generic feedforward - """ - ffn = build_ffn( - hidden_dim=64, - ffn_cfg={ - "ffn_type": "generic", - "ffn_dim": 128, - "bias": False, - "activation": "relu", - }, - ) - x = torch.randn(2, 16, 64) - y = ffn(x) - - assert y.shape == (2, 16, 64) - - -def test_swiglue(): - """ - Simple test to check that the swiglue feedforward - """ - ffn = build_ffn( - hidden_dim=64, - ffn_cfg={ - "ffn_type": "swiglu", - "ffn_dim": 128, - "bias": False, - }, - ) - x = torch.randn(2, 16, 64) - y = ffn(x) - - assert y.shape == (2, 16, 64) diff --git a/debugging/debugging_models/debugging_components/debugging_layers/test_normalization.py b/debugging/debugging_models/debugging_components/debugging_layers/test_normalization.py deleted file mode 100644 index 77f8fe70..00000000 --- a/debugging/debugging_models/debugging_components/debugging_layers/test_normalization.py +++ /dev/null @@ -1,33 +0,0 @@ -""" -Pytest functions for the individual components of the different normalization layers. -""" - -# pylint: disable=unused-import -import pytest - -# pylint: enable=unused-import -import torch - -from models.components.layers.normalization import build_normalization - - -def test_rms_norm(): - """Just test that it runs...""" - normalization = build_normalization( - normalization_name="rms_norm", dim=64, bias=False - ) - x = torch.rand(2, 10, 64) - out = normalization(x) - - assert out.shape == (2, 10, 64) - - -def test_layer_norm(): - """Just test that it runs...""" - normalization = build_normalization( - normalization_name="layer_norm", dim=64, bias=False - ) - x = torch.rand(2, 10, 64) - out = normalization(x) - - assert out.shape == (2, 10, 64) diff --git a/debugging/debugging_models/debugging_components/debugging_layers/test_transformer_block.py b/debugging/debugging_models/debugging_components/debugging_layers/test_transformer_block.py deleted file mode 100644 index 273d7225..00000000 --- a/debugging/debugging_models/debugging_components/debugging_layers/test_transformer_block.py +++ /dev/null @@ -1,38 +0,0 @@ -""" -Pytest for the full transformer blocks. -""" - -import pytest -import torch - -from models.components.layers.transformer_blocks import GenericTransformerBlock - - -def test_generic_transformer_block(): - """ - Simple test to check that the generic transformer block - """ - block = GenericTransformerBlock( - hidden_dim=64, - context_window=16, - use_rope=True, - ffn_cfg={ - "ffn_type": "generic", - "ffn_dim": 128, - "bias": False, - "activation": "relu", - "normalization": "layer_norm", - }, - attn_cfg={ - "attn_type": "generic", - "num_heads": 8, - "bias": False, - "is_causal": True, - "group_size": 1, - "normalization": "rms_norm", - }, - ) - x = torch.randn(2, 16, 64) - y = block(x) - - assert y.shape == (2, 16, 64) diff --git a/debugging/debugging_models/debugging_components/debugging_tokenizers/__init__.py b/debugging/debugging_models/debugging_components/debugging_tokenizers/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/debugging/debugging_models/debugging_components/debugging_tokenizers/test_bpe_tokenizer.py b/debugging/debugging_models/debugging_components/debugging_tokenizers/test_bpe_tokenizer.py deleted file mode 100644 index 1f169adc..00000000 --- a/debugging/debugging_models/debugging_components/debugging_tokenizers/test_bpe_tokenizer.py +++ /dev/null @@ -1,32 +0,0 @@ -""" -For the BPE tokenizer, test training, and inference. -""" - -# import os - -# import pytest -# import torch - -# from models.components.tokenizers import build_tokenizer -# from models.components.tokenizers.utils import get_tokenizer_path - -# def test_train_bpe_tokenizer(): -# """ -# Train the BPE tokenizer. -# """ -# tokenizer = build_tokenizer( -# tokenizer_type="bpe", vocab_size=259, dataset_name="simple_en_wiki" -# ) - -# text = "Hello, world!" - -# tokens = tokenizer.encode(text) -# assert tokenizer.decode(tokens) == text - -# # delete the trained tokenizer file - -# os.remove( -# get_tokenizer_path( -# tokenizer_type="bpe", vocab_size=259, dataset_name="simple_en_wiki" -# )[1] -# ) diff --git a/debugging/debugging_models/debugging_components/debugging_tokenizers/test_gpt2_tokenizer.py b/debugging/debugging_models/debugging_components/debugging_tokenizers/test_gpt2_tokenizer.py deleted file mode 100644 index bd483bac..00000000 --- a/debugging/debugging_models/debugging_components/debugging_tokenizers/test_gpt2_tokenizer.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -Pytest for the GPT2 tokenizer. -""" - -import pytest -import torch - -from models.components.tokenizers import build_tokenizer - - -def test_gpt2_tokenizer(): - """ - Build the GPT2 tokenizer and encode decode text. - """ - tokenizer = build_tokenizer( - tokenizer_type="gpt2", vocab_size=50257, dataset_name=None - ) - - text = "Hello, world!" - tokens = tokenizer.encode(text) - assert tokenizer.decode(tokens) == text - - text = "This is a test." - tokens = tokenizer.encode(text) - assert tokenizer.decode(tokens) == text diff --git a/debugging/debugging_models/debugging_components/test_positional_encoding.py b/debugging/debugging_models/debugging_components/test_positional_encoding.py deleted file mode 100644 index 919ed4d1..00000000 --- a/debugging/debugging_models/debugging_components/test_positional_encoding.py +++ /dev/null @@ -1,27 +0,0 @@ -""" -Test the positional encoding -""" - -import pytest -import torch - -from models.components.positional_encoding import build_positional_encodings - - -def test_learned_positional_encodings(): - """ - Test that the learned positional encodings - return the correct shape. - """ - pos_encodings = build_positional_encodings( - model_cfg={ - "positional_encoding_type": "learned", - "hidden_dim": 64, - "context_window": 16, - } - ) - - x = torch.randn(2, 16, 64) - y = pos_encodings(x) - - assert y.shape == (2, 16, 64) diff --git a/debugging/debugging_models/test_core_models.py b/debugging/debugging_models/test_core_models.py deleted file mode 100644 index 29e00600..00000000 --- a/debugging/debugging_models/test_core_models.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Pytest for core models. -""" - -import pytest -import torch - -from models.build_models import build_core_model - - -def test_generic_core_model(): - """ - Test the generic core model. - """ - model = build_core_model( - model_cfg={ - "hidden_dim": 64, - "context_window": 64, - "vocab_size": 50257, - "positional_encoding_type": "rope", - "core_model": { - "core_model_type": "generic", - "norm_bias": True, - "ffn": { - "ffn_type": "generic", - "ffn_dim": 128, - "activation": "relu", - "normalization": "layer_norm", - "bias": True, - }, - "attn": { - "attn_type": "generic", - "num_heads": 8, - "group_size": 8, - "bias": True, - "is_causal": False, - "normalization": "rms_norm", - }, - "num_layers": 2, - }, - } - ) - - x = torch.randn(2, 16, 64) - y = model(x) - - assert y.shape == (2, 16, 64) diff --git a/debugging/debugging_trainers/test_loss.py b/debugging/debugging_trainers/test_loss.py deleted file mode 100644 index 13197fbd..00000000 --- a/debugging/debugging_trainers/test_loss.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Tests for trainers/loss_fn.py""" -import pytest -import torch - -from trainers.loss_fn import compute_perplexity - -def test_compute_perplexity(): - input_batch = torch.randn(2, 16, 64) - target_batch = torch.randint(0, 63, (2, 16)) - char_lengths = [16, 16] - mask = torch.ones_like(target_batch).float() - mask[0, 8:] = 0 - perp = compute_perplexity(input_batch, target_batch, char_lengths, mask=mask) - assert perp > 0 diff --git a/debugging/test_evals.py b/debugging/test_evals.py deleted file mode 100644 index 46566db9..00000000 --- a/debugging/test_evals.py +++ /dev/null @@ -1,63 +0,0 @@ -# """ -# Pytest for debugging the llm eval code -# And verifying correctness of path probs. -# """ -# from dataclasses import dataclass - -# import pytest -# import torch - -# from models.experimental import hugging_face -# from models import model_shell -# from evals.eval_wrapper import LMEvalWrappedModel -# from lm_eval.models import huggingface - -# @dataclass -# class RequestModel: -# """Class for testing the LLM model.""" -# args: tuple[str, str] - - -# def test_lm_eval_wrapper(): -# """ -# Test the generic core model. -# """ -# # if torch.cuda.is_available(): -# # device = torch.device("cuda") -# modelpath = "microsoft/Phi-3-mini-4k-instruct" -# hf_model = huggingface.HFLM( -# pretrained=modelpath, -# trust_remote_code=True, -# attn_implementation="flash_attention_2", -# dtype=torch.float16, -# max_length=512 -# ) -# model_config = { -# "model_string": modelpath -# } -# shell = model_shell.ModelShell( -# hugging_face.HFEmbedder(model_cfg=model_config), -# hugging_face.HFTransformerCore(model_cfg=model_config), -# hugging_face.HFLMHead(model_cfg=model_config) -# ) -# shell.to(torch.device("cuda")) -# shell.eval() -# model = LMEvalWrappedModel(shell) -# requests = [RequestModel(args=("context"*512, "target"*512))] -# results = model.loglikelihood(requests) -# results_target = hf_model.loglikelihood(requests) -# assert abs(results[0][0] - results_target[0][0]) < 1e-2 - -# context_str = "The capital of France is" -# target_str = "Paris, the city of light" -# requests = [RequestModel(args=("oh", "Paris, you beast of a city"))] -# results = model.loglikelihood(requests) -# results_target = hf_model.loglikelihood(requests) -# assert abs(results[0][0] - results_target[0][0]) < 1e-2 - -# requests = [ -# RequestModel(args=(context_str, target_str)) -# ] -# results = model.loglikelihood(requests) -# results_target = hf_model.loglikelihood(requests) -# assert abs(results[0][0] - results_target[0][0]) < 1e-2 diff --git a/debugging/test_mcqs.py b/debugging/test_mcqs.py deleted file mode 100644 index 741ec054..00000000 --- a/debugging/test_mcqs.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Tests for each of the benchmarks""" - -# pylint: disable=unused-import -import pytest - -from evals.mcqs.load_benchmarks import load_benchmark - -# pylint: enable=unused-import - - -def test_load_benchmark(): - """loads in the benchmarks to check they work""" - benchmark_names = ["arc", "winograd", "mmlu", "hellaswag", "blimp"] - for benchmark_name in benchmark_names: - for split in ["test", "validation"]: - benchmark = load_benchmark(benchmark_name, split) - assert benchmark is not None - for item in benchmark: - assert len(item) == 3 - assert isinstance(item[0], str) - assert isinstance(item[1], str) - assert isinstance(item[2], list) - assert all(isinstance(option, str) for option in item[2]) - break diff --git a/eval.py b/eval.py index 0a1c08ff..24e08cdd 100644 --- a/eval.py +++ b/eval.py @@ -18,10 +18,11 @@ def main(cfg): # set the checkpoint path to absolute path cfg["model_ckpt"] = hydra.utils.to_absolute_path(cfg["model_ckpt"]) - model = build_model(checkpoint=torch.load(cfg["model_ckpt"])) + model, _ = build_model(checkpoint=torch.load(cfg["model_ckpt"])) # otherwise build the model from scratch (e.g. for external pretrained models) else: - model = build_model(model_cfg=cfg["model"]) + model, _ = build_model(model_cfg=cfg["model"]) + model.eval() # load the evaluator diff --git a/evals/__init__.py b/evals/__init__.py index e69de29b..e19eee5e 100644 --- a/evals/__init__.py +++ b/evals/__init__.py @@ -0,0 +1,210 @@ +""" +Import all and register them in the registry + +(i.e. import benchmarks and evaluators) + + +ultimately should be able to call .make and .evaluate +""" +from evals.benchmark_registry import register, make + + +from evals.benchmarks.yield_functions import * +from evals.benchmarks.extraction_functions import * + +from evals.model_wrappers import * + +from models.generators import * + + +# Register the MCQ benchmarks + +# ARC-Easy +register( + id="ArcEasy", + entry_point="evals.evaluators:MCQEvaluator", + model_wrapper=LoglikelihoodMCQModelWrapper, + yield_fn=load_arc_easy, + yield_fn_params={ + "version": "original", + "num_samples": None, # i.e. all samples + } +) +register( + id="ArcEasySubset", + entry_point="evals.evaluators:MCQEvaluator", + model_wrapper=LoglikelihoodMCQModelWrapper, + yield_fn=load_arc_easy, + yield_fn_params={ + "version": "original", + "num_samples": 100, # i.e. all samples + "seed": 489 + } +) +register( + id="ArcEasyLinearSubset", + entry_point="evals.evaluators:MCQEvaluator", + model_wrapper=LoglikelihoodMCQModelWrapper, + yield_fn=load_arc_easy, + yield_fn_params={ + "version": "stlm_eval", + "num_samples": None, # i.e. all samples + "seed": 489 + } +) + + +# Register the text-modeling benchmarks +register( + id="STLM-Text-Modeling (full)", + entry_point="evals.evaluators:TextModelingEvaluator", + model_wrapper=TextModelingModelWrapper, + yield_fn=load_stlm_synthetic_text_modeling, + yield_fn_params={ + "topics": None, + "difficulties": None # i.e. full dataset + } +) +register( + id="STLM-Text-Modeling (Science-Hard)", + entry_point="evals.evaluators:TextModelingEvaluator", + model_wrapper=TextModelingModelWrapper, + yield_fn=load_stlm_synthetic_text_modeling, + yield_fn_params={ + "topics": ["Science"], + "difficulties": ["Hard"] # i.e. full dataset + } +) + + +# Register the Free Form benchmarks +register( + id="MATH", + entry_point="evals.evaluators:FreeFormEvaluator", + model_wrapper=FreeFormModelWrapper, + model_generator=StandardGenerator, + answer_extraction_function=answer_extraction_math, + yield_fn=load_math, + yield_fn_params={ + "num_samples": None, # all + "seed": 489 + }, +) +register( + id="MATH-small", + entry_point="evals.evaluators:FreeFormEvaluator", + model_wrapper=FreeFormModelWrapper, + model_generator=StandardGenerator, + answer_extraction_function=answer_extraction_math, + yield_fn=load_math, + yield_fn_params={ + "num_samples": 100, # all + "seed": 489 + }, +) + + + + +register( + id="GSM8K", + entry_point="evals.evaluators:FreeFormEvaluator", + model_wrapper=FreeFormModelWrapper, + model_generator=StandardGenerator, + answer_extraction_function=answer_extraction_gsm8k, + yield_fn=load_gsm8k, + yield_fn_params={ + "num_samples": None, # all + "seed": 489 # few-shot etc. should be mentioned here + } +) + + + + + + +# EVALS_DICT = { +# "arc_easy": lambda num_samples: load_arc_easy( +# version="original", +# num_samples=num_samples +# ), +# "stlm_eval_arc_easy": lambda num_samples: load_arc_easy( +# version="stlm_eval", +# num_samples=num_samples +# ), +# "hellaswag": lambda num_samples: load_hellaswag( +# version="original", +# num_samples=num_samples +# ), +# "stlm_eval_hellaswag": lambda num_samples: load_hellaswag( +# version="stlm_eval", +# num_samples=num_samples +# ), +# "winogrande": lambda num_samples: load_winogrande( +# version="original", +# num_samples=num_samples +# ), +# "stlm_eval_winogrande": lambda num_samples: load_winogrande( +# version="stlm_eval", +# num_samples=num_samples +# ), +# "truthful_qa": lambda num_samples: load_truthful_qa_m2( +# version="original", +# num_samples=num_samples +# ), +# "stlm_eval_truthful_qa": lambda num_samples: load_truthful_qa_m2( +# version="stlm_eval", +# num_samples=num_samples +# ), +# "blimp": lambda num_samples: load_blimp( +# num_samples=num_samples +# ), +# "mmlu": lambda num_samples: load_mmlu( +# num_samples=num_samples +# ), +# "piqa": lambda num_samples: load_piqa( +# num_samples=num_samples +# ), +# "boolq": lambda num_samples: load_boolq( +# num_samples=num_samples +# ), +# "race_middle": lambda num_samples: load_race( +# version="middle", +# num_samples=num_samples +# ), +# "race_high": lambda num_samples: load_race( +# version="high", +# num_samples=num_samples +# ), +# "openbook_qa_open": lambda num_samples: load_openbook_qa( +# version="open", +# num_samples=num_samples +# ), +# "openbook_qa_closed": lambda num_samples: load_openbook_qa( +# version="closed", +# num_samples=num_samples +# ), +# "copa": lambda num_samples: load_copa( +# num_samples=num_samples +# ), +# "commonsense_qa": lambda num_samples: load_commonsense_qa( +# num_samples=num_samples +# ), +# "ewok": lambda num_samples: load_ewok( +# num_samples=num_samples +# ) +# } + + + + +# def load_benchmark(benchmark_name, num_samples): +# """ +# Given the benchmark name, build the benchmark +# """ +# assert benchmark_name in EVALS_DICT, \ +# f"Benchmark {benchmark_name} not found. The available benchmarks are: {list(EVALS_DICT.keys())}" +# return EVALS_DICT[benchmark_name]( +# num_samples=num_samples +# ) diff --git a/evals/benchmark_registry.py b/evals/benchmark_registry.py new file mode 100644 index 00000000..f64240e9 --- /dev/null +++ b/evals/benchmark_registry.py @@ -0,0 +1,169 @@ +import re +import importlib +from typing import Any, Callable, Dict, Tuple, Optional +from dataclasses import dataclass, field + +# Global environment registry +BENCHMARK_REGISTRY: Dict[str, 'BenchmarkSpec'] = {} + +@dataclass +class BenchmarkSpec: + """A specification for creating environments.""" + id: str + entry_point: Callable + kwargs: Dict[str, Any] = field(default_factory=dict) + + def make(self, **kwargs) -> Any: + """Create an benchmark instance.""" + all_kwargs = {**self.kwargs, **kwargs} + return self.entry_point(**all_kwargs) + + + +def register(id: str, entry_point: Callable, **kwargs: Any): + """Register an Benchmark with a given ID.""" + if id in BENCHMARK_REGISTRY: + raise ValueError(f"Benchmark {id} already registered.") + BENCHMARK_REGISTRY[id] = BenchmarkSpec(id=id, entry_point=entry_point, kwargs=kwargs) + +def pprint_registry(): + """Pretty print the current registry of benchmarks.""" + if not BENCHMARK_REGISTRY: + print("No Benchmarks registered.") + else: + print("Benchmark Benchmarks:") + for env_id, env_spec in BENCHMARK_REGISTRY.items(): + print(f" - {env_id}: {env_spec.entry_point}") + +def pprint_registry_detailed(): + """Pretty print the registry with additional details like kwargs.""" + if not ENV_REGISTRY: + print("No benchmarks registered.") + else: + print("Detailed Registered Benchmarks:") + for env_id, env_spec in BENCHMARK_REGISTRY.items(): + print(f" - {env_id}:") + print(f" Entry Point: {env_spec.entry_point}") + print(f" Kwargs: {env_spec.kwargs}") + + +def check_env_exists(env_id: str): + """Check if an benchmark exists in the registry.""" + if env_id not in BENCHMARK_REGISTRY: + raise ValueError(f"Benchmark {env_id} is not registered.") + else: + print(f"Benchmark {env_id} is registered.") + + +def make(env_id: str, **kwargs) -> Any: + """Create an benchmark instance using the registered ID.""" + if env_id not in BENCHMARK_REGISTRY: + raise ValueError(f"Benchmark {env_id} not found in registry.") + + env_spec = BENCHMARK_REGISTRY[env_id] + + # Resolve the entry point if it's a string + if isinstance(env_spec.entry_point, str): + module_name, class_name = env_spec.entry_point.split(":") + module = importlib.import_module(module_name) + env_class = getattr(module, class_name) + else: + env_class = env_spec.entry_point + + # Pass additional keyword arguments + env = env_class(**{**env_spec.kwargs, **kwargs}) + # set id + env.set_env_id(env_id=env_id) + return env + + + +# from dataclasses import dataclass, field +# from typing import Any, Callable, Dict, Optional, Type + +# # Global benchmark registry +# BENCHMARK_REGISTRY: Dict[str, 'BenchmarkSpec'] = {} + + + + + +# @dataclass +# class BenchmarkSpec: +# """A specification for benchmarks.""" +# name: str +# evaluator_class: Type +# load_fn: Optional[Callable] = None +# kwargs: Dict[str, Any] = field(default_factory=dict) + +# def make_evaluator(self, model, **kwargs) -> Any: +# """Create an evaluator instance.""" +# all_kwargs = {**self.kwargs, **kwargs} +# return self.evaluator_class(model, benchmark_spec=self, **all_kwargs) + +# def register( +# name: str, +# evaluator_class: Type, +# load_fn: Optional[Callable] = None, +# **kwargs: Any +# ): +# """Register a benchmark with a given name.""" +# if name in BENCHMARK_REGISTRY: +# raise ValueError(f"Benchmark {name} already registered.") +# BENCHMARK_REGISTRY[name] = BenchmarkSpec( +# name=name, +# evaluator_class=evaluator_class, +# load_fn=load_fn, +# kwargs=kwargs +# ) + +# def get_benchmark_spec(name: str) -> BenchmarkSpec: +# """Retrieve a benchmark specification.""" +# if name not in BENCHMARK_REGISTRY: +# raise ValueError(f"Benchmark {name} not found in registry.") +# return BENCHMARK_REGISTRY[name] + +# def list_registered_benchmarks(): +# """List all registered benchmarks.""" +# return list(BENCHMARK_REGISTRY.keys()) + +# # # Register benchmarks + +# # # MCQ Benchmarks +# # register_benchmark( +# # name='arc_easy', +# # evaluator_class=MCQEvaluator, +# # load_fn=load_arc_easy, +# # num_samples=100 # Default parameters +# # ) + +# # register_benchmark( +# # name='hellaswag', +# # evaluator_class=MCQEvaluator, +# # load_fn=load_hellaswag, +# # num_samples=100 +# # ) + +# # # Math Word Problem Benchmarks +# # register_benchmark( +# # name='gsm8k', +# # evaluator_class=MathWordProblemEvaluator, +# # load_fn=load_gsm8k, +# # num_samples=100 +# # ) + +# # register_benchmark( +# # name='aqua_rat', +# # evaluator_class=MathWordProblemEvaluator, +# # load_fn=load_aqua_rat, +# # num_samples=100 +# # ) + +# # # Text Generation Benchmark +# # register_benchmark( +# # name='text_generation', +# # evaluator_class=TextGenerationEvaluator, +# # prompts=None # Default prompts +# # ) + +# # # Add more benchmarks as needed... diff --git a/__init__.py b/evals/benchmarks/__init__.py similarity index 100% rename from __init__.py rename to evals/benchmarks/__init__.py diff --git a/evals/benchmarks/extraction_functions.py b/evals/benchmarks/extraction_functions.py new file mode 100644 index 00000000..e573880a --- /dev/null +++ b/evals/benchmarks/extraction_functions.py @@ -0,0 +1,52 @@ +import sympy +import re +from evals.benchmarks.utils import * + +def answer_extraction_math(text: str) -> str: + """ + Extracts the final answer from the generated text for the MATH dataset. + Supports formats like '\\boxed{...}' and extracts expressions between the first and last '$' signs. + """ + if "\\boxed" in text: + return remove_boxed(last_boxed_only_string(text)) + elif "Answer:" in text: + return text.split("Answer:")[-1] + elif "###" in text: + return text.split("###")[-1] + else: + return text + + + +def answer_extraction_gsm8k(generated_text: str) -> str: + """ + Extracts the final answer from the generated text for the GSM8k dataset. + """ + # Try to find patterns like 'Answer: 42' or '#### 42' + match = re.findall(r'####\s*(.*)', generated_text) + if match: + return match[-1].strip() + match = re.findall(r'Answer:\s*(.*)', generated_text) + if match: + return match[-1].strip() + # If no pattern matched, return the last numerical value + numbers = re.findall(r'[-+]?\d*\.\d+|\d+', generated_text) + if numbers: + return numbers[-1].strip() + # Fallback: Return the entire text + return generated_text.strip() + +def answer_extraction_aqua_rat(generated_text: str) -> str: + """ + Extracts the selected option from the generated text for the AQUA-RAT dataset. + """ + # Look for 'Answer: A', 'Answer: B', etc. + match = re.findall(r'Answer:\s*([A-E])', generated_text, re.IGNORECASE) + if match: + return match[-1].upper() + # Fallback: Return the last capital letter between A and E + match = re.findall(r'\b([A-E])\b', generated_text.upper()) + if match: + return match[-1] + # Fallback: Return the entire text + return generated_text.strip() diff --git a/evals/benchmarks/utils.py b/evals/benchmarks/utils.py new file mode 100644 index 00000000..66c66830 --- /dev/null +++ b/evals/benchmarks/utils.py @@ -0,0 +1,336 @@ +""" +Most of this should be moved to huggingface ASAP. TODO +""" +GENERATION_PROMPTS = [ + { + "difficulty": "easy", + "prompt": "The morning sun peeked through the curtains, waking Sarah gently from her sleep. She stretched lazily and thought about the day ahead" + }, + { + "difficulty": "easy", + "prompt": "Walking along the beach, the soft sand beneath his feet, Jake found a mysterious bottle washed up on the shore. Curious, he picked it up and" + }, + { + "difficulty": "easy", + "prompt": "In a small village surrounded by mountains, there was a legend about a hidden treasure that no one had ever found. Many had searched, but" + }, + { + "difficulty": "easy", + "prompt": "Emma opened the old, dusty book she found in her grandmother's attic. As she turned the pages, she discovered it was a diary from decades ago that revealed" + }, + { + "difficulty": "easy", + "prompt": "The crowd cheered loudly as the final seconds ticked away. The underdog team was about to win the championship for the first time, and the players could hardly believe" + }, + { + "difficulty": "easy", + "prompt": "Late at night, a distant howl echoed through the forest. James glanced nervously at the fire, wondering what might be lurking in the dark shadows beyond the trees" + }, + { + "difficulty": "easy", + "prompt": "As the spaceship approached the glowing nebula, Captain Lee felt a strange sense of déjà vu. The swirling colors seemed oddly familiar, like a dream she couldn’t quite remember" + }, + { + "difficulty": "easy", + "prompt": "The old clock tower chimed midnight as Mia tiptoed through the library, searching for the secret door that, according to legend, led to a hidden world" + }, + { + "difficulty": "hard", + "prompt": "Earth is the third planet from the sun. It is home to a diverse range of life forms, and its atmosphere plays a key role in maintaining life by trapping heat and providing oxygen" + }, + { + "difficulty": "hard", + "prompt": "The complex interplay between dark matter and visible matter in the universe presents one of the most intriguing puzzles in modern astrophysics. Researchers continue to explore" + }, + { + "difficulty": "hard", + "prompt": "Amidst the rapidly evolving landscape of artificial intelligence, ethical considerations have become increasingly paramount. One of the most pressing issues is" + }, + { + "difficulty": "hard", + "prompt": "The socioeconomic impacts of climate change are profound and far-reaching, affecting everything from agriculture to global migration patterns. Policymakers are challenged to" + }, + { + "difficulty": "hard", + "prompt": "Exploring the depths of human consciousness, philosophers have long debated the nature of reality and perception. The concept of subjective experience suggests that" + }, + { + "difficulty": "hard", + "prompt": "Advancements in quantum computing promise to revolutionize various industries by solving complex problems at unprecedented speeds. However, one of the major challenges that remains is" + }, + { + "difficulty": "hard", + "prompt": "The structure of DNA is a double helix, consisting of two strands that wind around each other. This discovery revolutionized our understanding of genetics and" + }, + { + "difficulty": "hard", + "prompt": "Photosynthesis is the process by which plants convert light energy into chemical energy, enabling them to produce food. This process is vital for life on Earth and" + }, + { + "difficulty": "hard", + "prompt": "Black holes are regions of spacetime where gravity is so strong that nothing, not even light, can escape. They are formed when massive stars collapse, and their study provides insight into" + }, + { + "difficulty": "hard", + "prompt": "A farmer has to cross a river with a wolf, a goat, and a cabbage. He can only take one item across at a time. If left alone together, the wolf will eat the goat, and the goat will eat the cabbage. How can he safely get all three across the river?" + }, + { + "difficulty": "hard", + "prompt": "You are in a room with two doors. One door leads to certain doom, and the other leads to freedom. There are two guards, one who always tells the truth and one who always lies. You can ask one question to determine which door is safe. What do you ask?" + }, + { + "difficulty": "hard", + "prompt": "There are three light switches outside a room. Inside the room, there are three light bulbs. You can only enter the room once. How can you determine which switch controls which light bulb?" + }, + { + "difficulty": "hard", + "prompt": "A prisoner is told: 'If you tell a lie, you will be hanged. If you tell the truth, you will be shot.' What can he say to avoid being hanged or shot?" + }, + { + "difficulty": "hard", + "prompt": "There are two identical jugs, one with a capacity of 5 liters and one with a capacity of 3 liters. How can you measure exactly 4 liters using only these two jugs and an unlimited water supply?" + }, + { + "difficulty": "hard", + "prompt": "A man is looking at a picture of someone. His friend asks, 'Who is that?' The man replies, 'Brothers and sisters, I have none. But that man's father is my father's son.' Who is the man in the picture?" + }, + { + "difficulty": "hard", + "prompt": "You have 9 coins, one of which is slightly heavier than the others. You have a balance scale, and you can only use it twice. How can you determine which coin is the heavier one?" + } + ] + + + +# taken from https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/hendrycks_math/utils.py +from typing import Dict, List + +import datasets + + +def process_docs(dataset: datasets.Dataset) -> datasets.Dataset: + def _process_doc(doc: dict) -> dict: + out_doc = { + "problem": doc["problem"], + "solution": doc["solution"], + "answer": remove_boxed(last_boxed_only_string(doc["solution"])), + } + return out_doc + + return dataset.map(_process_doc) + + +def process_results(doc: dict, results: List[str]) -> Dict[str, int]: + retval = 0 + indices = [pos for pos, char in enumerate(results[0]) if char == "$"] + if len(indices) <= 1: + answer = results[0] + else: + answer = results[0][indices[0] + 1 : indices[-1]] + + if is_equiv(answer, remove_boxed(last_boxed_only_string(doc["solution"]))): + retval = 1 + + results = { + "exact_match": retval, + } + return results + + +# string normalization from https://github.com/EleutherAI/lm-evaluation-harness/blob/master/lm_eval/tasks/hendrycks_math.py +def is_equiv(str1, str2, verbose=False): + if str1 is None and str2 is None: + print("WARNING: Both None") + return True + if str1 is None or str2 is None: + return False + + try: + ss1 = strip_string(str1) + ss2 = strip_string(str2) + if verbose: + print(ss1, ss2) + return ss1 == ss2 + except Exception: + return str1 == str2 + + +def remove_boxed(s): + if "\\boxed " in s: + left = "\\boxed " + assert s[: len(left)] == left + return s[len(left) :] + + left = "\\boxed{" + + assert s[: len(left)] == left + assert s[-1] == "}" + + return s[len(left) : -1] + + +def last_boxed_only_string(string): + idx = string.rfind("\\boxed") + if "\\boxed " in string: + return "\\boxed " + string.split("\\boxed ")[-1].split("$")[0] + if idx < 0: + idx = string.rfind("\\fbox") + if idx < 0: + return None + + i = idx + right_brace_idx = None + num_left_braces_open = 0 + while i < len(string): + if string[i] == "{": + num_left_braces_open += 1 + if string[i] == "}": + num_left_braces_open -= 1 + if num_left_braces_open == 0: + right_brace_idx = i + break + i += 1 + + if right_brace_idx is None: + retval = None + else: + retval = string[idx : right_brace_idx + 1] + + return retval + + +def fix_fracs(string): + substrs = string.split("\\frac") + new_str = substrs[0] + if len(substrs) > 1: + substrs = substrs[1:] + for substr in substrs: + new_str += "\\frac" + if substr[0] == "{": + new_str += substr + else: + try: + assert len(substr) >= 2 + except AssertionError: + return string + a = substr[0] + b = substr[1] + if b != "{": + if len(substr) > 2: + post_substr = substr[2:] + new_str += "{" + a + "}{" + b + "}" + post_substr + else: + new_str += "{" + a + "}{" + b + "}" + else: + if len(substr) > 2: + post_substr = substr[2:] + new_str += "{" + a + "}" + b + post_substr + else: + new_str += "{" + a + "}" + b + string = new_str + return string + + +def fix_a_slash_b(string): + if len(string.split("/")) != 2: + return string + a = string.split("/")[0] + b = string.split("/")[1] + try: + a = int(a) + b = int(b) + assert string == "{}/{}".format(a, b) + new_string = "\\frac{" + str(a) + "}{" + str(b) + "}" + return new_string + except AssertionError: + return string + + +def remove_right_units(string): + # "\\text{ " only ever occurs (at least in the val set) when describing units + if "\\text{ " in string: + splits = string.split("\\text{ ") + assert len(splits) == 2 + return splits[0] + else: + return string + + +def fix_sqrt(string): + if "\\sqrt" not in string: + return string + splits = string.split("\\sqrt") + new_string = splits[0] + for split in splits[1:]: + if split[0] != "{": + a = split[0] + new_substr = "\\sqrt{" + a + "}" + split[1:] + else: + new_substr = "\\sqrt" + split + new_string += new_substr + return new_string + + +def strip_string(string): + # linebreaks + string = string.replace("\n", "") + + # remove inverse spaces + string = string.replace("\\!", "") + + # replace \\ with \ + string = string.replace("\\\\", "\\") + + # replace tfrac and dfrac with frac + string = string.replace("tfrac", "frac") + string = string.replace("dfrac", "frac") + + # remove \left and \right + string = string.replace("\\left", "") + string = string.replace("\\right", "") + + # Remove circ (degrees) + string = string.replace("^{\\circ}", "") + string = string.replace("^\\circ", "") + + # remove dollar signs + string = string.replace("\\$", "") + + # remove units (on the right) + string = remove_right_units(string) + + # remove percentage + string = string.replace("\\%", "") + string = string.replace("\%", "") # noqa: W605 + + # " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string + string = string.replace(" .", " 0.") + string = string.replace("{.", "{0.") + # if empty, return empty string + if len(string) == 0: + return string + if string[0] == ".": + string = "0" + string + + # to consider: get rid of e.g. "k = " or "q = " at beginning + if len(string.split("=")) == 2: + if len(string.split("=")[0]) <= 2: + string = string.split("=")[1] + + # fix sqrt3 --> sqrt{3} + string = fix_sqrt(string) + + # remove spaces + string = string.replace(" ", "") + + # \frac1b or \frac12 --> \frac{1}{b} and \frac{1}{2}, etc. Even works with \frac1{72} (but not \frac{72}1). Also does a/b --> \\frac{a}{b} + string = fix_fracs(string) + + # manually change 0.5 --> \frac{1}{2} + if string == "0.5": + string = "\\frac{1}{2}" + + # NOTE: X/Y changed to \frac{X}{Y} in dataset, but in simple cases fix in case the model output is X/Y + string = fix_a_slash_b(string) + + return string \ No newline at end of file diff --git a/evals/benchmarks/yield_functions.py b/evals/benchmarks/yield_functions.py new file mode 100644 index 00000000..5a8384cf --- /dev/null +++ b/evals/benchmarks/yield_functions.py @@ -0,0 +1,455 @@ +""" +Load a benchmark loader, given the benchmark name. +""" +import numpy as np +from datasets import load_dataset +from tqdm import tqdm +from typing import Optional, List + +def get_idx_list(dataset_length, num_samples, seed=None, verbose=True): + """ + Given the dataset length and the number of samples, + return a list of indices to sample from the dataset + """ + # re-set seed every time for consistency + if seed: + np.random.seed(42) + idx_list = np.random.choice( + dataset_length, + dataset_length if num_samples is None else min(num_samples, dataset_length), + replace=False, + ).tolist() + + if verbose: + idx_list = tqdm(idx_list, desc="Evaluating samples") + + return idx_list + +def load_arc_easy(version, num_samples=None, seed=None): + """ + Load ARC easy eval set + (https://huggingface.co/datasets/allenai/ai2_arc/viewer/ARC-Easy) + """ + if version == "original": + dataset = load_dataset("ai2_arc", "ARC-Easy", trust_remote_code=True)["test"] + elif version == "stlm_eval": + raise NotImplementedError("STLM eval version not implemented yet") + + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + correct_idx = sample["choices"]["label"].index(sample["answerKey"]) + """correct_idx = next( + (i for i, choice in enumerate(sample["choices"]["text"]) if choice == sample["answerKey"]), + None + )""" + yield ( + sample["question"], + sample["choices"]["text"][correct_idx], + [ + sample["choices"]["text"][i] + for i in range(len(sample["choices"]["text"])) + if i != correct_idx + ], + ) + +def load_blimp(num_samples=None, seed=None): + """ + Load BLIMP eval set + https://huggingface.co/datasets/WillHeld/blimp + """ + dataset = load_dataset("WillHeld/blimp", trust_remote_code=True)["train"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + yield ( + "", + sample["sentence_good"], + [sample["sentence_bad"]], + ) + + +def load_hellaswag(version, num_samples=None, seed=None): + """ + Load hellaswag eval set + https://huggingface.co/datasets/Rowan/hellaswag + """ + if version == "original": + dataset = load_dataset("Rowan/hellaswag", trust_remote_code=True)["validation"] # standard to use val + elif version == "stlm_eval": + raise NotImplementedError("STLM eval version not implemented yet") + + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + ground_truth_idx = int(sample["label"]) + yield ( + sample["ctx"], + sample["endings"][ground_truth_idx], + [ending for i,ending in enumerate(sample["endings"]) if i != ground_truth_idx], + ) + + +def load_mmlu(num_samples=None, seed=None): + """ + Load MMLU eval set + https://huggingface.co/datasets/cais/mmlu + """ + dataset = load_dataset("cais/mmlu", "all", trust_remote_code=True)["test"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + correct_idx = sample["answer"] + yield ( + sample["question"], + f" Answer: {sample['choices'][correct_idx]}", + [f" Answer: {choice}" for i, choice in enumerate(sample["choices"]) if i != correct_idx], + ) + +def load_winogrande(version, num_samples=None, seed=None): + """ + Load Winogrande eval set + https://huggingface.co/datasets/allenai/winogrande + """ + if version == "original": + dataset = load_dataset( + "allenai/winogrande", + "winogrande_xs", + trust_remote_code=True + )["validation"] + elif version == "stlm_eval": + raise NotImplementedError("STLM eval version not implemented yet") + + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + correct_opt_num = sample["answer"] + yield ( + "", + sample["sentence"].replace("_", sample[f"option{correct_opt_num}"]), + [sample["sentence"].replace("_", sample[f"option{1 if correct_opt_num == 2 else 2}"])] + ) + +def load_truthful_qa_m2(version, num_samples=None, seed=None): + """ + Load the truthful QA eval set + https://huggingface.co/datasets/TruthfulQA + """ + if version == "original": + dataset = load_dataset("truthful_qa", "multiple_choice", trust_remote_code=True)["validation"] + elif version == "stlm_eval": + raise NotImplementedError("STLM eval version not implemented yet") + + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + yield ( + sample["question"], + sample["correct_answer"], + sample["incorrect_answers"] + ) + +def load_piqa(num_samples=None, seed=None): + """ + Load the PIQA eval set + https://arxiv.org/abs/1911.11641 + https://huggingface.co/datasets/ybisk/piqa + """ + dataset = load_dataset("ybisk/piqa", trust_remote_code=True)["validation"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + yield ( + sample["goal"], + sample[f"sol{sample['label']+1}"], + [sample[f"sol{1 if sample['label'] == 2 else 2}"]] + ) + +def load_boolq(num_samples=None, seed=None): + """ + Load the BoolQ eval set + https://arxiv.org/abs/1905.10044 + https://huggingface.co/datasets/google/boolq + """ + dataset = load_dataset("google/boolq", trust_remote_code=True)["validation"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + yield ( + sample["question"], + "yes" if sample["answer"] else "no", + ["no"] if sample["answer"] else ["yes"] + ) + +def load_race(version, num_samples=None, seed=None): + """ + Load the RACE eval set + https://aclanthology.org/D17-1082/ + https://huggingface.co/datasets/ehovy/race + """ + ANS_TO_IDX = {"A": 0, "B": 1, "C": 2, "D": 3} + dataset = load_dataset( + "ehovy/race", + version, # middle or high school + trust_remote_code=True + )["validation"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + correct_idx = ANS_TO_IDX[sample["answer"]] + yield ( + sample["article"] + f"Question: {sample['question']}", + f"Answer: {sample['options'][correct_idx]}", + #sample["options"][correct_idx], + [option for i, option in enumerate(sample["options"]) if i != correct_idx] + ) + +def load_openbook_qa(version, num_samples=None, seed=None): + """ + Load the OpenbookQA eval set + https://huggingface.co/datasets/allenai/openbookqa + """ + ANS_TO_IDX = {"A": 0, "B": 1, "C": 2, "D": 3} + dataset = load_dataset("allenai/openbookqa", "additional", trust_remote_code=True)["validation"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + correct_idx = ANS_TO_IDX[sample["answerKey"]] + yield ( + sample["question_stem"] if version=="closed" else f"{sample['fact1']} {sample['question_stem']}", + sample["choices"]["text"][correct_idx], + [choice for i, choice in enumerate(sample["choices"]["text"]) if i != correct_idx] + ) + +def load_copa(num_samples=None, seed=None): + """ + Load the Copa eval set (balanced) + https://aclanthology.org/S12-1052/ + https://huggingface.co/datasets/pkavumba/balanced-copa + """ + dataset = load_dataset("pkavumba/balanced-copa", trust_remote_code=True)["train"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + correct_idx = sample["label"] + incorrect_idx = 1 if correct_idx == 0 else 0 + yield ( + sample["premise"], + sample[f"choice{correct_idx+1}"], + [sample[f"choice{incorrect_idx+1}"]], + ) + + +def load_commonsense_qa(num_samples=None, seed=None): + """ + Load the Commonsense QA eval set + https://aclanthology.org/N19-1421/ + https://huggingface.co/datasets/tau/commonsense_qa + """ + ANS_TO_IDX = {"A": 0, "B": 1, "C": 2, "D": 3, "E": 4, "F": 5, "G": 6, "H": 7} + + dataset = load_dataset("tau/commonsense_qa", trust_remote_code=True)["validation"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + correct_idx = ANS_TO_IDX[sample["answerKey"]] + yield ( + sample["question"], + f"Answer: {sample['choices']['text'][correct_idx]}", + [f"Answer: {choice}" for i, choice in enumerate(sample["choices"]["text"]) if i != correct_idx] + ) + +def load_ewok(num_samples=None, seed=None): + """ + Load the Ewok eval set + https://arxiv.org/abs/2405.09605v1 + https://huggingface.co/datasets/ewok-core + """ + dataset = load_dataset("ewok-core/ewok-core-1.0", trust_remote_code=True)["test"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + yield( + sample["Context1"], + sample["Target1"], + [sample["Target2"]] + ) + + +# Free Form Eval yield functions +def load_gsm8k(num_samples=None, seed=None): + """ + Load the GSM8K eval set + https://huggingface.co/datasets/gsm8k + + Args: + num_samples (Optional[int]): Number of samples to load. If None, load all. + seed (Optional[int]): Seed for random sampling. + + Yields: + Tuple[str, str, List[str]]: (question, answer) + """ + dataset = load_dataset("gsm8k", "main", trust_remote_code=True)["test"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + yield ( + sample["question"], + sample["answer"], + ) + + + +def load_math(num_samples=None, seed=None): + """ + Load the MATH eval set + https://huggingface.co/datasets/lighteval/MATH + + Args: + num_samples (Optional[int]): Number of samples to load. If None, load all. + seed (Optional[int]): Seed for random sampling. + + Yields: + Tuple[str, str, List[str]]: (question, answer) + """ + dataset = load_dataset("lighteval/MATH", "all", trust_remote_code=True)["test"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + yield ( + sample["problem"], + sample["solution"], + ) + + +def load_drop(num_samples=None, seed=None): + """ + Load the DROP eval set + https://huggingface.co/datasets/drop + + Args: + num_samples (Optional[int]): Number of samples to load. If None, load all. + seed (Optional[int]): Seed for random sampling. + + Yields: + Tuple[str, str, List[str]]: (question + passage, answer) + """ + dataset = load_dataset("drop", "drop", trust_remote_code=True)["validation"] + index_list = get_idx_list( + dataset_length=len(dataset), + num_samples=num_samples, + seed=seed + ) + for i in index_list: + sample = dataset[i] + combined_question = f"Passage: {sample['passage']} Question: {sample['question']}" + yield ( + combined_question, + sample["answer"], + ) + + + + +# Text Modeling yield functions +def load_stlm_synthetic_text_modeling( + topics: Optional[List[str]] = None, + difficulties: Optional[List[str]] = None, + verbose: Optional[bool] = True, +): + """ + Load the STLM text modeling evaluation set and optionally subsample based on topic and difficulty. + + Args: + topic (Optional[List[str]]): List of topics to include. If None, all topics are included. + difficulty (Optional[List[str]]): List of difficulty levels to include. If None, all difficulty levels are included. + + Yields: + str: Text samples from the dataset that match the specified criteria. + + Dataset Source: + https://huggingface.co/datasets/SuperTinyLanguageModels/text-modeling-eval + """ + # Load the dataset + dataset = load_dataset("SuperTinyLanguageModels/text-modeling-eval")["train"] + + # Apply filtering based on topics if provided + if topics is not None: + dataset = dataset.filter(lambda example: example["topic"] in topics) + + # Apply filtering based on difficulties if provided + if difficulties is not None: + dataset = dataset.filter(lambda example: example["difficulty"] in difficulties) + + if verbose: + iterator = tqdm(dataset, desc="Evaluating Text Modeling samples") + else: + iterator = dataset + + # Yield the 'text' field from each filtered sample + for sample in iterator: + yield sample["text"] + + + +# Text Generation yield functions \ No newline at end of file diff --git a/evals/core.py b/evals/core.py new file mode 100644 index 00000000..8a7ac95c --- /dev/null +++ b/evals/core.py @@ -0,0 +1,28 @@ +from typing import Dict, Union, Any + +class BaseEvaluator: + """Base class for all evaluators.""" + + def __init__(self) -> None: + # for better logging + self.eval_metric: str = ... + self.eval_logging_path: str = ... + + def set_env_id(self, env_id: str) -> None: + """ TODO """ + self.env_id = env_id + + def evaluate(self, model): # -> Dict[str: Any]: + """Each evaluator must implement its own evaluate method.""" + raise NotImplementedError("Each evaluator must implement its own evaluate method.") + + +class BaseModelWrapper: + """ Base class for all model wrapper. """ + + def __init__(self): + pass + + def __call__(self, **kwargs): + """ pass the forward call through the model """ + raise NotImplementedError("Each ModelWrapper must implement its own call method.") \ No newline at end of file diff --git a/evals/eval_wrapper.py b/evals/eval_wrapper.py deleted file mode 100644 index 04425e0c..00000000 --- a/evals/eval_wrapper.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Integration code""" - -import torch - -from models import embedding_models, generator, model_shell - - -def batch(data, batch_size): - for i in range(0, len(data), batch_size): - yield data[i : i + batch_size] - - -class EvalWrapper: - def __init__(self, model_shell: model_shell.ModelShell): - self.model_shell = model_shell - super().__init__() - - def loglikelihood(self, prefixes, continuations) -> list[float]: - """ - Compute the loglikelihood of given inputs - """ - device_str = "cuda" if torch.cuda.is_available() else "cpu" - device = torch.device(device_str) - self.model_shell = self.model_shell.to(device) - results = [] - with torch.no_grad(): - with torch.autocast(device_type=device_str): - for prefix_batch, cont_batch in zip( - batch(prefixes, 32), batch(continuations, 32) - ): - ll = self.model_shell.loglikelihood(prefix_batch, cont_batch) - results.extend(ll.cpu().numpy()) - return results - - def generate(self, prefixes) -> list[str]: - """ - Generate a continuation for a given prefix - """ - model_generator = generator.StandardGenerator( - self.model_shell, - generate_cfg={ - "max_new_tokens": 128, - "temperature": 0.7, - "top_k": 0.9, - }, - ) - for prefix in prefixes: - # tokenize the inputs - yield model_generator.default_generate(prefix) diff --git a/evals/evaluator_interface.py b/evals/evaluator_interface.py deleted file mode 100644 index 7211747f..00000000 --- a/evals/evaluator_interface.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Defines the EvaluatorInterface class.""" - - -class EvaluationInterface: - """Interface for evaluating a model.""" - - def __init__(self, model): - pass - - def evaluate(self): - """Evaluate the model performance on a list - of benchmarks.""" - raise NotImplementedError() diff --git a/evals/evaluators/__init__.py b/evals/evaluators/__init__.py new file mode 100644 index 00000000..d18050dc --- /dev/null +++ b/evals/evaluators/__init__.py @@ -0,0 +1,3 @@ +from evals.evaluators.mcq_evaluator import MCQEvaluator +from evals.evaluators.text_modeling_evaluator import TextModelingEvaluator +from evals.evaluators.free_form_evaluator import FreeFormEvaluator \ No newline at end of file diff --git a/evals/evaluators/free_form_evaluator.py b/evals/evaluators/free_form_evaluator.py new file mode 100644 index 00000000..35017abe --- /dev/null +++ b/evals/evaluators/free_form_evaluator.py @@ -0,0 +1,76 @@ +from evals.benchmarks.yield_functions import * +from models.generators import BaseGenerator + +from evals.core import BaseEvaluator, BaseModelWrapper +from typing import Optional, Callable, Dict, Any + +import sympy + +class FreeFormEvaluator(BaseEvaluator): + """ Evaluator for free form questions. """ + + def __init__( + self, + yield_fn: Callable, + answer_extraction_function: Callable, + model_wrapper: BaseModelWrapper, + model_generator, #: Optional[BaseGenerator] = None, # if no generated is provided, the model must be wrapped outside + generator_params: Optional[Dict[str, Any]] = None, + yield_fn_params: Optional[Dict[str, Any]] = None, + eval_logging_path: Optional[str] = "FreeForm" + ): + super().__init__() + """ TODO """ + self.eval_logging_path = eval_logging_path + self.yield_fn = yield_fn(**yield_fn_params) + self.generator_params = generator_params + self.extract_answer = answer_extraction_function + self.model_wrapper = model_wrapper + self.model_generator = model_generator + + def _compare_math_answers(self, true_answer: str, model_answer: str) -> bool: + """ + Compares two mathematical expressions for equivalence. + """ + try: + true_expr = sympy.sympify(true_answer) + model_expr = sympy.sympify(model_answer) + return sympy.simplify(true_expr - model_expr) == 0 + except (sympy.SympifyError, TypeError): + # Fallback to string comparison + return true_answer.strip() == model_answer.strip() + + + def evaluate(self, model): + """ TODO """ + # wrap the model + model = self.model_wrapper( + model=model, + model_generator=self.model_generator, + generator_params=self.generator_params + ) + total, correct = 0, 0 + + for question, answer in self.yield_fn: + # generate model's answer + generated_answer = model(question) + + + # extract final answers + true_answer = self.extract_answer(answer) + model_answer = self.extract_answer(generated_answer) + + # comopare answers (numerical comparison) + if self._compare_math_answers( + true_answer=true_answer, + model_answer=model_answer + ): + correct += 1 + total += 1 + + + accuracy = correct / total if total > 0 else 0 + return { + f"{self.eval_logging_path}/{self.env_id} (Acc.)": accuracy + } + diff --git a/evals/evaluators/mcq_evaluator.py b/evals/evaluators/mcq_evaluator.py new file mode 100644 index 00000000..bb69bf6c --- /dev/null +++ b/evals/evaluators/mcq_evaluator.py @@ -0,0 +1,43 @@ +from evals.benchmarks.yield_functions import * + +from evals.core import BaseEvaluator, BaseModelWrapper + +from typing import Optional, Callable, Dict, Any + +class MCQEvaluator(BaseEvaluator): + """Evaluator for multiple-choice questions.""" + + def __init__( + self, + yield_fn: Callable, + model_wrapper: BaseModelWrapper, + yield_fn_params: Optional[Dict[str, Any]] = None, + eval_logging_path: Optional[str] = "MCQ", + ): + super().__init__() + """ TODO """ + self.eval_logging_path = eval_logging_path + self.yield_fn = yield_fn(**yield_fn_params) + self.model_wrapper = model_wrapper + + + def evaluate(self, model): + """ TODO """ + model = self.model_wrapper(model) # wrap the model + + total, correct = 0, 0 + for prefix, ground_truth, false_options in self.yield_fn: + # Prediction logic + loglikelihoods = model( + prefixes=[prefix] * (len(false_options) + 1), + continuations=[ground_truth] + false_options + ) + if loglikelihoods.index(max(loglikelihoods)) == 0: + correct += 1 + total += 1 + accuracy = correct / total if total > 0 else 0 + return { + f"{self.eval_logging_path}/{self.env_id} (Acc.)": accuracy + } + + diff --git a/evals/evaluators/text_generation_evaluator.py b/evals/evaluators/text_generation_evaluator.py new file mode 100644 index 00000000..f3a2353c --- /dev/null +++ b/evals/evaluators/text_generation_evaluator.py @@ -0,0 +1,16 @@ +import torch +from evals.core import BaseModelWrapper +from typing import Callable, List, Dict, Any + +class TextGenerationEvaluator: + def __init__( + self, + model_wrapper: BaseModelWrapper, + yield_fn: Callable, + yield_fn_params: Optional[Dict[str, Any]] = None, + eval_metric: Optional[str] = "LLM-PPL", + eval_logging_path: Optional[str] = "Text Generation" + ): + super().__init__() + pass + # TODO \ No newline at end of file diff --git a/evals/evaluators/text_modeling_evaluator.py b/evals/evaluators/text_modeling_evaluator.py new file mode 100644 index 00000000..d44e6268 --- /dev/null +++ b/evals/evaluators/text_modeling_evaluator.py @@ -0,0 +1,88 @@ +from evals.core import BaseEvaluator, BaseModelWrapper +from typing import Optional, Callable, Dict, Any +from tqdm import tqdm +import torch + +# Define the metrics and their computations +METRIC_EVALUATIONS = { + "Byte Accuracy": lambda results_dict: ( + results_dict["total_correct_bytes"] / results_dict["total_bytes"] + if results_dict["total_bytes"] > 0 else 0.0 + ), + "Byte Perplexity": lambda results_dict: ( + torch.exp(torch.tensor(results_dict["total_loss"] / results_dict["total_tokens"])).item() + if results_dict["total_tokens"] > 0 else float('inf') + ), + "Byte Levenshtein": lambda results_dict: ( + results_dict["total_edit_distance"] / results_dict["total_bytes"] + if results_dict["total_bytes"] > 0 else float('inf') + ), +} + +class TextModelingEvaluator(BaseEvaluator): + """Evaluator for text modeling capabilities.""" + + def __init__( + self, + model_wrapper: BaseModelWrapper, + yield_fn: Callable, + yield_fn_params: Optional[Dict[str, Any]] = None, + chunk_size: Optional[int] = 100, + eval_logging_path: Optional[str] = "Text Modeling" + ): + super().__init__() + self.eval_logging_path = eval_logging_path + self.model_wrapper = model_wrapper + self.yield_fn = yield_fn(**(yield_fn_params or {})) + self.chunk_size = chunk_size + + + def evaluate(self, model): + """ + Evaluate the model's text modeling capabilities. + + Args: + model: The model to be evaluated. + + Returns: + Dict[str, float]: A dictionary with the evaluation results. + """ + # Wrap the model using the provided model wrapper + model = self.model_wrapper(model, chunk_size=self.chunk_size) + + results = { + "total_bytes": 0, + "total_correct_bytes": 0, + "total_edit_distance": 0, + "total_loss": 0.0, + "total_tokens": 0 + } + + # Iterate over the reference texts + iterator = self.yield_fn + + for reference_text in tqdm(iterator, desc="Evaluating Text Modeling"): + # The wrapped model will return a dict with + # bytes, correct_bytes, edit_distance, loss, tokens + local_results = model(reference_text) + # Expected to return: + # { + # 'edit_distance': total_edit_distance, + # 'correct_bytes': total_correct_bytes, + # 'bytes': total_bytes, + # 'loss': total_loss, + # 'tokens': total_tokens + # } + + # Accumulate results + results["total_bytes"] += local_results["bytes"] + results["total_correct_bytes"] += local_results["correct_bytes"] + results["total_edit_distance"] += local_results["edit_distance"] + results["total_loss"] += local_results["loss"] + results["total_tokens"] += local_results["tokens"] + + return { + f"{self.eval_logging_path}/Byte Accuracy": METRIC_EVALUATIONS["Byte Accuracy"](results), + f"{self.eval_logging_path}/Byte Perplexity": METRIC_EVALUATIONS["Byte Perplexity"](results), + f"{self.eval_logging_path}/Byte Levenshtein": METRIC_EVALUATIONS["Byte Levenshtein"](results), + } diff --git a/evals/finetuning/__init__.py b/evals/finetuning/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/evals/finetuning/glue.py b/evals/finetuning/glue.py deleted file mode 100644 index 9e4e16e1..00000000 --- a/evals/finetuning/glue.py +++ /dev/null @@ -1,142 +0,0 @@ -"""GLUE eval?""" -from datasets import load_dataset -from sklearn.linear_model import LogisticRegression, Ridge -from sklearn.metrics import accuracy_score, f1_score, matthews_corrcoef -from scipy.stats import pearsonr, spearmanr - -import torch -import numpy as np - -from models.model_shell import ModelShell -from evals.evaluator_interface import EvaluationInterface - -GLUE_SUBSETS = [ - "cola", - # "mnli", - "mrpc", - # "qnli", - # "qqp", - "rte", - # "sst2", - "stsb", - "wnli", -] -GLUE_SENTENCE_MAPPINGS = { - "cola": ["sentence", 2], - "mnli": ["premise", "hypothesis", 3], - "mrpc": ["sentence1", "sentence2", 2], - "qnli": ["question", "sentence", 2], - "qqp": ["question1", "question2", 2], - "rte": ["sentence1", "sentence2", 2], - "sst2": ["sentence", 2], - "stsb": ["sentence1", "sentence2", 1], - "wnli": ["sentence1", "sentence2", 2], -} -GLUE_METRIC = { - "cola": ["matthews_correlation"], - "sst2": ["accuracy"], - "mrpc": ["accuracy", "f1"], - "stsb": ["pearson", "spearmanr"], - "qqp": ["accuracy", "f1"], - "mnli": ["accuracy"], - "qnli": ["accuracy"], - "rte": ["accuracy"], - "wnli": ["accuracy"], -} - -METRIC_MAP = { - "accuracy": accuracy_score, - "f1": f1_score, - "pearson": pearsonr, - "spearmanr": spearmanr, - "matthews_correlation": lambda a, b: matthews_corrcoef(b, a), - # matthews_corrcoef expects the labels to be the second argument - # for some reason -} - - -class FinetuningEvaluator(EvaluationInterface): - """Evaluator class for training and evaluating models. - Currently just for GLUE finetuning.""" - - def __init__(self, model): - super().__init__(model) - self.model: ModelShell = model - self.train_datasets = {} - self.eval_datasets = {} - self.load_dataset() - - def load_dataset(self): - """Load the dataset for finetuning the model""" - for subset in GLUE_SUBSETS: - dataset = load_dataset("glue", subset) - self.train_datasets[subset] = dataset["train"] - validation_str = "validation_matched" if subset == "mnli" else "validation" - self.eval_datasets[subset] = dataset[validation_str] - - def evaluate(self, *args, **kwargs): - """Evaluate the base model on the dataset""" - results = {} - for subset in GLUE_SUBSETS: - model = self.train(subset) - results[subset] = self.evaluate_model(model, subset) - return results - - def train(self, subset): - """Train SKLearn model on the dataset""" - train_features, train_labels = self.extract_subset(subset, "train") - if subset == "stsb": - model = Ridge() - else: - model = LogisticRegression(max_iter=1000) - model.fit(train_features, train_labels) - return model - - def evaluate_model(self, model, subset): - """Evaluate sklearn model""" - results = {} - eval_features, eval_labels = self.extract_subset(subset, "validation") - - predictions = model.predict(eval_features) - for metric in GLUE_METRIC[subset]: - metric_func = METRIC_MAP[metric] - if metric == "pearson": - metric_val = metric_func(eval_labels, predictions)[0] - elif metric == "spearmanr": - metric_val = metric_func(eval_labels, predictions)[1] - else: - metric_val = metric_func(eval_labels, predictions) - results[metric] = metric_val - return results - - def extract_subset(self, subset, split): - """Extract all features and labels from the dataset""" - subset_features = [] - subset_labels = [] - - dataset = self.train_datasets if split == "train" else self.eval_datasets - - for sample in dataset[subset]: - sample_struct = GLUE_SENTENCE_MAPPINGS[subset] - features, label = self.extract_features( - self.embedding_func, sample_struct, sample - ) - features = features.cpu().numpy() - subset_features.append(features) - subset_labels.append(label) - return subset_features, subset_labels - - @torch.no_grad() - def embedding_func(self, input_str): - """Embed the input string""" - init_embeds = self.model.embedding_model.inference(input_str) - embeds = self.model.core_model(init_embeds) - embeds = embeds.mean(dim=1) # mean pooling - return embeds[0] - - def extract_features(self, embedding_func, sample_struct, sample): - """Extract features from a sample""" - strs = [sample[sample_struct[i]] for i in range(len(sample_struct) - 1)] - embeddings = embedding_func("\n".join(strs)) - label = sample["label"] - return embeddings, label \ No newline at end of file diff --git a/evals/finetuning/qa.py b/evals/finetuning/qa.py deleted file mode 100644 index 9b73ad4d..00000000 --- a/evals/finetuning/qa.py +++ /dev/null @@ -1,98 +0,0 @@ -"""Training a QA model with finetuning on """ - -import random - -import numpy as np -import torch -from sklearn.linear_model import LogisticRegression -from sklearn.metrics import accuracy_score - -from evals.evaluator_interface import EvaluationInterface -from evals.mcqs import load_benchmarks -from models.model_shell import ModelShell - - -def form_prompt(question, all_options): - """Form a prompt for the model""" - option_list = "\n".join( - [f"{i+1}. {option}" for i, option in enumerate(all_options)] - ) - return f"{question}\n{option_list}\nAnswer: " - - -class FinetuningQA(EvaluationInterface): - """Evaluator class for training and evaluating models. - Currently just for GLUE finetuning.""" - - def __init__(self, model, benchmarks, max_train_samples=1000, max_eval_samples=1000): - super().__init__(model) - self.model: ModelShell = model - self.train_datasets = {} - self.eval_datasets = {} - self.max_train_samples = max_train_samples - self.max_eval_samples = max_eval_samples - self.load_dataset(benchmarks) - - def load_dataset(self, qa_benchmarks): - """Load the dataset""" - for benchmark in qa_benchmarks: - train_dataset = load_benchmarks.load_benchmark(benchmark, split="train") - eval_dataset = load_benchmarks.load_benchmark(benchmark, split="validation") - self.train_datasets[benchmark] = train_dataset - self.eval_datasets[benchmark] = eval_dataset - - def evaluate(self): - """Evaluate the base model on the dataset""" - results = {} - for benchmark in self.train_datasets: - model = self.train(benchmark) - results[benchmark] = self.evaluate_model(model, benchmark) - return results - - def train(self, benchmark): - """Train SKLearn model on the dataset""" - train_features, train_labels = self.get_features_labels(benchmark, "train", self.max_train_samples) - model = LogisticRegression(max_iter=1000) - model.fit(train_features, train_labels) - return model - - def evaluate_model(self, model, benchmark): - """Evaluate sklearn model""" - print(f"Evaluating Benchmark: {benchmark}") - eval_features, eval_labels = self.get_features_labels(benchmark, "validation", self.max_eval_samples) - predictions = model.predict(eval_features) - accuracy = accuracy_score(eval_labels, predictions) - return accuracy - - def get_features_labels(self, benchmark, split, max_samples): - """Extract all features and labels from the dataset""" - benchmark_features = [] - benchmark_labels = [] - - dataset = self.train_datasets if split == "train" else self.eval_datasets - - for i, sample in enumerate(dataset[benchmark]): - if i >= max_samples: - break - features, label = self.extract_features(self.embedding_func, *sample) - features = features.cpu().numpy() - benchmark_features.append(features) - benchmark_labels.append(label) - return benchmark_features, benchmark_labels - - @torch.no_grad() - def embedding_func(self, input_str): - """Embed the input string""" - init_embeds = self.model.embedding_model.inference(input_str) - embeds = self.model.core_model(init_embeds)[0] - embeds = embeds[-1] # Take last one - return embeds - - def extract_features(self, embedding_func, question, answer, other_options): - """Extract features from a sample""" - placement = random.randint(0, len(other_options)) - options = other_options[:placement] + [answer] + other_options[placement:] - prompt = form_prompt(question, options) - embeddings = embedding_func(prompt) - label = placement - return embeddings, label diff --git a/evals/load_evaluators.py b/evals/load_evaluators.py deleted file mode 100644 index 5ce42ad8..00000000 --- a/evals/load_evaluators.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -Given an evaluator name, load the evaluator -""" - -from evals.evaluator_interface import EvaluationInterface -from evals.mcqs.mcq_evaluator import MCQEvaluator -from evals.finetuning.glue import FinetuningEvaluator -from evals.finetuning.qa import FinetuningQA -from evals.mcqs.benchmarks.stories_progression import ProgressionEvaluator - -EVALUATORS_DICT = { - "mcq": MCQEvaluator, - "glue": FinetuningEvaluator, - "ft_qa": FinetuningQA, - "prog": ProgressionEvaluator -} - - -def load_evaluator(evaluator_name, model, **kwargs) -> EvaluationInterface: - """ - Given the evaluator name, load the evaluator - """ - return EVALUATORS_DICT[evaluator_name](model, **kwargs) diff --git a/evals/mcqs/__init__.py b/evals/mcqs/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/evals/mcqs/benchmarks/__init__.py b/evals/mcqs/benchmarks/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/evals/mcqs/benchmarks/arc.py b/evals/mcqs/benchmarks/arc.py deleted file mode 100644 index fa9d9811..00000000 --- a/evals/mcqs/benchmarks/arc.py +++ /dev/null @@ -1,37 +0,0 @@ -"""ARC Benchmark: https://arxiv.org/abs/1803.05457""" - -import random - -from datasets import load_dataset - - -def _split_options(options, labels, answer_key): - """Split the options according to whether label matches answer key""" - true_idx = ... # assume there is only one correct option - for idx, label in enumerate(labels): - if label == answer_key: - true_idx = idx - return options[true_idx], options[:true_idx] + options[true_idx + 1 :] - - -def load_arc(split="test"): - """Load and process the benchmark - - Returns a geneator of: - (prompt, ground_truth, fake_options)""" - base_dataset = load_dataset("allenai/ai2_arc", "ARC-Easy")[split] - index = list(range(len(base_dataset))) - random.shuffle(index) - - for i in index: - sample = base_dataset[i] - ground_truth, fake_options = _split_options( - sample["choices"]["text"], - sample["choices"]["label"], - sample["answerKey"], - ) - yield ( - sample["question"], - ground_truth, - fake_options, - ) diff --git a/evals/mcqs/benchmarks/blimp.py b/evals/mcqs/benchmarks/blimp.py deleted file mode 100644 index c12b737b..00000000 --- a/evals/mcqs/benchmarks/blimp.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Blimp dataset https://aclanthology.org/2020.tacl-1.25/""" - -import random - -from datasets import load_dataset - -# OPTIONS = ['adjunct_island', 'anaphor_gender_agreement', 'anaphor_number_agreement', -# 'animate_subject_passive', 'animate_subject_trans', 'causative', -# 'complex_NP_island', 'coordinate_structure_constraint_complex_left_branch', -# 'coordinate_structure_constraint_object_extraction', 'determiner_noun_agreement_1', -# 'determiner_noun_agreement_2', 'determiner_noun_agreement_irregular_1', -# 'determiner_noun_agreement_irregular_2', 'determiner_noun_agreement_with_adj_2', -# 'determiner_noun_agreement_with_adj_irregular_1', 'determiner_noun_agreement_with_adj_irregular_2', -# 'determiner_noun_agreement_with_adjective_1', 'distractor_agreement_relational_noun', -# 'distractor_agreement_relative_clause', 'drop_argument', 'ellipsis_n_bar_1', -# 'ellipsis_n_bar_2', 'existential_there_object_raising', 'existential_there_quantifiers_1', -# 'existential_there_quantifiers_2', 'existential_there_subject_raising', -# 'expletive_it_object_raising', 'inchoative', 'intransitive', 'irregular_past_participle_adjectives', -# 'irregular_past_participle_verbs', 'irregular_plural_subject_verb_agreement_1', -# 'irregular_plural_subject_verb_agreement_2', 'left_branch_island_echo_question', -# 'left_branch_island_simple_question', 'matrix_question_npi_licensor_present', 'npi_present_1', -# 'npi_present_2', 'only_npi_licensor_present', 'only_npi_scope', 'passive_1', 'passive_2', -# 'principle_A_c_command', 'principle_A_case_1', 'principle_A_case_2', 'principle_A_domain_1', -# 'principle_A_domain_2', 'principle_A_domain_3', 'principle_A_reconstruction', -# 'regular_plural_subject_verb_agreement_1', 'regular_plural_subject_verb_agreement_2', -# 'sentential_negation_npi_licensor_present', 'sentential_negation_npi_scope', -# 'sentential_subject_island', 'superlative_quantifiers_1', 'superlative_quantifiers_2', -# 'tough_vs_raising_1', 'tough_vs_raising_2', 'transitive', 'wh_island', 'wh_questions_object_gap', -# 'wh_questions_subject_gap', 'wh_questions_subject_gap_long_distance', 'wh_vs_that_no_gap', -# 'wh_vs_that_no_gap_long_distance', 'wh_vs_that_with_gap', 'wh_vs_that_with_gap_long_distance'] - -# TOTAL SIZE OF 67K, need to split into train, test, val - -def load_blimp(split="test"): - """Load and process the benchmark""" - base_dataset = load_dataset("WillHeld/blimp")["train"] - index = list(range(len(base_dataset))) - random.shuffle(index) - ds_size = len(index) - INDEX_MAP = { - "train": (0, int(0.1 * ds_size)), - "test": (int(0.1 * ds_size), int(0.6 * ds_size)), - "validation": (int(0.6 * ds_size), ds_size), - } - for i in index: - sample = base_dataset[i] - if i not in range(*INDEX_MAP[split]): - continue - yield ("", sample["sentence_good"], [sample["sentence_bad"]]) diff --git a/evals/mcqs/benchmarks/hellaswag.py b/evals/mcqs/benchmarks/hellaswag.py deleted file mode 100644 index df48cc0a..00000000 --- a/evals/mcqs/benchmarks/hellaswag.py +++ /dev/null @@ -1,30 +0,0 @@ -"""Hella Swag Benchmark Code: https://arxiv.org/pdf/1905.07830.pdf""" - -import random - -from datasets import load_dataset - - -def load_hellaswag(split="test"): - """Load and process the benchmark""" - split_map = { - "test": "validation", - "train": "train", - "validation": "train", - } - split = split_map[split] - base_dataset = load_dataset("Rowan/hellaswag")[split] - index = list(range(len(base_dataset))) - if split == "train": - index = index[len(index)//2:] - elif split == "validation": - index = index[:len(index)//2] - random.shuffle(index) - for i in index: - sample = base_dataset[i] - ground_truth = sample["endings"][int(sample["label"])] - yield ( - sample["ctx"], - ground_truth, - [ending for ending in sample["endings"] if ending != ground_truth], - ) diff --git a/evals/mcqs/benchmarks/mmlu.py b/evals/mcqs/benchmarks/mmlu.py deleted file mode 100644 index b3824f13..00000000 --- a/evals/mcqs/benchmarks/mmlu.py +++ /dev/null @@ -1,37 +0,0 @@ -"""MMLU Benchmark: https://arxiv.org/abs/2009.03300""" - -import random - -from datasets import load_dataset - - -def option_prompt(choice, choices): - prompt = f"Options: {';'.join(choices)}\n Answer: {choice}" - return prompt - - -def load_mmlu(split="test"): - """Load and process the benchmark - - Returns a geneator of: - (prompt, ground_truth, fake_options)""" - base_dataset = load_dataset("cais/mmlu", "all") - if split == "train": - split = "dev" - index = list(range(len(base_dataset[split]))) - random.shuffle(index) - for i in index: - sample = base_dataset[split][i] - ground_truth = sample["choices"][sample["answer"]] - ground_truth = option_prompt(ground_truth, sample["choices"]) - fake_options = [ - choice for choice in sample["choices"] if choice != ground_truth - ] - fake_options = [ - option_prompt(choice, sample["choices"]) for choice in fake_options - ] - yield ( - sample["question"], - ground_truth, - fake_options, - ) diff --git a/evals/mcqs/benchmarks/stories_progression.py b/evals/mcqs/benchmarks/stories_progression.py deleted file mode 100644 index 3f6df773..00000000 --- a/evals/mcqs/benchmarks/stories_progression.py +++ /dev/null @@ -1,82 +0,0 @@ -"""The tinystories training progression dataset""" - -import random -from evals import evaluator_interface -from evals import eval_wrapper -from datasets import load_dataset -from evals.metrics import MCQ_METRIC_DICT -import torch - -TASKS = [ - "byte_shuffle", - "word_shuffle", - "ngram_shuffle_3", - "ngram_shuffle_5", - "ngram_shuffle_7", - "ngram_shuffle_12", - "random_byte_deletion", - "random_word_deletion", - "random_byte_insertion", - "random_word_insertion", - "random_byte_substitution", - "random_word_substitution", - "parse_tree_shuffle" -] - - -def option_prompt(choice, choices): - prompt = f"Options: {';'.join(choices)}\n Answer: {choice}" - return prompt - - -def load_stories_progression(split="test"): - """Load and process the benchmark - - Returns a geneator of: - (prompt, ground_truth, fake_options)""" - base_dataset = load_dataset("LeonGuertler/TinyStories_stlm_training_progress") - base_dataset = base_dataset["train"] - index = list(range(len(base_dataset))) - assert split in ["validation", "test"] - if split == "validation": - index = index[: int(0.5 * len(index))] - elif split == "test": - index = index[int(0.5 * len(index)) :] - random.shuffle(index) - for i in index: - sample = base_dataset[i] - prompt = sample["first_n_words"] - actual_continuation = sample["actual_continuation"] - options = { - task: sample[task] for task in TASKS - } - yield prompt, actual_continuation, options - -class ProgressionEvaluator(evaluator_interface.EvaluationInterface): - """Evaluator for the stories progression set""" - - def __init__(self, model): - super().__init__(model) - self.model_wrapper = eval_wrapper.EvalWrapper(model) - - def evaluate(self, split="test"): - """Evaluate the model on the stories progression dataset""" - dataset = load_stories_progression(split) - likelihoods = [] - results = {} - for prompt, ground_truth, options in dataset: - all_options = list(options.items()) - options = [ground_truth] + [option for _, option in all_options] # (B+1)*T - prompts = [prompt for _ in options] - likelihoods.append(self.model_wrapper.loglikelihood(prompts, options)) - likelihoods = torch.tensor(likelihoods) # shape B * T - gt = likelihoods[:,0] # shape B - for i, (task) in enumerate(TASKS): - task_results = {} - for metric_name, metric_func in MCQ_METRIC_DICT.items(): - metric_input = torch.stack([gt, likelihoods[:,i + 1]], dim=-1) - # shape B x 2 - task_results[metric_name]=(metric_func(metric_input)) - results[task] = task_results - return results - diff --git a/evals/mcqs/benchmarks/winogrande.py b/evals/mcqs/benchmarks/winogrande.py deleted file mode 100644 index e776c405..00000000 --- a/evals/mcqs/benchmarks/winogrande.py +++ /dev/null @@ -1,33 +0,0 @@ -"""Winogrande benchmark""" - -from datasets import load_dataset - -SPLIT_REMAP = {"test": "validation", "validation": "train", "train": "train"} -INDEX_MAP = {"1": 0, "2": 1, "3": 2, "4": 3, "A": 0, "B": 1, "C": 2, "D": 3, "E": 4} -import random - - -def load_winogrande(split="test"): - """Load and process the benchmark""" - base_dataset = load_dataset( - "allenai/winogrande", "winogrande_xs", trust_remote_code=True - )[SPLIT_REMAP[split]] - index = list(range(len(base_dataset))) - if split == "train": - index = index[: len(index) // 2] - elif split == "validation": - index = index[len(index) // 2 :] - random.shuffle(index) - for i in index: - sample = base_dataset[i] - sentence = sample["sentence"] - options = [sample["option1"], sample["option2"]] - ground_truth = options[INDEX_MAP[sample["answer"]]] - options.remove(ground_truth) - ground_truth = sentence.replace("_", ground_truth) - options = [sentence.replace("_", option) for option in options] - yield ( - "", - ground_truth, - options, - ) diff --git a/evals/mcqs/load_benchmarks.py b/evals/mcqs/load_benchmarks.py deleted file mode 100644 index 1e8f1a17..00000000 --- a/evals/mcqs/load_benchmarks.py +++ /dev/null @@ -1,24 +0,0 @@ -""" -Load a bechmark loader, given the benchmark name. -""" - -from evals.mcqs.benchmarks.arc import load_arc -from evals.mcqs.benchmarks.blimp import load_blimp -from evals.mcqs.benchmarks.hellaswag import load_hellaswag -from evals.mcqs.benchmarks.mmlu import load_mmlu -from evals.mcqs.benchmarks.winogrande import load_winogrande - -EVALS_DICT = { - "arc": load_arc, - "winograd": load_winogrande, - "mmlu": load_mmlu, - "hellaswag": load_hellaswag, - "blimp": load_blimp, -} - - -def load_benchmark(benchmark_name, split): - """ - Given the benchmark name, build the benchmark - """ - return EVALS_DICT[benchmark_name](split=split) diff --git a/evals/mcqs/mcq_evaluator.py b/evals/mcqs/mcq_evaluator.py deleted file mode 100644 index 113fd7b0..00000000 --- a/evals/mcqs/mcq_evaluator.py +++ /dev/null @@ -1,93 +0,0 @@ -""" -Evaluator class for evaluating models. -""" - -import torch -import tqdm - -from evals import eval_wrapper -from evals.evaluator_interface import EvaluationInterface -from evals.mcqs.load_benchmarks import load_benchmark -from evals.metrics import MCQ_METRIC_DICT - - -class MCQEvaluator(EvaluationInterface): - """ - Base Evaluator class the evaluates models - and prints/logs the results. - """ - - def __init__(self, model, num_samples=None, benchmarks=None): - self.model = model - self.wrapper = eval_wrapper.EvalWrapper(model) - self.num_samples = num_samples - self.benchmarks = benchmarks - # make sure the model is in eval model - self.model.eval() - - @torch.no_grad() - def predict(self, prefix, ground_truth, false_options): - """ - Given a prompt, use the model to predict the output - Returns the loglikelihood of the ground truth and the options - """ - prefixes = [prefix] * (len(false_options) + 1) - continuations = [ground_truth] + false_options - loglikelihoods = self.wrapper.loglikelihood(prefixes=prefixes, continuations=continuations) - loglikelihoods = torch.tensor(loglikelihoods) - return loglikelihoods - - def _calculate_metrics(self, confidences): - """ - Calculate the metrics for the model - """ - score_dict = {} - - for metric_name, metric in MCQ_METRIC_DICT.items(): - score_dict[metric_name] = metric(confidences) - - return score_dict - - def evaluate_benchmark(self, benchmark_name, num_samples=None): - """Evaluate model performance on a specific benchmark""" - # load the benchmark_loader - benchmark_loader = load_benchmark(benchmark_name, split="test") - confidences = [] - for i, (prefix, ground_truth, false_options) in tqdm.tqdm( - enumerate(benchmark_loader) - ): - if num_samples is not None and i > num_samples: - break - loglikelihoods = self.predict(prefix, ground_truth, false_options) - confidences.append(loglikelihoods) - # find the maximum dimension and pad the confidences up to that dimension - max_length = max([len(confidence) for confidence in confidences]) - for i, confidence in enumerate(confidences): - confidences[i] = torch.nn.functional.pad( - confidence, (0, max_length - len(confidence)), value=-1e10 - ) - - score_dict = self._calculate_metrics(torch.stack(confidences)) - - return score_dict - - def evaluate(self): - """Given a list of benchmark names, load and evaluate them - - Only do so on {num_samples} for each benchmark""" - results = {} - for benchmark_name in self.benchmarks: - print(f"evalling benchmark {benchmark_name}") - score_dict = self.evaluate_benchmark( - benchmark_name=benchmark_name, num_samples=self.num_samples - ) - results[benchmark_name] = score_dict - - return results - - def _pretty_print_results(self, results): - """Pretty print the results""" - for benchmark_name, score_dict in results.items(): - print(f"{benchmark_name}:") - for metric_name, score in score_dict.items(): - print(f"\t{metric_name}: {score}") diff --git a/evals/metrics.py b/evals/metrics.py deleted file mode 100644 index 2c1065cf..00000000 --- a/evals/metrics.py +++ /dev/null @@ -1,52 +0,0 @@ -""" -A collection of metrics for evaluating models -""" - -import torch - -from trainers.utils import aggregate_value - -def accuracy_metric(confidences): - """ - Calculate the accuracy of the model over a path_prob - Assume that the ground truth is the first element in the list - Args: - confidences: (B, N) tensor of confidences - Outputs: - accuracy: float of accuracy - """ - _, predicted = torch.max(confidences, 1) - ## aggregate the tensor values - return aggregate_value((predicted == 0).float().mean()) - - -def path_confidence(confidences): - """ - Calculate the path confidence of the model. - Assume that the ground truth is the first element in the list - Args: - confidences: (B, N) tensor of confidences - Returns: - path_confidence: float of path confidences - """ - softmaxed = torch.nn.functional.softmax(confidences, dim=-1) - softmaxed = softmaxed[:, 0] - ## aggregate the tensor values - return aggregate_value(softmaxed.mean()) - -def ground_confidence(confidences): - """ - Calculate the confidence of the model on the ground truth - Assume that the ground truth is the first element in the list - Args: - confidences: (B, N) tensor of confidences - Returns: - ground_confidence: float of confidence on ground truth - See: https://arxiv.org/pdf/2406.04391 - this is equivalent to - $$P_\\theta^{\\text{Choices}}(\\text{Ground Truth})$$ over the - Path probabilities. (takeaway 3) - """ - return confidences[:, 0].mean() - - -MCQ_METRIC_DICT = {"accuracy": accuracy_metric, "path_confidence": path_confidence, "ground_confidence": ground_confidence} diff --git a/evals/model_wrappers.py b/evals/model_wrappers.py new file mode 100644 index 00000000..83497e01 --- /dev/null +++ b/evals/model_wrappers.py @@ -0,0 +1,165 @@ +import torch +from Levenshtein import distance as levenshtein_distance +from evals.core import BaseModelWrapper +from itertools import zip_longest + +from typing import List, Dict, Any + + +def batch(data, batch_size): + for i in range(0, len(data), batch_size): + yield data[i : i + batch_size] + + +class LoglikelihoodMCQModelWrapper(BaseModelWrapper): + """ TODO """ + def __init__(self, model): + """ TODO """ + super().__init__() + self.model = model + self.device = model.device + + @torch.no_grad() + def __call__( + self, + prefixes: List[str], + continuations: List[str] + ) -> List[float]: + """ Compute the loglikelihood of given inputs """ + results = [] + with torch.no_grad(): + with torch.autocast(device_type=self.device, dtype=torch.bfloat16): + for prefix_batch, cont_batch in zip( + batch(prefixes, 32), batch(continuations, 32) # TODO (legacy) should not be hardcoded + ): + ll = self.model.loglikelihood(prefix_batch, cont_batch) + results.extend(ll.cpu().numpy()) + return results + + +class TextModelingModelWrapper(BaseModelWrapper): + """ TODO """ + def __init__(self, model, chunk_size): + """ TODO """ + super().__init__() + self.model = model + self.device = model.device + self.chunk_size = chunk_size + + self.encoding_function = self.model.embedding_model.tokenize_input + self.decoding_function = self.model.embedding_model.decode + + def _split_into_chunks(self, text): + """ + Split the text into chunks of 'chunk_size' words. + """ + words = text.split() + return [' '.join(words[i:i + self.chunk_size]) for i in range(0, len(words), self.chunk_size)] + + @torch.no_grad() + def _process_chunk(self, chunk): + """ TODO """ + input_ids = self.encoding_function(chunk) + + input_ids = torch.tensor(input_ids).unsqueeze(0).to(self.model.device) + + logits, _ = self.model(token_ids=input_ids) + + # Shift the input tokens to align them with the predicted tokens + shift_labels = input_ids[:, 1:].contiguous() + shift_logits = logits[:, :-1, :].contiguous() + + # Get the predicted tokens (the ones with the highest logit) + predicted_token_ids = torch.argmax(shift_logits, dim=-1) + + return shift_labels, predicted_token_ids, shift_logits + + @torch.no_grad() + def __call__(self, reference_text: str) -> Dict[str, Any]: + """ + Process the reference text and compute metrics. + + Args: + reference_text (str): The text to process. + + Returns: + Dict[str, Any]: A dictionary containing metrics. + """ + # Split the input text into chunks + chunks = self._split_into_chunks(reference_text) + + # Initialize accumulators + total_edit_distance = 0 + total_bytes = 0 + total_correct_bytes = 0 + total_loss = 0.0 + total_tokens = 0 + + for chunk in chunks: + shift_labels, predicted_token_ids, shift_logits = self._process_chunk(chunk) + + # Decode input and predicted tokens + input_text = ''.join(self.decoding_function([shift_labels.squeeze(0).cpu().tolist()])) + predicted_text = ''.join(self.decoding_function([predicted_token_ids.squeeze(0).cpu().tolist()])) + + # Encode texts to bytes + input_bytes = input_text.encode("utf-8") + predicted_bytes = predicted_text.encode("utf-8") + + # Calculate Levenshtein distance + total_edit_distance += levenshtein_distance(input_bytes, predicted_bytes) + + # Calculate byte accuracy + for input_byte, predicted_byte in zip_longest(input_bytes, predicted_bytes): + if input_byte == predicted_byte: + total_correct_bytes += 1 + total_bytes += 1 + + # Calculate loss (negative log-likelihood) + shift_logits = shift_logits.view(-1, shift_logits.size(-1)) # Shape: (seq_len - 1, vocab_size) + shift_labels = shift_labels.view(-1) # Shape: (seq_len - 1) + loss_fct = torch.nn.CrossEntropyLoss(reduction='sum') + loss = loss_fct(shift_logits, shift_labels) + total_loss += loss.item() + total_tokens += shift_labels.numel() + + # Return accumulated results + return { + 'edit_distance': total_edit_distance, + 'correct_bytes': total_correct_bytes, + 'bytes': total_bytes, + 'loss': total_loss, + 'tokens': total_tokens + } + + + + +class FreeFormModelWrapper(BaseModelWrapper): + """ TODO """ + def __init__(self, model, model_generator, generator_params): + """ TODO """ + super().__init__() + self.model = model + + # check if a generator was provided, if not, + # assume the model is already wrapped in one + if model_generator is not None: + # wrap the model in the generator + if generator_params is None: + self.model = model_generator( + model=self.model + ) + else: + self.model = model_generator( + model=self.model, + **generator_params + ) + + + def __call__(self, input_text:str) -> str: + """ TODO """ + return self.model.generate( + input_text=input_text + )[0] # assume sample-by-sample for now + diff --git a/generate.py b/generate.py index cc5b8ea5..0443e5c1 100644 --- a/generate.py +++ b/generate.py @@ -6,7 +6,7 @@ import torch from models.build_models import build_model -from models.generator import StandardGenerator +from models.generators import build_generator @hydra.main(config_path="configs", config_name="generate") @@ -17,16 +17,28 @@ def main(cfg): cfg["model_ckpt"] = hydra.utils.to_absolute_path(cfg["model_ckpt"]) # load checkpoint from the path - model = build_model(checkpoint=torch.load(cfg["model_ckpt"])) - - generator = StandardGenerator(model=model, generate_cfg=cfg["generator"]) - - while True: - # generate the text + device = "cpu" if not torch.cuda.is_available() else "cuda" + model, _ = build_model( + checkpoint_path=cfg["model_ckpt"], + device=device, + attention_type=cfg["attention_type"] + ) + + # put model into eval mode + model.eval() + + generator = build_generator( + model=model, + generate_cfg=cfg["generator"], + device=device + ) + + # generate the text + for _ in range(5): # generate 5 samples generated_text = generator.default_generate( - input_text=input("Enter the input text: ") + input_text=cfg["generator"]["input_text"] ) - print(generated_text) + print("".join(generated_text)) if __name__ == "__main__": diff --git a/hackathon_evaluation.py b/hackathon_evaluation.py new file mode 100644 index 00000000..60126d78 --- /dev/null +++ b/hackathon_evaluation.py @@ -0,0 +1,63 @@ +""" +Evaluation Code. +""" + +import torch +import sympy +from hackathon_utils import compare_answers +from datasets import load_dataset +from models.build_models import build_model + + +def load_math(): + """ + Load the MATH eval set + https://huggingface.co/datasets/lighteval/MATH + + Args: + num_samples (Optional[int]): Number of samples to load. If None, load all. + seed (Optional[int]): Seed for random sampling. + + Yields: + Tuple[str, str, List[str]]: (question, answer) + """ + dataset = load_dataset("lighteval/MATH", "all", trust_remote_code=True)["test"] + for i in range(len(dataset)): + yield( + sample[i]["problem"], + sample[i]["solution"] + ) + + +# load the model +model, _ = build_model(model_cfg={ + "model_string": "openai-community/gpt2", + "core_model_type": "hf_core", + "embedding_model_type": "hf_embedder", + "tokenizer_name": "hf_tokenizer", + "model_shell_type": "standard", + "lm_head_type": "hf_head" +}) + + +def generate_reply(problem): + return problem + + + + +total, correct = 0, 0 +for i, (problem, solution) in enumerate(load_math()): + # get model answer + model_answer = generate_reply(problem) + + # evaluate model answer + correct += compare_answers( + y_true=solution, + y_pred=model_answer + ) + total += 1 + + + +print(f"Accuracy: {correct/total}") \ No newline at end of file diff --git a/hackathon_utils.py b/hackathon_utils.py new file mode 100644 index 00000000..b7956226 --- /dev/null +++ b/hackathon_utils.py @@ -0,0 +1,267 @@ + +# taken from https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/hendrycks_math/utils.py +from typing import Dict, List + +import datasets + + +def process_docs(dataset: datasets.Dataset) -> datasets.Dataset: + def _process_doc(doc: dict) -> dict: + out_doc = { + "problem": doc["problem"], + "solution": doc["solution"], + "answer": remove_boxed(last_boxed_only_string(doc["solution"])), + } + return out_doc + + return dataset.map(_process_doc) + + +def process_results(doc: dict, results: List[str]) -> Dict[str, int]: + retval = 0 + indices = [pos for pos, char in enumerate(results[0]) if char == "$"] + if len(indices) <= 1: + answer = results[0] + else: + answer = results[0][indices[0] + 1 : indices[-1]] + + if is_equiv(answer, remove_boxed(last_boxed_only_string(doc["solution"]))): + retval = 1 + + results = { + "exact_match": retval, + } + return results + + +# string normalization from https://github.com/EleutherAI/lm-evaluation-harness/blob/master/lm_eval/tasks/hendrycks_math.py +def is_equiv(str1, str2, verbose=False): + if str1 is None and str2 is None: + print("WARNING: Both None") + return True + if str1 is None or str2 is None: + return False + + try: + ss1 = strip_string(str1) + ss2 = strip_string(str2) + if verbose: + print(ss1, ss2) + return ss1 == ss2 + except Exception: + return str1 == str2 + + +def remove_boxed(s): + if "\\boxed " in s: + left = "\\boxed " + assert s[: len(left)] == left + return s[len(left) :] + + left = "\\boxed{" + + assert s[: len(left)] == left + assert s[-1] == "}" + + return s[len(left) : -1] + + +def last_boxed_only_string(string): + idx = string.rfind("\\boxed") + if "\\boxed " in string: + return "\\boxed " + string.split("\\boxed ")[-1].split("$")[0] + if idx < 0: + idx = string.rfind("\\fbox") + if idx < 0: + return None + + i = idx + right_brace_idx = None + num_left_braces_open = 0 + while i < len(string): + if string[i] == "{": + num_left_braces_open += 1 + if string[i] == "}": + num_left_braces_open -= 1 + if num_left_braces_open == 0: + right_brace_idx = i + break + i += 1 + + if right_brace_idx is None: + retval = None + else: + retval = string[idx : right_brace_idx + 1] + + return retval + + +def fix_fracs(string): + substrs = string.split("\\frac") + new_str = substrs[0] + if len(substrs) > 1: + substrs = substrs[1:] + for substr in substrs: + new_str += "\\frac" + if substr[0] == "{": + new_str += substr + else: + try: + assert len(substr) >= 2 + except AssertionError: + return string + a = substr[0] + b = substr[1] + if b != "{": + if len(substr) > 2: + post_substr = substr[2:] + new_str += "{" + a + "}{" + b + "}" + post_substr + else: + new_str += "{" + a + "}{" + b + "}" + else: + if len(substr) > 2: + post_substr = substr[2:] + new_str += "{" + a + "}" + b + post_substr + else: + new_str += "{" + a + "}" + b + string = new_str + return string + + +def fix_a_slash_b(string): + if len(string.split("/")) != 2: + return string + a = string.split("/")[0] + b = string.split("/")[1] + try: + a = int(a) + b = int(b) + assert string == "{}/{}".format(a, b) + new_string = "\\frac{" + str(a) + "}{" + str(b) + "}" + return new_string + except AssertionError: + return string + + +def remove_right_units(string): + # "\\text{ " only ever occurs (at least in the val set) when describing units + if "\\text{ " in string: + splits = string.split("\\text{ ") + assert len(splits) == 2 + return splits[0] + else: + return string + + +def fix_sqrt(string): + if "\\sqrt" not in string: + return string + splits = string.split("\\sqrt") + new_string = splits[0] + for split in splits[1:]: + if split[0] != "{": + a = split[0] + new_substr = "\\sqrt{" + a + "}" + split[1:] + else: + new_substr = "\\sqrt" + split + new_string += new_substr + return new_string + + +def strip_string(string): + # linebreaks + string = string.replace("\n", "") + + # remove inverse spaces + string = string.replace("\\!", "") + + # replace \\ with \ + string = string.replace("\\\\", "\\") + + # replace tfrac and dfrac with frac + string = string.replace("tfrac", "frac") + string = string.replace("dfrac", "frac") + + # remove \left and \right + string = string.replace("\\left", "") + string = string.replace("\\right", "") + + # Remove circ (degrees) + string = string.replace("^{\\circ}", "") + string = string.replace("^\\circ", "") + + # remove dollar signs + string = string.replace("\\$", "") + + # remove units (on the right) + string = remove_right_units(string) + + # remove percentage + string = string.replace("\\%", "") + string = string.replace("\%", "") # noqa: W605 + + # " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string + string = string.replace(" .", " 0.") + string = string.replace("{.", "{0.") + # if empty, return empty string + if len(string) == 0: + return string + if string[0] == ".": + string = "0" + string + + # to consider: get rid of e.g. "k = " or "q = " at beginning + if len(string.split("=")) == 2: + if len(string.split("=")[0]) <= 2: + string = string.split("=")[1] + + # fix sqrt3 --> sqrt{3} + string = fix_sqrt(string) + + # remove spaces + string = string.replace(" ", "") + + # \frac1b or \frac12 --> \frac{1}{b} and \frac{1}{2}, etc. Even works with \frac1{72} (but not \frac{72}1). Also does a/b --> \\frac{a}{b} + string = fix_fracs(string) + + # manually change 0.5 --> \frac{1}{2} + if string == "0.5": + string = "\\frac{1}{2}" + + # NOTE: X/Y changed to \frac{X}{Y} in dataset, but in simple cases fix in case the model output is X/Y + string = fix_a_slash_b(string) + + return string + + + + + + + + + + + + + +def extract_answer(text: str) -> str: + if "\\boxed" in text: + return remove_boxed(last_boxed_only_string(text)) + elif "Answer:" in text: + return text.split("Answer:")[-1] + elif "###" in text: + return text.split("###")[-1] + else: + return text + +def compare_answers(y_true, y_pred): + # preprocess both answers + y_true = extract_answer(y_true) + y_pred = extract_answer(y_pred) + try: + true_expr = sympy.sympify(true_answer) + model_expr = sympy.sympify(model_answer) + return sympy.simplify(true_expr - model_expr) == 0 + except (sympy.SympifyError, TypeError): + # Fallback to string comparison + return true_answer.strip() == model_answer.strip() \ No newline at end of file diff --git a/models/build_models.py b/models/build_models.py index 36236091..f4124a4a 100644 --- a/models/build_models.py +++ b/models/build_models.py @@ -2,21 +2,30 @@ Contains the build functions for the embedder, core model, lm head and the model shell. """ +import torch +from omegaconf import OmegaConf -from models.core_models import GenericFFNSharedTransfomer, GenericTransformer + +from models.core_models import GenericTransformer from models.embedding_models import GenericEmbedder from models.experimental.byte_level.embedding_model import ByteLevelEmbedder from models.experimental.byte_level.model_heads import ByteLevelDecoder from models.experimental.byte_level.byte_model_shell import ByteModelShell from models.experimental.hugging_face import HFEmbedder, HFLMHead, HFTransformerCore -from models.experimental.next_thought.embedding_models import HierarchicalEncoder -from models.experimental.next_thought.model_heads import VariableLengthLatentDecoder -from models.experimental.next_thought.core_models import BaselineCoreModel, Conv1dCoreModel from models.model_heads import AutoregressiveLMHead from models.model_shell import ModelShell +from models.experimental.weight_sharing import ( + SharedInteriorFFNLoraAndCProj, + SharedInteriorFFNLora, +) + +from models.experimental.moe_weight_sharing import ( + SharedMoE +) + -def build_model(model_cfg=None, checkpoint=None): +def build_model(model_cfg=None, checkpoint_path=None, device="cuda", **kwargs): """ Either initialize or load a model, depending on whether a config or checkpoint was provided @@ -30,25 +39,43 @@ def build_model(model_cfg=None, checkpoint=None): """ # check if model is to be loaded - if checkpoint is not None: + if checkpoint_path is not None: # load model with the correct architecture + checkpoint = torch.load( + checkpoint_path, + map_location=torch.device(device), + ) + + # update the attention type if provided + if checkpoint["config"]["model"].get("attention_type", None) is None: + cfg = OmegaConf.create({"attention_type": f"{kwargs.get('attention_type', 'standard')}"}) + checkpoint['config']['model'] = OmegaConf.merge(cfg, checkpoint['config']['model']) + elif checkpoint["config"]["model"].get("attention_type", None) == "standard": + checkpoint["config"]["model"]["attention_type"] = f"{kwargs.get('attention_type', 'standard')}" + model = initialize_model(checkpoint["config"]["model"]) # load the model weights model.load_state_dict(checkpoint["model"]) + loaded_train_config = { + "optimizer": checkpoint["optimizer"], + "iter_num": checkpoint["iter_num"], + "config": checkpoint["config"] + } + else: # initialize model model = initialize_model(model_cfg) + loaded_train_config = None - return model + return model, loaded_train_config EMBEDDING_MODEL_DICT = { "generic": GenericEmbedder, "byte_level": ByteLevelEmbedder, "hf_embedder": HFEmbedder, - "hierarchical": HierarchicalEncoder, } @@ -60,17 +87,19 @@ def build_embedding_model(model_cfg): Returns: embedding_model: embedding_model_instance """ - return EMBEDDING_MODEL_DICT[model_cfg["embedder"]["embedding_model_type"]]( + return EMBEDDING_MODEL_DICT[model_cfg["embedding_model_type"]]( model_cfg=model_cfg ) CORE_MODEL_DICT = { "generic": GenericTransformer, - "generic_ffn_sharing": GenericFFNSharedTransfomer, "hf_core": HFTransformerCore, - "next_thought_baseline": BaselineCoreModel, - "conv": Conv1dCoreModel + + # experimental + "ffn_lora_sharing": SharedInteriorFFNLora, + "ffn_lora_sharing": SharedInteriorFFNLoraAndCProj, + "ffn_lora_sharing_moe": SharedMoE, } @@ -82,7 +111,7 @@ def build_core_model(model_cfg): Returns: core_model: core_model_instance """ - return CORE_MODEL_DICT[model_cfg["core_model"]["core_model_type"]]( + return CORE_MODEL_DICT[model_cfg["core_model_type"]]( model_cfg=model_cfg ) @@ -106,7 +135,7 @@ def build_model_head(model_cfg, embedding_model=None): Returns: model_head: model_head_instance """ - return MODEL_HEAD_DICT[model_cfg["lm_head"]["lm_head_type"]]( + return MODEL_HEAD_DICT[model_cfg["lm_head_type"]]( model_cfg=model_cfg, embedding_model=embedding_model ) @@ -127,10 +156,27 @@ def build_model_shell(model_cfg, embedding_model, core_model, model_head): model_shell: model_shell_instance """ return MODEL_SHELL_DICT[model_cfg["model_shell_type"]]( - embedding_model=embedding_model, core_model=core_model, model_head=model_head + model_cfg=model_cfg, + embedding_model=embedding_model, + core_model=core_model, + model_head=model_head, ) +MODEL_WEIGHT_INIT_DICT = { + "xavier": torch.nn.init.xavier_normal_, + "kaiming": torch.nn.init.kaiming_normal_, + "none": None, +} +def build_model_initialization_function(model_cfg): + """ + Given the model initialiation config, build it. + """ + return MODEL_WEIGHT_INIT_DICT[ + model_cfg.get("initialization_fn", "kaiming") + ] + + def initialize_model(model_cfg): """ Initialize the model given the configuration. @@ -151,11 +197,6 @@ def initialize_model(model_cfg): embedding_model=embedding_model ) - # check if embedding model weights are to be shared with the model head - if model_cfg["embedding_weight_tying"]: - # share the weights between the token embeddings and the final - # logit layer, following: https://paperswithcode.com/method/weight-tying - embedding_model.token_embedder.weight = model_head.linear.weight # build the model shell model = build_model_shell( @@ -165,4 +206,18 @@ def initialize_model(model_cfg): model_head=model_head, ) + # initialize the model weights + init_fn_type = model_cfg.get("initialization_fn", "kaiming") + if init_fn_type != None: + init_fn = build_model_initialization_function(model_cfg) + + def init_weights(m): + if isinstance(m, (torch.nn.Linear, torch.nn.Conv2d, torch.nn.Conv1d)): + init_fn(m.weight) + if m.bias is not None: + torch.nn.init.zeros_(m.bias) + + model.apply(init_weights) + + return model diff --git a/models/components/activations.py b/models/components/activations.py new file mode 100644 index 00000000..1a589df0 --- /dev/null +++ b/models/components/activations.py @@ -0,0 +1,28 @@ +""" +A collection of common activation functions. +""" + +import torch + + +ACTIVATIONS_DICT = { + "gelu": torch.nn.GELU(), + "relu": torch.nn.ReLU(), + "leakyrelu": torch.nn.LeakyReLU(), + "tanh": torch.nn.Tanh(), + "sigmoid": torch.nn.Sigmoid(), + "silu": torch.nn.SiLU(), + "none": torch.nn.Identity(), +} + + +def build_activation(activation_name: str): + """ + Given the name of the activation function, + build it. + Args: + activation_name: str + Returns: + activation: torch.nn.Module + """ + return ACTIVATIONS_DICT[activation_name.lower()] diff --git a/models/components/layers/attention.py b/models/components/attention.py similarity index 68% rename from models/components/layers/attention.py rename to models/components/attention.py index 41b06b37..5982cdb4 100644 --- a/models/components/layers/attention.py +++ b/models/components/attention.py @@ -3,6 +3,7 @@ """ import torch +from models.components.utils.attention_utils import use_attention_type class Attention(torch.nn.Module): @@ -12,16 +13,23 @@ class Attention(torch.nn.Module): def __init__( self, + attention_type, hidden_dim, - num_heads, + num_q_heads, + num_kv_heads, bias, use_rope, context_window, is_causal, - group_size, ): super().__init__() - assert hidden_dim % num_heads == 0, "Hidden dim must be divisible by num heads" + assert hidden_dim % num_kv_heads == 0, "Hidden dim must be divisible by num heads" + assert num_kv_heads % num_q_heads == 0, "num_kv_heads must be divisible by num_q_heads" + + group_size = num_kv_heads // num_q_heads + + # set the attention_type + self.attention_type = attention_type # key, query, value projections for all heads self.c_attn = torch.nn.Linear( @@ -34,7 +42,7 @@ def __init__( # attention dropout self.attn_dropout = torch.nn.Dropout() - self.num_heads = num_heads + self.num_kv_heads = num_kv_heads self.group_size = group_size self.is_causal = is_causal @@ -43,7 +51,7 @@ def __init__( if self.use_rope: assert context_window % 2 == 0 self.freqs_cis = compute_freqs_cis( - seq_len=context_window, head_dim=hidden_dim // num_heads + seq_len=context_window, head_dim=hidden_dim // num_kv_heads ) def forward(self, x, attention_mask=None): @@ -52,15 +60,15 @@ def forward(self, x, attention_mask=None): """ assert attention_mask is None, "Not implemented yet" B, S, H = x.size() - num_grouped_heads = self.num_heads // self.group_size + num_grouped_heads = self.num_kv_heads // self.group_size group_hidden_dim = H // self.group_size # calculate query, key, values for all heads in batch # move head forward to be the batch dim q, k, v = self.c_attn(x).split([H, group_hidden_dim, group_hidden_dim], dim=-1) - k = k.view(B, S, num_grouped_heads, H // self.num_heads) # (B, T, nh, hs) - q = q.view(B, S, self.num_heads, H // self.num_heads) # (B, T, nh, hs) - v = v.view(B, S, num_grouped_heads, H // self.num_heads).transpose( + k = k.view(B, S, num_grouped_heads, H // self.num_kv_heads) # (B, T, nh, hs) + q = q.view(B, S, self.num_kv_heads, H // self.num_kv_heads) # (B, T, nh, hs) + v = v.view(B, S, num_grouped_heads, H // self.num_kv_heads).transpose( 1, 2 ) # (B, nh, T, hs) @@ -76,7 +84,8 @@ def forward(self, x, attention_mask=None): # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T) # flash attention # pylint: disable=not-callable - y = torch.nn.functional.scaled_dot_product_attention( + y, attn_components = use_attention_type( + attention_type=self.attention_type, query=q, key=k, value=v, @@ -84,6 +93,10 @@ def forward(self, x, attention_mask=None): dropout_p=self.attn_dropout.p if self.training else 0, is_causal=self.is_causal, ) + + # store the attention components + self.attn_components = attn_components + # pylint: enable=not-callable y = ( y.transpose(1, 2).contiguous().view(B, S, H) @@ -127,19 +140,30 @@ def compute_freqs_cis(seq_len, head_dim): ATTENTION_DICT = { - "generic": lambda hidden_dim, context_window, use_rope, attn_cfg: Attention( + "causal": lambda attention_type, hidden_dim, context_window, use_rope, attn_cfg: Attention( + attention_type=attention_type, + hidden_dim=hidden_dim, + num_kv_heads=attn_cfg["num_kv_heads"], + num_q_heads=attn_cfg.get("num_q_heads", attn_cfg["num_kv_heads"]), + bias=attn_cfg.get("bias", False), # Default to False + use_rope=use_rope, + context_window=context_window, + is_causal=True, + ), + "bidirectional": lambda attention_type, hidden_dim, context_window, use_rope, attn_cfg: Attention( + atttention_type=attention_type, hidden_dim=hidden_dim, - num_heads=attn_cfg["num_heads"], - bias=attn_cfg["bias"], + num_kv_heads=attn_cfg["num_kv_heads"], + num_q_heads=attn_cfg.get("num_q_heads", attn_cfg["num_kv_heads"]), + bias=attn_cfg.get("bias", False), # Default to False use_rope=use_rope, context_window=context_window, - is_causal=attn_cfg["is_causal"], - group_size=attn_cfg["group_size"], + is_causal=False, ) } -def build_attention(hidden_dim, context_window, use_rope, attn_cfg): +def build_attention(attention_type, hidden_dim, context_window, use_rope, attn_cfg): """ Build an attention layer @@ -150,6 +174,7 @@ def build_attention(hidden_dim, context_window, use_rope, attn_cfg): attn_cfg: attention config """ return ATTENTION_DICT[attn_cfg["attn_type"]]( + attention_type=attention_type, hidden_dim=hidden_dim, context_window=context_window, use_rope=use_rope, diff --git a/models/components/layers/feedforward.py b/models/components/feedforward.py similarity index 68% rename from models/components/layers/feedforward.py rename to models/components/feedforward.py index 580f5deb..c335da50 100644 --- a/models/components/layers/feedforward.py +++ b/models/components/feedforward.py @@ -5,7 +5,7 @@ import torch import torch.nn.functional as F -from models.components.layers.activations import build_activation +from models.components.activations import build_activation class GenericFFN(torch.nn.Module): @@ -19,6 +19,7 @@ def __init__( ffn_dim, bias, ffn_activation, + ffn_dropout ): super().__init__() # build the ffn block @@ -28,17 +29,22 @@ def __init__( self.linear_2 = torch.nn.Linear(ffn_dim, hidden_dim, bias=bias) + self.dropout = torch.nn.Dropout( + p=ffn_dropout + ) + def forward(self, x): """ A simple forward pass through the FFN """ + x = self.dropout(x) x = self.linear_1(x) x = self.activation(x) x = self.linear_2(x) return x -class SwiGLUFFN(torch.nn.Module): +class SiluFFN(torch.nn.Module): """ Implementation based on: https://github.com/meta-llama/llama3/blob/main/llama/model.py @@ -52,6 +58,7 @@ def __init__( hidden_dim, ffn_dim, bias, + ffn_dropout ): super().__init__() # build the linear functions @@ -61,10 +68,15 @@ def __init__( self.linear_3 = torch.nn.Linear(hidden_dim, ffn_dim, bias=bias) + self.dropout = torch.nn.Dropout( + p=ffn_dropout + ) + def forward(self, x): """ A simple forward pass through the FFN """ + x = self.dropout(x) return self.linear_2(F.silu(self.linear_1(x)) * self.linear_3(x)) @@ -72,13 +84,15 @@ def forward(self, x): "generic": lambda hidden_dim, ffn_cfg: GenericFFN( hidden_dim=hidden_dim, ffn_dim=ffn_cfg["ffn_dim"], - bias=ffn_cfg["bias"], - ffn_activation=ffn_cfg["activation"], + bias=ffn_cfg.get("bias", False), # Default to False + ffn_activation=ffn_cfg.get("activation", "gelu"), # Default to 'gelu + ffn_dropout=ffn_cfg.get("ffn_dropout", 0.0) # Default to 0.0 ), - "swiglu": lambda hidden_dim, ffn_cfg: SwiGLUFFN( + "silu_ffn": lambda hidden_dim, ffn_cfg: SiluFFN( hidden_dim=hidden_dim, ffn_dim=ffn_cfg["ffn_dim"], - bias=ffn_cfg["bias"], + bias=ffn_cfg.get("bias", False), # Default to False + ffn_dropout=ffn_cfg.get("ffn_dropout", 0.0) # Default to 0.0 ), } @@ -87,4 +101,7 @@ def build_ffn(hidden_dim, ffn_cfg): """ Build a feedforward network """ + assert ffn_cfg["ffn_type"] in FFN_DICT, \ + f"FFN type {ffn_cfg['ffn_type']} not found. Available types: {FFN_DICT.keys()}" + return FFN_DICT[ffn_cfg["ffn_type"]](hidden_dim=hidden_dim, ffn_cfg=ffn_cfg) diff --git a/models/components/layers/activations.py b/models/components/layers/activations.py deleted file mode 100644 index e9b89868..00000000 --- a/models/components/layers/activations.py +++ /dev/null @@ -1,60 +0,0 @@ -""" -A collection of common activation functions. -""" - -import torch - -class LearnedActivation(torch.nn.Module): - def __init__(self, hidden_size=10): - super(LearnedActivation, self).__init__() - self.fc1 = torch.nn.Linear(1, hidden_size) - self.fc2 = torch.nn.Linear(hidden_size, 1) - self.initialize_weights() - - def initialize_weights(self): - # Initialize weights to the learned parameters - self.fc1.weight.data = torch.tensor([[-0.3478], - [-0.3444], - [-0.9863], - [-0.8657], - [-0.0148], - [ 0.1085], - [-0.5282], - [-0.1138], - [-1.1070], - [-0.1035]]) - self.fc1.bias.data = torch.tensor([ 1.4480, 1.4610, -0.8526, 0.0151, -0.1249, -0.7658, 2.2386, -0.8884, 1.0032, -0.6235]) - self.fc2.weight.data = torch.tensor([[-0.4762, -1.2194, 0.4155, 0.3927, -0.2778, 0.0986, -0.9284, 0.2070, 0.3586, -0.2143]]) - self.fc2.bias.data = torch.tensor([4.1740]) - - def forward(self, x): - # Flatten the input to apply the learned activation element-wise - orig_shape = x.shape - x = x.view(-1, 1) # Flatten to (N, 1) - x = torch.relu(self.fc1(x)) - x = self.fc2(x) - return x.view(orig_shape) # Reshape back to original shape - - -ACTIVATIONS_DICT = { - "gelu": torch.nn.GELU(), - "relu": torch.nn.ReLU(), - "leakyrelu": torch.nn.LeakyReLU(), - "tanh": torch.nn.Tanh(), - "sigmoid": torch.nn.Sigmoid(), - "silu": torch.nn.SiLU(), - "learned": LearnedActivation(hidden_size=10), - "none": torch.nn.Identity(), -} - - -def build_activation(activation_name: str): - """ - Given the name of the activation function, - build it. - Args: - activation_name: str - Returns: - activation: torch.nn.Module - """ - return ACTIVATIONS_DICT[activation_name.lower()] diff --git a/models/components/layers/normalization.py b/models/components/normalization.py similarity index 91% rename from models/components/layers/normalization.py rename to models/components/normalization.py index 69b87605..df6020af 100644 --- a/models/components/layers/normalization.py +++ b/models/components/normalization.py @@ -53,4 +53,6 @@ def build_normalization(normalization_name, dim, bias=None): Available options: rmsnorm, layernorm - Bias is ignored for RMSNorm """ + assert normalization_name in NORMALIZATION_DICT, \ + f"Normalization {normalization_name} not found! 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"ĠTracy": 17390, + "ĠSatellite": 17391, + "ĠEnterprise": 17392, + "adeshiko": 17393, + "Ġdigit": 17394, + "Ġradius": 17395, + "Ġpotassium": 17396, + "Ġwaiting": 17397, + "ikovsky": 17398, + "ĠCrimea": 17399, + "ĠEmergency": 17400, + "ĠVaucluse": 17401, + "PP": 17402, + "Ġtuber": 17403, + "Ġbapt": 17404, + "Ġnerve": 17405, + "ĠGale": 17406, + "ĠVince": 17407, + "ĠViolet": 17408, + "1919": 17409, + "Ġcanon": 17410, + "909": 17411, + "Ġdivide": 17412, + "Ġchemists": 17413, + "ĠEsther": 17414, + "ĠCherry": 17415, + "Ġdominated": 17416, + "rophys": 17417, + "modern": 17418, + "Ġspiral": 17419, + "capital": 17420, + "Ġsubsidiary": 17421, + "amation": 17422, + "ĠHale": 17423, + "ĠECW": 17424, + "agin": 17425, + "Ġheter": 17426, + "athi": 17427, + "ricke": 17428, + "henko": 17429, + "ropod": 17430, + "tenberg": 17431, + "ĠCarne": 17432, + "annah": 17433, + "904": 17434, + "Ġstatistical": 17435, + "391": 17436, + "396": 17437, + "Ġvarieties": 17438, + "Ġacademy": 17439, + "ĠProducer": 17440, + 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17811, + "ĠTarn": 17812, + "ĠFrib": 17813, + "ĠJub": 17814, + "ĠKant": 17815, + "estag": 17816, + "Ġtelev": 17817, + "ĠCommercial": 17818, + "ĠAmadeus": 17819, + "ĠAircraft": 17820, + "ĠSynd": 17821, + "453": 17822, + "ĠProvidence": 17823, + "Ġafford": 17824, + "Ġadministered": 17825, + "ĠSaar": 17826, + "ĠJenny": 17827, + "Ġcontainer": 17828, + "Ġmillennium": 17829, + "ĠRecordings": 17830, + "ĠCandid": 17831, + "ĠChevrolet": 17832, + "Aquitaine": 17833, + "Out": 17834, + "nick": 17835, + "atops": 17836, + "ĠSI": 17837, + "ĠSons": 17838, + "Ġpill": 17839, + "ĠTet": 17840, + "ĠCave": 17841, + "ĠMller": 17842, + "Ġtowers": 17843, + "ĠBri": 17844, + "ĠDunn": 17845, + "ĠNgu": 17846, + "ceae": 17847, + "ĠKv": 17848, + "ĠKiy": 17849, + "Ġbeef": 17850, + "ĠChicken": 17851, + "cliffe": 17852, + "ĠElton": 17853, + "ĠHowe": 17854, + "elfth": 17855, + "706": 17856, + "681": 17857, + "682": 17858, + "686": 17859, + "456": 17860, + "696": 17861, + "Ġsucceeds": 17862, + "rology": 17863, + "476": 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17967, + "ĠSpike": 17968, + "ĠLeigh": 17969, + "ibo": 17970, + "Ġestimate": 17971, + "iera": 17972, + "ĠEmilia": 17973, + "Ġslide": 17974, + "Ġaccord": 17975, + "Ġcommemor": 17976, + "ĠAntio": 17977, + "ĠMelissa": 17978, + "ĠCorb": 17979, + "ĠCongressman": 17980, + "Ġpreserve": 17981, + "ĠDetective": 17982, + "ĠRaja": 17983, + "ĠTakah": 17984, + "Ġsleeping": 17985, + "ĠNortheast": 17986, + "Ġarchaeological": 17987, + "Italian": 17988, + "Ġhemorrhage": 17989, + "ĠAgreement": 17990, + "315": 17991, + "Ġog": 17992, + "igrated": 17993, + "ĠPrav": 17994, + "Ġtoile": 17995, + "ĠRag": 17996, + "ĠHollow": 17997, + "adan": 17998, + "ĠGN": 17999, + "ĠWade": 18000, + "Ġstuck": 18001, + "ocated": 18002, + "ookie": 18003, + "even": 18004, + "Ġenvironments": 18005, + "902": 18006, + "906": 18007, + "bergh": 18008, + "ĠHoliday": 18009, + "574": 18010, + "ĠAgnes": 18011, + "ĠStevie": 18012, + "Ġinfant": 18013, + "Ġholidays": 18014, + "Ġfiring": 18015, + "ĠMagnus": 18016, + "Ġcontinuing": 18017, + "Hungary": 18018, + "ĠFerguson": 18019, + "Ġterminology": 18020, + "ĠRhythm": 18021, + "fran": 18022, + "enbach": 18023, + "roads": 18024, + "Ġmaid": 18025, + "ĠCover": 18026, + "ĠMorm": 18027, + "ĠPill": 18028, + "ĠHr": 18029, + "ĠLief": 18030, + "Ġgifts": 18031, + "Ġeagle": 18032, + "eboard": 18033, + "solete": 18034, + "ĠMarines": 18035, + "Ġwarn": 18036, + "byter": 18037, + "451": 18038, + "484": 18039, + "ĠLabr": 18040, + "ĠAnnual": 18041, + "ĠFormation": 18042, + "Ġsecretly": 18043, + "Ġperspective": 18044, + "books": 18045, + "ĠStrauss": 18046, + "Ġteenagers": 18047, + "Ġsailed": 18048, + "ĠPinoc": 18049, + "uncredited": 18050, + "recce": 18051, + "ĠAntarctic": 18052, + "Australian": 18053, + "Ġsynthesis": 18054, + "ĠRappers": 18055, + "Ġbreathe": 18056, + "ĠJacqueline": 18057, + "Ġheadquartered": 18058, + "313": 18059, + "KA": 18060, + "frecce": 18061, + "Ġtoll": 18062, + "Ġtsun": 18063, + "Ġcad": 18064, + "Ġfu": 18065, + "Ġ220": 18066, + "ĠHaving": 18067, + "ĠNab": 18068, + 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"ihu": 18120, + "wareness": 18121, + "ĠAbdullah": 18122, + "Ġagrees": 18123, + "ĠBattles": 18124, + "Ġcircumstances": 18125, + "ĠCheshire": 18126, + "Ġsupernatural": 18127, + "Pigott": 18128, + "ĠFribourg": 18129, + "Ġicon": 18130, + "itiba": 18131, + "Ġcav": 18132, + "ĠCork": 18133, + "ĠBres": 18134, + "ĠFry": 18135, + "ĠNils": 18136, + "ĠGould": 18137, + "estrian": 18138, + "illac": 18139, + "005": 18140, + "soon": 18141, + "ĠAlto": 18142, + "Ġknife": 18143, + "Ġgrouped": 18144, + "ĠEditor": 18145, + "Ġmonks": 18146, + "ĠCups": 18147, + "aneous": 18148, + "Ġcondem": 18149, + "ĠSamsung": 18150, + "Ġhandball": 18151, + "Ġvictories": 18152, + "Ġmissed": 18153, + "ĠBosnian": 18154, + "Ġmigration": 18155, + "Ġdramatic": 18156, + "ĠSylvia": 18157, + "ĠIsabel": 18158, + "Ġairplanes": 18159, + "ĠFrost": 18160, + "ĠBundestag": 18161, + "ĠAuschwitz": 18162, + "ĠEsperanto": 18163, + "ĠWimbledon": 18164, + "yst": 18165, + "Ġtram": 18166, + "anza": 18167, + "Ġmol": 18168, + "ĠHT": 18169, + "ĠHast": 18170, + "ĠOu": 18171, + "isto": 18172, + "ovna": 18173, + "Ġecosystem": 18174, + "ĠKoll": 18175, + "ĠVog": 18176, + "1914": 18177, + "ounce": 18178, + "Ġconven": 18179, + "Ġshipping": 18180, + "ribe": 18181, + "991": 18182, + "487": 18183, + "663": 18184, + "ĠDonna": 18185, + "ĠByr": 18186, + "Ġcoaching": 18187, + "ĠInstitution": 18188, + "Ġboyfriend": 18189, + "ĠGerard": 18190, + "inguished": 18191, + "ĠRapids": 18192, + "Ġsculptures": 18193, + "ĠDogs": 18194, + "Ġdonated": 18195, + "Ġwildlife": 18196, + "ĠCreation": 18197, + "Ġconducting": 18198, + "Ġhorns": 18199, + "ĠWarrior": 18200, + "ĠBrighton": 18201, + "ĠCrisis": 18202, + "Ġproposal": 18203, + "Ġevacu": 18204, + "Ġfabric": 18205, + "Tunes": 18206, + "ĠSidd": 18207, + "ĠAal": 18208, + "lemy": 18209, + "ĠPapers": 18210, + "stand": 18211, + "ĠOra": 18212, + "Ġgap": 18213, + "opa": 18214, + "ĠKof": 18215, + "Ġrig": 18216, + "endium": 18217, + "Ġ750": 18218, + "eve": 18219, + "ĠProf": 18220, + "ĠItalians": 18221, + "993": 18222, + "692": 18223, + "666": 18224, + "ĠWeber": 18225, + "Ġperiodic": 18226, + "ĠIslanders": 18227, + "ĠHammond": 18228, + "Ġconfusion": 18229, + "Ġmurderer": 18230, + "Ġpropag": 18231, + "Ġfootage": 18232, + "Ġemerged": 18233, + "Ġapplies": 18234, + "ĠAPO": 18235, + "ĠiPod": 18236, + "Ġlymphoma": 18237, + "ĠMichaels": 18238, + "510": 18239, + "660": 18240, + "ĠSiege": 18241, + "Ġpod": 18242, + "ĠMurders": 18243, + "ĠFors": 18244, + "ĠGia": 18245, + "ĠEvel": 18246, + "ĠStall": 18247, + "acon": 18248, + "arde": 18249, + "Ġspokes": 18250, + "Ġ970": 18251, + "Ġ1785": 18252, + "Ġaggressive": 18253, + "Ġbladder": 18254, + "994": 18255, + "ushu": 18256, + "Ġdevoted": 18257, + "ĠArmenians": 18258, + "ĠTreasure": 18259, + "Ġshoots": 18260, + "Ġsignificance": 18261, + "ĠVicente": 18262, + "Ġtissues": 18263, + "ĠArrondissements": 18264, + "ĠCelebrity": 18265, + "Ġmascot": 18266, + "RO": 18267, + "Ġoath": 18268, + "Ġcaves": 18269, + "ĠMales": 18270, + "ĠOman": 18271, + "Ġgospel": 18272, + "ĠKens": 18273, + "Ġsteep": 18274, + "raits": 18275, + "ĠYv": 18276, + "auss": 18277, + "ĠAnita": 18278, + "axter": 18279, + "Ġmarries": 18280, + "ĠWeap": 18281, + "ĠRonnie": 18282, + "Ġroyalty": 18283, + "Ġmeasurements": 18284, + "Ġseriously": 18285, + "Ġvegetation": 18286, + "ĠVaugh": 18287, + "Ġboroughs": 18288, + "ĠHavana": 18289, + "Ġcassette": 18290, + "ĠKarnataka": 18291, + "ĠGaelic": 18292, + "province": 18293, + "Ġpredecessor": 18294, + "640": 18295, + "hini": 18296, + "path": 18297, + "viol": 18298, + "wat": 18299, + "Ġta": 18300, + "itas": 18301, + "reens": 18302, + "Ġmisc": 18303, + "ĠCA": 18304, + "ĠCte": 18305, + "ĠPeoples": 18306, + "ĠBian": 18307, + "ĠHanna": 18308, + "ĠFork": 18309, + "ĠDssel": 18310, + "htt": 18311, + "ulates": 18312, + "007": 18313, + "eburg": 18314, + "Ġarsen": 18315, + "neg": 18316, + "ĠShannon": 18317, + "482": 18318, + "488": 18319, + "478": 18320, + "Ġprotocol": 18321, + "449": 18322, + "ĠMetall": 18323, + "ognitive": 18324, + "ĠJulio": 18325, + "banded": 18326, + "ĠIntroduction": 18327, + "Ġrevol": 18328, + "ĠBonaparte": 18329, + "ĠCardiff": 18330, + "Ġpunished": 18331, + "onomic": 18332, + "Blue": 18333, + "ĠBarton": 18334, + "ĠSSR": 18335, + "communications": 18336, + "Ġessayist": 18337, + "Ġmerchant": 18338, + "Ġbotanist": 18339, + "ĠPanthers": 18340, + "Ġphenomenon": 18341, + "GO": 18342, + "XX": 18343, + "bys": 18344, + "Ġpier": 18345, + "ĠCandy": 18346, + "adic": 18347, + "stroke": 18348, + "agedy": 18349, + "Ġbeats": 18350, + "ĠChrys": 18351, + "Ġoverd": 18352, + "ĠAdri": 18353, + "ĠEminem": 18354, + "995": 18355, + "Ġprotons": 18356, + "ĠAntig": 18357, + "Ġmagical": 18358, + "ĠUrs": 18359, + "Ġhoped": 18360, + "racuse": 18361, + "Ġobservation": 18362, + "ĠFerrari": 18363, + "ĠTroms": 18364, + "Ġvinyl": 18365, + "dimensional": 18366, + "fb": 18367, + "lake": 18368, + "member": 18369, + "nikov": 18370, + "Ġtin": 18371, + "oric": 18372, + "ĠSuicides": 18373, + "ashes": 18374, + "ĠCats": 18375, + "ilan": 18376, + "ĠPey": 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"ĠHampton": 18430, + "Ġarguments": 18431, + "ĠLaurence": 18432, + "Ġtargets": 18433, + "ĠRenault": 18434, + "optera": 18435, + "ĠLionel": 18436, + "ĠJoyce": 18437, + "Ġcommissioned": 18438, + "Ġdecrease": 18439, + "ĠTitanic": 18440, + "ĠCowboys": 18441, + "ĠHertford": 18442, + "ĠGiorgio": 18443, + "Ġaunt": 18444, + "Ġwick": 18445, + "Ġsam": 18446, + "Ġcure": 18447, + "ĠTuc": 18448, + "Ġdressed": 18449, + "amorph": 18450, + "ĠHust": 18451, + "chner": 18452, + "Ġecol": 18453, + "orted": 18454, + "Ġrider": 18455, + "site": 18456, + "ĠAnime": 18457, + "Ġ1707": 18458, + "Ġbutton": 18459, + "Ġmusk": 18460, + "Ġmeg": 18461, + "Ġrecru": 18462, + "ĠElm": 18463, + "ĠGrampus": 18464, + "ĠHarbour": 18465, + "454": 18466, + "459": 18467, + "694": 18468, + "ĠTomorrow": 18469, + "ĠVictory": 18470, + "ĠVolks": 18471, + "Ġseparately": 18472, + "Ġsquares": 18473, + "ĠInteractive": 18474, + "Ġeras": 18475, + "ĠEverything": 18476, + "Ġflee": 18477, + "ĠTreatment": 18478, + "brother": 18479, + "ĠEscape": 18480, + "Ġrotation": 18481, + "Ġgathered": 18482, + "Ġlocomotive": 18483, + "ĠHussein": 18484, + "nio": 18485, + "ĠCM": 18486, + "ĠCreed": 18487, + "ĠPupp": 18488, + "ĠRising": 18489, + "ĠGaza": 18490, + "008": 18491, + "ewell": 18492, + "ogi": 18493, + "Ġchron": 18494, + "ĠMarqu": 18495, + "qual": 18496, + "forming": 18497, + "ĠEmm": 18498, + "ĠEnrique": 18499, + "573": 18500, + "662": 18501, + "ĠCherok": 18502, + "Ġposthum": 18503, + "ĠBetter": 18504, + "ĠRajas": 18505, + "Ġneighb": 18506, + "ĠFaust": 18507, + "ĠNagar": 18508, + "Ġdrought": 18509, + "ĠHubert": 18510, + "ĠJenkins": 18511, + "ĠCherokee": 18512, + "460": 18513, + "Ġbark": 18514, + "ĠSept": 18515, + "Ġfed": 18516, + "ĠAB": 18517, + "ĠBrett": 18518, + "Ġlord": 18519, + "ĠFunk": 18520, + "adeth": 18521, + "ĠNiz": 18522, + "ĠNih": 18523, + "ĠGuns": 18524, + "ianism": 18525, + "uti": 18526, + "006": 18527, + "asted": 18528, + "quito": 18529, + "eye": 18530, + "Ġtrap": 18531, + "Ġmonsters": 18532, + "Ġskiers": 18533, + 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18584, + "beat": 18585, + "Ġsettle": 18586, + "Ġknowing": 18587, + "ĠBoyd": 18588, + "ĠFortune": 18589, + "Ġsatir": 18590, + "ĠWiki": 18591, + "eddy": 18592, + "ĠRicky": 18593, + "ĠOwens": 18594, + "Germany": 18595, + "Ġcalculated": 18596, + "Ġvacuum": 18597, + "ĠCromwell": 18598, + "ĠDsseldorf": 18599, + "Gr": 18600, + "uya": 18601, + "aniel": 18602, + "Ġcipher": 18603, + "ĠSaid": 18604, + "ĠDram": 18605, + "story": 18606, + "stable": 18607, + "ĠJob": 18608, + "owitz": 18609, + "upe": 18610, + "ĠProb": 18611, + "ukary": 18612, + "ĠGlory": 18613, + "Ġexposure": 18614, + "hiro": 18615, + "Chinese": 18616, + "Ġmotiv": 18617, + "Ġimmigration": 18618, + "Ġmissionary": 18619, + "arlberg": 18620, + "Ġsurrendered": 18621, + "ĠBremen": 18622, + "Bay": 18623, + "Her": 18624, + "Nord": 18625, + "bank": 18626, + "titled": 18627, + "inom": 18628, + "arck": 18629, + "inga": 18630, + "ĠRih": 18631, + "ĠDund": 18632, + "ĠKram": 18633, + "Ġanxiety": 18634, + "ĠVad": 18635, + "ĠZam": 18636, + "998": 18637, + "Ġintent": 18638, + "589": 18639, + "Ġpublishes": 18640, + "umps": 18641, + "ĠPetrov": 18642, + "ĠHonolulu": 18643, + "Ġsporting": 18644, + "Art": 18645, + "ĠCasey": 18646, + "Ġwalked": 18647, + "ĠOmaha": 18648, + "Ġsulfate": 18649, + "Ġmomentum": 18650, + "ĠAnaheim": 18651, + "Ġpuppet": 18652, + "Ġhormone": 18653, + "ĠPreparation": 18654, + "ĠMozambique": 18655, + ")(": 18656, + "515": 18657, + "570": 18658, + "710": 18659, + "NL": 18660, + "ĠTik": 18661, + "Ġ230": 18662, + "Ġdiversity": 18663, + "ĠRaz": 18664, + "ĠLub": 18665, + "ĠLai": 18666, + "ĠNominated": 18667, + "ĠGM": 18668, + "ĠWide": 18669, + "ĠKick": 18670, + "Ġants": 18671, + "Ġvibr": 18672, + "ĠUnits": 18673, + "Ġ707": 18674, + "Ġfilming": 18675, + "Ġlightning": 18676, + "Ġpromise": 18677, + "iyah": 18678, + "ĠFernand": 18679, + "ĠLibertarian": 18680, + "rupted": 18681, + "Ġreserves": 18682, + "ĠPackers": 18683, + "ĠLearning": 18684, + "Ġvulner": 18685, + "480": 18686, + "Oh": 18687, + "aan": 18688, + "Ġtough": 18689, + "arma": 18690, + "ĠTian": 18691, + "ĠMim": 18692, + "adier": 18693, + "ĠNue": 18694, + "ĠOsh": 18695, + "009": 18696, + "Ġ940": 18697, + "Ġ1740": 18698, + "Ġ1784": 18699, + "ĠShot": 18700, + "Ġphon": 18701, + "gerald": 18702, + "ĠSyracuse": 18703, + "Ġcharacterized": 18704, + "Ġauto": 18705, + "ĠCatholicism": 18706, + "Ġsportscaster": 18707, + "Ġprimitive": 18708, + "Ġliteracy": 18709, + "ĠLucerne": 18710, + "Ġderiv": 18711, + "ĠFilipp": 18712, + "ĠSergeant": 18713, + "Ġmistake": 18714, + "Connor": 18715, + "Ġmirror": 18716, + "Ġsupercentenarian": 18717, + "ĠLiefering": 18718, + "cue": 18719, + "code": 18720, + "jav": 18721, + "slow": 18722, + "erland": 18723, + "ichel": 18724, + "Ġhaz": 18725, + "omas": 18726, + "iak": 18727, + "Ġshorts": 18728, + "asses": 18729, + "Ġcompact": 18730, + "Ġ910": 18731, + "ĠCann": 18732, + "ĠCarth": 18733, + "Ġnewer": 18734, + "Ġediting": 18735, + "471": 18736, + "Ġvariab": 18737, + "Ġindependently": 18738, + "Ġzinc": 18739, + "ĠFeature": 18740, 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18792, + "ĠKus": 18793, + "Ġproceed": 18794, + "Ġchorus": 18795, + "ahr": 18796, + "Ġacres": 18797, + "ancock": 18798, + "Ġrelax": 18799, + "Ġammon": 18800, + "ophone": 18801, + "571": 18802, + "546": 18803, + "Ġarchbishop": 18804, + "ĠErin": 18805, + "ĠAirports": 18806, + "Ġmissile": 18807, + "ĠBollywood": 18808, + "Ġemployee": 18809, + "Ġjunction": 18810, + "Ġdisabled": 18811, + "Ġinstallation": 18812, + "ĠTerritories": 18813, + "featuring": 18814, + "322": 18815, + "810": 18816, + "End": 18817, + "Ġaware": 18818, + "orum": 18819, + "ĠBenson": 18820, + "ĠRuther": 18821, + "ĠLily": 18822, + "irm": 18823, + "ĠJude": 18824, + "unge": 18825, + "ĠKak": 18826, + "Ġstating": 18827, + "osely": 18828, + "acin": 18829, + "ortex": 18830, + "Ġspac": 18831, + "ĠSham": 18832, + "ĠFlu": 18833, + "ĠPartners": 18834, + "unga": 18835, + "Ġadds": 18836, + "ophil": 18837, + "582": 18838, + "ĠCountess": 18839, + "ĠIntercontinental": 18840, + "Ġclosing": 18841, + "ĠPuy": 18842, + "Ġfacto": 18843, + "ĠPresbyter": 18844, + "zyme": 18845, + "ĠKatie": 18846, + "Ġvertebrates": 18847, + "Ġdollar": 18848, + "ĠSophia": 18849, + "Ġsurfaces": 18850, + "rehensive": 18851, + "izophrenia": 18852, + "390": 18853, + "512": 18854, + "590": 18855, + "jor": 18856, + "ĠCable": 18857, + "ĠIst": 18858, + "ĠRC": 18859, + "ĠDF": 18860, + "Ġnoun": 18861, + "1911": 18862, + "quiry": 18863, + "unders": 18864, + "ĠNewport": 18865, + "ĠOrt": 18866, + "Ġprehistoric": 18867, + "Ġmargin": 18868, + "Ġconsumer": 18869, + "iewicz": 18870, + "cheon": 18871, + "Ġinvade": 18872, + "992": 18873, + "588": 18874, + "oek": 18875, + "441": 18876, + "Ġbehalf": 18877, + "Ġcoral": 18878, + "ĠGalile": 18879, + "Ġsexuality": 18880, + "Ġfunding": 18881, + "ĠSquarePants": 18882, + "ĠHemings": 18883, + "ĠBoxing": 18884, + "Ġthrowing": 18885, + "ĠNegro": 18886, + "Ġdictator": 18887, + "Ġmilitia": 18888, + "ishnu": 18889, + "Ġsovereignty": 18890, + "ĠVorarlberg": 18891, + "Ġturtle": 18892, + "Ġcarefully": 18893, + "Just": 18894, + "onte": 18895, + "esp": 18896, + "Ġcorporation": 18897, + "ĠTut": 18898, + "Ġdried": 18899, + "Ġhate": 18900, + "ĠBing": 18901, + "arten": 18902, + "1912": 18903, + "ĠYus": 18904, + "ĠThink": 18905, + "Ġ1768": 18906, + "Ġ1783": 18907, + "prises": 18908, + "ĠCliff": 18909, + "578": 18910, + "Ġextent": 18911, + "Ġextends": 18912, + "ĠContin": 18913, + "ĠCampus": 18914, + "Ġpresents": 18915, + "ĠTimeline": 18916, + "ĠCamden": 18917, + "Ġpayment": 18918, + "ĠMidnight": 18919, + "ĠNagasaki": 18920, + "Ġfortress": 18921, + "ĠWorcester": 18922, + "ĠFitzgerald": 18923, + "Ġcarnivore": 18924, + "Ġmanuscript": 18925, + "Ġwithdrawal": 18926, + "ĠNatalie": 18927, + "ĠSturm": 18928, + "ĠProblems": 18929, + ".[": 18930, + "best": 18931, + "zk": 18932, + "oris": 18933, + "ĠRT": 18934, + "ĠRide": 18935, + "ĠHess": 18936, + "ĠNumbers": 18937, + "Ġhex": 18938, + "Ġcomments": 18939, + "orean": 18940, + "ĠAlien": 18941, + "ikon": 18942, + "munition": 18943, + "ĠAsc": 18944, + "ĠPride": 18945, + "ĠParticip": 18946, + "Ġsubprefecture": 18947, + "Ġmothers": 18948, + "ĠActs": 18949, + "culosis": 18950, + "Ġrenal": 18951, + "Ġheroes": 18952, + "ĠMegadeth": 18953, + "ĠProtestants": 18954, + "Ġplateau": 18955, + "ĠMaiden": 18956, + "Ġrestrictions": 18957, + "SV": 18958, + "Som": 18959, + "enov": 18960, + "Ġink": 18961, + "Ġcens": 18962, + "ĠCron": 18963, + "ilion": 18964, + "ĠPto": 18965, + "amel": 18966, + "ĠHitch": 18967, + "Ġlens": 18968, + "ĠDante": 18969, + "hter": 18970, + "opters": 18971, + "avior": 18972, + "arded": 18973, + "Ġ1758": 18974, + "oulder": 18975, + "sonzo": 18976, + "Ġrabb": 18977, + "Ġrecovery": 18978, + "ĠDeal": 18979, + "ĠCalder": 18980, + "aira": 18981, + "ĠOrlans": 18982, + "Ġnaturalist": 18983, + "Ġachievement": 18984, + "Ġbuying": 18985, + "ĠOccup": 18986, + "Ġhydroxide": 18987, + "ĠHousing": 18988, + "Ġimprisonment": 18989, + "Ġadvocate": 18990, + "eonato": 18991, + "Ġvagina": 18992, + "312": 18993, + "RL": 18994, + "both": 18995, + "human": 18996, + "sous": 18997, + "Ġaims": 18998, + "ĠCany": 18999, + "ĠBant": 19000, + "ĠDover": 19001, + "ĠGle": 19002, + "ĠWen": 19003, + "Ġgamb": 19004, + "ĠKuz": 19005, + "Ġsticks": 19006, + "tera": 19007, + "Ġ727": 19008, + "Ġoutstanding": 19009, + "ĠParan": 19010, + "ĠTej": 19011, + "ĠSeal": 19012, + "Ġremoving": 19013, + "Ġlasting": 19014, + "587": 19015, + "959": 19016, + "ĠAcademic": 19017, + "ĠSimmons": 19018, + "ĠBrowns": 19019, + "ĠSubway": 19020, + "ĠCurry": 19021, + "Ġtempor": 19022, + "Ġterrorism": 19023, + "Ġjewel": 19024, + "Ġhemisphere": 19025, + "ĠFellowship": 19026, + "ĠAndorra": 19027, + "830": 19028, + "Benz": 19029, + "may": 19030, + "alp": 19031, + "ĠSens": 19032, + "ĠSanga": 19033, + "ĠIUCN": 19034, + "ĠBord": 19035, + "ĠBunny": 19036, + "ĠHons": 19037, + "ĠLing": 19038, + "Ġbeam": 19039, + "Ġorphan": 19040, + "201": 19041, + "Ġestimates": 19042, + "ĠCaliph": 19043, + "Ġassemb": 19044, + "Ġlegacy": 19045, + "579": 19046, + "978": 19047, + "962": 19048, + "Ġpossession": 19049, + "Ġposts": 19050, + "Ġrebels": 19051, + "Ġlandsl": 19052, + "ĠBridg": 19053, + "Ġharmful": 19054, + "Ġaquatic": 19055, + "Ġtubes": 19056, + "ĠInspector": 19057, + "Ġrectang": 19058, + "ĠStrasbourg": 19059, + "Eredivisie": 19060, + "Green": 19061, + "cie": 19062, + "lant": 19063, + "publ": 19064, + "hell": 19065, + "along": 19066, + "ĠTheme": 19067, + "irn": 19068, + "ĠWyn": 19069, + "agos": 19070, + "assa": 19071, + "cler": 19072, + "ĠComing": 19073, + "Ġ1590": 19074, + "Ġtradem": 19075, + "ĠEls": 19076, + "Ġdecoration": 19077, + "Ġintention": 19078, + "Ġdefine": 19079, + "ĠDrum": 19080, + "Ġcyan": 19081, + "ĠHonour": 19082, + "ĠAnnabeth": 19083, + "ĠMatilda": 19084, + "ĠiTunes": 19085, + "Ġpromoting": 19086, + "ophyll": 19087, + "ĠBoliv": 19088, + "rapers": 19089, + "Ġattempting": 19090, + "ĠMandarin": 19091, + "thesis": 19092, + "Ġvenues": 19093, + "Seine": 19094, + "Ġkitchen": 19095, + "ĠDortmund": 19096, + "514": 19097, + "Dutch": 19098, + "birth": 19099, + "jatjara": 19100, + "oney": 19101, + "Ġhab": 19102, + "ĠHash": 19103, + "stick": 19104, + "Ġforth": 19105, + "ecution": 19106, + "ulating": 19107, + "ĠShab": 19108, + "Ġcharter": 19109, + "ĠAssam": 19110, + "Ġincorrect": 19111, + "ĠDesc": 19112, + "ĠGeorgetown": 19113, + "Ġavo": 19114, + "ĠArchd": 19115, + "Ġprimaries": 19116, + "Ġinhab": 19117, + "ĠJonas": 19118, + "Ġdisputes": 19119, + "Ġworshipped": 19120, + "Ġphilanthropists": 19121, + "Ġfeminism": 19122, + "Ġvessel": 19123, + "ĠCitizens": 19124, + "ĠBabylon": 19125, + "Ġvoyage": 19126, + "ĠKonstantin": 19127, + "630": 19128, + "Geor": 19129, + "map": 19130, + "Ġlays": 19131, + "Ġthy": 19132, + "Ġgig": 19133, + "Ġgates": 19134, + "raj": 19135, + "1915": 19136, + "ĠZak": 19137, + "Ġunderneath": 19138, + "zek": 19139, + "itsu": 19140, + "ĠBean": 19141, + "ometime": 19142, + "987": 19143, + "974": 19144, + "ĠDoom": 19145, + "ĠLinz": 19146, + "ĠParliamentary": 19147, + "ĠSilent": 19148, + "Ġministry": 19149, + "Ġargue": 19150, + "ĠSharp": 19151, + 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"Ġm ain", + "it s", + "ĠA fter", + "ther n", + "amp ions", + "ut ion", + "Ġ200 4", + "3 0", + "ĠW h", + "ĠA m", + "ĠS e", + "ĠG u", + "ograph y", + "el s", + "ĠCom mun", + "Ġ3 0", + "f ect", + "in k", + "c c", + "v ent", + "Ġsong s", + "19 7", + "Ġ201 8", + "Ġs ame", + "Ġsing er", + "ĠB ar", + "ctor s", + "ul l", + "olog y", + "Ġsm all", + "Ġb and", + "O S", + "Ġc ar", + "Ġm ake", + "Ġl oc", + "ĠH er", + "Ġ200 5", + "re en", + "ĠGe or", + "Ġ201 4", + "ĠCh rist", + "Ġfor mer", + "Ġf our", + "Ġs ub", + "d om", + "ly mp", + "ĠB e", + "g er", + "ĠM an", + "g est", + "Ġre t", + "5 0", + "in o", + "re t", + "ian s", + "c om", + "Ġdep artment", + "Ġcon s", + "Ġl angu", + "Ġk ill", + "Ġf e", + "Ġpro v", + "ĠSp ace", + "Ġus e", + "Ġa m", + "ĠO lymp", + "Ġl ife", + "l p", + "Ġm et", + "ak es", + "ĠW ill", + "if orn", + "ĠC ent", + "Ġa ir", + "is c", + "oun c", + "ov e", + "c ent", + "ĠCal iforn", + "Ġbe ing", + "Ġ19 0", + "ur al", + "y l", + "\" ,", + "Ġc r", + "ĠH ar", + "ĠCaliforn ia", + "Ġg ame", + "f ess", + "ĠPart y", + "Ġw ell", + "Ġ20 21", + "Ġprod uc", + "Ġ ed", + "oll ow", + "b le", + "Ġm od", + "Ġb ack", + "j ect", + "ĠR e", + "Ġn o", + "Ġp ages", + "Ġrec ord", + "ĠH ow", + "Ġd id", + "Ġof ten", + "Ġb el", + "y n", + "an k", + "Ġ2 1", + "Ġre p", + "Ġn amed", + "ie w", + "2 4", + "at ing", + "ĠB ra", + "land s", + "om et", + "Ġstart ed", + "6 0", + "iel d", + "Ġg u", + "r est", + "Ġ .", + "ĠCh ar", + "Ġo ld", + "ĠP ol", + "ĠO ff", + "Ġ201 6", + "a e", + "Ġreg ion", + "Ġb ased", + "Ġ2 3", + "2 5", + "st it", + "ĠGerman y", + "ĠN or", + "il le", + "ug ht", + "Ġb efore", + "Ġsy stem", + "al d", + "Ġ201 5", + "ĠA ng", + "8 0", + "Ġm unicipal", + "r ies", + "Ġfam ily", + "ĠM inist", + "Ġ2 9", + "ĠJapan ese", + "ician s", + "om ar", + "our n", + "ĠEng land", + "Ġs aid", + "Ġp ar", + "Ġre m", + "ĠInd ian", + "ĠRel ated", + "Ġo wn", + "ĠR oman", + "ener al", + "Ġ200 6", + "ĠPal omar", + "Ġagain st", + "Ġs ince", + "el f", + "Ġund er", + "ĠT r", + "if ic", + "ir d", + "Ġc areer", + "ond on", + "uc k", + "Ġact ress", + "on y", + "et her", + "Ġcommun e", + "ill ion", + "Ġt ran", + "Ġn orth", + "Ġl aw", + "b urg", + "k e", + "4 0", + "en g", + "Ġg ames", + "2 7", + "ĠB ro", + "ĠP eak", + "Ġn ear", + "ĠMov ies", + "ĠItal ian", + "Ġ X", + "ĠE m", + "ĠK itt", + "ĠL ondon", + "2 9", + "Ġ2 6", + "ĠE n", + "ĠA ir", + "Ġp o", + "ĠDe ath", + "st r", + "b s", + "at a", + "ĠH istory", + "Ġpl ace", + "Ġchar act", + "as k", + "Ġan y", + "Ġw e", + "Ġ2 8", + "Ġhe lp", + "w atch", + "2 8", + "ĠE x", + "ĠC up", + "Ġf ollow", + "c he", + "Ġw ater", + "Ġ201 1", + "i ation", + "2 6", + "at ure", + "is on", + "Ġas s", + "a f", + "Ġw ar", + "Ġ2 7", + "ĠSpace watch", + "Ġgovern ment", + "Ġ ent", + "Ġdirect ed", + "Ġb est", + "Ġin ter", + "ampions hip", + "ĠE ar", + "Ġt yp", + "in ess", + "r ing", + "Ġg r", + "Ġthe se", + "Ġan im", + "g ram", + "l ing", + "Ġ200 7", + "ul ar", + "al a", + "Ġcent ury", + "EA T", + "b y", + "u th", + "ar a", + "ain s", + "h am", + "ĠU S", + "ĠN EAT", + "c on", + "ide o", + "ĠAs s", + "Ġpopul ation", + "ĠS ome", + "an a", + "ed y", + "3 3", + "in t", + "Ġe ver", + "Ġse ason", + "od y", + "Ġm il", + "ram a", + "Ġ20 22", + "o on", + "Ġcount ry", + "Ġs ur", + "at or", + "Ġ &", + "ow s", + "ĠKing dom", + "Ġapp ear", + "ord s", + "am a", + "ro g", + "al t", + "Ġ201 3", + "Ġa round", + "Ġinclud ing", + "ut e", + "ĠW hen", + "Ġor ig", + "Ġl and", + "st er", + "Ġor gan", + "Ġ199 0", + "ĠM e", + "f l", + "9 0", + "Ġb oth", + "Ġse ver", + "oin t", + "T he", + "od e", + "a ul", + "Ġwebsit e", + "Ġ200 8", + "aj or", + "7 0", + "t t", + "an ish", + "Ġs chool", + "re m", + "Ġc ould", + "Ġin v", + "Ġ2 2", + "am b", + "q ue", + "r id", + "al es", + "ĠM c", + "ip p", + "d e", + "ĠB el", + "z er", + "on es", + "Ġ em", + "Ġs et", + "Ġd istrict", + "ĠG overn", + "il t", + "n er", + "ist an", + "ĠIn ternational", + "ur ch", + "us iness", + "ĠCanad ian", + "iz ed", + "emb ers", + "Ġim port", + "ĠWill iam", + "m s", + "ĠAustral ia", + "Ġbu ild", + "Ġ201 2", + "Ġ( )", + "Ġl ist", + "oug ht", + "ĠM ed", + "Ġpro fess", + "ag o", + "Ġe ach", + "Ġh ow", + "Ġr un", + "ĠAmer ica", + "a ur", + "Ġs l", + "ak ing", + "Ġh igh", + "um b", + "3 4", + "Ġus ually", + "n ey", + "Ġ right", + "Ġl ived", + "ĠAnd erson", + "ĠCom p", + "ĠCommun es", + "uct ion", + "o ard", + "ĠM es", + "Ġex amp", + "vel op", + "ĠPh il", + "Ġs im", + "ĠThe se", + "ort s", + "Ġ200 9", + "al k", + "ros s", + "9 9", + "Ġchild ren", + "ĠM on", + "3 7", + "Ġh um", + "i ence", + "6 5", + "ra ct", + "om in", + "u es", + "um mer", + "ĠM ich", + "ib le", + "ĠH ouse", + "w rit", + "Ġl ast", + "o le", + "Ġde velop", + "col span", + "ĠAust r", + "6 4", + "ad em", + "et er", + "Ġcre ated", + "Ġro ck", + "Ġcomp os", + "6 8", + "ĠO ne", + "6 7", + "ist or", + "Ġc aus", + "N E", + "\"| -", + "Ġser v", + "ĠInd ia", + "3 5", + "ĠMinist er", + "ĠL ou", + "Ġs k", + "Ġ ran", + "ĠSw ed", + "urr ent", + "le x", + "Ġc ounty", + "Ġ '", + "Ġbe gan", + "Ġad d", + "Ġsever al", + "Ġpro gram", + "\"|- ||", + "Ġw inn", + "Ġs ign", + "ĠS an", + "Ġcl ub", + "ĠP er", + "Ġs outh", + "Ġst at", + "ĠD em", + "Ġatt ack", + "en e", + "Ġwh ile", + "Ġo per", + "ĠSt ate", + "Ġcom mon", + "ĠS ec", + "in c", + "an e", + "Ġwrit er", + "3 8", + "Ġ198 0", + "ĠD av", + "Ġv ers", + "ap p", + "ĠG l", + "ed er", + "f or", + "f ul", + "ĠS up", + "Ġlar ge", + "c hes", + "Ġt erm", + "us h", + "ĠS y", + "it ary", + "Ġimport ant", + "Ġl ive", + "v en", + "ens us", + "s ide", + "ing ton", + "Ġoff icial", + "ĠHow ever", + "4 5", + "Ġsing le", + "ĠS ch", + "Ġ if", + "Ġp ol", + "Ġhe ad", + "ĠDeath s", + "Ġd rama", + "re w", + "ĠAustral ian", + "Ġdis c", + "ir ed", + "Ġac c", + "d ay", + "ĠC ities", + "6 9", + "Ġw ent", + "Ġ199 7", + "Ġf ilm", + "n a", + "l er", + "Ġin t", + "att le", + "Ġpopul ar", + "st e", + "a ught", + "as ter", + "Ġs uc", + "ĠA c", + "Ġm illion", + "ber g", + "t he", + "ĠMes a", + "Ġd ef", + "Ġmunicipal ity", + "ĠOff icial", + "Ġd iv", + "ĠRuss ian", + "Ġlangu age", + "ic o", + "z il", + "3 9", + "a ut", + "id d", + "Ġn ow", + "o ice", + "ro l", + "Ġs oc", + "ĠM iss", + "Ġle g", + "4 8", + "Ġexamp le", + "4 7", + "Ġm at", + "an ge", + "ce pt", + "Ġdes ign", + "Ġ199 6", + "om b", + ". \"", + "Ġp ower", + "Ġf in", + "ĠS er", + "Ġch ang", + "Ġcount ries", + "Ġm in", + "Ġear ly", + "Ġe p", + "Ġan n", + "ĠCh ampionship", + "Ġp resident", + "ĠBra zil", + "Ġd ist", + "omet imes", + "iv en", + "Ġh ome", + "ĠM ex", + "Ġg et", + "w est", + "Ġen g", + "ĠH ol", + "ĠL O", + "ĠQ u", + "Ġcomp et", + "Ġw est", + "ĠC o", + "Ġgroup s", + "ock ey", + "Ġinclud e", + "ic es", + "ĠP ark", + "ĠR ec", + "Ġo pen", + "Ġd ay", + ". .", + "iv il", + "Ġv ideo", + "Ġin c", + "op h", + "i ef", + "l in", + "Ġex p", + "Ġtran s", + "ber t", + "ĠR ober", + "Ġcap ital", + "p le", + "Ġspec ies", + "Ġme ans", + "ĠS m", + "f ord", + "NE OS", + "ĠLO NEOS", + "Ġ196 0", + "Ġwrit ten", + "ĠP olit", + "ri end", + "i j", + "ĠSp anish", + "con om", + "5 7", + "Ġle ft", + "g es", + "i en", + "ĠS ing", + "Ġw ay", + "id ed", + "ĠJ ames", + "ĠS chool", + "Ġex t", + "ĠT ur", + "ro d", + "ĠP aul", + "oc rat", + "Ġever y", + "ĠS en", + "ĠM or", + "g in", + "Ġh istory", + "Ġ199 5", + "a ces", + "Ġ ,", + "ĠD r", + "9 8", + "Ġm embers", + "ĠT ex", + "our t", + "ĠP ort", + "ĠCanad a", + "Ġp ass", + "ĠA ctors", + "i od", + "Ġt imes", + "ĠE ast", + "c o", + "ĠAng el", + "ĠF ootball", + "e al", + "l ed", + "i us", + "Ġ197 0", + "Ġd own", + "F A", + "r is", + "ĠS aint", + "le ge", + "u ff", + "Ġm uch", + "ĠG ree", + "ch n", + "ov er", + "Ġman ag", + "Ġmar ried", + "Ġact iv", + "ar n", + "Ġwh at", + "9 7", + "5 8", + "an ia", + "id es", + "m a", + "ra in", + "Ġpro t", + "ep end", + "oun g", + "ro te", + "ĠRep ublic", + "Ġfam ous", + "it ar", + "|||| ||||", + "it er", + "ist ics", + "Ġcan cer", + "Ġsh ort", + "Ġ199 4", + "Ġw omen", + "e an", + "i or", + "Ġv ar", + "os p", + "ĠM il", + "ĠR eg", + "id a", + "ĠS ov", + "Ġst ill", + "Ġcom edy", + "Ġm ajor", + "a el", + "ĠFl or", + "or p", + "ĠN ot", + "Ġcl ass", + "ĠT own", + "y le", + "u el", + "Ġre f", + "o e", + "ĠPro v", + "Ġbu ilt", + "ct ion", + "Ġf ather", + "h an", + "Ġ3 1", + "y a", + "ol ution", + "al th", + "Ġj oin", + "v iew", + "Ġc urrent", + "ill a", + "ĠGeor ge", + "ĠIt s", + "Ġre ce", + "k y", + "ĠN Y", + "Ġ1 00", + "g y", + "v es", + "6 6", + "Ġst ar", + "as tern", + "ĠLou is", + "Ġs old", + "as es", + "Ġ18 8", + "Ġ199 2", + "op e", + "Ġb re", + "ĠPr ime", + "Ġp ubl", + "ĠSov iet", + "9 5", + "ĠP re", + "he l", + "Ġt it", + "o f", + "Ġ199 3", + "Ġv ill", + "ric k", + "Ġdo es", + "ĠJ os", + "Ġsup port", + "ut ch", + "ĠJ ack", + "Ġlar gest", + "Ġcomp any", + "Ġto ok", + "Ġs on", + "Ġa ward", + "ĠAr t", + "Ġp ublic", + "ĠR ed", + "ĠCh ic", + "ĠC at", + "ans as", + "Ġb usiness", + "Ġg ood", + "ĠItal y", + "ĠT w", + "Ġw rote", + "ĠV al", + "ĠUn ion", + "ĠM ount", + "Ġmov ed", + "ĠEurope an", + "ĠV ir", + "ĠK ore", + "ĠM any", + "en a", + "Ġan other", + "Ġbec ome", + "ĠCom m", + "Ġl a", + "Ġfootball er", + "ĠR ich", + "ĠTex as", + "n ess", + "Ġth ird", + "ĠA g", + "ad io", + "is ed", + "ĠCh ina", + "Ġc ame", + "Ġsuc cess", + "Ġo b", + "oc iation", + "Ġhe ld", + "ĠChic ago", + "Ġdire ctor", + "Ġto p", + "Ġb ody", + "Ġst age", + "Ġ199 1", + "c y", + "ĠR o", + "enc y", + "w ork", + "3 6", + "Ġb ig", + "ad es", + "Ġn eed", + "ĠNY S", + "4 4", + "ot s", + "Ġev en", + "ĠDem ocrat", + "ric a", + "Ġpro ble", + "Ġcont in", + "ourn al", + "uth or", + "Ġalbum s", + "Ġg iven", + "Ġprofess ional", + "Ġp os", + "Ġw ant", + "ur ed", + "Ġg en", + "iv al", + "ag n", + "Ġb as", + "Ġme an", + "ad y", + "Ġph ys", + "ĠC ast", + "Ġbook s", + "ĠP ak", + "ĠP ri", + "Ġpolit ical", + "Ġcon d", + "ĠD on", + "ex t", + "ĠRober t", + "ĠM ont", + "ac ed", + "os ed", + "ra ft", + "ĠSp ort", + "ĠC ap", + "ĠL os", + "r ican", + "ĠD uring", + "Ġd eb", + "5 5", + "ĠM y", + "Ġj ust", + "ar m", + "Ġcom m", + "ĠS l", + "Ġs ix", + "ir l", + "ĠD istrict", + "Ġwork ed", + "ut er", + "Ġo cc", + "ĠY ou", + "k i", + "Ġn ov", + "ĠBl ack", + "Ġpolitician s", + "7 8", + "ĠM ost", + "ĠDav id", + "Ġc ensus", + "and er", + "ĠL ist", + "ĠB est", + "h i", + "ĠD ep", + "Ġdes c", + "ĠT ra", + "av ing", + "Ġc ult", + "iet y", + "Ġcharact er", + "il ity", + "t ain", + "Ġth ings", + "ĠCl ub", + "ul a", + "ĠJ ew", + "Ġkill ed", + "at ural", + "er a", + "ĠAf rican", + "Ġres p", + "ou thern", + "Ġp resent", + "3 1", + "ĠCh in", + "ĠM al", + "Ġep is", + "ĠN e", + "ĠH igh", + "Ġal ong", + "Ġm en", + "v ille", + "Ġr ul", + "Ġf ive", + "um p", + "ĠF rom", + "ut ed", + "ĠD utch", + "ĠSc ott", + "m en", + "Ġl ight", + "r u", + "Ġ| }", + "ĠB as", + "ra b", + "it ions", + "m ed", + "Ġw ord", + "o o", + "ĠW e", + "Ġf em", + "ĠR es", + "or ies", + "s c", + "ĠH en", + "ĠR oy", + "ĠW ash", + "Ġ198 9", + "Ġl iving", + "Ġs w", + "m e", + "ĠG ro", + "id s", + "ĠAs ia", + "ear ch", + "Ġper iod", + "Ġmil itary", + "il ar", + "Ġr ed", + "Ġro le", + "ĠB y", + "ĠAc adem", + "ĠS al", + "av y", + "ĠS ummer", + "9 4", + "ĠAf rica", + "ĠE mp", + "writ er", + "ĠB er", + "Ġ198 8", + "Ġfound ed", + "Ġplay s", + "an o", + "Ġ198 1", + "Ġt em", + "Ġvers ion", + "ĠD es", + "Ġser ved", + "ol ic", + "v al", + "itt le", + "3 2", + "Ġpubl ished", + "t y", + "ĠH al", + "Ġh istor", + "our s", + "Ġ ef", + "ĠM ad", + "Ġprov ince", + "e um", + "Ġrep resent", + "Ġus ing", + "ĠI ll", + "n ect", + "ĠQ ue", + "o ff", + "ant ic", + "Ġex pl", + "u x", + "ĠTh om", + "re g", + "ot a", + "Ġl ook", + "Ġwork s", + "ell s", + "ical ly", + "Ġm y", + "Ġor der", + "ounc il", + "' t", + "Ġf rog", + "ir c", + "ĠC ath", + "ĠM art", + "Ġfollow ing", + "ĠWash ington", + "l ine", + "Ġc amp", + "7 7", + "ĠGr and", + "ra el", + "Ġpart s", + "Ġf ew", + "Ġag ed", + "le ments", + "Ġret urn", + "Ġto g", + "ĠS am", + "Ġdis e", + "Ġ 0", + "Ġspec ial", + "Ġtog ether", + "Ġev ent", + "ĠAngel es", + "al ity", + "ĠChin ese", + "ĠB ut", + "Ġeng ine", + "ĠB u", + "ĠA lex", + "t al", + "ĠD iv", + "at ors", + "ĠPak istan", + "Ġa uthor", + "it zer", + "iz e", + "Ġ195 0", + "Ġ ide", + "ĠW rit", + "9 6", + "re am", + "k a", + "Ġhe art", + "Ġg reat", + "Ġcaus ed", + "ou l", + "ĠChar les", + "Ġf ood", + "h a", + "ĠChrist ian", + "iss ion", + "L O", + "od es", + "ĠM unicipal", + "ĠF irst", + "Ġbec om", + "ĠG reat", + "ĠE v", + "Ġs ometimes", + "iz ation", + "if ied", + "ĠH all", + "Ġelect ion", + "ounc ed", + "ĠMich ael", + "r ip", + "Ġe qu", + "or ed", + "ict ion", + "S t", + "Ġg ot", + "on a", + "op s", + "Ġ es", + "Ġr est", + "ĠN o", + "Ġse e", + "ĠD ay", + "Ġhum an", + "lect ion", + "Ġt er", + "Ġim p", + "Ġ198 6", + "Ġar r", + "Ġh ig", + "Ġt ake", + "Ġper form", + "Ġv oice", + "ĠVir gin", + "and s", + "c ed", + "Ġp ut", + "ĠG old", + "sp eople", + "ro v", + "i ol", + "5 4", + "ĠK ar", + "ĠP at", + "m inist", + "Ġth ought", + "ĠM ary", + "ĠPl ay", + "Ġg ener", + "g ian", + "ĠRich ard", + "Ġs ol", + "Ġbel ie", + "ĠOlymp ics", + "idd le", + "ĠB ill", + "ĠSp ain", + "Ġb i", + "i ers", + "ĠB en", + "ĠM ass", + "Ġf ight", + "B C", + "ĠL aw", + "ĠM et", + "Ġtr ad", + "ar ch", + "un t", + "Ġv ot", + "Ġ198 7", + "Ġse at", + "Ġgu itar", + "A S", + "ĠIs rael", + "Ġm other", + "ord ing", + "ĠI r", + "um ent", + "ĠCar ol", + "w ays", + "ĠG od", + "l o", + "Ġar ch", + "r ation", + "Ġ198 4", + "Ġle vel", + "Ġtyp e", + "ud e", + "Ġre pl", + "Ġ197 9", + "ĠS im", + "ar ed", + "ĠT er", + "8 8", + "Ġs ent", + "Ġv ol", + "Ġst ars", + "ĠS o", + "Ġf riend", + "Ġe conom", + "ĠT om", + "Ġin ternational", + "ĠPar is", + "in i", + "Ġn ext", + "Ġfe at", + "eren ce", + "ĠY ear", + "ĠAward s", + "Ġr iver", + "ĠU K", + "un n", + "ĠCh urch", + "ĠDemocrat ic", + "Ġg eneral", + "u ed", + "ĠF LO", + "at ives", + "5 9", + "Ġk ing", + "Ġl ine", + "ĠFran k", + "Ġm aking", + "Ġsc ient", + "ial ly", + "Ġ197 3", + "Ġm ark", + "Ġst ory", + "ĠTown s", + "ĠI f", + "Ġc rit", + "ĠTur k", + "Ġ198 5", + "m b", + "ĠAr g", + "ĠIs land", + "Ġd ata", + "Ġvill age", + "m ing", + "ĠL at", + "Ġy ou", + "ĠG eneral", + "et work", + "Ġ197 6", + "Ġ198 2", + "l iam", + "Ġorig in", + "Ġrel ig", + "ur a", + "ĠK n", + "per or", + "Ġf ind", + "Ġh ouse", + "ĠFlor ida", + "ar i", + "Ġan t", + "Ġ198 3", + "ĠS ant", + "ĠCol lege", + "Ġc hem", + "ĠAcadem y", + "form ation", + "ĠEmp ire", + "Ġ5 0", + "Ġv is", + "Ġlead er", + "I D", + "rop ical", + "Ġ197 7", + "u le", + "Ġf ail", + "i ber", + "Ġstr uct", + "ĠR ock", + "Ġj ournal", + "om a", + "Ġrece ived", + "ino is", + "Ġsim ilar", + "c ast", + "ĠAt l", + "ĠD an", + "ĠG o", + "st on", + "m ost", + "Ġloc ated", + "Ġn e", + "ĠH am", + "ĠK h", + "liam ent", + "ain ed", + "l ic", + "6 3", + "ĠT V", + "c her", + "ĠG ra", + ") :", + "ĠAustr ian", + "ĠIll inois", + "c ient", + "Ġ197 2", + "ĠB i", + "itzer land", + "ĠR ev", + "Ġart ist", + "s y", + "Ġproduc er", + "ert ain", + "Ġa ut", + "ig a", + "Ġnov el", + "ov ered", + "Ġd ays", + "Ġst ation", + "ĠD is", + "Ġed uc", + "Ġst op", + "ĠGree k", + "ĠMus ic", + "im ate", + "Ġp aint", + "ĠI m", + "ĠGovern or", + "Ġse en", + "ĠZ eal", + "ĠGeor g", + "Ġh ost", + "Ġall ow", + "Ġa v", + "a im", + "he st", + "Ġp rom", + "ad s", + "end ed", + "Ġstat istics", + "er ing", + "Ġ197 8", + "ĠP eter", + "Ġv al", + "ĠZeal and", + "Ġg l", + "Ġl ess", + "ĠSw itzerland", + "ar ian", + "Ġm ount", + "Ġe ast", + "Ġgro w", + "ĠSport speople", + "ĠAr ch", + "r or", + "Ġmod ern", + "Ġt urn", + "av es", + "y r", + "ĠT ran", + "ol f", + "ap e", + "ĠK ansas", + "Ġm akes", + "Ġcons id", + "0 8", + "ĠFran c", + "Ġdise ase", + "a ff", + "ir on", + "ĠD el", + "ĠOlymp ic", + "ĠU p", + "ĠAss ociation", + "Ġ193 0", + "ĠRuss ia", + "al f", + "200 6", + "4 9", + "im a", + "Ġ18 7", + "y d", + "cent ury", + "ar ia", + "ĠPolit icians", + "ĠSwed en", + "Ġis land", + "Ġst yle", + "ask et", + "200 5", + "Ġproduc ed", + "u z", + "Ġ197 4", + "aught er", + "ĠF ilm", + "Ġth ose", + "Ġ197 5", + "Ġbl ack", + "Ġl ed", + "est s", + "Ġev ents", + "Ġbe g", + "Ġind epend", + "ĠWrit ers", + "ian a", + "Ġelect ed", + "Ġteam s", + "ul ts", + "ĠT o", + "ĠProv ince", + "200 7", + "ĠCath olic", + "ĠM er", + "Ġcont rol", + "ud d", + "Ġbro ther", + "Ġpart y", + "ĠMex ico", + "Ġse x", + "un k", + "Ġst ates", + "Ġsh ould", + "Ġform ed", + "ition al", + "Ġ197 1", + "u ce", + "ĠG reen", + "th ough", + "ak en", + "re y", + "Ġ194 0", + "Ġd el", + "Ġcharact ers", + "in ter", + "4 6", + "it es", + "le ar", + "Ġg od", + "S S", + "in ed", + "l am", + "Ġs ound", + "uk e", + "Ġ #", + "gy pt", + "0 7", + "ur t", + "erg y", + "Ġwith out", + "Ġ :", + "Ġn omin", + "ĠEar th", + "I I", + "b oard", + "t ed", + "Ġmon ey", + "w ood", + "Ġph il", + "ĠA ct", + "ad a", + "Ġcon f", + "Ġtit le", + "Ġs ay", + "ĠVirgin ia", + "an i", + "Ġorig inal", + "Ġpl aces", + "f ield", + "Ġproble ms", + "o z", + "ap er", + "Ġd i", + "Ġs ide", + "ol s", + "ong ress", + "Ġann ounced", + "Ġ /", + "fect ure", + "if f", + "hem at", + "re et", + "ĠB ec", + "Ġdesc rib", + "ad u", + "ĠAr my", + "ĠWest ern", + "at ory", + "200 4", + "Ġw ife", + "Ġ ice", + "ent al", + "ight s", + "ĠE r", + "ubl ican", + "ĠRoy al", + "Ġd ue", + "s elf", + "ĠB C", + "ĠAn t", + "Ġa way", + "e ep", + "Ġex per", + "ill s", + "ĠHen ry", + "5 6", + "Ġshow s", + "Ġo pp", + "Ġl ot", + "ap pen", + "Ġjoin ed", + "ĠM ac", + "ĠE gypt", + "ic le", + "ĠThom as", + "Ġstr ong", + "Ġd est", + "Ġs il", + "Ġk ind", + "eb all", + "Ġmed al", + "Ġpo et", + "en se", + "A mer", + "Ġh ockey", + "ĠBrazil ian", + "Ġ el", + "asket ball", + "k n", + "ard s", + "ĠT or", + "ĠI ran", + "ur s", + "Ġst and", + "Ġto tal", + "ĠO l", + "ĠV ict", + "Ġ196 8", + "ist er", + "Ġc y", + "Ġmus ician", + "ol k", + "ĠArg ent", + "Ġm atch", + "ĠCent ral", + "ain e", + "ro y", + "ac hed", + "ĠO h", + "ĠW omen", + "ĠF in", + "Ġcompos er", + "ĠW il", + "ĠW ind", + "it a", + "ĠA cc", + "ĠC O", + "rid ge", + "x t", + "Ġd efe", + "g o", + "ep h", + "r ont", + "ste ad", + "Ġbuild ing", + "Ġc ities", + "Ġlangu ages", + "Ġs ite", + "as ons", + "Ġother s", + "Ġl ost", + "Ġchang ed", + "ers t", + "ĠB ook", + "ur b", + "ra w", + "200 3", + "Ġpl an", + "Ġloc al", + "yl v", + "ĠF I", + "ĠW ith", + "ĠV ill", + "Ġf ire", + "200 1", + "ord er", + "Ġdif f", + "Ġpro cess", + "Ġw rest", + "ĠPri ze", + "Ġm ust", + "Ġare as", + "urr ican", + "Ġp oss", + "em ents", + "Ġright s", + "ĠB ay", + "orp or", + "ĠC ouncil", + "Amer ican", + "ĠSt ud", + "Ġch ange", + "ĠD o", + "200 8", + "Ġstud io", + "ĠR ob", + "b e", + "re ed", + "Ġ196 9", + "ĠM in", + "Ġw ee", + "Ġd em", + "re land", + "Ġre al", + "c hed", + "end er", + "fl u", + "Ġre port", + "or ia", + "ĠRep ublican", + "8 7", + "ĠP op", + "Ġm ult", + "Ġwh ite", + "F F", + "Ġ196 4", + "Ġbe h", + "ĠSt ar", + "Ġl ate", + "ĠRec ords", + "n ot", + "ĠG ames", + "ĠP et", + "ad o", + "ĠCat al", + "Ġl ives", + "e le", + "ĠSwed ish", + "ward s", + "Ġ =", + "9 3", + "ether lands", + "Ġinclud es", + "Ġp at", + "Ġp oint", + "Ġh appen", + "ers ey", + "ĠD ev", + "om s", + "ĠI reland", + "Ġplay ing", + "ĠO k", + "ĠM ic", + "200 2", + "Ġ196 7", + "ĠC ont", + "el and", + "b or", + "Ġsoc ial", + "Ġh ard", + "ĠWh ite", + "Ġ $", + "Ġef fect", + "ĠP enn", + "Ġbro ad", + "Ġsc ience", + "ĠGro up", + "ĠA v", + "r d", + "ic les", + "rip t", + "Ġc ivil", + "ĠSc ot", + "al y", + "l ished", + "ar ies", + "Ġd et", + "Ġf un", + "Ġn on", + "ĠCarol ina", + "Ġy oung", + "Ġg ave", + "Ġinclud ed", + "ĠAustr ia", + "ĠSup er", + ". ,", + "ill er", + "ip s", + "Ġ )", + "Ġm ix", + "4 3", + "Ġre ad", + "ĠSec ret", + "aw a", + "Ġr adio", + "Ġmost ly", + "og n", + "ĠO s", + "200 0", + "an ies", + "Ġm ag", + "re l", + "i ro", + "Ġanim als", + "6 1", + "n ing", + "Ġh and", + "i qu", + "sh ire", + "Ġph ot", + "p art", + "ĠL ife", + "Ġ4 0", + "U n", + "Ġappear ed", + "Ġp ain", + "Ġg old", + "ak er", + "Ġf ield", + "eder al", + "am m", + "ĠM r", + "Ġte chn", + "ib r", + "Ġa ff", + "Ġf inal", + "c le", + "4 1", + "z a", + "Ġh old", + "all s", + "Ġra ce", + "Ġad v", + "Ġres ult", + "ĠC ro", + "b on", + "Ġn or", + "ant on", + "ĠM el", + "ĠH on", + "ĠS ur", + "Ġw ords", + "ĠN etherlands", + "ad or", + "ĠA rab", + "y m", + "ĠEar ly", + "p s", + "c raft", + "Ġs ett", + "ĠM ag", + "angu age", + "Ġ194 5", + "l i", + "ig er", + "ĠB o", + "9 2", + "ĠR h", + "Ġse a", + "ĠA pp", + "ect ed", + "Ġcol or", + "at o", + "il es", + "b r", + "Ġd aughter", + "ec ut", + "lect ed", + "ep ar", + "le ment", + "ĠC he", + "sp ort", + "Ġdeb ut", + "inn ing", + "200 9", + "9 1", + "Ġc or", + "199 9", + "Ġcomp uter", + "op her", + "a ud", + "os aur", + "Ġcom es", + "Ġc al", + "ĠL ab", + "he ast", + "it her", + "Ġstud y", + "ĠMar k", + "Ġco ach", + "Ġus es", + "uc ed", + "ĠC r", + "Ġt rib", + "Ġt aken", + "Ġ z", + "Ġwant ed", + "w w", + "id ing", + "6 2", + "Ġg ra", + "ĠCon f", + "ĠOh io", + "i que", + "Ġ196 6", + "is l", + "ĠF am", + "l or", + "ce an", + "Ġ( ;", + "ĠH a", + "5 3", + "ĠS ince", + "ĠV ol", + "Ġfem ale", + "st ate", + "Ġoff ice", + "ĠTh at", + "it ect", + "ub e", + "ĠB attle", + "ĠD en", + "in ation", + "ĠDiv ision", + "5 1", + "Ġre fer", + "ĠG ar", + "Ġ [", + "n y", + "it ch", + "Ġinv ol", + "i y", + "4 2", + "c a", + "ĠH ung", + "Ġ194 7", + "ell ow", + "e h", + "g en", + "Ġh aving", + "Ġbir th", + "at ic", + "Ġsc reen", + "ĠPort ug", + "Ġn atural", + "g r", + "w are", + "ĠJ er", + "ĠS ol", + "Ġwith in", + "le te", + "C h", + "ann el", + "ĠN ob", + "G B", + "ĠM od", + "ĠU k", + "Ġro und", + "Ġsp orts", + "k ed", + "s el", + "ĠLe g", + "ict ures", + "l a", + "ĠMus eum", + "Ġd am", + "ig an", + "ri al", + "ĠGe ography", + "Ġbet ter", + "Ġdevelop ed", + "Ġp ost", + "on ia", + "r ia", + "ĠGeorg ia", + "es se", + "Ġ196 5", + "Ġsur v", + "ĠJ ersey", + "Ġp ort", + "ĠJ r", + "ab it", + "ĠScott ish", + "Ġkn ow", + "ĠH el", + "ĠM os", + "Ġfootball ers", + "ies t", + "ĠPol ish", + "ĠJew ish", + "ĠH um", + "Ġ194 8", + "Ġs om", + "Ġh alf", + "Ġs ays", + "Ġv oc", + "ĠNor thern", + "Ġth ink", + "g ed", + "Ġpr ison", + "ĠB oy", + "Ġ196 3", + "ĠG en", + "Ġ195 6", + "he im", + "ĠS ong", + "ĠL o", + "Ġin formation", + "ĠP e", + "Ġcom e", + "ĠB ur", + "sy ch", + "V ID", + "Ġf ree", + "Ġs epar", + "Ġm ass", + "Ġle arn", + "ĠL ake", + "Ġestab lished", + "Ġdist rib", + "ĠG all", + "as y", + "Ġv iol", + "an ces", + "ĠL ove", + "ĠK ent", + "ĠLe e", + "u a", + "y o", + "ific ation", + "Ġconsid ered", + "b ers", + "urrican e", + "olog ical", + "Ġbecom es", + "Ġsp eak", + "Ġam ong", + "Ġstud ied", + "ĠS ett", + "ĠCO VID", + "Ġt our", + "ac y", + "Ġcon nect", + "ĠSt ev", + "ip le", + "Ġto o", + "ĠB or", + "osp ital", + "Ġad minist", + "ĠRep resent", + "ĠS un", + "Ġf ish", + "um n", + "Ġh on", + "Ġs outhern", + "ag on", + "Ġin flu", + "il s", + "ĠJ ul", + "our ces", + "ol a", + "et ts", + "Ġab le", + "ĠC amp", + "Ġl ab", + "u f", + "l im", + "Ġ20 23", + "ĠPre fecture", + "ro ll", + "ĠCent er", + "Ġcommun ity", + "it or", + "Ġ196 1", + "ĠC or", + "it z", + "ac her", + "l ess", + "ell ing", + "Ġs qu", + "Ġepis ode", + "ĠQue en", + "Ġd one", + "ĠEm peror", + "ou ri", + "ut es", + "Ġ196 2", + "ĠPr ince", + "ĠR os", + "t a", + "am ent", + "ĠAn n", + "Ġ194 6", + "ĠB ang", + "Ġind ust", + "et ic", + "em s", + "kn own", + "s ylv", + "ĠS k", + "ĠC ount", + "ĠC ivil", + "ond iss", + "ash i", + "Ġtra in", + "v ious", + "ĠIn ter", + "Ġcent er", + "ĠSm ith", + "l ike", + "Ġw oman", + "ur der", + "ĠP an", + "ĠIn stit", + "Ġsp ace", + "Ġab ove", + "us ed", + "ĠIr ish", + "\" )", + "Ġt re", + "j an", + "ĠC ourt", + "ĠCol umb", + "am s", + "ĠM at", + "s ong", + "Ġm ot", + "Ġl ow", + "ĠJ ust", + "amp ion", + "ap s", + "ĠIs lam", + "for man", + "ĠS illa", + "Ġmod el", + "ĠL in", + "m ar", + "Ġin str", + "ĠO bs", + "Ġmat hemat", + "Ġpos ition", + "r ie", + "Ġwrit ers", + "ĠMunicipal ities", + "ach us", + "it iz", + "Ġ194 2", + "Ġco ast", + "ĠGovern ment", + "sylv ania", + "ĠII I", + "ĠFI FA", + "g round", + "o or", + "ap t", + "Ġtra ck", + "Ġ194 9", + "Ġphil os", + "s ur", + "ak a", + "Ġc op", + "Ġn ever", + "Ġwork ing", + "Ġ195 7", + "Ġ19 20", + "az z", + "ĠC ongress", + "b and", + "Ġth ough", + "ĠB ern", + "199 8", + "ent ly", + "ĠE OS", + "Ġnor thern", + "Ġ194 1", + "Ġinc re", + "Ġt ro", + "Ġt reat", + "at ely", + "Ġcount ies", + "ĠMart in", + "Ġc irc", + "Ġ194 4", + "Ġis s", + "rit ory", + "el t", + "Ġwinn ers", + "ĠSe a", + "achus etts", + "Ġ193 6", + "ĠPar liament", + "Ġmus ical", + "ĠJ im", + "Ġ195 1", + "5 2", + "Ġob ject", + "ĠM a", + "pl ay", + "Ġint rod", + "Ġ et", + "ĠTe levision", + "ĠW W", + "ef f", + "Ġk eep", + "Ġal most", + "Ġf ar", + "d s", + "v ers", + "b ack", + "ĠScot land", + "ĠF red", + "Ġb r", + "Ġ193 9", + "ĠMass achusetts", + "Ġch art", + "Ġ ill", + "ĠThe ir", + "Ġoff ic", + "Ġpl ants", + "w h", + "ver age", + "et te", + "Ġf ull", + "re ad", + "ĠC ons", + "Ġtyp es", + "Ġh or", + "Ġsing ers", + "Ġserv ice", + "Ġ193 4", + "Ġhig hest", + "w a", + "Ġf a", + "ĠL and", + "Ġ8 8", + "ĠEd ward", + "Ġn ames", + "ish op", + "ĠHal eak", + "ĠW ales", + "ĠDis ney", + "Ġmus icians", + "H L", + "ĠJos eph", + "ĠSt an", + "Ġ195 8", + "ot o", + "ĠSett lements", + "Ġp res", + "Ġth r", + "Ġc ast", + "ĠBec ause", + "ĠB ob", + "ĠS at", + "p ec", + "ĠH ot", + "ell a", + "ater ial", + "Ġact ion", + "Ġde v", + "Ġc and", + "ĠS il", + "Ġret ired", + "Ġend ed", + "ĠPenn sylvania", + "c ul", + "ĠHaleak ala", + "Ġh it", + "ĠKore an", + "n ow", + "Ġwinn ing", + "p her", + "Ġb asketball", + "Ġcom b", + "Ġ193 8", + "Ġle ast", + "id er", + "b a", + "p e", + "ze ch", + "ĠE U", + "A R", + "ran ch", + "Ġk ey", + "ĠT ok", + "Ġsuccess ful", + "olog ist", + "ĠFor mer", + "ĠW rest", + "Ġ195 2", + "Ġin f", + "199 7", + "Ġopen ed", + "ĠU s", + "Ġen ergy", + "Ġ( \"", + "Ġ195 4", + "istric ts", + "m ark", + "Ġc he", + "Ġw in", + "ĠCar l", + "Ġar my", + "ress ion", + "Ġt en", + "est ival", + "ow a", + "ĠJ o", + "Ġpr im", + "Ġ192 9", + "Ġapp ro", + "u it", + "Ġ195 9", + "Ġmet al", + "ĠKore a", + "ĠB ir", + "ĠNot es", + "ĠS om", + "Ġcon stit", + "Ġpol ice", + "ĠSen ate", + "Ġ rom", + "Ġra il", + "Ġm id", + "Ġ193 3", + "a ign", + "Ġhim self", + "ĠR ail", + "ĠMiss ouri", + "b our", + "Ġmean ing", + "ĠJack son", + "or es", + "Ġfail ure", + "Ġm inist", + "Ġtem per", + "ĠB ig", + "T V", + "ul f", + "ĠS ar", + "or f", + "Ġcomp let", + "ĠAs ian", + "Ġjournal ist", + "n es", + "o ch", + "le g", + "Ġ194 3", + "ĠLat in", + "ĠT im", + "Ġfor ces", + "Ġcult ure", + "ov a", + "ĠC zech", + "Ġg ive", + "ĠD isc", + "ĠPhil ipp", + "ĠEn ter", + "ĠO b", + "Ġl ik", + "ĠF C", + "Ġtrans l", + "ĠMex ican", + "ir es", + "Ġpl ant", + "Ġ195 5", + "Ġmov e", + "Ġre qu", + "ow ers", + "Ġvar ious", + "Ġlaw y", + "ar io", + "ĠL ater", + "ecut ive", + "Ġ i", + "ian o", + "ĠIs lands", + "y e", + "ĠG al", + "v iron", + "g ar", + "iv ely", + "Ġt est", + "Ġc ause", + "ĠV er", + "Ġ8 0", + "yn ast", + "Ġle t", + "Ġ193 5", + "Ġmanag er", + "Ġ192 8", + "ir a", + "Ġg irl", + "ĠH ockey", + "Ġ193 1", + "Ġsy mb", + "Ġn etwork", + "ĠCatal ina", + "Ġj ob", + "it te", + "ĠS ir", + "Ġgro und", + "ĠE s", + "Ġa verage", + "Ġl iter", + "it ive", + "Ġac ross", + "Ġbuild ings", + "ĠAcc ording", + "Ġrecord ed", + "Ġl im", + "ĠTw o", + "Ġt ry", + "20 10", + "Ġb ad", + "ĠBro wn", + "Ġin stead", + "ĠO ver", + "Ġperform ed", + "Ġ193 7", + "ĠU r", + "Ġcon c", + "Ġst orm", + "L S", + "Ġt akes", + "ĠT ime", + "ĠJ ean", + "ĠU E", + "ĠEd uc", + "Ġarr ondiss", + "Ġ6 0", + "Ġprod uct", + "Ġcent ral", + "ĠM P", + "h ar", + "eth ing", + "ĠP ac", + "Ġcurrent ly", + "ĠH aw", + "Ġprot ect", + "i os", + "Ġtra vel", + "ĠF ox", + "g g", + "as c", + "Ġex ist", + "Ġmain ly", + "ĠO ld", + "199 6", + "Ġb ar", + "ab ly", + "ĠF ar", + "Ġme as", + "Ġm aterial", + "f ace", + "p ed", + "Ġcl aim", + "ĠC SS", + "Ġtown s", + "and a", + "ec k", + "ĠU nd", + "ste in", + "ĠC am", + "ĠM o", + "ĠK e", + "Ġro les", + "eth od", + "ĠSec ond", + "Ġcr ime", + "Ġdest roy", + "l anguage", + "Ġun ivers", + "ĠM inn", + "ĠL uc", + "Ġam ount", + "Ġ195 3", + "Ġp ark", + "ĠT od", + "Ġhe alth", + "Ġ193 2", + "j a", + "Ġs ug", + "199 5", + "Ġw ind", + "Ġmov ement", + "Ġrel ations", + "ab eth", + "Ġe ither", + "Ġpro per", + "az ine", + "ades h", + "Ġse ats", + "Ġ18 0", + "Ġc ertain", + "Ġn orm", + "st ron", + "Ġrec ogn", + "Ġw he", + "O R", + "Ġbl ood", + "ĠSc ient", + "t ime", + "Ġmon ths", + "Ġe at", + "rem e", + "ĠA p", + "ĠW ood", + "Ġch urch", + "Ġv iew", + "k o", + "Ġhelp ed", + "Ġse ven", + "Ġm ight", + "our ce", + "Ġwest ern", + "iv id", + "Ġcl ose", + "Ġ192 6", + "Ġb all", + "pec ially", + "Ġc reat", + "Ġchem ical", + "Ġstruct ures", + "Ġarch itect", + "y ear", + "ĠI owa", + "Ġal ways", + "isc o", + "ĠSt ep", + "Ġs it", + "Ġv ict", + "Ġbroad cast", + "Un ited", + "ĠD ire", + "ĠSp ring", + "air s", + "Ġc ase", + "Ġc at", + "8 9", + "N A", + "i h", + "ang er", + "end ing", + "Ġwrit ing", + "ent ion", + "ĠSt reet", + "Ġre ached", + "ĠSecret ary", + "Ġp ast", + "ĠCl ass", + "el d", + "Ġ ident", + "ad ium", + "Ġon ce", + "w an", + "Ġg e", + "Ġ192 7", + "Ġcomp anies", + "Ġto day", + "Ġprod uction", + "Ġ( ,", + "Ġcand id", + "ur ther", + "Ġde g", + "7 5", + "Ġe ight", + "ĠNob el", + "al i", + "ĠJ ud", + "end s", + "ĠH ill", + "oin ts", + "ĠBu ild", + "Ġfour th", + "ak i", + "ĠAn ton", + "ĠSoc ial", + "Ġm urder", + "ĠPol and", + "ĠS ub", + "ĠB ad", + "Ġnum bers", + "ĠMich igan", + "Ġsh own", + "Ġanim ated", + "Ġcompet ed", + "viron ment", + "Ġrepl aced", + "um ents", + "Ġp e", + "st ant", + "Ġt ree", + "ĠOr der", + "Ġc anton", + "Ġpain ter", + "uck y", + "Ġin h", + "Ġp ress", + "i as", + "emb ly", + "ĠO ut", + "ĠB efore", + "Ġpro p", + "Ġmon th", + "ĠA re", + "Ġide a", + "ĠSp orts", + "ĠH ind", + "us e", + "Ġgo al", + "Ġt alk", + "Ġcap t", + "ibr ary", + "Ġc ard", + "Ġdevelop ment", + "if t", + "ĠIn t", + "Ġsign ed", + "Ġre v", + "ĠUn ivers", + "ĠL u", + "Ġ7 0", + "ĠC areer", + "Ġin vent", + "Ġso ft", + "rem ier", + "Ġchang es", + "Ġc itiz", + "c el", + "Ġh ot", + "Ġcom ed", + "ĠGold en", + "Ġprogram s", + "Ġc ourt", + "ut y", + "Ġc ross", + "ĠK enn", + "iet n", + "um e", + "ed s", + "ĠW al", + "7 2", + "ĠBrit ain", + "ĠS erv", + "o is", + "ent h", + "ĠD ar", + "Ġre act", + "ĠSer ies", + "ĠSoc iety", + "Ġdec ided", + "ynast y", + "ĠAl b", + "at re", + "Ġde ad", + "Ġun iversity", + "Ġstud ents", + "ĠT enn", + "ĠInstit ute", + "ĠAn im", + "ĠMc C", + "Ġprom ot", + "Ġin j", + "ĠY oung", + "id ge", + "Ġan cient", + "Ġp ract", + "Ġo x", + "Ġd er", + "tern et", + "Ġn ight", + "Ġrele ase", + "ĠTe am", + "ĠM iddle", + "ĠB av", + "Ġpro ject", + "Ġ192 5", + "I n", + "ĠS and", + "id ence", + "ĠOr gan", + "Ġreturn ed", + "Ġ !", + "Ġar rest", + "ĠR ome", + "ok e", + "oc i", + "ĠAtl antic", + "s en", + "Ġp ay", + "Ġl ittle", + "ĠL y", + "or a", + "Ġes pecially", + "em p", + "Ġgo es", + "Ġb oy", + "ĠB usiness", + "es ota", + "ogra pher", + "Ġpre vious", + "Ġadd ed", + "Ġin side", + "Ġ9 0", + "Ġout side", + "ur d", + "Ġj ud", + "ed ia", + "om er", + "ip l", + "Ġear l", + "Ġgr adu", + "ĠThe n", + "at i", + "ĠF e", + "ĠRepresent atives", + "in ces", + "Ġf iction", + "Ġb attle", + "ĠMus lim", + "ĠL ittle", + "Ġindepend ent", + "Ġf ig", + "ĠB ab", + "st ra", + "ĠG ood", + "ĠAb out", + "ĠM ax", + "ĠV ietn", + "anc he", + "ask a", + "ul ation", + "ĠW ork", + "ĠMinn esota", + "ĠP ress", + "ate g", + "199 4", + "Ġper forman", + "Ġallow ed", + "ĠDep artment", + "Ġbas eball", + "8 6", + "Ġs en", + "Ġdr ug", + "Ġf all", + "Ġf re", + "Ġmunicipal ities", + "ĠE ver", + "Ġart ists", + "Ġlead ers", + "ĠE p", + "ĠS a", + "ĠM ah", + "Ġh om", + "Ġb ox", + "ĠG h", + "Ġsom ething", + "Ġen ough", + "Ġf if", + "m ond", + "Ġla un", + "eng th", + "Ġnomin ated", + "Ġcan not", + "r ich", + "Ġmount ain", + "Ġsouth west", + "Ġra p", + "al so", + "ĠP ers", + "un s", + "Ġme et", + "Ġf ront", + "Ġinter est", + "Ġrel ated", + "Ġfor ce", + "l ah", + "ĠT our", + "ĠAr men", + "ĠComp any", + "p eople", + "ĠWrest ling", + "ĠFranc isco", + "Ġres earch", + "ic ular", + "ri z", + "ad el", + "Ġposs ible", + "Ġb oard", + "8 5", + "ost on", + "Ġthe ory", + "is ing", + "ound s", + "w in", + "Ġsystem s", + "ĠW ay", + "Ġse qu", + "ĠJ ac", + "ĠB ul", + "Ġc ele", + "ĠR on", + "ĠF er", + "ĠD uke", + "h in", + "Ġa th", + "ĠColumb ia", + "ĠP ictures", + "ĠG ram", + "Ġpar ents", + "Ġband s", + "Ġair craft", + "ĠN az", + "ĠEnter tain", + "Ġfriend s", + "itte e", + "Ġ192 4", + "Ġactiv ist", + "ĠLouis iana", + "it ing", + "Ġgo ing", + "ĠV an", + "est ab", + "iz ations", + "ĠAlex ander", + "ag ed", + "Ġc oll", + "ĠF orm", + "Ġv ir", + "iv ate", + "C A", + "Ġorigin ally", + "Ġst ay", + "Ġear th", + "ĠT re", + "rat ive", + "ĠE lect", + "in son", + "c an", + "Ġra c", + "Ġwee k", + "ĠP LS", + "ĠAir port", + "Ġ19 22", + "ad d", + "hes s", + "ay er", + "ĠLe on", + "Ġm em", + "ĠSp ec", + "Ġt ropical", + "p ly", + "199 3", + "ĠWind ows", + "ay a", + "Ġlong er", + "ĠFootball ers", + "il ly", + "ar g", + "ĠA D", + "Ġres ults", + "ĠBi ography", + "in cess", + "is ions", + "j i", + "ien ces", + "Ġbre ak", + "ut s", + "7 4", + "Ġd ig", + "am i", + "Ġnorth west", + "r as", + "ing er", + "ĠF ame", + "Ġse asons", + "ĠE astern", + "ens ive", + "ĠCh ief", + "Ġgr and", + "im b", + "lah oma", + "Ġsh oot", + "m in", + "Ġr en", + "GB T", + "Ġcamp aign", + "ĠI d", + "ĠFam ily", + "7 9", + "us es", + "Ġre view", + "ail able", + "ĠH istor", + "y an", + "z o", + "ĠCh ild", + "Ġp ur", + "ĠP erson", + "h ood", + "ĠN ight", + "if y", + "Ġl ove", + "Ġfin ished", + "ĠOk lahoma", + "v a", + "Ġc rick", + "ĠM u", + "ĠSh ow", + "ĠJ eff", + "Ġc ell", + "Ġs ize", + "Ġ192 3", + "il a", + "um m", + "Ġold est", + "or ial", + "Ġm ale", + "olit an", + "ĠT am", + "ĠC ub", + "Ġdiv ided", + "ĠM ajor", + "0 5", + "c est", + "Ġepis odes", + "ĠD et", + "id ae", + "ro wn", + "/ /", + "w ar", + "or g", + "ra ine", + "Ġ19 00", + "ĠB oston", + "ĠL ong", + "7 6", + "Ġm iss", + "ou d", + "ĠChar l", + "Ġhow ever", + "ĠAr k", + "Ġd oc", + "ĠA k", + "val ue", + "s ort", + "ĠCh ile", + "p resent", + "ĠWar s", + "ĠM em", + "Ġh ospital", + "Ġle ague", + "Ġpro b", + "ĠN ord", + "l av", + "ĠPac ific", + "ut t", + "Ġmed ia", + "Ġm ach", + "ĠEl iz", + "ĠTok yo", + "ra ck", + "ĠM att", + "ĠWh ile", + "Ġb o", + "Ġe astern", + "Ġcon v", + "Ġgo als", + "ĠL GBT", + "Ġ er", + ": //", + "Ġcy cl", + "ĠWW E", + "Ġelect ric", + "Ġw id", + "ĠP ope", + "el le", + "Ġperson al", + "Ġch ampionship", + "Ġnew sp", + "enc ed", + "ĠO cean", + "ĠB al", + "Ġqu ick", + "l ers", + "ĠNew s", + "ain ing", + "ac hes", + "um i", + "Ġcontin ued", + "Ġeduc ation", + "ĠR ay", + "ĠBer lin", + "Ġl o", + "Ġc ases", + "Ġp sych", + "ĠMar ia", + "ĠL i", + "ĠJohn son", + "Ġm ethod", + "Ġsup er", + "ĠG ame", + ". )", + "el a", + "Ġac adem", + "ĠN ick", + "Ġst ations", + "Ġf ac", + "ĠC a", + "Ġ ;", + "Ġs uff", + "ĠSt e", + "Ġsmall er", + "Ġlaw s", + "0 6", + "ĠRo ad", + "Ġbelie ved", + "it o", + "writ ers", + "ur ity", + "Ġform s", + "ĠP as", + "Ġaward ed", + "ic ult", + "Ġlawy er", + "th ur", + "w ith", + "ĠT a", + "ut ure", + "Ġacc ident", + "Ġfeat ures", + "ch an", + "Ġdisc overed", + "Ġb order", + "Ġcrit ic", + "ĠJ un", + "ĠI v", + "Ġserv ices", + "ĠN ations", + "ĠG irl", + "Ġcl os", + "g u", + "ri age", + "Ġro ad", + "Ġn uc", + "ĠD ef", + "ĠP o", + "Ġd og", + "ĠC op", + "Ġl ower", + "ĠBel gian", + "r ay", + "Ġav ailable", + "in et", + "em ic", + "ĠS qu", + "20 11", + "ĠC amb", + "ĠN a", + "ĠJ oe", + "ĠDan iel", + "ĠS outhern", + "ĠReg ion", + "Ġran ge", + "Ġh app", + "ot al", + "ĠE nd", + "Ġcaus es", + "ĠAl bert", + "ĠSt at", + "il i", + "ĠAl ab", + "en ed", + "Ġmat ches", + "at ter", + "Ġkill ing", + "ĠF ort", + "Ġp oints", + "ĠScient ists", + "ĠL es", + "ag an", + "Ġc over", + "ĠE st", + "ĠW ater", + "rop olitan", + "Ġdesign ed", + "ĠD i", + "b ach", + "Ġsold iers", + "Ġb ass", + "ĠL ord", + "ĠW all", + "ĠB BC", + "ĠEU N", + "Ġintrod uced", + "ĠVict oria", + "w er", + "l as", + "ĠM en", + "ĠSw iss", + "ob ile", + "Ġcol on", + "ĠW at", + "he ad", + "k en", + "Ġrelig ious", + "ĠAlab ama", + "ĠE conom", + "Ġw ood", + "199 2", + "ourn ament", + "th ing", + "ĠK ong", + "ĠMar io", + "ĠAss embly", + "ic ine", + "enn a", + "ĠMus ical", + "ĠK ob", + "ĠC y", + "ipp i", + "ov ed", + "Ġreg ular", + "Ġschool s", + "ĠO f", + "ak h", + "act er", + "ĠEl st", + "Ġt old", + "Ġind ivid", + "ĠB on", + "ĠJ ones", + "op er", + "enn is", + "Ġs ister", + "ĠN ic", + "ĠP u", + "l ar", + "Ġdis estab", + "Ġd anc", + "ĠMiss iss", + "Ġbusiness man", + "ĠEntertain ment", + "w ell", + "il ies", + "Ġh ous", + "Ġscient ists", + "ĠR og", + "Ġst ories", + "F ran", + "l ines", + "Ġd ate", + "ĠPro d", + "and ing", + "ĠBav aria", + "ĠR om", + "Ġpr ed", + "d en", + "ĠMov ie", + "ĠMed al", + "Ġa stron", + "ict ional", + "Ġpart icip", + "ult ure", + "ĠBar b", + "ri k", + "Ġte xt", + "ĠBang l", + "ĠUE FA", + "Ġb ur", + "lic ations", + "in als", + "B S", + "is her", + "Ġmil es", + "0 4", + "Ġsp ort", + "b it", + "ĠTra ck", + "ĠM ir", + "Ġy oun", + "0 3", + "Ġg as", + "ĠV in", + "ian t", + ".. .", + "ĠM ot", + "itt ed", + "Ġdescrib ed", + "8 4", + "Ġstar ring", + "Ġbl ue", + "Ġsh ip", + "ic ed", + "ran ge", + "sel ves", + "om y", + "Ġcont ains", + "ĠN iger", + "ĠAl though", + "ĠHar ry", + "Ġinv est", + "Ġthem selves", + "Ġo bs", + "m as", + "stit ution", + "u ic", + "ĠC orn", + "ĠMus icians", + "Ġg ets", + "ĠO x", + "Ġg reen", + "Ġpresident ial", + "ĠB re", + "ĠKent ucky", + "Ġm iddle", + "in ary", + "Ġr ule", + "Ġhappen ed", + "Ġre b", + "Ġt ried", + "Ġ19 21", + "ug e", + "Ġte acher", + "ĠK en", + "Ġsh ot", + "Ġcl imate", + "ĠB h", + "ĠBl ue", + "Ġc ou", + "Ġinh abit", + "m ir", + "ĠAmerican s", + "Ġc ur", + "ĠInd ones", + "ĠOn t", + "end o", + "ĠPh ys", + "Ġt ax", + "Ġp en", + "ĠVal ley", + "ĠV en", + "Ġcol lege", + "r ad", + "Ġapp oint", + "7 3", + "ĠT H", + "ov ers", + "Ġe gg", + "Ġh tt", + "i i", + "ĠC her", + "Ġb ank", + "esse e", + "ph y", + "Ġvoc als", + "ĠR am", + "ĠSant a", + "ĠH or", + "Ġe as", + "ĠAl so", + "ĠL ar", + "ĠEliz abeth", + "h ib", + "Ġf oc", + "Ġpart icular", + "8 3", + "ĠWilliam s", + "ĠP ublic", + "up t", + "Ġcon str", + "ĠRev olution", + "on to", + "ie ce", + "ĠT ro", + "song writer", + "ĠL em", + "ĠMississ ippi", + "an ks", + "Ġa ud", + "Ġr ad", + "v ing", + "ĠB udd", + "el ly", + "ĠG ard", + "ĠTenn essee", + "Ġs ch", + "o id", + "0 1", + "Ġex cept", + "Ġmark et", + "Ġdistrib uted", + "em pt", + "Ġhum ans", + "Ġbeg inning", + "w ide", + "Ġc er", + "Ġoper a", + "ĠB et", + "Ġcommon ly", + "ĠL ine", + "Ġrom antic", + "ĠJ on", + "ĠOnt ario", + "ol es", + "ĠW ild", + "Ġlar ger", + "ala is", + "Ġcitiz ens", + "ĠR od", + "199 0", + "0 9", + "ĠC D", + "ĠM ore", + "ĠIn c", + "b ai", + "ĠH y", + "Ġsp eed", + "ĠArgent ine", + "Ġsur face", + "ĠPro t", + "ĠTe chn", + "ell ed", + "Ġch ampion", + "burg h", + "ĠAt t", + "our g", + "Ġsil ver", + "ph ia", + "in ct", + "ĠE ach", + "Ġh us", + "Ġbro ught", + "th rop", + "20 12", + "ann ed", + "oph y", + "Ġra in", + "ĠGall ery", + "Ġphilos opher", + "av en", + "Ġs n", + "Ġ19 18", + "Ġbel ong", + "Ġc ells", + "se x", + "av a", + "ist ry", + "Ġan g", + "is es", + "ĠNor way", + "in ks", + "ĠE ll", + "ĠS on", + "Ġits elf", + "Ġaut om", + "e z", + "ĠA zer", + "os ition", + "Ġb omb", + "Ġd ou", + "s k", + "Ġf act", + "Ġg rew", + "ĠGl ob", + "Ġis lands", + "ĠAl f", + "ĠF ound", + "ĠB us", + "ĠBel g", + "ĠB ack", + "Ġcre ate", + "Ġdiff icult", + "ent y", + "ĠT y", + "ĠD oug", + "ĠAn other", + "ĠB at", + "Ġown ed", + "ĠEduc ation", + "Ġn ort", + "ĠO tt", + "199 1", + "Ġl ength", + "ĠW inter", + "r ian", + "Ġra ised", + "ad er", + "Ġc ost", + "c ow", + "Ġf ast", + "Ġwinn er", + "Ġtrad itional", + "ĠD ie", + "Ġsome one", + "Ġsub ject", + "Ġrelations hip", + "ĠB ow", + "ĠF re", + "and y", + "ach ing", + "Ġs aw", + "it age", + "ĠJ es", + "ĠM ain", + "ĠOr ig", + "Ġfollow ed", + "Ġw ays", + "is a", + "Ġoffic er", + "Ġal though", + "Ġdiv ision", + "ar ter", + "Ġm erg", + "eder ation", + "Ġhe re", + "ĠCol or", + "ot e", + "im ent", + "ĠHum an", + "8 1", + "Ġvot e", + "ent ial", + "Ġreport ed", + "adel phia", + "Ġqu al", + "Ġwe ap", + "Ġhe avy", + "ĠT op", + "ĠG er", + "Ġbelie ve", + "f ile", + "d ess", + "ic y", + "h old", + "ĠL iber", + "pl oy", + ", \"", + "ĠSc ience", + "ĠTod ay", + "ĠC ensus", + "ĠAzer bai", + "ok en", + "ĠK im", + "Ġmed ical", + "N S", + "ĠUS A", + "b re", + "Ġtemper ature", + "int endo", + "ĠAr thur", + "Ġact ive", + "ĠB ell", + "ĠIndian a", + "or ary", + "Ġp age", + "Ġarrondiss ement", + "Ġnew s", + "Ġst e", + "Ġf arm", + "man n", + "ĠBill board", + "ĠObs erv", + "ĠS us", + "ĠAnd rew", + "Ġlead ing", + "ĠE th", + "A r", + "he t", + "Ġfe et", + "Ġcent re", + "Ġent ire", + "Ġsw im", + "av al", + "ĠA z", + "ef ul", + "rel ated", + "Ġd u", + "Ġd istricts", + "ur ies", + "air man", + "b ury", + "Ġclub s", + "ĠJan e", + "ĠF ire", + "vent ure", + "Ġhig her", + "Ġbl ock", + "a is", + "Ġ19 19", + "ĠW el", + "ĠFor ce", + "ĠAd d", + "Ġen vironment", + "d es", + "h y", + "Ġrul es", + "ĠU t", + "ab ility", + "ĠCast le", + "ett ing", + "7 1", + "ĠTurk ey", + "aly mp", + "ron ic", + "v ey", + "un a", + "Ġanim al", + "ĠV i", + "Ġold er", + "os h", + "Ġgen us", + "Ġdefe ated", + "ĠTor onto", + "ing u", + "Ġcompet ition", + "Ġh yd", + "ect s", + "ĠIsrael i", + "Ġd ark", + "ĠO per", + "ĠPar alymp", + "Ġc our", + "ĠC ard", + "ĠC ross", + "Ġhus band", + "ch ie", + "ĠK ir", + "Ġb rain", + "er o", + "ĠN intendo", + "mer c", + "Ġass ist", + "am in", + "Ġ7 5", + "Ġdisestab lishments", + "ĠAm b", + "F L", + "ĠCh ris", + "ree k", + "ĠA h", + "ĠPhil adelphia", + "Ġso on", + "ĠR aj", + "ut en", + "ap ore", + "Ġmag azine", + "Ġcommun es", + "ĠR adio", + "ĠPro fess", + "Ġmin or", + "Ġinhabit ants", + "l ad", + "le ctor", + "Ġfound er", + "T h", + "Ġengine er", + "Ġsec ret", + "Ġc art", + "Ġminist er", + "Ġk il", + "ĠSy stem", + "it ude", + "Ġfe el", + "ĠMos cow", + "p ar", + "198 9", + "ign ed", + "ĠL ady", + "Ġbig gest", + "l iga", + "20 17", + "u ese", + "ĠM id", + "ag er", + "Ġgovern or", + "ĠVietn am", + "Ġelect ions", + "Ġo il", + "ia c", + "ver t", + "ĠDire ctor", + "ĠT it", + "ĠF our", + "ic ated", + "ĠP it", + "Ġneed ed", + "ĠK OR", + "n ic", + "um s", + "Ġbir ds", + "ĠS el", + "ĠG re", + "ĠChampionship s", + "ĠN ik", + "Ġh ar", + "20 13", + "ear s", + "20 14", + "Ġdire ctors", + "n ed", + "y es", + "Ġquick ly", + "r ine", + "u an", + "ĠTh ree", + "we gian", + "ag a", + "ĠBro ok", + "Ġ3 5", + "ĠCon nect", + "Ġsp read", + "ĠB a", + "ĠL ind", + "und es", + "ĠVill ages", + "v in", + "Ġide as", + "ur i", + "Ġocc up", + "Ġag o", + "ĠP res", + "Ġv o", + "k h", + "Ġp ot", + "Ġm agn", + "ĠGree ce", + "Ġsex ual", + "ord ers", + "Ġaward s", + "S A", + "ĠP en", + "Ġf ly", + "Ġproduc ers", + "is hes", + "Ġin fect", + "ĠF ederal", + "ĠN ep", + "Ġmult iple", + "Ġes c", + "uct ed", + "ĠT ay", + "in em", + "ĠL ight", + "Ġrelig ion", + "z y", + "ren ce", + "Ġsug gest", + "l ast", + "ĠF inn", + "ĠDon ald", + "Ġcomed ian", + "c u", + "Ġphys ic", + "Ġg ives", + "ons in", + "ul pt", + "Ġreg ions", + "ir it", + "ĠM embers", + "Ġ8 5", + "Ġproble m", + "ĠStep hen", + "Ġthrough out", + "ro ke", + "ĠU l", + "ĠMar c", + "0 2", + "ĠB ank", + "ĠBelg ium", + "ed a", + "ĠCol omb", + "isc onsin", + "ĠH ard", + "em i", + "B A", + "ĠArk ansas", + "i ents", + "Ġsc ored", + "ĠS av", + "Ġsl ow", + "ĠPhilipp ines", + "ĠAr ts", + "Ġmy th", + "Ġcompos ers", + "ĠW ebsit", + "ĠC as", + "Ġv on", + "ĠComm ittee", + "ĠNor wegian", + "Ġre ason", + "ĠSl ov", + "ant asy", + "Ġprofess or", + "il ities", + "ĠP ost", + "ĠW isconsin", + "Ġ +", + "re al", + "ĠA ut", + "ant a", + "Ġh urricane", + "ĠN avy", + "Ġch o", + "Ġsk in", + "it i", + "ĠV ar", + "ĠInd epend", + "Ġofficial ly", + "Ġs ource", + "ĠSt r", + "C alais", + "Ġdestroy ed", + "Ġleg al", + "Ġs at", + "u ation", + "ĠPr incess", + "Ġr ivers", + "g n", + "te en", + "Ġse lected", + "Ġfam ilies", + "Ġpr ivate", + "Ġne igh", + "ĠL ew", + "ĠArgent ina", + "Ġ eth", + "Ġperforman ce", + "Ġk ept", + "Ġle ave", + "g han", + "ĠT ony", + "ĠB all", + "che stra", + "Ġc are", + "ĠL ive", + "el op", + "Ġorgan ization", + "Ġrun s", + "Ġleg isl", + "Ġc ode", + "ĠIn ternet", + "i ec", + "ĠMay or", + "k ins", + "Ġrun ning", + "Ġguitar ist", + "Ġf ederal", + "ĠUk raine", + "ĠMil itary", + "g ress", + "Ġbecom ing", + "Ġt ells", + "ĠN ap", + "ĠW ik", + "ĠTurk ish", + "Ġdeg ree", + "ĠB ol", + "out heast", + "ĠF a", + "und red", + "ric s", + "em y", + "Ġfl ag", + "ut ions", + "m ad", + "ik h", + "Ġterm s", + "h ire", + "ĠP ier", + "ay ashi", + "ĠK han", + "Ġresp ons", + "Ġh ours", + "ĠB ah", + "ist ic", + "Ġass oci", + "ĠSy d", + "ĠM ur", + "A l", + "ĠA le", + "ĠT imes", + "Ġ19 17", + "Ġcont ract", + "Ġt able", + "ĠAn cient", + "Ġtr ue", + "Ġappoint ed", + "ĠAs h", + "Ġbel ow", + "Ġwrit e", + "ĠS ab", + "ĠS ev", + "Ġas ked", + "Ġj azz", + "ĠComm ission", + "Ġb ron", + "ĠM as", + "Ġac cept", + "ou ch", + "ĠT s", + "ĠSt ory", + "ĠF estival", + "8 2", + "as ion", + "Ġsur round", + "ĠD eb", + "le ep", + "ĠTH M", + "Ġb illion", + "Ġsy n", + "anche ster", + "g a", + "ĠUnd er", + "ĠPerson al", + "Ġ190 1", + "ic ation", + "M ar", + "er ve", + "Ġ19 10", + "ĠCom mon", + "Ġstart ing", + "Ġs af", + "ĠD ou", + "re ady", + "Ġpr int", + "Ġex ecutive", + "ĠPortug al", + "ĠGram my", + "Ġwho le", + "Ġ8 7", + "ĠS ometimes", + "Ġocc ur", + "ĠJew s", + "ĠW inn", + "Ġsoft ware", + "ĠStan ley", + "ĠS ax", + "olog ists", + "Ġf ought", + "ĠP un", + "F C", + "im s", + "ĠK an", + "k ey", + "ĠN atural", + "Ġqu est", + "198 7", + "ĠSing apore", + "Ġtran sport", + "Ġbeh ind", + "Ġb urn", + "ĠG reg", + "Ġmus eum", + "he w", + "ian g", + "20 15", + "ĠI ra", + "ĠH ong", + "Ġl ines", + "Ġsqu are", + "on ne", + "eb ec", + "anc y", + "ĠSer b", + "Ġv an", + "Ġac cess", + "Ġin st", + "ĠN etwork", + "vent ion", + "S er", + "ĠS n", + "Ġsp oken", + "Ġp il", + "ĠK r", + "ĠAf ghan", + "Ġter ritory", + "s s", + "Ġsing les", + "ĠR ap", + "ĠD ak", + "ip e", + "ĠHung arian", + "Ġs elf", + "ĠV ideo", + "Ġt ournament", + "ĠBuild ings", + "ĠW eb", + "ra p", + "ĠM ike", + "Ġw eb", + "Ġpo or", + "ĠM un", + "ĠCent ury", + "ĠCons erv", + "ct ions", + "ĠG ab", + "Ġbeh av", + "Ġdam age", + "Ġindust ry", + "Ġo p", + "ail s", + "ĠIslam ic", + "ĠH ome", + "Ġl y", + "ĠW olf", + "Ġinvol ved", + "d ied", + "ĠP remier", + "r ated", + "Ġread ing", + "ĠL ike", + "ĠB oth", + "ĠN ow", + "Ġlet ter", + "Ġmet ers", + "ĠSing ers", + "i ot", + "Ġv eh", + "and ed", + "le m", + "Ġgener ally", + "Ġprob ably", + "u ctor", + "ĠV ice", + "merc ial", + "ĠSyd ney", + "ĠN HL", + "ĠR et", + "v is", + "end ar", + "Ġass ociation", + "Ġ4 5", + "Ġdoc ument", + "all ed", + "ict ure", + "ĠD omin", + "Ġpart ies", + "ipl om", + "ĠD own", + "u v", + "Ġof fer", + "Ġ5 00", + "ĠWil son", + "or ter", + "Ġtr ade", + "Ġcele br", + "ov ery", + "Ġ8 4", + "k er", + "ro it", + "ĠS und", + "ĠL or", + "ĠQue ens", + "ĠT em", + "ĠH art", + "Ġadd ition", + "k ing", + "com p", + "in y", + "ict ed", + "Ġrul ed", + "reed om", + "Ġ7 7", + "Ġ6 4", + "ĠSen ator", + "ript ion", + "ĠK y", + "ĠIran ian", + "se y", + "od ies", + "Ġhistor ical", + "Ġcol lection", + "k in", + "Ġpl at", + "ĠAl bum", + "ug g", + "A n", + "Ġfif th", + "ĠL en", + "ne um", + "ĠFin land", + "uic ide", + "inc ip", + "Ġt all", + "Ġar m", + "Ġt aking", + "Ġp ers", + "Ġsp ent", + "Ġmar riage", + "ĠR em", + "ĠL ibrary", + "ĠPortug uese", + "Ġnewsp aper", + "ĠJes us", + "Ġc overed", + "ĠH aut", + "Ġsc ulpt", + "ĠCh annel", + "ĠMic ro", + "Ġp and", + "ĠB alt", + "Ġs ummer", + "ad ed", + "ĠO pen", + "Ġb ase", + "Ġst ep", + "ĠHe art", + "Ġch ief", + "Ġch annel", + "it ation", + "ath an", + "ĠB and", + "Ġl ung", + "ĠN ar", + "Ġn eg", + "ĠT ai", + "Ġh op", + "Ġle tt", + "ĠServ ice", + "en ces", + "ĠB erg", + "Ġal ready", + "Ġthr iller", + "ĠP ower", + "Ġinter view", + "Ġw ide", + "ĠF il", + "Ġ6 5", + "ĠChrist mas", + "Ġatt ract", + "20 19", + "198 8", + "ĠBro ad", + "ĠJust ice", + "u ay", + "ĠCro at", + "Ġf ru", + "Ġd ance", + "ann a", + "Ġde ep", + "Ġr ather", + "orpor ated", + "Ġad op", + "ic ut", + "ĠN ag", + "Ġsch ol", + "ĠK az", + "ĠAn th", + "ĠWal ter", + "il ton", + "Ġl og", + "ell o", + "e es", + "Ġturn ed", + "a o", + "h ol", + "Ġact ing", + "ran g", + "Ġpower ful", + "ĠO t", + "ed d", + "Ġconstit u", + "Ġle aves", + "pp ed", + "Ġstop ped", + "uk i", + "Ġbeg ins", + "ĠA ff", + "T otal", + "w ater", + "ĠF ore", + "Ġ( ),", + "ĠDen mark", + "Ġsymb ol", + "Ġm ole", + "ĠObserv atory", + "ĠP ot", + "enc ies", + "ĠHe alth", + "if ican", + "Ġrail way", + "h o", + "Ġth ous", + "ĠStev e", + "em an", + "ik a", + "l it", + "v i", + "Ġ19 14", + "Ġmanag ers", + "f ort", + "ĠM anchester", + "Ġpass ed", + "Ġf uture", + "st an", + "ĠAir lines", + ") ;", + "ĠSt ock", + "ab y", + "Ġy ellow", + "E E", + "T A", + "ĠR ac", + "Ġro w", + "ĠBangl adesh", + "Ġt al", + "ĠAn ne", + "Ġatt empt", + "Ġfor e", + "ĠCl ark", + "Ġnorm al", + "Ġd raw", + "Ġtre es", + "Ġp aper", + "Ġn ine", + "Ġad ult", + "er al", + "al ign", + "Ġfun ction", + "ĠC oll", + "Ġin s", + "ce ed", + "al ia", + "Ġpur p", + "ĠAb b", + "Ġn ative", + "Ġtro ops", + "ĠBook s", + "ĠL oc", + "ric e", + "au x", + "ĠTay lor", + "iec es", + "Ġp ick", + "ĠN ev", + "ĠLo ire", + "Ġfor ced", + "Ġ8 6", + "Ġund erst", + "Ġwh ose", + "ĠDak ota", + "am ber", + "ĠSup reme", + "Ġpand emic", + "rog en", + "L e", + "ĠK ings", + "Ġ3 2", + "ĠTran s", + "l s", + "ĠM oh", + "os es", + "ee ch", + "ĠH ay", + "ab les", + "Ġbron ze", + "ĠPl an", + "ĠCo ast", + "ĠOx ford", + "ĠMary land", + "ĠWebsit e", + "Ġstart s", + "D on", + "h ouse", + "ors hip", + "ĠEv ents", + "20 16", + "ar o", + "ĠI ce", + "ĠVi enna", + "ĠKob ayashi", + "ĠTran sport", + "Ġstand ard", + "ĠA riz", + "Ġed itor", + "l ight", + "ap ers", + "ĠConnect icut", + "ens ion", + "ash ion", + "|||||||| ||", + "ĠSpec ial", + "ie uten", + "amp les", + "Ġcont roll", + "m et", + "te xt", + "r im", + "Ġto wards", + "ĠH an", + "Ġacc ording", + "S C", + "Ġstruct ure", + "Ġprim ary", + "Ġpl aced", + "uf act", + "Ġsupport ed", + "ĠC reek", + "Ġdr iver", + "undes liga", + "w ick", + "Ġelect r", + "ĠAb d", + "Ġhistor ian", + "Ġgod dess", + "Ġe lements", + "Ġsepar ate", + "Ġy our", + "Ġscreen writer", + "Ġconf ir", + "Ġc ut", + "um ber", + "Ġ7 8", + "hed ral", + "Ġstud ies", + "ĠLaw rence", + "ĠAriz ona", + "ĠL im", + "ĠK am", + "ĠPolit ical", + "Ġun its", + "rain ian", + "Ġw eak", + "Ġen c", + "urb an", + "ĠC urrent", + "Ġreview s", + "ly n", + "le an", + "Ġmerg ed", + "Ġwrest ler", + "or i", + "u j", + "at ers", + "ĠCan cer", + "Ġfeat ured", + "Ġindepend ence", + "Ġb al", + "ĠWh at", + "ww w", + "Ġg un", + "Ġro y", + "Ġd iss", + "we ight", + "al o", + "Ġ7 9", + "S h", + "ĠD am", + "ĠBr idge", + "ley ball", + "Ġsoc iety", + "ad os", + "ĠH amp", + "ĠDet roit", + "p ro", + "Ġcomp lex", + "Ġme ant", + "Ġadminist rative", + "Ġin sp", + "ĠRoman ia", + "ol is", + "Ġed ition", + "ĠK at", + "ĠMar sh", + "ĠColor ado", + "Ġ19 12", + "ug by", + "per ial", + "Ġar g", + "orn ing", + "Ġevent ually", + "Ġkil omet", + "ĠC ur", + "Ġcandid ate", + "Ġattack s", + "Ġmedal ists", + "ĠIra q", + "ĠC hem", + "ĠD ream", + "Ġcar ry", + "u y", + "ard o", + "ĠMunicipal ity", + "neum onia", + "Ġem ploy", + "ene z", + "ĠK al", + "ĠK er", + "b l", + "Ġkind s", + "ĠD un", + "Ġmin utes", + "Ġm ic", + "Ġmay or", + "l an", + "ĠSh in", + "198 3", + "ĠMod ern", + "Ġlaun ched", + "or ation", + "Ġd en", + "p ing", + "ĠC ost", + "ĠAd minist", + "Ġair port", + "ĠL ast", + "Ġg etting", + "Ġmot or", + "Ġn ick", + "ĠF ree", + "ĠO d", + "ĠJoh ann", + "ĠEgypt ian", + "Ġrepresent ed", + "u h", + "ang a", + "Ġearl ier", + "Ġl ake", + "Ġc lear", + "Ġtechn ology", + "ĠD or", + "198 6", + "Ġactiv ists", + "ĠSe ason", + "Ġvis it", + "Ġwee ks", + "le ph", + "bour ne", + "ĠNep al", + "ell ig", + "Ġcomp ut", + "ĠC irc", + "ieuten ant", + "n o", + "us p", + "Ġde al", + "Ġegg s", + "is ation", + "ĠH urricane", + "ĠM AS", + "Ġlist ed", + "se ct", + "Ġchar ge", + "ap ed", + "iz umi", + "ĠS her", + "us c", + "Ġn ar", + "A F", + "ĠR ang", + "ĠSt orm", + "col n", + "ĠHol ly", + "St ation", + "r ig", + "ar es", + "Ġm er", + "Ġcar bon", + "ĠMet ropolitan", + "Ġhor ror", + "t ies", + "ĠB os", + "Ġst adium", + "b ox", + "Ġpos itive", + "art ers", + "Ġd om", + "orpor ation", + "u i", + "ident s", + "tern al", + "l ov", + "ĠB ull", + "Ġfight ing", + "Ġrac ing", + "ĠC ab", + "w orth", + "ist ance", + "Ġorgan izations", + "ĠLem mon", + "Ġd iplom", + "ig en", + "Ġt ell", + "ch ange", + "ĠE ag", + "ĠPresident s", + "ĠAzerbai jan", + "ĠW alk", + "ut her", + "eng ers", + "ĠBul g", + "198 5", + "Ġfig ure", + "in ated", + "Ġf av", + "ĠEr n", + "ĠE ven", + "Ġsign ifican", + "ĠRes earch", + "Ġeconom ic", + "Ġb us", + "en burg", + "Ġe lev", + "Ġmix ed", + "Ġshow ed", + "ĠJ ord", + "ĠF ord", + "qu es", + "Ġ9 5", + "ĠTr ump", + "ĠBe at", + "Ġme chan", + "ĠT ar", + "Ġ era", + "20 18", + "ĠOff ice", + "ĠG il", + "Ġnort heast", + "Ġp neumonia", + "ĠHer itage", + "Ġcrick et", + "Ġb ridge", + "ĠFred er", + "Ġcompos ed", + "Ġn ature", + "Ġsong writer", + "os en", + "so ft", + "ĠFound ation", + "ĠLin coln", + "ok a", + "Ġf oss", + "ĠT ropical", + "Ġpre m", + "Ġmount ains", + "Ġfre qu", + "ect ion", + "Ġfl ight", + "ĠT an", + "Ġart icle", + "Ġpress ure", + "Ġcol lect", + "Ġ19 16", + "Ġ emb", + "Ġ1 20", + "ren g", + "Ġmov ing", + "or er", + "ist a", + "Ġab s", + "Ġun it", + "1 00", + "ĠFl ight", + "Ġliter ature", + "n s", + "ĠF air", + "ĠCon stitution", + "ĠCent re", + "ĠI V", + "ĠUk rainian", + "Ġp iece", + "ĠForm ula", + "us ion", + "og y", + "Ġo ur", + "ion e", + "Ġ7 4", + "Ġh undred", + "ast ers", + "Ġunivers ities", + "ĠDoug las", + "ĠR oll", + "ĠRail way", + "Ġw alk", + "Ġp ian", + "ĠR oss", + "' .", + "and o", + "d istrict", + "Ġw ear", + "Ġcomp ounds", + "ĠT ak", + "ĠD og", + "Ġnuc lear", + "Ġf urther", + "ress ed", + "ĠY e", + "ar ing", + "Ġcomp lications", + "L a", + "Ġst roke", + "Ġstar red", + "Ġc old", + "ĠD anish", + "Ġcont rib", + "ĠPlay Station", + "Ġp eak", + "ĠFranc is", + "m ore", + "ann ing", + "Ġhtt p", + "Ġfore ign", + "v o", + "ĠM aine", + "ĠH ans", + "v est", + "Ġ x", + "5 00", + "ĠBr ian", + "reg on", + "as ing", + "in osaur", + "Ġp ri", + "Ġm ess", + "Ġl oss", + "Ġre ign", + "A t", + "ĠA qu", + "p son", + "Ġ3 4", + "Ġappear ance", + "is k", + "ail y", + "ĠBas eball", + "o a", + "ĠEx p", + "ĠVict or", + "Ġ5 7", + "Ġlist ing", + "Ġn ation", + "ĠBusiness people", + "ĠMount ains", + "Ġf oot", + "Ġd ro", + "Ġf ace", + "Ġdo ctor", + "ĠB ird", + "Ġpl ane", + "Ġcr im", + "Ġvers ions", + "ian ce", + "ĠY our", + "Ġ19 15", + "Ġnear ly", + "Ġ19 13", + "Ġmach ine", + "V D", + "en z", + "ill ed", + "ĠMP s", + "f all", + "Ġa p", + "Ġan al", + "ĠTai wan", + "Ġsh ape", + "ĠCount ry", + "ĠMar ie", + "E F", + "if orm", + "Ġrecord s", + "ĠAr m", + "ast ic", + "Ġsim ply", + "Ġ3 3", + "Y G", + "rat or", + "Ġwe ight", + "ĠMad rid", + "Ġc ust", + "Ġp un", + "Ġlett ers", + "Ġt ennis", + "Ġcl osed", + "Ġplan et", + "198 0", + "ĠHol y", + "S p", + "ĠN ative", + "Ġ8 3", + "Ġworld wide", + "Ġ7 6", + "ĠO regon", + "c hen", + "Ġex ec", + "ĠN AS", + "ĠT al", + "ĠM oon", + "itt ing", + "ĠDev elop", + "ul ly", + "Ġcom mercial", + "ĠTer ritory", + "ĠEver y", + "ĠOn ly", + "Ġet c", + "Ġd on", + "Ġcomplet ed", + "f ore", + "f ound", + "Ġd ie", + "Ġon line", + "og ne", + "ell er", + "d am", + "Ġa chie", + "ib b", + "Ġd anger", + "ĠH ug", + "ĠD er", + "ĠAl i", + "umn i", + "u ro", + "oth ing", + "ĠMinist ers", + "Ġchart s", + "Ġd ynasty", + "Ġrecord ing", + "Ġ190 8", + "Ġ19 11", + "ĠHung ary", + "ĠM and", + "Ġwh y", + "L A", + "ĠH om", + "197 9", + "ro ad", + "198 4", + "ĠW alt", + "Ġ4 8", + "Ġbro wn", + "iqu es", + "Ġan cest", + "Ġlik ely", + "Ġhous es", + "Ġ9 3", + "Ser ie", + "Ġor d", + "Ġmajor ity", + "Ġb ring", + "Ġp al", + "Ġbe aut", + "Ġprod uce", + "Ġphysic ist", + "Ġval ue", + "ĠSt one", + "Ġcomp lete", + "s ky", + "os is", + "Ġrem oved", + "ell i", + "ĠH YG", + "Ġmon arch", + "Ġb acter", + "ĠG ord", + "ĠV enez", + "ĠCh ap", + "Ġac id", + "ĠR en", + "u als", + "ĠLew is", + "he astern", + "Ġproduct s", + "Ġs usp", + "Ġ8 1", + "Ġ3 6", + "c ar", + "Ġl ay", + "Ġsh ips", + "ys is", + "ĠMic hel", + "ĠKenn edy", + "ĠBro ther", + "ig e", + "c oh", + "Ġappear s", + "Ġresp ect", + "ĠL iga", + "os ing", + "yp e", + "Ġtrain ing", + "at ures", + "up p", + "198 1", + "Ġb ranch", + "b gcolor", + "ĠH ills", + "we et", + "re ct", + "Ġpar liament", + "ĠB ush", + "Ġk id", + "ĠSt and", + "Ġdist ance", + "p ite", + "Ġh air", + "ĠW in", + "Ġ ess", + "Ġstud ent", + "ĠK l", + "Ġdo ing", + "ĠDep uty", + "Ġeffect s", + "ĠHind u", + "ĠP ass", + "Ġsim ple", + "Ġhead qu", + "d a", + "Ġth reat", + "Ġphys ical", + "Ġking dom", + "ĠPat rick", + "Ġwid ely", + "est er", + "ph ab", + "Ġfa ctor", + "Ġant i", + "ĠHe ad", + "Ġrefer red", + "Ġf olk", + "ĠJ enn", + "Ġun ion", + "ĠWel sh", + "ĠAfghan istan", + "Ġp ieces", + "ĠV lad", + "ĠY am", + "Ġearth qu", + "z burg", + "ĠConf erence", + "ĠK elly", + "Ġr ing", + "Ġal tern", + "Ġend s", + "led ge", + "ch a", + "ateg ory", + "ĠSh ar", + "Ġnick n", + "Ġd ial", + "ĠK le", + "ĠAd am", + "ĠEx t", + "o en", + "ĠD ark", + "Ġim m", + "oc a", + "Ġbe at", + "Ġfl ows", + "ĠBe ach", + "Ġstat us", + "cept ion", + "Ġhe at", + "ĠHaw ai", + "Ġdec l", + "Ġmathemat ician", + "n ers", + "Ġw ild", + "ing en", + "Ġup on", + "Ġcop ies", + "ĠP ur", + "ĠAl an", + "Ġw or", + "Ġscient ist", + "Ġcon fl", + "Ġcond itions", + "if ul", + "iz es", + "row s", + "ĠQu ebec", + "ĠJ am", + "Ġcrit ics", + "Ġ9 1", + "Ġprov inces", + "b ased", + "Ġt aught", + "Ġs uicide", + "ĠP ic", + "al ed", + "t own", + "ch i", + "Ġfilm s", + "ĠAnth ony", + "ĠS ever", + "Ġ8 9", + "ĠBr ad", + "Ġc ars", + "Ġf und", + "ĠG i", + "Ġacc ount", + "Ġc ouncil", + "z en", + "ĠMicro soft", + "Ġp iano", + "ĠIn f", + "rov ers", + "ex ual", + "ĠMont real", + "ot te", + "Ġcommun ities", + "Ġdr ink", + "ĠH ou", + "Ġro om", + "ĠBudd h", + "ĠB urn", + "ĠChrist opher", + "ĠT ag", + "b ook", + "ĠB ros", + "ĠF ederation", + "Ġweap ons", + "ĠAnd re", + "j e", + "Ġch ampions", + "amm als", + "ĠDes ert", + "Ġh ol", + "R hin", + "Ġb ott", + "Ġact ually", + "b i", + "le ts", + "ĠR ad", + "ig g", + "Ġs al", + "Ġrem ains", + "Ġre ach", + "Ġre ve", + "Ġmeas ure", + "lev eland", + "cel ona", + "d y", + "Ġm ission", + "ĠK or", + "Ġperson ality", + "Ġdiv or", + "ĠCh ampions", + "ond e", + "ĠZ h", + "Ġcont est", + "ĠC ra", + "Ġprogram ming", + "ĠMed ia", + "f eld", + "on ic", + "ĠS ri", + "av er", + "ĠEm my", + "ĠOlymp ians", + "Ġbur ied", + "it ter", + "Ġlab el", + "e en", + "cy cl", + "198 2", + "Ġindivid ual", + "ee k", + "ĠWh o", + "Ġgreat est", + "ar us", + "Ġdis p", + "ĠFinn ish", + "ĠB oard", + "ĠG ir", + "ĠMount ain", + "ĠH o", + "ĠF rog", + "th a", + "ĠPar ish", + "ĠB eng", + "ĠN at", + "ĠSt ra", + "Ġobject s", + "ĠCamb ridge", + "ĠJord an", + "i at", + "ĠF r", + "ia b", + "ĠAn na", + "d orf", + "on om", + "Ġent ertain", + "j ab", + "w right", + "ĠL ower", + "ĠK ash", + "r ison", + "ĠBr uce", + "Ġ3 8", + "Ġman ufact", + "ĠT un", + "Ġpre vent", + "ĠAlf red", + "Ġb ought", + "Ġe yes", + "ĠV ik", + "ĠR io", + "Ġcom une", + "ond s", + "Ġ9 2", + "Ġyoun ger", + "ĠD a", + "ĠO izumi", + "ĠAlex and", + "en ger", + "Ġ8 2", + "Ġtem p", + "I A", + "ĠR a", + "Ġconfir med", + "u ly", + "Ġst reng", + "Ġent ered", + "Ġattack ed", + "oun ter", + "Ġ5 5", + "ĠEar l", + "Ġf le", + "im ated", + "res h", + "Ġ3 7", + "orn ey", + "Ġnear by", + "es h", + "n el", + "ĠD om", + "ĠA th", + "Ġmyth ology", + "iv ity", + "Ġcom ic", + "er to", + "Ġs un", + "Ġfl ood", + "g al", + "ig r", + "Ġsup p", + "Ġin sect", + "Ġp oll", + "ĠT reat", + "st one", + "le ges", + "ĠP ap", + "ult ural", + "Ġsol o", + "Ġd ry", + "ick s", + "ĠMal ays", + "ĠD al", + "ĠSt ation", + "y gen", + "ĠVenez uel", + "Ġf ictional", + "all as", + "Ġe lement", + "Ġse ction", + "ĠI c", + "ĠJu an", + "ĠPhil ip", + "ĠM aur", + "Ġn ob", + "Ġiss ues", + "ograph ic", + "Ġcal endar", + "Ġinflu ence", + "ri er", + "Ġ9 4", + "Ġneed s", + "ĠYou T", + "ĠAnton io", + "ĠTam il", + "ĠKar l", + "iv a", + "ĠDo ctor", + "Ġgradu ated", + "Ġl iqu", + "ĠPol ice", + "for mer", + "Ġcer em", + "Ġun known", + "Ġwe ather", + "ĠParalymp ics", + "Ġtw ice", + "Ġhelp s", + "Ġcult ural", + "Ġinvest ig", + "ac c", + "A lp", + "Ġwhe ther", + "ĠM aster", + "Ġall ows", + "Ġfe ature", + "ĠHow ard", + "u ins", + "ĠIndones ia", + "Ġf r", + "in th", + "ol as", + "ĠB ible", + "ĠWar ner", + "ĠK ey", + "ens ity", + "ad ing", + "Ġcons erv", + "Ġeconom y", + "ĠO ak", + "ĠCh art", + "- -", + "B l", + "Ġcr as", + "anc ed", + "anc ial", + "ĠL iter", + "ĠDevelop ment", + "ĠEng ine", + "e k", + "Ġte en", + "ĠAr n", + "Ġh unt", + "os lav", + "ĠA ge", + "Ġd ies", + "Ġ6 6", + "Ġf antasy", + "Ġch osen", + "ĠI l", + "Ġ4 4", + "ĠLab our", + "a ult", + "Ġd a", + "Ġc red", + "ĠR ivers", + "Ġref ers", + "ĠUt ah", + "ol ved", + "ĠF inal", + "197 7", + "ĠR ose", + "ĠV ers", + "ĠAl aska", + "Ġcons ist", + "uct ions", + "Ġev olution", + "ĠAre a", + "ĠP y", + "ĠD ise", + "em en", + "ac he", + "eg et", + "ĠDav is", + "s ince", + "ĠI S", + "Ġg al", + "Ġair ed", + "ĠIv an", + "Ġsignifican t", + "it als", + "Ġ6 7", + "ograph ical", + "Ġtry ing", + "ĠS iding", + "Ġhe ro", + "ĠD C", + "Ġr at", + "ĠT est", + "ond er", + "Ġpolit ics", + "Ġsay ing", + "Ġran ked", + "Ġc ook", + "ĠV o", + "Ġr ate", + "Ġ6 8", + "Ġag re", + "Ġsequ el", + "en h", + "Ġcom ment", + "ĠPal ace", + "Ġon es", + "Ġem erg", + "Ġm ention", + "ĠC art", + "el ine", + "Ġcont ain", + "Ġhapp ens", + "ĠFore ign", + "ĠC E", + "Ġsc ore", + "Ġgra ph", + "on se", + "med i", + "C om", + "Ġ6 9", + "Ġtrad ition", + "Ġarrest ed", + "l ong", + "er ved", + "ĠF ield", + "Ġs ides", + "Ġad venture", + "Ġev idence", + "Ġl if", + "Ġ7 3", + "Ġfl u", + "ĠProfess or", + "et y", + "ĠRe al", + "\" ).", + "r upt", + "Ġs ail", + "Ġcre w", + "Ġbel ief", + "eg a", + "Ġpr ime", + "Ġtra ins", + "Ġbu y", + "ĠConf eder", + "ĠK ev", + "Ġeas ily", + "i op", + "ip ur", + "ĠAll en", + "ĠA is", + "ĠThe atre", + "c ont", + "Ġapp l", + "lector al", + "ĠH it", + "Ġear ned", + "i ya", + "ĠW y", + "Ġro b", + "wh ich", + "g or", + "ĠL ux", + "Ġem peror", + "ĠRon ald", + "ĠChild ren", + "ol i", + "ot hes", + "Ġimp ro", + "Ġill ust", + "Ġpaint ing", + "Ġs urg", + "d ie", + "I S", + "ĠF urther", + "p a", + "ĠC orporation", + "o ons", + "ĠS ix", + "im ore", + "Ġw inter", + "Ġfor est", + "r ong", + "ĠJ ay", + "Ġrec ent", + "Ġreg ard", + "alt y", + "Ġactiv ities", + "ec ess", + "ĠPu erto", + "ĠC re", + "ĠL eb", + "ĠE sp", + "ĠH un", + "ign ated", + "ĠLab or", + "ĠJ ournal", + "ĠAd ams", + "ĠRog er", + "Ġm ill", + "phab et", + "C D", + "ĠPro ject", + "ĠSim on", + "ĠDisc ography", + "Ġath let", + "Ġequ al", + "Ġroy al", + "Ġst ream", + "Ġdet erm", + "Ġdou ble", + "Alp es", + "Ġc overs", + "ik i", + "Ġg iving", + "Ġpro te", + "Ġrem ained", + "ĠNaz i", + "ĠC ook", + "Ġcons ists", + "ĠMil an", + "p ool", + "Ġl ink", + "ab ad", + "Ġsp lit", + "Ġem p", + "Ġproper ty", + "Ġpe ace", + "ĠPit ts", + "Ġc am", + "ĠT el", + "ĠPal est", + "Ġspec ific", + "ĠAr d", + "Ġ190 9", + "ĠMar gar", + "Ġsp eech", + "ĠArmen ian", + "ant e", + "Ġ4 7", + "ark s", + "ock s", + "Ġdefe at", + "ĠL ank", + "ĠMar s", + "Ġconstr uction", + "' '", + "I T", + "ism s", + "ĠF ern", + "im ately", + "Ġplat form", + "Ġ3 9", + "iz ing", + "Ġconc ert", + "g ers", + "ĠInd ust", + "ĠB art", + "Ġwork ers", + "Ġath lete", + "Ġ190 7", + "ĠUp per", + "d own", + "ĠS ud", + "uc ks", + "cul es", + "Ġ9 6", + "Ġdrug s", + "m ont", + "Ġb att", + "osaur us", + "Ġassoci ated", + "mp t", + "ĠPun jab", + "Ġcapt ured", + "ĠT en", + "II I", + "Ġf ashion", + "ĠB illy", + "Ġdecl ared", + "o ir", + "or ing", + "ĠGu ard", + "cher s", + "ra c", + "b o", + "Ġcon qu", + "ĠBar celona", + "ik o", + "Ġf eed", + "ĠR ey", + "ĠRoman ian", + "Ġleg s", + "Ġcond uctor", + "ĠCap tain", + "Ġfrog s", + "ĠS ources", + "ĠA st", + "Ġup per", + "At l", + "ĠM ap", + "ĠK on", + "Ġcr ash", + "c ano", + "ing ham", + "Ġth ing", + "n i", + "ĠS ite", + "Ġscient ific", + "Ġre asons", + "Ġrecogn ized", + "ic ations", + "oc ol", + "Ġbir d", + "ĠR ub", + "ĠPri x", + "al em", + "ĠDe ad", + "ĠMel bourne", + "Ġpract ice", + "ĠB ishop", + "im ir", + "197 4", + "Ġo pt", + "ri ef", + "ĠSh ah", + "Ġw ants", + "ĠV as", + "Ġserv ing", + "E R", + "z h", + "Ġlevel s", + "v ard", + "ur ches", + "ĠGu ine", + "Ġconnect ed", + "ĠHamp shire", + "Ġrespons ible", + "Ġthe atre", + "Ġb odies", + "Ġcent uries", + "M an", + "o es", + "ar ily", + "Ġr ich", + "ry st", + "Ġs outheast", + "Ġex act", + "cycl op", + "at ar", + "ĠE cu", + "ĠAis ne", + "rod uction", + "Ġacadem ic", + "Ġrap per", + "at in", + "Ġinstr ument", + "Ġassist ant", + "ĠD ynasty", + "Ġv s", + "ĠCommun ity", + "r m", + "Ġmet res", + "on ent", + "ĠN on", + "ĠE ric", + "os a", + "Ġlook s", + "ĠA y", + "ĠT urn", + "Ġfl ow", + "i ology", + "ĠD ick", + "aw n", + "ĠLa ur", + "Ġtreat ment", + "ĠKev in", + "ĠPe ace", + "197 3", + "Ġter rit", + "Ġjud ge", + "ĠK it", + "Ġ190 6", + "ĠMem orial", + "des ignated", + "Ġvot es", + "ĠO p", + "Ġown er", + "Ġval ley", + "os hi", + "ins ula", + "Ġal coh", + "ke ep", + "Ġopen ing", + "Ġdi agn", + "ĠM ill", + "id el", + "Ġfor t", + "re ll", + "Ġpro file", + "Ġpar ish", + "Ġinstr uments", + "s a", + "ĠEx amples", + "Ġloc ation", + "ĠHou ston", + "n et", + "ĠH u", + "Ġper cent", + "Ġlong est", + "197 6", + "ĠJim my", + "erg round", + "ĠHam ilton", + "ĠFrank lin", + "Ġprevious ly", + "Ġj ump", + "ĠMu ham", + "ann y", + "Ġpaint ings", + "ĠD h", + "h att", + "ĠD ig", + "ĠV erm", + "ĠPers ian", + "Ġvict ory", + "Ġ1 21", + "Ġcre ation", + "Ġdirect ly", + "ĠC leveland", + "ĠSc iences", + "ĠAff airs", + "Ġnum er", + "m es", + "Ġle aving", + "Ġ15 0", + "Ġpre c", + "ĠB ib", + "Ġoper ating", + "Ġgl ob", + "ĠM AR", + "Ġre ally", + "Ġengine ering", + "Ġ3 00", + "Ġse vent", + "ĠD NA", + "Ġal t", + "yn am", + "Ġ urban", + "ebr aska", + "Ġbas ic", + "m outh", + "m ission", + "k m", + "l ay", + "ĠHar ris", + "ĠN ich", + "|||| ||", + "ĠApp le", + "ĠR ick", + "Ġ18 90", + "Ġgod s", + "g et", + "Ġmiss ing", + "Ġfru it", + "Ġ que", + "ĠF all", + "ĠSh ort", + "ĠBr and", + "ĠC er", + "Ġ9 7", + "197 8", + "Ġc ab", + "ch t", + "Ġst ra", + "ub lish", + "ĠF le", + "Ġ190 5", + "ĠIc eland", + "Ġ5 4", + "ib a", + "Ġlim ited", + "p ut", + "if er", + "ev a", + "Ġst ore", + "ĠB ry", + "Ġqu ite", + "20 20", + "hen s", + "Ġ190 3", + "f ish", + "Ġsuc ceed", + "Ġfl at", + "se qu", + "Ġ9 8", + "ĠCap e", + "ĠAtl anta", + "Ġ iron", + "Ġkey board", + "ĠTe h", + "ĠGord on", + "N ew", + "ĠBalt imore", + "ĠS id", + "Ġf ix", + "ĠN am", + "ĠN orm", + "ĠCam er", + "h at", + "j o", + "Ġp aid", + "Ġm aster", + "ĠI mp", + "Ġtrans fer", + "Ġadop ted", + "b ed", + "ĠS ound", + "ĠR ot", + "ĠSqu are", + "ĠEcu ador", + "ĠF riend", + "ag en", + "Ġex c", + "oy d", + "ĠM ember", + "Ġback ground", + "Ġrest aur", + "id en", + "Ġwrest ling", + "ellig ence", + "ĠH i", + "Ġter ror", + "ĠL ow", + "ak ers", + "ĠCal v", + "Ġprov ide", + "ĠGlob e", + "Ġf estival", + "ĠB oe", + "und er", + "k ov", + "Ġjournal ists", + "ĠFreder ick", + "M A", + "ic ate", + "ĠHe avy", + "Ġdesign er", + "ol t", + "ĠK a", + "ra v", + "Ġro ll", + "Ġc orn", + "ĠI NS", + "ĠD u", + "r in", + "ĠE lection", + "ob a", + "Ġcol umn", + "Ġbro ke", + "b ro", + "Ġhold s", + "Ġill ness", + "Ġneigh bor", + "W est", + "Ġp owers", + "Ġat om", + "n ia", + "ĠG ers", + "Ġmanag ed", + "Ġ ille", + "at ab", + "197 2", + "ĠCh ampion", + "ĠGu y", + "Ġo cean", + "Ġf ir", + "ĠM ach", + "ĠP ed", + "ak u", + "ric t", + "ĠRe agan", + "Ġw ins", + "Ġpl anned", + "Ġsp irit", + "Ġatt ended", + "Ġsix th", + "Ġcomplet ely", + "ĠDie go", + "ĠM iller", + "Ġser ious", + "Ġland s", + "p re", + "ĠF ried", + "l ow", + "h ard", + "Ġp ath", + "ĠG ulf", + "Ġ7 2", + "ĠBet ween", + "ĠTechn ology", + "or o", + "Ġex hib", + "iam i", + "Ġcomput ers", + "ĠS ky", + "ĠP a", + "Ġdebut s", + "Ġar ms", + "ib ility", + "197 5", + "osp here", + "Ġren amed", + "ĠS ee", + "ĠRang ers", + "Ġindust rial", + "el son", + "l ife", + "ĠD allas", + "part ement", + "ĠE ston", + "Fran ce", + "Ġh y", + "ĠN ebraska", + "ĠOr th", + "ĠJer ry", + "Ġm m", + ") \"", + "Ġwh om", + "Ġclaim ed", + "ĠD VD", + "ĠCl in", + "um a", + "ĠC our", + "ir ty", + "ĠG on", + "ĠSong s", + "ĠAmb ass", + "im um", + "Ġgener ation", + "ĠSom me", + "E N", + "Ġin it", + "ĠL as", + "od ox", + "ĠFor est", + "ĠMer c", + "Ġt rial", + "ĠF el", + "ĠF em", + "ax y", + "Ġpass engers", + "ĠIm perial", + "Ġhost ed", + "ĠL ith", + "ĠEag le", + "ĠPitts burgh", + "M ay", + "on str", + "Ġ4 6", + "Ġim age", + "Ġheadqu arters", + "ĠY ug", + "l aw", + "ĠL yn", + "ĠYouT ube", + "Ġmod els", + "Ġsec urity", + "Ġeth nic", + "Ġcont rovers", + "Ġef f", + "es c", + "Ġs ac", + "Ġd ed", + "Ġnot able", + "Ġexamp les", + "w ald", + "Ġf air", + "ĠC ru", + "Ġvar iety", + "Ġs leep", + "Ġin te", + "ol ph", + "Ġ4 00", + "ĠJac ob", + "Ġpil ot", + "ĠMuham mad", + "Ġent er", + "ĠV is", + "Ġcon cent", + "Ġox ygen", + "r h", + "ĠS u", + "le ase", + "Ġ4 3", + "ult y", + "|||||||| ||||||||", + "Ġgrow th", + "ĠBl ues", + "ĠStat istics", + "Ġcou ple", + "ĠLux emb", + "ĠB urg", + "Ġst ated", + "Ġearthqu ake", + "ĠR y", + "Ġfor ests", + "Ġf ile", + "ĠC all", + "ĠC rit", + "Ġab ility", + "Ġcar ried", + "Ġvoc al", + "ĠUnivers al", + "ĠINS EE", + "est ic", + "ud a", + "ace ous", + "ĠWh it", + "ĠTem ple", + "ill es", + "ol o", + "Ġmaterial s", + "ĠJohn ny", + "ĠMed icine", + "Ġcond ition", + "ĠB ure", + "Ġb ound", + "ĠHolly wood", + "ĠS S", + "ĠO cc", + "orp s", + "ĠStud io", + "s m", + "ĠCub a", + "ĠS aud", + "ad i", + "ach i", + "og en", + "D ec", + "cent er", + "ĠMuslim s", + "ĠN FL", + "ap h", + "ic i", + "il ed", + "ĠSing h", + "Ġdig ital", + "Ġb ul", + "Ġm oon", + "Ġar ts", + "Ġtw enty", + "Ġdef in", + "Ġfin ish", + "g ing", + "Ġs ources", + "ĠRuss ell", + "ot ic", + "ĠSal v", + "mpt oms", + "Ġplay wright", + "Ġ190 4", + "Ġactiv ity", + "in er", + "Ġthe or", + "se e", + "Ġpo ets", + "ĠGuine a", + "ĠPr im", + "ĠR ab", + "ĠSer ge", + "I V", + "Ġb orders", + "Ġdanc er", + "b an", + "ĠL am", + "ost er", + "ĠFrog s", + "Ġr ugby", + "Ġsymb ols", + "ĠMargar et", + "J an", + "d iv", + "ĠPro gram", + "ĠCarl os", + "ĠR ights", + "od a", + "Ġqu arter", + "Ġw all", + "ĠM ong", + "Ġch all", + "Ġincre ased", + "av el", + "Ġcom ing", + "t rack", + "al le", + "Ġb right", + "ĠR ain", + "Ġfin ally", + "Ġd partement", + "ĠM it", + "ĠPl ot", + "ĠWork s", + "ĠCar ter", + "Ġw atch", + "el ve", + "Ġdis play", + "Ġprison ers", + "Ġs earch", + "ĠP ok", + "ĠLe v", + "Ġmed icine", + "us alem", + "ĠSt adium", + "Ġmat ter", + "m at", + "Ġst one", + "Ġincre ase", + "Ġinvent ed", + "ĠM ix", + "C C", + "Ġp ack", + "ĠZ e", + "Ġpr on", + "Ġlast ed", + "Ġ4 2", + "ile y", + "197 0", + "Ġdev ice", + "hatt an", + "Ġ5 1", + "ah o", + "Ġdeb uted", + "ĠR ud", + "ov es", + "ĠSam uel", + "Ġm outh", + "Ġdirect ion", + "197 1", + "ail and", + "Ġdisc uss", + "Ġser ve", + "ourn ey", + "Ġsol id", + "ĠSur vey", + "ĠHawai i", + "or ough", + "Ġdep art", + "ĠHon or", + "ĠColomb ia", + "or ph", + "ĠCalv ados", + "Ġdec ision", + "Ġext ra", + "Ġw ave", + "ĠF ive", + "ass ic", + "Ġtrib ut", + "Ġsouthwest ern", + "ĠHe b", + "Ġ18 00", + "f ather", + "Ġcontroll ed", + "Ġg ard", + "ut ional", + "ug uay", + "p an", + "ĠT oy", + "Ġsil ent", + "or ig", + "ĠR ic", + "Ġe ye", + "um an", + "Ġp et", + "ĠO m", + "os ite", + "vent ures", + "ĠT ri", + "Ġtr ies", + "Ġhouse hold", + "Ġoffic ers", + "ĠT ower", + "ĠM iami", + "ide os", + "Ġ6 3", + "ĠPh ill", + "Ġgirl s", + "Ġcol our", + "Ġc hess", + "b oy", + "ĠC ass", + "ĠL ib", + "Ġl ic", + "Ġal phabet", + "ats u", + "Ġphys ics", + "d in", + "ĠMun ich", + "ĠGovern ors", + "ĠPier re", + "ĠT rib", + "W A", + "ĠJer usalem", + "Ġag reed", + "hip s", + "Ġappear ances", + "hol m", + "ĠPh oen", + "ĠCommun ist", + "igh th", + "Ġb en", + "ĠF ront", + "ĠO sc", + "ap es", + "Ġkill s", + "Ġvol leyball", + "Ġnovel ist", + "ĠB ud", + "ĠD ave", + "ĠD ance", + "ra cy", + "19 69", + "ĠComp anies", + "Ġpat tern", + "ĠO w", + "Ġ5 6", + "Ġpian ist", + "Ġclass ical", + "ĠVlad imir", + "ĠR un", + "ĠL et", + "Ġsh are", + "ĠMan hattan", + "Ġnorm ally", + "an throp", + "st ers", + "Ġsing ing", + "ĠMo ore", + "in ent", + "en ge", + "Ġy et", + "Ġ5 9", + "ĠCol on", + "b ar", + "ĠSc reen", + "Ġfollow s", + "Ġnov els", + "G erman", + "im al", + "ab e", + "ĠMed ical", + "Ġra ces", + "Ġcol ors", + "ĠPakistan i", + "Ġdescrib e", + "ĠMatt hew", + "ĠF al", + "ĠB an", + "ĠPet ers", + "Ġbacter ia", + "Ġl ists", + "Ġmeet ing", + "ĠJun ior", + "en ing", + "Ġs ites", + "Ġv er", + "Ġv eget", + "Ġcon cept", + "ist ers", + "Ġpr incip", + "Ġcour se", + "Ġunderst and", + "Ġen emy", + "ĠBure au", + "ĠL ad", + "ĠOtt oman", + "ob i", + "Ġorgan ized", + "ĠLiber al", + "ĠM agn", + "ant ed", + "ĠB uff", + "ĠA ud", + "we alth", + "Ġappro x", + "as a", + "ĠB ou", + "R A", + "ĠUr uguay", + "ĠM ess", + "ĠM eg", + "Ġart icles", + "ĠCost a", + "ĠYug oslav", + "w ich", + "ĠTh ird", + "oot h", + "Ġact s", + "ĠL ang", + "act ive", + "Ġmem ory", + "c i", + "ĠN ash", + "Ġexper ience", + "; \"", + "Ġs ons", + "Ġc ant", + "Ġf ell", + "Ġ190 2", + "Ġch airman", + "Ġtit les", + "Ġfail ed", + "Ġfor ward", + "ĠPer u", + "Ġbrother s", + "Ġarchitect ure", + "Ġc ool", + "Ġg ained", + "Ġover all", + "Ġ4 9", + "ĠHar vard", + "ĠMor oc", + "ĠSal zburg", + "Ġcal cul", + "Ġ7 1", + "Ġwrest lers", + "ĠL ieutenant", + "Ġal umni", + "Ġexpl os", + "ĠFil ip", + "h aw", + "ip ment", + "ather ine", + "ĠChrist ians", + "inc orporated", + "Ġdef end", + "Ġgrow ing", + "J uly", + "Ġw rong", + "Ġs and", + "ĠNor man", + "m ph", + "Ġd inosaur", + "ĠG ran", + "ĠComp uter", + "c ott", + "is er", + "ĠS in", + "Ġm om", + "ĠC ong", + "Ġun c", + "Ġthem e", + "umb ent", + "Ġc oin", + "ĠD ur", + "ĠK o", + "ĠBr on", + "19 68", + "Ġorder ed", + "n ie", + "il ia", + "ir t", + "rop s", + "ĠAd v", + "Ġt arg", + "ug s", + "Ġgl ass", + "ĠNiger ia", + "ĠBoe ing", + "ĠS uff", + "ĠM ol", + "ĠJ ama", + "ĠTr in", + "Ġprot est", + "Ġs emi", + "N ov", + "Ġa part", + "Ġiss ue", + "y ing", + "th m", + "Ġcr imes", + "ym n", + "Ġquest ion", + "ra k", + "Ġhe ight", + "A pril", + "Ġreact ion", + "ag h", + "og le", + "ibb ean", + "f rom", + "r ast", + "Ġc m", + "ĠBen j", + "Ġan s", + "Ġy outh", + "Ġvill ages", + "Ġrev olution", + "p ur", + "t ic", + "ĠB undesliga", + "ad t", + "Ġ18 80", + "Ġrec ip", + "j oy", + "ĠRel ig", + "f it", + "aw are", + "i ens", + "it an", + "Ġcontin ue", + "Ġly rics", + "Ġp erman", + "Ġ14 0", + "ett y", + "heim er", + "ĠC ret", + "ĠAl ger", + "Ġbig ger", + "ent e", + "ĠSt ew", + "Ġsh r", + "Ġe asy", + "ĠRob inson", + "ĠTh ailand", + "Ġact ed", + "P h", + "Ġg re", + "ĠR yan", + "ire ment", + "Ġm ap", + "anc ell", + "ĠGr iff", + "ĠMc G", + "Ġbre ed", + "Ġhon or", + "ĠP oint", + "Ġst aff", + "ĠOrth odox", + "u ces", + "Ġs now", + "Ġp icture", + "Ġh ands", + "ĠW ard", + "Ġmov es", + "Ġpresent ed", + "Ġconstitu ency", + "ĠB asketball", + "ĠH unt", + "Ġv ice", + "Ġgr ass", + "ĠPlay er", + "Ġbr and", + "Ġh uge", + "ĠF uk", + "ĠN av", + "Ġwar m", + "Ġfoc us", + "ag ing", + "Ġpl ans", + "Ġcomp ared", + "re es", + "Ġ9 9", + "Ġcont ent", + "S eptember", + "ĠO range", + "igen ous", + "Ġ ir", + "ĠCon stant", + "ĠGirl s", + "ĠI ron", + "ĠH em", + "ĠV I", + "ĠAg ain", + "Ġrepresent atives", + "Ġv ert", + "w ig", + "ĠM ick", + "ĠH ospital", + "ĠW he", + "Ġr are", + "Ġconc ern", + "Ġs umm", + "Ġb aby", + "ĠA ctor", + "od s", + "og ue", + "C l", + "ĠKash mir", + "ĠR oc", + "ect ive", + "Ġdr ive", + "Ġpol icy", + "Ġphot ographer", + "ĠCom ics", + "back ground", + "Ġp ictures", + "ĠBel arus", + "ĠHol land", + "Ġinj ured", + "ĠCommon wealth", + "ĠSax ony", + "Jan uary", + "ĠO ber", + "Ġus ers", + "m und", + "Ġw a", + "ir ates", + "Ġsc ript", + "Ġest imated", + "Ġsound s", + "ĠWay ne", + "Ġobs erv", + "ov ich", + "Ġre form", + "ĠBor ough", + "en os", + "Ġc ongress", + "ou ver", + "oc racy", + "Ġdr ums", + "um in", + "ren e", + "M arch", + "act ion", + "Ġprov ided", + "ĠP in", + "Ġv ia", + "Ġ5 3", + "Ġro ute", + "Ġatt ention", + "ĠOl iver", + "Ġstay ed", + "ĠAl t", + "ĠArmen ia", + "ro c", + "ĠE P", + "Ġpr iest", + "ĠCo oper", + "Ġevery one", + "ming ham", + "ĠCon c", + "as ia", + "gress ive", + "\" ),", + "as p", + "Ġran k", + "Ġfem ales", + "ĠCh amber", + "A T", + "ul s", + "Ġpol o", + "Ġearl iest", + "Ġf ans", + "ĠG or", + "ang el", + "ĠCar ibbean", + "O ctober", + "Ġs ense", + "ill o", + "ĠBar ry", + "vers e", + "ĠSt ars", + "Ġliqu id", + "Ġresp onse", + "Ġlearn ed", + "A ugust", + "a ire", + "f riend", + "Ġm ind", + "ĠIn formation", + "ĠSp r", + "ĠIndepend ence", + "ĠK u", + "ĠFlor ence", + "Ġcor rect", + "Ġaccept ed", + "ĠHigh way", + "ĠPop ulation", + "Ġim ages", + "Ġtra ff", + "ĠSar ah", + "ess ion", + "Ġunivers e", + "m us", + "ĠD istricts", + "res p", + "Ġ18 70", + "Ġlik ed", + "Ġst reet", + "Ġam b", + "Ġopp osite", + "Ġshoot ing", + "ĠWik ip", + "iv ia", + "ĠC os", + "ĠM ak", + "ĠH ere", + "ĠJ ur", + "Ġreg ional", + "ĠDise ase", + "m itted", + "Ġp it", + "ĠN ort", + "uss ia", + "ĠAl ice", + "Ġinflu enced", + "Ġs ure", + "ĠW eek", + "ry pt", + "Ġthr one", + "ĠEp is", + "Ġcart oon", + "d ale", + "ĠG ian", + "ĠQueens land", + "ĠN elson", + "b ishop", + "z z", + "ĠT og", + "om ot", + "ok u", + "Ġhor se", + "Ġsc ale", + "Ġmention ed", + "Ġt ri", + "on i", + "19 67", + "ail ed", + "Ġsen ior", + "Ġconfl ict", + "Ġalcoh ol", + "le ans", + "ĠH ell", + "Ġhigh ly", + "Ġthous ands", + "p op", + "ch ar", + "ĠJ ason", + "Ġelect ronic", + "Ġf ranch", + "Ġp an", + "id ency", + "Ġl ots", + "ric ulture", + "Ġh ip", + "Ġcap tain", + "ĠSing les", + "ĠId aho", + "y ers", + "ĠSec urity", + "Ġmathemat ics", + "Ġc ert", + "ĠC ulture", + "ĠL anc", + "iv or", + "ov en", + "Ġ18 99", + "Ġvir us", + "Ġpurp ose", + "R S", + "ĠS oul", + "Ġcreat ing", + "s et", + "is ch", + "ĠL iver", + "Ġal one", + "f e", + "Ġm aint", + "ĠC BS", + "Ġd ensity", + "ĠDomin ican", + "ak ia", + "ult an", + "ĠPer cy", + "Ġm orning", + "Ġ18 60", + "Ġpart ner", + "ĠAth let", + "C an", + "Ġb ishop", + "Ġc lean", + "Ġp ull", + "Ġl iber", + "Ġeng ines", + "or gan", + "ĠConserv ative", + "it ors", + "ĠProt est", + "ite i", + "ad ers", + "ĠSoc cer", + "Ġ10 7", + "Ġsoc cer", + "ĠSil ver", + "H e", + "Ġm ales", + "ĠC old", + "Ġag ent", + "Dec ember", + "ĠBenj amin", + "is p", + "ly ing", + "ap olis", + "Ġs oul", + "Ġfin ancial", + "ĠB ass", + "Ġl ack", + "ĠAnd r", + "ĠLuxemb ourg", + "ĠLeb an", + "c ription", + "ĠE ug", + "ĠOs aka", + "I s", + "ĠChristian ity", + "Ġrequ ired", + "y g", + "Ġb it", + "ĠL og", + "Ġres igned", + "ĠOn ce", + "ĠBar on", + "Ġsub st", + "Ġaff ected", + "Ġphilos ophy", + "Ġbott om", + "Ġadd itional", + "ĠSaud i", + "Ġh abit", + "ĠB ed", + "Ġshe ll", + "ĠMont ana", + "Ġtrib es", + "id ay", + "atab ase", + "ĠG EF", + "Ġant hem", + "Ġpersonal ities", + "ĠSever al", + "Ġd omin", + "az a", + "Ġdanger ous", + "w o", + "Ġrul er", + "Ġcast le", + "ĠClin ton", + "Ġh its", + "ct ic", + "Ġ4 1", + "ĠArab ic", + "Ġlab or", + "o om", + "Ġc rown", + "ĠR aw", + "20 21", + "ĠAm az", + "ĠKn ight", + "Ġpromot ed", + "in ian", + "ĠS r", + "Ġf alls", + "ĠSt aff", + "Ġpresent er", + "ĠJeff erson", + "Ġt ail", + "ain ts", + "Ġus er", + "Ġco al", + "E ng", + "Ġm art", + "ĠC aus", + "ĠG am", + "amp le", + "idd en", + "ublish ing", + "ven ue", + "ĠCret aceous", + "Ġm el", + "Ġtem ple", + "3 00", + "r and", + "y les", + "iz u", + "Ġdiff erence", + "ĠMalays ia", + "w all", + "Ġtransl ated", + "ar p", + "anc ouver", + "Ġlook ing", + "Ġmethod s", + "Ġneg ative", + "Ġass ass", + "ĠB our", + "Ġl ibrary", + "Ġsc hed", + "ĠLi bert", + "ĠAlbum s", + "Ġcrim inal", + "m it", + "ĠL l", + "Ġit ems", + "ĠAir es", + "Ġann ual", + "Ġequ ipment", + "ĠEs sex", + "Ġbehav ior", + "ĠH and", + "ĠL ud", + "ĠN BC", + "ĠCap ital", + "ĠYear s", + "ĠHistor ical", + "ĠBrook lyn", + "Ġf at", + "ĠMon te", + "Ġinsp ired", + "it ness", + "ĠA ld", + "ĠP ays", + "Ġdis app", + "at ively", + "ĠB enn", + "id ers", + "ub le", + "ĠPaul o", + "ĠCharl ie", + "ĠEth iop", + "ĠSp eak", + "ĠLe ader", + "T C", + "ĠV a", + "Ġac qu", + "Ġcomb ined", + "Ġw orship", + "ĠK ath", + "ran ean", + "' m", + "Ġf uel", + "Ġext rem", + "Ġocc urs", + "Ġb ow", + "Ġcont act", + "Ġrock s", + "ag ues", + "ĠOr chestra", + "Ġrem ain", + "ĠGab ri", + "ĠFran ces", + "ĠCl imate", + "Ġcomm and", + "S P", + "rom e", + "Ġ18 96", + "ĠY outh", + "Ġsp ring", + "Ġdid n", + "Ġinv asion", + "A C", + "Ġk W", + "Ġmov ements", + "ob iles", + "ĠTun is", + "c alled", + "Ġb rief", + "el ess", + "ĠG ener", + "ich i", + "ĠBern ard", + "p es", + "ĠP ul", + "res ents", + "Ġnot es", + "ĠSund ay", + "Nov ember", + "ĠA M", + "Ġcl othing", + "Ġdog s", + "H O", + "Ġinsect s", + "ĠR out", + "Ġad apt", + "ĠStud ios", + "d rama", + "ĠMet ro", + "Ġarrondiss ements", + "Ġterrit ories", + "w ing", + "ind er", + "Ġdiplom at", + "Ġth ick", + "eop les", + "ĠOb last", + "ĠR ena", + "Ġfl oor", + "ĠBl ood", + "Ġsit com", + "ic er", + "Ġsold ier", + "ĠOrig in", + "J une", + "P remier", + "ĠM ans", + "ur er", + "Ġnot ed", + "aus en", + "Ġsepar ated", + "Ġs ell", + "Ġim medi", + "Ġrep resents", + "ĠPhoen ix", + "Ġh yp", + "Ġbe gin", + "Ġact ions", + "ĠT yp", + "ra h", + "ĠMor gan", + "Ġw ing", + "song writers", + "Ġtrack s", + "ĠN i", + "Ġformer ly", + "ĠMinist ry", + "Ġveh icles", + "ro t", + "Ġh ang", + "ĠX box", + "ĠEn t", + "ĠSy ria", + "ĠDr agon", + "Ġfeat uring", + "ĠAct ress", + "ĠSuff olk", + "em et", + "ĠGo ogle", + "ĠB uck", + "ĠPar am", + "ĠMov ement", + "ij i", + "Ġeconom ist", + "U S", + "ret t", + "Ġhistor ians", + "Ġv ideos", + "Ġdivor ced", + "d on", + "he ro", + "ĠChile an", + "ĠBarb ara", + "ĠW right", + "ĠMr s", + "w here", + "ĠPr adesh", + "ĠBir ths", + "Ġjob s", + "or us", + "Ġd raft", + "Ġ18 95", + "Ġbirth day", + "ĠG ary", + "ĠMin or", + "ĠSev en", + "ĠH op", + "ĠRober ts", + "ĠDel aware", + "Ġprop osed", + "Ġm ist", + "ows ki", + "ĠMe itei", + "Ġsett lement", + "ĠL uther", + "ra ham", + "ĠV ancouver", + "Ġsp in", + "iter ranean", + "Ġnomin ation", + "Ġknow ledge", + "Ġh p", + "et e", + "Ġun ique", + "Ġpos itions", + "ĠPlay ers", + "ro om", + "ag g", + "uk a", + "ĠFilm ography", + "ĠVerm ont", + "F ebruary", + "Ġon to", + "ett a", + "Ġt ouch", + "en ne", + "Ġgu ard", + "Ġviol ence", + "ĠCharl otte", + "ĠUl t", + "Ġc odes", + "st ery", + "ĠVin jan", + "a ug", + "ĠR al", + "ak ed", + "ony m", + "Ġp ra", + "Ġrepresent ative", + "ĠAust in", + "Ġsurg ery", + "ĠT ol", + "Ġd iam", + "un es", + "Ġprov ides", + "Ġc atch", + "ĠT ob", + "iv als", + "Ġor chestra", + "ast e", + "urr ay", + "ĠDou bs", + "ĠMarsh all", + "ĠM other", + "ag ement", + "Ġesc ape", + "Ġreve aled", + "an ne", + "Ġin stit", + "Ġtit led", + "Ġend ing", + "ĠMan ipur", + "ĠEn cyclop", + "Ġagre ement", + "om ers", + "ĠPh ot", + "Ġperson nel", + "Ġelectric ity", + "Ġm ax", + "ĠR and", + "Ġstr ip", + "ĠSe attle", + "ĠBoy s", + "ĠS af", + "Ġoper ations", + "S rie", + "ro ph", + "Ġ10 4", + "Ġille gal", + "' ,", + "ĠLiter ature", + "as aki", + "Ġm ut", + "ĠB ac", + "ĠR eed", + "Ġfield s", + "ĠR at", + "ĠOn line", + "ĠSup port", + "emp orary", + "ut ies", + "G eneral", + "h ing", + "un c", + "ell ite", + "ĠCat hedral", + "ĠC ole", + "ĠG ay", + "oth y", + "ĠLaw yers", + "ĠS ah", + "et ime", + "ĠD ays", + "ĠW a", + "Ġpoet ry", + "ĠLeg isl", + "m ers", + "Ġstra ight", + "f am", + "Ġsy mptoms", + "Ġleg end", + "b al", + "id i", + "Ġdocument ary", + "el er", + "ĠJ ess", + "Ġ5 2", + "Ġclass ification", + "ĠStew art", + "Ġd oll", + "Ġch ain", + "Ġch urches", + "Ġte eth", + "ie val", + "Ġtrib e", + "Ġwhe el", + "Ġsuff ered", + "Ġev il", + "ĠGr ant", + "ĠPolit ics", + "Ġs alt", + "ĠT ib", + "ĠP ont", + "ad ow", + "Ġte ach", + "iel der", + "ĠAdminist ration", + "ĠC ow", + "ĠN BA", + "Ġsh op", + "Ġdis order", + "ĠMed iterranean", + "us ter", + "Ġrec ently", + "e or", + "Ġs av", + "Ġor bit", + "k ar", + "om ing", + "ĠF ather", + "ut o", + "Ġhe ard", + "ĠH as", + "ik ov", + "Ġrem ember", + "FF FF", + "emet ery", + "ĠM oz", + "ĠI an", + "inn er", + "ĠBr id", + "Ġqu ant", + "Ġparticular ly", + "ĠTreat y", + "av ery", + "19 66", + "ĠKn ow", + "R I", + "et a", + "ĠW oman", + "Ġre ality", + "ern e", + "Ġen joy", + "Ġany thing", + "Ġs ick", + "ĠB ox", + "ri x", + "Ġconv icted", + "Ġded icated", + "ĠS ard", + "ĠSocial ist", + "ĠWikip edia", + "ĠW ell", + "Ġpre gn", + "Ġfind s", + "Ġd ates", + "ĠR ound", + "Ġarr ived", + "Ġadminist ration", + "Ġdef ined", + "Ġphys ician", + "Ġinfect ion", + "M S", + "Ġsug ar", + "ĠNev ada", + "onom ous", + "e ctor", + "om m", + "Ġch lor", + "Ġr hy", + "Ġs ales", + "Ġte aching", + "ĠEx pl", + "Ġcontin u", + "t op", + "Ġs ave", + "Ġp air", + "Ġh all", + "ĠF ictional", + "uss ian", + "Ġestab lish", + "b ell", + "c aster", + "d o", + "or al", + "Ġg iant", + "ĠK os", + "ab a", + "Ġmanag ement", + "Ġne ut", + "ĠS C", + "ĠArt ist", + "en es", + "Ġelect ron", + "inc ial", + "ĠRec ord", + "unn y", + "ĠU N", + "Ġdise ases", + "ol ia", + "ĠF rid", + "ann er", + "Ġdep ression", + "ĠBrother s", + "is in", + "Ġgen etic", + "it is", + "ace ae", + "Ġres ign", + "ĠNot able", + "Ġyoun gest", + "U K", + "ig i", + "Ġfl ower", + "Ġw ars", + "ĠAl z", + "ĠFor ces", + "re p", + "Ġmil k", + "Ġcaus ing", + "ĠGre ater", + "Ġexec uted", + "ĠCh airman", + "ĠB eg", + "ĠDig ital", + "il ing", + "um es", + "ĠVenezuel a", + "ĠS em", + "Atl ant", + "t es", + "19 64", + "Ġsl ight", + "St ar", + "ify ing", + "Ġparticip ated", + "ĠK id", + "Ġsp ect", + "Ġsc ene", + "I t", + "ĠS um", + "ĠN et", + "Ġcy cle", + "Ġdistrib ution", + "Ġ18 93", + "Ġsh ared", + "ĠBir mingham", + "ĠGir onde", + "ĠLiver pool", + "en berg", + "re c", + "ĠF ood", + "Ġj un", + "Ġvis ited", + "ar ians", + "ĠH end", + "ĠG ot", + "Ġrepl ac", + "ĠBat man", + "ĠB orn", + "ĠAn at", + "Ġdiagn osed", + "Ġa x", + "Ġh ors", + "ĠH orn", + "Ġoper ation", + "Ġ <", + "ĠP ad", + "ĠUnivers e", + "B E", + "ĠL anguage", + "ĠG ust", + "Ġext inct", + "80 72", + "ĠSat urn", + "Ġprem ier", + "Ġl ies", + "ames e", + "ĠIs a", + "Ġshow ing", + "Ġtre ated", + "ĠP sych", + "Ġat m", + "r um", + "as ure", + "Ġrem aining", + "Ġcop y", + "ĠLank a", + "ĠS uz", + "Ġschol ar", + "ĠF u", + "ĠJ e", + "Ġse g", + "ĠBab y", + "ĠG a", + "Ġ18 98", + "Ġun s", + "Ġautom obiles", + "ĠCroat ia", + "W e", + "ĠK um", + "Ġgro wn", + "ĠOr leans", + "Ġinc ome", + "er c", + "ĠK ur", + "Ġrep e", + "ĠBu enos", + "ic ts", + "Ġf resh", + "Ġbl ues", + "ancell or", + "Ġb orough", + "Ġ1 000", + "Ġh our", + "Ġph r", + "ĠYe ung", + "c les", + "ĠA ra", + "Ġcrick eter", + "ĠS i", + "ot es", + "Ġ18 97", + "Ġcomm ander", + "Ġfranch ise", + "Ġoper ated", + "ĠStock holm", + "Ġanal ysis", + "ĠOsc ar", + "ĠE sc", + "ĠEu ro", + "Ġsl aves", + "Ġprotect ion", + "ĠFilip ino", + "A D", + "Ġel se", + "ĠStud ies", + "Ġmole c", + "Ġf reedom", + "ĠD ub", + "ĠE qu", + "ac o", + "Ġcandid ates", + "Ġcras hes", + "ĠO ur", + "an es", + "cl us", + "inn ed", + "ĠNap ole", + "' re", + "Ġm ammals", + "ĠN ame", + "Ġn om", + "st anding", + "ĠIn g", + "Ġcall s", + "ĠWalk er", + "Ġto ols", + "app y", + "Ġfav or", + "P S", + "ĠT rain", + "ri ke", + "ĠGard en", + "Ġbeaut iful", + "ĠS now", + "ĠSh im", + "Ġadd ress", + "ĠA BC", + "Ġstreng th", + "d i", + "ĠM ort", + "ĠK ap", + "ĠPl ace", + "ĠMc K", + "Ġpaint ers", + "s ch", + "ag u", + "Ġdam aged", + "ĠMat hemat", + "ĠK aw", + "Ġad ults", + "Ġres idents", + "lic ation", + "Ġp anc", + "ep p", + "ĠIn stead", + "ĠCh ang", + "ĠPhys ics", + "ĠNAS A", + "hel m", + "ĠLeg end", + "Ġlearn ing", + "R h", + "Ġro t", + "pl ace", + "Ġmed als", + "ĠIndones ian", + "Ġs ets", + "ĠL ost", + "ran ce", + "Ġvot ed", + "in ing", + "ĠC orps", + "ĠHon ours", + "ĠEconom ic", + "l ie", + "Ġ11 0", + "Ġconsid er", + "Ġpri ze", + "s ki", + "le ton", + "Ġphil anthrop", + "ber to", + "Ġse em", + "com mun", + "ĠVin cent", + "ĠF ollow", + "ous ly", + "Ġgu est", + "ĠFriend s", + "O r", + "Ġ18 50", + "Ġad vert", + "ĠNik ol", + "Ġfoss il", + "ĠP ear", + "Ġpo ison", + "Ġcon stant", + "Ġcomm itted", + "b u", + "Ġ ens", + "Ġc ounter", + "n ij", + "ĠAb raham", + "x ide", + "ĠB ach", + "ĠD iam", + "ĠCl ar", + "Ġboy s", + "Ġhom es", + "4 00", + "ĠR av", + "end e", + "Ġfun ctions", + "m el", + "ĠR ing", + "Ġg all", + "ĠLar ry", + "ĠHeb rew", + "Ġin n", + "ĠS or", + "Ġpart icles", + "ĠNa uch", + "Ġindivid uals", + "ĠM i", + "Ġpaint ed", + "ĠCr ime", + "Ġident ity", + "Ġsculpt or", + "op es", + "ber ry", + "Ġpl ate", + "Ġcol ony", + "che ll", + "Ġaltern ative", + "Ġtra v", + "ol y", + "Ġor b", + "Ġimp act", + "A ll", + "Ġh urt", + "Ġ18 61", + "Ġqu ality", + "ĠSerb ia", + "Ġch ose", + "Ġdr ummer", + "c ia", + "in ental", + "Ġf ully", + "ĠC hen", + "Ġh am", + "ĠR eb", + "op ter", + "19 65", + "Ġ188 9", + "Ġbusiness people", + "ĠThom pson", + "ĠD J", + "Ġlook ed", + "ĠLu is", + "ĠHit ler", + "] ]", + "Ġw alls", + "ĠMar vel", + "Ġdev ices", + "ĠBib li", + "in ch", + "Ġwith d", + "Ġhyd rogen", + "ĠNauch nij", + "Ġl iver", + "Ġn ecess", + "Ġth row", + "ac re", + "Ġcl imb", + "Ġres ear", + "Ġme at", + "oc ard", + "Ġr isk", + "Ġrepl ace", + "Ġsett led", + "Ġoccur red", + "ĠSerb ian", + "i ago", + "ĠA round", + "in ia", + "ĠWar ren", + "Ġcou p", + "h r", + "ĠA u", + "ĠM urray", + "Ġher b", + "Ġvict ims", + "ĠSel ena", + "ran ches", + "Ġdiv isions", + "Ġsub t", + "ĠD od", + "ip er", + "Ġcon cer", + "lev i", + "iss ance", + "clud ing", + "Ġsk i", + "H ex", + "Ġm ental", + "ĠC ant", + "ĠL ion", + "ĠG ib", + "eg as", + "Ġevery thing", + "ĠR ow", + "Ġbi ography", + "Ġeduc ator", + "Ġf elt", + "ĠCon vention", + "ĠUs ually", + "Ġnumer ous", + "Ġb atter", + "ĠIn n", + "Ġ188 8", + "Ġvol cano", + "ĠOper a", + "C on", + "ĠL angu", + "ĠO pp", + "ĠW or", + "ib ly", + "Ġhistor ic", + "ĠLat v", + "ien ne", + "Ġglob al", + "Ġapprox imately", + "Ġb ra", + "Ġ *", + "Ġst ores", + "Ġfl owers", + "ĠPen insula", + "m aker", + "Ġ( ).", + "ĠW onder", + "Ġclass es", + "ĠBor is", + "ĠOrgan ization", + "z i", + "ĠB ond", + "ĠR an", + "ock ed", + "Ġext ended", + "Ġpass es", + "ĠBulg aria", + "l on", + "Ġus eful", + "orro w", + "ĠPl at", + "Ġtyp ically", + "ĠJos h", + "ĠJon athan", + "Ġenvironment al", + "Ġf aster", + "ĠL enn", + "ĠG un", + "ĠRev iew", + "Ġdeg rees", + "ĠBeat les", + "Ġp le", + "ĠF ight", + "ul es", + "ĠCh and", + "se a", + "ĠMar ine", + "Ġ6 2", + "Ġcompet itions", + "Ġcerem ony", + "ĠF un", + "Ġhe av", + "Ġproper ties", + "ĠThe od", + "ĠRh ine", + "ĠB aker", + "im ents", + "Ġst rik", + "Ġ5 8", + "Ġ6 1", + "Ġresp ir", + "Ġclos ely", + "ĠE mer", + "ĠYork shire", + "Ġiss ued", + "Ġcho ose", + "w ise", + "ĠS ide", + "Ġf t", + "ĠM aced", + "ĠD iet", + "Ġ16 5", + "Ġalong side", + "Ġvo iced", + "d u", + "ĠD ist", + "ish a", + "Ġcon ver", + "Ġso il", + "ĠPl aces", + "ĠInter view", + "Ġminist ers", + "Ġinvent or", + "am an", + "om on", + "ĠGra ham", + "ĠSlov en", + "ig o", + "Ġsol ar", + "Ġveh icle", + "Ġ1 19", + "ĠB att", + "ĠOrig inal", + "o j", + "om p", + "Ġinf ar", + "Ġeas ier", + "t hes", + "ot an", + "ĠDes pite", + "ĠCro wn", + "ĠW ien", + "ĠHer z", + "ĠStev en", + "Ġschol ars", + "Ġinfar ction", + "b es", + "ĠF reedom", + "Ġher self", + "ĠRes ults", + "ĠHistor ic", + "F irst", + "ul u", + "Ġown ers", + "Ġben ef", + "Ġrun ner", + "R GB", + "es ar", + "ĠA FC", + "Ġn ations", + "Ġsy nd", + "ĠR ou", + "ĠW ag", + "Ġth us", + "Ġ18 92", + "ĠEn ergy", + "ĠAlz heimer", + "Ġd aily", + "Ġg ang", + "Ġe lectoral", + "ĠSt u", + "ĠUn like", + "Ġpo em", + "Ġc ous", + "iv an", + "Ġwind s", + "en cer", + "Ġro ugh", + "Ġpo ems", + "Ġfly ing", + "ran e", + "ĠCub an", + "19 60", + "Ġac comp", + "Ġturn s", + "Ġnuc le", + "ĠBird s", + "Ġgre ater", + "Ġs end", + "Ġs ulf", + "Ġc ategory", + "ĠS ak", + "ĠL av", + "ip h", + "sp eak", + "ĠDe an", + "Ġoffer ed", + "ĠO g", + "Ġgu ilt", + "Ġsevent h", + "alt a", + "Ġdisc over", + "ĠDes ign", + "ĠA uthor", + "ĠT on", + "ĠHe in", + "ĠV ic", + "ĠJac ques", + "ĠL is", + "id o", + "ĠF ish", + "Ġofficial s", + "ĠArab ia", + "ĠCroat ian", + "Ġsucceed ed", + "w inning", + "ĠI b", + "amb a", + "aught ers", + "Ġexp ected", + "Ġb ill", + "am ing", + "Ġbre ast", + "ĠMag azine", + "Ġtarg et", + "E S", + "Ġw aters", + "ĠA ges", + "Ġg ay", + "ĠSo ft", + "ĠS ic", + "om i", + "ĠV el", + "Ġnot e", + "ĠGr ay", + "ĠBob by", + "ĠNiger ian", + "Ġkilomet ers", + "ĠExp ress", + "Ġro ads", + "Ġmy ocard", + "Ġav oid", + "Ġmot ion", + "l ia", + "ster dam", + "Ġz one", + "Ġche ck", + "v ement", + "Ġm obile", + "orn ado", + "Ġret irement", + "Ġmom ent", + "Ġpre p", + "ĠPresident ial", + "Ġaud ience", + "Ġste el", + "T S", + "m o", + "ĠP age", + "Ġkn ew", + "Ġbehav i", + "ap ted", + "Ġ11 4", + "ĠDel hi", + "al ing", + "ĠS oph", + "ĠM ig", + "ĠP C", + "ir k", + "ĠD ol", + "Ġ18 94", + "Ġres c", + "Ġque en", + "I nd", + "ĠM ind", + "us hed", + "Ġ18 91", + "Ġchem istry", + "ĠBuild ing", + "Ġrad iation", + "n ight", + "Ġc inem", + "ĠH ond", + "Ġtw elve", + "Ġres ources", + "aur us", + "Ġsupp osed", + "ĠC ond", + "ĠGu ide", + "f urt", + "ĠS ign", + "Ġv en", + "ant ine", + "ens is", + "Ġrece ive", + "g om", + "ĠT u", + "ĠDe ep", + "Ġsaf ety", + "t ing", + "ĠB ert", + "ĠB ald", + "rov ision", + "ĠJo an", + "ie g", + "Ġbro ken", + "Ġpun ish", + "Ġp p", + "un o", + "Ġserv es", + "Ġdisc overy", + "ĠKor levi", + "Ġappl ied", + "ĠS ac", + "Ġat oms", + "Ġthere fore", + "ĠL omb", + "ĠO ri", + "im ens", + "Ġdescrib es", + "Ġinterest ed", + "Ġcele b", + "ĠElect ric", + "Ġmyocard ial", + "il o", + "Ġst ock", + "Ġag ree", + "ram s", + "Ġmar ry", + "off s", + "ĠIndepend ent", + "ĠA in", + "ĠJ ose", + "ord in", + "Ġdes cent", + "Ġperform ing", + "ĠL ag", + "Ġprofess ion", + "ĠAdd itional", + "h u", + "at ur", + "Ġpre y", + "Ġ. ..", + "en o", + "Ġg uns", + "ia ble", + "Ġcl othes", + "! \"", + "ĠF ull", + "Ġinvol ving", + "on ian", + "Ġim mun", + "Ġbi ology", + "Ġelectr ical", + "ĠT as", + "ĠLe o", + "Ġgood s", + "ĠHen ri", + "ĠNick el", + "ĠP rom", + "Ġch rom", + "Ġend ings", + "Ġsit uation", + "ĠN ak", + "od ium", + "Ġrap id", + "ĠWinn ers", + "Ġas p", + "cl aim", + "ĠArn old", + "on ents", + "ĠD uc", + "ĠBad en", + "p ret", + "s uch", + "ĠA ar", + "ĠP ied", + "Ġre ar", + "all ow", + "ĠAg ency", + "Ġfig ures", + "Ġsurround ed", + "Ġprot ests", + "udd en", + "ĠSte in", + "Ġnar row", + "ĠPer ry", + "ĠI P", + "ĠSh a", + "pr ise", + "ĠS ug", + "ĠM ikh", + "ir ing", + "et es", + "Ġchang ing", + "Ġdist inct", + "ĠCont est", + "Ġacadem ics", + "ĠNich olas", + "E C", + "ĠP ink", + "eb ra", + "ub b", + "aw ay", + "Ġemp ire", + "m i", + "ĠM ond", + "em a", + "ĠW ii", + "ĠTh rough", + "uch i", + "Ġref used", + "Ġhot el", + "Ġcongress ional", + "ur u", + "uc cess", + "ar l", + "Ġem ot", + "Ġguitar ists", + "Ġdom estic", + "Ġr ural", + "uc lear", + "ĠBack ground", + "ĠPark er", + "Ġob tain", + "Ġrequ ire", + "G N", + "S R", + "Ġre ject", + "end ment", + "Ġentertain ment", + "ĠTib et", + "ar th", + "ĠS eb", + "un ch", + "ak ov", + "Ġeat en", + "CA P", + "ĠLangu ages", + "b urn", + "Ġf ear", + "ĠL ot", + "ĠHar rison", + "ĠRob in", + "ĠP il", + "ĠA C", + "st ream", + "Ġsign s", + "ĠRepresent ative", + "Ġp unk", + "ĠL ov", + "ĠD S", + "ul ed", + "Ġser ial", + "ĠAm sterdam", + "Ġpubl isher", + "b at", + "b ul", + "ĠM ult", + "ĠK ill", + "se ud", + "Ġex change", + "ĠCom edy", + "ans ion", + "ann els", + "Ġtran sit", + "Ġprot ected", + "ĠKaz akh", + "Ġin ternet", + "ĠG ang", + "19 63", + "Ġcompet e", + "Ġg ender", + "ens es", + "ĠTr ade", + "et on", + "Ġvo ices", + "A ust", + "am oto", + "Ġsecond s", + "Ġ188 7", + "Ġc ong", + "Ġst re", + "Ġdec ide", + "Ġto x", + "Ġcon st", + "Ġsup ply", + "Ġph one", + "Ġview s", + "Ġtraff ic", + "ĠPr ague", + "Ġun us", + "Ġbo at", + "Ġneighbor hood", + "Y ou", + "ab or", + "yl er", + "Ġsurv ived", + "ic ient", + "ĠL isa", + "ot ten", + "em ia", + "ĠStand ard", + "Ġcant ons", + "Ġmed ium", + "m or", + "n on", + "Ġn ose", + "ist ol", + "out heastern", + "Ġchem ist", + "Ġs elling", + "Ġch ance", + "Ġfound ing", + "urn ame", + "Ġsk y", + "ĠPok mon", + "Ġval ues", + "Ġcap ac", + "ĠSm all", + "Ġarch ae", + "Ġdro pped", + "Ġgovern ments", + "ĠRap id", + "Le ague", + "ĠChem istry", + "Eng lish", + "o i", + "Ġg ar", + "ĠOb ama", + "Ġastron omer", + "ĠB ey", + "ir i", + "ĠEu rovision", + "Ġbas is", + "ĠBy z", + "Ġw orth", + "ĠP yr", + "ĠK ids", + "Ġhyd ro", + "Ġemerg ency", + "Ġw ound", + "ix on", + "lo ad", + "ĠAp oll", + "ĠParam ount", + "ĠV oice", + "ĠHar old", + "Ġslow ly", + "em ies", + "ist ical", + "Ġg ather", + "ĠDef ense", + "Ġperman ent", + "I C", + "ĠN y", + "Ġme ets", + "Ġlaun ch", + "gom ery", + "Ġl at", + "et o", + "ĠD ennis", + "og a", + "ok i", + "ĠMar in", + "Ġdis ab", + "ĠVal ent", + "iction ary", + "Ġel im", + "ĠJul ian", + "oca ust", + ": #", + "S e", + "Ġacc used", + "Ġclaim s", + "ĠAnim ation", + "Ġop in", + "ro id", + "ĠH ann", + "ĠNot e", + "Ġleaders hip", + "ad ian", + "ere ign", + "Ġcomp ound", + "amp s", + "ivers ary", + "ĠAd ventures", + "ĠNatural ized", + "ĠBroad cast", + "P r", + "f ielder", + "ĠD ra", + "ubl in", + "Ġcond uct", + "ĠTro phy", + "ĠHun ter", + "ĠHi ros", + "ĠT at", + "ĠM end", + "Ġdes ert", + "Ġform ula", + "Ġref erence", + "Ġauthor ity", + "Ġsuggest ed", + "R T", + "ag ne", + "Ġslight ly", + "ĠB ourg", + "ĠSat ur", + "Ġcelebr ated", + "ĠConfeder ate", + "Ġp urch", + "ĠF at", + "ens on", + "ĠFran z", + "ĠX ing", + "Ġpol ic", + "ĠGlob al", + "ĠS ay", + "Ġf ired", + "ĠC and", + "st ar", + "Ġrepresent ing", + "t ure", + "ĠTur in", + "Ġbatt les", + "ĠHeavy weight", + "ĠRout e", + "ĠC atherine", + "ĠR ight", + "ĠJ ar", + "ier ra", + "Ġstr ugg", + "ĠSant iago", + "ĠGra ce", + "ĠFam ous", + "Ġperforman ces", + "p oint", + "t i", + "Ġ18 65", + "Ġpar ishes", + "ĠEd die", + "ipp ed", + "Ġpark s", + "ĠBuff alo", + "Rh ne", + "k es", + "ĠM ik", + "iv ore", + "Ġgra v", + "Ġcapt ure", + "idel berg", + "g ie", + "t hen", + "Ġd ra", + "Ġal ive", + "Ġr ise", + "ĠZ oo", + "mph ony", + "ĠT ig", + "ce p", + "est on", + "Ġed ge", + "iform es", + "O T", + "c op", + "z le", + "or age", + "Ġf ur", + "Ġ13 0", + "Ġsent enced", + "ĠT ed", + "ag i", + "ĠCom ed", + "Ġexist ed", + "Ġw aves", + "Ġn eck", + "ĠPro file", + "ĠLou ise", + "ĠBeng al", + "ĠFried rich", + "Ġk ings", + "ĠNew ton", + "ĠAm ong", + "ĠMad ison", + "Ġar ran", + "Ġconstit ution", + "ĠM az", + "ĠN ation", + "ue z", + "ĠGh ost", + "s il", + "Ġt iss", + "Ġst ands", + "Ġextrem ely", + "up iter", + "ann i", + "ĠWil helm", + "ĠOrgan izations", + "Ġchampionship s", + "ĠAt hens", + "rid ges", + "Ġmole cules", + "SC O", + "S aint", + "Ġvol ume", + "Ġdou b", + "orig inal", + "ĠFollow ing", + "n ell", + "il ation", + "ĠN umber", + "ith m", + "Ġ18 63", + "ĠHot el", + "Ġoccup ied", + "le ft", + "ĠH old", + "Ġ10 1", + "Ġres istance", + "ĠCl ay", + "Ġnomin ations", + "Ġsaf e", + "ĠD ue", + "ign y", + "ĠBe aut", + "ĠHol ocaust", + "Ġsurv ive", + "Ġbal let", + "ĠC rick", + "ĠH erman", + "st ic", + "ĠProd uction", + "Ġexact ly", + "ĠMoroc co", + "b ody", + "h ost", + "om es", + "Ġst yles", + "ie v", + "iet ies", + "Ġsup pl", + "Ġcard iac", + "Ġ ult", + "ĠS v", + "ĠS ultan", + "ĠM n", + "ĠSp eed", + "ĠCol leges", + "ĠAnd y", + "ĠBas ic", + "Ġreport s", + "Ġor ange", + "Ġ18 64", + "Ġaff ect", + "ĠH ud", + "ĠW ald", + "ĠPed ro", + "ĠStu art", + "t ers", + "ĠEd in", + "Ġchar ged", + "Ġdr ivers", + "Ġcamp s", + "urd ers", + "ak s", + "ĠSt ef", + "ff ee", + "ah n", + "ĠPhil os", + "Ġpopular ity", + "Ġkid ney", + "b ow", + "um er", + "Ġtra ined", + "Ġmic ro", + "Ġan ime", + "orm s", + "Ġprint ed", + "Ġenc our", + "ĠMit chell", + "ĠRal ph", + "ĠD anny", + "Ġ18 40", + "ĠSh ir", + "Ġent ry", + "Ġmurder ed", + "Ġs ang", + "ol ves", + "alle l", + "ĠS yn", + "Ġpl us", + "Ġpart ners", + "Ġcont aining", + "Ġport ray", + "l ate", + "ar an", + "ĠR ico", + "Ġlead s", + "ĠEn vironment", + "Ġany one", + "Ġint elligence", + "forman ce", + "Ġord ers", + "el ia", + "Ġguilt y", + "on ed", + "ĠO D", + "ĠArch bishop", + "c ious", + "Ġr if", + "Ġsmall est", + "ĠHer bert", + "Ġdepart ments", + "haw ks", + "Ġl ayer", + "Ġform at", + "Ġdel iver", + "ĠWall ace", + "Ġfir m", + "Ġc os", + "Ġsurround ing", + "S L", + "ĠM aj", + "ĠTe le", + "Ġwe alth", + "Ġliter ary", + "ĠBibli ography", + "r ate", + "Ġc up", + "Ġsl ave", + "Ġproject s", + "ĠAle ks", + "r uct", + "it aine", + "ht e", + "ĠShin to", + "c ore", + "Ġm ig", + "ĠJ a", + "ĠTer ry", + "erst ate", + "Ġtreat y", + "ĠSpr ings", + "ĠSatur day", + "ĠN ext", + "Ġ18 62", + "Ġviol in", + "R C", + "Ġb an", + "ĠK ab", + "ge on", + "Ġj ur", + "ĠP icture", + "ate ur", + "ĠArch itect", + "ĠAmbass ador", + "ĠMick ey", + "at ra", + "ĠC ob", + "ĠM ack", + "Ġsp ons", + "ĠComm ander", + "bor ough", + "ĠMot or", + "P D", + "Ġal g", + "ĠJ oy", + "ĠHe idelberg", + "ant ry", + "ver y", + "sp ec", + "Ġform ation", + "Ġstr ing", + "Ġexp ensive", + "ĠHel en", + "Ġtransl ation", + "e ast", + "Ġ ur", + "ĠA ction", + "ĠG oth", + "ĠAll iance", + "Ġsl avery", + "ĠBen ed", + "ĠFer r", + "u in", + "Ġc aught", + "Ġc ards", + "ig s", + "ĠDiv isin", + "Ġdiff erences", + "ĠErn est", + "N o", + "p ear", + "ĠS ad", + "ĠB og", + "ĠL t", + "ra ph", + "h aps", + "ol ics", + "ĠL uke", + "Ġsp r", + "ĠPot ter", + "c ome", + "Ġd ram", + "ĠL ists", + "if ferent", + "ĠY onne", + "Ġm os", + "ĠFer din", + "ĠRay mond", + "ĠLoc al", + "it ol", + "ĠP eng", + "au er", + "Ġlim it", + "Ġmart ial", + "w orld", + "ĠY ellow", + "Ġcond ucted", + "Ġhapp y", + "ĠRom ans", + "ĠPied mont", + "in us", + "Ġb ones", + "Ġse lection", + "Ġad apted", + "Ġorgan isms", + "Ġcrit ical", + "r ical", + "Ġe ating", + "Ġ188 5", + "ĠAtt orney", + "Ġd aughters", + "Ġ188 2", + "Ġcycl ist", + "Ġconcer ts", + "M L", + "ĠB ear", + "Ġtr ip", + "ĠXing long", + "O C", + "b b", + "f are", + "Ġswim mer", + "ĠMikh ail", + "H A", + "Ġa ster", + "Ġf al", + "il er", + "ĠF ac", + "Ġsh rine", + "ĠCon stit", + "ĠAv iation", + "Ġmach ines", + "Ġpers ons", + "ĠR ah", + "ist ed", + "ĠU NE", + "b us", + "Ġl ose", + "ĠJ upiter", + "Ġper fect", + "Ġoper as", + "ĠPat ri", + "Ġexper iment", + "ĠS CAP", + "ific ations", + "Ġshort ly", + "u is", + "on na", + "ist ant", + "if icial", + "Ġte x", + "ash a", + "inc inn", + "Ġexp ress", + "Ġ188 3", + "ryst al", + "Ġpanc reat", + "ĠS art", + "Ġf an", + "Ġh arm", + "Ġplan ets", + "ĠKe ith", + "Ġl osing", + "ish i", + "ling ton", + "ĠMag ic", + "oc al", + "hel or", + "ĠEdin burgh", + "ot ton", + "Ġlawy ers", + "min ster", + "Ġnickn ame", + "r n", + "ĠS ylv", + "ĠP rem", + "ĠF ab", + "ĠProv inces", + "Ġb ranches", + "ĠP HO", + "ĠR ace", + "ĠK ush", + "ĠEx ecutive", + "Ġelectr ons", + "incinn ati", + "F ootball", + "p erson", + "Ġp eoples", + "ort un", + "ph a", + "Ġloc omot", + "ĠSchool s", + "Ġimp rov", + "Ġthous and", + "ĠF ace", + "Ġn it", + "os ev", + "ĠShe l", + "Ġinj ury", + "ĠBroad way", + "E P", + "ĠK un", + "oy a", + "Ġ187 3", + "ĠPeters burg", + "ĠM aps", + "Ġ15 9", + "Ġcommun ication", + "Ġfl o", + "Ġ188 6", + "Ġnovel ists", + "Ġrespir atory", + "Ġf ert", + "am o", + "ĠL an", + "ĠN ever", + "Ġcomm ission", + "Ġsent ence", + "ĠProd uctions", + "ĠM ul", + "ĠO st", + "ard y", + "ort ion", + "ĠGi ov", + "% .", + "ĠS ul", + "ick en", + "akes pear", + "r up", + "ĠG n", + "ul ated", + "Ġscreen writers", + "ish ops", + "ĠMc N", + "Ġhtt ps", + "Don ald", + "Ġimmedi ately", + "t ual", + "ĠO z", + "Ġch ap", + "ple ment", + "ĠComm ons", + "Ġsuper hero", + "Ġastron aut", + "ĠEngine ering", + "cl ass", + "anc ing", + "Ġcontin ues", + "ĠGrand e", + "SA C", + "ett i", + "Ġsub urb", + "Ġback ing", + "Ġmark ed", + "Ġhold ing", + "Ġcous in", + "ĠP ra", + "Ġl inks", + "ĠSt art", + "ars h", + "Ġsign al", + "ĠOt to", + "ĠColl ins", + "o ire", + "Ġt el", + "Ġa er", + "est yle", + "ĠSp ider", + "Ġrem ix", + "Ġfood s", + "ĠBos nia", + "ĠProtest ant", + "ĠS ony", + "ĠA my", + "ĠN eg", + "os lov", + "av ia", + "ie u", + "Ġcour ts", + "m od", + "Ġar med", + "ĠIs ab", + "Ġsever e", + "ĠCamp bell", + "Ġrespect ively", + "ĠCaus s", + "Ġhundred s", + "ĠCE O", + "Ġto wer", + "ĠG et", + "Ġ25 0", + "ĠUS D", + "ĠTom my", + "Ġc row", + "ĠA S", + "ĠM ust", + "ĠF alls", + "Ġst ick", + "amp a", + "Ġen emies", + "ym ph", + "Ġfish ing", + "ĠAD E", + "ĠNash ville", + "ĠL ock", + "ĠW here", + "Ġdo ctors", + "ĠPl ant", + "Ġopp onent", + "ĠJenn ifer", + "an ian", + "ig ation", + "Ġth in", + "ong e", + "Ġch oice", + "ĠMe an", + "Ġcat hedral", + "ĠAnim ated", + "ĠTog ether", + "Ġ18 68", + "oh n", + "Ġpass enger", + "ĠW ings", + "Ġse eds", + "lev el", + "Ġexpl orer", + "ĠSol ar", + "om ach", + "Ġch annels", + "Ġr ating", + "ĠSh akespear", + "Ġchild hood", + "Ġmess age", + "ĠA P", + "Ġh orn", + "ag s", + "ĠK ang", + "ĠGen eva", + "g ate", + "Ġs ed", + "ĠB iden", + "ĠG ill", + "Ġ18 30", + "ĠY ank", + "Ġj oint", + "Ġsub div", + "ĠServ ices", + "Ġbelong s", + "Ġweap on", + "Ġatm osphere", + "% )", + "j u", + "ul ate", + "Ġsecond ary", + "Ġathlet es", + "ĠA ur", + "ĠP rop", + "ĠU tt", + "ĠAn y", + "ĠCath olics", + "l en", + "Ġr ival", + "Ġstand ing", + "ĠUNE SCO", + "Ġs uit", + "Ġp rice", + "ht m", + "ow der", + "Ġind ic", + "Ġdec re", + "Ġann oun", + "ĠSus an", + "Ġs olution", + "ĠB uch", + "ĠH us", + "ĠL amb", + "mer ce", + "Ġsp ot", + "ren cy", + "ĠReg ional", + "Ġ188 1", + "Ġpancreat ic", + "ĠI ch", + "ĠAng lo", + "] ,", + "Ġs um", + "ĠC urt", + "ĠC inem", + "ou x", + "Ġfl av", + "p ent", + "p rod", + "Ġa im", + "ĠH im", + "umb ria", + "ĠPlan et", + "ĠC hel", + "am ents", + "ĠK rist", + "ĠBrit t", + "Ġident ified", + "Ġgoal keep", + "ĠYugoslav ia", + "f a", + "ar in", + "ĠGl as", + "ĠComm and", + "ĠColumb us", + "ocol ate", + "ĠSalv ador", + "ĠApoll o", + "b ird", + "p ine", + "ir us", + "Ġim pr", + "ĠPer formance", + "ĠHug o", + "Ġt im", + "so le", + "ok oh", + "ĠRe in", + "ĠLeon ard", + "Ġalt itude", + "Ġconcent ration", + "Ġ ),", + "ĠT ax", + "et ics", + "Ġcirc uit", + "Ġfoss ils", + "J apan", + "M P", + "Ġc ats", + "ĠS V", + "ĠN ear", + "ĠMan uel", + "ĠArt ists", + "ĠHam burg", + "ĠTurn er", + "c ell", + "ĠD raft", + "ĠK urd", + "Ġ3 60", + "ĠSh ang", + "Ġ187 9", + "ĠBow l", + "Ġb anks", + "e lection", + "Ġin her", + "Ġf ellow", + "ĠTw in", + "ĠEv ans", + "Ġg ene", + "Ġpol y", + "ĠDes cription", + "Ġthink ing", + "ĠLl oyd", + "ol ar", + "ĠF arm", + "ĠRo osev", + "Ġcult ures", + "Ġcard inal", + "ĠVen ice", + "ĠCauss ols", + "M y", + "h ai", + "Ġt end", + "ĠV it", + "Ġch air", + "Ġoff ices", + "Ġdep ending", + "ĠKn ights", + "ĠFrid ay", + "H S", + "ĠCast ile", + "Ġvis ual", + "ĠLin ux", + "Ġrom ance", + "ĠNaz is", + "ĠInd ians", + "ĠGerman s", + "Ġchar ges", + "ĠNapole on", + "okoh ama", + "l ock", + "ĠA ude", + "ĠD ublin", + "ĠG rey", + "ill on", + "19 62", + "ok es", + "sp ir", + "Ġfoc used", + "Ġkilomet res", + "ĠC rist", + "ĠOther s", + "Ġdis aster", + "Ġform al", + "Ġanim ation", + "Ġ188 4", + "ĠRog ers", + "Ġrow span", + "Ġbelief s", + "w oman", + "Ġd ream", + "ell ion", + "Ġsp ir", + "Ġ12 5", + "Ġfac ulty", + "ĠUn til", + "uss ion", + "Ġso ap", + "Ġsupport ing", + "ĠDep ression", + "ĠCzech oslov", + "Ġsit u", + "m other", + "ĠL il", + "ĠZ one", + "ĠNov a", + "ĠUr ban", + "Ġnewsp apers", + "Ġbi ographical", + "Ġfa ith", + "ĠTyp es", + "ĠA er", + "ĠC yr", + "ode on", + "ĠHol t", + "Ġimm igr", + "c os", + "ĠBe h", + "ĠMont gomery", + "Ġconserv ative", + "ĠComed ians", + "ount ain", + "ok o", + "amp ton", + "Ġphot os", + "A N", + "f inals", + "off icial", + "B ar", + "C W", + "Ġd ict", + "im ar", + "Ġnot hing", + "Ġund erground", + "ĠSch w", + "Ġcor resp", + "ĠUE SAC", + "ĠWag ner", + "ub a", + "Ġprim arily", + "Ġlo an", + "ĠParalymp ic", + "ĠRoosev elt", + "Ġw ings", + "Ġl akes", + "ĠF ur", + "ĠSp irit", + "oss ible", + "Ġdesc end", + "ĠNe il", + "r us", + "Ġ rit", + "re is", + "ch o", + "ember g", + "ĠAnt ar", + "Ġappro ach", + "Ġtalk ing", + "ĠVietn amese", + "Ġpal ace", + "Ġact ual", + "zer o", + "Ġbroadcast ers", + "A z", + "E M", + "ĠS oon", + "aw s", + "Ġdoes n", + "ĠVill age", + "Ġdem onstr", + "ĠEconom ics", + "Ġswim ming", + "Ġ187 1", + "Ġcor on", + "ĠSte el", + "ĠHaut s", + "ĠE t", + "ĠE is", + "ov i", + "ai ro", + "Ġw et", + "Ġs em", + "Ġf er", + "ĠA venue", + "Ġhe l", + "Ġmult i", + "wh o", + "ĠDar win", + "Ġang ry", + "6 00", + "Ġp ione", + "rat a", + "ĠMor ris", + "Ġmagn etic", + "edd ing", + "s ec", + "Ġm is", + "Ġl ingu", + "Ġ18 48", + "Ġ12 8", + "Ġincre asing", + "ĠWars aw", + "ĠC av", + "end ers", + "ĠCl a", + "Ġdec or", + "ĠBlack hawks", + "Ġadv ant", + "ĠBuddh ist", + "ĠS z", + "ĠS ig", + "ag awa", + "Ġregard ed", + "ĠEncyclop edia", + "speak ing", + "B ob", + "r ent", + "v an", + "ub s", + "Ġtr ust", + "Ġcolon ial", + "ĠMur phy", + "Ġinst all", + "Ġabs or", + "C ol", + "Ġf illed", + "Ġab bre", + "ĠRe lease", + "uck er", + "unn ing", + "Ġmeas ured", + "Ġox id", + "Ġcolon ies", + "Ġimpro ve", + "ĠK ate", + "19 59", + "se ason", + "Ġ18 67", + "is is", + "ĠIn vest", + "ĠV ern", + "ĠGe off", + "Ġsk ills", + "s er", + "ĠR af", + "ĠH ir", + "ĠTh ose", + "Ġtour ist", + "Ġpremier ed", + "ed o", + "ĠM obile", + "land ers", + "Ġest ate", + "Ġsqu ad", + "apt ist", + "ĠAleks and", + "u its", + "ĠG w", + "ab i", + "iz z", + "L C", + "ĠS ierra", + "Ġ18 57", + "Ġdo or", + "Ġmet ropolitan", + "ĠEconom y", + "ĠRac ing", + "P as", + "ĠP el", + "Ġn ut", + "Ġv ac", + "Ġor g", + "ĠSh rine", + "Ġquest ions", + "ĠJohann es", + "ĠC hess", + "iz ard", + "Ġj ourney", + "ĠSe an", + "ĠCy pr", + "Ġ ri", + "ĠS K", + "Ġd iet", + "Ġh ill", + "ĠL ef", + "Ġan throp", + "Ġab use", + "Ġpat ients", + "ĠOtt awa", + "Ġpot ential", + "ĠPyr nes", + "ar at", + "ic on", + "ĠG and", + "ack s", + "Ġ8 00", + "Ġsubject s", + "ĠCra ig", + "ĠAss oci", + "Ġtribut ary", + "ĠCong o", + "Ġrhy thm", + "ĠIsa ac", + "ĠFerdin and", + "ĠP ay", + "ĠK i", + "ĠOff icer", + "Ġjud ges", + "Ġnob le", + "ĠNickel odeon", + "us a", + "ĠCh a", + "te enth", + "ter y", + "urrican es", + "ĠSus sex", + "ĠJur assic", + "Ġin ternal", + "ĠH av", + "ĠCol lection", + "ĠNorth west", + "Ġred uced", + "Ġamount s", + "ic as", + "le e", + "ĠR est", + "ĠD oc", + "Ġprod uces", + "ĠDen ver", + "ĠJane iro", + "B N", + "Ġ10 3", + "Ġreg ist", + "Ġdec lar", + "Ġadv anced", + "Ġconnect ion", + "r tt", + "Ġw at", + "ro at", + "Ġen for", + "Ġ187 5", + "war f", + "ĠGreg ory", + "U N", + "ĠP le", + "op p", + "Ġeconom ics", + "ĠInt erstate", + "Ġtal ks", + "ĠS ind", + "ch ild", + "end a", + "ine e", + "ĠCl aud", + "ĠEv olution", + "Ġresult ed", + "ĠBulg arian", + "Ġlink ed", + "ol ished", + "ĠTr ust", + "ĠLind a", + "Ġunus ual", + "e o", + "Ġb one", + "ic ia", + "and al", + "19 61", + "ĠPop ular", + "ĠC ultural", + "Ġst omach", + "ĠU g", + "ĠFilm s", + "Ġp ed", + "Ġm i", + "ĠD aily", + "act ers", + "Ġspeak ing", + "Ġtemper atures", + "ĠSeb ast", + "in cluding", + "ĠA stron", + "ĠF uture", + "ter m", + "ĠV egas", + "k an", + "Ġp ink", + "ĠC ec", + "am ental", + "ĠH ad", + "man uel", + "ĠHa ute", + "ĠRena issance", + "ĠTas man", + "A A", + "ĠT ang", + "ack er", + "Ġrel atively", + "D P", + "Ġh ole", + "ĠH at", + "ĠL ane", + "ĠD ifferent", + "ur as", + "ur st", + "ĠN uclear", + "ĠAn a", + "ins ky", + "Ġcab inet", + "ĠShakespear e", + "Ġt ornado", + "ĠH az", + "Ġatt end", + "ĠN ancy", + "Ġlar g", + "Ġinter pret", + "Ġimport ance", + "Ġox ide", + "Ġphilosopher s", + "S c", + "Ġmar ine", + "Ġgen re", + "Ġcirc le", + "Ġreb u", + "ĠVers ion", + "un i", + "Ġr ice", + "ĠAr r", + "b ie", + "ĠA j", + "ĠM iy", + "ĠN eed", + "ĠK ay", + "ew here", + "ĠAnt i", + "Ġcomb ination", + "ĠGil bert", + "ĠWy oming", + "g ne", + "ĠL ah", + "id ad", + "ĠW es", + "ĠPr incip", + "ĠEd ition", + "int ers", + "ĠSy rian", + "Ġ187 2", + "Ġfix ed", + "ĠLegisl ative", + "Ġp ul", + "ot i", + "Ġst ages", + "uc ing", + "Ġbut ter", + "Ġup d", + "ĠTit an", + "ĠSud an", + "rtt emberg", + "Ġs ung", + "ĠF ly", + "ĠK iss", + "Ġ18 78", + "Ġro se", + "ier re", + "ĠPort land", + "Ġfem in", + "Ġspeak ers", + "ĠCa esar", + "ĠC elt", + "ĠP ep", + "ep lay", + "Ġdevelop ing", + "Ġrelig ions", + "Ġa st", + "Ġb anned", + "Ġg am", + "Ġ18 69", + "ĠAd olf", + "Ġgrow s", + "ĠRh ode", + "Ġcoast al", + "Ġbox er", + "rep rene", + "D S", + "Ġapp lications", + "16 6", + "Ġauthor ities", + "ĠJud ge", + "ot ed", + "ĠO ce", + "ĠCh ron", + "Ġsh ark", + "ĠChar acters", + "ocrat ic", + "Ġ187 6", + "ĠPic ard", + "Ġcapac ity", + "ĠD est", + "Ġhelp ing", + "ĠMiss ion", + "ĠDemocrat s", + "B e", + "ĠS eg", + "ĠS ites", + "Ġn aval", + "ie ge", + "Ġoff ers", + "ĠWest minster", + "ĠPhys iology", + "ĠMaur ice", + "Com t", + "ĠEthiop ia", + "Ġmax imum", + "Ġc as", + "Ġv ess", + "ĠY ang", + "ĠTh under", + "Ġclass ified", + "Ġnetwork s", + "AF TA", + "7 00", + "d r", + "al us", + "Ġb ed", + "Ġm ac", + "ran o", + "ĠKenn eth", + "Ġcycl one", + "ĠCrick et", + "Ġin ches", + "Ġro ots", + "ĠSh o", + "aj a", + "Ġcamp us", + "Ġopp osition", + "ĠRevolution ary", + "fam ily", + "Ġnecess ary", + "ĠM ut", + "ent o", + "ĠE ight", + "ĠY u", + "Ġcol leges", + "ĠArt icle", + "unn el", + "ĠCy cl", + "ĠMar x", + "Ġmark s", + "Ġstudy ing", + "Ġess ay", + "ĠAb u", + "mir al", + "ĠFem ale", + "g ow", + "ĠH yd", + "Ġun able", + "Ġpresent ers", + "ĠMah ar", + "Ġburn ed", + "re te", + "um ed", + "ert y", + "Ġdoc uments", + "ĠCru z", + "ĠSpeak er", + "A m", + "ĠD j", + "Ġpl astic", + "io let", + "Ġdep end", + "Ġexist ence", + "Ġfactor y", + "Ġg ain", + "ĠBar n", + "Ġair pl", + "ato es", + "ĠAnim al", + "Ġgal axy", + "8 00", + "l ive", + "le ased", + "Ġd io", + "Ġrel ative", + "Ġbusiness es", + "Ġw orn", + "Ġd ress", + "ĠH ugh", + "ov o", + "est ivals", + "ĠSh aw", + "Ġatt orney", + "enc ia", + "Ġkid n", + "est one", + "ĠAr c", + "az ines", + "ather s", + "Ġsequ ence", + "Ġphr ase", + "ĠA x", + "ur bs", + "Ġhigh way", + "Ġt ests", + "Ġdep uty", + "ij ing", + "ĠAbd ul", + "u ania", + "ĠM ann", + "ĠG ene", + "ĠW as", + "Ġcr isis", + "Ġwe igh", + "ĠSch m", + "ps ons", + "sur ance", + "Ġdial ect", + "Ġdisapp ear", + "ĠHer o", + "Ġair line", + "Ġ15 6", + "Ġadd ing", + "ĠPark inson", + "ĠCart oon", + "at em", + "Ġs weet", + "it led", + "ĠU m", + "uc er", + "ĠFran co", + "Ġgr anted", + "ĠBourg ogne", + "ĠB AFTA", + "ĠH ous", + "id al", + "og s", + "ĠBr ig", + "Ġfa ctors", + "g i", + "ĠM ade", + "ĠMan agement", + "ĠM all", + "ch ing", + "olog ies", + "Ġexp anded", + "Ġjust ice", + "Ġinj uries", + "ographer s", + "ĠLeban on", + "ro e", + "am as", + "ĠR us", + "ĠHe aven", + "Ġopp ortun", + "ĠTest ament", + "Ġchall eng", + "Ġdinosaur s", + "Ġs ort", + "Ġs udden", + "ĠD rama", + "Ġtal ent", + "t or", + "Ġe ighth", + "iss a", + "omet ry", + "ĠMod el", + "Ġball ads", + "ĠRac hel", + "f ly", + "ro up", + "ĠC P", + "ĠN adu", + "Ġth inks", + "19 56", + "Ġdis orders", + "ke ley", + "Ġequ ation", + "Ġh ired", + "Ġl in", + "Ġr ar", + "Ġun incorporated", + "Ġland sc", + "Ġred uce", + "ĠSqu ad", + "Ġ ign", + "Ġs urr", + "it us", + "as i", + "Ġm ir", + "ĠV ery", + "Ġsim pl", + "Ġphot ograph", + "ĠByz antine", + "ĠGiov anni", + "Ġhe ar", + "Ġ18 77", + "Ġwrit es", + "cent ral", + "Ġstat ement", + "Ġdef ense", + "ĠEs s", + "end orf", + "Ġ6 00", + "ĠSc ar", + "ract ion", + "Ġhead s", + "ĠTy rol", + "ĠHud son", + "Ġp ool", + "Ġm anga", + "ĠM ouse", + "ĠP att", + "ĠF itz", + "Ġder ived", + "ĠSand ers", + "Fran che", + "ĠBrook s", + "ĠKy oto", + "B undesliga", + "f olk", + "Ġb ud", + "ĠJ ung", + "oc ese", + "ĠIn s", + "Ġpurp oses", + "ial s", + "ric ultural", + "iz er", + "Ġatt ached", + "j ani", + "on omy", + "ĠS ou", + "ĠH ope", + "ir s", + "Ġk ick", + "ac le", + "Ġab d", + "Ġres erv", + "ĠAng l", + "Ġsuccess or", + "Ġnickn amed", + "eren d", + "ĠWar ri", + "ĠTw enty", + "ĠRet urn", + "ĠPur ple", + "Ġw ine", + "Ġb ord", + "Ġd ating", + "ĠP ast", + "Ġ18 59", + "Ġtall est", + "ĠC row", + "ĠH ig", + "ĠN an", + "Ġrem ove", + "itar ian", + "Ġclass ic", + "gr ade", + "ĠAlbert a", + "Ġans wer", + "ĠE vent", + "ĠThere fore", + "ĠCr im", + "Ġaud io", + "V al", + "ĠR ica", + "ĠG ren", + "ut a", + "Ġr ub", + "Ġ17 0", + "Ġdes pite", + "Ġmy stery", + "ĠKir k", + "found er", + "Ġhol iday", + "Ġlarg ely", + "A nd", + "h um", + "Ġt ube", + "ĠM ale", + "ĠJ ava", + "ov ina", + "ĠGabri el", + "on so", + "ch air", + "Ġg one", + "Ġcar n", + "Ġbehavi our", + "T e", + "in stein", + "ĠP ow", + "ĠCh ancellor", + "Ġsp oke", + "ĠBe ijing", + "Ġaut obi", + "Ġste am", + "ĠAmaz on", + "Ġt y", + "ĠN ature", + "ut ing", + "ĠHar vey", + "Ġref ere", + "Ġhors es", + "A M", + "Ġthe ater", + "ĠE ld", + "od on", + "ap est", + "Ġrep rod", + "Ġorgan ic", + "ĠArch ae", + "ĠWind s", + "ĠGen us", + "Ġmid fielder", + "Ġcritic ized", + "ĠColomb ian", + "in ations", + "ĠB omb", + "ĠK ra", + "20 22", + "15 0", + "Ġtyp ical", + "ĠSy mphony", + "ĠWat an", + "ymn ast", + "Ġst ops", + "ĠMar a", + "Ġbl ind", + "ĠSim psons", + "Ġconf erence", + "Ġstrong er", + "Ġin form", + "ĠRep ort", + "ĠPol y", + "Ġperiod s", + "ĠHa iti", + "ĠCop a", + "e i", + "at ics", + "Ġb ull", + "ĠD uck", + "Ġpr iv", + "Ġmil it", + "ĠBec k", + "ĠArd che", + "ĠS alt", + "ia h", + "Ġse es", + "ĠM ats", + "ĠJ azz", + "Ġre ady", + "ans k", + "Ġfind ing", + "bre ak", + "Ġsat ellite", + "leph one", + "h and", + "Ġpl anning", + "ĠV e", + "aj i", + "Ġpre fer", + "can ic", + "ĠM ang", + "ĠSard inia", + "h ima", + "ĠP ack", + "ĠK el", + "Ġacc ur", + "ĠBer keley", + "ĠMerc ury", + "B I", + "l ass", + "ir teen", + "st adt", + "19 58", + "app ed", + "Ġliber al", + "u ctors", + "ro ck", + "Ġad j", + "Ġpr ince", + "Ġmed ieval", + "Ġsound track", + "ĠCamer on", + "u il", + "Ġev olved", + "ĠSing er", + "ades hi", + "ĠEston ia", + "Ġdio xide", + "? \"", + "in ction", + "ish n", + "ĠTh ai", + "Ġcl im", + "Ġtr iang", + "Ġprincip al", + "ĠH ob", + "ĠD y", + "Ġn erv", + "os exual", + "Ġkill er", + "Ġvot ing", + "ĠFern ando", + "ĠMig uel", + "ĠF if", + "ĠMar ion", + "Ġres ident", + "Ġengine ers", + "Ġallow ing", + "Ġdet ails", + "ĠWat son", + "M ont", + "es is", + "ĠL ate", + "ĠCh an", + "ĠEd wards", + "Ġpre fecture", + "ĠRoll ing", + "Ġcam era", + "ĠSupport ing", + "at on", + "Ġs au", + "ĠT ree", + "Ġd iab", + "ĠR ush", + "ur ation", + "oc hem", + "Ġcomp ar", + "Ġim ag", + "ĠDisc overy", + "Ġfarm ing", + "ĠAbb ey", + "ĠWatan abe", + "z ig", + "is ations", + "em aker", + "19 45", + "ĠHind i", + "Ġspirit ual", + "Ġcontrovers ial", + "B O", + "ĠA ber", + "ĠM uch", + "ĠL akes", + "ĠAl tern", + "Ġex ists", + "ah a", + "ĠMc M", + "Ġfight er", + "ĠSim pson", + "ĠTran sit", + "Ġinit ially", + "Ġdefin ition", + "O n", + "Ġsc enes", + "ĠGu atem", + "ĠPicard ie", + "ĠProv ence", + "Ġhost s", + "Ġcomb at", + "ĠGh ana", + "Ġhunt ing", + "n ik", + "Ġc ore", + "ĠS weet", + "Ġm ort", + "ĠB ran", + "ĠK urt", + "Ġout er", + "Ġcricket ers", + "ĠLith uania", + "et ry", + "Ġpro gress", + "old s", + "Ġag ency", + "Ġcre am", + "ĠX V", + "ĠAlger ia", + "Ġtex ts", + "a a", + "ĠC anton", + "ĠE lement", + "out s", + "ipp ing", + "Ġsurv ey", + "ĠTod d", + "Ġimpr ison", + "in ja", + "ĠS old", + "ĠH ip", + "Ġg ram", + "Ġab and", + "ĠAr ena", + "Ġ11 7", + "Ġtrad ed", + "Ġresult ing", + "Ġfrequ ently", + "u ber", + "ĠT yler", + "Ġbe ar", + "Ġgu ide", + "ĠGer ald", + "n or", + "Ġ ib", + "Ġb in", + "Ġc oc", + "ĠT erm", + "ĠC incinnati", + "our ed", + "Ġshe l", + "Ġeduc ational", + "Ġer upt", + "Ġ ut", + "Ġt ick", + "ĠH EN", + "qu er", + "ern al", + "Ġ7 37", + "ah l", + "Ġcreat or", + "ĠBangl adeshi", + "Ġrestaur ant", + "d is", + "m aster", + "s l", + "ĠM is", + "ĠP and", + "ĠI z", + "ĠLa ura", + "Ġpar ent", + "ĠGl en", + "ĠSl av", + "Ġprom inent", + "Ġdiam eter", + "Ġtox ic", + "Ġchemical s", + "Ġb om", + "Ġin fl", + "ĠS umm", + "ĠH ab", + "ĠO il", + "ĠInd o", + "Ġgr ay", + "ĠAh med", + "Ġsecret ary", + "ĠJama ica", + "Ġs ens", + "ĠR ican", + "Ġan ch", + "ĠTer rit", + "Ġposs ibly", + "Ġpres ence", + "Ġ zero", + "on ies", + "ĠS uch", + "ov ing", + "ok ia", + "Ġpr inc", + "Ġgen es", + "Ġcop per", + "Ġcorresp ond", + "re cht", + "ĠC emetery", + "ĠF landers", + "ĠRef orm", + "ĠSh oot", + "ĠTr ad", + "ĠMon ey", + "Ġcitiz en", + "ĠRem ix", + "ĠChem ical", + "Ġelev en", + "Ġterror ist", + "ĠEpis ode", + "ĠBened ict", + "C AR", + "Ġthe olog", + "Ġl it", + "un te", + "ĠSp encer", + "ĠHel in", + "Ġgradu ating", + "Ġsen ator", + "1 10", + "Ġthe rm", + "ĠS ex", + "ĠP ut", + "ĠCh ad", + "orn e", + "per ors", + "oz o", + "ĠOper ation", + "ĠDuc hess", + "ĠM old", + "Ġal le", + "ac a", + "ĠEduc ators", + "N e", + "ĠP av", + "ra it", + "Ġopp osed", + "Ġsubst ance", + "E L", + "p et", + "t wo", + "Ġs outheastern", + "ay an", + "Ġv e", + "ĠGreat est", + "ĠProfess ional", + "Ġphilanthrop ist", + "ĠL ess", + "ĠV II", + "ant o", + "Ġle ct", + "Ġdef ensive", + "ĠSur v", + "c at", + "ch ang", + "Ġint ended", + "ĠCont rol", + "Ġgard en", + "Ġm aps", + "ĠF und", + "ver gne", + "ĠBra h", + "Ġexp ed", + "in ity", + "ĠC le", + "ĠB es", + "Ġv acc", + "Ġpri or", + "ĠGriff ith", + "ĠF ro", + "ĠLe af", + "com ing", + "emp ts", + "Ġsport speople", + "Ġteacher s", + "D own", + "F rench", + "Ġstat ue", + "ĠNe igh", + "Ġadv oc", + "Ġlow est", + "ĠKer ala", + "ov ereign", + "ĠWe ather", + "c ussion", + "Ġc ateg", + "rit z", + "ell ation", + "osp el", + "Ġwear ing", + "Ġeff orts", + "en zo", + "ĠC ord", + "il is", + "os ph", + "Ġpart ly", + "str uction", + "Ġinc ident", + "be k", + "Ġtechn iques", + "Ġpres idency", + "ĠCypr us", + "or ers", + "ĠP oll", + "ĠB ent", + "ib i", + "Ġaut onomous", + "ĠJul ia", + "ĠAar on", + "Ġa qu", + "Ġe leph", + "Ġ18 20", + "Ġatom ic", + "Ġshr ines", + "Ġin aug", + "ĠM TV", + "Ġd rop", + "Ġbe y", + "Ġv ary", + "Ġpar allel", + "Ġgoddess es", + "FF C", + "Ġspace craft", + "ĠPalest inian", + "ĠNorm andy", + "Ġf ib", + "ĠM ath", + "ag ar", + "Ġtw in", + "Ġnew ly", + "Ġcol lected", + "ĠBas il", + "Ġstates man", + "ĠM urder", + "im p", + "Ġst rike", + "art a", + "ey er", + "Ġex erc", + "ĠEd mund", + "ĠAb original", + "Ġeffect ive", + "Ġcorn er", + "ĠBroadcast ing", + "Ġb ond", + "ĠF isher", + "Ġinv ited", + "Atlant iques", + "Ġ {", + "it u", + "Ġp hen", + "ĠA w", + "ĠR uth", + "Ġper haps", + "Ġsupport s", + "adu ate", + "ĠSol omon", + "P al", + "19 55", + "Ġse ems", + "ĠCan al", + "Ġrul ers", + "ĠBut ler", + "ĠBuddh ism", + "Ġ1 18", + "ĠJ ak", + "Ġrele ases", + "ĠEx per", + "Ġpass ing", + "Ġpromot e", + "ĠCher ny", + "Aust ral", + "ag ua", + "ĠAd venture", + "Ġrecord ings", + "ĠBry an", + "9 00", + "Ġh at", + "Ġ8 50", + "Ġ12 7", + "Ġproduc ing", + "Th is", + "rupt ion", + "ĠGust av", + "h ausen", + "ĠS ent", + "Ġ- -", + "Ġind igenous", + "Ġdec ides", + "inc umbent", + "ĠSpec ies", + "ĠErn st", + "ĠUtt ar", + "Ġf it", + "ĠI w", + "oc ene", + "Ġpr incess", + "ĠPalest ine", + "ill ing", + "Ġpart icle", + "Ġland ing", + "Ġmin ing", + "ĠPan ama", + "ĠEston ian", + "ĠA ri", + "ĠL eop", + "Ġv ow", + "ph is", + "ĠPro per", + "Ġtran sp", + "ĠMark et", + "Ġsuccessful ly", + "Ġc able", + "Ġh ills", + "Ġsp end", + "21 3", + "Ġapp ar", + "ĠRe ception", + "Ġappro ved", + "Ġpeak ed", + "Ġun cle", + "ĠSon ic", + "Ġentire ly", + "Ġshe ep", + "Ġsk ull", + "Ġrul ing", + "Ġmass ive", + "Ġviol ent", + "Ġpen alty", + "ĠAuthor ity", + "ĠHiros hima", + "ĠGlas gow", + "h al", + "ĠP ick", + "ĠR he", + "ĠK as", + "ip per", + "Ġbey ond", + "m ith", + "Ġp orn", + "ĠB ak", + "ĠB ath", + "Ġl es", + "ĠJ or", + "ĠSe oul", + "Ġcomm ittee", + "Ġinflu ential", + "rie ved", + "ĠLy rics", + "ig ue", + "st yle", + "ĠV iol", + "Ġar rang", + "ĠPr inc", + "ĠRich mond", + "Ġrelationship s", + "Ġinstit utions", + "P ar", + "S up", + "s i", + "ĠF olk", + "Ġsuggest s", + "Ġvisit ors", + "ĠMichel le", + "Ġfal se", + "ĠT all", + "ĠC ad", + "ĠM orning", + "od ia", + "Ġapp lication", + "sh aped", + "Ġreact ions", + "Ġqual ified", + "an th", + "ĠC N", + "Ġal gor", + "ec a", + "und a", + "Ġsub sequ", + "ĠJust in", + "ĠOak land", + "Ġsched uled", + "Ġs isters", + "ĠH erm", + "og an", + "ĠMar g", + "Ġro of", + "Ġ17 90", + "Ġdec isions", + "Ġrecogn ition", + "Ġtalk ed", + "stan bul", + "vo iced", + "isp here", + "E n", + "r ag", + "ĠB il", + "Ġch amber", + "Ġ18 66", + "ĠD oll", + "Ġst ret", + "Ġinc orporated", + "Ġ187 4", + "Ġrestaur ants", + "Ġhousehold s", + "ĠSart he", + "l ings", + "v ar", + "ĠM ethod", + "ad ors", + "ĠAmer icas", + "Ġ18 54", + "Ġtrans form", + "agon ist", + "Ġmem orial", + "Ġbeaut y", + "l u", + "r uction", + "v oice", + "ĠNe u", + "ĠHug hes", + "ĠW ang", + "im i", + "Ġrec omm", + "mon ary", + "Ġmet als", + "uth ors", + "c ing", + "m ade", + "Ġt ank", + "Ġc rops", + "ĠS aints", + "ĠL ak", + "ess ions", + "Ġcarry ing", + "ĠLud wig", + "ĠCherny kh", + "v or", + "w ers", + "Ġt ort", + "er r", + "Ġm al", + "Ġ10 9", + "ĠCon st", + "ĠGu ild", + "amb ia", + "Ġprogram me", + "arch y", + "Ġel der", + "Ġtest ed", + "ĠU z", + "ĠCar lo", + "Ġloc ations", + "Ġhy per", + "ĠOcc itan", + "ĠB ranch", + "and om", + "Ġv eter", + "Ġdesign s", + "ĠR AF", + "ĠTe en", + "ĠTre as", + "Ġsynd rome", + "ĠB ag", + "ber ger", + "Ġte a", + "yl a", + "Ġmod e", + "ĠHal f", + "Ġpat ient", + "E D", + "I R", + "W rttemberg", + "Ġdist ingu", + "Ġmix ing", + "Ġsett ing", + "f ootball", + "Ġst ress", + "ĠV III", + "Ġr id", + "Ġ13 5", + "ull ivan", + "Ġent reprene", + "Ġrock et", + "Ġbre ad", + "Ġcontrol s", + "ĠNap les", + "Ġsyn thes", + "Ġprote in", + "Ġf ine", + "ĠR i", + "ĠL ink", + "Ġas ks", + "Ġbe ach", + "ĠZ imb", + "ĠNor folk", + "Ġass ault", + "Ġann iversary", + "ĠEth nic", + "G u", + "on c", + "Ġp upp", + "ĠF oot", + "ĠPar ag", + "Ġvis ible", + "Ġcirc um", + "Ġnort heastern", + "E l", + "I P", + "ĠP ent", + "Ġpre late", + "ĠEm ma", + "Ġgr ad", + "ival ent", + "Ġwor st", + "c ons", + "ĠS ey", + "ag ers", + "Ġst able", + "ĠPr ison", + "enn es", + "Ġbl og", + "ĠFin ance", + "ĠVol leyball", + "pro fit", + "I tal", + "Ġc attle", + "Ġp up", + "ĠT ake", + "ĠB elf", + "Ġle af", + "ĠEu rop", + "ĠRe ich", + "Ġfun eral", + "Ġhous ing", + "cep ts", + "or ian", + "Ġs overeign", + "ing o", + "Ġto ol", + "ĠL ic", + "iv ision", + "ber y", + "app ing", + "Ġtechn ical", + "Ġprec ip", + "c ount", + "re f", + "ĠA LA", + "om o", + "ĠR u", + "eg ovina", + "Ġdep ends", + "Ġart ificial", + "ĠGl enn", + "Ġtro uble", + "Ġbreak s", + "ĠCab inet", + "Ġstre ets", + "v ideo", + "Ġp urs", + "ĠI TV", + "Ġdr iving", + "ĠCorn wall", + "Ġconstitu encies", + "l ater", + "er ts", + "Ġto ward", + "ĠI stanbul", + "ĠL az", + "Ġch ore", + "Ġ18 58", + "ph alia", + "Ġag riculture", + "ĠPr ussia", + "ĠBr uins", + "Ġadv is", + "Ġsuff ering", + "Ġorg ans", + "g ang", + "im ental", + "Ġar cher", + "Ġcont rast", + "ten eg", + "ĠAll ied", + "Ġclos er", + "ĠF ra", + "ĠE ff", + "Ġ13 8", + "Ġatt empts", + "Ġmod er", + "Ġeven ing", + "Ġs ax", + "ĠA ube", + "oc c", + "ĠY osh", + "ild a", + "Ġ10 6", + "Ġcont emporary", + "Ġmag ic", + "Ġmag azines", + "ĠDef ence", + "ĠCamer oon", + "Ġrough ly", + "ĠP ath", + "ch w", + "ĠCh uck", + "ina e", + "uk ee", + "let t", + "Ġpresident s", + "ĠUnd erground", + "al in", + "Ġst ored", + "ĠAd ela", + "Ġgener a", + "bor o", + "Ġtechn ique", + "Ġmeas ures", + "ĠVo ices", + "Ġflu id", + "O M", + "ax ies", + "Ġreturn s", + "Ġjun ior", + "a fter", + "Ġc e", + "ĠD atabase", + "Ġcan c", + "oy al", + "ĠOr d", + "Ġpre par", + "Ġconver ted", + "ĠBritt any", + "u rope", + "ĠN okia", + "if ically", + "Ġco at", + "2 50", + "Ġa id", + "ĠM ammals", + "ir th", + "ĠW i", + "ĠK ot", + "Ġas k", + "ĠCh all", + "Ġex clus", + "Ġph ase", + "ĠAll ier", + "Ġexpl ain", + "ĠTowns hip", + "ĠTour ism", + "s ar", + "ĠN aval", + "ok er", + "Ġund erg", + "Ġmay ors", + "ĠFl ash", + "ĠKen ya", + "ĠS ister", + "ĠS outheast", + "Ġis ol", + "im o", + "un ct", + "art e", + "Ġpl ot", + "Ġinc idents", + "Ġdrink ing", + "Ġpunish ment", + "ĠH omer", + "ĠDel ta", + "Ġmathemat ical", + "Ġgrand father", + "ĠRelig ion", + "ĠAu vergne", + "ĠLenn on", + "im et", + "Ġ18 56", + "Ġprocess es", + "Ġtransfer red", + "M on", + "ed e", + "ĠThe ater", + "ĠF oster", + "Ġv ent", + "ĠCh i", + "Ġ17 5", + "ĠAlb ania", + "ĠMach ine", + "ĠCla ude", + "Ġadvant age", + "Ġdiab etes", + "A E", + "at ore", + "Ġp ale", + "ĠC hes", + "ĠP ubl", + "ĠR ider", + "ĠL on", + "ep age", + "ric ks", + "Ġpr ay", + "Ġsm art", + "Ġs ample", + "urr ing", + "ĠBra b", + "ĠAlexand ria", + "ĠB anks", + "el lect", + "Ġth irty", + "Ġre aching", + "ĠV illa", + "ud es", + "ath ing", + "Ġsp y", + "Ġpo le", + "ĠClass ical", + "Ġpron ounced", + "ĠRoc he", + "e enth", + "Ġb oss", + "ro se", + "Ġan aly", + "19 54", + "Ġ16 8", + "ĠNAS CAR", + "ĠSoft ware", + "Ġm o", + "av an", + "Ġhe aring", + "ĠAn to", + "act s", + "ĠCommission er", + "Ġrepe ated", + "as m", + "oot s", + "Ġund ers", + "Ġrequ est", + "former ly", + "h of", + "as o", + "Ġh osp", + "ĠCh ase", + "Ġdis asters", + "13 0", + "ij a", + "Ġobs erved", + "Ġhar mon", + "Ġt ill", + "er als", + "Ġc ra", + "ĠT ab", + "ĠF antasy", + "ĠD ictionary", + "Ġk g", + "ut ter", + "rat ing", + "Ġbi ological", + "Ġconstr ucted", + "Ġinvestig ation", + "d est", + "av i", + "Ġex am", + "ĠAr ctic", + "ĠSc he", + "Q u", + "Ġl atter", + "et z", + "and ie", + "Ġ14 5", + "az e", + "Ġpopul ations", + "Ġmix ture", + "ĠCa uc", + "ĠLatv ia", + "b ridge", + "en arians", + "Ġp ush", + "ĠP ly", + "ay e", + "ph ib", + "ĠRes erve", + "ĠSant os", + "ĠStan ford", + "ĠBir th", + "Ġmagn itude", + "S an", + "ic us", + "Ġm ate", + "ĠC S", + "Ġcom ics", + "Ġco ffee", + "Ġsup erv", + "Ġbroad caster", + "Ġinvol ves", + "Ġcrow d", + "ĠF ell", + "ag re", + "Ġocc asion", + "Ġi P", + "ĠSpring field", + "ĠIndust ry", + "A d", + "B est", + "e j", + "h l", + "Ġf iles", + "Ġm ent", + "oc ent", + "Ġmain land", + "omin ated", + "Ġexper t", + "M art", + "h ill", + "ĠE ve", + "Ġst orage", + "Ġ18 15", + "ĠAl berto", + "isc her", + "wa ukee", + "ĠCirc le", + "ĠHerz egovina", + "ĠLef t", + "ĠT usc", + "Ġm oons", + "ĠC el", + "Ġd ivers", + "ell es", + "b ian", + "Ġw ire", + "Ġh orm", + "ess ed", + "ĠAir ways", + "ĠLu igi", + "Ġoccup ation", + "Ġimpro ved", + "Ġwound ed", + "1 20", + "F or", + "Ġs aint", + "Ġfor g", + "Ġres idence", + "ĠCanad iens", + "ĠMon teneg", + "ush ima", + "Ġbomb ing", + "ĠOw en", + "Ġo d", + "ĠH ass", + "ĠF uj", + "art s", + "ap a", + "Ġsp ots", + "Ġdec ade", + "ĠMet al", + "Ġro utes", + "pt ion", + "Ġdec ades", + "Ġep ic", + "Ġsett lers", + "Ġconqu ered", + "Ġkeyboard s", + "ĠKazakh stan", + "Ġdisab ilities", + "ĠParag uay", + "ĠL ie", + "19 57", + "ĠMan it", + "ĠFrank furt", + "Ġthe at", + "ĠF ul", + "uk h", + "ĠX X", + "ĠCap itol", + "Ġche ese", + "ĠMap le", + "s is", + "ĠT ampa", + "ĠP ublishing", + "19 50", + "ĠEd gar", + "Ġorgan isation", + "Ġmin ute", + "ĠAcadem ics", + "ĠP iet", + "ĠB od", + "ĠN athan", + "est ive", + "ac ional", + "ys c", + "Ġbelie ves", + "ĠVol ume", + "ĠTeh ran", + "B rit", + "ĠT aj", + "ĠC ategory", + "ĠP unk", + "ĠH ed", + "ĠW end", + "ut ation", + "Ġcommun ist", + "ĠPet e", + "Ġfle et", + "g art", + "ĠJ in", + "Ġsp elled", + "Ġ15 5", + "Ġ14 7", + "ĠAm endment", + "fect ures", + "ught on", + "ĠLuc as", + "utt le", + "ĠOce ania", + "Ġrar ely", + "N orm", + "c ement", + "e on", + "ĠC ode", + "ĠG ate", + "Ġcon sole", + "ĠMer ced", + "Ġdanc ing", + "Ġmer ch", + "ĠPear l", + "ĠF A", + "Ġ7 00", + "ĠJul ius", + "Ġfac ilities", + "Ġregular ly", + "ĠSic ily", + "G M", + "Ġs ke", + "ĠK D", + "ul i", + "ab we", + "Ġrep ublic", + "Ġinv aded", + "Ġext reme", + "Ġdanc ers", + "ĠBurn s", + "Ġo v", + "ĠM alta", + "ac les", + "Ġus ual", + "Ġpr ies", + "Ġgold en", + "ĠAzerbai jani", + "Az ur", + "Ġsax oph", + "Ġt urb", + "Ġc hest", + "oc ide", + "red ited", + "che ster", + "ĠWith out", + "ĠRh odes", + "Ġfrequ ency", + "ub ble", + "Ġ18 21", + "Ġloc ality", + "Ġpar as", + "Ġfew er", + "ĠOrigin ally", + "Ġarran ged", + "c ope", + "ĠA ch", + "ĠL ear", + "Ġre aches", + "Ġse lect", + "ĠAl g", + "ement ia", + "Ġcompos ition", + "ĠCard inal", + "Ġexplos ion", + "ĠEug ene", + "ep pe", + "Ġ18 44", + "Ġkn ows", + "pp ing", + "Ġhor iz", + "Ġsen ators", + "Ġpit cher", + "uccess ful", + "ĠOD AS", + "A s", + "I M", + "f ight", + "h n", + "Ġp ure", + "ĠT i", + "el in", + "ag ram", + "Ġst eal", + "Ġbe ings", + "ĠV ent", + "Ġim plement", + "Ġper cussion", + "ĠQ ur", + "Ġtax es", + "ĠBud apest", + "ĠT ul", + "ĠT oul", + "ĠMem phis", + "e a", + "ĠT ong", + "ĠM am", + "ĠO ct", + "ip her", + "Ġ18 37", + "Ġtr ump", + "ĠFl ag", + "Ġjoin ing", + "ĠJo el", + "Ġcomed ians", + "Ġattempt ed", + "ĠBald win", + "ĠS ed", + "ĠC u", + "ĠC ou", + "ol and", + "Ġh a", + "Ġsh orter", + "Ġtr uck", + "Ġeduc ated", + "Ġexper i", + "ĠBol ivia", + "Ġthe rap", + "Ġl ieutenant", + "col m", + "Ġ18 10", + "Ġ15 8", + "21 0", + "Ġ14 8", + "ĠIs le", + "mon ton", + "Ġjoin s", + "Ġh oles", + "ĠG P", + "Ġcon vers", + "Ġbre aking", + "ĠFin ally", + "r un", + "ĠV aud", + "ond o", + "Ġstand ards", + "ĠPres cott", + "Ġcomment ator", + "ĠGian ts", + "Ġprecip itation", + "A L", + "P resident", + "o an", + "at che", + "Ġc od", + "as ant", + "as ks", + "Ġp y", + "ĠLe eds", + "ĠBr istol", + "ĠTr ip", + "Ġem pt", + "ĠOf ten", + "Ġstep s", + "Ġunderst anding", + "Ġheav ily", + "m iss", + "Ġf ung", + "ĠA ub", + "ĠR iv", + "ĠN ixon", + "Ġsp aces", + "Ġpred ators", + "Ġobtain ed", + "Ġtiss ue", + "ĠElement ary", + "g ary", + "ĠM om", + "ĠP aint", + "ĠN ine", + "ĠO nd", + "ĠW ol", + "ord e", + "ĠY as", + "Ġ13 7", + "Ġstr uck", + "utt ing", + "ĠFa ctor", + "ĠTransport ation", + "Ġpolic ies", + "ĠLt d", + "g l", + "s ol", + "er as", + "ĠC airo", + "ĠB aptist", + "ur ai", + "Ġre un", + "ĠLe aders", + "Ġro ot", + "uct ive", + "ĠPr ivate", + "ĠCar n", + "ĠAnd ers", + "ĠTr iple", + "e lected", + "er k", + "ĠU eda", + "ire ct", + "ind a", + "Ġcre ates", + "ourn aments", + "Ġexp ression", + "Ġexp ansion", + "ĠTra vel", + "Ġop ens", + "ĠSebast ian", + "er i", + "Ġb ush", + "ĠT ournament", + "Ġ18 51", + "ĠMar ath", + "Ġj ail", + "Ġwrit ings", + "Ġref lect", + "-- --", + "Ġdeterm ined", + "ĠAthlet ics", + "ĠDiam ond", + "h or", + "p at", + "r ates", + "ĠB ever", + "ĠGu j", + "ĠTer ror", + "Ġspeak er", + "ĠTre k", + "Ġpsych ology", + "ĠHein rich", + "ĠS ag", + "Ġf ires", + "ov erty", + "ĠK in", + "19 53", + "19 52", + "Ġpro state", + "ĠZ o", + "rip ts", + "ĠGal axy", + "\" :", + "r ons", + "at us", + "Ġcomp lic", + "Ġcl oud", + "uss els", + "ull ah", + "ĠRock y", + "Ġgal axies", + "Ġprote ins", + "Ġsav ed", + "ĠT ir", + "ĠFran ois", + "ĠEd u", + "ĠHis pan", + "ĠSm ack", + "Ġport ion", + "Ġlegisl ature", + "C ar", + "an ion", + "ĠL iv", + "Ġ18 55", + "ĠMil waukee", + "ĠMal colm", + "Ġant ib", + "Ġfeel ing", + "ĠPul itzer", + "' ll", + "P ort", + "c in", + "l ik", + "t ype", + "av ier", + "ĠLad ies", + "ĠAgain st", + "O N", + "as is", + "ian e", + "Ġv ision", + "Ġpro ved", + "ĠY a", + "ĠY ale", + "Ġra ise", + "ĠBl oom", + "Ġdem ocracy", + "ĠCont inental", + "ĠPhill ips", + "Ġdraft ed", + "S ec", + "Ġs urname", + "ĠC umbria", + "ĠF ind", + "ĠD aw", + "ĠE k", + "and ro", + "ĠIn v", + "ap se", + "Ġtour ists", + "ĠElect ronic", + "ic z", + "ĠH ers", + "th al", + "Ġ18 12", + "ĠDor othy", + "Ġdra wn", + "Ġd warf", + "ĠMan ager", + "Ġstorm s", + "Ġwor se", + "ĠBenn ett", + "ĠAngl ican", + "Ġbord ered", + "ĠOccitan ie", + "l ain", + "s sel", + "ĠS aw", + "Ġm urders", + "ĠM umb", + "ĠE b", + "ĠE lectoral", + "ĠAl ong", + "ah an", + "Ġear n", + "ĠMc L", + "Ġcur rency", + "ĠTechn ical", + "ĠCard inals", + "Ġancest ry", + "ĠChart s", + "Ġrecip ient", + "en stein", + "Ġin g", + "ĠH appy", + "et ical", + "Ġse ctions", + "ĠPal m", + "Ġappear ing", + "ĠMon ster", + "Ġcharacter istics", + "ĠHigh ness", + "ĠBas se", + "Ġtrad ing", + "ĠJul ie", + "Ġnorthwest ern", + "Ġfarm ers", + "o ise", + "s elling", + "ĠSt re", + "ograph ics", + "ĠOr land", + "Ġplan es", + "wh ile", + "ĠBre ak", + "C te", + "E urope", + "u oka", + "an z", + "ĠS ang", + "Ġl oved", + "Ġpl ain", + "ric es", + "Ġag es", + "Ġ11 6", + "Ġbre eds", + "Ġreturn ing", + "Ġneigh bour", + "ĠSerge i", + "Ġg ymnast", + "ĠSh adow", + "ĠAdela ide", + "e qu", + "in ts", + "ĠA T", + "ĠM atch", + "ĠP on", + "ov al", + "ĠU C", + "ĠEm il", + "Ġfall ing", + "Ġexhib ition", + "ĠZimb abwe", + "n ew", + "Ġb otan", + "Ġn inth", + "ĠE RI", + "Ġsc iences", + "Ġintrod uction", + "Ġtest ing", + "ĠFle et", + "Ġopin ion", + "Ġc le", + "ĠT alk", + "ob e", + "Ġco aches", + "Ġocc as", + "Ġmechan ical", + "Ġcrim inals", + "U C", + "g ard", + "t r", + "as ht", + "ĠC ele", + "ĠW ak", + "ĠK ham", + "ĠHe at", + "16 0", + "Ġprov incial", + "Ġcr ust", + "west ern", + "Ġtrad itions", + "ĠMax im", + "ĠManit oba", + "a uc", + "i u", + "Ġs ad", + "Ġc orpor", + "ĠR um", + "Ġg rey", + "ĠK om", + "ĠArch ive", + "ĠMarc us", + "n oon", + "Ġs itting", + "og o", + "enc ing", + "str ong", + "h all", + "ĠC it", + "ĠV or", + "ĠV il", + "ock et", + "Ġdis k", + "Ġwar ri", + "Ġfilm ed", + "ĠMor i", + "Ġgen res", + "Ġexec ution", + "Ġpregn ant", + "Ġimmigr ants", + "Ġin ner", + "ĠS et", + "ĠH ur", + "19 0", + "Ġdefe ating", + "Ġarm ies", + "Ġemploy ees", + "ĠKush iro", + "z an", + "ol ith", + "Ġn et", + "Ġn est", + "fl ower", + "Ġconnect s", + "1 13", + "g io", + "Ġd ementia", + "ĠCh amp", + "own ed", + "ĠAl c", + "ĠAl pes", + "ĠAr med", + "Ġ10 2", + "az i", + "Ġoper ates", + "Ġang le", + "Ġf ra", + "ĠC reat", + "Ġwas te", + "ĠG as", + "ang ered", + "Ġcall ing", + "Ġ15 00", + "ĠCal vin", + "Ġsw ord", + "Ġacqu ired", + "Norm andie", + "S w", + "g ender", + "Ġform ally", + "Ġsurv iving", + "asc ar", + "Ġqual ification", + "Ġfru its", + "Ġwalk ing", + "Ġcert ified", + "Ġexped ition", + "ĠPly mouth", + "it ic", + "ĠV iew", + "Ġkeep ing", + "Ġbad ly", + "Ġwood en", + "Ġcook ing", + "Ġresc ue", + "M I", + "Ġs u", + "ĠF iction", + "Ġexper iments", + "ĠCamp aign", + "ĠHindu ism", + "CD P", + "f ried", + "it ches", + "ol ve", + "Ġl aid", + "ĠPl ate", + "Ġfollow ers", + "ĠEmp ress", + "Ġgener als", + "ĠAv iv", + "verse as", + "ĠCelt ic", + "Ġbud get", + "Ġentreprene ur", + "ĠS of", + "ĠS leep", + "ol an", + "ĠCh iba", + "Ġ18 47", + "ĠMar tha", + "Ġmon aster", + "ĠMal ay", + "ĠRod rig", + "ĠKan eda", + "Ġnom inee", + "ĠSloven ia", + "Ġ ions", + "en ess", + "Ġp ig", + "ent le", + "ian ts", + "ill ery", + "Ġhe ir", + "18 0", + "ĠCal gary", + "ĠDev on", + "a ctor", + "ar u", + "it ime", + "ĠR ugby", + "Ġpro ced", + "Ġland fall", + "Ġphysic ists", + "Ġchrom os", + "claim ed", + "Ġcomplic ated", + "Ġt ask", + "Ġw ides", + "ĠT otal", + "ĠM ason", + "ĠD ame", + "ĠN ou", + "Ġsh ut", + "21 8", + "enn ium", + "Ġdesc ription", + "ĠPhys ical", + "ĠMond ay", + "Japan ese", + "M D", + "Ġt ied", + "ĠM asters", + "ĠB etty", + "em e", + "Ġhe lic", + "Ġag encies", + "Ġ16 7", + "lin ed", + "Ġdem and", + "ĠSom ers", + "L ove", + "he v", + "ĠT able", + "Ġr ic", + "ĠEx change", + "Ġfeel ings", + "ĠConstant in", + "c ase", + "on ly", + "Ġm ph", + "Ġbe et", + "Ġcon vention", + "uc l", + "ĠBr ussels", + "ĠGu itar", + "Ġent ers", + "ĠOut standing", + "ĠStat istical", + "Ġminor ity", + "ipe i", + "Ġparliament ary", + "ĠTunis ia", + "Ġib n", + "Ġd ated", + "Ġsc hem", + "Ġwork er", + "Ġtr uth", + "ĠEd monton", + "Ġed ited", + "Ġef fort", + "Ġsol ve", + "ĠCr us", + "Ġkeep s", + "Ġthreat ened", + "ĠSoph ie", + "ĠGuatem ala", + "R eg", + "d al", + "w al", + "er ia", + "ĠN ed", + "Ġran ges", + "ĠSlov akia", + "Ġtel esc", + "b ur", + "o ids", + "Ġthe ories", + "Ġin iti", + "Ġf il", + "Ġp apers", + "ĠL ions", + "ĠD iana", + "ok ing", + "Ġcol ours", + "str ument", + "Ġhum id", + "Ġconf used", + "ĠPhilipp ine", + "Ġtravel ed", + "ĠLes lie", + "Ġrain fall", + "uv ian", + "ĠVenezuel an", + "Ġcoin s", + "O ne", + "re ck", + "ep hew", + "ĠSt age", + "if a", + "Ġun iform", + "eh ogne", + "ĠDeb ehogne", + "ĠGuard ian", + "itu aries", + "t ran", + "Ġs an", + "ĠJ ura", + "ĠW ed", + "Ġres emb", + "ĠPap ua", + "Ġcras hed", + "Ġsac red", + "ĠLibert y", + "ĠOrland o", + "i ologist", + "p read", + "y ard", + "ĠL ett", + "Ġn av", + "ĠCom merce", + "ĠAng els", + "ĠMin ne", + "ĠOpp osition", + "ĠAleksand r", + "N HL", + "b rid", + "ĠT roy", + "ĠC ry", + "ĠC ome", + "Ġsh aped", + "Ġfight s", + "Ġprison er", + "ĠAqu itaine", + "Ġap artment", + "Ġb ishops", + "ĠJ esse", + "Ġpro of", + "Ġ17 7", + "ĠPar ad", + "Ġdu o", + "Ġinit ial", + "b ane", + "m ate", + "Ġt ournaments", + "ĠD ale", + "ĠE g", + "ĠW atch", + "Ġ9 00", + "uch y", + "Ġmet er", + "ĠWW F", + "Ġmechan ics", + "Ġconst ellation", + "h ab", + "Ġa head", + "Ġf ame", + "ĠF ont", + "Ġor ient", + "ant i", + "Ġsh ops", + "Ġthem es", + "ĠMc Donald", + "ĠStev ens", + "Ġhon our", + "Ġfast est", + "ĠGeoff rey", + "is abeth", + "Ġ18 45", + "Ġdis pl", + "ĠCar r", + "Ġsk ier", + "ibr aries", + "Ġvict im", + "ĠJud a", + "M us", + "R ussian", + "l io", + "p roduction", + "ĠT ales", + "ĠD im", + "ist le", + "Ġpl ac", + "ab ies", + "ĠRef erence", + "ern er", + "ett es", + "Ġcons ult", + "fl ix", + "ĠScot ia", + "Ġhealth y", + "ĠLim ited", + "ĠLomb ardy", + "3 20", + "W W", + "ĠC ay", + "ĠM S", + "el ing", + "Ġr andom", + "ry s", + "ens en", + "Ġ17 8", + "Ġfl ights", + "ĠJer emy", + "Ġindust ries", + "ĠSid ney", + "Ġor che", + "og ical", + "Ġcl ock", + "ĠCar son", + "ĠMon ica", + "Ġvers us", + "B r", + "e lect", + "ĠD in", + "ĠSt ill", + "Ġse ed", + "ĠBr is", + "Ġsub s", + "ĠMan ila", + "Ġsign als", + "Ġconstit utional", + "ĠPhilipp e", + "Ġexist ing", + "ĠTa ipei", + "Ġachie ved", + "ĠBrab ant", + "b in", + "ĠX I", + "Ġent rance", + "Ġequ ivalent", + "Ġfriend ly", + "ĠUs es", + "ĠFa ith", + "al am", + "ĠC ell", + "ad al", + "Ġth roat", + "19 51", + "Ġ14 4", + "ck en", + "12 5", + "Ġrel atives", + "ĠGeor gian", + "Ġserv ant", + "Ġpublic ation", + "Ġtransport ation", + "Ġpick ed", + "Ġarg ument", + "ĠMumb ai", + "Ġ id", + "Ġc ow", + "ĠS iber", + "ĠL ook", + "ĠO S", + "est y", + "Ġ18 38", + "Ġ18 49", + "ĠSp onge", + "ond a", + "Ġdis ability", + "17 0", + "ĠVen us", + "Ġtribut aries", + "H ol", + "f ast", + "ou stic", + "ab out", + "Ġte lephone", + "ĠTh ough", + "Ġj ury", + "Ġexper iences", + "ĠAnim als", + "ĠA E", + "ĠB right", + "ĠW u", + "Ġ12 4", + "Ġem ph", + "ĠAg riculture", + "cast le", + "ĠAtl as", + "Ġski ing", + "Ġimmun e", + "E d", + "Ġd ish", + "il ian", + "op rano", + "ĠK ul", + "ah i", + "ĠCar ey", + "Ġman usc", + "ĠEm ily", + "amm u", + "ĠArab ian", + "ĠZe us", + "ĠColon el", + "ĠP rice", + "ĠCol in", + "Ġdr um", + "ĠComp et", + "Ġcost s", + "ĠRet rieved", + "Ġneut ral", + "ĠNikol ai", + "Ġprep ared", + "ĠSmack Down", + "p ass", + "Ġa uthors", + "Ġf inger", + "ra ce", + "iz a", + "amb ig", + "Ġstreng then", + "h is", + "ĠP or", + "ĠB eth", + "ĠR oh", + "ĠN u", + "ĠW ells", + "ul us", + "ate gy", + "ĠY emen", + "Ġsh arp", + "Ġag ents", + "ĠPark s", + "cy cle", + "ID S", + "ĠUs ing", + "Ġbound ary", + "ĠLib ya", + "Ġsuppl ies", + "ĠPhilos ophy", + "is ons", + "ain te", + "ath y", + "Ġsh ore", + "Ġfound ation", + "ash ire", + "ĠBr as", + "Ġco oper", + "ĠKar en", + "go ing", + "Ġmill ions", + "Ġamb ass", + "ĠBour bon", + "m n", + "ĠS ox", + "ĠC itiz", + "ĠB erm", + "ir ate", + "st ances", + "Ġ18 36", + "ĠFl oyd", + "Ġprov iding", + "ĠPer iod", + "Ġdeb ate", + "Ġvol canic", + "Ġclos est", + "West phalia", + "Ġa f", + "ĠInd ivid", + "Ġmus cle", + "yl um", + "ĠKh al", + "ĠDev il", + "Ġarg ued", + "ĠNet flix", + "ĠMerced es", + "M e", + "P ro", + "Ġb at", + "ĠR anger", + "ip el", + "uk u", + "Ġown s", + "ĠBro ughton", + "ĠSen ators", + "ĠPat ric", + "Ġdest ruction", + "Ġblock s", + "ĠLyn n", + "Ġwithd raw", + "Ġrif le", + "r ors", + "t el", + "us ing", + "Ġ18 35", + "Ġher itage", + "ĠSh ak", + "ĠInd igenous", + "ĠCar roll", + "Ġsur f", + "Ġfin als", + "Ġtour ism", + "ĠPan ther", + "ĠBh ut", + "ĠTeh sil", + "bu ild", + "1 15", + "is f", + "al ysis", + "ĠL ap", + "ĠV iv", + "19 48", + "20 3", + "Ġother wise", + "Ġcontin ental", + "Ġexpl ained", + "Ġcover ing", + "Ġelim inated", + "A P", + "z el", + "Ġt iny", + "Ġb ridges", + "ĠA GN", + "ĠD ob", + "ĠN g", + "rit e", + "ĠTh us", + "ĠTh ings", + "che l", + "Ġmedal ist", + "ĠLuc y", + "ĠLanc ashire", + "Ġuns uccessful", + "ĠLis bon", + "ĠTig ers", + "ĠS ullivan", + "re ts", + "ĠM aid", + "ĠL ok", + "ĠG ius", + "Ġsh ares", + "ĠQ ual", + "ĠMon roe", + "ĠQu arter", + "Ġbas in", + "f our", + "ĠR ice", + "ĠCh air", + "Ġsc oring", + "Ġcont ained", + "18 2", + "Ġind icate", + "ĠHar per", + "Ġass embly", + "ĠPer uvian", + "ĠJos ef", + "Ġorigin ated", + "Ġstrong ly", + "Ġfar ms", + "Ġsn ake", + "Ġmess ages", + "O F", + "k ina", + "ĠH off", + "av irus", + "ĠHe ath", + "amp ire", + "eg al", + "10 5", + "Ġpre f", + "Ġcontrovers y", + "Ġveget ables", + "Ġchlor ide", + "3 30", + "Ġb rows", + "Ġp ublishing", + "ĠB ark", + "Ġl ock", + "ĠN as", + "Ġcent ers", + "ush i", + "ĠSen ior", + "Ġsuccess ion", + "ĠRelig ious", + "S outh", + "ust ers", + "Ġph ones", + "Ġturn ing", + "ĠClass ic", + "Ġpsych iat", + "ĠNat al", + "ĠIS BN", + "Ġgraph ics", + "Ġpattern s", + "Ġbrief ly", + "ĠRav ens", + "Ġb on", + "Ġp ump", + "ĠT iger", + "10 4", + "ĠNor se", + "ĠCo hen", + "ĠIS O", + "ĠFuk uoka", + "ĠPrinc eton", + "Ġt urt", + "Ġw ore", + "Ġb le", + "ĠB le", + "Ġl as", + "Ġ18 18", + "ĠAr sen", + "ĠCon n", + "ean ing", + "gen re", + "Ġregist ered", + "Ġwides pread", + "Ġo w", + "ĠS ut", + "Ġf aced", + "ĠL ug", + "ĠF oss", + "ĠD ata", + "Ġ3 50", + "Ġch ocolate", + "Ġform ing", + "ĠIndian apolis", + "osaur s", + "Ġcoll aps", + "Ġmuseum s", + "Ġhang ing", + "Ġrefere e", + "ĠR ural", + "ĠL ac", + "Ġgo alt", + "ott a", + "rim ination", + "Ġtemp orary", + "Ġchall enge", + "Ġmolec ule", + "ĠMathemat ics", + "ĠLeop old", + "ĠD id", + "mun icipal", + "Ġ12 9", + "ĠBl u", + "Ġmon key", + "Ġmass acre", + "Ġbo ats", + "Ġmer c", + "ĠCzechoslov akia", + "ĠMinne apolis", + "ĠJuda ism", + "h orn", + "m ic", + "ĠI N", + "ĠF ountain", + "ĠN AT", + "ĠMar cel", + "ach t", + "Ġun less", + "Ġag ricultural", + "Ġ13 4", + "oon ey", + "Ġland ed", + "Ġaff airs", + "Ġreb ellion", + "Ġcred ited", + "Ġresign ation", + "Ġ ion", + "Ġt elling", + "ĠT ucker", + "ĠR idge", + "ĠR andy", + "ĠG av", + "Ġcomp l", + "ĠOs lo", + "Ġgradu ate", + "ĠMong ol", + ") )", + "m aking", + "Ġm ile", + "Ġh idden", + "ĠN ames", + "ĠSt rat", + "ĠBr uns", + "14 0", + "ĠGl ad", + "Ġrequ ires", + "Ġf estivals", + "ig ar", + "ĠB uc", + "ĠK w", + "Ġ15 4", + "ĠBr uno", + "Ġne u", + "Ġdel ay", + "ĠAndre a", + "M C", + "g overn", + "h us", + "s im", + "ĠS ter", + "Ġp seud", + "ĠC rystal", + "rom agn", + "ĠK il", + "Ġr ated", + "Ġ18 46", + "Ġle ts", + "Ġcar go", + "ĠTra il", + "Ġrail road", + "Ġpen is", + "ĠKit ami", + "ĠWinds or", + "Ġing red", + "at ically", + "ĠA sp", + "ĠUn incorporated", + "Ġ18 53", + "Ġro oms", + "ink ing", + "Ġorgan ism", + "Ġput ting", + "ĠClark e", + "Ġherb ivore", + "ambig uation", + "1 12", + "r ant", + "Ġb old", + "Ġd rain", + "ĠL ords", + "ĠComp osition", + "ours es", + "cest er", + "ĠLiber ation", + "Ġesc aped", + "Ġinst ance", + "ĠIraq i", + "Ġcollect ions", + "ĠDer by", + "ĠBac helor", + "Ġremember ed", + "ĠLah ore", + "D e", + "P l", + "k u", + "en za", + "ĠS ection", + "ĠR oma", + "Ġl ady", + "ag ons", + "if ies", + "ie ve", + "ĠAl most", + "ass y", + "Ġj ew", + "ĠDem ographics", + "Ġtravel s", + "Ġcreat ures", + "Ġvir uses", + "Ġlay ers", + "Ġsubt ropical", + "Ġaccomp an", + "Ġgrav ity", + "m m", + "he y", + "ur se", + "av ian", + "ĠHe ights", + "Ġte le", + "ĠBl ake", + "ĠBar ack", + "ĠBe ing", + "Ġsw itch", + "add y", + "Ġpur ple", + "ĠKam en", + "ĠNam ib", + "E T", + "m are", + "v ements", + "w yn", + "ĠS ask", + "ĠB rew", + "ter bury", + "ĠV ende", + "Ġj u", + "Ġacc eler", + "ĠWrit ing", + "ĠLeg acy", + "ĠLy on", + "ĠMot ion", + "Ġsac r", + "ĠTheod ore", + "spec ies", + "ĠChel sea", + "R e", + "s ometimes", + "ĠN ept", + "Ġn one", + "ĠK oh", + "col ogy", + "Ġv it", + "ri o", + "ĠTh orn", + "Ġar c", + "Ġoff ensive", + "ĠPer th", + "ĠTw itter", + "Ġvol unte", + "ĠTan z", + "ĠShang hai", + "V B", + "nd ez", + "ĠTh r", + "ĠTh ames", + "ang o", + "aw i", + "Ġ13 9", + "ĠSing le", + "Ġprocess ing", + "Ġreplac ing", + "ĠMaced onia", + "htm l", + "Ġphen omen", + "E x", + "ig ration", + "ĠH ok", + "ĠW ord", + "19 2", + "Ġ18 52", + "ĠTh or", + "ng en", + "Ġab olished", + "ĠSur rey", + "ĠFrances co", + "Ġprinc iple", + "g reen", + "Ġf ever", + "Ġ18 24", + "Ġnot ably", + "Ġab st", + "ĠPr ed", + "Ġfl our", + "Ġfe athers", + "ĠAss istant", + "ĠCirc uit", + "ĠJess ica", + "ĠEvent ually", + "Ġf old", + "im mer", + "Ġapp eal", + "ĠTr ue", + "Ġass igned", + "Ġmin i", + "Ġmult ip", + "ĠBo hem", + "ĠApp ear", + "ĠAnn ie", + "ĠAh mad", + "Ġcrown ed", + "ĠGener ation", + "ĠTusc any", + "W hat", + "Ġs we", + "ĠB ott", + "ri ers", + "Ġro d", + "act ic", + "Ġrec re", + "Ġdown load", + "Ġgreat ly", + "Ġbroadcast ing", + "Ġdial ects", + "unc iation", + "c z", + "en ko", + "Ġb ats", + "Ġp ushed", + "ĠV oy", + "Ġplay offs", + "Ġ16 4", + "ĠDec lar", + "ĠAd rian", + "Ġcho ir", + "Ġtemp les", + "ĠAmbass adors", + "Ġcanc elled", + "W h", + "Ġs ale", + "ĠS au", + "ĠR ange", + "ad der", + "Ġ12 6", + "10 9", + "23 0", + "ĠReg ions", + "ĠHor se", + "ĠSav oy", + "Ġquarter back", + "Ġcateg ories", + "Ġpries ts", + "m ail", + "ĠR A", + "ct ica", + "Ġre ven", + "Ġv el", + "if ts", + "qu in", + "ident al", + "ĠQ atar", + "ĠSlov ak", + "Ġmolec ular", + "T ok", + "p o", + "Ġw ww", + "op al", + "Ġ18 19", + "Ġ18 33", + "10 3", + "Ġwar ning", + "ĠMil ano", + "ĠNic ar", + "Ġdraw ing", + "L and", + "i bert", + "ĠC ool", + "ĠR aid", + "ĠF ig", + "ĠN em", + "ess a", + "ĠQu est", + "Ġdefe ats", + "Ġpra ised", + "Ġtheolog ian", + "ĠGius eppe", + "n ear", + "y stem", + "Ġt aste", + "es i", + "ĠT ic", + "ĠB un", + "ĠR oth", + "im an", + "ĠEd win", + "Ġstr ings", + "ĠJack ie", + "ĠWolf gang", + "S he", + "ĠS ource", + "ĠK ol", + "ac co", + "ĠMar ian", + "ition ally", + "Ġreg ul", + "Ġann ounc", + "amm ad", + "ĠAp ost", + "Ġabs ol", + "C ap", + "ak o", + "21 4", + "Ġeng aged", + "ĠChe ster", + "ĠMod els", + "Ġlik es", + "Ġparticip ate", + "Ġsn akes", + "Ġancest ors", + "j r", + "ĠP irates", + "ass ium", + "ev ich", + "ĠHer cules", + "ĠMon aco", + "Ġint ellect", + "ĠSecret aries", + "Ġren ew", + "uan ian", + "i ate", + "y er", + "ĠV at", + "qu is", + "ĠLe ad", + "ĠEm irates", + "Ġrece iving", + "Ġincre ases", + "ĠWrest le", + "Ġsculpt ure", + "Ġord inary", + "Ġrat io", + "ĠS ki", + "ĠC ris", + "et ch", + "ĠD ion", + "Ġn ephew", + "oun s", + "Ġcar ries", + "Ġsee ing", + "ĠSim ilar", + "Ġhard ware", + "Ġdivor ce", + "is ie", + "re a", + "Ġst im", + "ĠArt icles", + "ĠHaw ks", + "Ġwind ow", + "Bl ack", + "Ġaster oid", + "Ġindic ates", + "ĠSho emaker", + "ĠEurop a", + "ĠSponge Bob", + "m ates", + "m eaning", + "v ol", + "Ġs ight", + "ing ers", + "ĠG em", + "Ġar chers", + "21 7", + "Ġac ids", + "10 7", + "rib ution", + "ĠCon struction", + "Ġrep orter", + "Ġdesign ated", + "Ġcontrib utions", + "Ġfr ag", + "ĠUruguay an", + "Ġw edding", + "ĠA chie", + "ĠD ynam", + "ĠJ en", + "Ġcon text", + "Ġ18 39", + "ĠLe ip", + "ne x", + "ah u", + "Ġmus cles", + "Ġmet re", + "Ġran king", + "yr gy", + "ĠInt elligence", + "lar gest", + "ĠIndust rial", + "Ġhabit at", + "Ġreplac ement", + "ĠB ears", + "ĠDe uts", + "idd les", + "ĠSil va", + "b one", + "w omen", + "ar r", + "Ġd atabase", + "ĠH ave", + "ĠK ane", + "Ġsupport ers", + "Ġident ify", + "Ġfoc uses", + "ruct ure", + "Ġlandsc ape", + "H ung", + "ĠC ed", + "Ġst range", + "ast s", + "Ġind ucted", + "Ġcar eful", + "Ġcontin ent", + "Ġcoll abor", + "Ġattract ion", + "ĠAdminist rative", + "ĠVik tor", + "Ġstream s", + "Ġdoll ars", + "Ġlocomot ives", + "ĠSomers et", + "h istor", + "h wa", + "z zo", + "Ġs ession", + "ĠM ake", + "Ġl oud", + "oc o", + "ĠU RS", + "Ġall iance", + "Ġstr ateg", + "ĠFour th", + "Ġcartoon ist", + "Ġadapt ation", + "Ġreject ed", + "ĠJor ge", + "ĠUz bek", + "f in", + "ĠP ret", + "ĠG round", + "all i", + "pr on", + "ebr ates", + "10 2", + "G old", + "m ons", + "Ġb es", + "Ġm ild", + "Ġh ide", + "ĠB ast", + "ĠR ut", + "Ġsh all", + "Ġim pl", + "Ġdef ender", + "ĠCorn ell", + "ĠHard y", + "ĠAbb ott", + "ĠHop kins", + "M ania", + "c ap", + "ĠT omb", + "ĠC ort", + "ab lo", + "ĠBrit ann", + "ĠSw an", + "24 0", + "ĠConf ederation", + "Ġmonarch y", + "ĠNAT O", + "1 14", + "Ġin surance", + "ĠA way", + "ĠI gn", + "ĠThe ory", + "Ġn inet", + "ĠW ant", + "Ġst ab", + "Ġ18 13", + "Ġover l", + "uk es", + "Ġmin eral", + "Ġ[ [", + "Ġpromot ion", + "hib ition", + "ĠSher man", + "Ġdeterm ine", + "Ġplatform s", + "ĠAthlet ic", + "ĠS ikh", + "ĠAl umni", + "Ġrele g", + "Ġsup ern", + "Ġinter act", + "ĠGro ve", + "Ġsett lements", + "ĠEll en", + "ĠArr ondiss", + "ĠBris bane", + "Ġw itness", + "ĠT in", + "op le", + "olit ion", + "Ġim perial", + "Ġequ ations", + "rd u", + "ĠCamp o", + "Ġlung s", + "Ġfle w", + "ĠEpis odes", + "ĠSind h", + "Ġaband oned", + "dis ambiguation", + "C O", + "d ef", + "Ġd uties", + "Ġ17 89", + "att ers", + "ĠFor bes", + "ĠAll an", + "Ġcelebr ate", + "ĠLor enzo", + "Ġtrav elled", + "ĠFac ulty", + "ĠMonteneg ro", + "! ,", + "p ower", + "ĠC ore", + "Ġd im", + "ĠP od", + "ĠB ot", + "Ġbe ating", + "Ġ11 3", + "Ġdep os", + "ott ed", + "Ġresp ond", + "ĠRepublican s", + "ĠMedal ists", + "Ġinfect ions", + "Ġbring ing", + "Ġlic ense", + "Ġgoalkeep er", + "is exual", + "Ġf urn", + "us ually", + "ĠR ank", + "ov an", + "ĠCan terbury", + "Ġreg ime", + "ins k", + "Ġfl ed", + "Ġattack ing", + "vious ly", + "Ġdiss olved", + "ĠReed y", + "Ġprofession ally", + "ĠTasman ia", + "D F", + "a illes", + "d oor", + "z es", + "ar ant", + "ĠF ir", + "Ġg host", + "ph al", + "rat ors", + "Ġbel t", + "Ġpol ar", + "unn ers", + "ĠWood s", + "Ġpe ac", + "Ġweek ly", + "count ry", + "ĠDeclar ation", + "M G", + "v et", + "Ġc ave", + "Ġf aces", + "Ġd ub", + "am er", + "if ted", + "Ġr ide", + "Ġres erve", + "ont in", + "ae k", + "Ġexpl an", + "Ġimp ossible", + "Ġcivil ian", + "ĠStr ong", + "Ġlog ic", + "ĠH ern", + "ĠN ott", + "ot ion", + "Ġre pt", + "19 40", + "ĠMar l", + "Ġtw inned", + "ĠTe ams", + "ĠEm b", + "ĠBrad ley", + "hte au", + "ĠCurt is", + "W ork", + "Ġp itch", + "Ġm and", + "ĠP am", + "Ġoper ate", + "Ġmark ets", + "Ġmathemat icians", + "Ġcritic ism", + "Ġcinem a", + "s ized", + "re leased", + "st ad", + "ag ascar", + "Ġcon son", + "Ġde er", + "Ġle ap", + "cl usion", + "lev en", + "Ġco ordin", + "Ġdem ocratic", + "ĠAltern ative", + "Ġexperi enced", + "ĠC ov", + "ĠG y", + "ĠJ ag", + "ĠU rata", + "Ġ18 43", + "ĠTh an", + "Ġsh apes", + "clud ed", + "Ġout put", + "ĠCar bon", + "60 7", + "que z", + "ĠComp os", + "ĠGree ks", + "ĠCarol ine", + "Ġbelong ed", + "Ġstrugg le", + "b et", + "u able", + "il ogy", + "om ical", + "ĠD w", + "orn o", + "Ġam ateur", + "ĠEv il", + "Ġer ror", + "Ġcer eb", + "Ġburn ing", + "Ġathlet ics", + "ĠAdv ance", + "Ġresear chers", + "Ġrebu ilt", + "w ana", + "Ġp ap", + "ol en", + "ent ed", + "ĠB alk", + "ĠF ine", + "ĠW ing", + "Ġ18 4", + "Ġnot ice", + "isc he", + "Ġexp osed", + "Ġvol t", + "Ġaccident s", + "ĠMot ors", + "Ġconserv ation", + "olith ic", + "1 18", + "l iving", + "r ish", + "ĠS A", + "ĠR NA", + "Ġk it", + "19 49", + "14 3", + "Ġpre t", + "Ġsub urbs", + "ĠMil ton", + "u en", + "Ġc ass", + "ĠT emp", + "ĠP os", + "ĠI gor", + "ĠH els", + "eb e", + "21 6", + "ĠCl if", + "ĠComp lete", + "play ing", + "Ġinterest ing", + "ĠKob e", + "Ġweak ened", + "Ġs ust", + "al og", + "it ched", + "ĠI F", + "ĠF av", + "ust rated", + "ob y", + "Ġref erend", + "Ġpione er", + "it ars", + "ol er", + "ĠB oh", + "ĠF eder", + "ĠE y", + "Ġde leg", + "Ġ18 14", + "Ġ18 41", + "Ġun like", + "Ġ14 9", + "Ġdr agon", + "ounc ill", + "Ġvar iable", + "Ġtop ics", + "Ġ0 4", + "ĠEag les", + "ĠAntar ctica", + "f ts", + "Ġb ath", + "Ġn urs", + "pp en", + "17 2", + "elf are", + "work s", + "ĠGard ens", + "vi ated", + "Ġconfl icts", + "ĠFace book", + "l ans", + "Ġs izes", + "ĠP i", + "ĠP ere", + "Ġsh ield", + "Ġ17 4", + "Ġ14 6", + "erv ation", + "Ġfem inist", + "Ġpsych ological", + "ĠLabor atory", + "I f", + "es a", + "ĠS elf", + "ard i", + "ĠY okohama", + "10 6", + "ĠAd ult", + "ĠEd ge", + "Ġsk ater", + "Ġfilm maker", + "Ġgl ac", + "Ġel dest", + "Ġcust om", + "ĠShir ley", + "ĠLeip zig", + "g ra", + "r ina", + "s hip", + "t ec", + "z ing", + "al bum", + "ĠK ind", + "ia z", + "os ure", + "ĠSt ories", + "ĠCh op", + "to od", + "amb ers", + "Ġexp ressed", + "ĠGod s", + "ĠInter ior", + "Ġlegisl ators", + "Ġbal ance", + "Ġgraph ic", + "Ġgard ens", + "c are", + "an mar", + "ĠH od", + "Ġr ings", + "ĠAnd roid", + "Ġ. ...", + "ĠMad agascar", + "Brit ish", + "ĠKD OT", + "c ular", + "Ġs amples", + "ĠC ox", + "Ġd ying", + "Ġg ate", + "ag le", + "19 4", + "Ġex port", + "Ġsm ooth", + "Ġchar ity", + "ĠMong olia", + "Ġnerv ous", + "f oot", + "m met", + "n an", + "y t", + "is se", + "ĠC raw", + "ĠM ull", + "ĠB ody", + "ĠH ood", + "ĠF inist", + "Ġre in", + "ĠU TC", + "Ġpl ates", + "ĠMar co", + "ĠAr ist", + "Ġspec ifically", + "Ġcivil ization", + "ĠVictor ian", + "ĠBruns wick", + "f ire", + "y u", + "it ure", + "ing le", + "ĠL od", + "ct ional", + "art ney", + "rest rial", + "80 9", + "Ġinternational ly", + "ĠDun can", + "ĠYam ag", + "C o", + "l oo", + "s burg", + "ro b", + "ĠC lement", + "ĠF ay", + "Ġst rict", + "our se", + "anc a", + "ĠHer oes", + "ĠPol o", + "ling en", + "Ġput s", + "Ġpres erved", + "Ġtransit ion", + "Ġvess els", + "Ġbom bs", + "u ve", + "19 5", + "ook er", + "born e", + "|||||||| ||||||", + "Ġbi ologist", + "G C", + "J ohn", + "X T", + "ĠT ouch", + "le en", + "ĠP BS", + "om ic", + "Ġcan n", + "ob s", + "au th", + "ĠEv an", + "Ġcivil ians", + "ĠKir by", + "ĠDou ble", + "ĠMoz art", + "Ġphotograph s", + "G ar", + "W orld", + "Ġ ~", + "an as", + "im on", + "Ġst ones", + "ĠHol mes", + "Ġexp ect", + "Ġphot ography", + "ĠBah amas", + "Ġportray ed", + "f irst", + "ĠH ain", + "ĠF lem", + "ĠD awn", + "ers e", + "Ġcent enarians", + "ru it", + "Ġhappen ing", + "Ġhard er", + "ĠPo etry", + "Ġconsist ing", + "ĠDh aka", + "ĠCrit ics", + "ĠStef an", + "1 17", + "r s", + "w alk", + "Ġ ic", + "ĠK arn", + "ĠIn side", + "Ġ16 0", + "Ġ16 9", + "ĠIs h", + "ĠSch les", + "Ġshort ened", + "Ġartist ic", + "ĠCatal onia", + "ĠLar ge", + "ĠArm strong", + "Ġmanufact urer", + "Ġpull ed", + "ansk rit", + "H C", + "ĠS earch", + "ap or", + "Ġus age", + "ĠCl ara", + "Ġbi ochem", + "ĠRh in", + "Ġmiss ions", + "enh agen", + "Ġtradition ally", + "Ġambass ador", + "ĠIndivid ual", + "N or", + "s he", + "ĠH aven", + "ĠD ais", + "st op", + "ĠUn ter", + "Ġfl ash", + "Ġstud ios", + "Ġdr inks", + "ĠGeor ges", + "raw a", + "ĠChe v", + "Ġinstr uctions", + "ĠLeon ardo", + "Ġcoal ition", + "Ġquant um", + "ĠArchitect ure", + "Ġhoriz ont", + "L ou", + "Ġd ust", + "Ġd rown", + "ĠJ ourney", + "Ġr ates", + "Ġ17 9", + "pp ers", + "21 5", + "ĠRec ording", + "leg raph", + "Ġpract ices", + "ĠBrand on", + "Ġdescend ants", + "Ġs ib", + "ĠR ams", + "Ġl oves", + "ign e", + "ri en", + "any a", + "ph oon", + "ĠMay enne", + "Ġmon ument", + "Ġhead ed", + "ĠMil es", + "ĠScient ific", + "ĠAndrew s", + "ĠBever ly", + "ĠS end", + "Ġm aker", + "ĠM R", + "ĠF ras", + "all a", + "12 3", + "let te", + "ĠBe a", + "Ġlist ings", + "ĠRos a", + "Ġtransl ator", + "Ġgirl friend", + "Ġpen insula", + "Ġassass ination", + "Ġp owder", + "ĠG iant", + "ĠJ ammu", + "ĠK re", + "ab oration", + "Ġor n", + "Ġit em", + "ĠLe x", + "Ġag g", + "ĠZ rich", + "Ġover th", + "ĠAir bus", + "|||||||| ||||", + "ĠLeon id", + "ĠBow ell", + "ĠFinist re", + "n ut", + "z on", + "is i", + "Ġc rypt", + "ĠB und", + "ĠJ as", + "Ġan at", + "Ġab ortion", + "ock out", + "Ġafter wards", + "Ġdr ives", + "Ġgr ade", + "Ġran ks", + "Ġsitu ations", + "Ġopportun ity", + "ozo ic", + "t z", + "t ra", + "t ico", + "Ġv ul", + "Ġmet eor", + "Ġrep air", + "ĠTr ent", + "Ġz o", + "wh ite", + "Ġlegisl ative", + "ĠJama ican", + "ĠShim izu", + "Ġasp ects", + "n amed", + "Ġp airs", + "ĠW r", + "Ġth rown", + "Ġg ases", + "ĠK ad", + "Ġpro ve", + "ug u", + "Ġsh ock", + "Ġcl ay", + "Ġad mitted", + "Ġ13 6", + "ĠRo om", + "Ġfinish ing", + "ĠCow boy", + "C ent", + "Ġt ight", + "Ġh ung", + "oc he", + "Ġser ver", + "Ġret ail", + "Ġref erences", + "ĠWinn er", + "Ġneg oti", + "Ġconsist ed", + "ĠRot ter", + "rast ructure", + "ĠUlt imate", + "ĠSaf ety", + "Ġm ud", + "ĠP ig", + "Ġhe m", + "19 39", + "ĠAr agon", + "ĠSh r", + "Ġsc andal", + "au f", + "Ġrem ake", + "Ġhum or", + "ĠDep uties", + "Ġphot o", + "Ġhosp itals", + "m al", + "Ġ ere", + "Ġt ip", + "an ium", + "ĠB oss", + "ĠL ed", + "Ġpop e", + "13 5", + "ĠChrist ine", + "ĠEm manuel", + "Ġdef ence", + "Ġinte g", + "ĠPatric ia", + "ro x", + "ĠM IT", + "ĠD OR", + "ĠU ran", + "ug a", + "Ġsh arks", + "ĠLe agues", + "Ġcl an", + "Ġper mission", + "erm o", + "reg ation", + "Ġrest rict", + "Ġgener ations", + "ĠIm per", + "Ġsex ually", + "Ġhon orary", + "ĠInt el", + "Ġbass ist", + "Ġancest or", + "ĠAlexand ra", + "ĠConstant ine", + "Ġshop ping", + "ĠKum ar", + "Ġdeliver ed", + "Ġalle ged", + "L oire", + "j un", + "s un", + "Ġc ub", + "Ġto m", + "ĠEurope ans", + "ĠSen egal", + "ĠMor rison", + "Ġcult iv", + "Ġexpl ains", + "ĠIm port", + "ĠSuz uki", + "ĠGib son", + "ĠDra ke", + "spir acy", + "m ill", + "Ġp overty", + "ĠD ew", + "ers hire", + "um ont", + "Ġgro ss", + "ĠBl ock", + "ĠBra in", + "Ġadv ance", + "Ġviol inist", + "Ġadminist rator", + "ĠImp act", + "Ġfavor ite", + "ĠNept une", + "G A", + "r ations", + "ĠN ina", + "af e", + "ĠMc D", + "Ġopen ly", + "irc hen", + "Ġhon ored", + "Ġlim its", + "uge es", + "' ve", + "in as", + "is o", + "ĠS as", + "ĠC ran", + "ĠD ere", + "ĠW als", + "est e", + "ĠSp y", + "oy s", + "Ġ12 3", + "Ġcred it", + "Ġt ou", + "ĠA ce", + "ol ly", + "ĠF ut", + "ad ows", + "ip es", + "Ġex ecut", + "ĠUS SR", + "Ġorig ins", + "istan ces", + "ĠCap itals", + "Ġcomm it", + "ĠHon ey", + "Ġview ers", + "ĠAut o", + "ĠP resent", + "ĠR ole", + "ĠD as", + "19 6", + "19 46", + "Ġcons um", + "ĠJeff rey", + "ĠWeek ly", + "h ra", + "Ġ rib", + "Ġt ub", + "an ese", + "Ġo ct", + "Ġf art", + "ĠL ope", + "et i", + "ĠD re", + "ot ype", + "ĠV es", + "Ġ18 31", + "Ġle agues", + "Ġdep th", + "15 1", + "writ ten", + "Ġaff ects", + "ĠPers ia", + "ĠPo ems", + "Ġinst alled", + "Ġfund amental", + "ĠIceland ic", + "Ġgall ery", + "Ġlat est", + "ĠPow ell", + "W I", + "i ere", + "Ġt ong", + "Ġb uses", + "ĠF BI", + "ĠN ile", + "em on", + "Ġ18 29", + "Ġse ctor", + "Ġcl oth", + "ook s", + "ĠMay a", + "Ġres ort", + "ĠPl ains", + "16 2", + "14 5", + "Ġmon ster", + "ĠProv incial", + "Ġpred ict", + "Ġqual ifying", + "i ations", + "Ġt ape", + "ch io", + "un ar", + "Ġ18 25", + "Ġcomp onents", + "ĠCor on", + "ĠStra it", + "ĠGand hi", + "oun sel", + "ran ger", + "ass er", + "Ġ17 6", + "Ġpopul ous", + "Ġext ension", + "Ġrest ored", + "Ġfire arm", + "ĠOver view", + "Ġillust rator", + "Ġalg ebra", + "Ġfra ud", + "P ierre", + "Ġ ).", + "Ġs ou", + "Ġin ject", + "ĠT aut", + "ĠG olf", + "ĠW orth", + "ĠW itch", + "ip eg", + "ure ate", + "29 3", + "amm al", + "ĠMos es", + "Ġmaint ain", + "ĠFell ows", + "ĠWrestle Mania", + "P art", + "b ut", + "g el", + "ĠM ant", + "ĠE le", + "ĠMar se", + "11 1", + "ĠBer ry", + "bour g", + "ĠEnd ate", + "b el", + "f ree", + "k aid", + "Ġb abies", + "Ġd uty", + "ĠCast ro", + "ym es", + "Ġprop os", + "ĠBah rain", + "ĠMoh ammad", + "ĠRey n", + "ĠCour age", + "ĠPrim ary", + "Ġbatter y", + "Ġcas ual", + "w eb", + "it ama", + "ĠP ale", + "ĠL ars", + "ĠSt alin", + "Ġle uk", + "iz ards", + "rop ri", + "ĠZ ur", + "16 3", + "16 5", + "ĠAll ies", + "ĠRel ations", + "28 3", + "Ġdesign ers", + "Ġadv ice", + "Ġwid ow", + "Sp anish", + "Ġwat ched", + "ĠLett ers", + "ĠRotter dam", + "H a", + "c ium", + "l ag", + "w il", + "ar ium", + "Ġin stitution", + "Ġc otton", + "ic it", + "ĠR ise", + "up s", + "ĠAl pha", + "ĠNew sp", + "Ġapp ly", + "Ġend angered", + "ĠCo al", + "ĠSystem s", + "ĠDirector y", + "Ġt ag", + "er ies", + "ĠH D", + "ĠF an", + "st a", + "ight y", + "ĠV ald", + "Ġcon vert", + "Ġ18 32", + "ick ing", + "Ġcap s", + "ĠMy anmar", + "ĠArch ives", + "ĠKenn y", + "ĠHeart s", + "ĠBed ford", + "u o", + "Ġc ryst", + "ĠP om", + "ĠB ash", + "Ġ18 34", + "Ġev ac", + "enn y", + "Ġcol ored", + "13 6", + "ĠRock ef", + "Ch rist", + "Ġfif teen", + "Ġcoll apse", + "Ġwid th", + "ĠProt ection", + "Ġexc ell", + "Ġinte gr", + "ĠKurd ish", + "ĠLeaf s", + "L B", + "S o", + "Ġb icy", + "Ġm ine", + "ĠG C", + "ĠJ al", + "th y", + "Ġst ood", + "row ing", + "ink i", + "ĠSup erman", + "ĠVir tual", + "bor g", + "Ġgrand son", + "Ġlog o", + "ĠFel ix", + "ĠRud olf", + "f s", + "he a", + "ĠS erg", + "Ġan nex", + "ĠSt ur", + "ĠIn fect", + "ĠY uk", + "Ġ17 00", + "Ġall ies", + "ĠPro gressive", + "14 2", + "12 4", + "ise i", + "ator ial", + "Ġcompet ing", + "ĠMer it", + "Ġcross ed", + "ĠLight ning", + "Ġrevolution ary", + "Ġpul monary", + "Europe an", + "N C", + "id ays", + "ĠN ad", + "ĠW ich", + "Ġg ol", + "ow ing", + "19 33", + "uc ci", + "Ġsc ope", + "21 1", + "ann on", + "13 4", + "Ġcor ruption", + "Ġlab els", + "Ġdom ain", + "Ġrapid ly", + "ĠRaf ael", + "Ġairpl ane", + "b ing", + "i ott", + "m ov", + "o que", + "r t", + "u ous", + "Ġm emb", + "19 47", + "ĠLe ic", + "Ġcl ar", + "ĠAn ast", + "Ġ17 95", + "pr int", + "ĠSw ift", + "27 3", + "Ġcross ing", + "Ġadop t", + "Ġcred its", + "Ġbul let", + "ĠBelarus ian", + "Ġnucle us", + "ĠTibet an", + "Ġreferend um", + "ĠL if", + "ch us", + "ll er", + "ĠCamb odia", + "Ġautom obile", + "ĠTaiwan ese", + "ĠWin ston", + "ĠLeban ese", + "ĠEnvironment al", + "R ed", + "h ow", + "ĠM t", + "ĠM ills", + "ĠK och", + "Ġ18 28", + "ĠFran con", + "99 9", + "ĠRober to", + "Ġspecial ized", + "Ġi Ph", + "Ġhol y", + "Ġdies el", + "ĠVo iv", + "Ġlif etime", + "ĠRot ten", + "Ġtheor etical", + "Ġsudden ly", + "Ġgrad ually", + "b en", + "p m", + "Ġc rack", + "Ġl ymph", + "ĠK art", + "Ġsp elling", + "12 8", + "ĠWill ie", + "ĠAss ass", + "ici ency", + "E ast", + "g it", + "g un", + "Ġm ask", + "ĠH esse", + "id t", + "ĠW IT", + "ter ed", + "ĠV ille", + "ant es", + "ĠMar ina", + "Ġsc ores", + "Ġcons ecutive", + "ĠBel grade", + "ĠBur ton", + "ĠTim othy", + "roc od", + "ĠHend erson", + "Ġempt y", + "Ġb ac", + "Ġin put", + "ĠP owers", + "ĠL al", + "Ġ18 26", + "21 2", + "17 4", + "17 5", + "13 3", + "ĠAd vis", + "ĠSant o", + "Ġgrand mother", + "ĠKu wa", + "P aul", + "d istance", + "f r", + "ĠR ib", + "iv ery", + "ĠCom ba", + "ĠSh iva", + "Ġafter noon", + "10 8", + "Ġcent res", + "ĠDe e", + "ĠPal mer", + "Ġstrong est", + "roll ed", + "har mon", + "Ġfactor ies", + "Ġadvert ising", + "ĠBeaut y", + "strument al", + "A ng", + "S ax", + "f iction", + "l ist", + "t ons", + "Ġp rices", + "ĠT os", + "ĠC oy", + "Ġ18 42", + "15 4", + "ĠSe lected", + "39 4", + "ĠNic ole", + "ĠToul ouse", + "Ġt um", + "Ġs odium", + "ĠS ega", + "Ġm ad", + "ĠC are", + "ou ds", + "ĠB ase", + "ĠN WA", + "Ġn atur", + "ĠG el", + "Ġex ile", + "Ġra b", + "ĠEst ab", + "f o", + "ed ing", + "Ġw itch", + "ĠB agh", + "th ree", + "Ġsp l", + "ĠBe au", + "yl an", + "ring ton", + "Ġevery day", + "Ġrece ives", + "ĠStud ents", + "ĠRail road", + "ĠCurrent ly", + "Ġevolution ary", + "ro us", + "ĠH NS", + "ĠG ol", + "Ġhe n", + "ĠCh oice", + "iet h", + "15 2", + "ara oh", + "Ġmin erals", + "Ġpublic ations", + "gr im", + "ĠStep han", + "ĠAre as", + "ĠStaff ord", + "Ġestablish ment", + "ĠTon ight", + "ĠRockef eller", + "ĠM ugh", + "ĠH ert", + "ĠE SP", + "ĠO v", + "ĠW er", + "ov el", + "ack ed", + "ĠQu int", + "Ġflood ing", + "Ġsubst ances", + "ĠOnd ej", + "f inal", + "n am", + "Ġt et", + "in ite", + "ic ism", + "ĠC ock", + "ĠM uk", + "ip uri", + "Ġex ternal", + "Ġ13 3", + "Ġent ering", + "ĠGl ou", + "ĠCo ach", + "ĠUp on", + "ĠWork ers", + "Ġaccount s", + "bro ok", + "er ver", + "ĠP M", + "ĠH ep", + "ĠD il", + "Ġpro port", + "ĠUn ified", + "ĠAugust us", + "ĠCol ony", + "15 5", + "Ġsoc ieties", + "ĠAct ing", + "ĠGar ca", + "ĠJam ie", + "ĠTrin ity", + "Ġhyp othes", + "Ġpregn ancy", + "Ġtherap y", + "municipal ity", + "S T", + "ĠT ype", + "ĠH oll", + "ir ts", + "Ġal umin", + "Ġre organ", + "od or", + "ĠAl ps", + "ach ment", + "ne z", + "Ġper former", + "ĠBl air", + "Ġmed ic", + "Ġwatch ing", + "ĠGriff in", + "Ġreven ge", + "\" ;", + "2 60", + "P rim", + "p age", + "Ġl os", + "Ġfor wards", + "ath a", + "Ġ10 5", + "13 1", + "Ġstr ategy", + "ĠBar nes", + "Ġfe ud", + "ĠDr ug", + "Ġprom ised", + "ĠBC E", + "gr and", + "ĠLope z", + "at to", + "ĠE aster", + "Ġpro gressive", + "20 4", + "ph an", + "Ġsub mar", + "amb o", + "ĠSch war", + "ĠEv angel", + "ĠGra ph", + "rel s", + "Ġpsych ologist", + "Ġcust omers", + "Ġpurch ased", + "ĠI g", + "ell ar", + "ĠY ev", + "ĠZ ar", + "18 5", + "17 3", + "Ġwar fare", + "Ġpublic ly", + "Ġvis iting", + "ĠKh y", + "Ġge ometry", + "ĠConstit utional", + "Ġerupt ion", + "atche wan", + "Ġgol fer", + "I X", + "Ġt enth", + "at ivity", + "ĠS ally", + "Ġbe aches", + "ĠUn cle", + "Ġdo ors", + "att i", + "Ġph osph", + "12 9", + "Ġep id", + "Ġext ensive", + "CA A", + "Ġexcept ion", + "Ġbomb ings", + "B ig", + "h our", + "ĠT ell", + "ĠB R", + "ĠG ul", + "un ter", + "ew hat", + "ous s", + "ĠPr iv", + "Ġinter ests", + "Ġmin imum", + "Ġmean ings", + "ĠEv a", + "Ġ180 1", + "ĠRom ance", + "Ġmanufact uring", + "c ard", + "ĠS ainte", + "Ġf reed", + "ĠG ur", + "18 1", + "ĠX ia", + "ĠRes p", + "ĠFin als", + "Ġtransl ations", + "ĠMcC artney", + "Ġvir t", + "ĠCop enhagen", + "Ġrecip ients", + "Ġclimb ing", + "L P", + "Ġt ons", + "ĠS eren", + "ĠS ara", + "ĠN ut", + "ĠK yle", + "Ġat he", + "ex p", + "att a", + "ĠCommun ications", + "Ġpat ron", + "ĠPlat form", + "ĠConstantin ople", + "d ing", + "g ent", + "or ious", + "ĠCh ak", + "ĠY an", + "ĠY uri", + "Ġcl in", + "12 6", + "ĠGu est", + "ĠBut ter", + "Ġexper ts", + "Ġfif ty", + "ĠMaur it", + "Ġvert ical", + "ĠSug ar", + "ĠOndej ov", + "1 22", + "l ore", + "w he", + "Ġw el", + "al is", + "Ġb ases", + "Ġf ro", + "Ġm a", + "Ġm or", + "ĠE ra", + "Ġ18 16", + "20 5", + "13 7", + "ĠRichard son", + "Ġinflu ences", + "aval ry", + "Ġachie ve", + "Ġlegend ary", + "' )", + "B el", + "in ese", + "ĠA R", + "Ġg olf", + "ĠV all", + "19 34", + "Ġcon sequ", + "ĠEl isabeth", + "Ġconc rete", + "ĠKy iv", + "Ġcoron avirus", + "Ġunders tood", + "D C", + "O P", + "d isc", + "in um", + "ĠM au", + "ĠR ules", + "ĠF alk", + "ĠE gg", + "19 18", + "Ġsh ift", + "ind e", + "Ġsc ar", + "ĠPro ble", + "15 9", + "Ġtrib ute", + "Ġshoot er", + "Ġmotor cycle", + "ĠNott ingham", + "h op", + "in ate", + "ĠS ieg", + "ĠM its", + "ĠF ear", + "ul ia", + "Ġcon vent", + "og un", + "ib an", + "ĠSh ield", + "ĠBe eth", + "uck land", + "38 4", + "Ġeconom ists", + "ĠHel ena", + "ĠSom alia", + "ĠWinn ipeg", + "Ġcontrib uted", + "Ġbreed ing", + "Ġflower ing", + "L ive", + "j ing", + "k os", + "he i", + "on aut", + "Ġs ends", + "Ġd istances", + "ir ie", + "ĠF erg", + "ĠE ye", + "ĠW ilm", + "Ġfor ty", + "Ġ18 17", + "ĠTh ing", + "ĠAn s", + "ĠJan et", + "ĠZ en", + "ĠGr im", + "ĠGu adal", + "Ġem perors", + "Ġdown town", + "ript ions", + "Ġkey s", + "Ġproper ly", + "Ġrecogn ised", + "Ġattract ed", + "ĠBeng ali", + "Ġleuk emia", + "i w", + "p ost", + "ĠS P", + "am med", + "ĠW ins", + "od ed", + "og g", + "Ġ15 3", + "18 4", + "ĠSand y", + "Can adian", + "ĠJosh ua", + "Ġmoder ate", + "ĠWich ita", + "c s", + "Ġt or", + "Ġf ram", + "ro le", + "Ġm igr", + "th an", + "Ġe ats", + "Ġas h", + "19 42", + "ĠNew man", + "17 9", + "16 8", + "26 3", + "ĠDem ocracy", + "ĠAngel a", + "ĠWild life", + "ĠAtt ack", + "Ġelev ation", + "Ġsusp ected", + "Ġvocal ist", + "ĠBron ze", + "Ġwheel chair", + "ĠHond uras", + "Ġalgor ithm", + "yrgy z", + "c ourt", + "g roup", + "Ġc ourses", + "Ġm ouse", + "ĠC i", + "Ġd iver", + "Ġ20 24", + "iv o", + "ĠG unn", + "op ing", + "20 2", + "Ġout break", + "ĠChrist ina", + "ĠGl oria", + "Ġterm inal", + "ĠLeg al", + "ĠBab yl", + "Ġhom osexual", + "Ġprincip les", + "Ġdomin ant", + "Col umb", + "ĠSask atchewan", + "Ġb onds", + "ĠM iz", + "Ġd ances", + "ĠB oul", + "el i", + "ĠJ n", + "ov sky", + "ĠV ista", + "19 36", + "Ġ18 27", + "Ġqu iet", + "ĠHar bor", + "Ġvers e", + "Ġbi ologists", + "uz zle", + "Ġbox ing", + "ĠGon z", + "Ġto mb", + "ĠD ust", + "ch ief", + "ce eds", + "and ra", + "ĠK end", + "19 3", + "ant is", + "ri ages", + "17 7", + "Ġlist en", + "ĠAc cess", + "ĠConserv ation", + "W hen", + "l ived", + "o ibi", + "he id", + "ĠR ip", + "ĠH il", + "ĠCh ange", + "cl ip", + "Ġatt rib", + "rib ed", + "12 7", + "ron es", + "Ġsub stit", + "ĠRe ading", + "24 5", + "ĠReg ular", + "Ġvot ers", + "ĠChap el", + "ĠTic ino", + "c io", + "j in", + "ĠS ik", + "ĠS hen", + "Ġf ill", + "Ġf ab", + "ĠH IV", + "ay as", + "ĠG amb", + "Ġse al", + "ah on", + "16 4", + "Ġsub species", + "Ġinc umbent", + "aff e", + "fe at", + "ĠClar ence", + "ĠCauc as", + "Ġcereb ral", + "ĠT oo", + "ĠD ylan", + "ĠK ub", + "ĠY ar", + "iz za", + "18 6", + "ĠSw im", + "rib le", + "14 1", + "Ġanim ator", + "ĠMon th", + "ĠIm m", + "ĠAnt wer", + "Ġhon ey", + "Ġliter ally", + "Ġrespons ibility", + "ĠHaut es", + "ĠMalays ian", + "Ġsau ce", + "nd en", + "Ġk ids", + "Ġ18 22", + "20 9", + "ĠWar wick", + "Ġcol span", + "14 9", + "ĠChar acter", + "ĠPol icy", + "ĠAng ola", + "ĠMe chan", + "ĠGreen land", + "ĠTrans l", + "ĠConc ert", + "Ġfert il", + "ĠWarri ors", + "Ġtriang le", + "in ho", + "Ġis ot", + "el o", + "Ġfl ies", + "Ġlight s", + "Ġsom ewhat", + "ih ara", + "ĠMir anda", + "atin ate", + "ĠiPh one", + "ĠG ott", + "ĠW on", + "19 37", + "Ġpro sp", + "ure l", + "ff ield", + "ĠInd ies", + "Ġ14 3", + "Ġtrans mission", + "Ġphys icians", + "odes hip", + "Ġvol umes", + "ellig ent", + "ĠDoc ument", + "h art", + "k reis", + "Ġb ell", + "ĠC ly", + "ĠH OF", + "ĠD mit", + "ĠJ ake", + "ag ic", + "ra id", + "19 30", + "qu el", + "ĠNew castle", + "Ġj et", + "ĠSh ip", + "14 8", + "br uck", + "ĠSa itama", + "arg au", + "Ġcycl ists", + "ĠTs ar", + "atin um", + "Ġexerc ise", + "ĠBerm uda", + "N orth", + "b as", + "c ity", + "Ġb inary", + "ĠL ima", + "ĠG ior", + "Ġit al", + "Ġr iding", + "ĠCon rad", + "13 9", + "25 5", + "Ġsee k", + "Ġarch ive", + "ĠWith in", + "ĠEll is", + "ĠDal mat", + "ĠBeaut iful", + "I L", + "ĠB har", + "ĠF ritz", + "ĠN ice", + "Ġth irteen", + "Ġst yl", + "ĠV inc", + "Ġpr ayer", + "14 4", + "ĠEl vis", + "ĠStud y", + "ĠBa iley", + "ĠNepal ese", + "ĠNav ar", + "Ġceleb ration", + "ĠSurv ivor", + "ĠDere k", + "ĠF ant", + "iv isie", + "st own", + "ĠE c", + "ĠE rik", + "th rough", + "ri et", + "Ġ18 23", + "Ġsong writers", + "30 3", + "25 3", + "Ġsign ature", + "ĠRos en", + "Ġident ical", + "Ġdict ators", + "ĠVat ican", + "K ing", + "r ill", + "ĠN acional", + "Ġre ward", + "19 41", + "rit ies", + "ĠMar riage", + "ĠMay ors", + "10 1", + "ĠCar s", + "Ġrep utation", + "Ġvill ain", + "ĠVer de", + "ĠKr ish", + "Ġdisp ute", + "Ġstrik es", + "ĠFlem ish", + "ĠKhy ber", + "Ġt une", + "Ġt anks", + "ĠS ap", + "Ġh o", + "Ġh al", + "ĠR app", + "ĠK yrgyz", + "ul se", + "Ġsh oes", + "Ġra ppers", + "red ivisie", + "13 8", + "ĠEl ite", + "ster ious", + "Ġrun ners", + "ĠPres idency", + "ĠEnt ry", + "Ġbenef its", + "ĠHerman n", + "ĠPeng uins", + "at u", + "Ġo vers", + "ĠC H", + "ĠB ug", + "ĠB io", + "ĠB ren", + "ĠR if", + "ĠH BO", + "el en", + "ĠK ok", + "ak k", + "ĠV ocal", + "ous es", + "ib u", + "ĠJud y", + "Ġrac ial", + "ĠSax e", + "ĠFore ver", + "ĠAndre as", + "ĠLith uanian", + "ĠNort on", + "D o", + "D r", + "P er", + "ĠA us", + "ĠP AD", + "ĠB am", + "ĠD eg", + "20 7", + "ens ions", + "18 3", + "ĠKing ston", + "Ġco le", + "Ġind ex", + "ĠWh y", + "fl ies", + "ĠGl ass", + "Ġarr ives", + "Ġcitizens hip", + "Ġpoll ution", + "ĠTrin idad", + "ĠMean while", + "Ġenfor cement", + "ĠTaut enburg", + "h ang", + "at as", + "ĠN C", + "iv ores", + "Ġde als", + "ĠAn j", + "Ġ17 80", + "18 9", + "ĠIs s", + "17 8", + "16 9", + "ĠWill is", + "24 3", + "ĠFI VB", + "Ġbelong ing", + "Ġmeasure ment", + "ĠBrid ges", + "ĠLot us", + "A rab", + "Ġto t", + "ur ers", + "ĠO mar", + "im edia", + "Ġr um", + "Ġ17 88", + "18 8", + "Ġover view", + "ĠPl ain", + "Ġco ached", + "23 5", + "25 6", + "Ġorgan ised", + "Ġvar iant", + "Ġav iation", + "ĠVern on", + "on n", + "Ġb reat", + "ĠM ask", + "Ġart illery", + "ĠMal i", + "ĠDire ct", + "ĠWat ers", + "ĠKle in", + "ĠWes ley", + "Ġanch or", + "N ational", + "c al", + "Ġw ish", + "Ġm eth", + "ĠH ank", + "ĠL ay", + "Ġl ion", + "ĠF lying", + "ĠU rdu", + "Ġv ag", + "sit ive", + "ĠSc and", + "ĠCl aus", + "16 1", + "14 7", + "Ġman age", + "Ġgu ards", + "Ġprogram mes", + "ĠIm age", + "ĠTor res", + "ĠHart ford", + "Ġdisappear ed", + "ĠAntwer p", + "S ch", + "V P", + "k ir", + "m ut", + "at z", + "Ġo verseas", + "Ġs ings", + "ĠS U", + "Ġp ounds", + "ĠL ey", + "ad p", + "19 35", + "Ġpro f", + "ĠNew found", + "ĠSh re", + "16 7", + "13 2", + "Ġdifferent ly", + "Ġpian ists", + "Ġdeclar es", + "E m", + "l is", + "Ġs in", + "ĠS eth", + "Ġf ran", + "Ġm ail", + "Ġh atch", + "ĠE instein", + "ĠSt op", + "ĠV ed", + "ĠCom o", + "ĠBr ngen", + "ĠAt hen", + "15 8", + "iff s", + "Ġmonaster y", + "ĠNamib ia", + "ĠHels inki", + "ĠReyn olds", + "O L", + "a ise", + "Ġt unnel", + "Ġb ay", + "ic ing", + "ĠS anskrit", + "ol ism", + "iv ors", + "olog na", + "Ġrel ation", + "ĠGo als", + "Ġwind ows", + "ĠEsp er", + "Ġjur is", + "g ame", + "en i", + "ĠA uckland", + "ĠC C", + "ĠP eg", + "ĠH imal", + "ad h", + "ĠK ron", + "ab el", + "Ġse as", + "20 23", + "Ġcomp onent", + "Ġcl ient", + "Ġcl ergy", + "18 7", + "oth s", + "25 4", + "ĠFin ancial", + "Ġfac ing", + "ĠLank an", + "ĠDur ham", + "D A", + "m ath", + "r g", + "ĠC ul", + "ĠM ing", + "ĠF ris", + "th at", + "ap ur", + "Ġr ising", + "22 5", + "Ġaff ili", + "ĠVar ious", + "Ġprint ing", + "ynam ics", + "ĠCN N", + "D ivision", + "m uth", + "Ġw ider", + "ĠN eb", + "ep ort", + "ĠAl ess", + "ne e", + "ĠGerman ic", + "22 2", + "ĠPl uto", + "emb le", + "umb ers", + "Ġgen etics", + "ĠOl ga", + "ĠEU P", + "ĠAqu atics", + "ĠPhill ip", + "Ġmaint ained", + "ĠEld er", + "ĠVoiv odeship", + "J SL", + "P rov", + "w as", + "ĠL P", + "ber n", + "19 1", + "20 6", + "Ġj ack", + "Ġ17 93", + "ev ille", + "22 3", + "Ġra pe", + "ung s", + "ĠGu ang", + "Ġed it", + "Ġorgan ist", + "Ġsil k", + "Ġmeas uring", + "Ġaccident ally", + "Ġinvest ment", + "ĠHy per", + "Ġess ential", + "char ge", + "ĠShoot ing", + "Ġnatur ally", + ". -", + "A qu", + "u er", + "Ġb er", + "ĠS ass", + "ĠT j", + "ĠT IR", + "ĠP ole", + "ĠR as", + "ay ama", + "ĠE ch", + "ong o", + "Ġch icken", + "te in", + "Ġso le", + "ĠCal endar", + "sh ort", + "af a", + "ĠTex t", + "Ġarr ive", + "Ġ180 6", + "ĠBudd ha", + "cont inent", + "|||||||||||||||| ||||", + "Ġstrip es", + "Ġteach ings", + "O ff", + "ĠS emi", + "ĠH urricanes", + "Ġl ob", + "em or", + "ĠW erner", + "19 25", + "ĠCh al", + "se in", + "22 4", + "Ġsy mmet", + "Ġfour teen", + "ene uve", + "ĠTom atoes", + "Ġbr ings", + "Ġclear ly", + "Ġabsol ute", + "b ad", + "ro k", + "ĠH ort", + "oc y", + "Ġ16 00", + "itt a", + "ros cop", + "24 4", + "Ġent itled", + "uic ides", + "ĠBudd y", + "Ġsusp ended", + "onym ous", + "ĠWor st", + "Ġlin ear", + "A f", + "n t", + "u um", + "Ġt one", + "am ura", + "ĠR ita", + "id ian", + "ĠD ul", + "ĠG ates", + "im ov", + "ĠU b", + "ĠV y", + "Ġde ity", + "20 8", + "Ġ9 78", + "Ġ17 70", + "Ġ17 98", + "22 6", + "15 6", + "ĠSe ine", + "69 7", + "ĠRail ways", + "ĠNic olas", + "Ġinfect ed", + "ĠChap ter", + "Ġsubdiv isions", + "ĠNewfound land", + "2 80", + "m u", + "or ide", + "ĠA ster", + "ht unk", + "ment al", + "Ġpopul ated", + "Ġmon k", + "Ġcar rier", + "24 9", + "ĠVal le", + "ĠNo ah", + "gr aded", + "ĠBal let", + "ĠNic ol", + "Ġastron omers", + "ĠGoth ic", + "ĠUzbek istan", + "r ise", + "om orph", + "ĠW ick", + "Ġpart ially", + "ĠCal d", + "Ġval id", + "Ġfac ility", + "Ġden omin", + "Ġcontest ant", + "ĠWhit ney", + "ĠSloven ian", + "ĠJung le", + "Sup er", + "htunk hwa", + "M al", + "h as", + "Ġ q", + "Ġw al", + "ĠM ine", + "Ġpre ced", + "Ġpar ad", + "Ġpoint ed", + "Ġ180 3", + "Ġsumm ers", + "Ġshell s", + "ĠGam eplay", + "ĠProper ties", + "asht ra", + "h ot", + "p x", + "v y", + "Ġs ew", + "Ġc emetery", + "ĠS ank", + "ĠB rom", + "ĠL ig", + "ĠG ros", + "Ġab road", + "sp ring", + "14 6", + "ĠAm anda", + "ĠBel ize", + "Ġz ones", + "Ġlearn s", + "Ġconnect ing", + "Ġastron omy", + "he art", + "Ġc ock", + "ĠH ost", + "ĠN ure", + "ĠG ael", + "ov ic", + "ut or", + "oc ity", + "ie ux", + "ok ed", + "Ġar ena", + "ĠJohn s", + "Ġdec ay", + "Ġgu itars", + "Ġdisc ontin", + "ĠSam an", + "ĠKn ox", + "Ġcreat ive", + "Ġpract ical", + "ĠElect ron", + "isher s", + "Ġdiplom atic", + "Ġnit rogen", + "Prim era", + "Ġc yl", + "Ġc rocod", + "Ġm ice", + "ĠP ablo", + "ĠI C", + "ĠB rem", + "ĠN it", + "Ġn avy", + "Ġg a", + "Ġbe er", + "Ġres olution", + "Ġrec overed", + "ĠFor um", + "Ġmod ified", + "Ġinter ior", + "ĠMe h", + "Ġint ers", + "ĠGra z", + "udd y", + "Ġi OS", + "Ġwhe at", + "Ġarchitect s", + "ĠEll iott", + "ĠBalt ic", + "ĠFif th", + "Ġpeac eful", + "ĠSchles wig", + "f ive", + "g ae", + "ĠP ir", + "ent in", + "Ġl ap", + "ĠD ud", + "im er", + "op ot", + "Ġe ars", + "ĠV ul", + "Ġ8 80", + "17 6", + "ris ing", + "ĠBer ks", + "ĠBay ern", + "ĠLog an", + "ĠStart ing", + "3 10", + "3 40", + "w ear", + "at j", + "ĠR ebec", + "eb ack", + "az ar", + "15 3", + "ĠBel le", + "Ġhand le", + "Ġge ography", + "Ġmeet ings", + "ĠBarb ados", + "Ġdifficult y", + "ĠTit les", + "ĠWell ington", + "Ġimprison ed", + "c ut", + "d an", + "ar en", + "ĠI T", + "ĠH ut", + "ĠL ip", + "ir ation", + "ĠF ast", + "ĠJ ets", + "ist on", + "ian i", + "ĠIn te", + "19 44", + "ram id", + "Ġsub sc", + "ĠDen is", + "ĠJul iet", + "Ġrecogn ize", + "utt gart", + "imens ional", + "r il", + "Ġs iege", + "ĠM ou", + "im ates", + "Ġst em", + "ĠCh urches", + "Ġle ather", + "ade us", + "Ġkn ight", + "Ġ17 97", + "ĠSh op", + "Ġsc ales", + "ĠMon sters", + "ĠDr ive", + "Ġaff air", + "Ġcover age", + "Ġmarket ing", + "found ed", + "ugs burg", + "Ġresident ial", + "ĠBeeth oven", + "m berg", + "ur us", + "ĠE yes", + "ĠK eep", + "Ġas king", + "ens ed", + "15 7", + "Ġtr ucks", + "Ġsk ill", + "Ġhistor ically", + "Ġrepresent ation", + "Ġground s", + "Ġreform s", + "ĠNikol ay", + "ĠUg anda", + "Ġoccasion ally", + "B ad", + "Ġw ears", + "Ġp el", + "ĠM GM", + "Ġh ope", + "ĠB M", + "ir y", + "ĠSt rip", + "Ġsp ending", + "Ġdis ag", + "ĠCl aire", + "Ġorgan isations", + "Ġvar iations", + "ĠAz tec", + "ĠScreen writers", + "ĠKaw asaki", + "ĠESP N", + "f red", + "p y", + "t ar", + "Ġ iod", + "Ġt ie", + "er os", + "ĠM A", + "am y", + "ĠD res", + "ch ten", + "Ġv ector", + "ĠV id", + "Ġtr ig", + "Ġcap itals", + "Ġne ither", + "ĠRou ge", + "Ġoxid ation", + ". :", + "3 80", + "u bert", + "in j", + "ĠS unn", + "Ġm oral", + "ĠL ives", + "ul in", + "ĠU se", + "art z", + "Ġbe ars", + "Ġch ronic", + "Ġsh allow", + "und erst", + "ĠAl m", + "olog ne", + "Ġac com", + "Ġinter f", + "the tic", + "Ġsurv ival", + "Ġver b", + "Ġenjoy ed", + "ĠAber de", + "Ġarrang ement", + "ĠWed nes", + "ĠNicar agua", + "v ae", + "ĠC ert", + "ĠM s", + "ĠM ald", + "ĠP ages", + "ĠP iano", + "Ġl ux", + "et own", + "ut ers", + "ĠIn sp", + "ĠUn it", + "Ġnot iced", + "ĠQ ing", + "ĠPl ants", + "Ġdec line", + "ĠAb el", + "ĠBe ast", + "Ġed itions", + "27 0", + "ĠPre par", + "ĠGar cia", + "ĠTenn is", + "ĠFe atures", + "ĠPier ce", + "ĠAsh ley", + "ĠBron x", + "Ġtong ue", + "ĠKrish na", + "H ar", + "p in", + "p rov", + "v as", + "ol ine", + "ĠR unners", + "ĠH ang", + "ĠF M", + "ĠG ret", + "Ġre ef", + "ĠZ ambia", + "Ġ25 5", + "Ġext inction", + "ĠDay ton", + "iff e", + "ĠCr ash", + "ĠMax well", + "ĠStr uct", + "Ġpron unciation", + "ĠKos ovo", + "ĠBeg inning", + "Ġsubs idi", + "Ġhorizont al", + "k el", + "n ame", + "is en", + "ĠA ugsburg", + "ĠB ess", + "ĠF o", + "ĠF resh", + "ĠD ong", + "ĠN ig", + "19 38", + "Ġ18 08", + "Ġ18 07", + "und y", + "Ġcomp ilation", + "22 7", + "vel le", + "ĠSouth west", + "ĠTur ks", + "Ġbre athing", + "Ġob ituaries", + "ĠBav arian", + "Ġpil ots", + "Ġregard ing", + "Ġcontinu ous", + "ĠTanz ania", + "Ġabst ract", + "Ġb io", + "Ġto pped", + "ĠB aku", + "Ġl ift", + "eb ody", + "ri que", + "ĠY ad", + "Ġsp erm", + "Ġelect romagn", + "anc o", + "ĠTe acher", + "ĠAm ar", + "ĠMad onna", + "Ġround s", + "ĠGen esis", + "Ġloss es", + "Ġeff icient", + "rah im", + "Ġalg ae", + "Ġinher ited", + "ĠChall enge", + "ĠKuwa it", + "th is", + "Ġg rain", + "19 29", + "Ġ7 47", + "ob ia", + "ib al", + "Ġrec over", + "ily n", + "ĠJohn ston", + "Ġsm ell", + "Ġgovern ed", + "26 4", + "ĠPak htunkhwa", + "ĠAv atar", + "Ġdet ailed", + "Ġten or", + "ĠJo ey", + "Ġrain for", + "ĠLor raine", + "mus ic", + "S D", + "b age", + "k ers", + "s ay", + "Ġs ending", + "ĠF iji", + "ĠD ad", + "ĠG ast", + "em ann", + "ĠJ ong", + "ĠJ oint", + "Ġas c", + "ĠCl oud", + "ool s", + "ĠEl im", + "Ġrep roduction", + "ĠPhil harmon", + "ĠRed s", + "Ġlight er", + "ĠIr ving", + "Ġless ons", + "ĠGreen e", + "ĠOl iv", + "Ġgra ve", + "Ġdiscuss ion", + "Ġresear cher", + "Ġsulf ur", + "Ġaccur ate", + "D ay", + "F l", + "p ers", + "y ll", + "Ġs oprano", + "al an", + "al ion", + "Ġl oses", + "ĠD rew", + "ĠO pt", + "Ġre ct", + "ew ay", + "ri um", + "end ered", + "ide a", + "amp us", + "Ġun ited", + "Ġj aw", + "Ġ17 92", + "23 8", + "ĠGr ass", + "Ġdr unk", + "Ġed ges", + "Ġdef ending", + "Ġbeg un", + "ĠWood y", + "Ġdefend ers", + "Atlant ique", + "Ġpartners hip", + "ĠFras er", + "Ġ 00", + "Ġb its", + "ad ays", + "Ġe y", + "Ġam phib", + "29 0", + "ĠMe et", + "Ġpower ed", + "esse x", + "Ġsuff er", + "ĠRom antic", + "Ġsumm it", + "ĠYank ees", + "Ġhelic opter", + "T o", + "Ġf asc", + "Ġm atters", + "ĠH of", + "Ġhe ated", + "se i", + "ry ing", + "Ġab ilities", + "ĠCar men", + "Ġbel ly", + "umb o", + "ĠHay es", + "ĠDise ases", + "Ġsched ule", + "Mart in", + "3 50", + "c od", + "c ock", + "h aus", + "n ian", + "n ai", + "Ġt on", + "Ġb ars", + "ĠM ail", + "ĠR iley", + "ĠG ross", + "st ed", + "19 32", + "Ġy ards", + "Ġ11 5", + "ĠAd miral", + "23 3", + "sh a", + "Ġcons cious", + "37 4", + "ĠQu ant", + "ĠKar achi", + "ĠBur ke", + "eld a", + "Mar ne", + "Ġattract ions", + "Ġnob ility", + "Ġrub ber", + "Ġaccompan ied", + "Ġrept iles", + ". ).", + "J ack", + "e als", + "an ch", + "Ġc uis", + "ĠC ut", + "ĠM ini", + "ĠP avel", + "am ine", + "ĠB amb", + "ĠR up", + "ĠL ige", + "et us", + "ag us", + "Ġcons ort", + "40 5", + "Ġtop ic", + "Ġ180 4", + "Ġqual ify", + "ĠAut onomous", + "umber land", + "d ad", + "v iation", + "ĠS ail", + "ĠC ash", + "ĠB ie", + "ĠJ et", + "im ony", + "if ice", + "ry an", + "ĠSh izu", + "22 9", + "ĠBr ent", + "ĠHer ald", + "28 6", + "ij n", + "ĠLaw s", + "pe z", + "ĠLy nd", + "Ġtheor em", + "Ġspir its", + "Ġtick et", + "ĠBerks hire", + "a q", + "Ġp ent", + "ĠT rom", + "ĠM iddles", + "ly wood", + "Ġhe mor", + "end ra", + "ĠShe ffield", + "Ġcol le", + "17 1", + "Ġsm oke", + "23 2", + "Ġfe els", + "28 5", + "33 4", + "ĠSch n", + "Ġlab our", + "ĠDi ocese", + "Ġhar vest", + "Ġcerem onies", + "ivor ous", + "Ġhabit ats", + "Ġadvert is", + "ĠCinem a", + "football er", + "Ġmo ist", + "Ġingred ients", + "> [", + "s erv", + "al u", + "ĠT ale", + "Ġm ines", + "ĠC rom", + "ol in", + "ĠF K", + "ab l", + "Ġterm in", + "Ġvar ies", + "ĠMy th", + "ĠCont emporary", + "Ġpercent age", + "Ġbound aries", + "ĠSac ram", + "histor ic", + "Ġc ash", + "Ġp ine", + "le ader", + "ĠL up", + "ul um", + "Ġyear ly", + "ĠSc r", + "ĠPr eston", + "de ath", + "ĠDep art", + "Ġred ucing", + "Ġmult ipl", + "not es", + "ĠEp ic", + "Ġprob ability", + "Ġpred ecess", + "ĠTunis ian", + "ĠFu ji", + "ĠCraw ford", + "Ġt rick", + "Ġt iger", + "Ġb ip", + "ot ing", + "ĠG iven", + "ĠAn k", + "24 1", + "67 7", + "Ġread ers", + "ĠOs wald", + "ĠLoc ation", + "pre fecture", + "Ġtri als", + "Ġbenef it", + "ĠOri ental", + "h anded", + "s ung", + "w y", + "ar ms", + "ion i", + "19 23", + "Ġfound ers", + "ĠEd o", + "ĠBe aver", + "24 7", + "Ġunder water", + "26 8", + "33 5", + "ĠDav ies", + "Ġdisc rimination", + "oph ical", + "Ġtrans gender", + "ĠBas el", + "ĠSat an", + "Ġplaywright s", + "ĠConc ord", + "Ġcopy right", + "Ġveter an", + "% ,", + "T ur", + "w ord", + "Ġb alls", + "ĠM LB", + "ĠH ag", + "ĠH ague", + "ĠH iro", + "ĠO wn", + "and e", + "os o", + "Ġan arch", + "ĠSh i", + "22 8", + "Ġra ced", + "aj o", + "Ġrel ief", + "29 7", + "Ġdevelop er", + "ĠTur t", + "Ġarr ival", + "ĠHam mer", + "ĠOl ive", + "Ġsocial ist", + "ĠHy der", + "Mar ie", + "Ġflow ing", + "Can ada", + "Ġencour aged", + "ĠConstit u", + "ĠTaj ik", + "ĠMR X", + "P C", + "R eal", + "h ou", + "s up", + "Ġt ack", + "Ġb rom", + "us hes", + "ĠH ipp", + "ĠD ancing", + "ĠG os", + "st en", + "ep per", + "Ġelect ro", + "Ġdr iven", + "Ġdef enc", + "ĠStud ent", + "ĠClass ification", + "Ġhot els", + "ĠMcC l", + "Ġlegisl ation", + "ĠBerg er", + "Ġhy brid", + "Ġchap ter", + "Ġnut ri", + "P R", + "k ind", + "ĠT ip", + "ĠH ul", + "id an", + "ĠN CAA", + "ĠSt ore", + "ĠV ER", + "ess es", + "Ġ10 8", + "ev es", + "ĠSc ots", + "Ġany more", + "48 5", + "Ġspeak s", + "ĠHind us", + "ĠAlb any", + "ĠFre eman", + "Ġmechan ism", + "ĠVik ings", + "3 60", + "s outh", + "Ġt adp", + "ĠS M", + "Ġm ob", + "ĠM ast", + "ĠP aper", + "ĠG az", + "th on", + "ĠV era", + "qu est", + "omet own", + "39 5", + "ĠLiter ary", + "Ġlabor atory", + "Ġfresh water", + "G o", + "N FL", + "at ist", + "ĠW essex", + "th ird", + "ill ar", + "19 31", + "ri ka", + "IN E", + "ĠAr lington", + "omet ric", + "25 8", + "ĠSch ol", + "ĠPre z", + "ĠIm ag", + "Ġradio active", + "ĠSol o", + "Ġtravel ing", + "Ġreact s", + "zle z", + "ĠSend ai", + "ĠInfect ious", + "role um", + "1 16", + "N T", + "f ilm", + "m ology", + "Ġt asks", + "Ġf iled", + "ĠT up", + "ĠH ors", + "ĠD inosaur", + "ĠN ipp", + "ĠTh ous", + "Ġ17 99", + "Ġret iring", + "28 4", + "Ġmembers hip", + "ĠHall ow", + "sm ith", + "ĠWhe el", + "ĠCrim inal", + "Ġeleph ant", + "Ġintellect ual", + "ĠMarse ille", + "Q ue", + "W ill", + "Ġthe ology", + "ed i", + "ol id", + "ĠI de", + "ĠD anger", + "23 9", + "29 6", + "ĠSl am", + "Ġview ed", + "Ġbroadcast s", + "Ġcandid acy", + "Ġcelebr ity", + "ĠBerg en", + "ĠHom o", + "ĠInf antry", + "ĠVers ailles", + "Ġrequire ments", + "ĠSof ia", + "Ġc utting", + "Ġh urricanes", + "us on", + "ĠH ak", + "ul er", + "ĠSt ones", + "Ġcon cepts", + "Ġsc orer", + "23 4", + "23 6", + "Ġqu it", + "26 5", + "ĠMe iji", + "Ġdisc ip", + "Ġcoll aboration", + "ili ar", + "Ġmagn et", + "Ġrat ings", + "ĠGon zlez", + "ĠMoroc can", + "Ph il", + "Ġemot ional", + "Ġmos que", + "Ġexclus ive", + "Ġantib iot", + "ipel ago", + "j ee", + "m art", + "m ie", + "re ads", + "ĠH ack", + "ĠL ec", + "Ġl ie", + "ĠG aul", + "19 27", + "ant le", + "ĠY ak", + "Ġte aches", + "23 7", + "ĠAb e", + "50 6", + "Ġany where", + "ĠSim ple", + "Ġcong estive", + "ĠSlav ic", + "Ġsubsequ ently", + "pron ounced", + ">[ [", + "it imes", + "ĠT ues", + "ĠB ard", + "ĠR ash", + "ĠThe res", + "ĠE lean", + "Ġon going", + "erm ain", + "27 5", + "Ġsur geon", + "57 6", + "osp her", + "ye ong", + "Ġachie vements", + "ĠBrad ford", + "Ġaim ed", + "Gar onne", + "it ian", + "ic ious", + "ĠS isters", + "Ġf isher", + "Ġl ibraries", + "od ont", + "ary a", + "ĠInd us", + "Ġ2019 20", + "24 6", + "25 1", + "29 5", + "Ġsw orn", + "ĠHon orary", + "asc al", + "Ġvir tual", + "Ġcook ed", + "Ġcolumn ist", + "Ġowners hip", + "Ġabbre viated", + "Ġoccas ions", + "R oman", + "Ġp in", + "ĠM og", + "ĠH yp", + "ce ived", + "att y", + "23 1", + "Ġcap able", + "ox ide", + "26 2", + "by e", + "Ġfun ctional", + "Ġport rait", + "Ġ180 5", + "Ġbank rupt", + "ĠGard ner", + "ĠLanc aster", + "Ġseg ment", + "ĠRebec ca", + "p ective", + "t urn", + "Ġs its", + "ĠC red", + "ĠG ospel", + "ind ing", + "Ġtra ct", + "ĠRe id", + "Ġhospital ized", + "Ġcart oons", + "ĠMand ela", + "ĠExt reme", + "Ġconqu est", + "arth y", + "ĠSold ier", + "he ld", + "ĠM oss", + "ĠM aking", + "ĠM RT", + "ĠI ber", + "ent ieth", + "ĠL uk", + "Ġl ib", + "ĠN inja", + "Ġcom fort", + "ĠCol lect", + "30 5", + "yn es", + "ĠEx ample", + "Ġprocess or", + "ĠBur ma", + "Ġsuper cent", + "ĠTit ans", + "ĠLatv ian", + "Ġceleb rities", + "G od", + "L I", + "h yd", + "l ord", + "is dom", + "ĠC yn", + "ĠR ost", + "ĠH undred", + "ĠN one", + "ĠK ag", + "se v", + "Ġ9 80", + "ten se", + "Ġcount ed", + "Ġcommun ications", + "Ġsur pr", + "ĠRo ads", + "ĠBas que", + "uz z", + "Ġexper imental", + "Ġz oo", + "Ġwhe els", + "ĠCele br", + "ĠAppear ance", + "Ġexcell ent", + ". '", + "1 21", + "n is", + "ĠS uk", + "Ġdis hes", + "Ġra cer", + "Ġsy ll", + "Ġass umed", + "Ġland mark", + "ĠBas in", + "ux ili", + "Ġeduc ators", + "ĠHor iz", + "Ġautom atically", + "Ġassass inated", + "Ġdivers e", + "k inson", + "in os", + "ĠS ob", + "ĠT yr", + "Ġl oyal", + "ĠV oc", + "rit ion", + "Ġsh aring", + "ik awa", + "Ġro pe", + "30 6", + "iven ess", + "ĠMy stery", + "pher d", + "Ġproduct ions", + "Ġfre estyle", + "Ġfact s", + "ĠTit le", + "cha ft", + "hard t", + "ĠAlger ian", + "ĠMahar ashtra", + "Ġhypothes is", + "ĠShizu oka", + "P A", + "Ġt ur", + "ĠS ue", + "Ġm elt", + "ĠD uchy", + "ĠJ ump", + "ost a", + "Ġ17 50", + "ĠLa f", + "30 4", + "25 7", + "28 1", + "ĠVal encia", + "ĠAg u", + "ĠSam oa", + "ĠCount ies", + "ĠBern ie", + "enz ie", + "ĠShar on", + "onstr uction", + "Ġfair ly", + "ĠVis ual", + "Ġtrav elling", + "Port ug", + "Ġschem e", + "ĠNure mberg", + "opot am", + "Ġt ours", + "Ġa w", + "Ġm anner", + "ĠM uss", + "Ġn aming", + "ĠK em", + "ĠK ai", + "um per", + "19 43", + "ile e", + "ĠSc hed", + "let cher", + "ald i", + "28 8", + "ĠMed ieval", + "ush ing", + "osp ace", + "Ġthought s", + "ĠTurk ic", + "Ġstop ping", + "Ġval uable", + "ĠVill eneuve", + "ĠStar r", + "ĠLi u", + "ĠMoh ammed", + "Ġcomput ing", + "Ġkingdom s", + "Ġfair y", + "Ġcompar ison", + "Austral ia", + "Ġske leton", + "Ġvolt age", + "L et", + "x iety", + "he us", + "ĠS oria", + "Ġf ingers", + "ĠT ah", + "ĠM ist", + "ĠP orter", + "Ġto b", + "oc ial", + "27 7", + "28 7", + "26 1", + "47 5", + "Ġmanag ing", + "Ġsix teen", + "Ġcampaign s", + "ĠMoh amed", + "R ep", + "b ound", + "Ġt act", + "as so", + "Ġm it", + "ĠP orts", + "om ed", + "ĠV rh", + "ub nden", + "ell ites", + "Ġ4 50", + "Ġ17 76", + "Ġ14 00", + "ĠJu els", + "ĠWest on", + "Ġdef ended", + "ĠSer ie", + "Ġopp onents", + "ĠPublic ations", + "Ġsitcom s", + "ĠThrough out", + "Ġreleg ated", + "Ġere ct", + "V er", + "l ift", + "n ational", + "Ġc a", + "ĠS F", + "av id", + "ĠY oshi", + "ĠSp ect", + "Ġ16 3", + "inn ess", + "Ġcommun icate", + "ĠPart s", + "24 8", + "27 6", + "ĠSil v", + "Ġcreat ure", + "Ġmotor way", + "ĠBrand enburg", + "ĠEug en", + "ĠCec il", + "ĠSiber ia", + "G rand", + "or c", + "Ġb id", + "ĠB ologna", + "ĠJ ill", + "ĠO le", + "Ġfor b", + "av ed", + "ri ot", + "29 9", + "af ia", + "ĠMon key", + "37 5", + "68 7", + "35 5", + "ĠAg ent", + "ĠEcuador ian", + "ĠRic ardo", + "Hol stein", + "Ġhemor rh", + "4 50", + "J ean", + "l ied", + "Ġm ammal", + "ĠP ine", + "ĠR w", + "ĠH ull", + "ĠD ow", + "Ġe lector", + "ew ork", + "ĠAnd or", + "Ġrel ay", + "ĠRuss ians", + "let ter", + "Ġret reat", + "com mon", + "25 9", + "ĠGra ubnden", + "Ġsurv ivors", + "Ġhon ors", + "atter ed", + "Mar itimes", + "ĠIndust ries", + "Ġsuit able", + "ĠWals h", + "M en", + "r ens", + "Ġm asc", + "ĠC ust", + "ig ata", + "ĠL ines", + "ĠG an", + "00 1", + "ĠAr ms", + "ĠInd ex", + "Ġad ventures", + "ĠGr ig", + "25 2", + "writ ing", + "38 5", + "the l", + "ĠSm ash", + "Ġsw itched", + "ĠHum ph", + "Ġ180 9", + "Ġpract iced", + "igg ins", + "Ġfr ame", + "Ġcam eras", + "ĠAud io", + "Ġspect rum", + "Ġri ots", + "ĠFly ers", + "ĠNeigh bor", + "ĠRoche ster", + "; \"|", + "A b", + "O ise", + "at ro", + "re ction", + "ĠM eyer", + "iv ic", + "Ġal ien", + "ĠK ara", + "ĠK atherine", + "Ġtw ins", + "30 7", + "ĠBar ber", + "26 6", + "26 9", + "Ġleg ally", + "ĠIll ustrated", + "ĠChurch ill", + "Ġdet ail", + "Ġinf antry", + "mov ie", + "P ak", + "b eck", + "ĠC od", + "ĠR ex", + "ĠN othing", + "ĠG ut", + "ĠW id", + "ĠSt uttgart", + "Ġwh ales", + "ĠMar ilyn", + "ĠHar mon", + "40 6", + "29 4", + "of ten", + "ĠBy ron", + "ĠJud ith", + "Ġmerg er", + "ĠLet ter", + "Ġconcern s", + "Ġorb ital", + "Ġadvis or", + "Ġcollaps ed", + "it he", + "ĠS D", + "ĠS exual", + "ĠN ights", + "19 21", + "Ġr ni", + "Ġ11 2", + "ĠBr ady", + "ĠPh oto", + "27 1", + "28 2", + "28 9", + "Ġob last", + "ĠArch ie", + "udd in", + "ĠCatal an", + "ĠRh y", + "ĠSol id", + "ĠLeg ends", + "Ġconnect ions", + "ĠAlf onso", + "Ġdisp uted", + "Ġteen ager", + "ĠFal con", + "Ġanthem s", + "ĠEthiop ian", + "D en", + "i ad", + "Ġf ox", + "Ġf ract", + "Ġh ij", + "ĠL us", + "ĠD ry", + "ĠV ia", + "Ġ17 94", + "Ġmar ch", + "26 7", + "70 5", + "Ġoper ator", + "ĠMad h", + "Ġhost ing", + "ĠMic he", + "ĠSecond ary", + "cest ershire", + "Ġcycl ones", + "ĠToy ota", + "ĠExpl orer", + "Ġric hest", + "ĠElean or", + "U SA", + "he ng", + "al om", + "al and", + "Ġin stant", + "ĠM ia", + "ĠP is", + "ĠB end", + "ĠL ill", + "ct rine", + "Ġl ibert", + "Ġn aked", + "ĠNew ark", + "33 3", + "70 4", + "Ġge ographical", + "ĠPerson nel", + "ĠOd ys", + "ĠField s", + "ĠHann ah", + "Ġsib lings", + "ropri ate", + "ĠMugh al", + "Ġd rew", + "Ġl iv", + "ĠF ashion", + "ĠF ischer", + "ĠD olph", + "ĠCh itt", + "Ġ17 77", + "ĠAust en", + "ĠCar pent", + "67 4", + "ĠReg ister", + "ĠBi ology", + "ĠJac qu", + "Ġly ric", + "Ġemploy ed", + "Ġjump ing", + "ĠCer ro", + "Ġwa it", + "ĠCole man", + "ĠSent ai", + "Ġtob acco", + "i ors", + "n orth", + "Ġf lex", + "ĠC oc", + "ent ry", + "19 22", + "ĠCh u", + "ant om", + "27 2", + "27 9", + "29 1", + "str m", + "ĠUS S", + "Ġpass age", + "ĠTurk men", + "ĠLeg ion", + "Ġspeed s", + "lyn n", + "Ġearthqu akes", + "ĠShort ly", + "Ġdisplay ed", + "Ġorb its", + "cos ystem", + "ĠImper atore", + "k ok", + "Ġs a", + "ĠC ologne", + "ĠP au", + "Ġto ys", + "ĠI X", + "Ġfor ever", + "ĠK rak", + "ĠV ij", + "te chn", + "ĠRob ot", + "ĠOd d", + "IA A", + "ĠLyn ch", + "ĠRun ner", + "ĠHem isphere", + "ĠTrain ing", + "Ġinn ov", + "Ġupd ated", + "Ġkidn apped", + "ĠHispan ic", + "Ġtelesc ope", + "ĠWednes day", + "B M", + "u ing", + "Ġs aving", + "ĠP S", + "Ġh ometown", + "ĠR oland", + "ĠD um", + "ers dorf", + "ĠJ h", + "ĠK ais", + "ac ent", + "ĠCh o", + "ĠPr iest", + "Ġtra its", + "30 8", + "30 9", + "ĠBel ow", + "64 4", + "Ġsk ating", + "ĠFlor a", + "Ġ0 5", + "ĠMass acre", + "Ġne ur", + "ĠGra f", + "ĠAE W", + "ĠScand in", + "Ġjuris d", + "O ld", + "h urst", + "it able", + "Ġf o", + "ĠC hteau", + "ĠM ina", + "Ġn a", + "ill ic", + "ĠIn ns", + "ĠV est", + "ĠTh urs", + "ĠAn ch", + "Ġ17 3", + "uct s", + "ĠPro cess", + "ĠBl ind", + "Ġmon uments", + "Ġmed ian", + "34 5", + "ĠDon key", + "Ġcomm anded", + "Ġshould er", + "ĠTim or", + "ĠAlb anian", + "Ġfriends hip", + "ĠUt recht", + "ĠAut om", + "Don nell", + "cont inental", + "Ġdiscover ies", + "Ġsurr ender", + "Ġbeet les", + "ĠParad ise", + "j p", + "l ig", + "x x", + "ĠA part", + "ĠC ors", + "ĠM EP", + "ĠB it", + "ĠH es", + "ĠG one", + "ĠW izard", + "ra per", + "Ġ17 91", + "ĠSw itch", + "Ġdif fer", + "ott i", + "40 3", + "64 5", + "58 6", + "ĠPri or", + "ĠEarth qu", + "Ġtechn ologies", + "ĠJer ome", + "ĠNob le", + "ĠVer dy", + "Ġtowns hip", + "ĠId ol", + "ĠFre ud", + "ĠDub ai", + "Ġseem ed", + "ĠEmer g", + "Ġgram mar", + "Ġrein for", + "p resident", + "an an", + "Ġthe aters", + "Ġ1 200", + "ĠC lear", + "Ġd ella", + "ĠB ism", + "ĠD up", + "ĠD iff", + "ĠV ision", + "Ġpro fit", + "Ġnot ation", + "ĠSh awn", + "ĠTe ch", + "27 8", + "lo aded", + "Ġpat ent", + "Ġdraw ings", + "Ġcontrib ution", + "Ġcouncil s", + "Ġham mer", + "Ġdoub les", + "ĠMold ova", + "T r", + "d em", + "ĠC sar", + "Ġal k", + "Ġr ays", + "ĠMar vin", + "les h", + "Ġen cl", + "ĠEl ena", + "Ġed itors", + "24 2", + "ĠMuseum s", + "ĠId ent", + "Ġparticip ants", + "Ġrad ical", + "Ġillust rated", + "Ġvow el", + "ĠBhut an", + "ĠZur ich", + "D T", + "on ial", + "ĠC e", + "Ġto y", + "ĠR id", + "ev o", + "27 4", + "ĠBro ck", + "35 7", + "Ġdist urb", + "Ġdeb t", + "Ġscreen play", + "Ġfar mer", + "Ġappro val", + "ĠClass ics", + "Ġsupp orter", + "ĠRein muth", + "ĠMcM ahon", + "ĠTrad itional", + "M E", + "b ot", + "as ma", + "ĠC ros", + "ĠM alt", + "ĠR ings", + "ur ia", + "st s", + "Ġg ran", + "ish ing", + "Ġv oy", + "ĠZ ach", + "Ġpar a", + "ĠTr uth", + "Ġnear est", + "Ġsol utions", + "Ġtour ed", + "ĠWater loo", + "Ġassist ance", + "ĠKan agawa", + "igg s", + "Ġsurg ical", + "Ġpan el", + "child ren", + "Ġchore ographer", + "ĠArsen al", + "o op", + "Ġw ard", + "ĠA K", + "ĠJ UN", + "Ġst olen", + "Ġbe ans", + "ĠZ ion", + "Ġlar vae", + "Ġwhere as", + "Ġinter face", + "ĠMil ky", + "Ġfight ers", + "ĠCra zy", + "Ġelement ary", + "ĠMoz amb", + "Ġadj ust", + "an ov", + "ro ve", + "ĠT av", + "ĠT ina", + "ĠC ase", + "ĠI ly", + "ĠH our", + "ĠH MS", + "ĠN iel", + "ĠW ool", + "Ġhe aven", + "ell an", + "iz ers", + "Ġpart ial", + "Ġ9 60", + "40 7", + "29 2", + "ĠPort o", + "sk aya", + "Ġdro ve", + "Ġheadqu arter", + "zh ou", + "Ġannoun ces", + "Ġappar ent", + "F I", + "Ġin sc", + "ĠM ec", + "id on", + "ĠD ana", + "ĠE leph", + "Ġv ast", + "Ġun f", + "Ġdirect ions", + "ĠMed ici", + "35 4", + "35 9", + "Ġsign ing", + "Ġsurv ivor", + "Ġinstr uction", + "ĠRome o", + "ĠKon stant", + "Ġcolumn s", + "Ġcinem at", + "Ġfung i", + "uxili ary", + "3 21", + "] .", + "r unning", + "Ġb ag", + "ic ht", + "ĠS ok", + "Ġp riz", + "ĠT ac", + "ĠD ukes", + "Ġ3 000", + "ĠZ ero", + "ĠMan ipuri", + "Ġpar am", + "40 4", + "ĠMich a", + "Ġgen ocide", + "ĠGod dess", + "Ġdi ary", + "ĠWork ing", + "ĠChap man", + "Ġvolcano es", + "ĠPatri arch", + "ĠAchie vement", + "Ġ u", + "on ist", + "ĠS ach", + "ĠS tern", + "ĠS SS", + "ĠL uz", + "Ġst er", + "Ġ18 3", + "Ġsp iders", + "are z", + "ier i", + "ĠAnd es", + "Ġsec ure", + "30 2", + "ae us", + "ĠSch l", + "Ġsoc iologist", + "ĠPaul a", + "Ġprot agonist", + "Ġvar iation", + "Ġinvent ion", + "ploy ment", + "Ġassoci ate", + "ĠLim burg", + "Ġden ied", + "ĠShar ma", + "ĠRoberts on", + "ĠMathemat ical", + "s ub", + "or ah", + "ĠB uk", + "ĠL ep", + "ĠN XT", + "im en", + "all ing", + "Ġrem ark", + "33 9", + "65 7", + "Ġserv ants", + "ĠSy mb", + "ĠHigh land", + "Ġbur ial", + "Ġautom atic", + "ĠMarc os", + "ynam ic", + "ĠRab bit", + "Ġsed iment", + "ĠWi ener", + "A c", + "G S", + "g ov", + "k top", + "Ġw ool", + "Ġw inters", + "it udes", + "ĠS app", + "as u", + "ĠP ione", + "ĠI OC", + "ĠE C", + "ĠE ur", + "Ġk iss", + "ap o", + "19 26", + "air es", + "ĠAll ah", + "50 9", + "ĠChar ter", + "ĠEn s", + "65 4", + "Ġcompet itive", + "ĠIm ages", + "enth al", + "ĠCorn el", + "Ġflood s", + "ĠLaur en", + "ĠNi igata", + "Ġprep are", + "P M", + "Ġb ib", + "Ġh ier", + "ĠB ella", + "ĠL und", + "ĠL uck", + "Ġst ays", + "ith ms", + "Ġen cyclop", + "sh ine", + "ĠBar oque", + "Ġcr uel", + "ae a", + "35 2", + "ĠOfficial s", + "Ġaut umn", + "ĠPenn y", + "Ġdet ect", + "ĠSur in", + "Ġrev olt", + "Ġdisestab lished", + "Ġabbre viation", + "ĠBelf ast", + "ĠQur an", + "ĠRhin eland", + "ĠHallow een", + "on o", + "or rect", + "es ch", + "Ġw inger", + "Ġf usion", + "ĠP A", + "Ġth rew", + "ĠAl leg", + "Ġcent imet", + "Ġnational ist", + "50 5", + "ĠSch ne", + "Ġunivers al", + "ĠMid lands", + "Ġinterview s", + "Ġinstrument al", + "ĠIsab ella", + "ĠHig her", + "agre b", + "ĠA ly", + "ĠT il", + "ĠC ult", + "ĠR ule", + "ĠN T", + "all iga", + "av o", + "og e", + "orm al", + "rop ods", + "Ġra w", + "Ġthan ks", + "40 8", + "ĠGar field", + "asc ular", + "ĠAle k", + "Ġax is", + "ĠHold en", + "U p", + "b ec", + "Ġs ap", + "Ġp eng", + "Ġp orts", + "Ġm ars", + "ĠP f", + "ĠP all", + "ĠP aw", + "ĠD iane", + "Ġg or", + "ĠU d", + "Ġ18 11", + "ĠY un", + "Ġcan al", + "Ġun h", + "Ġ9 90", + "clud es", + "Ġdec k", + "40 2", + "70 8", + "Ġint elligent", + "Ġdiv ine", + "Ġfin ance", + "Ġcond uctors", + "Ġhuman ity", + "ĠRichard s", + "Ch ar", + "ĠSand ra", + "Ġswim mers", + "ĠPunjab i", + "Ġprefer red", + "ĠKham ba", + "Ġvel ocity", + "s son", + "ĠN est", + "ĠJ i", + "Ġ3 166", + "ran k", + "ĠY oun", + "Ġ17 87", + "ĠPl atinum", + "Ġcons oles", + "80 8", + "Ġem er", + "65 3", + "67 5", + "Ġdisc overs", + "ĠTw elve", + "Ġreal ized", + "Ġche f", + "Ġprotect s", + "Ġpers u", + "Ġfund s", + "ĠSerge y", + "Ġpack age", + "ĠAdv anced", + "Ġwithd rew", + "Ġstrik er", + "ĠMcN aught", + "Ġchalleng es", + "ĠTues day", + "K O", + "h ad", + "m os", + "he nd", + "ĠS T", + "Ġm ath", + "Ġd ys", + "ĠL iz", + "ĠF ellow", + "her ry", + "ĠK ell", + "ĠSt a", + "Ġcon spiracy", + "Ġcan cel", + "ĠShe ikh", + "Ġcl ouds", + "Ġtr ilogy", + "Ġmain stream", + "29 8", + "70 9", + "unk er", + "ĠTre vor", + "Ġconv inc", + "ĠCop per", + "ĠDeb ut", + "ĠFair y", + "Ġfeed ing", + "Ġwealth y", + "ĠEdu ardo", + "ĠBohem ia", + "Ġbicy cle", + "Ġcuis ine", + "ĠHyder abad", + ". ),", + "is le", + "re hens", + "ĠP ain", + "ĠP ract", + "ent a", + "ĠR ik", + "Ġwas n", + "st ock", + "op us", + "ra ch", + "um ann", + "ĠHe y", + "ab ul", + "Ġad m", + "ict ions", + "ĠCon cer", + "ĠFl ames", + "eng ths", + "ĠSm art", + "iol a", + "ĠAcc idental", + "Ġinvol ve", + "Ġmonth ly", + "Ġsat isf", + "ĠTal iban", + "Ġpal m", + "Ġcontest ants", + "sec ond", + "ĠLeic ester", + "A v", + "L ife", + "on ing", + "Ġs ells", + "ĠT ill", + "ĠN ish", + "ĠN ied", + "th ia", + "Ġg ift", + "ut z", + "ac io", + "Ġ17 75", + "ĠJoh an", + "ĠBr un", + "ĠEd ith", + "Ġrem ote", + "ij k", + "ĠHor ror", + "fort un", + "oir s", + "ĠDiam onds", + "ĠChen nai", + "Sax on", + "Ġmedic ines", + "Ġin tern", + "ĠA argau", + "ĠT ate", + "ĠP ia", + "Ġn eb", + "ĠK ppen", + "ĠSt ras", + "Ġpro s", + "ud i", + "Ġcon fess", + "Ġde ities", + "Ġ17 96", + "ĠPl aza", + "Ġtra pped", + "ĠLa os", + "ĠGu ill", + "50 7", + "80 6", + "34 7", + "64 7", + "ĠJean ne", + "Ġemb ry", + "Ġsun light", + "Ġopt ion", + "ĠFight ing", + "Ġrecomm ended", + "Ġdistingu ished", + "Ġdispl ays", + "ĠHok kaid", + "ĠYev gen", + "R ock", + "l ength", + "m and", + "Ġt ables", + "en ic", + "ĠM ate", + "ec hes", + "19 20", + "ate ver", + "ass is", + "sp eed", + "Ġac oustic", + "uk o", + "erv ille", + "Ġcr uc", + "Ġgu arant", + "ĠUS B", + "33 7", + "34 3", + "65 1", + "ĠArt em", + "Ġsequ els", + "ĠGre ens", + "ĠLen in", + "Ġlif estyle", + "Is rael", + "ĠPeng uin", + "chw itz", + "Sec ond", + "Ġaf raid", + "Ġdiscontin ued", + "er ie", + "ĠS aul", + "re ated", + "Ġd ual", + "ĠD ies", + "ĠK ann", + "ĠK ness", + "ĠHe ads", + "ĠIn flu", + "ry n", + "Ġra ising", + "ĠTe legraph", + "Ġqu ad", + "70 3", + "rew s", + "Ġdem on", + "ĠConf uc", + "Ġhom eless", + "ĠBal och", + "ĠProgram ming", + "Ġrit ual", + "Ġtrump et", + "chten stein", + "ĠPhilharmon ic", + "! .", + "g irl", + "w ind", + "Ġc ake", + "Ġc uts", + "ĠS uicide", + "Ġm ang", + "ĠP WI", + "ĠH abit", + "ĠL j", + "ĠN ir", + "ĠN ur", + "Ġg lands", + "ĠSt ark", + "if ier", + "ĠV eter", + "ĠTh ir", + "Ġcl uster", + "Ġup coming", + "Ġtr iple", + "50 8", + "34 1", + "ĠRec ent", + "Ġdest ination", + "ĠMa o", + "ĠElect ions", + "Ġcer am", + "iser ies", + "ĠPhot os", + "Ġexplan ation", + "c as", + "i ary", + "z one", + "Ġs essions", + "ĠS add", + "ĠA IDS", + "Ġto oth", + "ĠI A", + "Ġn urse", + "ĠW ies", + "ĠIn st", + "Ġhe ating", + "ĠV isc", + "og ist", + "og ie", + "ern o", + "Ġar ist", + "cl ub", + "ĠSh ane", + "sh op", + "ĠGu err", + "60 6", + "36 2", + "Ġess ays", + "Ġconcern ed", + "ĠPsych ology", + "ĠInvest igation", + "ĠOil ers", + "ĠHass an", + "Ġbrows er", + "N BA", + "s ix", + "ar is", + "ĠC B", + "ĠCh ip", + "iss el", + "ubl ics", + "ĠPr ussian", + "ĠPh D", + "ĠAb h", + "40 9", + "33 1", + "33 6", + "34 4", + "38 1", + "45 5", + "Ġdist ant", + "36 3", + "ĠMarsh al", + "ĠHawai ian", + "ĠJournal ists", + "ĠColon ial", + "ĠMarin os", + "ĠAnto ine", + "rg uez", + "ĠKness et", + "er ic", + "Ġto es", + "ĠF letcher", + "ĠG etty", + "ign on", + "Ġwh ale", + "Ġor al", + "Ġr ises", + "ll a", + "Ġgr ant", + "33 8", + "ator ies", + "64 3", + "ĠPer fect", + "ĠDire ctors", + "ĠMah m", + "Ġlo op", + "head ed", + "Ġopin ions", + "ĠBritann ica", + "Prov ence", + "C ube", + "D R", + "S F", + "f i", + "g ren", + "m ain", + "ĠS es", + "Ġf ake", + "ĠP itt", + "ĠR ao", + "ĠV ale", + "Ġr uins", + "ĠZ agreb", + "Ġbl ow", + "33 2", + "Ġrun way", + "34 6", + "34 8", + "39 3", + "ĠNot re", + "Ġide al", + "Ġimp e", + "Ġvis its", + "ĠDan ube", + "Ġdescrib ing", + "isl av", + "ĠHum ans", + "ĠJackson ville", + "ĠOver all", + "ĠGram mar", + "ĠRod rguez", + "ĠHan over", + "Ġstadium s", + "Ġfrequ ent", + "ĠZh ang", + "ĠEngine ers", + "Ġteen age", + "ĠHond a", + "Ġven om", + "ĠLie chtenstein", + "ĠImport ant", + "ed es", + "Ġin land", + "ĠT ot", + "ing ly", + "ĠM oor", + "Ġd orm", + "ĠP ix", + "ĠB ret", + "ĠL it", + "Ġl over", + "ĠD ag", + "Ġfor ec", + "Ġst ere", + "Ġshe et", + "Ġmon itor", + "Ġgovern ors", + "67 3", + "ĠQu inn", + "ĠDo ctors", + "ĠJun ction", + "ĠDou b", + "Ġemot ions", + "ĠCyr us", + "ĠBrig ade", + "ĠSacram ento", + "Ġ els", + "er b", + "it one", + "Ġm as", + "ĠM C", + "ĠM ole", + "ĠH its", + "ĠE leven", + "Ġpl ural", + "ern ess", + "ision al", + "Ġdis miss", + "ĠTr uman", + "ĠMon arch", + "Ġlast s", + "Ġbusiness woman", + "ĠAg re", + "ĠGreen wich", + "ĠOk in", + "Ġbr ig", + "Ġreact ing", + "ĠSab ha", + "Ġcust oms", + "l at", + "v ill", + "an imated", + "en arian", + "Ġin tense", + "ĠC INE", + "Ġl ith", + "oc ation", + "man s", + "ĠSh arks", + "we ed", + "Ġapp le", + "ĠRiver a", + "ĠAll ison", + "60 3", + "ĠReg iment", + "ĠMet eor", + "ĠGo es", + "ĠOl ivia", + "Ġmid field", + "Ġlim estone", + "ĠEll iot", + "ĠPres ley", + "Ġflag s", + "Ġterrit orial", + "ĠDist ribution", + "ĠBey ond", + "ĠBes ides", + "Ġbon us", + "Work s", + "ĠUran us", + "Ġclar inet", + "it ational", + "ĠT ud", + "ĠM upp", + "ent on", + "ĠE rit", + "ĠW ander", + "ĠK amp", + "ap ing", + "if ax", + "=\" #", + "50 2", + "60 4", + "ĠX IV", + "37 7", + "65 5", + "38 7", + "Ġsepar ation", + "ĠAdd itionally", + "ĠDream s", + "ĠCrit ical", + "Ġinterpret ation", + "ĠTreas ury", + "Y okohama", + "d ia", + "m ese", + "en k", + "at aka", + "ĠC IA", + "ĠB j", + "ĠD ort", + "ch u", + "ĠG ert", + "ia o", + "19 24", + "Ġ17 78", + "60 2", + "60 8", + "che t", + "64 2", + "38 3", + "38 6", + "ĠTer esa", + "ĠDo or", + "ĠCher n", + "ĠHy de", + "ĠVik ing", + "Ġsumm ary", + "Ġobserv ations", + "Ġelectron ics", + "Ġseg ments", + "ic he", + "ic am", + "Ġm ast", + "od o", + "ĠAl pine", + "ric ted", + "ĠPl ata", + "Ġtra il", + "60 5", + "che z", + "35 8", + "ĠRes ources", + "ĠRes istance", + "sc ale", + "Ġamong st", + "apt ers", + "ĠChief s", + "ĠAbb as", + "Ġrob ot", + "ĠKap oor", + "ĠLag os", + "Ġanat omy", + "Ġtadp oles", + "ĠCINE OS", + "b ath", + "as co", + "ĠB order", + "ĠL ands", + "ĠD ora", + "ur ated", + "ĠG uru", + "ĠW ords", + "Ġre vers", + "um en", + "ĠHe ather", + "ĠSt yle", + "und i", + "ten ance", + "Ġbu ff", + "other ap", + "ĠFl ood", + "Ġdirect ing", + "Ġworld s", + "vent us", + "Ġpar ody", + "eng o", + "67 8", + "35 1", + "Ġstat ements", + "39 2", + "Ġexp and", + "led on", + "ĠBu zz", + "Ġperform s", + "ĠVol cano", + "back s", + "Ġplat inum", + "Ġroll er", + "ĠBuck ingham", + "ĠJohannes burg", + "ĠCP U", + "Ġles bian", + "orde aux", + "Ġwarri or", + "Ġchromos omes", + "ĠSapp oro", + "3 23", + "K ar", + "p iece", + "w ang", + "Ġ }", + "he ed", + "Ġs ought", + "Ġs aints", + "ĠC otton", + "ig ious", + "ĠP erm", + "am ar", + "ĠL pez", + "et to", + "ĠN EM", + "ep s", + "ĠV ega", + "ew ard", + "Ġch ains", + "ĠSh ore", + "Ġad miral", + "ĠNov el", + "ĠIs les", + "Ġup set", + "ins ki", + "Ġgovern ing", + "Ġdr ag", + "50 4", + "Ġgu ests", + "Ġsur prise", + "34 2", + "37 1", + "37 9", + "64 9", + "36 1", + "ĠNe o", + "Ġinf rastructure", + "ĠMcC arthy", + "Ġpay ing", + "Ġweek end", + "Ġwor ried", + "Ġvalley s", + "ĠLuther an", + "Ġflav or", + "ĠXV I", + "Ġtheat rical", + "atj ara", + "ĠNipp on", + "ospher ic", + "E very", + "M er", + "b ey", + "e us", + "l ical", + "m oon", + "in z", + "ĠB undes", + "ĠH os", + "ĠN om", + "ĠN og", + "Ġre venue", + "os c", + "ath am", + "Ġ11 1", + "air y", + "Ġair lines", + "90 8", + "ĠComp lex", + "68 3", + "68 9", + "35 6", + "Ġsk ysc", + "57 5", + "Ġexpl ore", + "Ġcross es", + "ĠAndre i", + "Ġentertain er", + "ĠDom ingo", + "Ġult imately", + "Ġjur ist", + "Ġdecor ated", + "Ġmerc ury", + "Pak istan", + "c ourse", + "g ia", + "m akers", + "en as", + "Ġw avel", + "Ġs le", + "Ġc ord", + "ĠM ighty", + "ent ially", + "ĠF ed", + "st et", + "ain es", + "Ġr ats", + "ere zo", + "ĠY ah", + "ull a", + "50 3", + "60 9", + "str as", + "65 2", + "ĠAct iv", + "ĠLuc ia", + "Ġdig its", + "ĠGame Cube", + "sm all", + "ĠVa ucl", + "otten ham", + "prod uced", + "Ġgoalt ender", + "l iest", + "Ġt oler", + "Ġd iving", + "ch urch", + "ĠW imb", + "Ġg ast", + "Ġpl ague", + "ĠAl s", + "ĠSp art", + "ress ive", + "Ġpre v", + "Ġno ise", + "80 5", + "ĠMon ter", + "Ġcontin ents", + "ĠMy ers", + "Ġsw ing", + "ĠGro ver", + "kn ow", + "ĠPet r", + "Ġaud iences", + "Ġfav ou", + "ĠSah ara", + "ĠDod gers", + "Ġflo ors", + "Ġgam eplay", + "Ġautobi ography", + "Ġisol ated", + "ĠFactor y", + "ĠElectron ics", + "4 70", + "ro oms", + "Ġe clip", + "ill as", + "ĠV ick", + "ĠEd en", + "35 3", + "Ġmy sterious", + "ĠBon nie", + "ĠGreg orian", + "Ġairport s", + "ĠIb rahim", + "Ġvacc ine", + "ĠWend y", + "n ar", + "ir as", + "ĠN ong", + "ass au", + "iz oph", + "Ġ17 60", + "ener ation", + "ets k", + "Ġdec lined", + "80 3", + "80 7", + "ĠEx hibition", + "34 9", + "Ġann ually", + "viron ments", + "ĠEduc ational", + "Ġanal og", + "ĠValent ine", + "Ġdream s", + "ĠIw ata", + "P ig", + "er d", + "ĠC ull", + "il and", + "Ġal leg", + "ĠE h", + "uc a", + "ĠLe one", + "oy le", + "ĠPro te", + "ĠCommun ities", + "37 2", + "ĠTra cy", + "ĠSat ellite", + "ĠEnter prise", + "adesh iko", + "Ġdig it", + "Ġrad ius", + "Ġpot assium", + "Ġwa iting", + "ikov sky", + "ĠCrime a", + "ĠEmerg ency", + "ĠVaucl use", + "P P", + "Ġt uber", + "Ġb apt", + "Ġn erve", + "ĠG ale", + "ĠV ince", + "ĠV iolet", + "19 19", + "Ġcan on", + "90 9", + "Ġdiv ide", + "Ġchem ists", + "ĠEst her", + "ĠCher ry", + "Ġdomin ated", + "roph ys", + "mod ern", + "Ġspir al", + "cap ital", + "Ġsubsidi ary", + "am ation", + "ĠH ale", + "ĠE CW", + "ag in", + "Ġhe ter", + "ath i", + "ric ke", + "hen ko", + "rop od", + "ten berg", + "ĠCar ne", + "ann ah", + "90 4", + "Ġstat istical", + "39 1", + "39 6", + "Ġvar ieties", + "Ġacadem y", + "ĠProd ucer", + "Ġly ing", + "Ġneighbor ing", + "angel o", + "ĠMaced onian", + "Ġlingu ist", + "Ġod d", + "' d", + "3 70", + "H ow", + "d ev", + "he e", + "Ġt un", + "ro let", + "ĠT D", + "Ġd os", + "ol ini", + "il st", + "ĠI rene", + "ĠF aso", + "ĠW it", + "Ġre ception", + "ag na", + "ĠCom unes", + "Ġph arm", + "ĠMus k", + "tt i", + "38 2", + "39 8", + "unn els", + "ĠTrack s", + "Ġcomment ators", + "ĠRain bow", + "ĠTob ago", + "ĠCompet ition", + "A ss", + "L ittle", + "d ate", + "h og", + "l isted", + "Ġt ale", + "ĠP iper", + "Ġh itting", + "un ts", + "Ġpl ains", + "Ġab olition", + "air ed", + "Ġman ages", + "ĠEn c", + "37 3", + "64 1", + "Ġstat ues", + "45 8", + "39 7", + "ĠSal am", + "Ġhold er", + "Ġcal cium", + "Ġsen sitive", + "Ġcoll ision", + "ĠTol edo", + "erne ath", + "ĠPrincip al", + "B o", + "d aughter", + "w id", + "x i", + "Ġw elfare", + "Ġm ature", + "ĠH og", + "id y", + "ch in", + "ĠN adeshiko", + "ap arte", + "Ġ16 20", + "az aki", + "ĠGe ographic", + "Ġrele asing", + "ograph ies", + "ĠMan ufact", + ".. ..", + "57 7", + "Ġexpl o", + "ĠGovernor ate", + "ĠJul es", + "Ġpe er", + "Ġrac ism", + "Ġspread ing", + "ĠHom epage", + "medi ate", + "ĠRub y", + "ĠRah man", + "ĠClif ford", + "C or", + "G P", + "T om", + "h ak", + "w en", + "Ġd olph", + "ĠP seud", + "ĠL amp", + "ĠL ingu", + "ad ia", + "ĠO leg", + "ĠK oz", + "ĠSt ro", + "Ġv in", + "00 3", + "Ġra id", + "ille m", + "90 5", + "68 5", + "69 5", + "47 3", + "ĠSal em", + "Ġtrib al", + "Ġtro phy", + "Ġcirc ulation", + "ĠOrgan isation", + "whe el", + "ĠDres den", + "s v", + "s an", + "ĠT ap", + "ĠM illion", + "ĠB ased", + "ĠG im", + "ĠG aga", + "ĠK av", + "19 28", + "ust ion", + "ĠSh an", + "Ġcount s", + "ank a", + "80 2", + "37 6", + "67 1", + "app a", + "ĠSer ver", + "of en", + "unt a", + "Ġsent ences", + "Ġpost ed", + "Ġbr ands", + "Ġche ap", + "Ġfall en", + "Ġlaws uit", + "ĠWeb ster", + "Ġlay out", + "Ġfolk lore", + "keep er", + "ser ies", + "ĠNou velle", + "Ġannounc ement", + "Ġnutri ents", + "ĠInns bruck", + "Ġb orrow", + "ĠT ou", + "ĠC ena", + "ĠI k", + "ĠF eld", + "ĠD ock", + "ay ev", + "art et", + "Ġbe i", + "if est", + "ĠV ampire", + "Ġ9 50", + "ĠSh uttle", + "ĠMay flower", + "Ġdes ire", + "Ġqu ar", + "ĠGu inness", + "38 9", + "reg ate", + "ĠBi har", + "ĠCub s", + "ĠLaur ent", + "ĠBey onc", + "ĠSchm idt", + "ĠGuj arat", + "Ġbac helor", + "Ġels ewhere", + "icam eral", + "d ays", + "g s", + "p ox", + "r ays", + "Ġt ent", + "Ġt ab", + "on ica", + "re z", + "ĠA val", + "ĠT akes", + "Ġm arsh", + "ig ator", + "ĠR ust", + "ĠH C", + "ot yp", + "00 4", + "ĠV and", + "up ta", + "ĠSw ord", + "ys h", + "erm ark", + "Ġ24 0", + "80 4", + "70 2", + "70 7", + "65 6", + "67 6", + "38 8", + "ĠDev ils", + "ĠMag gie", + "Ġinvol vement", + "apt or", + "ĠCongress ional", + "ĠAnton y", + "Ġdig est", + "Ġcur ve", + "Ġinsp iration", + "Ġannoun cer", + "ĠBasil ica", + "UC N", + "Ġfurn iture", + "Ġlux ury", + "S ing", + "b ands", + "as an", + "ig l", + "Ġh iding", + "ĠF ah", + "ĠD ut", + "ĠV on", + "ell ers", + "ĠAugust a", + "urn ames", + "Ġair ing", + "90 7", + "37 8", + "67 9", + "48 9", + "ĠNe ust", + "eck l", + "Ġdestroy ing", + "Ġblock ed", + "Ġbeat en", + "Ġcorrect ly", + "Ġtri o", + "Ġfat al", + "Ġmonkey s", + "izoph ren", + "l n", + "l ink", + "Ġt ies", + "al m", + "ĠS uf", + "ce ans", + "Ġup graded", + "Ġcr u", + "oint ed", + "65 9", + "Ġran ging", + "ĠMat ches", + "Ġtemper ate", + "Ġbank er", + "ĠWebsit es", + "ĠFull er", + "Ġgoalkeep ers", + "Ġreprod uctive", + "Pal atinate", + "Ġhorm ones", + "ĠDw ight", + "ĠAberde en", + "b rown", + "e u", + "h igh", + "p ose", + "t land", + "ĠH aj", + "ĠF y", + "ĠG ob", + "ific ate", + "90 3", + "tt y", + "65 8", + "67 2", + "69 3", + "48 1", + "47 4", + "Ġrece iver", + "ĠBur kina", + "ĠCount ries", + "Ġcirc ular", + "leg iate", + "ĠHaw k", + "min ute", + "Ġdiscuss ed", + "ĠTri assic", + "ĠTrib une", + "ĠMol ly", + "Ġeleph ants", + "Reg ional", + "Ġmemb rane", + "ĠBagh dad", + "t ail", + "Ġc avalry", + "ĠS uccess", + "ĠC orp", + "am on", + "ĠB ee", + "ĠB ordeaux", + "ĠR ox", + "ĠH enn", + "ĠL ance", + "et ary", + "Ġn am", + "ak ura", + "19 10", + "ĠUn known", + "ĠMar ino", + "ĠAr min", + "Ġkn ee", + "ild e", + "Ġ16 50", + "ĠZ immer", + "Ġcr ater", + "68 8", + "69 8", + "48 6", + "Ġequ ality", + "Ġbirth place", + "Ġdead ly", + "het ic", + "Ġenc ounter", + "Ġphr ases", + "Ġpurch ase", + "ĠKurd istan", + "Ġcorpor ate", + "M ad", + "g ment", + "j ud", + "w on", + "ĠS ib", + "re uth", + "ĠM urd", + "ĠH in", + "ĠL ob", + "ĠThe rm", + "ĠN aw", + "ĠV itt", + "19 17", + "ear ing", + "qu et", + "ĠLe vel", + "Ġac ute", + "uss y", + "99 6", + "64 8", + "68 4", + "48 3", + "ĠOut side", + "ĠFe el", + "Ġprem iere", + "ĠKl aus", + "Ġaltern ate", + "Ġsail ors", + "ĠThurs day", + "2 20", + "or io", + "at al", + "Ġc ater", + "ro ft", + "ĠT arn", + "ĠF rib", + "ĠJ ub", + "ĠK ant", + "est ag", + "Ġte lev", + "ĠCom mercial", + "ĠAm adeus", + "ĠAir craft", + "ĠSy nd", + "45 3", + "ĠProv idence", + "Ġaff ord", + "Ġadminist ered", + "ĠSa ar", + "ĠJenn y", + "Ġcontain er", + "Ġmill ennium", + "ĠRecord ings", + "ĠCand id", + "ĠChev rolet", + "Aqu itaine", + "O ut", + "n ick", + "at ops", + "ĠS I", + "ĠS ons", + "Ġp ill", + "ĠT et", + "ĠC ave", + "ĠM ller", + "Ġto wers", + "ĠB ri", + "ĠD unn", + "ĠN gu", + "ce ae", + "ĠK v", + "ĠK iy", + "Ġbe ef", + "ĠCh icken", + "cl iffe", + "ĠEl ton", + "ĠHow e", + "elf th", + "70 6", + "68 1", + "68 2", + "68 6", + "45 6", + "69 6", + "Ġsuc ceeds", + "rol ogy", + "47 6", + "Ġpast or", + "Ġparticip ating", + "ĠWin chester", + "ĠAnat omy", + "Ġporn ographic", + "ĠDais y", + "ĠCly de", + "ĠMiddles ex", + "4 30", + "6 50", + "s u", + "Ġd ense", + "ĠF res", + "ĠD epend", + "ĠN ing", + "ĠU rawa", + "og ram", + "Ġsp ider", + "Ġab und", + "sh an", + "ene ath", + "45 7", + "Ġint ensity", + "47 2", + "47 7", + "Ġ180 2", + "'' '", + "Ġimmedi ate", + "Ġchap el", + "ĠEis enh", + "Ġshel ter", + "Ġorient ation", + "k ping", + "u ana", + "Ġp ets", + "Ġ( '", + "ĠP oints", + "ĠF en", + "ĠW ong", + "Ġg entle", + "ey ed", + "Ġcomp anion", + "ĠAr cher", + "ĠPal ae", + "ĠOr b", + "45 2", + "69 1", + "Ġdesign ation", + "ĠHal ifax", + "St ars", + "Ġprop he", + "ĠGood ricke", + "Ġhom epage", + "Ġreb el", + "Ġoffer ing", + "Ġcarbon ate", + "ĠSerg io", + "G ood", + "W ar", + "ar ina", + "ĠL ucky", + "ĠL ester", + "el ic", + "ot he", + "Ġon wards", + "Ġe cc", + "ra pped", + "00 2", + "Ġr is", + "ern a", + "ĠCom b", + "ĠAm rica", + "ĠX avier", + "64 6", + "Ġ' '", + "ĠSch we", + "Ġevery where", + "Ġref ugees", + "66 1", + "Ġcharacter istic", + "ĠThom son", + "ĠApp lied", + "ĠLand mark", + "Ġcycl ing", + "ĠTa o", + "Ġpred icted", + "ĠOrig ins", + "ĠAz ad", + "ĠFuk ushima", + "Ġspons ored", + "Ġdecre ased", + "bird s", + "Ġdelay ed", + "Ġfart her", + "C al", + "M a", + "S I", + "v ana", + "w ords", + "ĠB oot", + "ĠD ate", + "ĠIn ner", + "ĠY uan", + "ĠSp ike", + "ĠLe igh", + "ib o", + "Ġest imate", + "ier a", + "ĠEm ilia", + "Ġsl ide", + "Ġacc ord", + "Ġcomm emor", + "ĠAnt io", + "ĠMel issa", + "ĠCor b", + "ĠCongress man", + "Ġpres erve", + "ĠDet ective", + "ĠRaj a", + "ĠTak ah", + "Ġsleep ing", + "ĠNort heast", + "Ġarchae ological", + "Ital ian", + "Ġhemorrh age", + "ĠAgre ement", + "3 15", + "Ġo g", + "ig rated", + "ĠP rav", + "Ġto ile", + "ĠR ag", + "ĠH ollow", + "ad an", + "ĠG N", + "ĠW ade", + "Ġst uck", + "oc ated", + "ook ie", + "ev en", + "Ġen vironments", + "90 2", + "90 6", + "berg h", + "ĠHol iday", + "57 4", + "ĠAg nes", + "ĠStev ie", + "Ġinf ant", + "Ġhol idays", + "Ġfir ing", + "ĠMagn us", + "Ġcontinu ing", + "Hung ary", + "ĠFerg uson", + "Ġtermin ology", + "ĠRhy thm", + "f ran", + "en bach", + "ro ads", + "Ġm aid", + "ĠC over", + "ĠM orm", + "ĠP ill", + "ĠH r", + "ĠL ief", + "Ġg ifts", + "Ġe agle", + "eb oard", + "so lete", + "ĠMar ines", + "Ġwar n", + "by ter", + "45 1", + "48 4", + "ĠLab r", + "ĠAnn ual", + "ĠForm ation", + "Ġsecret ly", + "Ġpers pective", + "book s", + "ĠStra uss", + "Ġteen agers", + "Ġsail ed", + "ĠPin oc", + "unc redited", + "rec ce", + "ĠAntar ctic", + "Austral ian", + "Ġsynthes is", + "ĠRapp ers", + "Ġbreat he", + "ĠJacqu eline", + "Ġheadquarter ed", + "3 13", + "K A", + "f recce", + "Ġt oll", + "Ġt sun", + "Ġc ad", + "Ġf u", + "Ġ2 20", + "ĠH aving", + "ĠN ab", + "ĠG og", + "Ġat e", + "rit ic", + "ĠY o", + "Ġac cent", + "ĠCan ucks", + "Ġfl ute", + "47 9", + "Ġtrans m", + "44 6", + "ĠWe imar", + "Ġarchitect ural", + "year s", + "Ġcollect ing", + "ĠAra bs", + "Ġconstant ly", + "Ġsitu ated", + "ĠKyrgyz stan", + "Ġelectromagn etic", + "Ġpriz es", + "ĠEisenh ower", + "3 14", + "B rien", + "G en", + "P ants", + "R om", + "w ara", + "ar oo", + "Ġo ceans", + "ĠS ep", + "ĠS aga", + "ĠP ah", + "ĠB ates", + "ĠB ots", + "ĠF ow", + "ĠG ond", + "ĠG ilm", + "if ter", + "ik es", + "Ġab oard", + "Ġun official", + "ex cept", + "ĠCar m", + "ĠOr ton", + "ĠFl owers", + "Ġla ureate", + "ĠMad ame", + "Ġperform ers", + "ĠBur mese", + "ĠSom ething", + "Ġbar rel", + "ih u", + "war eness", + "ĠAbd ullah", + "Ġagre es", + "ĠBatt les", + "Ġcircum stances", + "ĠChes hire", + "Ġsupern atural", + "Pig ott", + "ĠFrib ourg", + "Ġ icon", + "it iba", + "Ġc av", + "ĠC ork", + "ĠB res", + "ĠF ry", + "ĠN ils", + "ĠG ould", + "est rian", + "ill ac", + "00 5", + "so on", + "ĠAl to", + "Ġkn ife", + "Ġgroup ed", + "ĠEd itor", + "Ġmon ks", + "ĠCup s", + "ane ous", + "Ġcond em", + "ĠSam sung", + "Ġhand ball", + "Ġvict ories", + "Ġmiss ed", + "ĠBos nian", + "Ġmig ration", + "Ġdram atic", + "ĠSylv ia", + "ĠIsab el", + "Ġairpl anes", + "ĠFro st", + "ĠBund estag", + "ĠAus chwitz", + "ĠEsper anto", + "ĠWimb ledon", + "y st", + "Ġt ram", + "an za", + "Ġm ol", + "ĠH T", + "ĠH ast", + "ĠO u", + "ist o", + "ov na", + "Ġe cosystem", + "ĠK oll", + "ĠV og", + "19 14", + "oun ce", + "Ġcon ven", + "Ġsh ipping", + "rib e", + "99 1", + "48 7", + "66 3", + "ĠDon na", + "ĠBy r", + "Ġcoach ing", + "ĠInstit ution", + "Ġboy friend", + "ĠGer ard", + "ingu ished", + "ĠRap ids", + "Ġsculpt ures", + "ĠDog s", + "Ġdon ated", + "Ġwild life", + "ĠCre ation", + "Ġconduct ing", + "Ġhorn s", + "ĠWarri or", + "ĠBright on", + "ĠCris is", + "Ġpropos al", + "Ġevac u", + "Ġfab ric", + "T unes", + "ĠS idd", + "ĠA al", + "le my", + "ĠP apers", + "st and", + "ĠO ra", + "Ġg ap", + "op a", + "ĠK of", + "Ġr ig", + "end ium", + "Ġ7 50", + "ev e", + "ĠPro f", + "ĠItal ians", + "99 3", + "69 2", + "66 6", + "ĠWe ber", + "Ġperiod ic", + "ĠIsland ers", + "ĠHam mond", + "Ġconf usion", + "Ġmurder er", + "Ġprop ag", + "Ġfoot age", + "Ġemerg ed", + "Ġappl ies", + "ĠAP O", + "ĠiP od", + "Ġlymph oma", + "ĠMicha els", + "5 10", + "6 60", + "ĠS iege", + "Ġp od", + "ĠM urders", + "ĠF ors", + "ĠG ia", + "ĠE vel", + "ĠSt all", + "ac on", + "ard e", + "Ġsp okes", + "Ġ9 70", + "Ġ17 85", + "Ġag gressive", + "Ġbl adder", + "99 4", + "ush u", + "Ġdev oted", + "ĠArmen ians", + "ĠTre asure", + "Ġshoot s", + "Ġsignifican ce", + "ĠVic ente", + "Ġtiss ues", + "ĠArrondiss ements", + "ĠCelebr ity", + "Ġmasc ot", + "R O", + "Ġo ath", + "Ġc aves", + "ĠM ales", + "ĠO man", + "Ġg ospel", + "ĠK ens", + "Ġst eep", + "ra its", + "ĠY v", + "aus s", + "ĠAn ita", + "ax ter", + "Ġmar ries", + "ĠWe ap", + "ĠRon nie", + "Ġroy alty", + "Ġmeasure ments", + "Ġserious ly", + "Ġveget ation", + "ĠVa ugh", + "Ġborough s", + "ĠHav ana", + "Ġcass ette", + "ĠKarn ataka", + "ĠGael ic", + "prov ince", + "Ġpredecess or", + "6 40", + "h ini", + "p ath", + "v iol", + "w at", + "Ġt a", + "it as", + "re ens", + "Ġm isc", + "ĠC A", + "ĠC te", + "ĠP eoples", + "ĠB ian", + "ĠH anna", + "ĠF ork", + "ĠD ssel", + "ht t", + "ul ates", + "00 7", + "eb urg", + "Ġar sen", + "ne g", + "ĠSh annon", + "48 2", + "48 8", + "47 8", + "Ġprot ocol", + "44 9", + "ĠMet all", + "ogn itive", + "ĠJul io", + "band ed", + "ĠInt roduction", + "Ġrev ol", + "ĠBon aparte", + "ĠCard iff", + "Ġpun ished", + "onom ic", + "Bl ue", + "ĠBart on", + "ĠSS R", + "commun ications", + "Ġessay ist", + "Ġmerch ant", + "Ġbotan ist", + "ĠPanther s", + "Ġphenomen on", + "G O", + "X X", + "b ys", + "Ġp ier", + "ĠC andy", + "ad ic", + "st roke", + "ag edy", + "Ġbe ats", + "ĠCh rys", + "Ġover d", + "ĠAd ri", + "ĠEm inem", + "99 5", + "Ġprot ons", + "ĠAnt ig", + "Ġmag ical", + "ĠUr s", + "Ġhop ed", + "rac use", + "Ġobserv ation", + "ĠFerr ari", + "ĠTrom s", + "Ġvin yl", + "d imensional", + "f b", + "l ake", + "m ember", + "n ikov", + "Ġt in", + "or ic", + "ĠS uicides", + "as hes", + "ĠC ats", + "il an", + "ĠP ey", + "Ġh ind", + "ĠB ram", + "ch y", + "ĠN ara", + "ĠN ico", + "im ation", + "og ether", + "ach im", + "ĠCom ic", + "Ġme al", + "Ġform ats", + "ĠGu am", + "ĠHer r", + "78 8", + "ĠTra vis", + "ĠWe in", + "ĠMat th", + "ĠTre es", + "Ġlo ad", + "Ġburn s", + "Ġsail or", + "ĠPatri ots", + "Ġinher it", + "ĠCyr illic", + "Ġsubsequ ent", + "Ġsubmar ine", + "U T", + "h ara", + "j ar", + "n ake", + "s urname", + "Ġb isexual", + "Ġc it", + "Ġc rop", + "Ġp epper", + "Ġh ub", + "ĠJ ol", + "ĠU ly", + "ich ael", + "Ġr h", + "Ġr ally", + "rop olis", + "ons cious", + "ĠZ elda", + "Ġcar b", + "Ġsub way", + "Ġam endment", + "99 7", + "Ġposs ibility", + "azz o", + "ynast ies", + "ĠCoast al", + "ĠHamp ton", + "Ġarg uments", + "ĠLaur ence", + "Ġtarg ets", + "ĠRena ult", + "opter a", + "ĠLion el", + "ĠJoy ce", + "Ġcommission ed", + "Ġdecre ase", + "ĠTitan ic", + "ĠCowboy s", + "ĠHert ford", + "ĠGior gio", + "Ġa unt", + "Ġw ick", + "Ġs am", + "Ġc ure", + "ĠT uc", + "Ġd ressed", + "am orph", + "ĠH ust", + "ch ner", + "Ġe col", + "ort ed", + "Ġr ider", + "sit e", + "ĠAn ime", + "Ġ17 07", + "Ġbut ton", + "Ġmus k", + "Ġme g", + "Ġrec ru", + "ĠEl m", + "ĠGr ampus", + "ĠHar bour", + "45 4", + "45 9", + "69 4", + "ĠTom orrow", + "ĠVict ory", + "ĠVol ks", + "Ġsepar ately", + "Ġsqu ares", + "ĠInter active", + "Ġer as", + "ĠEvery thing", + "Ġfle e", + "ĠTreat ment", + "bro ther", + "ĠEsc ape", + "Ġrot ation", + "Ġgather ed", + "Ġlocomot ive", + "ĠHus sein", + "n io", + "ĠC M", + "ĠC reed", + "ĠP upp", + "ĠR ising", + "ĠG aza", + "00 8", + "ew ell", + "og i", + "Ġch ron", + "ĠMar qu", + "qu al", + "form ing", + "ĠEm m", + "ĠEn rique", + "57 3", + "66 2", + "ĠChe rok", + "Ġpost hum", + "ĠBet ter", + "ĠRaj as", + "Ġneigh b", + "ĠFa ust", + "ĠNag ar", + "Ġdro ught", + "ĠHu bert", + "ĠJen kins", + "ĠCherok ee", + "4 60", + "Ġb ark", + "ĠS ept", + "Ġf ed", + "ĠA B", + "ĠB rett", + "Ġl ord", + "ĠF unk", + "ad eth", + "ĠN iz", + "ĠN ih", + "ĠG uns", + "ian ism", + "ut i", + "00 6", + "ast ed", + "qu ito", + "ey e", + "Ġtra p", + "Ġmon sters", + "Ġsk iers", + "eal ous", + "44 4", + "ĠTra ins", + "Ġresp ected", + "Ġdel ivery", + "ĠBang kok", + "ĠUnivers ities", + "ĠSav age", + "ĠWeb b", + "Ġdeal ing", + "ĠExt ended", + "Ġsail ing", + "imens ions", + "ĠChron icles", + "ĠArchae ological", + "ĠDaw son", + "Tok yo", + "ĠNewsp apers", + "1 19", + "h ma", + "z illa", + "Ġt ap", + "Ġt ales", + "ar est", + "ed ge", + "Ġp or", + "ĠM itch", + "ion ale", + "ol ulu", + "ĠP ant", + "ĠF lynn", + "ĠW ave", + "ĠW ake", + "th ou", + "ht on", + "ul ance", + "ĠV ert", + "ord inary", + "ĠTh irteen", + "Ġj ersey", + "ĠSh iv", + "ĠFor rest", + "ĠPl us", + "Ġind icated", + "eed ing", + "ĠBel t", + "Ġhigh ways", + "Ġdevelop ers", + "Ġleg ends", + "Ġpass ion", + "Ġav i", + "be at", + "Ġsett le", + "Ġknow ing", + "ĠBoy d", + "ĠFort une", + "Ġsat ir", + "ĠWik i", + "edd y", + "ĠRick y", + "ĠOw ens", + "German y", + "Ġcalcul ated", + "Ġvac uum", + "ĠCrom well", + "ĠDssel dorf", + "G r", + "u ya", + "an iel", + "Ġc ipher", + "ĠS aid", + "ĠD ram", + "st ory", + "st able", + "ĠJ ob", + "ow itz", + "up e", + "ĠPro b", + "uk ary", + "ĠGl ory", + "Ġexp osure", + "hi ro", + "Ch inese", + "Ġmot iv", + "Ġimm igration", + "Ġmission ary", + "arl berg", + "Ġsurr endered", + "ĠBrem en", + "B ay", + "H er", + "N ord", + "b ank", + "t itled", + "in om", + "ar ck", + "ing a", + "ĠR ih", + "ĠD und", + "ĠK ram", + "Ġan xiety", + "ĠV ad", + "ĠZ am", + "99 8", + "Ġint ent", + "58 9", + "Ġpubl ishes", + "ump s", + "ĠPet rov", + "ĠHon olulu", + "Ġsport ing", + "Ar t", + "ĠCas ey", + "Ġwalk ed", + "ĠOm aha", + "Ġsulf ate", + "Ġmoment um", + "ĠAna heim", + "Ġpupp et", + "Ġhorm one", + "ĠPrepar ation", + "ĠMozamb ique", + ") (", + "5 15", + "5 70", + "7 10", + "N L", + "ĠT ik", + "Ġ2 30", + "Ġd iversity", + "ĠR az", + "ĠL ub", + "ĠL ai", + "ĠN ominated", + "ĠG M", + "ĠW ide", + "ĠK ick", + "Ġan ts", + "Ġv ibr", + "ĠUn its", + "Ġ7 07", + "Ġfilm ing", + "Ġlight ning", + "Ġprom ise", + "iy ah", + "ĠFern and", + "ĠLibert arian", + "rup ted", + "Ġreserv es", + "ĠPack ers", + "ĠLear ning", + "Ġvul ner", + "4 80", + "O h", + "a an", + "Ġt ough", + "ar ma", + "ĠT ian", + "ĠM im", + "ad ier", + "ĠN ue", + "ĠO sh", + "00 9", + "Ġ9 40", + "Ġ17 40", + "Ġ17 84", + "ĠSh ot", + "Ġph on", + "ger ald", + "ĠSy racuse", + "Ġcharacter ized", + "Ġaut o", + "ĠCatholic ism", + "Ġsports caster", + "Ġprim itive", + "Ġliter acy", + "ĠLuc erne", + "Ġder iv", + "ĠFil ipp", + "ĠSerge ant", + "Ġmist ake", + "Con nor", + "Ġmir ror", + "Ġsupercent enarian", + "ĠLief ering", + "c ue", + "c ode", + "j av", + "s low", + "er land", + "ic hel", + "Ġh az", + "om as", + "ia k", + "Ġsh orts", + "ass es", + "Ġcomp act", + "Ġ9 10", + "ĠCan n", + "ĠCar th", + "Ġnew er", + "Ġed iting", + "47 1", + "Ġvar iab", + "Ġindepend ently", + "Ġz inc", + "ĠFe ature", + "ĠBon n", + "ĠPlan ets", + "Ġnar rator", + "Ġmanufact ured", + "Ġmanufact urers", + "ĠVas ily", + "Ġexhib itions", + "Ġaster oids", + "ĠRum ble", + "Ġcasual ties", + "ĠStephan ie", + "4 10", + "H z", + "h alt", + "t ailed", + "y ar", + "Ġw olf", + "Ġs ep", + "Ġb rew", + "Ġf ee", + "ĠP ius", + "ĠE z", + "ĠE ure", + "and an", + "eb u", + "ĠCh rom", + "Ġpro hib", + "ĠTh reat", + "ĠAl ain", + "ik er", + "Ġfam iliar", + "Ġvers es", + "44 2", + "44 3", + "Ġgen ome", + "ĠAnt lers", + "ĠCont rovers", + "Ġbox es", + "ĠSax on", + "Ġmonarch s", + "Ġens ure", + "Ġastronaut s", + "Portug uese", + "5 13", + "7 50", + "f ur", + "l oy", + "am ous", + "ĠH ilton", + "ct uary", + "ĠF arn", + "ĠG ad", + "ĠK us", + "Ġpro ceed", + "Ġch orus", + "ah r", + "Ġac res", + "anc ock", + "Ġrel ax", + "Ġam mon", + "oph one", + "57 1", + "54 6", + "Ġarch bishop", + "ĠEr in", + "ĠAirport s", + "Ġmiss ile", + "ĠBol lywood", + "Ġemploy ee", + "Ġjun ction", + "Ġdisab led", + "Ġinstall ation", + "ĠTerrit ories", + "feat uring", + "3 22", + "8 10", + "E nd", + "Ġa ware", + "or um", + "ĠB enson", + "ĠR uther", + "ĠL ily", + "ir m", + "ĠJ ude", + "un ge", + "ĠK ak", + "Ġst ating", + "os ely", + "ac in", + "ort ex", + "Ġsp ac", + "ĠSh am", + "ĠFl u", + "ĠPart ners", + "ung a", + "Ġadd s", + "oph il", + "58 2", + "ĠCount ess", + "ĠInter continental", + "Ġclos ing", + "ĠPu y", + "Ġfact o", + "ĠPres byter", + "zy me", + "ĠKat ie", + "Ġvert ebrates", + "Ġdoll ar", + "ĠSoph ia", + "Ġsurf aces", + "rehens ive", + "izophren ia", + "3 90", + "5 12", + "5 90", + "j or", + "ĠC able", + "ĠI st", + "ĠR C", + "ĠD F", + "Ġn oun", + "19 11", + "qu iry", + "und ers", + "ĠNew port", + "ĠOr t", + "Ġpre historic", + "Ġmar gin", + "Ġcons umer", + "iew icz", + "che on", + "Ġinv ade", + "99 2", + "58 8", + "oe k", + "44 1", + "Ġbeh alf", + "Ġcor al", + "ĠGal ile", + "Ġsexual ity", + "Ġfund ing", + "ĠSquare Pants", + "ĠHem ings", + "ĠBox ing", + "Ġthrow ing", + "ĠNeg ro", + "Ġdict ator", + "Ġmilit ia", + "ishn u", + "Ġsovereign ty", + "ĠVor arlberg", + "Ġturt le", + "Ġcareful ly", + "J ust", + "on te", + "es p", + "Ġc orporation", + "ĠT ut", + "Ġd ried", + "Ġh ate", + "ĠB ing", + "art en", + "19 12", + "ĠY us", + "ĠTh ink", + "Ġ17 68", + "Ġ17 83", + "pr ises", + "ĠCl iff", + "57 8", + "Ġext ent", + "Ġext ends", + "ĠCont in", + "ĠCamp us", + "Ġpres ents", + "ĠTim eline", + "ĠCam den", + "Ġpay ment", + "ĠMid night", + "ĠNag asaki", + "Ġfort ress", + "ĠWor cester", + "ĠFitz gerald", + "Ġcarn ivore", + "Ġmanusc ript", + "Ġwithdraw al", + "ĠNatal ie", + "ĠStur m", + "ĠProble ms", + ". [", + "b est", + "z k", + "or is", + "ĠR T", + "ĠR ide", + "ĠH ess", + "ĠN umbers", + "Ġhe x", + "Ġcom ments", + "ore an", + "ĠAl ien", + "ik on", + "mun ition", + "ĠAs c", + "ĠPr ide", + "ĠPart icip", + "Ġsub prefecture", + "Ġmother s", + "ĠAct s", + "cul osis", + "Ġren al", + "Ġhero es", + "ĠMeg adeth", + "ĠProtest ants", + "Ġplate au", + "ĠMaid en", + "Ġrestrict ions", + "S V", + "S om", + "en ov", + "Ġin k", + "Ġc ens", + "ĠC ron", + "il ion", + "ĠP to", + "am el", + "ĠH itch", + "Ġl ens", + "ĠD ante", + "ht er", + "op ters", + "av ior", + "ard ed", + "Ġ17 58", + "ould er", + "son zo", + "Ġra bb", + "Ġrec overy", + "ĠDe al", + "ĠCal der", + "air a", + "ĠOr lans", + "Ġnatural ist", + "Ġachie vement", + "Ġbuy ing", + "ĠOcc up", + "Ġhydro xide", + "ĠHous ing", + "Ġimprison ment", + "Ġadvoc ate", + "eon ato", + "Ġvag ina", + "3 12", + "R L", + "b oth", + "h uman", + "s ous", + "Ġa ims", + "ĠC any", + "ĠB ant", + "ĠD over", + "ĠG le", + "ĠW en", + "Ġg amb", + "ĠK uz", + "Ġst icks", + "ter a", + "Ġ7 27", + "Ġout standing", + "ĠPar an", + "ĠTe j", + "ĠSe al", + "Ġrem oving", + "Ġlast ing", + "58 7", + "95 9", + "ĠAcadem ic", + "ĠSim mons", + "ĠBrown s", + "ĠSub way", + "ĠCur ry", + "Ġtemp or", + "Ġterror ism", + "Ġjew el", + "Ġhem isphere", + "ĠFellows hip", + "ĠAndor ra", + "8 30", + "B enz", + "m ay", + "al p", + "ĠS ens", + "ĠS anga", + "ĠI UCN", + "ĠB ord", + "ĠB unny", + "ĠH ons", + "ĠL ing", + "Ġbe am", + "Ġor phan", + "20 1", + "Ġest imates", + "ĠCal iph", + "Ġass emb", + "Ġleg acy", + "57 9", + "97 8", + "96 2", + "Ġposs ession", + "Ġpost s", + "Ġreb els", + "Ġlands l", + "ĠBrid g", + "Ġharm ful", + "Ġaqu atic", + "Ġtub es", + "ĠInsp ector", + "Ġrect ang", + "ĠStras bourg", + "E redivisie", + "G reen", + "c ie", + "l ant", + "p ubl", + "he ll", + "al ong", + "ĠThe me", + "ir n", + "ĠW yn", + "ag os", + "ass a", + "cl er", + "ĠCom ing", + "Ġ15 90", + "Ġtr adem", + "ĠEl s", + "Ġdec oration", + "Ġint ention", + "Ġdef ine", + "ĠDr um", + "Ġcy an", + "ĠHon our", + "ĠAnn abeth", + "ĠMat ilda", + "Ġi Tunes", + "Ġpromot ing", + "ophy ll", + "ĠBol iv", + "rap ers", + "Ġattempt ing", + "ĠMand arin", + "thes is", + "Ġven ues", + "Se ine", + "Ġkit chen", + "ĠDort mund", + "5 14", + "D utch", + "b irth", + "j atjara", + "on ey", + "Ġh ab", + "ĠH ash", + "st ick", + "Ġfor th", + "ec ution", + "ul ating", + "ĠSh ab", + "Ġchar ter", + "ĠAss am", + "Ġinc orrect", + "ĠDes c", + "ĠGeorg etown", + "Ġav o", + "ĠArch d", + "Ġprim aries", + "Ġinh ab", + "ĠJon as", + "Ġdisp utes", + "Ġworship ped", + "Ġphilanthrop ists", + "Ġfemin ism", + "Ġvess el", + "ĠCitiz ens", + "ĠBabyl on", + "Ġvoy age", + "ĠKonstant in", + "6 30", + "G eor", + "m ap", + "Ġl ays", + "Ġth y", + "Ġg ig", + "Ġg ates", + "ra j", + "19 15", + "ĠZ ak", + "Ġund erneath", + "ze k", + "its u", + "ĠBe an", + "omet ime", + "98 7", + "97 4", + "ĠDo om", + "ĠLin z", + "ĠParliament ary", + "ĠSil ent", + "Ġminist ry", + "Ġarg ue", + "ĠShar p", + "atsu ura", + "ĠNort e", + "ĠLamb org", + "Ġnoble man", + "Ġunderg r", + "wid th", + "eckl enburg", + "6 70", + "P G", + "c over", + "e eches", + "y ch", + "or ient", + "ĠT art", + "ĠM ega", + "Ġd uck", + "Ġh unter", + "ĠH utch", + "Ġl ip", + "ĠD ug", + "ĠD ial", + "ĠD uff", + "ĠG ore", + "ĠJ oo", + "ĠV eh", + "Ġor e", + "Ġsp ell", + "Ġsh irt", + "Ġ17 86", + "inn ers", + "az y", + "ĠCon way", + "ĠGr aduate", + "Ġair s", + "af i", + "Ġ0 3", + "Ġsom ewhere", + "Ġmot to", + "ĠFred dy", + "Ġcell o", + "Ġcolon ists", + "Ġsignificant ly", + "ĠFle ming", + "ĠDest iny", + "Ġchalleng ed", + "Ġautobi ographers", + "ĠCed ar", + "ĠSunn i", + "ĠCarne gie", + "l ich", + "in ally", + "is an", + "it on", + "ĠS odium", + "Ġd rops", + "ĠL inn", + "ch id", + "Ġg est", + "im oto", + "ain able", + "ich o", + "ost al", + "Ġmain tenance", + "Ġprov en", + "ject ive", + "Ġdevelop s", + "98 1", + "ĠReg ent", + "ĠSoviet s", + "55 6", + "Ġdesc ended", + "Ġter restrial", + "Ġrefer ring", + "ĠCamp eonato", + "ĠRos es", + "ĠIv ory", + "ĠObserv ances", + "ĠMerc ia", + "Ġresc ued", + "Ġrebu ild", + "7 40", + "8 40", + "l ies", + "s ize", + "s ils", + "v ation", + "z ymes", + "an u", + "ĠT ort", + "ĠC rd", + "ĠM ons", + "ĠP oc", + "ĠD art", + "ĠD ex", + "Ġn ests", + "Ġst uff", + "ĠU ll", + "od us", + "ĠV alais", + "Ġcon cluded", + "ĠY es", + "ĠMar i", + "ern s", + "ind ers", + "ĠZ el", + "ĠBr at", + "Ġear ning", + "Ġmet ro", + "Ġsl ower", + "Ġvar ied", + "Ġvar iants", + "reg ular", + "Ġstruct ural", + "ĠRob b", + "Ġtour ing", + "Ġbar rier", + "ĠLam bert", + "Ġdistinct ive", + "ĠPubl ishers", + "Ġintegr ated", + "ĠNig el", + "ĠOliv ier", + "6 80", + "G ermain", + "c us", + "n itz", + "r age", + "er re", + "ĠS ale", + "Ġf itness", + "Ġm im", + "ĠC yp", + "ĠD ix", + "ĠD ixon", + "Ġre cept", + "ĠTh irty", + "Ġsh ots", + "ĠSc o", + "ĠAss yr", + "ĠMes opotam", + "Ġtrans mitted", + "58 5", + "ĠTra ff", + "Ġspecial ist", + "ĠBo om", + "Ġmass es", + "ashi wa", + "ĠFer ry", + "ĠHistor ians", + "ĠLind say", + "Ġabs ence", + "Ġsusp ect", + "Ġcomment ary", + "ĠGuy ana", + "ĠPa olo", + "Ġperman ently", + "Ġbatter ies", + "ĠCzechoslov ak", + "ĠPiet ro", + "ĠArtem is", + "- )", + "z in", + "it uary", + "ĠM ood", + "ĠM ile", + "ig hed", + "ĠH ancock", + "ct al", + "Ġn ud", + "ĠG is", + "ĠJ ab", + "ec he", + "ĠCh ir", + "ĠCh ow", + "ĠTh ur", + "ik ing", + "Ġ9 30", + "Ġdep icted", + "Ġbl amed", + "Ġsup reme", + "Ġass ets", + "97 1", + "ĠSl ayer", + "ĠBen in", + "unt ed", + "Ġtrain er", + "Ġparticip ation", + "ĠTem per", + "Ġbus y", + "Ġthreat s", + "Ġdepend ent", + "WW E", + "Ġstrengthen ed", + "ĠVoy ager", + "ĠHumph rey", + "5 80", + "T here", + "t ical", + "w ari", + "Ġc raft", + "ĠS d", + "ĠS ales", + "as hed", + "ĠT ow", + "Ġm ood", + "Ġ2 10", + "ĠM ud", + "ĠB ooth", + "ĠF ey", + "Ġre verse", + "ĠV eget", + "ĠAn alysis", + "te am", + "iss an", + "Ġshe ar", + "Ġoff spring", + "ĠPro gress", + "Ġpr inces", + "ĠPh antom", + "Ġend ors", + "Ġsm oking", + "Ġmet ab", + "Ġleg it", + "Ġeffect ively", + "ĠTim ber", + "Ġprotect ing", + "ĠEver ett", + "Ġbank ing", + "ĠTar zan", + "Ġveget able", + "ĠAM O", + "Ġrif les", + "Ġinaug uration", + "Ġsmart ph", + "Ġmanusc ripts", + "Ġmidfield ers", + "0 60", + "5 40", + "v re", + "an um", + "Ġb rack", + "Ġc ounsel", + "Ġp ublish", + "ĠP athan", + "ĠB otan", + "el ong", + "Ġl unar", + "ĠU pp", + "ĠSt range", + "ĠCh am", + "ĠY el", + "hen t", + "Ġ15 60", + "ĠPar ade", + "ĠBro ken", + "ĠEx ternal", + "fl at", + "Ġmat rix", + "97 2", + "58 1", + "95 1", + "sc reen", + "Ġexpl orers", + "ĠGall en", + "ĠBab a", + "ĠFern ndez", + "ĠAnat olia", + "ĠHed ge", + "Ġcooper ation", + "ĠCaucas us", + "0 50", + "0 80", + "8 21", + "d et", + "l ook", + "Ġ >", + "le igh", + "Ġan ger", + "ud er", + "ric o", + "ĠBr end", + "Ġcar riers", + "Ġide ology", + "ĠAv engers", + "Ġsequ ences", + "ĠAle j", + "ĠSn chez", + "rang ement", + "Ġcontroll er", + "Ġreve als", + "ĠGran ada", + "Ġsaxoph onist", + "ĠMarath on", + "Ġneighbour ing", + "ĠSched ule", + "ĠRuther ford", + "0 24", + "6 33", + "s ons", + "Ġ uter", + "ĠS op", + "Ġp our", + "ĠT ope", + "ĠC ic", + "ĠP ip", + "om ens", + "ĠW ink", + "ht i", + "ber ra", + "ag ger", + "ill i", + "if u", + "19 13", + "ab is", + "sp orts", + "ĠCom bat", + "act ions", + "inn y", + "ĠSc out", + "ĠSc ream", + "ĠPr int", + "ĠPr inces", + "Ġcent ered", + "Ġrem oval", + "57 2", + "Ġob vious", + "ĠRes cue", + "94 6", + "ha o", + "ĠGo ing", + "ĠMel an", + "Ġche aper", + "Ġclaim ing", + "Ġcost ume", + "Ġflood ed", + "ĠSix th", + "Ġstream ing", + "ĠBry ant", + "Ġju ice", + "4 12", + "d ong", + "h ouses", + "l og", + "l und", + "n u", + "Ġt ension", + "as ian", + "as se", + "ĠM D", + "il ight", + "et ically", + "ĠN S", + "ĠW ere", + "ra f", + "ĠV ac", + "ie ves", + "Ġ17 30", + "Ġdis charge", + "ĠPr att", + "Ġam pl", + "Ġint est", + "98 5", + "58 3", + "94 9", + "ĠDiv isions", + "ĠUs her", + "Ġboard s", + "ĠGi ul", + "Ġterror ists", + "Ġans wers", + "ĠAmaz ing", + "ithm etic", + "Ġabsor bed", + "Ġtornado es", + "ĠPatt erson", + "Ġreprod uce", + "ĠEff ects", + "Wh ite", + "ĠRaid ers", + "ĠConcer to", + "Ġtempor arily", + "7 13", + "M ayer", + "T O", + "U P", + "e astern", + "y am", + "z u", + "Ġt atto", + "ĠA ks", + "ĠT b", + "ĠP ik", + "ĠH yl", + "ĠD ro", + "ot r", + "ĠG us", + "qu i", + "ĠEd it", + "omet er", + "98 4", + "97 7", + "58 4", + "Ġwant ing", + "55 3", + "94 2", + "Ġmy el", + "ĠThat cher", + "ĠMem ory", + "rang ers", + "ĠJenn ings", + "ĠKey s", + "ache v", + "ĠBurg undy", + "Ġbul lets", + "Ġgrav itational", + "ĠAssoci ate", + "Ġfil ter", + "abul ary", + "ĠBots wana", + "5 20", + "5 96", + "7 15", + "7 14", + "N D", + "P ac", + "g reat", + "Ġ ell", + "ar b", + "it ely", + "ĠP omp", + "ĠB old", + "ĠD ord", + "est own", + "Ġv ib", + "Ġch ol", + "ĠAl one", + "Ġex chang", + "ĠFl ower", + "ĠX en", + "ste en", + "54 3", + "Ġtreat ments", + "ĠSn ake", + "Ġdraw s", + "Ġcoup les", + "ĠTheres a", + "fortun ately", + "5 21", + "8 60", + "8 70", + "I sonzo", + "f ront", + "g ood", + "m our", + "y on", + "is ible", + "Ġc ob", + "ĠA e", + "ĠB ick", + "ĠD ino", + "ĠE up", + "ĠW itt", + "ĠW CW", + "ĠK ah", + "ĠHe ar", + "Ġv om", + "Ġ8 88", + "ĠCan berra", + "Ġsec ular", + "Ġbl ank", + "ĠFl at", + "ĠTe k", + "Ġdifferent ial", + "ĠAm ber", + "ink a", + "Ġinter action", + "97 3", + "Ġequ ator", + "ĠEr ich", + "Ġinf inite", + "Ġrail ways", + "ĠProt ocol", + "ĠOper ations", + "ĠLow e", + "Ġinte ger", + "Ġunc onscious", + "ĠWhe eler", + "ĠSumm ary", + "Ġpup ils", + "ĠRodrig uez", + "ĠSter ling", + "Ġvit amin", + "Ġpros ecut", + "ĠLamborg hini", + "0 90", + "0 70", + "5 50", + "8 90", + "ar ov", + "ĠS it", + "ĠT rial", + "ĠI ro", + "ĠW ife", + "ĠK iller", + "ĠK iev", + "ld on", + "ore ctal", + "ĠTh uring", + "Ġen h", + "ĠPar ma", + "its ch", + "ĠBra un", + "ĠComp anion", + "ĠPet erson", + "aly pt", + "ĠGar rett", + "Ġcirc uits", + "acter ia", + "osh ima", + "ĠSev enth", + "ĠDown town", + "ĠCirc um", + "ĠAlfred o", + "Ġopt ions", + "ĠCour se", + "Ġexplos ive", + "Ġdrain age", + "Ġattrib uted", + ". ;", + "5 23", + "H am", + "d oc", + "p ol", + "s z", + "Ġs id", + "ĠA ston", + "ĠC ertain", + "ĠC umm", + "ĠM oy", + "ĠI MD", + "ĠR aven", + "ĠN olan", + "ĠK ik", + "ign an", + "Ġ\" \"", + "Ġr idge", + "Ġind oor", + "95 3", + "94 3", + "Ġgot ten", + "ĠRev enge", + "Ġhon ours", + "ĠCor inth", + "ĠMat ter", + "ĠDef in", + "ĠBow ie", + "Ġpage ant", + "Ġprevent ed", + "ĠReb ellion", + "ĠPrem iers", + "Ġstrateg ic", + "ĠEstab lishments", + "Ġtrig ger", + "Que en", + "ĠPorts mouth", + "0 10", + "7 30", + "he lp", + "on an", + "Ġf ins", + "Ġp m", + "ĠL una", + "Ġl v", + "ĠG um", + "ĠG ron", + "ĠE ly", + "ĠO uter", + "ra wn", + "Ġse iz", + "Ġ17 63", + "Ġtw entieth", + "ĠSh ia", + "ock ing", + "Ġdis ks", + "ĠCan ary", + "enn ial", + "Ġrem n", + "98 9", + "97 5", + "55 5", + "Ġsw allow", + "Ġequ ally", + "ĠLat ino", + "Ġround ed", + "Ġvir al", + "Ġoccup ations", + "Sp ace", + "Ġpoison ous", + "0 30", + "p os", + "Ġp ub", + "Ġp ound", + "Ġm ating", + "ĠC ot", + "ĠM O", + "ĠM ai", + "ĠM ys", + "Ġl amp", + "ĠD ir", + "ĠG erm", + "ĠK olk", + "ill iant", + "ĠCh it", + "ĠCh ung", + "Ġr ay", + "ib ilities", + "form ers", + "Ġind irect", + "Ġsl ang", + "ĠPer kins", + "lin er", + "Ġtrans c", + "95 2", + "ĠAp ache", + "Ġer otic", + "ĠGab on", + "Ġcontroll ing", + "ĠAlexand re", + "ĠTel ugu", + "Cl ass", + "ĠDist inguished", + "Ġspr inter", + "Lou is", + "ĠFrancon ia", + "ĠShre k", + "ĠLabr ador", + "5 45", + "G I", + "G overn", + "l ights", + "ar ius", + "Ġb rick", + "ĠA V", + "ĠT cha", + "ĠC ody", + "ĠM um", + "ou b", + "ef ield", + "ĠW ine", + "ĠK ras", + "ul ous", + "ĠHe w", + "Ġch im", + "Ġ16 80", + "Ġcol orectal", + "Ġbl ade", + "Ġspec imens", + "sh i", + "54 2", + "ĠSim one", + "ĠBo one", + "ĠFar ra", + "Ġmiss iles", + "Th at", + "Ġtal uk", + "ĠHo hen", + "ĠLev y", + "ĠRand olph", + "ĠAur ora", + "Ġproced ure", + "Ġsou p", + "ĠTajik istan", + "ĠYoun ger", + "ĠOkin awa", + "b cken", + "d ig", + "j ac", + "w ent", + "Ġt unnels", + "Ġs urnames", + "Ġc rystal", + "ĠM aterial", + "Ġd ictionary", + "Ġh arsh", + "Ġl ava", + "ĠF i", + "ĠG era", + "ĠW illem", + "and i", + "ity a", + "ĠZ ack", + "ĠCar ac", + "ĠPar a", + "98 2", + "95 5", + "96 3", + "ĠHon ors", + "ĠFred die", + "ĠHill ary", + "ĠAtt acks", + "Ġdom est", + "Ġconcent rated", + "Ġham let", + "Ġplac ing", + "ĠCoal ition", + "ĠYad av", + "ĠCany on", + "3 24", + "is ode", + "ĠM ob", + "ĠR ig", + "ĠH air", + "ĠN aj", + "st ral", + "ow l", + "ĠU st", + "Ġbe es", + "Ġv ine", + "Ġsp here", + "Ġsh ogun", + "ĠShe pherd", + "Ġ17 64", + "Ġpop es", + "erm aid", + "ron ym", + "Ġqu ot", + "ĠAfter wards", + "Ġexp elled", + "98 8", + "ours elf", + "Ġfail s", + "Ġconf idence", + "oz a", + "56 2", + "Ġprocess ors", + "Ġge ology", + "Ġins ert", + "Ġdiss olve", + "Ġexc av", + "Ġsacr ifice", + "ĠPione er", + "ĠBaloch istan", + "ĠNeust adt", + "0 27", + "4 20", + "C ity", + "J P", + "s n", + "t it", + "Ġt ired", + "ĠC z", + "ĠM itt", + "ĠR iding", + "ĠH ogan", + "ir ang", + "ir ling", + "ĠF ol", + "ĠF erm", + "ec uted", + "sp ur", + "ĠSh u", + "Ġac know", + "Ġreg ister", + "Ġmet aph", + "ĠRe ality", + "ĠX II", + "Ġcompos itions", + "ĠDr agons", + "95 4", + "ĠSam i", + "49 3", + "46 1", + "Ġmix t", + "ĠSun shine", + "Ġcop ied", + "ĠDar ling", + "Ġfav our", + "iling ual", + "ĠKab ul", + "Ġri ot", + "Ġwarri ors", + "ĠPlate au", + "gra ve", + "auth or", + "Ġprosp er", + "7 12", + "G reat", + "f al", + "f rey", + "p it", + "u ire", + "Ġa wareness", + "in ery", + "Ġf unny", + "as ants", + "ĠT ich", + "et ers", + "ĠN im", + "ĠG ent", + "ĠW et", + "ĠIn to", + "ĠLe vi", + "per t", + "Ġj ung", + "Ġ17 65", + "ĠPh one", + "ys s", + "sh aw", + "ĠMon ument", + "96 1", + "Ġhuman itarian", + "Ġmount ed", + "ĠMac Donald", + "Ġcur ved", + "ĠNep ali", + "Ġelev enth", + "chen ko", + "epp ers", + "ĠClay ton", + "prod ucer", + "Ġfemin ists", + "ouncill ors", + "0 23", + "M ex", + "P ol", + "j oin", + "y cl", + "Ġs ear", + "ic ist", + "ĠS amp", + "ĠM B", + "ĠM ih", + "ĠL A", + "ĠF ury", + "ĠV ie", + "ant ly", + "aw ks", + "Ġ17 74", + "Ġ17 79", + "ĠPh arm", + "ĠMus e", + "ĠFl int", + 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+ "ĠI s", + "ĠP r", + "ĠC l", + "Ġm ore", + "ĠC an", + "ĠA pril", + "Ġ im", + "ag ue", + "ur y", + "ĠF ebruary", + "res ident", + "un e", + "Ġd o", + "Ġof f", + "Ġw hen", + "Ġ up", + "if e", + "o ol", + "c k", + "Ġpolit ician", + "e ak", + "ĠW orld", + "Ġd ire", + "he s", + "re at", + "Ġp op", + "ĠP ro", + "e g", + "Ġ ra", + "Ġp r", + "Ġp er", + "ĠJ u", + "Ġc ount", + "1 0", + "i x", + "b um", + "ro w", + "|| ||", + "Ġe v", + "Ġ est", + "Ġte am", + "Ġc ent", + "ĠJ oh", + "h ip", + "f t", + "Ġre c", + "p t", + "o in", + "i er", + "as e", + "ĠBrit ish", + "Ġre g", + "ĠL iving", + "he re", + "en n", + "Ġb et", + "ĠW ar", + "Ġa pp", + "Ġbec ame", + "in ist", + "Ġo ut", + "us s", + "Ġ199 9", + "ig h", + "Ġthe m", + "l l", + "ĠC ar", + "ivers ity", + "Ġn um", + "i et", + "in s", + "Ġf am", + "Ġo ver", + "og ra", + "Ġyear s", + "an n", + "an ce", + "v el", + "ist ric", + "ur n", + "ĠFran ce", + "os e", + "ĠD e", + "ĠB r", + "Ġ200 2", + "Ġn ew", + "il y", + "Ġcom mun", + "ĠK ing", + "Ġactor s", + "Ġal bum", + "Ġ20 20", + "Ġpro d", + "ĠJu ly", + "ic ip", + "at t", + "Ġn ame", + "Ġser ies", + "Ġs ome", + "Ġ19 2", + "ĠJoh n", + "ĠS w", + "ĠA t", + "ĠG e", + "Ġgro up", + "Ġs y", + "at ch", + "Ġp h", + "Ġpop ul", + "Ġf l", + "Ġd ep", + "ĠC ol", + "Ġs o", + "Ġplay ed", + "Ġest ab", + "ĠA nd", + "ĠY ork", + "le y", + "Ġc ol", + "Ġe lect", + "ĠP h", + "en er", + "Ġd if", + "a j", + "Ġre le", + "1 7", + "ĠB l", + "Ġon ly", + "al e", + "im es", + "is m", + "Ġth an", + "Ġs ec", + "ĠP ar", + "we en", + "e ver", + "Ġd es", + "a i", + "i am", + "ĠF or", + "ot h", + "Ġh im", + "Ġ Q", + "ĠLe ague", + "Ġl ar", + "u k", + "Ġst art", + "ic ial", + "ar s", + "ĠS outh", + "ĠE u", + "ab le", + "ist ory", + "Ġplay er", + "Ġat t", + "ro und", + "res ent", + "Ġb u", + "ĠJ une", + "ĠP l", + "r ed", + "ĠAust ral", + "ĠC al", + "er t", + "ug h", + "ow er", + "k s", + "ĠIt al", + "om an", + "Ġfor m", + "1 5", + "ou se", + "ĠP al", + "Ġb l", + "a ir", + "ri b", + "m on", + "Ġst ud", + "ogra ph", + "ren ch", + "ĠUn iversity", + "Ġt r", + "ur es", + "d er", + "el y", + "\" .", + "ĠM us", + "i el", + "em b", + "et t", + "1 6", + "iv ed", + "ang u", + "an c", + "ch ool", + "v e", + "ces s", + "1 4", + "ĠC on", + "ĠCan ad", + "lish ments", + "Ġbet ween", + "y s", + "1 3", + "istric t", + "icip al", + "Ġbec ause", + "1 2", + "ĠThe re", + "ic a", + "ĠP eople", + "Ġt ra", + "ĠC ity", + "er m", + "w ay", + "r on", + "ĠF rench", + "Ġst ate", + "ĠA d", + "i ent", + "un icipal", + "at her", + "19 8", + "ion al", + "ĠE d", + "Ġg o", + "Ġnum ber", + "ĠR ep", + "Ġag ain", + "ubl ic", + "ot her", + "as on", + "v ed", + "ĠN ational", + "in al", + "Ġw here", + "Ġd uring", + "rop e", + "r at", + "em ent", + "et h", + "Ġsh ow", + "ĠO r", + "Ġm on", + "a u", + "Ġc o", + "a id", + "Ġv ery", + "iv es", + "m y", + "Ġ und", + "Ġp re", + "Ġ end", + "Ġw ould", + "Ġ201 0", + "ur g", + "ur r", + "ĠN orth", + "t il", + "it al", + "Ġsp ec", + "u ally", + "Ġplay ers", + "ĠEu rope", + "oug h", + "y p", + "e e", + "Ġm ay", + "Ġm an", + "Ġw on", + "Ġl ike", + "ro s", + "art ment", + "r ist", + "ĠA ward", + "f orm", + "Ġs m", + "Ġe ar", + "ain t", + "Ġs uch", + "a x", + "he m", + "00 0", + "Ġto wn", + "fer ent", + "Ġre l", + "ĠF l", + "Ġar t", + "Ġmus ic", + "er ed", + "i ous", + "Ġin d", + "2 3", + "u al", + "Ġrele ased", + "Ġc re", + "am ed", + "ĠT e", + "in es", + "Ġ2 4", + "is hed", + "Ġestab lishments", + "Ġch ar", + "i um", + "ĠP resident", + "ro ugh", + "ĠP art", + "tern ational", + "Ġb ro", + "ĠA f", + "p en", + "s h", + "et s", + "Ġth ree", + "Ġdif ferent", + "Ġp erson", + "un g", + "Ġsec ond", + "Ġdire ct", + "Ġun til", + "ĠM ov", + "c er", + "ĠR iver", + "ĠR uss", + "Ġs up", + "Ġl ong", + "row span", + "ĠW est", + "ef ore", + "Ġst r", + "ot t", + "ĠE l", + "Ġg overn", + "Ġ200 3", + "er g", + "is e", + "Ġw ill", + "os s", + "Ġ2 5", + "il m", + "Ġ qu", + "Ġc ap", + "Ġ199 8", + "ĠL a", + "Ġw orld", + "ĠI I", + "e ed", + "o x", + "Ġle ad", + "st em", + "Ġl ater", + "Ġm ed", + "ĠG r", + "Ġthe n", + "1 1", + "Ġb ook", + "ĠAl l", + "are er", + "ant s", + "Ġch ild", + "ĠH is", + "Ġ201 9", + "ri ed", + "at ive", + "le t", + "er v", + "Ġd r", + "Ġ201 7", + "Ġd ec", + "en c", + "z e", + "Ġn ational", + "Ġm ember", + "Ġa ge", + "Ġth rough", + "ĠR el", + "Ġm ar", + "Ġare a", + "at s", + "om en", + "ĠA b", + "Ġm ain", + "it s", + "ĠA fter", + "ther n", + "amp ions", + "ut ion", + "Ġ200 4", + "3 0", + "ĠW h", + "ĠA m", + "ĠS e", + "ĠG u", + "ograph y", + "el s", + "ĠCom mun", + "Ġ3 0", + "f ect", + "in k", + "c c", + "v ent", + "Ġsong s", + "19 7", + "Ġ201 8", + "Ġs ame", + "Ġsing er", + "ĠB ar", + "ctor s", + "ul l", + "olog y", + "Ġsm all", + "Ġb and", + "O S", + "Ġc ar", + "Ġm ake", + "Ġl oc", + "ĠH er", + "Ġ200 5", + "re en", + "ĠGe or", + "Ġ201 4", + "ĠCh rist", + "Ġfor mer", + "Ġf our", + "Ġs ub", + "d om", + "ly mp", + "ĠB e", + "g er", + "ĠM an", + "g est", + "Ġre t", + "5 0", + "in o", + "re t", + "ian s", + "c om", + "Ġdep artment", + "Ġcon s", + "Ġl angu", + "Ġk ill", + "Ġf e", + "Ġpro v", + "ĠSp ace", + "Ġus e", + "Ġa m", + "ĠO lymp", + "Ġl ife", + "l p", + "Ġm et", + "ak es", + "ĠW ill", + "if orn", + "ĠC ent", + "Ġa ir", + "is c", + "oun c", + "ov e", + "c ent", + "ĠCal iforn", + "Ġbe ing", + "Ġ19 0", + "ur al", + "y l", + "\" ,", + "Ġc r", + "ĠH ar", + "ĠCaliforn ia", + "Ġg ame", + "f ess", + "ĠPart y", + "Ġw ell", + "Ġ20 21", + "Ġprod uc", + "Ġ ed", + "oll ow", + "b le", + "Ġm od", + "Ġb ack", + "j ect", + "ĠR e", + "Ġn o", + "Ġp ages", + "Ġrec ord", + "ĠH ow", + "Ġd id", + "Ġof ten", + "Ġb el", + "y n", + "an k", + "Ġ2 1", + "Ġre p", + "Ġn amed", + "ie w", + "2 4", + "at ing", + "ĠB ra", + "land s", + "om et", + "Ġstart ed", + "6 0", + "iel d", + "Ġg u", + "r est", + "Ġ .", + "ĠCh ar", + "Ġo ld", + "ĠP ol", + "ĠO ff", + "Ġ201 6", + "a e", + "Ġreg ion", + "Ġb ased", + "Ġ2 3", + "2 5", + "st it", + "ĠGerman y", + "ĠN or", + "il le", + "ug ht", + "Ġb efore", + "Ġsy stem", + "al d", + "Ġ201 5", + "ĠA ng", + "8 0", + "Ġm unicipal", + "r ies", + "Ġfam ily", + "ĠM inist", + "Ġ2 9", + "ĠJapan ese", + "ician s", + "om ar", + "our n", + "ĠEng land", + "Ġs aid", + "Ġp ar", + "Ġre m", + "ĠInd ian", + "ĠRel ated", + "Ġo wn", + "ĠR oman", + "ener al", + "Ġ200 6", + "ĠPal omar", + "Ġagain st", + "Ġs ince", + "el f", + "Ġund er", + "ĠT r", + "if ic", + "ir d", + "Ġc areer", + "ond on", + "uc k", + "Ġact ress", + "on y", + "et her", + "Ġcommun e", + "ill ion", + "Ġt ran", + "Ġn orth", + "Ġl aw", + "b urg", + "k e", + "4 0", + "en g", + "Ġg ames", + "2 7", + "ĠB ro", + "ĠP eak", + "Ġn ear", + "ĠMov ies", + "ĠItal ian", + "Ġ X", + "ĠE m", + "ĠK itt", + "ĠL ondon", + "2 9", + "Ġ2 6", + "ĠE n", + "ĠA ir", + "Ġp o", + "ĠDe ath", + "st r", + "b s", + "at a", + "ĠH istory", + "Ġpl ace", + "Ġchar act", + "as k", + "Ġan y", + "Ġw e", + "Ġ2 8", + "Ġhe lp", + "w atch", + "2 8", + "ĠE x", + "ĠC up", + "Ġf ollow", + "c he", + "Ġw ater", + "Ġ201 1", + "i ation", + "2 6", + "at ure", + "is on", + "Ġas s", + "a f", + "Ġw ar", + "Ġ2 7", + "ĠSpace watch", + "Ġgovern ment", + "Ġ ent", + "Ġdirect ed", + "Ġb est", + "Ġin ter", + "ampions hip", + "ĠE ar", + "Ġt yp", + "in ess", + "r ing", + "Ġg r", + "Ġthe se", + "Ġan im", + "g ram", + "l ing", + "Ġ200 7", + "ul ar", + "al a", + "Ġcent ury", + "EA T", + "b y", + "u th", + "ar a", + "ain s", + "h am", + "ĠU S", + "ĠN EAT", + "c on", + "ide o", + "ĠAs s", + "Ġpopul ation", + "ĠS ome", + "an a", + "ed y", + "3 3", + "in t", + "Ġe ver", + "Ġse ason", + "od y", + "Ġm il", + "ram a", + "Ġ20 22", + "o on", + "Ġcount ry", + "Ġs ur", + "at or", + "Ġ &", + "ow s", + "ĠKing dom", + "Ġapp ear", + "ord s", + "am a", + "ro g", + "al t", + "Ġ201 3", + "Ġa round", + "Ġinclud ing", + "ut e", + "ĠW hen", + "Ġor ig", + "Ġl and", + "st er", + "Ġor gan", + "Ġ199 0", + "ĠM e", + "f l", + "9 0", + "Ġb oth", + "Ġse ver", + "oin t", + "T he", + "od e", + "a ul", + "Ġwebsit e", + "Ġ200 8", + "aj or", + "7 0", + "t t", + "an ish", + "Ġs chool", + "re m", + "Ġc ould", + "Ġin v", + "Ġ2 2", + "am b", + "q ue", + "r id", + "al es", + "ĠM c", + "ip p", + "d e", + "ĠB el", + "z er", + "on es", + "Ġ em", + "Ġs et", + "Ġd istrict", + "ĠG overn", + "il t", + "n er", + "ist an", + "ĠIn ternational", + "ur ch", + "us iness", + "ĠCanad ian", + "iz ed", + "emb ers", + "Ġim port", + "ĠWill iam", + "m s", + "ĠAustral ia", + "Ġbu ild", + "Ġ201 2", + "Ġ( )", + "Ġl ist", + "oug ht", + "ĠM ed", + "Ġpro fess", + "ag o", + "Ġe ach", + "Ġh ow", + "Ġr un", + "ĠAmer ica", + "a ur", + "Ġs l", + "ak ing", + "Ġh igh", + "um b", + "3 4", + "Ġus ually", + "n ey", + "Ġ right", + "Ġl ived", + "ĠAnd erson", + "ĠCom p", + "ĠCommun es", + "uct ion", + "o ard", + "ĠM es", + "Ġex amp", + "vel op", + "ĠPh il", + "Ġs im", + "ĠThe se", + "ort s", + "Ġ200 9", + "al k", + "ros s", + "9 9", + "Ġchild ren", + "ĠM on", + "3 7", + "Ġh um", + "i ence", + "6 5", + "ra ct", + "om in", + "u es", + "um mer", + "ĠM ich", + "ib le", + "ĠH ouse", + "w rit", + "Ġl ast", + "o le", + "Ġde velop", + "col span", + "ĠAust r", + "6 4", + "ad em", + "et er", + "Ġcre ated", + "Ġro ck", + "Ġcomp os", + "6 8", + "ĠO ne", + "6 7", + "ist or", + "Ġc aus", + "N E", + "\"| -", + "Ġser v", + "ĠInd ia", + "3 5", + "ĠMinist er", + "ĠL ou", + "Ġs k", + "Ġ ran", + "ĠSw ed", + "urr ent", + "le x", + "Ġc ounty", + "Ġ '", + "Ġbe gan", + "Ġad d", + "Ġsever al", + "Ġpro gram", + "\"|- ||", + "Ġw inn", + "Ġs ign", + "ĠS an", + "Ġcl ub", + "ĠP er", + "Ġs outh", + "Ġst at", + "ĠD em", + "Ġatt ack", + "en e", + "Ġwh ile", + "Ġo per", + "ĠSt ate", + "Ġcom mon", + "ĠS ec", + "in c", + "an e", + "Ġwrit er", + "3 8", + "Ġ198 0", + "ĠD av", + "Ġv ers", + "ap p", + "ĠG l", + "ed er", + "f or", + "f ul", + "ĠS up", + "Ġlar ge", + "c hes", + "Ġt erm", + "us h", + "ĠS y", + "it ary", + "Ġimport ant", + "Ġl ive", + "v en", + "ens us", + "s ide", + "ing ton", + "Ġoff icial", + "ĠHow ever", + "4 5", + "Ġsing le", + "ĠS ch", + "Ġ if", + "Ġp ol", + "Ġhe ad", + "ĠDeath s", + "Ġd rama", + "re w", + "ĠAustral ian", + "Ġdis c", + "ir ed", + "Ġac c", + "d ay", + "ĠC ities", + "6 9", + "Ġw ent", + "Ġ199 7", + "Ġf ilm", + "n a", + "l er", + "Ġin t", + "att le", + "Ġpopul ar", + "st e", + "a ught", + "as ter", + "Ġs uc", + "ĠA c", + "Ġm illion", + "ber g", + "t he", + "ĠMes a", + "Ġd ef", + "Ġmunicipal ity", + "ĠOff icial", + "Ġd iv", + "ĠRuss ian", + "Ġlangu age", + "ic o", + "z il", + "3 9", + "a ut", + "id d", + "Ġn ow", + "o ice", + "ro l", + "Ġs oc", + "ĠM iss", + "Ġle g", + "4 8", + "Ġexamp le", + "4 7", + "Ġm at", + "an ge", + "ce pt", + "Ġdes ign", + "Ġ199 6", + "om b", + ". \"", + "Ġp ower", + "Ġf in", + "ĠS er", + "Ġch ang", + "Ġcount ries", + "Ġm in", + "Ġear ly", + "Ġe p", + "Ġan n", + "ĠCh ampionship", + "Ġp resident", + "ĠBra zil", + "Ġd ist", + "omet imes", + "iv en", + "Ġh ome", + "ĠM ex", + "Ġg et", + "w est", + "Ġen g", + "ĠH ol", + "ĠL O", + "ĠQ u", + "Ġcomp et", + "Ġw est", + "ĠC o", + "Ġgroup s", + "ock ey", + "Ġinclud e", + "ic es", + "ĠP ark", + "ĠR ec", + "Ġo pen", + "Ġd ay", + ". .", + "iv il", + "Ġv ideo", + "Ġin c", + "op h", + "i ef", + "l in", + "Ġex p", + "Ġtran s", + "ber t", + "ĠR ober", + "Ġcap ital", + "p le", + "Ġspec ies", + "Ġme ans", + "ĠS m", + "f ord", + "NE OS", + "ĠLO NEOS", + "Ġ196 0", + "Ġwrit ten", + "ĠP olit", + "ri end", + "i j", + "ĠSp anish", + "con om", + "5 7", + "Ġle ft", + "g es", + "i en", + "ĠS ing", + "Ġw ay", + "id ed", + "ĠJ ames", + "ĠS chool", + "Ġex t", + "ĠT ur", + "ro d", + "ĠP aul", + "oc rat", + "Ġever y", + "ĠS en", + "ĠM or", + "g in", + "Ġh istory", + "Ġ199 5", + "a ces", + "Ġ ,", + "ĠD r", + "9 8", + "Ġm embers", + "ĠT ex", + "our t", + "ĠP ort", + "ĠCanad a", + "Ġp ass", + "ĠA ctors", + "i od", + "Ġt imes", + "ĠE ast", + "c o", + "ĠAng el", + "ĠF ootball", + "e al", + "l ed", + "i us", + "Ġ197 0", + "Ġd own", + "F A", + "r is", + "ĠS aint", + "le ge", + "u ff", + "Ġm uch", + "ĠG ree", + "ch n", + "ov er", + "Ġman ag", + "Ġmar ried", + "Ġact iv", + "ar n", + "Ġwh at", + "9 7", + "5 8", + "an ia", + "id es", + "m a", + "ra in", + "Ġpro t", + "ep end", + "oun g", + "ro te", + "ĠRep ublic", + "Ġfam ous", + "it ar", + "|||| ||||", + "it er", + "ist ics", + "Ġcan cer", + "Ġsh ort", + "Ġ199 4", + "Ġw omen", + "e an", + "i or", + "Ġv ar", + "os p", + "ĠM il", + "ĠR eg", + "id a", + "ĠS ov", + "Ġst ill", + "Ġcom edy", + "Ġm ajor", + "a el", + "ĠFl or", + "or p", + "ĠN ot", + "Ġcl ass", + "ĠT own", + "y le", + "u el", + "Ġre f", + "o e", + "ĠPro v", + "Ġbu ilt", + "ct ion", + "Ġf ather", + "h an", + "Ġ3 1", + "y a", + "ol ution", + "al th", + "Ġj oin", + "v iew", + "Ġc urrent", + "ill a", + "ĠGeor ge", + "ĠIt s", + "Ġre ce", + "k y", + "ĠN Y", + "Ġ1 00", + "g y", + "v es", + "6 6", + "Ġst ar", + "as tern", + "ĠLou is", + "Ġs old", + "as es", + "Ġ18 8", + "Ġ199 2", + "op e", + "Ġb re", + "ĠPr ime", + "Ġp ubl", + "ĠSov iet", + "9 5", + "ĠP re", + "he l", + "Ġt it", + "o f", + "Ġ199 3", + "Ġv ill", + "ric k", + "Ġdo es", + "ĠJ os", + "Ġsup port", + "ut ch", + "ĠJ ack", + "Ġlar gest", + "Ġcomp any", + "Ġto ok", + "Ġs on", + "Ġa ward", + "ĠAr t", + "Ġp ublic", + "ĠR ed", + "ĠCh ic", + "ĠC at", + "ans as", + "Ġb usiness", + "Ġg ood", + "ĠItal y", + "ĠT w", + "Ġw rote", + "ĠV al", + "ĠUn ion", + "ĠM ount", + "Ġmov ed", + "ĠEurope an", + "ĠV ir", + "ĠK ore", + "ĠM any", + "en a", + "Ġan other", + "Ġbec ome", + "ĠCom m", + "Ġl a", + "Ġfootball er", + "ĠR ich", + "ĠTex as", + "n ess", + "Ġth ird", + "ĠA g", + "ad io", + "is ed", + "ĠCh ina", + "Ġc ame", + "Ġsuc cess", + "Ġo b", + "oc iation", + "Ġhe ld", + "ĠChic ago", + "Ġdire ctor", + "Ġto p", + "Ġb ody", + "Ġst age", + "Ġ199 1", + "c y", + "ĠR o", + "enc y", + "w ork", + "3 6", + "Ġb ig", + "ad es", + "Ġn eed", + "ĠNY S", + "4 4", + "ot s", + "Ġev en", + "ĠDem ocrat", + "ric a", + "Ġpro ble", + "Ġcont in", + "ourn al", + "uth or", + "Ġalbum s", + "Ġg iven", + "Ġprofess ional", + "Ġp os", + "Ġw ant", + "ur ed", + "Ġg en", + "iv al", + "ag n", + "Ġb as", + "Ġme an", + "ad y", + "Ġph ys", + "ĠC ast", + "Ġbook s", + "ĠP ak", + "ĠP ri", + "Ġpolit ical", + "Ġcon d", + "ĠD on", + "ex t", + "ĠRober t", + "ĠM ont", + "ac ed", + "os ed", + "ra ft", + "ĠSp ort", + "ĠC ap", + "ĠL os", + "r ican", + "ĠD uring", + "Ġd eb", + "5 5", + "ĠM y", + "Ġj ust", + "ar m", + "Ġcom m", + "ĠS l", + "Ġs ix", + "ir l", + "ĠD istrict", + "Ġwork ed", + "ut er", + "Ġo cc", + "ĠY ou", + "k i", + "Ġn ov", + "ĠBl ack", + "Ġpolitician s", + "7 8", + "ĠM ost", + "ĠDav id", + "Ġc ensus", + "and er", + "ĠL ist", + "ĠB est", + "h i", + "ĠD ep", + "Ġdes c", + "ĠT ra", + "av ing", + "Ġc ult", + "iet y", + "Ġcharact er", + "il ity", + "t ain", + "Ġth ings", + "ĠCl ub", + "ul a", + "ĠJ ew", + "Ġkill ed", + "at ural", + "er a", + "ĠAf rican", + "Ġres p", + "ou thern", + "Ġp resent", + "3 1", + "ĠCh in", + "ĠM al", + "Ġep is", + "ĠN e", + "ĠH igh", + "Ġal ong", + "Ġm en", + "v ille", + "Ġr ul", + "Ġf ive", + "um p", + "ĠF rom", + "ut ed", + "ĠD utch", + "ĠSc ott", + "m en", + "Ġl ight", + "r u", + "Ġ| }", + "ĠB as", + "ra b", + "it ions", + "m ed", + "Ġw ord", + "o o", + "ĠW e", + "Ġf em", + "ĠR es", + "or ies", + "s c", + "ĠH en", + "ĠR oy", + "ĠW ash", + "Ġ198 9", + "Ġl iving", + "Ġs w", + "m e", + "ĠG ro", + "id s", + "ĠAs ia", + "ear ch", + "Ġper iod", + "Ġmil itary", + "il ar", + "Ġr ed", + "Ġro le", + "ĠB y", + "ĠAc adem", + "ĠS al", + "av y", + "ĠS ummer", + "9 4", + "ĠAf rica", + "ĠE mp", + "writ er", + "ĠB er", + "Ġ198 8", + "Ġfound ed", + "Ġplay s", + "an o", + "Ġ198 1", + "Ġt em", + "Ġvers ion", + "ĠD es", + "Ġser ved", + "ol ic", + "v al", + "itt le", + "3 2", + "Ġpubl ished", + "t y", + "ĠH al", + "Ġh istor", + "our s", + "Ġ ef", + "ĠM ad", + "Ġprov ince", + "e um", + "Ġrep resent", + "Ġus ing", + "ĠI ll", + "n ect", + "ĠQ ue", + "o ff", + "ant ic", + "Ġex pl", + "u x", + "ĠTh om", + "re g", + "ot a", + "Ġl ook", + "Ġwork s", + "ell s", + "ical ly", + "Ġm y", + "Ġor der", + "ounc il", + "' t", + "Ġf rog", + "ir c", + "ĠC ath", + "ĠM art", + "Ġfollow ing", + "ĠWash ington", + "l ine", + "Ġc amp", + "7 7", + "ĠGr and", + "ra el", + "Ġpart s", + "Ġf ew", + "Ġag ed", + "le ments", + "Ġret urn", + "Ġto g", + "ĠS am", + "Ġdis e", + "Ġ 0", + "Ġspec ial", + "Ġtog ether", + "Ġev ent", + "ĠAngel es", + "al ity", + "ĠChin ese", + "ĠB ut", + "Ġeng ine", + "ĠB u", + "ĠA lex", + "t al", + "ĠD iv", + "at ors", + "ĠPak istan", + "Ġa uthor", + "it zer", + "iz e", + "Ġ195 0", + "Ġ ide", + "ĠW rit", + "9 6", + "re am", + "k a", + "Ġhe art", + "Ġg reat", + "Ġcaus ed", + "ou l", + "ĠChar les", + "Ġf ood", + "h a", + "ĠChrist ian", + "iss ion", + "L O", + "od es", + "ĠM unicipal", + "ĠF irst", + "Ġbec om", + "ĠG reat", + "ĠE v", + "Ġs ometimes", + "iz ation", + "if ied", + "ĠH all", + "Ġelect ion", + "ounc ed", + "ĠMich ael", + "r ip", + "Ġe qu", + "or ed", + "ict ion", + "S t", + "Ġg ot", + "on a", + "op s", + "Ġ es", + "Ġr est", + "ĠN o", + "Ġse e", + "ĠD ay", + "Ġhum an", + "lect ion", + "Ġt er", + "Ġim p", + "Ġ198 6", + "Ġar r", + "Ġh ig", + "Ġt ake", + "Ġper form", + "Ġv oice", + "ĠVir gin", + "and s", + "c ed", + "Ġp ut", + "ĠG old", + "sp eople", + "ro v", + "i ol", + "5 4", + "ĠK ar", + "ĠP at", + "m inist", + "Ġth ought", + "ĠM ary", + "ĠPl ay", + "Ġg ener", + "g ian", + "ĠRich ard", + "Ġs ol", + "Ġbel ie", + "ĠOlymp ics", + "idd le", + "ĠB ill", + "ĠSp ain", + "Ġb i", + "i ers", + "ĠB en", + "ĠM ass", + "Ġf ight", + "B C", + "ĠL aw", + "ĠM et", + "Ġtr ad", + "ar ch", + "un t", + "Ġv ot", + "Ġ198 7", + "Ġse at", + "Ġgu itar", + "A S", + "ĠIs rael", + "Ġm other", + "ord ing", + "ĠI r", + "um ent", + "ĠCar ol", + "w ays", + "ĠG od", + "l o", + "Ġar ch", + "r ation", + "Ġ198 4", + "Ġle vel", + "Ġtyp e", + "ud e", + "Ġre pl", + "Ġ197 9", + "ĠS im", + "ar ed", + "ĠT er", + "8 8", + "Ġs ent", + "Ġv ol", + "Ġst ars", + "ĠS o", + "Ġf riend", + "Ġe conom", + "ĠT om", + "Ġin ternational", + "ĠPar is", + "in i", + "Ġn ext", + "Ġfe at", + "eren ce", + "ĠY ear", + "ĠAward s", + "Ġr iver", + "ĠU K", + "un n", + "ĠCh urch", + "ĠDemocrat ic", + "Ġg eneral", + "u ed", + "ĠF LO", + "at ives", + "5 9", + "Ġk ing", + "Ġl ine", + "ĠFran k", + "Ġm aking", + "Ġsc ient", + "ial ly", + "Ġ197 3", + "Ġm ark", + "Ġst ory", + "ĠTown s", + "ĠI f", + "Ġc rit", + "ĠTur k", + "Ġ198 5", + "m b", + "ĠAr g", + "ĠIs land", + "Ġd ata", + "Ġvill age", + "m ing", + "ĠL at", + "Ġy ou", + "ĠG eneral", + "et work", + "Ġ197 6", + "Ġ198 2", + "l iam", + "Ġorig in", + "Ġrel ig", + "ur a", + "ĠK n", + "per or", + "Ġf ind", + "Ġh ouse", + "ĠFlor ida", + "ar i", + "Ġan t", + "Ġ198 3", + "ĠS ant", + "ĠCol lege", + "Ġc hem", + "ĠAcadem y", + "form ation", + "ĠEmp ire", + "Ġ5 0", + "Ġv is", + "Ġlead er", + "I D", + "rop ical", + "Ġ197 7", + "u le", + "Ġf ail", + "i ber", + "Ġstr uct", + "ĠR ock", + "Ġj ournal", + "om a", + "Ġrece ived", + "ino is", + "Ġsim ilar", + "c ast", + "ĠAt l", + "ĠD an", + "ĠG o", + "st on", + "m ost", + "Ġloc ated", + "Ġn e", + "ĠH am", + "ĠK h", + "liam ent", + "ain ed", + "l ic", + "6 3", + "ĠT V", + "c her", + "ĠG ra", + ") :", + "ĠAustr ian", + "ĠIll inois", + "c ient", + "Ġ197 2", + "ĠB i", + "itzer land", + "ĠR ev", + "Ġart ist", + "s y", + "Ġproduc er", + "ert ain", + "Ġa ut", + "ig a", + "Ġnov el", + "ov ered", + "Ġd ays", + "Ġst ation", + "ĠD is", + "Ġed uc", + "Ġst op", + "ĠGree k", + "ĠMus ic", + "im ate", + "Ġp aint", + "ĠI m", + "ĠGovern or", + "Ġse en", + "ĠZ eal", + "ĠGeor g", + "Ġh ost", + "Ġall ow", + "Ġa v", + "a im", + "he st", + "Ġp rom", + "ad s", + "end ed", + "Ġstat istics", + "er ing", + "Ġ197 8", + "ĠP eter", + "Ġv al", + "ĠZeal and", + "Ġg l", + "Ġl ess", + "ĠSw itzerland", + "ar ian", + "Ġm ount", + "Ġe ast", + "Ġgro w", + "ĠSport speople", + "ĠAr ch", + "r or", + "Ġmod ern", + "Ġt urn", + "av es", + "y r", + "ĠT ran", + "ol f", + "ap e", + "ĠK ansas", + "Ġm akes", + "Ġcons id", + "0 8", + "ĠFran c", + "Ġdise ase", + "a ff", + "ir on", + "ĠD el", + "ĠOlymp ic", + "ĠU p", + "ĠAss ociation", + "Ġ193 0", + "ĠRuss ia", + "al f", + "200 6", + "4 9", + "im a", + "Ġ18 7", + "y d", + "cent ury", + "ar ia", + "ĠPolit icians", + "ĠSwed en", + "Ġis land", + "Ġst yle", + "ask et", + "200 5", + "Ġproduc ed", + "u z", + "Ġ197 4", + "aught er", + "ĠF ilm", + "Ġth ose", + "Ġ197 5", + "Ġbl ack", + "Ġl ed", + "est s", + "Ġev ents", + "Ġbe g", + "Ġind epend", + "ĠWrit ers", + "ian a", + "Ġelect ed", + "Ġteam s", + "ul ts", + "ĠT o", + "ĠProv ince", + "200 7", + "ĠCath olic", + "ĠM er", + "Ġcont rol", + "ud d", + "Ġbro ther", + "Ġpart y", + "ĠMex ico", + "Ġse x", + "un k", + "Ġst ates", + "Ġsh ould", + "Ġform ed", + "ition al", + "Ġ197 1", + "u ce", + "ĠG reen", + "th ough", + "ak en", + "re y", + "Ġ194 0", + "Ġd el", + "Ġcharact ers", + "in ter", + "4 6", + "it es", + "le ar", + "Ġg od", + "S S", + "in ed", + "l am", + "Ġs ound", + "uk e", + "Ġ #", + "gy pt", + "0 7", + "ur t", + "erg y", + "Ġwith out", + "Ġ :", + "Ġn omin", + "ĠEar th", + "I I", + "b oard", + "t ed", + "Ġmon ey", + "w ood", + "Ġph il", + "ĠA ct", + "ad a", + "Ġcon f", + "Ġtit le", + "Ġs ay", + "ĠVirgin ia", + "an i", + "Ġorig inal", + "Ġpl aces", + "f ield", + "Ġproble ms", + "o z", + "ap er", + "Ġd i", + "Ġs ide", + "ol s", + "ong ress", + "Ġann ounced", + "Ġ /", + "fect ure", + "if f", + "hem at", + "re et", + "ĠB ec", + "Ġdesc rib", + "ad u", + "ĠAr my", + "ĠWest ern", + "at ory", + "200 4", + "Ġw ife", + "Ġ ice", + "ent al", + "ight s", + "ĠE r", + "ubl ican", + "ĠRoy al", + "Ġd ue", + "s elf", + "ĠB C", + "ĠAn t", + "Ġa way", + "e ep", + "Ġex per", + "ill s", + "ĠHen ry", + "5 6", + "Ġshow s", + "Ġo pp", + "Ġl ot", + "ap pen", + "Ġjoin ed", + "ĠM ac", + "ĠE gypt", + "ic le", + "ĠThom as", + "Ġstr ong", + "Ġd est", + "Ġs il", + "Ġk ind", + "eb all", + "Ġmed al", + "Ġpo et", + "en se", + "A mer", + "Ġh ockey", + "ĠBrazil ian", + "Ġ el", + "asket ball", + "k n", + "ard s", + "ĠT or", + "ĠI ran", + "ur s", + "Ġst and", + "Ġto tal", + "ĠO l", + "ĠV ict", + "Ġ196 8", + "ist er", + "Ġc y", + "Ġmus ician", + "ol k", + "ĠArg ent", + "Ġm atch", + "ĠCent ral", + "ain e", + "ro y", + "ac hed", + "ĠO h", + "ĠW omen", + "ĠF in", + "Ġcompos er", + "ĠW il", + "ĠW ind", + "it a", + "ĠA cc", + "ĠC O", + "rid ge", + "x t", + "Ġd efe", + "g o", + "ep h", + "r ont", + "ste ad", + "Ġbuild ing", + "Ġc ities", + "Ġlangu ages", + "Ġs ite", + "as ons", + "Ġother s", + "Ġl ost", + "Ġchang ed", + "ers t", + "ĠB ook", + "ur b", + "ra w", + "200 3", + "Ġpl an", + "Ġloc al", + "yl v", + "ĠF I", + "ĠW ith", + "ĠV ill", + "Ġf ire", + "200 1", + "ord er", + "Ġdif f", + "Ġpro cess", + "Ġw rest", + "ĠPri ze", + "Ġm ust", + "Ġare as", + "urr ican", + "Ġp oss", + "em ents", + "Ġright s", + "ĠB ay", + "orp or", + "ĠC ouncil", + "Amer ican", + "ĠSt ud", + "Ġch ange", + "ĠD o", + "200 8", + "Ġstud io", + "ĠR ob", + "b e", + "re ed", + "Ġ196 9", + "ĠM in", + "Ġw ee", + "Ġd em", + "re land", + "Ġre al", + "c hed", + "end er", + "fl u", + "Ġre port", + "or ia", + "ĠRep ublican", + "8 7", + "ĠP op", + "Ġm ult", + "Ġwh ite", + "F F", + "Ġ196 4", + "Ġbe h", + "ĠSt ar", + "Ġl ate", + "ĠRec ords", + "n ot", + "ĠG ames", + "ĠP et", + "ad o", + "ĠCat al", + "Ġl ives", + "e le", + "ĠSwed ish", + "ward s", + "Ġ =", + "9 3", + "ether lands", + "Ġinclud es", + "Ġp at", + "Ġp oint", + "Ġh appen", + "ers ey", + "ĠD ev", + "om s", + "ĠI reland", + "Ġplay ing", + "ĠO k", + "ĠM ic", + "200 2", + "Ġ196 7", + "ĠC ont", + "el and", + "b or", + "Ġsoc ial", + "Ġh ard", + "ĠWh ite", + "Ġ $", + "Ġef fect", + "ĠP enn", + "Ġbro ad", + "Ġsc ience", + "ĠGro up", + "ĠA v", + "r d", + "ic les", + "rip t", + "Ġc ivil", + "ĠSc ot", + "al y", + "l ished", + "ar ies", + "Ġd et", + "Ġf un", + "Ġn on", + "ĠCarol ina", + "Ġy oung", + "Ġg ave", + "Ġinclud ed", + "ĠAustr ia", + "ĠSup er", + ". ,", + "ill er", + "ip s", + "Ġ )", + "Ġm ix", + "4 3", + "Ġre ad", + "ĠSec ret", + "aw a", + "Ġr adio", + "Ġmost ly", + "og n", + "ĠO s", + "200 0", + "an ies", + "Ġm ag", + "re l", + "i ro", + "Ġanim als", + "6 1", + "n ing", + "Ġh and", + "i qu", + "sh ire", + "Ġph ot", + "p art", + "ĠL ife", + "Ġ4 0", + "U n", + "Ġappear ed", + "Ġp ain", + "Ġg old", + "ak er", + "Ġf ield", + "eder al", + "am m", + "ĠM r", + "Ġte chn", + "ib r", + "Ġa ff", + "Ġf inal", + "c le", + "4 1", + "z a", + "Ġh old", + "all s", + "Ġra ce", + "Ġad v", + "Ġres ult", + "ĠC ro", + "b on", + "Ġn or", + "ant on", + "ĠM el", + "ĠH on", + "ĠS ur", + "Ġw ords", + "ĠN etherlands", + "ad or", + "ĠA rab", + "y m", + "ĠEar ly", + "p s", + "c raft", + "Ġs ett", + "ĠM ag", + "angu age", + "Ġ194 5", + "l i", + "ig er", + "ĠB o", + "9 2", + "ĠR h", + "Ġse a", + "ĠA pp", + "ect ed", + "Ġcol or", + "at o", + "il es", + "b r", + "Ġd aughter", + "ec ut", + "lect ed", + "ep ar", + "le ment", + "ĠC he", + "sp ort", + "Ġdeb ut", + "inn ing", + "200 9", + "9 1", + "Ġc or", + "199 9", + "Ġcomp uter", + "op her", + "a ud", + "os aur", + "Ġcom es", + "Ġc al", + "ĠL ab", + "he ast", + "it her", + "Ġstud y", + "ĠMar k", + "Ġco ach", + "Ġus es", + "uc ed", + "ĠC r", + "Ġt rib", + "Ġt aken", + "Ġ z", + "Ġwant ed", + "w w", + "id ing", + "6 2", + "Ġg ra", + "ĠCon f", + "ĠOh io", + "i que", + "Ġ196 6", + "is l", + "ĠF am", + "l or", + "ce an", + "Ġ( ;", + "ĠH a", + "5 3", + "ĠS ince", + "ĠV ol", + "Ġfem ale", + "st ate", + "Ġoff ice", + "ĠTh at", + "it ect", + "ub e", + "ĠB attle", + "ĠD en", + "in ation", + "ĠDiv ision", + "5 1", + "Ġre fer", + "ĠG ar", + "Ġ [", + "n y", + "it ch", + "Ġinv ol", + "i y", + "4 2", + "c a", + "ĠH ung", + "Ġ194 7", + "ell ow", + "e h", + "g en", + "Ġh aving", + "Ġbir th", + "at ic", + "Ġsc reen", + "ĠPort ug", + "Ġn atural", + "g r", + "w are", + "ĠJ er", + "ĠS ol", + "Ġwith in", + "le te", + "C h", + "ann el", + "ĠN ob", + "G B", + "ĠM od", + "ĠU k", + "Ġro und", + "Ġsp orts", + "k ed", + "s el", + "ĠLe g", + "ict ures", + "l a", + "ĠMus eum", + "Ġd am", + "ig an", + "ri al", + "ĠGe ography", + "Ġbet ter", + "Ġdevelop ed", + "Ġp ost", + "on ia", + "r ia", + "ĠGeorg ia", + "es se", + "Ġ196 5", + "Ġsur v", + "ĠJ ersey", + "Ġp ort", + "ĠJ r", + "ab it", + "ĠScott ish", + "Ġkn ow", + "ĠH el", + "ĠM os", + "Ġfootball ers", + "ies t", + "ĠPol ish", + "ĠJew ish", + "ĠH um", + "Ġ194 8", + "Ġs om", + "Ġh alf", + "Ġs ays", + "Ġv oc", + "ĠNor thern", + "Ġth ink", + "g ed", + "Ġpr ison", + "ĠB oy", + "Ġ196 3", + "ĠG en", + "Ġ195 6", + "he im", + "ĠS ong", + "ĠL o", + "Ġin formation", + "ĠP e", + "Ġcom e", + "ĠB ur", + "sy ch", + "V ID", + "Ġf ree", + "Ġs epar", + "Ġm ass", + "Ġle arn", + "ĠL ake", + "Ġestab lished", + "Ġdist rib", + "ĠG all", + "as y", + "Ġv iol", + "an ces", + "ĠL ove", + "ĠK ent", + "ĠLe e", + "u a", + "y o", + "ific ation", + "Ġconsid ered", + "b ers", + "urrican e", + "olog ical", + "Ġbecom es", + "Ġsp eak", + "Ġam ong", + "Ġstud ied", + "ĠS ett", + "ĠCO VID", + "Ġt our", + "ac y", + "Ġcon nect", + "ĠSt ev", + "ip le", + "Ġto o", + "ĠB or", + "osp ital", + "Ġad minist", + "ĠRep resent", + "ĠS un", + "Ġf ish", + "um n", + "Ġh on", + "Ġs outhern", + "ag on", + "Ġin flu", + "il s", + "ĠJ ul", + "our ces", + "ol a", + "et ts", + "Ġab le", + "ĠC amp", + "Ġl ab", + "u f", + "l im", + "Ġ20 23", + "ĠPre fecture", + "ro ll", + "ĠCent er", + "Ġcommun ity", + "it or", + "Ġ196 1", + "ĠC or", + "it z", + "ac her", + "l ess", + "ell ing", + "Ġs qu", + "Ġepis ode", + "ĠQue en", + "Ġd one", + "ĠEm peror", + "ou ri", + "ut es", + "Ġ196 2", + "ĠPr ince", + "ĠR os", + "t a", + "am ent", + "ĠAn n", + "Ġ194 6", + "ĠB ang", + "Ġind ust", + "et ic", + "em s", + "kn own", + "s ylv", + "ĠS k", + "ĠC ount", + "ĠC ivil", + "ond iss", + "ash i", + "Ġtra in", + "v ious", + "ĠIn ter", + "Ġcent er", + "ĠSm ith", + "l ike", + "Ġw oman", + "ur der", + "ĠP an", + "ĠIn stit", + "Ġsp ace", + "Ġab ove", + "us ed", + "ĠIr ish", + "\" )", + "Ġt re", + "j an", + "ĠC ourt", + "ĠCol umb", + "am s", + "ĠM at", + "s ong", + "Ġm ot", + "Ġl ow", + "ĠJ ust", + "amp ion", + "ap s", + "ĠIs lam", + "for man", + "ĠS illa", + "Ġmod el", + "ĠL in", + "m ar", + "Ġin str", + "ĠO bs", + "Ġmat hemat", + "Ġpos ition", + "r ie", + "Ġwrit ers", + "ĠMunicipal ities", + "ach us", + "it iz", + "Ġ194 2", + "Ġco ast", + "ĠGovern ment", + "sylv ania", + "ĠII I", + "ĠFI FA", + "g round", + "o or", + "ap t", + "Ġtra ck", + "Ġ194 9", + "Ġphil os", + "s ur", + "ak a", + "Ġc op", + "Ġn ever", + "Ġwork ing", + "Ġ195 7", + "Ġ19 20", + "az z", + "ĠC ongress", + "b and", + "Ġth ough", + "ĠB ern", + "199 8", + "ent ly", + "ĠE OS", + "Ġnor thern", + "Ġ194 1", + "Ġinc re", + "Ġt ro", + "Ġt reat", + "at ely", + "Ġcount ies", + "ĠMart in", + "Ġc irc", + "Ġ194 4", + "Ġis s", + "rit ory", + "el t", + "Ġwinn ers", + "ĠSe a", + "achus etts", + "Ġ193 6", + "ĠPar liament", + "Ġmus ical", + "ĠJ im", + "Ġ195 1", + "5 2", + "Ġob ject", + "ĠM a", + "pl ay", + "Ġint rod", + "Ġ et", + "ĠTe levision", + "ĠW W", + "ef f", + "Ġk eep", + "Ġal most", + "Ġf ar", + "d s", + "v ers", + "b ack", + "ĠScot land", + "ĠF red", + "Ġb r", + "Ġ193 9", + "ĠMass achusetts", + "Ġch art", + "Ġ ill", + "ĠThe ir", + "Ġoff ic", + "Ġpl ants", + "w h", + "ver age", + "et te", + "Ġf ull", + "re ad", + "ĠC ons", + "Ġtyp es", + "Ġh or", + "Ġsing ers", + "Ġserv ice", + "Ġ193 4", + "Ġhig hest", + "w a", + "Ġf a", + "ĠL and", + "Ġ8 8", + "ĠEd ward", + "Ġn ames", + "ish op", + "ĠHal eak", + "ĠW ales", + "ĠDis ney", + "Ġmus icians", + "H L", + "ĠJos eph", + "ĠSt an", + "Ġ195 8", + "ot o", + "ĠSett lements", + "Ġp res", + "Ġth r", + "Ġc ast", + "ĠBec ause", + "ĠB ob", + "ĠS at", + "p ec", + "ĠH ot", + "ell a", + "ater ial", + "Ġact ion", + "Ġde v", + "Ġc and", + "ĠS il", + "Ġret ired", + "Ġend ed", + "ĠPenn sylvania", + "c ul", + "ĠHaleak ala", + "Ġh it", + "ĠKore an", + "n ow", + "Ġwinn ing", + "p her", + "Ġb asketball", + "Ġcom b", + "Ġ193 8", + "Ġle ast", + "id er", + "b a", + "p e", + "ze ch", + "ĠE U", + "A R", + "ran ch", + "Ġk ey", + "ĠT ok", + "Ġsuccess ful", + "olog ist", + "ĠFor mer", + "ĠW rest", + "Ġ195 2", + "Ġin f", + "199 7", + "Ġopen ed", + "ĠU s", + "Ġen ergy", + "Ġ( \"", + "Ġ195 4", + "istric ts", + "m ark", + "Ġc he", + "Ġw in", + "ĠCar l", + "Ġar my", + "ress ion", + "Ġt en", + "est ival", + "ow a", + "ĠJ o", + "Ġpr im", + "Ġ192 9", + "Ġapp ro", + "u it", + "Ġ195 9", + "Ġmet al", + "ĠKore a", + "ĠB ir", + "ĠNot es", + "ĠS om", + "Ġcon stit", + "Ġpol ice", + "ĠSen ate", + "Ġ rom", + "Ġra il", + "Ġm id", + "Ġ193 3", + "a ign", + "Ġhim self", + "ĠR ail", + "ĠMiss ouri", + "b our", + "Ġmean ing", + "ĠJack son", + "or es", + "Ġfail ure", + "Ġm inist", + "Ġtem per", + "ĠB ig", + "T V", + "ul f", + "ĠS ar", + "or f", + "Ġcomp let", + "ĠAs ian", + "Ġjournal ist", + "n es", + "o ch", + "le g", + "Ġ194 3", + "ĠLat in", + "ĠT im", + "Ġfor ces", + "Ġcult ure", + "ov a", + "ĠC zech", + "Ġg ive", + "ĠD isc", + "ĠPhil ipp", + "ĠEn ter", + "ĠO b", + "Ġl ik", + "ĠF C", + "Ġtrans l", + "ĠMex ican", + "ir es", + "Ġpl ant", + "Ġ195 5", + "Ġmov e", + "Ġre qu", + "ow ers", + "Ġvar ious", + "Ġlaw y", + "ar io", + "ĠL ater", + "ecut ive", + "Ġ i", + "ian o", + "ĠIs lands", + "y e", + "ĠG al", + "v iron", + "g ar", + "iv ely", + "Ġt est", + "Ġc ause", + "ĠV er", + "Ġ8 0", + "yn ast", + "Ġle t", + "Ġ193 5", + "Ġmanag er", + "Ġ192 8", + "ir a", + "Ġg irl", + "ĠH ockey", + "Ġ193 1", + "Ġsy mb", + "Ġn etwork", + "ĠCatal ina", + "Ġj ob", + "it te", + "ĠS ir", + "Ġgro und", + "ĠE s", + "Ġa verage", + "Ġl iter", + "it ive", + "Ġac ross", + "Ġbuild ings", + "ĠAcc ording", + "Ġrecord ed", + "Ġl im", + "ĠTw o", + "Ġt ry", + "20 10", + "Ġb ad", + "ĠBro wn", + "Ġin stead", + "ĠO ver", + "Ġperform ed", + "Ġ193 7", + "ĠU r", + "Ġcon c", + "Ġst orm", + "L S", + "Ġt akes", + "ĠT ime", + "ĠJ ean", + "ĠU E", + "ĠEd uc", + "Ġarr ondiss", + "Ġ6 0", + "Ġprod uct", + "Ġcent ral", + "ĠM P", + "h ar", + "eth ing", + "ĠP ac", + "Ġcurrent ly", + "ĠH aw", + "Ġprot ect", + "i os", + "Ġtra vel", + "ĠF ox", + "g g", + "as c", + "Ġex ist", + "Ġmain ly", + "ĠO ld", + "199 6", + "Ġb ar", + "ab ly", + "ĠF ar", + "Ġme as", + "Ġm aterial", + "f ace", + "p ed", + "Ġcl aim", + "ĠC SS", + "Ġtown s", + "and a", + "ec k", + "ĠU nd", + "ste in", + "ĠC am", + "ĠM o", + "ĠK e", + "Ġro les", + "eth od", + "ĠSec ond", + "Ġcr ime", + "Ġdest roy", + "l anguage", + "Ġun ivers", + "ĠM inn", + "ĠL uc", + "Ġam ount", + "Ġ195 3", + "Ġp ark", + "ĠT od", + "Ġhe alth", + "Ġ193 2", + "j a", + "Ġs ug", + "199 5", + "Ġw ind", + "Ġmov ement", + "Ġrel ations", + "ab eth", + "Ġe ither", + "Ġpro per", + "az ine", + "ades h", + "Ġse ats", + "Ġ18 0", + "Ġc ertain", + "Ġn orm", + "st ron", + "Ġrec ogn", + "Ġw he", + "O R", + "Ġbl ood", + "ĠSc ient", + "t ime", + "Ġmon ths", + "Ġe at", + "rem e", + "ĠA p", + "ĠW ood", + "Ġch urch", + "Ġv iew", + "k o", + "Ġhelp ed", + "Ġse ven", + "Ġm ight", + "our ce", + "Ġwest ern", + "iv id", + "Ġcl ose", + "Ġ192 6", + "Ġb all", + "pec ially", + "Ġc reat", + "Ġchem ical", + "Ġstruct ures", + "Ġarch itect", + "y ear", + "ĠI owa", + "Ġal ways", + "isc o", + "ĠSt ep", + "Ġs it", + "Ġv ict", + "Ġbroad cast", + "Un ited", + "ĠD ire", + "ĠSp ring", + "air s", + "Ġc ase", + "Ġc at", + "8 9", + "N A", + "i h", + "ang er", + "end ing", + "Ġwrit ing", + "ent ion", + "ĠSt reet", + "Ġre ached", + "ĠSecret ary", + "Ġp ast", + "ĠCl ass", + "el d", + "Ġ ident", + "ad ium", + "Ġon ce", + "w an", + "Ġg e", + "Ġ192 7", + "Ġcomp anies", + "Ġto day", + "Ġprod uction", + "Ġ( ,", + "Ġcand id", + "ur ther", + "Ġde g", + "7 5", + "Ġe ight", + "ĠNob el", + "al i", + "ĠJ ud", + "end s", + "ĠH ill", + "oin ts", + "ĠBu ild", + "Ġfour th", + "ak i", + "ĠAn ton", + "ĠSoc ial", + "Ġm urder", + "ĠPol and", + "ĠS ub", + "ĠB ad", + "Ġnum bers", + "ĠMich igan", + "Ġsh own", + "Ġanim ated", + "Ġcompet ed", + "viron ment", + "Ġrepl aced", + "um ents", + "Ġp e", + "st ant", + "Ġt ree", + "ĠOr der", + "Ġc anton", + "Ġpain ter", + "uck y", + "Ġin h", + "Ġp ress", + "i as", + "emb ly", + "ĠO ut", + "ĠB efore", + "Ġpro p", + "Ġmon th", + "ĠA re", + "Ġide a", + "ĠSp orts", + "ĠH ind", + "us e", + "Ġgo al", + "Ġt alk", + "Ġcap t", + "ibr ary", + "Ġc ard", + "Ġdevelop ment", + "if t", + "ĠIn t", + "Ġsign ed", + "Ġre v", + "ĠUn ivers", + "ĠL u", + "Ġ7 0", + "ĠC areer", + "Ġin vent", + "Ġso ft", + "rem ier", + "Ġchang es", + "Ġc itiz", + "c el", + "Ġh ot", + "Ġcom ed", + "ĠGold en", + "Ġprogram s", + "Ġc ourt", + "ut y", + "Ġc ross", + "ĠK enn", + "iet n", + "um e", + "ed s", + "ĠW al", + "7 2", + "ĠBrit ain", + "ĠS erv", + "o is", + "ent h", + "ĠD ar", + "Ġre act", + "ĠSer ies", + "ĠSoc iety", + "Ġdec ided", + "ynast y", + "ĠAl b", + "at re", + "Ġde ad", + "Ġun iversity", + "Ġstud ents", + "ĠT enn", + "ĠInstit ute", + "ĠAn im", + "ĠMc C", + "Ġprom ot", + "Ġin j", + "ĠY oung", + "id ge", + "Ġan cient", + "Ġp ract", + "Ġo x", + "Ġd er", + "tern et", + "Ġn ight", + "Ġrele ase", + "ĠTe am", + "ĠM iddle", + "ĠB av", + "Ġpro ject", + "Ġ192 5", + "I n", + "ĠS and", + "id ence", + "ĠOr gan", + "Ġreturn ed", + "Ġ !", + "Ġar rest", + "ĠR ome", + "ok e", + "oc i", + "ĠAtl antic", + "s en", + "Ġp ay", + "Ġl ittle", + "ĠL y", + "or a", + "Ġes pecially", + "em p", + "Ġgo es", + "Ġb oy", + "ĠB usiness", + "es ota", + "ogra pher", + "Ġpre vious", + "Ġadd ed", + "Ġin side", + "Ġ9 0", + "Ġout side", + "ur d", + "Ġj ud", + "ed ia", + "om er", + "ip l", + "Ġear l", + "Ġgr adu", + "ĠThe n", + "at i", + "ĠF e", + "ĠRepresent atives", + "in ces", + "Ġf iction", + "Ġb attle", + "ĠMus lim", + "ĠL ittle", + "Ġindepend ent", + "Ġf ig", + "ĠB ab", + "st ra", + "ĠG ood", + "ĠAb out", + "ĠM ax", + "ĠV ietn", + "anc he", + "ask a", + "ul ation", + "ĠW ork", + "ĠMinn esota", + "ĠP ress", + "ate g", + "199 4", + "Ġper forman", + "Ġallow ed", + "ĠDep artment", + "Ġbas eball", + "8 6", + "Ġs en", + "Ġdr ug", + "Ġf all", + "Ġf re", + "Ġmunicipal ities", + "ĠE ver", + "Ġart ists", + "Ġlead ers", + "ĠE p", + "ĠS a", + "ĠM ah", + "Ġh om", + "Ġb ox", + "ĠG h", + "Ġsom ething", + "Ġen ough", + "Ġf if", + "m ond", + "Ġla un", + "eng th", + "Ġnomin ated", + "Ġcan not", + "r ich", + "Ġmount ain", + "Ġsouth west", + "Ġra p", + "al so", + "ĠP ers", + "un s", + "Ġme et", + "Ġf ront", + "Ġinter est", + "Ġrel ated", + "Ġfor ce", + "l ah", + "ĠT our", + "ĠAr men", + "ĠComp any", + "p eople", + "ĠWrest ling", + "ĠFranc isco", + "Ġres earch", + "ic ular", + "ri z", + "ad el", + "Ġposs ible", + "Ġb oard", + "8 5", + "ost on", + "Ġthe ory", + "is ing", + "ound s", + "w in", + "Ġsystem s", + "ĠW ay", + "Ġse qu", + "ĠJ ac", + "ĠB ul", + "Ġc ele", + "ĠR on", + "ĠF er", + "ĠD uke", + "h in", + "Ġa th", + "ĠColumb ia", + "ĠP ictures", + "ĠG ram", + "Ġpar ents", + "Ġband s", + "Ġair craft", + "ĠN az", + "ĠEnter tain", + "Ġfriend s", + "itte e", + "Ġ192 4", + "Ġactiv ist", + "ĠLouis iana", + "it ing", + "Ġgo ing", + "ĠV an", + "est ab", + "iz ations", + "ĠAlex ander", + "ag ed", + "Ġc oll", + "ĠF orm", + "Ġv ir", + "iv ate", + "C A", + "Ġorigin ally", + "Ġst ay", + "Ġear th", + "ĠT re", + "rat ive", + "ĠE lect", + "in son", + "c an", + "Ġra c", + "Ġwee k", + "ĠP LS", + "ĠAir port", + "Ġ19 22", + "ad d", + "hes s", + "ay er", + "ĠLe on", + "Ġm em", + "ĠSp ec", + "Ġt ropical", + "p ly", + "199 3", + "ĠWind ows", + "ay a", + "Ġlong er", + "ĠFootball ers", + "il ly", + "ar g", + "ĠA D", + "Ġres ults", + "ĠBi ography", + "in cess", + "is ions", + "j i", + "ien ces", + "Ġbre ak", + "ut s", + "7 4", + "Ġd ig", + "am i", + "Ġnorth west", + "r as", + "ing er", + "ĠF ame", + "Ġse asons", + "ĠE astern", + "ens ive", + "ĠCh ief", + "Ġgr and", + "im b", + "lah oma", + "Ġsh oot", + "m in", + "Ġr en", + "GB T", + "Ġcamp aign", + "ĠI d", + "ĠFam ily", + "7 9", + "us es", + "Ġre view", + "ail able", + "ĠH istor", + "y an", + "z o", + "ĠCh ild", + "Ġp ur", + "ĠP erson", + "h ood", + "ĠN ight", + "if y", + "Ġl ove", + "Ġfin ished", + "ĠOk lahoma", + "v a", + "Ġc rick", + "ĠM u", + "ĠSh ow", + "ĠJ eff", + "Ġc ell", + "Ġs ize", + "Ġ192 3", + "il a", + "um m", + "Ġold est", + "or ial", + "Ġm ale", + "olit an", + "ĠT am", + "ĠC ub", + "Ġdiv ided", + "ĠM ajor", + "0 5", + "c est", + "Ġepis odes", + "ĠD et", + "id ae", + "ro wn", + "/ /", + "w ar", + "or g", + "ra ine", + "Ġ19 00", + "ĠB oston", + "ĠL ong", + "7 6", + "Ġm iss", + "ou d", + "ĠChar l", + "Ġhow ever", + "ĠAr k", + "Ġd oc", + "ĠA k", + "val ue", + "s ort", + "ĠCh ile", + "p resent", + "ĠWar s", + "ĠM em", + "Ġh ospital", + "Ġle ague", + "Ġpro b", + "ĠN ord", + "l av", + "ĠPac ific", + "ut t", + "Ġmed ia", + "Ġm ach", + "ĠEl iz", + "ĠTok yo", + "ra ck", + "ĠM att", + "ĠWh ile", + "Ġb o", + "Ġe astern", + "Ġcon v", + "Ġgo als", + "ĠL GBT", + "Ġ er", + ": //", + "Ġcy cl", + "ĠWW E", + "Ġelect ric", + "Ġw id", + "ĠP ope", + "el le", + "Ġperson al", + "Ġch ampionship", + "Ġnew sp", + "enc ed", + "ĠO cean", + "ĠB al", + "Ġqu ick", + "l ers", + "ĠNew s", + "ain ing", + "ac hes", + "um i", + "Ġcontin ued", + "Ġeduc ation", + "ĠR ay", + "ĠBer lin", + "Ġl o", + "Ġc ases", + "Ġp sych", + "ĠMar ia", + "ĠL i", + "ĠJohn son", + "Ġm ethod", + "Ġsup er", + "ĠG ame", + ". )", + "el a", + "Ġac adem", + "ĠN ick", + "Ġst ations", + "Ġf ac", + "ĠC a", + "Ġ ;", + "Ġs uff", + "ĠSt e", + "Ġsmall er", + "Ġlaw s", + "0 6", + "ĠRo ad", + "Ġbelie ved", + "it o", + "writ ers", + "ur ity", + "Ġform s", + "ĠP as", + "Ġaward ed", + "ic ult", + "Ġlawy er", + "th ur", + "w ith", + "ĠT a", + "ut ure", + "Ġacc ident", + "Ġfeat ures", + "ch an", + "Ġdisc overed", + "Ġb order", + "Ġcrit ic", + "ĠJ un", + "ĠI v", + "Ġserv 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ent", + "et h", + "Ġsh ow", + "ĠO r", + "Ġm on", + "a u", + "Ġc o", + "a id", + "Ġv ery", + "iv es", + "m y", + "Ġ und", + "Ġp re", + "Ġ end", + "Ġw ould", + "Ġ201 0", + "ur g", + "ur r", + "ĠN orth", + "t il", + "it al", + "Ġsp ec", + "u ally", + "Ġplay ers", + "ĠEu rope", + "oug h", + "y p", + "e e", + "Ġm ay", + "Ġm an", + "Ġw on", + "Ġl ike", + "ro s", + "art ment", + "r ist", + "ĠA ward", + "f orm", + "Ġs m", + "Ġe ar", + "ain t", + "Ġs uch", + "a x", + "he m", + "00 0", + "Ġto wn", + "fer ent", + "Ġre l", + "ĠF l", + "Ġar t", + "Ġmus ic", + "er ed", + "i ous", + "Ġin d", + "2 3", + "u al", + "Ġrele ased", + "Ġc re", + "am ed", + "ĠT e", + "in es", + "Ġ2 4", + "is hed", + "Ġestab lishments", + "Ġch ar", + "i um", + "ĠP resident", + "ro ugh", + "ĠP art", + "tern ational", + "Ġb ro", + "ĠA f", + "p en", + "s h", + "et s", + "Ġth ree", + "Ġdif ferent", + "Ġp erson", + "un g", + "Ġsec ond", + "Ġdire ct", + "Ġun til", + "ĠM ov", + "c er", + "ĠR iver", + "ĠR uss", + "Ġs up", + "Ġl ong", + "row span", + "ĠW est", + "ef ore", + "Ġst r", + "ot t", + "ĠE l", + "Ġg overn", + "Ġ200 3", + "er g", + "is e", + "Ġw ill", + "os s", + "Ġ2 5", + "il m", + "Ġ qu", + "Ġc ap", + "Ġ199 8", + "ĠL a", + "Ġw orld", + "ĠI I", + "e ed", + "o x", + "Ġle ad", + "st em", + "Ġl ater", + "Ġm ed", + "ĠG r", + "Ġthe n", + "1 1", + "Ġb ook", + "ĠAl l", + "are er", + "ant s", + "Ġch ild", + "ĠH is", + "Ġ201 9", + "ri ed", + "at ive", + "le t", + "er v", + "Ġd r", + "Ġ201 7", + "Ġd ec", + "en c", + "z e", + "Ġn ational", + "Ġm ember", + "Ġa ge", + "Ġth rough", + "ĠR el", + "Ġm ar", + "Ġare a", + "at s", + "om en", + "ĠA b", + "Ġm ain", + "it s", + "ĠA fter", + "ther n", + "amp ions", + "ut ion", + "Ġ200 4", + "3 0", + "ĠW h", + "ĠA m", + "ĠS e", + "ĠG u", + "ograph y", + "el s", + "ĠCom mun", + "Ġ3 0", + "f ect", + "in k", + "c c", + "v ent", + "Ġsong s", + "19 7", + "Ġ201 8", + "Ġs ame", + "Ġsing er", + "ĠB ar", + "ctor s", + "ul l", + "olog y", + "Ġsm all", + "Ġb and", + "O S", + "Ġc ar", + "Ġm ake", + "Ġl oc", + "ĠH er", + "Ġ200 5", + "re en", + "ĠGe or", + "Ġ201 4", + "ĠCh rist", + "Ġfor mer", + "Ġf our", + "Ġs ub", + "d om", + "ly mp", + "ĠB e", + "g er", + "ĠM an", + "g est", + "Ġre t", + "5 0", + "in o", + "re t", + "ian s", + "c om", + "Ġdep artment", + "Ġcon s", + "Ġl angu", + "Ġk ill", + "Ġf e", + "Ġpro v", + "ĠSp ace", + "Ġus e", + "Ġa m", + "ĠO lymp", + "Ġl ife", + "l p", + "Ġm et", + "ak es", + "ĠW ill", + "if orn", + "ĠC ent", + "Ġa ir", + "is c", + "oun c", + "ov e", + "c ent", + "ĠCal iforn", + "Ġbe ing", + "Ġ19 0", + "ur al", + "y l", + "\" ,", + "Ġc r", + "ĠH ar", + "ĠCaliforn ia", + "Ġg ame", + "f ess", + "ĠPart y", + "Ġw ell", + "Ġ20 21", + "Ġprod uc", + "Ġ ed", + "oll ow", + "b le", + "Ġm od", + "Ġb ack", + "j ect", + "ĠR e", + "Ġn o", + "Ġp ages", + "Ġrec ord", + "ĠH ow", + "Ġd id", + "Ġof ten", + "Ġb el", + "y n", + "an k", + "Ġ2 1", + "Ġre p", + "Ġn amed", + "ie w", + "2 4", + "at ing", + "ĠB ra", + "land s", + "om et", + "Ġstart ed", + "6 0", + "iel d", + "Ġg u", + "r est", + "Ġ .", + "ĠCh ar", + "Ġo ld", + "ĠP ol", + "ĠO ff", + "Ġ201 6", + "a e", + "Ġreg ion", + "Ġb ased", + "Ġ2 3", + "2 5", + "st it", + "ĠGerman y", + "ĠN or", + "il le", + "ug ht", + "Ġb efore", + "Ġsy stem", + "al d", + "Ġ201 5", + "ĠA ng", + "8 0", + "Ġm unicipal", + "r ies", + "Ġfam ily", + "ĠM inist", + "Ġ2 9", + "ĠJapan ese", + "ician s", + "om ar", + "our n", + "ĠEng land", + "Ġs aid", + "Ġp ar", + "Ġre m", + "ĠInd ian", + "ĠRel ated", + "Ġo wn", + "ĠR oman", + "ener al", + "Ġ200 6", + "ĠPal omar", + "Ġagain st", + "Ġs ince", + "el f", + "Ġund er", + "ĠT r", + "if ic", + "ir d", + "Ġc areer", + "ond on", + "uc k", + "Ġact ress", + "on y", + "et her", + "Ġcommun e", + "ill ion", + "Ġt ran", + "Ġn orth", + "Ġl aw", + "b urg", + "k e", + "4 0", + "en g", + "Ġg ames", + "2 7", + "ĠB ro", + "ĠP eak", + "Ġn ear", + "ĠMov ies", + "ĠItal ian", + "Ġ X", + "ĠE m", + "ĠK itt", + "ĠL ondon", + "2 9", + "Ġ2 6", + "ĠE n", + "ĠA ir", + "Ġp o", + "ĠDe ath", + "st r", + "b s", + "at a", + "ĠH istory", + "Ġpl ace", + "Ġchar act", + "as k", + "Ġan y", + "Ġw e", + "Ġ2 8", + "Ġhe lp", + "w atch", + "2 8", + "ĠE x", + "ĠC up", + "Ġf ollow", + "c he", + "Ġw ater", + "Ġ201 1", + "i ation", + "2 6", + "at ure", + "is on", + "Ġas s", + "a f", + "Ġw ar", + "Ġ2 7", + "ĠSpace watch", + "Ġgovern ment", + "Ġ ent", + "Ġdirect ed", + "Ġb est", + "Ġin ter", + "ampions hip", + "ĠE ar", + "Ġt yp", + "in ess", + "r ing", + "Ġg r", + "Ġthe se", + "Ġan im", + "g ram", + "l ing", + "Ġ200 7", + "ul ar", + "al a", + "Ġcent ury", + "EA T", + "b y", + "u th", + "ar a", + "ain s", + "h am", + "ĠU S", + "ĠN EAT", + "c on", + "ide o", + "ĠAs s", + "Ġpopul ation", + "ĠS ome", + "an a", + "ed y", + "3 3", + "in t", + "Ġe ver", + "Ġse ason", + "od y", + "Ġm il", + "ram a", + "Ġ20 22", + "o on", + "Ġcount ry", + "Ġs ur", + "at or", + "Ġ &", + "ow s", + "ĠKing dom", + "Ġapp ear", + "ord s", + "am a", + "ro g", + "al t", + "Ġ201 3", + "Ġa round", + "Ġinclud ing", + "ut e", + "ĠW hen", + "Ġor ig", + "Ġl and", + "st er", + "Ġor gan", + "Ġ199 0", + "ĠM e", + "f l", + "9 0", + "Ġb oth", + "Ġse ver", + "oin t", + "T he", + "od e", + "a ul", + "Ġwebsit e", + "Ġ200 8", + "aj or", + "7 0", + "t t", + "an ish", + "Ġs chool", + "re m", + "Ġc ould", + "Ġin v", + "Ġ2 2", + "am b", + "q ue", + "r id", + "al es", + "ĠM c", + "ip p", + "d e", + "ĠB el", + "z er", + "on es", + "Ġ em", + "Ġs et", + "Ġd istrict", + "ĠG overn", + "il t", + "n er", + "ist an", + "ĠIn ternational", + "ur ch", + "us iness", + "ĠCanad ian", + "iz ed", + "emb ers", + "Ġim port", + "ĠWill iam", + "m s", + "ĠAustral ia", + "Ġbu ild", + "Ġ201 2", + "Ġ( )", + "Ġl ist", + "oug ht", + "ĠM ed", + "Ġpro fess", + "ag o", + "Ġe ach", + "Ġh ow", + "Ġr un", + "ĠAmer ica", + "a ur", + "Ġs l", + "ak ing", + "Ġh igh", + "um b", + "3 4", + "Ġus ually", + "n ey", + "Ġ right", + "Ġl ived", + "ĠAnd erson", + "ĠCom p", + "ĠCommun es", + "uct ion", + "o ard", + "ĠM es", + "Ġex amp", + "vel op", + "ĠPh il", + "Ġs im", + "ĠThe se", + "ort s", + "Ġ200 9", + "al k", + "ros s", + "9 9", + "Ġchild ren", + "ĠM on", + "3 7", + "Ġh um", + "i ence", + "6 5", + "ra ct", + "om in", + "u es", + "um mer", + "ĠM ich", + "ib le", + "ĠH ouse", + "w rit", + "Ġl ast", + "o le", + "Ġde velop", + "col span", + "ĠAust r", + "6 4", + "ad em", + "et er", + "Ġcre ated", + "Ġro ck", + "Ġcomp os", + "6 8", + "ĠO ne", + "6 7", + "ist or", + "Ġc aus", + "N E", + "\"| -", + "Ġser v", + "ĠInd ia", + "3 5", + "ĠMinist er", + "ĠL ou", + "Ġs k", + "Ġ ran", + "ĠSw ed", + "urr ent", + "le x", + "Ġc ounty", + "Ġ '", + "Ġbe gan", + "Ġad d", + "Ġsever al", + "Ġpro gram", + "\"|- ||", + "Ġw inn", + "Ġs ign", + "ĠS an", + "Ġcl ub", + "ĠP er", + "Ġs outh", + "Ġst at", + "ĠD em", + "Ġatt ack", + "en e", + "Ġwh ile", + "Ġo per", + "ĠSt ate", + "Ġcom mon", + "ĠS ec", + "in c", + "an e", + "Ġwrit er", + "3 8", + "Ġ198 0", + "ĠD av", + "Ġv ers", + "ap p", + "ĠG l", + "ed er", + "f or", + "f ul", + "ĠS up", + "Ġlar ge", + "c hes", + "Ġt erm", + "us h", + "ĠS y", + "it ary", + "Ġimport ant", + "Ġl ive", + "v en", + "ens us", + "s ide", + "ing ton", + "Ġoff icial", + "ĠHow ever", + "4 5", + "Ġsing le", + "ĠS ch", + "Ġ if", + "Ġp ol", + "Ġhe ad", + "ĠDeath s", + "Ġd rama", + "re w", + "ĠAustral ian", + "Ġdis c", + "ir ed", + "Ġac c", + "d ay", + "ĠC ities", + "6 9", + "Ġw ent", + "Ġ199 7", + "Ġf ilm", + "n a", + "l er", + "Ġin t", + "att le", + "Ġpopul ar", + "st e", + "a ught", + "as ter", + "Ġs uc", + "ĠA c", + "Ġm illion", + "ber g", + "t he", + "ĠMes a", + "Ġd ef", + "Ġmunicipal ity", + "ĠOff icial", + "Ġd iv", + "ĠRuss ian", + "Ġlangu age", + "ic o", + "z il", + "3 9", + "a ut", + "id d", + "Ġn ow", + "o ice", + "ro l", + "Ġs oc", + "ĠM iss", + "Ġle g", + "4 8", + "Ġexamp le", + "4 7", + "Ġm at", + "an ge", + "ce pt", + "Ġdes ign", + "Ġ199 6", + "om b", + ". \"", + "Ġp ower", + "Ġf in", + "ĠS er", + "Ġch ang", + "Ġcount ries", + "Ġm in", + "Ġear ly", + "Ġe p", + "Ġan n", + "ĠCh ampionship", + "Ġp resident", + "ĠBra zil", + "Ġd ist", + "omet imes", + "iv en", + "Ġh ome", + "ĠM ex", + "Ġg et", + "w est", + "Ġen g", + "ĠH ol", + "ĠL O", + "ĠQ u", + "Ġcomp et", + "Ġw est", + "ĠC o", + "Ġgroup s", + "ock ey", + "Ġinclud e", + "ic es", + "ĠP ark", + "ĠR ec", + "Ġo pen", + "Ġd ay", + ". .", + "iv il", + "Ġv ideo", + "Ġin c", + "op h", + "i ef", + "l in", + "Ġex p", + "Ġtran s", + "ber t", + "ĠR ober", + "Ġcap ital", + "p le", + "Ġspec ies", + "Ġme ans", + "ĠS m", + "f ord", + "NE OS", + "ĠLO NEOS", + "Ġ196 0", + "Ġwrit ten", + "ĠP olit", + "ri end", + "i j", + "ĠSp anish", + "con om", + "5 7", + "Ġle ft", + "g es", + "i en", + "ĠS ing", + "Ġw ay", + "id ed", + "ĠJ ames", + "ĠS chool", + "Ġex t", + "ĠT ur", + "ro d", + "ĠP aul", + "oc rat", + "Ġever y", + "ĠS en", + "ĠM or", + "g in", + "Ġh istory", + "Ġ199 5", + "a ces", + "Ġ ,", + "ĠD r", + "9 8", + "Ġm embers", + "ĠT ex", + "our t", + "ĠP ort", + "ĠCanad a", + "Ġp ass", + "ĠA ctors", + "i od", + "Ġt imes", + "ĠE ast", + "c o", + "ĠAng el", + "ĠF ootball", + "e al", + "l ed", + "i us", + "Ġ197 0", + "Ġd own", + "F A", + "r is", + "ĠS aint", + "le ge", + "u ff", + "Ġm uch", + "ĠG ree", + "ch n", + "ov er", + "Ġman ag", + "Ġmar ried", + "Ġact iv", + "ar n", + "Ġwh at", + "9 7", + "5 8", + "an ia", + "id es", + "m a", + "ra in", + "Ġpro t", + "ep end", + "oun g", + "ro te", + "ĠRep ublic", + "Ġfam ous", + "it ar", + "|||| ||||", + "it er", + "ist ics", + "Ġcan cer", + "Ġsh ort", + "Ġ199 4", + "Ġw omen", + "e an", + "i or", + "Ġv ar", + "os p", + "ĠM il", + "ĠR eg", + "id a", + "ĠS ov", + "Ġst ill", + "Ġcom edy", + "Ġm ajor", + "a el", + "ĠFl or", + "or p", + "ĠN ot", + "Ġcl ass", + "ĠT own", + "y le", + "u el", + "Ġre f", + "o e", + "ĠPro v", + "Ġbu ilt", + "ct ion", + "Ġf ather", + "h an", + "Ġ3 1", + "y a", + "ol ution", + "al th", + "Ġj oin", + "v iew", + "Ġc urrent", + "ill a", + "ĠGeor ge", + "ĠIt s", + "Ġre ce", + "k y", + "ĠN Y", + "Ġ1 00", + "g y", + "v es", + "6 6", + "Ġst ar", + "as tern", + "ĠLou is", + "Ġs old", + "as es", + "Ġ18 8", + "Ġ199 2", + "op e", + "Ġb re", + "ĠPr ime", + "Ġp ubl", + "ĠSov iet", + "9 5", + "ĠP re", + "he l", + "Ġt it", + "o f", + "Ġ199 3", + "Ġv ill", + "ric k", + "Ġdo es", + "ĠJ os", + "Ġsup port", + "ut ch", + "ĠJ ack", + "Ġlar gest", + "Ġcomp any", + "Ġto ok", + "Ġs on", + "Ġa ward", + "ĠAr t", + "Ġp ublic", + "ĠR ed", + "ĠCh ic", + "ĠC at", + "ans as", + "Ġb usiness", + "Ġg ood", + "ĠItal y", + "ĠT w", + "Ġw rote", + "ĠV al", + "ĠUn ion", + "ĠM ount", + "Ġmov ed", + "ĠEurope an", + "ĠV ir", + "ĠK ore", + "ĠM any", + "en a", + "Ġan other", + "Ġbec ome", + "ĠCom m", + "Ġl a", + "Ġfootball er", + "ĠR ich", + "ĠTex as", + "n ess", + "Ġth ird", + "ĠA g", + "ad io", + "is ed", + "ĠCh ina", + "Ġc ame", + "Ġsuc cess", + "Ġo b", + "oc iation", + "Ġhe ld", + "ĠChic ago", + "Ġdire ctor", + "Ġto p", + "Ġb ody", + "Ġst age", + "Ġ199 1", + "c y", + "ĠR o", + "enc y", + "w ork", + "3 6", + "Ġb ig", + "ad es", + "Ġn eed", + "ĠNY S", + "4 4", + "ot s", + "Ġev en", + "ĠDem ocrat", + "ric a", + "Ġpro ble", + "Ġcont in", + "ourn al", + "uth or", + "Ġalbum s", + "Ġg iven", + "Ġprofess ional", + "Ġp os", + "Ġw ant", + "ur ed", + "Ġg en", + "iv al", + "ag n", + "Ġb as", + "Ġme an", + "ad y", + "Ġph ys", + "ĠC ast", + "Ġbook s", + "ĠP ak", + "ĠP ri", + "Ġpolit ical", + "Ġcon d", + "ĠD on", + "ex t", + "ĠRober t", + "ĠM ont", + "ac ed", + "os ed", + "ra ft", + "ĠSp ort", + "ĠC ap", + "ĠL os", + "r ican", + "ĠD uring", + "Ġd eb", + "5 5", + "ĠM y", + "Ġj ust", + "ar m", + "Ġcom m", + "ĠS l", + "Ġs ix", + "ir l", + "ĠD istrict", + "Ġwork ed", + "ut er", + "Ġo cc", + "ĠY ou", + "k i", + "Ġn ov", + "ĠBl ack", + "Ġpolitician s", + "7 8", + "ĠM ost", + "ĠDav id", + "Ġc ensus", + "and er", + "ĠL ist", + "ĠB est", + "h i", + "ĠD ep", + "Ġdes c", + "ĠT ra", + "av ing", + "Ġc ult", + "iet y", + "Ġcharact er", + "il ity", + "t ain", + "Ġth ings", + "ĠCl ub", + "ul a", + "ĠJ ew", + "Ġkill ed", + "at ural", + "er a", + "ĠAf rican", + "Ġres p", + "ou thern", + "Ġp resent", + "3 1", + "ĠCh in", + "ĠM al", + "Ġep is", + "ĠN e", + "ĠH igh", + "Ġal ong", + "Ġm en", + "v ille", + "Ġr ul", + "Ġf ive", + "um p", + "ĠF rom", + "ut ed", + "ĠD utch", + "ĠSc ott", + "m en", + "Ġl ight", + "r u", + "Ġ| }", + "ĠB as", + "ra b", + "it ions", + "m ed", + "Ġw ord", + "o o", + "ĠW e", + "Ġf em", + "ĠR es", + "or ies", + "s c", + "ĠH en", + "ĠR oy", + "ĠW ash", + "Ġ198 9", + "Ġl iving", + "Ġs w", + "m e", + "ĠG ro", + "id s", + "ĠAs ia", + "ear ch", + "Ġper iod", + "Ġmil itary", + "il ar", + "Ġr ed", + "Ġro le", + "ĠB y", + "ĠAc adem", + "ĠS al", + "av y", + "ĠS ummer", + "9 4", + "ĠAf rica", + "ĠE mp", + "writ er", + "ĠB er", + "Ġ198 8", + "Ġfound ed", + "Ġplay s", + "an o", + "Ġ198 1", + "Ġt em", + "Ġvers ion", + "ĠD es", + "Ġser ved", + "ol ic", + "v al", + "itt le", + "3 2", + "Ġpubl ished", + "t y", + "ĠH al", + "Ġh istor", + "our s", + "Ġ ef", + "ĠM ad", + "Ġprov ince", + "e um", + "Ġrep resent", + "Ġus ing", + "ĠI ll", + "n ect", + "ĠQ ue", + "o ff", + "ant ic", + "Ġex pl", + "u x", + "ĠTh om", + "re g", + "ot a", + "Ġl ook", + "Ġwork s", + "ell s", + "ical ly", + "Ġm y", + "Ġor der", + "ounc il", + "' t", + "Ġf rog", + "ir c", + "ĠC ath", + "ĠM art", + "Ġfollow ing", + "ĠWash ington", + "l ine", + "Ġc amp", + "7 7", + "ĠGr and", + "ra el", + "Ġpart s", + "Ġf ew", + "Ġag ed", + "le ments", + "Ġret urn", + "Ġto g", + "ĠS am", + "Ġdis e", + "Ġ 0", + "Ġspec ial", + "Ġtog ether", + "Ġev ent", + "ĠAngel es", + "al ity", + "ĠChin ese", + "ĠB ut", + "Ġeng ine", + "ĠB u", + "ĠA lex", + "t al", + "ĠD iv", + "at ors", + "ĠPak istan", + "Ġa uthor", + "it zer", + "iz e", + "Ġ195 0", + "Ġ ide", + "ĠW rit", + "9 6", + "re am", + "k a", + "Ġhe art", + "Ġg reat", + "Ġcaus ed", + "ou l", + "ĠChar les", + "Ġf ood", + "h a", + "ĠChrist ian", + "iss ion", + "L O", + "od es", + "ĠM unicipal", + "ĠF irst", + "Ġbec om", + "ĠG reat", + "ĠE v", + "Ġs ometimes", + "iz ation", + "if ied", + "ĠH all", + "Ġelect ion", + "ounc ed", + "ĠMich ael", + "r ip", + "Ġe qu", + "or ed", + "ict ion", + "S t", + "Ġg ot", + "on a", + "op s", + "Ġ es", + "Ġr est", + "ĠN o", + "Ġse e", + "ĠD ay", + "Ġhum an", + "lect ion", + "Ġt er", + "Ġim p", + "Ġ198 6", + "Ġar r", + "Ġh ig", + "Ġt ake", + "Ġper form", + "Ġv oice", + "ĠVir gin", + "and s", + "c ed", + "Ġp ut", + "ĠG old", + "sp eople", + "ro v", + "i ol", + "5 4", + "ĠK ar", + "ĠP at", + "m inist", + "Ġth ought", + "ĠM ary", + "ĠPl ay", + "Ġg ener", + "g ian", + "ĠRich ard", + "Ġs ol", + "Ġbel ie", + "ĠOlymp ics", + "idd le", + "ĠB ill", + "ĠSp ain", + "Ġb i", + "i ers", + "ĠB en", + "ĠM ass", + "Ġf ight", + "B C", + "ĠL aw", + "ĠM et", + "Ġtr ad", + "ar ch", + "un t", + "Ġv ot", + "Ġ198 7", + "Ġse at", + "Ġgu itar", + "A S", + "ĠIs rael", + "Ġm other", + "ord ing", + "ĠI r", + "um ent", + "ĠCar ol", + "w ays", + "ĠG od", + "l o", + "Ġar ch", + "r ation", + "Ġ198 4", + "Ġle vel", + "Ġtyp e", + "ud e", + "Ġre pl", + "Ġ197 9", + "ĠS im", + "ar ed", + "ĠT er", + "8 8", + "Ġs ent", + "Ġv ol", + "Ġst ars", + "ĠS o", + "Ġf riend", + "Ġe conom", + "ĠT om", + "Ġin ternational", + "ĠPar is", + "in i", + "Ġn ext", + "Ġfe at", + "eren ce", + "ĠY ear", + "ĠAward s", + "Ġr iver", + "ĠU K", + "un n", + "ĠCh urch", + "ĠDemocrat ic", + "Ġg eneral", + "u ed", + "ĠF LO", + "at ives", + "5 9", + "Ġk ing", + "Ġl ine", + "ĠFran k", + "Ġm aking", + "Ġsc ient", + "ial ly", + "Ġ197 3", + "Ġm ark", + "Ġst ory", + "ĠTown s", + "ĠI f", + "Ġc rit", + "ĠTur k", + "Ġ198 5", + "m b", + "ĠAr g", + "ĠIs land", + "Ġd ata", + "Ġvill age", + "m ing", + "ĠL at", + "Ġy ou", + "ĠG eneral", + "et work", + "Ġ197 6", + "Ġ198 2", + "l iam", + "Ġorig in", + "Ġrel ig", + "ur a", + "ĠK n", + "per or", + "Ġf ind", + "Ġh ouse", + "ĠFlor ida", + "ar i", + "Ġan t", + "Ġ198 3", + "ĠS ant", + "ĠCol lege", + "Ġc hem", + "ĠAcadem y", + "form ation", + "ĠEmp ire", + "Ġ5 0", + "Ġv is", + "Ġlead er", + "I D", + "rop ical", + "Ġ197 7", + "u le", + "Ġf ail", + "i ber", + "Ġstr uct", + "ĠR ock", + "Ġj ournal", + "om a", + "Ġrece ived", + "ino is", + "Ġsim ilar", + "c ast", + "ĠAt l", + "ĠD an", + "ĠG o", + "st on", + "m ost", + "Ġloc ated", + "Ġn e", + "ĠH am", + "ĠK h", + "liam ent", + "ain ed", + "l ic", + "6 3", + "ĠT V", + "c her", + "ĠG ra", + ") :", + "ĠAustr ian", + "ĠIll inois", + "c ient", + "Ġ197 2", + "ĠB i", + "itzer land", + "ĠR ev", + "Ġart ist", + "s y", + "Ġproduc er", + "ert ain", + "Ġa ut", + "ig a", + "Ġnov el", + "ov ered", + "Ġd ays", + "Ġst ation", + "ĠD is", + "Ġed uc", + "Ġst op", + "ĠGree k", + "ĠMus ic", + "im ate", + "Ġp aint", + "ĠI m", + "ĠGovern or", + "Ġse en", + "ĠZ eal", + "ĠGeor g", + "Ġh ost", + "Ġall ow", + "Ġa v", + "a im", + "he st", + "Ġp rom", + "ad s", + "end ed", + "Ġstat istics", + "er ing", + "Ġ197 8", + "ĠP eter", + "Ġv al", + "ĠZeal and", + "Ġg l", + "Ġl ess", + "ĠSw itzerland", + "ar ian", + "Ġm ount", + "Ġe ast", + "Ġgro w", + "ĠSport speople", + "ĠAr ch", + "r or", + "Ġmod ern", + "Ġt urn", + "av es", + "y r", + "ĠT ran", + "ol f", + "ap e", + "ĠK ansas", + "Ġm akes", + "Ġcons id", + "0 8", + "ĠFran c", + "Ġdise ase", + "a ff", + "ir on", + "ĠD el", + "ĠOlymp ic", + "ĠU p", + "ĠAss ociation", + "Ġ193 0", + "ĠRuss ia", + "al f", + "200 6", + "4 9", + "im a", + "Ġ18 7", + "y d", + "cent ury", + "ar ia", + "ĠPolit icians", + "ĠSwed en", + "Ġis land", + "Ġst yle", + "ask et", + "200 5", + "Ġproduc ed", + "u z", + "Ġ197 4", + "aught er", + "ĠF ilm", + "Ġth ose", + "Ġ197 5", + "Ġbl ack", + "Ġl ed", + "est s", + "Ġev ents", + "Ġbe g", + "Ġind epend", + "ĠWrit ers", + "ian a", + "Ġelect ed", + "Ġteam s", + "ul ts", + "ĠT o", + "ĠProv ince", + "200 7", + "ĠCath olic", + "ĠM er", + "Ġcont rol", + "ud d", + "Ġbro ther", + "Ġpart y", + "ĠMex ico", + "Ġse x", + "un k", + "Ġst ates", + "Ġsh ould", + "Ġform ed", + "ition al", + "Ġ197 1", + "u ce", + "ĠG reen", + "th ough", + "ak en", + "re y", + "Ġ194 0", + "Ġd el", + "Ġcharact ers", + "in ter", + "4 6", + "it es", + "le ar", + "Ġg od", + "S S", + "in ed", + "l am", + "Ġs ound", + "uk e", + "Ġ #", + "gy pt", + "0 7", + "ur t", + "erg y", + "Ġwith out", + "Ġ :", + "Ġn omin", + "ĠEar th", + "I I", + "b oard", + "t ed", + "Ġmon ey", + "w ood", + "Ġph il", + "ĠA ct", + "ad a", + "Ġcon f", + "Ġtit le", + "Ġs ay", + "ĠVirgin ia", + "an i", + "Ġorig inal", + "Ġpl aces", + "f ield", + "Ġproble ms", + "o z", + "ap er", + "Ġd i", + "Ġs ide", + "ol s", + "ong ress", + "Ġann ounced", + "Ġ /", + "fect ure", + "if f", + "hem at", + "re et", + "ĠB ec", + "Ġdesc rib", + "ad u", + "ĠAr my", + "ĠWest ern", + "at ory", + "200 4", + "Ġw ife", + "Ġ ice", + "ent al", + "ight s", + "ĠE r", + "ubl ican", + "ĠRoy al", + "Ġd ue", + "s elf", + "ĠB C", + "ĠAn t", + "Ġa way", + "e ep", + "Ġex per", + "ill s", + "ĠHen ry", + "5 6", + "Ġshow s", + "Ġo pp", + "Ġl ot", + "ap pen", + "Ġjoin ed", + "ĠM ac", + "ĠE gypt", + "ic le", + "ĠThom as", + "Ġstr ong", + "Ġd est", + "Ġs il", + "Ġk ind", + "eb all", + "Ġmed al", + "Ġpo et", + "en se", + "A mer", + "Ġh ockey", + "ĠBrazil ian", + "Ġ el", + "asket ball", + "k n", + "ard s", + "ĠT or", + "ĠI ran", + "ur s", + "Ġst and", + "Ġto tal", + "ĠO l", + "ĠV ict", + "Ġ196 8", + "ist er", + "Ġc y", + "Ġmus ician", + "ol k", + "ĠArg ent", + "Ġm atch", + "ĠCent ral", + "ain e", + "ro y", + "ac hed", + "ĠO h", + "ĠW omen", + "ĠF in", + "Ġcompos er", + "ĠW il", + "ĠW ind", + "it a", + "ĠA cc", + "ĠC O", + "rid ge", + "x t", + "Ġd efe", + "g o", + "ep h", + "r ont", + "ste ad", + "Ġbuild ing", + "Ġc ities", + "Ġlangu ages", + "Ġs ite", + "as ons", + "Ġother s", + "Ġl ost", + "Ġchang ed", + "ers t", + "ĠB ook", + "ur b", + "ra w", + "200 3", + "Ġpl an", + "Ġloc al", + "yl v", + "ĠF I", + "ĠW ith", + "ĠV ill", + "Ġf ire", + "200 1", + "ord er", + "Ġdif f", + "Ġpro cess", + "Ġw rest", + "ĠPri ze", + "Ġm ust", + "Ġare as", + "urr ican", + "Ġp oss", + "em ents", + "Ġright s", + "ĠB ay", + "orp or", + "ĠC ouncil", + "Amer ican", + "ĠSt ud", + "Ġch ange", + "ĠD o", + "200 8", + "Ġstud io", + "ĠR ob", + "b e", + "re ed", + "Ġ196 9", + "ĠM in", + "Ġw ee", + "Ġd em", + "re land", + "Ġre al", + "c hed", + "end er", + "fl u", + "Ġre port", + "or ia", + "ĠRep ublican", + "8 7", + "ĠP op", + "Ġm ult", + "Ġwh ite", + "F F", + "Ġ196 4", + "Ġbe h", + "ĠSt ar", + "Ġl ate", + "ĠRec ords", + "n ot", + "ĠG ames", + "ĠP et", + "ad o", + "ĠCat al", + "Ġl ives", + "e le", + "ĠSwed ish", + "ward s", + "Ġ =", + "9 3", + "ether lands", + "Ġinclud es", + "Ġp at", + "Ġp oint", + "Ġh appen", + "ers ey", + "ĠD ev", + "om s", + "ĠI reland", + "Ġplay ing", + "ĠO k", + "ĠM ic", + "200 2", + "Ġ196 7", + "ĠC ont", + "el and", + "b or", + "Ġsoc ial", + "Ġh ard", + "ĠWh ite", + "Ġ $", + "Ġef fect", + "ĠP enn", + "Ġbro ad", + "Ġsc ience", + "ĠGro up", + "ĠA v", + "r d", + "ic les", + "rip t", + "Ġc ivil", + "ĠSc ot", + "al y", + "l ished", + "ar ies", + "Ġd et", + "Ġf un", + "Ġn on", + "ĠCarol ina", + "Ġy oung", + "Ġg ave", + "Ġinclud ed", + "ĠAustr ia", + "ĠSup er", + ". ,", + "ill er", + "ip s", + "Ġ )", + "Ġm ix", + "4 3", + "Ġre ad", + "ĠSec ret", + "aw a", + "Ġr adio", + "Ġmost ly", + "og n", + "ĠO s", + "200 0", + "an ies", + "Ġm ag", + "re l", + "i ro", + "Ġanim als", + "6 1", + "n ing", + "Ġh and", + "i qu", + "sh ire", + "Ġph ot", + "p art", + "ĠL ife", + "Ġ4 0", + "U n", + "Ġappear ed", + "Ġp ain", + "Ġg old", + "ak er", + "Ġf ield", + "eder al", + "am m", + "ĠM r", + "Ġte chn", + "ib r", + "Ġa ff", + "Ġf inal", + "c le", + "4 1", + "z a", + "Ġh old", + "all s", + "Ġra ce", + "Ġad v", + "Ġres ult", + "ĠC ro", + "b on", + "Ġn or", + "ant on", + "ĠM el", + "ĠH on", + "ĠS ur", + "Ġw ords", + "ĠN etherlands", + "ad or", + "ĠA rab", + "y m", + "ĠEar ly", + "p s", + "c raft", + "Ġs ett", + "ĠM ag", + "angu age", + "Ġ194 5", + "l i", + "ig er", + "ĠB o", + "9 2", + "ĠR h", + "Ġse a", + "ĠA pp", + "ect ed", + "Ġcol or", + "at o", + "il es", + "b r", + "Ġd aughter", + "ec ut", + "lect ed", + "ep ar", + "le ment", + "ĠC he", + "sp ort", + "Ġdeb ut", + "inn ing", + "200 9", + "9 1", + "Ġc or", + "199 9", + "Ġcomp uter", + "op her", + "a ud", + "os aur", + "Ġcom es", + "Ġc al", + "ĠL ab", + "he ast", + "it her", + "Ġstud y", + "ĠMar k", + "Ġco ach", + "Ġus es", + "uc ed", + "ĠC r", + "Ġt rib", + "Ġt aken", + "Ġ z", + "Ġwant ed", + "w w", + "id ing", + "6 2", + "Ġg ra", + "ĠCon f", + "ĠOh io", + "i que", + "Ġ196 6", + "is l", + "ĠF am", + "l or", + "ce an", + "Ġ( ;", + "ĠH a", + "5 3", + "ĠS ince", + "ĠV ol", + "Ġfem ale", + "st ate", + "Ġoff ice", + "ĠTh at", + "it ect", + "ub e", + "ĠB attle", + "ĠD en", + "in ation", + "ĠDiv ision", + "5 1", + "Ġre fer", + "ĠG ar", + "Ġ [", + "n y", + "it ch", + "Ġinv ol", + "i y", + "4 2", + "c a", + "ĠH ung", + "Ġ194 7", + "ell ow", + "e h", + "g en", + "Ġh aving", + "Ġbir th", + "at ic", + "Ġsc reen", + "ĠPort ug", + "Ġn atural", + "g r", + "w are", + "ĠJ er", + "ĠS ol", + "Ġwith in", + "le te", + "C h", + "ann el", + "ĠN ob", + "G B", + "ĠM od", + "ĠU k", + "Ġro und", + "Ġsp orts", + "k ed", + "s el", + "ĠLe g", + "ict ures", + "l a", + "ĠMus eum", + "Ġd am", + "ig an", + "ri al", + "ĠGe ography", + "Ġbet ter", + "Ġdevelop ed", + "Ġp ost", + "on ia", + "r ia", + "ĠGeorg ia", + "es se", + "Ġ196 5", + "Ġsur v", + "ĠJ ersey", + "Ġp ort", + "ĠJ r", + "ab it", + "ĠScott ish", + "Ġkn ow", + "ĠH el", + "ĠM os", + "Ġfootball ers", + "ies t", + "ĠPol ish", + "ĠJew ish", + "ĠH um", + "Ġ194 8", + "Ġs om", + "Ġh alf", + "Ġs ays", + "Ġv oc", + "ĠNor thern", + "Ġth ink", + "g ed", + "Ġpr ison", + "ĠB oy", + "Ġ196 3", + "ĠG en", + "Ġ195 6", + "he im", + "ĠS ong", + "ĠL o", + "Ġin formation", + "ĠP e", + "Ġcom e", + "ĠB ur", + "sy ch", + "V ID", + "Ġf ree", + "Ġs epar", + "Ġm ass", + "Ġle arn", + "ĠL ake", + "Ġestab lished", + "Ġdist rib", + "ĠG all", + "as y", + "Ġv iol", + "an ces", + "ĠL ove", + "ĠK ent", + "ĠLe e", + "u a", + "y o", + "ific ation", + "Ġconsid ered", + "b ers", + "urrican e", + "olog ical", + "Ġbecom es", + "Ġsp eak", + "Ġam ong", + "Ġstud ied", + "ĠS ett", + "ĠCO VID", + "Ġt our", + "ac y", + "Ġcon nect", + "ĠSt ev", + "ip le", + "Ġto o", + "ĠB or", + "osp ital", + "Ġad minist", + "ĠRep resent", + "ĠS un", + "Ġf ish", + "um n", + "Ġh on", + "Ġs outhern", + "ag on", + "Ġin flu", + "il s", + "ĠJ ul", + "our ces", + "ol a", + "et ts", + "Ġab le", + "ĠC amp", + "Ġl ab", + "u f", + "l im", + "Ġ20 23", + "ĠPre fecture", + "ro ll", + "ĠCent er", + "Ġcommun ity", + "it or", + "Ġ196 1", + "ĠC or", + "it z", + "ac her", + "l ess", + "ell ing", + "Ġs qu", + "Ġepis ode", + "ĠQue en", + "Ġd one", + "ĠEm peror", + "ou ri", + "ut es", + "Ġ196 2", + "ĠPr ince", + "ĠR os", + "t a", + "am ent", + "ĠAn n", + "Ġ194 6", + "ĠB ang", + "Ġind ust", + "et ic", + "em s", + "kn own", + "s ylv", + "ĠS k", + "ĠC ount", + "ĠC ivil", + "ond iss", + "ash i", + "Ġtra in", + "v ious", + "ĠIn ter", + "Ġcent er", + "ĠSm ith", + "l ike", + "Ġw oman", + "ur der", + "ĠP an", + "ĠIn stit", + "Ġsp ace", + "Ġab ove", + "us ed", + "ĠIr ish", + "\" )", + "Ġt re", + "j an", + "ĠC ourt", + "ĠCol umb", + "am s", + "ĠM at", + "s ong", + "Ġm ot", + "Ġl ow", + "ĠJ ust", + "amp ion", + "ap s", + "ĠIs lam", + "for man", + "ĠS illa", + "Ġmod el", + "ĠL in", + "m ar", + "Ġin str", + "ĠO bs", + "Ġmat hemat", + "Ġpos ition", + "r ie", + "Ġwrit ers", + "ĠMunicipal ities", + "ach us", + "it iz", + "Ġ194 2", + "Ġco ast", + "ĠGovern ment", + "sylv ania", + "ĠII I", + "ĠFI FA", + "g round", + "o or", + "ap t", + "Ġtra ck", + "Ġ194 9", + "Ġphil os", + "s ur", + "ak a", + "Ġc op", + "Ġn ever", + "Ġwork ing", + "Ġ195 7", + "Ġ19 20", + "az z", + "ĠC ongress", + "b and", + "Ġth ough", + "ĠB ern", + "199 8", + "ent ly", + "ĠE OS", + "Ġnor thern", + "Ġ194 1", + "Ġinc re", + "Ġt ro", + "Ġt reat", + "at ely", + "Ġcount ies", + "ĠMart in", + "Ġc irc", + "Ġ194 4", + "Ġis s", + "rit ory", + "el t", + "Ġwinn ers", + "ĠSe a", + "achus etts", + "Ġ193 6", + "ĠPar liament", + "Ġmus ical", + "ĠJ im", + "Ġ195 1", + "5 2", + "Ġob ject", + "ĠM a", + "pl ay", + "Ġint rod", + "Ġ et", + "ĠTe levision", + "ĠW W", + "ef f", + "Ġk eep", + "Ġal most", + "Ġf ar", + "d s", + "v ers", + "b ack", + "ĠScot land", + "ĠF red", + "Ġb r", + "Ġ193 9", + "ĠMass achusetts", + "Ġch art", + "Ġ ill", + "ĠThe ir", + "Ġoff ic", + "Ġpl ants", + "w h", + "ver age", + "et te", + "Ġf ull", + "re ad", + "ĠC ons", + "Ġtyp es", + "Ġh or", + "Ġsing ers", + "Ġserv ice", + "Ġ193 4", + "Ġhig hest", + "w a", + "Ġf a", + "ĠL and", + "Ġ8 8", + "ĠEd ward", + "Ġn ames", + "ish op", + "ĠHal eak", + "ĠW ales", + "ĠDis ney", + "Ġmus icians", + "H L", + "ĠJos eph", + "ĠSt an", + "Ġ195 8", + "ot o", + "ĠSett lements", + "Ġp res", + "Ġth r", + "Ġc ast", + "ĠBec ause", + "ĠB ob", + "ĠS at", + "p ec", + "ĠH ot", + "ell a", + "ater ial", + "Ġact ion", + "Ġde v", + "Ġc and", + "ĠS il", + "Ġret ired", + "Ġend ed", + "ĠPenn sylvania", + "c ul", + "ĠHaleak ala", + "Ġh it", + "ĠKore an", + "n ow", + "Ġwinn ing", + "p her", + "Ġb asketball", + "Ġcom b", + "Ġ193 8", + "Ġle ast", + "id er", + "b a", + "p e", + "ze ch", + "ĠE U", + "A R", + "ran ch", + "Ġk ey", + "ĠT ok", + "Ġsuccess ful", + "olog ist", + "ĠFor mer", + "ĠW rest", + "Ġ195 2", + "Ġin f", + "199 7", + "Ġopen ed", + "ĠU s", + "Ġen ergy", + "Ġ( \"", + "Ġ195 4", + "istric ts", + "m ark", + "Ġc he", + "Ġw in", + "ĠCar l", + "Ġar my", + "ress ion", + "Ġt en", + "est ival", + "ow a", + "ĠJ o", + "Ġpr im", + "Ġ192 9", + "Ġapp ro", + "u it", + "Ġ195 9", + "Ġmet al", + "ĠKore a", + "ĠB ir", + "ĠNot es", + "ĠS om", + "Ġcon stit", + "Ġpol ice", + "ĠSen ate", + "Ġ rom", + "Ġra il", + "Ġm id", + "Ġ193 3", + "a ign", + "Ġhim self", + "ĠR ail", + "ĠMiss ouri", + "b our", + "Ġmean ing", + "ĠJack son", + "or es", + "Ġfail ure", + "Ġm inist", + "Ġtem per", + "ĠB ig", + "T V", + "ul f", + "ĠS ar", + "or f", + "Ġcomp let", + "ĠAs ian", + "Ġjournal ist", + "n es", + "o ch", + "le g", + "Ġ194 3", + "ĠLat in", + "ĠT im", + "Ġfor ces", + "Ġcult ure", + "ov a", + "ĠC zech", + "Ġg ive", + "ĠD isc", + "ĠPhil ipp", + "ĠEn ter", + "ĠO b", + "Ġl ik", + "ĠF C", + "Ġtrans l", + "ĠMex ican", + "ir es", + "Ġpl ant", + "Ġ195 5", + "Ġmov e", + "Ġre qu", + "ow ers", + "Ġvar ious", + "Ġlaw y", + "ar io", + "ĠL ater", + "ecut ive", + "Ġ i", + "ian o", + "ĠIs lands", + "y e", + "ĠG al", + "v iron", + "g ar", + "iv ely", + "Ġt est", + "Ġc ause", + "ĠV er", + "Ġ8 0", + "yn ast", + "Ġle t", + "Ġ193 5", + "Ġmanag er", + "Ġ192 8", + "ir a", + "Ġg irl", + "ĠH ockey", + "Ġ193 1", + "Ġsy mb", + "Ġn etwork", + "ĠCatal ina", + "Ġj ob", + "it te", + "ĠS ir", + "Ġgro und", + "ĠE s", + "Ġa verage", + "Ġl iter", + "it ive", + "Ġac ross", + "Ġbuild ings", + "ĠAcc ording", + "Ġrecord ed", + "Ġl im", + "ĠTw o", + "Ġt ry", + "20 10", + "Ġb ad", + "ĠBro wn", + "Ġin stead", + "ĠO ver", + "Ġperform ed", + "Ġ193 7", + "ĠU r", + "Ġcon c", + "Ġst orm", + "L S", + "Ġt akes", + "ĠT ime", + "ĠJ ean", + "ĠU E", + "ĠEd uc", + "Ġarr ondiss", + "Ġ6 0", + "Ġprod uct", + "Ġcent ral", + "ĠM P", + "h ar", + "eth ing", + "ĠP ac", + "Ġcurrent ly", + "ĠH aw", + "Ġprot ect", + "i os", + "Ġtra vel", + "ĠF ox", + "g g", + "as c", + "Ġex ist", + "Ġmain ly", + "ĠO ld", + "199 6", + "Ġb ar", + "ab ly", + "ĠF ar", + "Ġme as", + "Ġm aterial", + "f ace", + "p ed", + "Ġcl aim", + "ĠC SS", + "Ġtown s", + "and a", + "ec k", + "ĠU nd", + "ste in", + "ĠC am", + "ĠM o", + "ĠK e", + "Ġro les", + "eth od", + "ĠSec ond", + "Ġcr ime", + "Ġdest roy", + "l anguage", + "Ġun ivers", + "ĠM inn", + "ĠL uc", + "Ġam ount", + "Ġ195 3", + "Ġp ark", + "ĠT od", + "Ġhe alth", + "Ġ193 2", + "j a", + "Ġs ug", + "199 5", + "Ġw ind", + "Ġmov ement", + "Ġrel ations", + "ab eth", + "Ġe ither", + "Ġpro per", + "az ine", + "ades h", + "Ġse ats", + "Ġ18 0", + "Ġc ertain", + "Ġn orm", + "st ron", + "Ġrec ogn", + "Ġw he", + "O R", + "Ġbl ood", + "ĠSc ient", + "t ime", + "Ġmon ths", + "Ġe at", + "rem e", + "ĠA p", + "ĠW ood", + "Ġch urch", + "Ġv iew", + "k o", + "Ġhelp ed", + "Ġse ven", + "Ġm ight", + "our ce", + "Ġwest ern", + "iv id", + "Ġcl ose", + "Ġ192 6", + "Ġb all", + "pec ially", + "Ġc reat", + "Ġchem ical", + "Ġstruct ures", + "Ġarch itect", + "y ear", + "ĠI owa", + "Ġal ways", + "isc o", + "ĠSt ep", + "Ġs it", + "Ġv ict", + "Ġbroad cast", + "Un ited", + "ĠD ire", + "ĠSp ring", + "air s", + "Ġc ase", + "Ġc at", + "8 9", + "N A", + "i h", + "ang er", + "end ing", + "Ġwrit ing", + "ent ion", + "ĠSt reet", + "Ġre ached", + "ĠSecret ary", + "Ġp ast", + "ĠCl ass", + "el d", + "Ġ ident", + "ad ium", + "Ġon ce", + "w an", + "Ġg e", + "Ġ192 7", + "Ġcomp anies", + "Ġto day", + "Ġprod uction", + "Ġ( ,", + "Ġcand id", + "ur ther", + "Ġde g", + "7 5", + "Ġe ight", + "ĠNob el", + "al i", + "ĠJ ud", + "end s", + "ĠH ill", + "oin ts", + "ĠBu ild", + "Ġfour th", + "ak i", + "ĠAn ton", + "ĠSoc ial", + "Ġm urder", + "ĠPol and", + "ĠS ub", + "ĠB ad", + "Ġnum bers", + "ĠMich igan", + "Ġsh own", + "Ġanim ated", + "Ġcompet ed", + "viron ment", + "Ġrepl aced", + "um ents", + "Ġp e", + "st ant", + "Ġt ree", + "ĠOr der", + "Ġc anton", + "Ġpain ter", + "uck y", + "Ġin h", + "Ġp ress", + "i as", + "emb ly", + "ĠO ut", + "ĠB efore", + "Ġpro p", + "Ġmon th", + "ĠA re", + "Ġide a", + "ĠSp orts", + "ĠH ind", + "us e", + "Ġgo al", + "Ġt alk", + "Ġcap t", + "ibr ary", + "Ġc ard", + "Ġdevelop ment", + "if t", + "ĠIn t", + "Ġsign ed", + "Ġre v", + "ĠUn ivers", + "ĠL u", + "Ġ7 0", + "ĠC areer", + "Ġin vent", + "Ġso ft", + "rem ier", + "Ġchang es", + "Ġc itiz", + "c el", + "Ġh ot", + "Ġcom ed", + "ĠGold en", + "Ġprogram s", + "Ġc ourt", + "ut y", + "Ġc ross", + "ĠK enn", + "iet n", + "um e", + "ed s", + "ĠW al", + "7 2", + "ĠBrit ain", + "ĠS erv", + "o is", + "ent h", + "ĠD ar", + "Ġre act", + "ĠSer ies", + "ĠSoc iety", + "Ġdec ided", + "ynast y", + "ĠAl b", + "at re", + "Ġde ad", + "Ġun iversity", + "Ġstud ents", + "ĠT enn", + "ĠInstit ute", + "ĠAn im", + "ĠMc C", + "Ġprom ot", + "Ġin j", + "ĠY oung", + "id ge", + "Ġan cient", + "Ġp ract", + "Ġo x", + "Ġd er", + "tern et", + "Ġn ight", + "Ġrele ase", + "ĠTe am", + "ĠM iddle", + "ĠB av", + "Ġpro ject", + "Ġ192 5", + "I n", + "ĠS and", + "id ence", + "ĠOr gan", + "Ġreturn ed", + "Ġ !", + "Ġar rest", + "ĠR ome", + "ok e", + "oc i", + "ĠAtl antic", + "s en", + "Ġp ay", + "Ġl ittle", + "ĠL y", + "or a", + "Ġes pecially", + "em p", + "Ġgo es", + "Ġb oy", + "ĠB usiness", + "es ota", + "ogra pher", + "Ġpre vious", + "Ġadd ed", + "Ġin side", + "Ġ9 0", + "Ġout side", + "ur d", + "Ġj ud", + "ed ia", + "om er", + "ip l", + "Ġear l", + "Ġgr adu", + "ĠThe n", + "at i", + "ĠF e", + "ĠRepresent atives", + "in ces", + "Ġf iction", + "Ġb attle", + "ĠMus lim", + "ĠL ittle", + "Ġindepend ent", + "Ġf ig", + "ĠB ab", + "st ra", + "ĠG ood", + "ĠAb out", + "ĠM ax", + "ĠV ietn", + "anc he", + "ask a", + "ul ation", + "ĠW ork", + "ĠMinn esota", + "ĠP ress", + "ate g", + "199 4", + "Ġper forman", + "Ġallow ed", + "ĠDep artment", + "Ġbas eball", + "8 6", + "Ġs en", + "Ġdr ug", + "Ġf all", + "Ġf re", + "Ġmunicipal ities", + "ĠE ver", + "Ġart ists", + "Ġlead ers", + "ĠE p", + "ĠS a", + "ĠM ah", + "Ġh om", + "Ġb ox", + "ĠG h", + "Ġsom ething", + "Ġen ough", + "Ġf if", + "m ond", + "Ġla un", + "eng th", + "Ġnomin ated", + "Ġcan not", + "r ich", + "Ġmount ain", + "Ġsouth west", + "Ġra p", + "al so", + "ĠP ers", + "un s", + "Ġme et", + "Ġf ront", + "Ġinter est", + "Ġrel ated", + "Ġfor ce", + "l ah", + "ĠT our", + "ĠAr men", + "ĠComp any", + "p eople", + "ĠWrest ling", + "ĠFranc isco", + "Ġres earch", + "ic ular", + "ri z", + "ad el", + "Ġposs ible", + "Ġb oard", + "8 5", + "ost on", + "Ġthe ory", + "is ing", + "ound s", + "w in", + "Ġsystem s", + "ĠW ay", + "Ġse qu", + "ĠJ ac", + "ĠB ul", + "Ġc ele", + "ĠR on", + "ĠF er", + "ĠD uke", + "h in", + "Ġa th", + "ĠColumb ia", + "ĠP ictures", + "ĠG ram", + "Ġpar ents", + "Ġband s", + "Ġair craft", + "ĠN az", + "ĠEnter tain", + "Ġfriend s", + "itte e", + "Ġ192 4", + "Ġactiv ist", + "ĠLouis iana", + "it ing", + "Ġgo ing", + "ĠV an", + "est ab", + "iz ations", + "ĠAlex ander", + "ag ed", + "Ġc oll", + "ĠF orm", + "Ġv ir", + "iv ate", + "C A", + "Ġorigin ally", + "Ġst ay", + "Ġear th", + "ĠT re", + "rat ive", + "ĠE lect", + "in son", + "c an", + "Ġra c", + "Ġwee k", + "ĠP LS", + "ĠAir port", + "Ġ19 22", + "ad d", + "hes s", + "ay er", + "ĠLe on", + "Ġm em", + "ĠSp ec", + "Ġt ropical", + "p ly", + "199 3", + "ĠWind ows", + "ay a", + "Ġlong er", + "ĠFootball ers", + "il ly", + "ar g", + "ĠA D", + "Ġres ults", + "ĠBi ography", + "in cess", + "is ions", + "j i", + "ien ces", + "Ġbre ak", + "ut s", + "7 4", + "Ġd ig", + "am i", + "Ġnorth west", + "r as", + "ing er", + "ĠF ame", + "Ġse asons", + "ĠE astern", + "ens ive", + "ĠCh ief", + "Ġgr and", + "im b", + "lah oma", + "Ġsh oot", + "m in", + "Ġr en", + "GB T", + "Ġcamp aign", + "ĠI d", + "ĠFam ily", + "7 9", + "us es", + "Ġre view", + "ail able", + "ĠH istor", + "y an", + "z o", + "ĠCh ild", + "Ġp ur", + "ĠP erson", + "h ood", + "ĠN ight", + "if y", + "Ġl ove", + "Ġfin ished", + "ĠOk lahoma", + "v a", + "Ġc rick", + "ĠM u", + "ĠSh ow", + "ĠJ eff", + "Ġc ell", + "Ġs ize", + "Ġ192 3", + "il a", + "um m", + "Ġold est", + "or ial", + "Ġm ale", + "olit an", + "ĠT am", + "ĠC ub", + "Ġdiv ided", + "ĠM ajor", + "0 5", + "c est", + "Ġepis odes", + "ĠD et", + "id ae", + "ro wn", + "/ /", + "w ar", + "or g", + "ra ine", + "Ġ19 00", + "ĠB oston", + "ĠL ong", + "7 6", + "Ġm iss", + "ou d", + "ĠChar l", + "Ġhow ever", + "ĠAr k", + "Ġd oc", + "ĠA k", + "val ue", + "s ort", + "ĠCh ile", + "p resent", + "ĠWar s", + "ĠM em", + "Ġh ospital", + "Ġle ague", + "Ġpro b", + "ĠN ord", + "l av", + "ĠPac ific", + "ut t", + "Ġmed ia", + "Ġm ach", + "ĠEl iz", + "ĠTok yo", + "ra ck", + "ĠM att", + "ĠWh ile", + "Ġb o", + "Ġe astern", + "Ġcon v", + "Ġgo als", + "ĠL GBT", + "Ġ er", + ": //", + "Ġcy cl", + "ĠWW E", + "Ġelect ric", + "Ġw id", + "ĠP ope", + "el le", + "Ġperson al", + "Ġch ampionship", + "Ġnew sp", + "enc ed", + "ĠO cean", + "ĠB al", + "Ġqu ick", + "l ers", + "ĠNew s", + "ain ing", + "ac hes", + "um i", + "Ġcontin ued", + "Ġeduc ation", + "ĠR ay", + "ĠBer lin", + "Ġl o", + "Ġc ases", + "Ġp sych", + "ĠMar ia", + "ĠL i", + "ĠJohn son", + "Ġm ethod", + "Ġsup er", + "ĠG ame", + ". )", + "el a", + "Ġac adem", + "ĠN ick", + "Ġst ations", + "Ġf ac", + "ĠC a", + "Ġ ;", + "Ġs uff", + "ĠSt e", + "Ġsmall er", + "Ġlaw s", + "0 6", + "ĠRo ad", + "Ġbelie ved", + "it o", + "writ ers", + "ur ity", + "Ġform s", + "ĠP as", + "Ġaward ed", + "ic ult", + "Ġlawy er", + "th ur", + "w ith", + "ĠT a", + "ut ure", + "Ġacc ident", + "Ġfeat ures", + "ch an", + "Ġdisc overed", + "Ġb order", + "Ġcrit ic", + "ĠJ un", + "ĠI v", + "Ġserv ices", + "ĠN ations", + "ĠG irl", + "Ġcl os", + "g u", + "ri age", + "Ġro ad", + "Ġn uc", + "ĠD ef", + "ĠP o", + "Ġd og", + "ĠC op", + "Ġl ower", + "ĠBel gian" + ] + } +} \ No newline at end of file diff --git a/models/components/tokenizers.py b/models/components/tokenizers.py new file mode 100644 index 00000000..8cc7cf42 --- /dev/null +++ b/models/components/tokenizers.py @@ -0,0 +1,324 @@ +""" A collection of different tokenizers. """ +from typing import List + +import torch +import tiktoken +from transformers import AutoTokenizer + +# local imports +from trainers.data_utils import load_data +from models.components import utils + +# text processing imports +import re +import string + +# Custom BPE tokenizer imports +from tokenizers import Tokenizer +from tokenizers.models import BPE +from tokenizers.trainers import BpeTrainer +from tokenizers.pre_tokenizers import PreTokenizer, WhitespaceSplit, Sequence, Split, ByteLevel +from tokenizers.normalizers import NFD, StripAccents, Replace, Sequence as NormalizerSequence +from tokenizers import decoders + + + +class TokenizerClass: + """Base class for tokenizers, defines the interface for tokenizers.""" + + def __init__(self, **_): + self.eot_token = 0 + self.pad_token = 0 + self.vocab_size = ... + + def encode(self, text): + """Encode a text into tokens.""" + raise NotImplementedError + + def encode_batch(self, texts): + """Encode a batch of texts into tokens. + + Default implementation is to loop over the texts""" + for text in texts: + yield self.encode(text) + + def pad_batch(self, token_lists, direction="right"): + """Pad a list of token lists to the same length, + and return the padded tensor, and mask tensor. + + Direction can be 'right' or 'left' to specify the padding direction. + """ + max_len = max(len(tokens) for tokens in token_lists) + padded_tokens = [] + mask = [] + for tokens in token_lists: + if direction == "right": + padded_tokens.append(tokens + [self.pad_token] * (max_len - len(tokens))) + mask.append([1] * len(tokens) + [0] * (max_len - len(tokens))) + elif direction == "left": + padded_tokens.append([self.pad_token] * (max_len - len(tokens)) + tokens) + mask.append([0] * (max_len - len(tokens)) + [1] * len(tokens)) + return torch.tensor(padded_tokens), torch.tensor(mask) + + def decode(self, tokens): + """Decode a list of tokens into a string.""" + raise NotImplementedError + + def decode_batch(self, token_lists): + """Decode a list of token lists into a list of strings. + + Default implementation is to loop over the token lists.""" + for tokens in token_lists: + yield self.decode(tokens) + + +class HuggingfaceTokenizer(TokenizerClass): + """A simple wrapper around a Huggingface Tokenizer.""" + + def __init__(self, tokenizer_path): + super().__init__() + # load the tokenizer + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + self.eot_token = self.tokenizer.eos_token_id + self.pad_token = self.tokenizer.pad_token_id + self.vocab_size = self.tokenizer.vocab_size + + def encode(self, text): + """Encode a string into tokens.""" + return self.tokenizer.encode(text, add_special_tokens=False) + + def encode_batch(self, texts): + """Encode a list of strings into tokens.""" + return [self.encode(text) for text in texts] + + def decode(self, tokens): + """Decode a list of tokens into a string.""" + if torch.is_tensor(tokens): + tokens = tokens.tolist() + return self.tokenizer.decode(tokens) + + def decode_batch(self, token_lists): + """Decode a list of token lists into a list of strings.""" + if torch.is_tensor(token_lists): + token_lists = token_lists.tolist() + return [self.decode(tokens) for tokens in token_lists] + +class TiktokenTokenizer(TokenizerClass): + """A simple wrapper around the GPT2 Tokenizer.""" + + def __init__(self, tokenizer_name: str): + super().__init__() + self.tokenizer = tiktoken.get_encoding(tokenizer_name) + self.eot_token = self.tokenizer.eot_token + self.pad_token = self.tokenizer.eot_token + self.vocab_size = self.tokenizer.max_token_value + 1 + + def encode(self, text): + """Encode a string into tokens.""" + return self.tokenizer.encode_ordinary(text) + + def encode_batch(self, texts): + """Encode a list of strings into tokens.""" + return self.tokenizer.encode_ordinary_batch(texts) + + def decode(self, tokens): + """Decode a list of tokens into a string.""" + # check if the tokens are a tensor + if torch.is_tensor(tokens): + tokens = tokens.tolist() + return self.tokenizer.decode(tokens) + + def decode_batch(self, token_lists): + """Decode a list of token lists into a list of strings.""" + if torch.is_tensor(token_lists): + token_lists = token_lists.tolist() + return self.tokenizer.decode_batch(token_lists) + + + +class BPETokenizer(TokenizerClass): + def __init__( + self, + vocab_size: int, + dataset_names: str, + simplify: bool = True, + num_reserved_tokens: int = 0, + ): + super().__init__() + self.vocab_size = vocab_size + self.dataset_names = dataset_names + self.simplify = simplify + self.reserved_tokens = [f"[Res{x}]" for x in range(num_reserved_tokens)] + + if not utils.check_if_tokenizer_exists( + tokenizer_type="bpe", + vocab_size=vocab_size, + dataset_names=dataset_names, + simplify=simplify, + num_reserved_tokens=len(self.reserved_tokens) + ): + self._train_tokenizer() + self._save() + else: + self._load() + + # Update special token IDs after loading or training + self.pad_token = self.tokenizer.token_to_id("[PAD]") + self.eot_token = self.tokenizer.token_to_id("[EOT]") + self.unk_token = self.tokenizer.token_to_id("[UNK]") + + def _train_tokenizer(self, verbose: bool = True): + raw_datasets = load_data(dataset_names=self.dataset_names) + + # Pattern string without compiling + non_english_char_pattern = r"[^a-zA-Z0-9\s" + re.escape(string.punctuation) + r"]" + + # Define special tokens, including reserved tokens + special_tokens = ["[PAD]", "[EOT]", "[UNK]"] + self.reserved_tokens + + # Define initial alphabet + initial_alphabet = list( + string.ascii_letters + string.digits + string.punctuation + " \n\t" + ) + + # Initialize a new tokenizer + self.tokenizer = Tokenizer(BPE(unk_token="[UNK]", dropout=0.1)) + + # Set the decoder to ByteLevel + self.tokenizer.decoder = decoders.ByteLevel() + + # Initialize the trainer + trainer = BpeTrainer( + vocab_size=self.vocab_size, + min_frequency=5, + special_tokens=special_tokens, + initial_alphabet=initial_alphabet, + show_progress=verbose, + ) + + if self.simplify: + # Set the normalizer to remove non-English characters + self.tokenizer.normalizer = NormalizerSequence( + [ + NFD(), # Decompose unicode characters + StripAccents(), # Remove accents + Replace(non_english_char_pattern, ""), # Use pattern string directly + ] + ) + + # Custom pre-tokenizer to split numbers into individual digits + self.tokenizer.pre_tokenizer = Sequence( + [ + WhitespaceSplit(), # Split on whitespace + # Split digits and isolate them + Split(r"\d", behavior="isolated"), # Each digit is a separate token + ByteLevel(), # Byte-level encoding + ] + ) + + # Prepare the training data with filtering + def batch_iterator(): + batch_size = 1000 + for i in range(0, len(raw_datasets["train"]), batch_size): + batch_texts = raw_datasets["train"][i:i+batch_size]["text"] + # Filter and clean texts + cleaned_texts = [] + for text in batch_texts: + text = text.strip() + # Remove non-English characters + cleaned_text = re.sub(non_english_char_pattern, '', text) + if cleaned_text: + cleaned_texts.append(cleaned_text) + if cleaned_texts: + yield cleaned_texts + + # Train the tokenizer + self.tokenizer.train_from_iterator( + batch_iterator(), + trainer=trainer + ) + + # print + if verbose: + print(f"Trained a BPE tokenizer with {self.vocab_size} tokens on the {self.dataset_names} dataset(s).") + + def encode(self, text: str) -> List[int]: + return self.tokenizer.encode(text).ids + + def encode_batch(self, texts: List[str]) -> List[List[int]]: + return [self.encode(text) for text in texts] + + def decode(self, tokens: List[int]) -> str: + if torch.is_tensor(tokens): + tokens = tokens.tolist() + return self.tokenizer.decode(tokens) + + def decode_batch(self, token_lists: List[List[int]]) -> List[str]: + if torch.is_tensor(token_lists): + token_lists = token_lists.tolist() + return [self.decode(tokens) for tokens in token_lists] + + def _save(self): + _, tokenizer_path = utils.get_tokenizer_path( + tokenizer_type="bpe", + vocab_size=self.vocab_size, + dataset_names=self.dataset_names, + simplify=self.simplify, + num_reserved_tokens=len(self.reserved_tokens), + ) + self.tokenizer.save(str(tokenizer_path)) + + def _load(self): + _, tokenizer_path = utils.get_tokenizer_path( + tokenizer_type="bpe", + vocab_size=self.vocab_size, + dataset_names=self.dataset_names, + simplify=self.simplify, + num_reserved_tokens=len(self.reserved_tokens), + ) + self.tokenizer = Tokenizer.from_file(str(tokenizer_path)) + self.vocab = self.tokenizer.get_vocab() + + # print vocab size + print(f"Loaded a BPE tokenizer with {len(self.vocab)} tokens.") + + + + + + + +TOKENIZER_DICT = { + # a number of standard tiktoken tokenizers + "o200k_base": lambda vocab_size, dataset_name, simplify: TiktokenTokenizer(tokenizer_name="o200k_base"), + "cl100k_base": lambda vocab_size, dataset_name, simplify: TiktokenTokenizer(tokenizer_name="cl100k_base"), + "p50k_base": lambda vocab_size, dataset_name, simplify: TiktokenTokenizer(tokenizer_name="p50k_base"), + "gpt2": lambda vocab_size, dataset_name, simplify: TiktokenTokenizer(tokenizer_name="gpt2"), + + # a number of standard huggingface tokenizers + "llama_32k": lambda vocab_size, dataset_name, simplify: HuggingfaceTokenizer(tokenizer_path="chavinlo/alpaca-native"), + "opt_50k": lambda vocab_size, dataset_name, simplify: HuggingfaceTokenizer(tokenizer_path="facebook/opt-1.3b"), + "mistral_32k": lambda vocab_size, dataset_name, simplify: HuggingfaceTokenizer(tokenizer_path="mistralai/Mistral-7B-v0.1"), + + # a custom BPE tokenizer (using the HF implementation) + "bpe": lambda vocab_size, dataset_names, simplify, num_reserved_tokens: BPETokenizer( + vocab_size=vocab_size, dataset_names=dataset_names, simplify=simplify, num_reserved_tokens=num_reserved_tokens + ), +} + + +def build_tokenizer( + tokenizer_type, + vocab_size, + dataset_names, + simplify, + num_reserved_tokens + ) -> TokenizerClass: + """ + Build the tokenizer. + """ + assert tokenizer_type in TOKENIZER_DICT, \ + f"Tokenizer type {tokenizer_type} not found. The available tokenizers are: {list(TOKENIZER_DICT.keys())}" + return TOKENIZER_DICT[tokenizer_type]( + vocab_size=vocab_size, dataset_names=dataset_names, simplify=simplify, num_reserved_tokens=num_reserved_tokens + ) diff --git a/models/components/tokenizers/__init__.py b/models/components/tokenizers/__init__.py deleted file mode 100644 index ca532447..00000000 --- a/models/components/tokenizers/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -Simplify imports -""" - -from models.components.tokenizers.setup import build_tokenizer diff --git a/models/components/tokenizers/base_class.py b/models/components/tokenizers/base_class.py deleted file mode 100644 index 673f7280..00000000 --- a/models/components/tokenizers/base_class.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Base Class for Tokenizers""" - -import torch - - -class Tokenizer: - """Base class for tokenizers, defines the interface for tokenizers.""" - - def __init__(self, **_): - self.eot_token = 0 - self.pad_token = 0 - self.vocab_size = ... - - def encode(self, text): - """Encode a text into tokens.""" - raise NotImplementedError - - def encode_batch(self, texts): - """Encode a batch of texts into tokens. - - Default implementation is to loop over the texts""" - for text in texts: - yield self.encode(text) - - def pad_batch(self, token_lists, direction="right"): - """Pad a list of token lists to the same length, - and return the padded tensor, and mask tensor. - - Direction can be 'right' or 'left' to specify the padding direction. - """ - max_len = max(len(tokens) for tokens in token_lists) - padded_tokens = [] - mask = [] - for tokens in token_lists: - if direction == "right": - padded_tokens.append(tokens + [self.pad_token] * (max_len - len(tokens))) - mask.append([1] * len(tokens) + [0] * (max_len - len(tokens))) - elif direction == "left": - padded_tokens.append([self.pad_token] * (max_len - len(tokens)) + tokens) - mask.append([0] * (max_len - len(tokens)) + [1] * len(tokens)) - return torch.tensor(padded_tokens), torch.tensor(mask) - - def decode(self, tokens): - """Decode a list of tokens into a string.""" - raise NotImplementedError - - def decode_batch(self, token_lists): - """Decode a list of token lists into a list of strings. - - Default implementation is to loop over the token lists.""" - for tokens in token_lists: - yield self.decode(tokens) diff --git a/models/components/tokenizers/bpe.py b/models/components/tokenizers/bpe.py deleted file mode 100644 index c39cea20..00000000 --- a/models/components/tokenizers/bpe.py +++ /dev/null @@ -1,222 +0,0 @@ -""" -A simple implementation of the Byte Pair Encoding tokenizer, based on -https://github.com/karpathy/minbpe and sped up using https://t.co/MkTecNoWNP -by https://twitter.com/lexandermorgan/status/1778793836929495098. - -Original Paper: https://arxiv.org/abs/1508.07909v5 -""" -import torch -import os -from heapq import nlargest - -from tqdm import tqdm - -from models.components.tokenizers import utils -from models.components.tokenizers.base_class import Tokenizer -from trainers.utils import load_data - - -class BPETokenizer(Tokenizer): - """Tokenizer for Byte Pair Encoding.""" - - def __init__(self, vocab_size, dataset_name): - """ - Check if the specific tokenizer already exists, if not, create it. - """ - super().__init__() - self.vocab_size = vocab_size - self.dataset_name = dataset_name - self.special_tokens = { - "<|pad|>": vocab_size - 2, - "<|endoftext|>": vocab_size - 1, - } - self.pad_token = self.special_tokens["<|pad|>"] - self.eot_token = self.special_tokens["<|endoftext|>"] - - assert self.vocab_size >= 256 + len( - self.special_tokens - ), f"Vocab size too small! Must be > {256+len(self.special_tokens)})" - - if not utils.check_if_tokenizer_exists( - tokenizer_type="bpe", vocab_size=vocab_size, dataset_name=dataset_name - ): - # train the tokenizer and save it - self._train_tokenizer() - self._save() - - else: - # load the stored tokenizer - self._load() - - self.eot_token = self.special_tokens["<|endoftext|>"] - - def encode(self, text): - """ - Encode the text into Byte Pair Encoding tokens. - """ - text_bytes = text.encode("utf-8") # raw bytes - ids = list(text_bytes) # list of integers in range 0..255 - while len(ids) >= 2: - # find the pair with the lowest merge index - stats = utils.get_stats(ids) - pair = min(stats, key=lambda p: self.merges.get(p, float("inf"))) - # subtle: if there are no more merges available, the key will - # result in an inf for every single pair, and the min will be - # just the first pair in the list, arbitrarily - # we can detect this terminating case by a membership check - if pair not in self.merges: - break # nothing else can be merged anymore - # otherwise let's merge the best pair (lowest merge index) - idx = self.merges[pair] - ids = utils.merge(ids, pair, idx) - return ids - - def encode_batch(self, texts): - """ - Encode a batch of texts into Byte Pair Encoding tokens. - """ - return [self.encode(text) for text in texts] - - def decode(self, tokens): - """ - Decode the Byte Pair Encoding tokens back into text. - """ - # if tensor, convert to list - if torch.is_tensor(tokens): - tokens = tokens.tolist() - text_bytes = b"".join(self.vocab[idx] for idx in tokens) - text = text_bytes.decode("utf-8", errors="replace") - return text - - def decode_batch(self, token_lists): - """ - Decode a batch of Byte Pair Encoding token lists back into text. - """ - # if tensor, convert to list - if torch.is_tensor(token_lists): - token_lists = token_lists.tolist() - return [self.decode(tokens) for tokens in token_lists] - - def _train_tokenizer(self, verbose=True): - """ - Train the Byte Pair Encoding tokenizer - on the given dataset. - """ - # load the dataset - dataset = load_data(dataset_name=self.dataset_name) - - # convert it into a large string - dataset_text = "".join(dataset["train"]["text"]) - - # preprocess the input text - text_bytes = dataset_text.encode("utf-8") - text_bytes = [*map(int, text_bytes)] - ids = list(text_bytes) - current_vocab_size = 256 - num_merges = self.vocab_size - current_vocab_size - len(self.special_tokens) - max_clutch_size = 64 - - # iteratively merge the most frequent pair - merges = {} # (int, int) -> int - - with tqdm(total=num_merges, desc="Training BPE", disable=not verbose) as pbar: - while num_merges > 0: - stats = utils.get_stats(ids) - top_pairs = nlargest( - min(max_clutch_size, num_merges), stats, key=stats.get - ) - pairs_to_merge = {} - first_seen = set() - second_seen = set() - for pair in top_pairs: - if pair[0] in second_seen or pair[1] in first_seen: - first_seen.add(pair[0]) - second_seen.add(pair[1]) - continue # skip this pair but keep looking for mergeable top_pairs - first_seen.add(pair[0]) - second_seen.add(pair[1]) - pairs_to_merge[pair] = current_vocab_size - current_vocab_size += 1 - num_merges -= 1 - pbar.update(1) - - ids = utils.multi_merge(ids, pairs_to_merge) - merges.update(pairs_to_merge) - - # save as class variable - self.merges = merges - self.vocab = self._build_vocab() - - def _build_vocab(self): - """ - Build the vocabulary from the merges. - """ - vocab = {idx: bytes([idx]) for idx in range(256)} - for (p0, p1), idx in self.merges.items(): - vocab[idx] = vocab[p0] + vocab[p1] - for special, idx in self.special_tokens.items(): - vocab[idx] = special.encode("utf-8") - return vocab - - def _save(self): - """ - Save the tokenizer as a .model file, and save the vocabulary - for easy debugging as a .vocab file. - """ - tokenizer_folder, tokenizer_path = utils.get_tokenizer_path( - tokenizer_type="bpe", - vocab_size=self.vocab_size, - dataset_name=self.dataset_name, - ) - # create folder if necessary - if not os.path.exists(tokenizer_folder): - os.makedirs(tokenizer_folder) - - # store the .model file - # pylint: disable=unspecified-encoding - with open(tokenizer_path, "w") as f: - # write the merges - for idx1, idx2 in self.merges: - f.write(f"{idx1} {idx2}\n") - # pylint: enable=unspecified-encoding - - # store the .vocab file - vocab_path = tokenizer_path.replace(".model", ".vocab") - inverted_merges = {idx: pair for pair, idx in self.merges.items()} - with open(vocab_path, "w", encoding="utf-8") as f: - for idx, token in self.vocab.items(): - # try rendering the tokens - s = utils.render_token(token) - # find the children of this token, if any - if idx in inverted_merges: - # if this token has children, render it nicely as a merge - idx0, idx1 = inverted_merges[idx] - s0 = utils.render_token(self.vocab[idx0]) - s1 = utils.render_token(self.vocab[idx1]) - f.write(f"[{s0}][{s1}] -> [{s}] {idx}\n") - else: - # otherwise this is leaf token, just print it - # (this should just be the first 256 tokens, the bytes) - f.write(f"[{s}] {idx}\n") - - def _load(self): - """ - Load the .model file of merges and build - the vocabulary. - """ - _, tokenizer_path = utils.get_tokenizer_path( - tokenizer_type="bpe", - vocab_size=self.vocab_size, - dataset_name=self.dataset_name, - ) - - merges = {} - idx = 256 - with open(tokenizer_path, "r", encoding="utf-8") as f: - # read the merges - for line in f: - idx1, idx2 = map(int, line.split()) - merges[(idx1, idx2)] = idx - idx += 1 - self.merges = merges - self.vocab = self._build_vocab() diff --git a/models/components/tokenizers/gpt2.py b/models/components/tokenizers/gpt2.py deleted file mode 100644 index a753b754..00000000 --- a/models/components/tokenizers/gpt2.py +++ /dev/null @@ -1,41 +0,0 @@ -""" -A simple wrapper around the GPT2 Tokenizer to -standardize the interface for tokenization. -""" - -import tiktoken -import torch - -from models.components.tokenizers.base_class import Tokenizer - - -class GPT2Tokenizer(Tokenizer): - """A simple wrapper around the GPT2 Tokenizer.""" - - def __init__(self, **_): - super().__init__() - self.tokenizer = tiktoken.get_encoding("gpt2") - self.eot_token = self.tokenizer.eot_token - self.pad_token = self.tokenizer.eot_token - self.vocab_size = self.tokenizer.max_token_value + 1 - - def encode(self, text): - """Encode a string into tokens.""" - return self.tokenizer.encode_ordinary(text) - - def encode_batch(self, texts): - """Encode a list of strings into tokens.""" - return self.tokenizer.encode_ordinary_batch(texts) - - def decode(self, tokens): - """Decode a list of tokens into a string.""" - # check if the tokens are a tensor - if torch.is_tensor(tokens): - tokens = tokens.tolist() - return self.tokenizer.decode(tokens) - - def decode_batch(self, token_lists): - """Decode a list of token lists into a list of strings.""" - if torch.is_tensor(token_lists): - token_lists = token_lists.tolist() - return self.tokenizer.decode_batch(token_lists) diff --git a/models/components/tokenizers/setup.py b/models/components/tokenizers/setup.py deleted file mode 100644 index 72e4cbe0..00000000 --- a/models/components/tokenizers/setup.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -A script for building the various tokenizers. -""" - -from models.components.tokenizers.base_class import Tokenizer -from models.components.tokenizers.bpe import BPETokenizer -from models.components.tokenizers.gpt2 import GPT2Tokenizer - -TOKENIZER_DICT = { - "gpt2": lambda vocab_size, dataset_name: GPT2Tokenizer(), - "bpe": lambda vocab_size, dataset_name: BPETokenizer( - vocab_size=vocab_size, dataset_name=dataset_name - ), -} - - -def build_tokenizer(tokenizer_type, vocab_size, dataset_name) -> Tokenizer: - """ - Build the tokenizer. - """ - return TOKENIZER_DICT[tokenizer_type]( - vocab_size=vocab_size, dataset_name=dataset_name - ) diff --git a/models/components/tokenizers/tokenizer_models/bpe_simple_en_wiki_10000.model 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+[t][i] -> [ti] 279 +[an][d ] -> [and ] 280 +[in][g] -> [ing] 281 +[1][9] -> [19] 282 +[2][0] -> [20] 283 +[ ][|| ] -> [ || ] 284 +[e][d ] -> [ed ] 285 +[i][n ] -> [in ] 286 +[i][c] -> [ic] 287 +[i][s] -> [is] 288 +[a][s ] -> [as ] 289 +[a][n ] -> [an ] 290 +[.][\u000a\u000a] -> [.\u000a\u000a] 291 +[\u000a][|] -> [\u000a|] 292 +[�][�] -> [�] 293 +[a][ ] -> [a ] 294 +[of][ ] -> [of ] 295 +[er][ ] -> [er ] 296 +[o][ ] -> [o ] 297 +[i][s ] -> [is ] 298 +[r][e] -> [re] 299 +[c][h] -> [ch] 300 +[T][h] -> [Th] 301 +[r][o] -> [ro] 302 +[i][t] -> [it] 303 +[a][t] -> [at] 304 +[\u000a][ ] -> [\u000a ] 305 +[e][s ] -> [es ] 306 +[e][l] -> [el] 307 +[a][m] -> [am] 308 +[s][t] -> [st] 309 +[ing][ ] -> [ing ] 310 +[al][ ] -> [al ] 311 +[or][ ] -> [or ] 312 +[w][as ] -> [was ] 313 +[Th][e ] -> [The ] 314 +[o][n ] -> [on ] 315 +[o][u] -> [ou] 316 +[o][l] -> [ol] 317 +[i][g] -> [ig] 318 +[i][l] -> [il] 319 +[t][o ] -> [to ] 320 +[a][s] -> [as] 321 +[of ][the ] -> [of the ] 322 +[20][0] -> [200] 323 +[o][m] -> [om] 324 +[a][c] -> [ac] 325 +[i][d] -> [id] 326 +[u][s] -> [us] 327 +[i][r] -> [ir] 328 +[p][l] -> [pl] 329 +[e][c] -> [ec] 330 +[u][r] -> [ur] 331 +[a][ti] -> [ati] 332 +[u][n] -> [un] 333 +[o][v] -> [ov] 334 +[o][w] -> [ow] 335 +[en][t] -> [ent] 336 +[ig][h] -> [igh] 337 +[�][�] -> [—] 338 +[in ][the ] -> [in the ] 339 +[e][m] -> [em] 340 +[e][f] -> [ef] 341 +[a][t ] -> [at ] 342 +[a][d] -> [ad] 343 +[a][g] -> [ag] 344 +[al][l] -> [all] 345 +[e][n ] -> [en ] 346 +[r][i] -> [ri] 347 +[s][\u000a] -> [s\u000a] 348 +[o][c] -> [oc] 349 +[—][ || ] -> [— || ] 350 +[en][c] -> [enc] 351 +[\u000a|][-] -> [\u000a|-] 352 +[ou][n] -> [oun] 353 +[. ][I] -> [. I] 354 +[igh][t ] -> [ight ] 355 +[ig][n] -> [ign] 356 +[en][t ] -> [ent ] 357 +[m][ ] -> [m ] 358 +[d][i] -> [di] 359 +[e][t] -> [et] 360 +[u][l] -> [ul] 361 +[o][s] -> [os] 362 +[ || ][— || ] -> [ || — || ] 363 +[a][y] -> [ay] 364 +[a][p] -> [ap] 365 +[a][b] -> [ab] 366 +[f][or ] -> [for ] 367 +[is][t] -> [ist] 368 +[c][ol] -> [col] 369 +[�][�] -> [–] 370 +[c][om] -> [com] 371 +[th][ ] -> [th ] 372 +[f][ro] -> [fro] 373 +[i][v] -> [iv] 374 +[l][e] -> [le] 375 +[o][t] -> [ot] 376 +[r][es] -> [res] 377 +[an][d] -> [and] 378 +[S][t] -> [St] 379 +[20][1] -> [201] 380 +[r][ight ] -> [right ] 381 +[. ][H] -> [. H] 382 +[19][9] -> [199] 383 +[col][or] -> [color] 384 +[E][9] -> [E9] 385 +[al][ign] -> [align] 386 +[ ][b] -> [ b] 387 +[fro][m ] -> [from ] 388 +[e][v] -> [ev] 389 +[e][d] -> [ed] 390 +[a][v] -> [av] 391 +[w][i] -> [wi] 392 +[l][i] -> [li] 393 +[align][=] -> [align=] 394 +[color][=] -> [color=] 395 +[th][er ] -> [ther ] 396 +[. ][The ] -> [. The ] 397 +[ar][e ] -> [are ] 398 +[ati][on] -> [ation] 399 +[h][ ] -> [h ] 400 +[k][ ] -> [k ] 401 +[)][ ] -> [) ] 402 +[b][er ] -> [ber ] 403 +[e][p] -> [ep] 404 +[b][y ] -> [by ] 405 +[\u000a|][ ] -> [\u000a| ] 406 +[g][color=] -> [gcolor=] 407 +[align=][right ] -> [align=right ] 408 +[id][=] -> [id=] 409 +['][s ] -> ['s ] 410 +[wi][th ] -> [with ] 411 +[is ][a ] -> [is a ] 412 +[th][at ] -> [that ] 413 +[ti][c] -> [tic] 414 +[)][\u000a ] -> [)\u000a ] 415 +[R][ef] -> [Ref] 416 +[w][h] -> [wh] 417 +[er][s] -> [ers] 418 +[p][e] -> [pe] 419 +[ ][k] -> [ k] 420 +[gcolor=][#] -> [gcolor=#] 421 +[\u000a|-][id=] -> [\u000a|-id=] 422 +[em][ber ] -> [ember ] 423 +[1][8] -> [18] 424 +[es][\u000a\u000a] -> [es\u000a\u000a] 425 +[f][or] -> [for] 426 +[. I][t ] -> [. It ] 427 +[U][n] -> [Un] 428 +[o][p] -> [op] 429 +[m][er] -> [mer] 430 +[a][k] -> [ak] 431 +[s][h] -> [sh] 432 +[ch][ ] -> [ch ] 433 +[|][ ] -> [| ] 434 +[e][b] -> [eb] 435 +[ b][gcolor=#] -> [ bgcolor=#] 436 +[e][, ] -> [e, ] 437 +[e][w] -> [ew] 438 +[e][y ] -> [ey ] 439 +[s][, ] -> [s, ] 440 +[o][pl] -> [opl] 441 +[u][p] -> [up] 442 +[o][d] -> [od] 443 +[l][y ] -> [ly ] 444 +[on][g] -> [ong] 445 +[t][r] -> [tr] 446 +[u][m] -> [um] 447 +[C][h] -> [Ch] 448 +[align=right ][| ] -> [align=right | ] 449 +[er][e ] -> [ere ] 450 +[e][a] -> [ea] 451 +[mer][ic] -> [meric] 452 +[l][ ] -> [l ] 453 +[e][ar] -> [ear] 454 +[e][x] -> [ex] 455 +[p][ro] -> [pro] 456 +[d][6] -> [d6] 457 +[u][c] -> [uc] 458 +[, ][200] -> [, 200] 459 +[u][g] -> [ug] 460 +[o][g] -> [og] 461 +[it][y ] -> [ity ] 462 +[ati][on ] -> [ation ] 463 +[pe][opl] -> [peopl] 464 +[N][E] -> [NE] 465 +[m][ || ] -> [m || ] 466 +[e ][of ] -> [e of ] 467 +[t][er] -> [ter] 468 +[A][meric] -> [Americ] 469 +[a][y ] -> [ay ] 470 +[ ][(] -> [ (] 471 +[a][in] -> [ain] 472 +[ar][y ] -> [ary ] 473 +[c][on] -> [con] 474 +[s][p] -> [sp] 475 +[u][b] -> [ub] 476 +[E9][E9] -> [E9E9] 477 +[, ][and ] -> [, and ] 478 +[ou][r] -> [our] 479 +[. ][Th] -> [. Th] 480 +[s][t ] -> [st ] 481 +[s][o ] -> [so ] 482 +[o][t ] -> [ot ] 483 +[m || ][\u000a|-id=] -> [m || \u000a|-id=] 484 +[er][enc] -> [erenc] 485 +[i][m] -> [im] 486 +[h][e ] -> [he ] 487 +[ || ][ || — || ] -> [ || || — || ] 488 +[Americ][an ] -> [American ] 489 +[. H][e ] -> [. He ] 490 +[an][c] -> [anc] 491 +[–][ ] -> [– ] 492 +[l][e ] -> [le ] 493 +[:][ ] -> [: ] 494 +[0][ ] -> [0 ] 495 +[ic][ ] -> [ic ] 496 +[ar][t] -> [art] 497 +[in][c] -> [inc] 498 +[-][ ] -> [- ] 499 +[u][t] -> [ut] 500 +[ || ][S] -> [ || S] 501 +[ || ][L] -> [ || L] 502 +[19][8] -> [198] 503 +[s][e] -> [se] 504 +[NE][A] -> [NEA] 505 +[es][t ] -> [est ] 506 +[y][, ] -> [y, ] 507 +[pl][ay] -> [play] 508 +[I][n] -> [In] 509 +[ k][m || \u000a|-id=] -> [ km || \u000a|-id=] 510 +[Ref][erenc] -> [Referenc] 511 +[er][s ] -> [ers ] 512 +[i][an ] -> [ian ] 513 +[an][g] -> [ang] 514 +[all][ ] -> [all ] 515 +[h][is ] -> [his ] 516 +[M][ar] -> [Mar] 517 +[t][er ] -> [ter ] 518 +[||][0] -> [||0] 519 +[ed ][by ] -> [ed by ] 520 +[am][e ] -> [ame ] 521 +[w][or] -> [wor] 522 +[u][d] -> [ud] 523 +[ti][on] -> [tion] 524 +[r][a] -> [ra] 525 +[A][n] -> [An] 526 +[s][it] -> [sit] 527 +[th][s\u000a] -> [ths\u000a] 528 +[ || — || ][align=right | ] -> [ || — || align=right | ] 529 +[o][o] -> [oo] 530 +[b][e] -> [be] 531 +[19][7] -> [197] 532 +[es][\u000a] -> [es\u000a] 533 +[al][so ] -> [also ] 534 +[i][t ] -> [it ] 535 +[m][an] -> [man] 536 +[.\u000a\u000a][Referenc] -> [.\u000a\u000aReferenc] 537 +[ed ][to ] -> [ed to ] 538 +[c][t] -> [ct] 539 +[q][u] -> [qu] 540 +[s ][of ] -> [s of ] 541 +[es][t] -> [est] 542 +[is][h] -> [ish] 543 +[b][u] -> [bu] 544 +[ac][t] -> [act] 545 +[ed ][in ] -> [ed in ] 546 +[ow][n ] -> [own ] 547 +[d6][d6] -> [d6d6] 548 +[ || L][I] -> [ || LI] 549 +[ti][on ] -> [tion ] 550 +[NEA][R] -> [NEAR] 551 +[F][r] -> [Fr] 552 +[. ][S] -> [. S] 553 +[er][, ] -> [er, ] 554 +[ov][i] -> [ovi] 555 +[il][l] -> [ill] 556 +[or][ro] -> [orro] 557 +[s ][in ] -> [s in ] 558 +[s ][and ] -> [s and ] 559 +[it][ed ] -> [ited ] 560 +[h][as ] -> [has ] 561 +[)][, ] -> [), ] 562 +[f][f] -> [ff] 563 +[at][e ] -> [ate ] 564 +[w][ere ] -> [were ] 565 +[w][eb] -> [web] 566 +[en][d] -> [end] 567 +[ou][t ] -> [out ] 568 +[av][e ] -> [ave ] 569 +[i][ ] -> [i ] 570 +[i][p] -> [ip] 571 +[i][on ] -> [ion ] 572 +[b][or] -> [bor] 573 +[m][ovi] -> [movi] 574 +[oc][orro] -> [ocorro] 575 +[l][d ] -> [ld ] 576 +[web][sit] -> [websit] 577 +[Un][ited ] -> [United ] 578 +[at][ed ] -> [ated ] 579 +[oun][t] -> [ount] 580 +[.\u000a\u000aReferenc][es\u000a\u000a] -> [.\u000a\u000aReferences\u000a\u000a] 581 +[is][h ] -> [ish ] 582 +[u][ary ] -> [uary ] 583 +[th][e] -> [the] 584 +[E][n] -> [En] 585 +[ar][ ] -> [ar ] 586 +[i][a] -> [ia] 587 +[i][on] -> [ion] 588 +[i][an] -> [ian] 589 +[p][r] -> [pr] 590 +[ || S][ocorro] -> [ || Socorro] 591 +[A][l] -> [Al] 592 +[or][t] -> [ort] 593 +[o][b] -> [ob] 594 +[ti][m] -> [tim] 595 +[e][t ] -> [et ] 596 +[el][l] -> [ell] 597 +[bor][n ] -> [born ] 598 +[to ][the ] -> [to the ] 599 +[n][ot ] -> [not ] 600 +[.][\u000a ] -> [.\u000a ] 601 +[1][ ] -> [1 ] 602 +[a][d ] -> [ad ] 603 +[ || LI][NEAR] -> [ || LINEAR] 604 +[r][ic] -> [ric] 605 +[on][e ] -> [one ] 606 +[b][e ] -> [be ] 607 +[i][a ] -> [ia ] 608 +[an][t] -> [ant] 609 +[p][ar] -> [par] 610 +[e][g] -> [eg] 611 +[or][e ] -> [ore ] 612 +[O][ther ] -> [Other ] 613 +[s][er] -> [ser] 614 +[m][un] -> [mun] 615 +[es][, ] -> [es, ] 616 +[on ][the ] -> [on the ] 617 +[ec][t] -> [ect] 618 +[c][l] -> [cl] 619 +[p][ol] -> [pol] 620 +[ ][and ] -> [ and ] 621 +[ || Socorro][ || LINEAR] -> [ || Socorro || LINEAR] 622 +[\u000a| ][1] -> [\u000a| 1] 623 +[st][r] -> [str] 624 +[a][w] -> [aw] 625 +[A][r] -> [Ar] 626 +[ic][h ] -> [ich ] 627 +[ar][d] -> [ard] 628 +[ef][e] -> [efe] 629 +[es][s] -> [ess] 630 +[p][er] -> [per] 631 +[g][l] -> [gl] 632 +[a][, ] -> [a, ] 633 +[St][at] -> [Stat] 634 +[as][s] -> [ass] 635 +[peopl][e ] -> [people ] 636 +[ || ][align=right | ] -> [ || align=right | ] 637 +[b][ec] -> [bec] 638 +[at ][the ] -> [at the ] 639 +[Other ][websit] -> [Other websit] 640 +[i][es ] -> [ies ] 641 +[ation][al ] -> [ational ] 642 +[h][ave ] -> [have ] 643 +[ew][ ] -> [ew ] 644 +[. ][ ] -> [. ] 645 +[2][ ] -> [2 ] 646 +[r][it] -> [rit] 647 +[19][6] -> [196] 648 +[wh][ich ] -> [which ] 649 +[1][0] -> [10] 650 +[f][ef] -> [fef] 651 +[a][st] -> [ast] 652 +[e][\u000a] -> [e\u000a] 653 +[p][h] -> [ph] 654 +[k][n] -> [kn] 655 +[os][t ] -> [ost ] 656 +[United ][Stat] -> [United Stat] 657 +[o][ther ] -> [other ] 658 +[ed ][the ] -> [ed the ] 659 +[ar][d ] -> [ard ] 660 +[i][ti] -> [iti] 661 +[th][er] -> [ther] 662 +[. I][n ] -> [. In ] 663 +[om][e ] -> [ome ] 664 +[m][ent] -> [ment] 665 +[, ][the ] -> [, the ] 666 +[all][y ] -> [ally ] 667 +[es][e ] -> [ese ] 668 +[o][ot] -> [oot] 669 +[is ][the ] -> [is the ] 670 +[E9E9][E9] -> [E9E9E9] 671 +[ ][ ] -> [ ] 672 +[fef][efe] -> [fefefe] 673 +[t][e] -> [te] 674 +[for][m] -> [form] 675 +[er][n] -> [ern] 676 +[ bgcolor=#][E9E9E9] -> [ bgcolor=#E9E9E9] 677 +[us][t ] -> [ust ] 678 +[ag][e ] -> [age ] 679 +[or][d] -> [ord] 680 +[es ][in ] -> [es in ] 681 +[, ][199] -> [, 199] 682 +[i][al ] -> [ial ] 683 +[th][ey ] -> [they ] 684 +[l][and] -> [land] 685 +[C][om] -> [Com] 686 +[ing ][the ] -> [ing the ] 687 +[i][tic] -> [itic] 688 +[s][c] -> [sc] 689 +[I][n ] -> [In ] 690 +[u][s ] -> [us ] 691 +[ir][ ] -> [ir ] 692 +[el][ ] -> [el ] 693 +[5][ ] -> [5 ] 694 +[4][ ] -> [4 ] 695 +[il][ ] -> [il ] 696 +["][ ] -> [" ] 697 +[3][ ] -> [3 ] 698 +[1][7] -> [17] 699 +[6][ ] -> [6 ] 700 +[A][ ] -> [A ] 701 +[ bgcolor=#][fefefe] -> [ bgcolor=#fefefe] 702 +[ep][t] -> [ept] 703 +[m][e] -> [me] 704 +[v][e ] -> [ve ] 705 +[in][e ] -> [ine ] 706 +[ || Socorro || LINEAR][ || — || align=right | ] -> [ || Socorro || LINEAR || — || align=right | ] 707 +[is][t ] -> [ist ] 708 +[L][e] -> [Le] 709 +[N][ew ] -> [New ] 710 +[ro][w] -> [row] 711 +[a][us] -> [aus] 712 +[En][gl] -> [Engl] 713 +[\u000a|-][\u000a|] -> [\u000a|-\u000a|] 714 +[y][ear] -> [year] 715 +[re][c] -> [rec] 716 +[i][z] -> [iz] 717 +[f][ir] -> [fir] 718 +[8][ ] -> [8 ] 719 +[ow][ ] -> [ow ] 720 +[7][ ] -> [7 ] 721 +[am][ ] -> [am ] 722 +[19][5] -> [195] 723 +[19][4] -> [194] 724 +[S][h] -> [Sh] 725 +[d][ea] -> [dea] 726 +[d][uc] -> [duc] 727 +[t][w] -> [tw] 728 +[19][3] -> [193] 729 +[as][t ] -> [ast ] 730 +[ept][ember ] -> [eptember ] 731 +[S][p] -> [Sp] 732 +[i][b] -> [ib] 733 +[1][6] -> [16] 734 +[u][re] -> [ure] 735 +[ar][e] -> [are] 736 +[was ][a ] -> [was a ] 737 +[m][ent ] -> [ment ] 738 +[s ][from ] -> [s from ] 739 +[a][in ] -> [ain ] 740 +[oot][b] -> [ootb] 741 +[wh][o ] -> [who ] 742 +[for ][the ] -> [for the ] 743 +[com][p] -> [comp] 744 +[ic][al ] -> [ical ] 745 +[c][an ] -> [can ] 746 +[)][\u000a] -> [)\u000a] 747 +[9][ ] -> [9 ] 748 +[the][ir ] -> [their ] 749 +[ong][ ] -> [ong ] 750 +[0][0] -> [00] 751 +[\u000a ][\u000a ] -> [\u000a \u000a ] 752 +[up][ ] -> [up ] 753 +[. ][A] -> [. A] 754 +[ac][k] -> [ack] 755 +[er][s\u000a] -> [ers\u000a] 756 +[J][an] -> [Jan] 757 +[fir][st ] -> [first ] 758 +[ b][ir] -> [ bir] 759 +[a][f] -> [af] 760 +[i][f] -> [if] 761 +[h][ad ] -> [had ] 762 +[w][e] -> [we] 763 +[un][d] -> [und] 764 +[s][u] -> [su] 765 +[p][res] -> [pres] 766 +[w][rit] -> [writ] 767 +[1][.] -> [1.] 768 +[S][eptember ] -> [September ] 769 +[h][er ] -> [her ] 770 +[at][t] -> [att] 771 +[�][�] -> [é] 772 +[l][iv] -> [liv] 773 +[G][er] -> [Ger] 774 +[it][s ] -> [its ] 775 +[es ][\u000a\u000a] -> [es \u000a\u000a] 776 +[i][d ] -> [id ] 777 +[O][ct] -> [Oct] 778 +[v][er] -> [ver] 779 +[c][all] -> [call] 780 +[a][ch] -> [ach] 781 +[m][an ] -> [man ] 782 +[ab][out ] -> [about ] 783 +[kn][own ] -> [known ] 784 +[m][on] -> [mon] 785 +[c][ent] -> [cent] 786 +[. It ][is ] -> [. It is ] 787 +[d6d6][d6] -> [d6d6d6] 788 +[o][ber ] -> [ober ] 789 +[N][or] -> [Nor] 790 +[ bir][ths\u000a] -> [ births\u000a] 791 +[d][is] -> [dis] 792 +[am][p] -> [amp] 793 +[e ][and ] -> [e and ] 794 +[e][.\u000a\u000a] -> [e.\u000a\u000a] 795 +[e ][(] -> [e (] 796 +[o][h] -> [oh] 797 +[||][1] -> [||1] 798 +[en][s] -> [ens] 799 +[of][f] -> [off] 800 +[ub][l] -> [ubl] 801 +[Oct][ober ] -> [October ] 802 +[ bgcolor=#][d6d6d6] -> [ bgcolor=#d6d6d6] 803 +[pol][itic] -> [politic] 804 +[m][us] -> [mus] 805 +[s ][(] -> [s (] 806 +[or][y ] -> [ory ] 807 +[eb][r] -> [ebr] 808 +[man][y ] -> [many ] 809 +[el][ev] -> [elev] 810 +[an][n] -> [ann] 811 +[an][t ] -> [ant ] 812 +[bu][t ] -> [but ] 813 +[ed ][on ] -> [ed on ] 814 +[. S][he ] -> [. She ] 815 +[ic][h] -> [ich] 816 +[ou][ld ] -> [ould ] 817 +[.][\u000a] -> [.\u000a] 818 +[A][ug] -> [Aug] 819 +[ac][k ] -> [ack ] 820 +[Mar][ch ] -> [March ] 821 +[in][d] -> [ind] 822 +[is][ion ] -> [ision ] 823 +[P][e] -> [Pe] 824 +[ad][e ] -> [ade ] 825 +[A][u] -> [Au] 826 +[1][5] -> [15] 827 +[f][l] -> [fl] 828 +[s][. ] -> [s. ] 829 +[s][ing] -> [sing] 830 +[ar][g] -> [arg] 831 +[b][o] -> [bo] 832 +[n][e] -> [ne] 833 +[Jan][uary ] -> [January ] 834 +[er ][(] -> [er (] 835 +[igh][t] -> [ight] 836 +[n][am] -> [nam] 837 +[oun][d ] -> [ound ] 838 +[20][2] -> [202] 839 +[=]["] -> [="] 840 +[N][ov] -> [Nov] 841 +[1][4] -> [14] 842 +[D][ec] -> [Dec] 843 +[th][is ] -> [this ] 844 +[||][2] -> [||2] 845 +[p][o] -> [po] 846 +[ou][s ] -> [ous ] 847 +[1][2] -> [12] 848 +[Fr][anc] -> [Franc] 849 +[iv][ers] -> [ivers] 850 +[. He ][was ] -> [. 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They ] 869 +[tw][o ] -> [two ] 870 +[W][h] -> [Wh] 871 +[1][3] -> [13] 872 +[2][.] -> [2.] 873 +[L][iv] -> [Liv] 874 +[, 200][0] -> [, 2000] 875 +[e][-] -> [e-] 876 +[elev][ision ] -> [elevision ] 877 +[u][e ] -> [ue ] 878 +[g][r] -> [gr] 879 +[s][y] -> [sy] 880 +[s][.\u000a\u000a] -> [s.\u000a\u000a] 881 +[con][t] -> [cont] 882 +[C][on] -> [Con] 883 +[ ][of ] -> [ of ] 884 +[Dec][ember ] -> [December ] 885 +[es ][of ] -> [es of ] 886 +[Nov][ember ] -> [November ] 887 +[t ][of ] -> [t of ] 888 +[er][al ] -> [eral ] 889 +[h][i] -> [hi] 890 +[J][ap] -> [Jap] 891 +[at][er ] -> [ater ] 892 +[B][ri] -> [Bri] 893 +[C][ount] -> [Count] 894 +[il][li] -> [illi] 895 +[c][o] -> [co] 896 +[A][pr] -> [Apr] 897 +[. Th][is ] -> [. 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It was ] 1009 +[Liv][ing ] -> [Living ] 1010 +[is ][an ] -> [is an ] 1011 +[p][ort] -> [port] 1012 +[ow][n] -> [own] 1013 +[t][elevision ] -> [television ] 1014 +[en][n] -> [enn] 1015 +[l][in] -> [lin] 1016 +[an][y ] -> [any ] 1017 +[d][ist] -> [dist] 1018 +[d][es] -> [des] 1019 +[I][I] -> [II] 1020 +[a][il] -> [ail] 1021 +[d][ep] -> [dep] 1022 +[establ][ish] -> [establish] 1023 +[d][. ] -> [d. ] 1024 +[with ][the ] -> [with the ] 1025 +[20][20] -> [2020] 1026 +[ bgcolor=#fefefe][\u000a| 1] -> [ bgcolor=#fefefe\u000a| 1] 1027 +[P][ro] -> [Pro] 1028 +[) ][is a ] -> [) is a ] 1029 +[Wor][ld ] -> [World ] 1030 +[c][ity ] -> [city ] 1031 +[sh][e ] -> [she ] 1032 +[id][e] -> [ide] 1033 +[P][res] -> [Pres] 1034 +[us][s] -> [uss] 1035 +[s ][to ] -> [s to ] 1036 +[Bri][tish ] -> [British ] 1037 +[i][k] -> [ik] 1038 +[di][rect] -> [direct] 1039 +[as][h] -> [ash] 1040 +[com][mun] -> [commun] 1041 +[Un][ivers] -> [Univers] 1042 +[1][, ] -> [1, ] 1043 +[P][h] -> [Ph] 1044 +[g][ ] -> [g ] 1045 +[og][rap] -> [ograp] 1046 +[2][4] -> [24] 1047 +[T][r] -> [Tr] 1048 +[C][ar] -> [Car] 1049 +[u][ro] -> [uro] 1050 +[||][||] -> [||||] 1051 +[at][ur] -> [atur] 1052 +[oo][d ] -> [ood ] 1053 +[ol][d ] -> [old ] 1054 +[Jul][y ] -> [July ] 1055 +[ bgcolor=#E9E9E9][\u000a| ] -> [ bgcolor=#E9E9E9\u000a| ] 1056 +[G][e] -> [Ge] 1057 +[A][f] -> [Af] 1058 +[3][.] -> [3.] 1059 +[y][p] -> [yp] 1060 +[el][l ] -> [ell ] 1061 +[re][l] -> [rel] 1062 +[g][en] -> [gen] 1063 +[il][l ] -> [ill ] 1064 +[es][\u000a ] -> [es\u000a ] 1065 +[n][or] -> [nor] 1066 +[ol][d] -> [old] 1067 +[C][al] -> [Cal] 1068 +[our][ ] -> [our ] 1069 +[2][5] -> [25] 1070 +[le][as] -> [leas] 1071 +[f][ ] -> [f ] 1072 +[a ][(] -> [a (] 1073 +[n][um] -> [num] 1074 +[o][y] -> [oy] 1075 +[.\u000a\u000aReferences\u000a\u000a][Other websit] -> [.\u000a\u000aReferences\u000a\u000aOther websit] 1076 +[ bgcolor=#E9E9E9][\u000a| 1] -> [ bgcolor=#E9E9E9\u000a| 1] 1077 +[s ][in the ] -> [s in the ] 1078 +[pop][ul] -> [popul] 1079 +[b][r] -> [br] 1080 +[h][el] -> [hel] 1081 +[s][id] -> [sid] 1082 +[u][ ] -> [u ] 1083 +[3][0] -> [30] 1084 +[s][k] -> [sk] 1085 +[at][ch] -> [atch] 1086 +[P][r] -> [Pr] 1087 +[Le][ag] -> [Leag] 1088 +[Engl][ish ] -> [English ] 1089 +[di][ff] -> [diff] 1090 +[as ][the ] -> [as the ] 1091 +[. ][\u000a\u000a] -> [. \u000a\u000a] 1092 +[u][t ] -> [ut ] 1093 +[G][r] -> [Gr] 1094 +[a][j] -> [aj] 1095 +[b][et] -> [bet] 1096 +[s][ti] -> [sti] 1097 +[in][ter] -> [inter] 1098 +[oo][k] -> [ook] 1099 +[S][w] -> [Sw] 1100 +[c][ount] -> [count] 1101 +[O][r] -> [Or] 1102 +[b][ur] -> [bur] 1103 +[ers][on ] -> [erson ] 1104 +[Living ][people\u000a] -> [Living people\u000a] 1105 +[es][.\u000a\u000a] -> [es.\u000a\u000a] 1106 +[P][al] -> [Pal] 1107 +[c][re] -> [cre] 1108 +[P][ol] -> [Pol] 1109 +[d][e ] -> [de ] 1110 +[or][t ] -> [ort ] 1111 +[l][and ] -> [land ] 1112 +[ograp][h] -> [ograph] 1113 +[es][s ] -> [ess ] 1114 +[s][ur] -> [sur] 1115 +[le][g] -> [leg] 1116 +[th][an ] -> [than ] 1117 +[bec][ame ] -> [became ] 1118 +[on][ly ] -> [only ] 1119 +[ro][ug] -> [roug] 1120 +[str][al] -> [stral] 1121 +[oo][k ] -> [ook ] 1122 +[we][en ] -> [ween ] 1123 +[o][st] -> [ost] 1124 +[li][ke ] -> [like ] 1125 +[I][tal] -> [Ital] 1126 +[\u000a\u000aReferenc][es\u000a\u000a] -> [\u000a\u000aReferences\u000a\u000a] 1127 +[ti][v] -> [tiv] 1128 +[ab][le ] -> [able ] 1129 +[er][\u000a ] -> [er\u000a ] 1130 +[ad][i] -> [adi] 1131 +[a][\u000a] -> [a\u000a] 1132 +[c][ri] -> [cri] 1133 +[E][uro] -> [Euro] 1134 +[actor][s\u000a] -> [actors\u000a] 1135 +[Jap][an] -> [Japan] 1136 +[O][n ] -> [On ] 1137 +[.\u000a\u000aReferenc][es \u000a\u000a] -> [.\u000a\u000aReferences \u000a\u000a] 1138 +[tic][ ] -> [tic ] 1139 +[c][oun] -> [coun] 1140 +[f][in] -> [fin] 1141 +[oc][i] -> [oci] 1142 +[id][ent ] -> [ident ] 1143 +[Fr][en] -> [Fren] 1144 +[G][re] -> [Gre] 1145 +[Y][or] -> [Yor] 1146 +[B][l] -> [Bl] 1147 +[e ][of the ] -> [e of the ] 1148 +[Au][stral] -> [Austral] 1149 +[s][on ] -> [son ] 1150 +[M][us] -> [Mus] 1151 +[Can][ad] -> [Canad] 1152 +[s ][the ] -> [s the ] 1153 +[was ][the ] -> [was the ] 1154 +[in][to ] -> [into ] 1155 +[ang][u] -> [angu] 1156 +[f][i] -> [fi] 1157 +[ch][ool] -> [chool] 1158 +[Pe][opl] -> [Peopl] 1159 +[sp][ec] -> [spec] 1160 +[politic][ian] -> [politician] 1161 +[y ][of ] -> [y of ] 1162 +[ || ][ || ] -> [ || || ] 1163 +[on][d ] -> [ond ] 1164 +[ic][ip] -> [icip] 1165 +[o][od] -> [ood] 1166 +[ri][v] -> [riv] 1167 +[d][ur] -> [dur] 1168 +[establish][ment] -> [establishment] 1169 +[ ][in ] -> [ in ] 1170 +[wh][ere ] -> [where ] 1171 +[2][9] -> [29] 1172 +[R][ep] -> [Rep] 1173 +[S][outh ] -> [South ] 1174 +[om][e] -> [ome] 1175 +[an][k] -> [ank] 1176 +[th][ere ] -> [there ] 1177 +[re][m] -> [rem] 1178 +[re][leas] -> [releas] 1179 +[l][arg] -> [larg] 1180 +[Jun][e ] -> [June ] 1181 +[inc][e ] -> [ince ] 1182 +[. ][M] -> [. M] 1183 +[a ][s] -> [a s] 1184 +[New ][Yor] -> [New Yor] 1185 +[ver][y ] -> [very ] 1186 +[iv][e ] -> [ive ] 1187 +[u][e] -> [ue] 1188 +[ar][i] -> [ari] 1189 +[An][d] -> [And] 1190 +[et][t] -> [ett] 1191 +[2][6] -> [26] 1192 +[A][w] -> [Aw] 1193 +[em][ent] -> [ement] 1194 +[born ][in ] -> [born in ] 1195 +[ist][or] -> [istor] 1196 +[2][8] -> [28] 1197 +[2][3] -> [23] 1198 +[o][s ] -> [os ] 1199 +[ || || — || ][September ] -> [ || || — || September ] 1200 +[c][ap] -> [cap] 1201 +[O][n] -> [On] 1202 +[el][ec] -> [elec] 1203 +[er][r] -> [err] 1204 +[\u000a|-\u000a|][200] -> [\u000a|-\u000a|200] 1205 +[ic][k] -> [ick] 1206 +[N][ational ] -> [National ] 1207 +[F][or] -> [For] 1208 +[I][t ] -> [It ] 1209 +[A][d] -> [Ad] 1210 +[icip][al] -> [icipal] 1211 +[Ch][r] -> [Chr] 1212 +[ov][er ] -> [over ] 1213 +[P][l] -> [Pl] 1214 +[s][\u000a\u000a] -> [s\u000a\u000a] 1215 +[M][in] -> [Min] 1216 +[re][f] -> [ref] 1217 +[i][e ] -> [ie ] 1218 +[s][ome ] -> [some ] 1219 +[M][on] -> [Mon] 1220 +[s][\u000a ] -> [s\u000a ] 1221 +[R][iv] -> [Riv] 1222 +[s ][that ] -> [s that ] 1223 +[C][ ] -> [C ] 1224 +[g][u] -> [gu] 1225 +[2][, ] -> [2, ] 1226 +[su][ch ] -> [such ] 1227 +[2][7] -> [27] 1228 +[cent][ur] -> [centur] 1229 +[ati][c] -> [atic] 1230 +[play][ed ] -> [played ] 1231 +[In][ter] -> [Inter] 1232 +[ ][ ] -> [ ] 1233 +[ births\u000a][Living people\u000a] -> [ births\u000aLiving people\u000a] 1234 +[ bgcolor=#d6d6d6][\u000a| ] -> [ bgcolor=#d6d6d6\u000a| ] 1235 +[4][.] -> [4.] 1236 +[em][b] -> [emb] 1237 +[w][ould ] -> [would ] 1238 +[g][am] -> [gam] 1239 +[y][\u000a] -> [y\u000a] 1240 +[us][ed ] -> [used ] 1241 +[pro][v] -> [prov] 1242 +[d][om] -> [dom] 1243 +[R][om] -> [Rom] 1244 +[st][art] -> [start] 1245 +[p][art] -> [part] 1246 +[footb][all ] -> [football ] 1247 +[20][19] -> [2019] 1248 +[ti][l ] -> [til ] 1249 +[)][\u000a\u000a] -> [)\u000a\u000a] 1250 +[p][ubl] -> [publ] 1251 +[G][u] -> [Gu] 1252 +[P][ri] -> [Pri] 1253 +[l][ow] -> [low] 1254 +[er ][of ] -> [er of ] 1255 +[f][e] -> [fe] 1256 +[m][ov] -> [mov] 1257 +[a][i] -> [ai] 1258 +[t][s ] -> [ts ] 1259 +[y][s] -> [ys] 1260 +[ ][S] -> [ S] 1261 +[19][0] -> [190] 1262 +[1][1] -> [11] 1263 +[enc][e ] -> [ence ] 1264 +[, 199][9] -> [, 1999] 1265 +[ b][y ] -> [ by ] 1266 +[ bgcolor=#d6d6d6][\u000a| 1] -> [ bgcolor=#d6d6d6\u000a| 1] 1267 +[, ][a ] -> [, a ] 1268 +[il][e ] -> [ile ] 1269 +[num][ber ] -> [number ] 1270 +[Fren][ch ] -> [French ] 1271 +[bet][ween ] -> [between ] 1272 +[H][e ] -> [He ] 1273 +[R][uss] -> [Russ] 1274 +[ati][ve ] -> [ative ] 1275 +[c][ur] -> [cur] 1276 +[n][ame ] -> [name ] 1277 +[st][ri] -> [stri] 1278 +[h][igh] -> [high] 1279 +[t ][of the ] -> [t of the ] 1280 +[ity ][of ] -> [ity of ] 1281 +[United Stat][es ] -> [United States ] 1282 +[off][ic] -> [offic] 1283 +[bec][aus] -> [becaus] 1284 +[diff][er] -> [differ] 1285 +[Joh][n ] -> [John ] 1286 +[span="][2] -> [span="2] 1287 +[it][e ] -> [ite ] 1288 +[un][til ] -> [until ] 1289 +[g][overn] -> [govern] 1290 +[Ger][man ] -> [German ] 1291 +[d][ec] -> [dec] 1292 +[st][em] -> [stem] 1293 +[E][l] -> [El] 1294 +[e][\u000a ] -> [e\u000a ] 1295 +[F][l] -> [Fl] 1296 +[e][as] -> [eas] 1297 +[ov][er] -> [over] 1298 +[t][on ] -> [ton ] 1299 +[t][ak] -> [tak] 1300 +[y ][(] -> [y (] 1301 +[s][m] -> [sm] 1302 +[span="2]["|] -> [span="2"|] 1303 +[s][ev] -> [sev] 1304 +[f][ul] -> [ful] 1305 +[amp][ion] -> [ampion] 1306 +[en][d ] -> [end ] 1307 +[s][up] -> [sup] 1308 +[ || ][K] -> [ || K] 1309 +[5][0] -> [50] 1310 +[fam][il] -> [famil] 1311 +[u][ally ] -> [ually ] 1312 +[os][e ] -> [ose ] 1313 +[an][s ] -> [ans ] 1314 +[A][c] -> [Ac] 1315 +[ch][ang] -> [chang] 1316 +[, ][M] -> [, M] 1317 +[ef][ore ] -> [efore ] 1318 +[op][ ] -> [op ] 1319 +[) ][and ] -> [) and ] 1320 +[f][ound ] -> [found ] 1321 +[anc][e ] -> [ance ] 1322 +[||][row] -> [||row] 1323 +[g][o] -> [go] 1324 +[w][ay ] -> [way ] 1325 +[p][re] -> [pre] 1326 +[, 200][2] -> [, 2002] 1327 +[, ][he ] -> [, he ] 1328 +[u][di] -> [udi] 1329 +[4][0] -> [40] 1330 +[ || align=right | ][1.] -> [ || align=right | 1.] 1331 +[ed by ][the ] -> [ed by the ] 1332 +[Euro][pe] -> [Europe] 1333 +[p][ag] -> [pag] 1334 +[, ][201] -> [, 201] 1335 +[l][angu] -> [langu] 1336 +[l][es ] -> [les ] 1337 +[el][y ] -> [ely ] 1338 +[ul][t] -> [ult] 1339 +[li][f] -> [lif] 1340 +[it][t] -> [itt] 1341 +[er][n ] -> [ern ] 1342 +[K][ing] -> [King] 1343 +[Peopl][e ] -> [People ] 1344 +[m][e ] -> [me ] 1345 +[di][v] -> [div] 1346 +[tim][e ] -> [time ] 1347 +[l][d] -> [ld] 1348 +[l][oc] -> [loc] 1349 +[th][roug] -> [throug] 1350 +[n][ew ] -> [new ] 1351 +[was ][an ] -> [was an ] 1352 +[Nor][th ] -> [North ] 1353 +[\u000a][The ] -> [\u000aThe ] 1354 +[one ][of the ] -> [one of the ] 1355 +[u][k] -> [uk] 1356 +[em][ent ] -> [ement ] 1357 +[, ][American ] -> [, American ] 1358 +[dep][art] -> [depart] 1359 +[se][as] -> [seas] 1360 +[differ][ent ] -> [different ] 1361 +[p][ ] -> [p ] 1362 +[New Yor][k ] -> [New York ] 1363 +[th][ree ] -> [three ] 1364 +[th][oug] -> [thoug] 1365 +[M][an] -> [Man] 1366 +[ed ][in the ] -> [ed in the ] 1367 +[dist][ric] -> [distric] 1368 +[. ][F] -> [. F] 1369 +[O][S] -> [OS] 1370 +[ic][t] -> [ict] 1371 +[ag][ain] -> [again] 1372 +[.][S] -> [.S] 1373 +[Com][mun] -> [Commun] 1374 +[M][ovi] -> [Movi] 1375 +[(][b. ] -> [(b. ] 1376 +[R][el] -> [Rel] 1377 +[s][el] -> [sel] 1378 +[a ][m] -> [a m] 1379 +[ar][m] -> [arm] 1380 +[o][th] -> [oth] 1381 +[f][ol] -> [fol] 1382 +[ia][, ] -> [ia, ] 1383 +[0][s ] -> [0s ] 1384 +[e][k] -> [ek] 1385 +[i][x] -> [ix] 1386 +[g][re] -> [gre] 1387 +[.\u000a\u000a][In ] -> [.\u000a\u000aIn ] 1388 +[h][t ] -> [ht ] 1389 +[P][art] -> [Part] 1390 +[Ch][in] -> [Chin] 1391 +[Ge][org] -> [Georg] 1392 +[ear][ ] -> [ear ] 1393 +[ || || — || ][October ] -> [ || || — || October ] 1394 +[mus][ic] -> [music] 1395 +[to ][be ] -> [to be ] 1396 +[t][ri] -> [tri] 1397 +[(][born ] -> [(born ] 1398 +[i][es] -> [ies] 1399 +[en][, ] -> [en, ] 1400 +['][ ] -> [' ] 1401 +[ac][e ] -> [ace ] 1402 +[ser][ies ] -> [series ] 1403 +[w][ay] -> [way] 1404 +[d][ev] -> [dev] 1405 +[ther][n ] -> [thern ] 1406 +[ag][e of ] -> [age of ] 1407 +[pres][ent] -> [present] 1408 +[ly][mp] -> [lymp] 1409 +[i][for] -> [ifor] 1410 +[b][el] -> [bel] 1411 +[United Stat][es] -> [United States] 1412 +[ro][s] -> [ros] 1413 +[am][a ] -> [ama ] 1414 +[m][ay ] -> [may ] 1415 +[ch][ar] -> [char] 1416 +[th][en ] -> [then ] 1417 +[c][ar] -> [car] 1418 +[d][o] -> [do] 1419 +[in][v] -> [inv] 1420 +[s ][were ] -> [s were ] 1421 +[er][t] -> [ert] 1422 +[,][00] -> [,00] 1423 +[\u000a \u000a ][\u000a \u000a ] -> [\u000a \u000a \u000a \u000a ] 1424 +[C][ent] -> [Cent] 1425 +[O][lymp] -> [Olymp] 1426 +[es][ter] -> [ester] 1427 +[in][e] -> [ine] 1428 +[it][al ] -> [ital ] 1429 +[ia][\u000a] -> [ia\u000a] 1430 +[ain][t] -> [aint] 1431 +[be][g] -> [beg] 1432 +[ation ][of ] -> [ation of ] 1433 +[footb][all] -> [football] 1434 +[p][or] -> [por] 1435 +[ifor][n] -> [iforn] 1436 +[i][, ] -> [i, ] 1437 +[I][ ] -> [I ] 1438 +[U][.S] -> [U.S] 1439 +[W][ar] -> [War] 1440 +[, 2000][ || Socorro || LINEAR || — || align=right | ] -> [, 2000 || Socorro || LINEAR || — || align=right | ] 1441 +[d][o ] -> [do ] 1442 +[i][re] -> [ire] 1443 +[an][, ] -> [an, ] 1444 +["][ (] -> [" (] 1445 +[2][ km || \u000a|-id=] -> [2 km || \u000a|-id=] 1446 +[R][ob] -> [Rob] 1447 +[B][ar] -> [Bar] 1448 +[s][ol] -> [sol] 1449 +[s][a] -> [sa] 1450 +[com][m] -> [comm] 1451 +[ed ][a ] -> [ed a ] 1452 +[te][am] -> [team] 1453 +[A][m] -> [Am] 1454 +[s][ame ] -> [same ] 1455 +[ac][c] -> [acc] 1456 +[Cal][iforn] -> [Californ] 1457 +[v][ers] -> [vers] 1458 +[s][ub] -> [sub] 1459 +[at][or] -> [ator] 1460 +[5][.] -> [5.] 1461 +[ing][, ] -> [ing, ] 1462 +[e][al] -> [eal] 1463 +[sec][ond ] -> [second ] 1464 +[pr][of] -> [prof] 1465 +[y ][and ] -> [y and ] 1466 +[Af][ric] -> [Afric] 1467 +[ig][in] -> [igin] 1468 +[z][ ] -> [z ] 1469 +[form][er ] -> [former ] 1470 +[w][om] -> [wom] 1471 +[ap][p] -> [app] 1472 +[b][um] -> [bum] 1473 +[can ][be ] -> [can be ] 1474 +[O][ff] -> [Off] 1475 +[8][0] -> [80] 1476 +[n][o ] -> [no ] 1477 +[Pres][ident ] -> [President ] 1478 +[wor][k ] -> [work ] 1479 +[t][own ] -> [town ] 1480 +[4][ km || \u000a|-id=] -> [4 km || \u000a|-id=] 1481 +[an][im] -> [anim] 1482 +[3][ km || \u000a|-id=] -> [3 km || \u000a|-id=] 1483 +[1][ km || \u000a|-id=] -> [1 km || \u000a|-id=] 1484 +[es ][the ] -> [es the ] 1485 +[E][d] -> [Ed] 1486 +[Ger][man] -> [German] 1487 +[D][ea] -> [Dea] 1488 +[s][ou] -> [sou] 1489 +[a ][and ] -> [a and ] 1490 +[a][ || ] -> [a || ] 1491 +[a][\u000a ] -> [a\u000a ] 1492 +[a][x] -> [ax] 1493 +[am][pl] -> [ampl] 1494 +[S][er] -> [Ser] 1495 +[am][m] -> [amm] 1496 +[l][aw] -> [law] 1497 +[c][aus] -> [caus] 1498 +[N][e] -> [Ne] 1499 +[p][ac] -> [pac] 1500 +[al][e ] -> [ale ] 1501 +[hi][m ] -> [him ] 1502 +[s][ome] -> [some] 1503 +[of][ten ] -> [often ] 1504 +[Ch][ar] -> [Char] 1505 +[sy][stem] -> [system] 1506 +[ap][pe] -> [appe] 1507 +[Rep][ubl] -> [Republ] 1508 +[f][ound] -> [found] 1509 +[b][ro] -> [bro] 1510 +[\u000a\u000a][ ] -> [\u000a\u000a ] 1511 +[G][en] -> [Gen] 1512 +[7][0] -> [70] 1513 +[) ][was a ] -> [) was a ] 1514 +[sti][t] -> [stit] 1515 +[f][r] -> [fr] 1516 +[or ][of ] -> [or of ] 1517 +[or][n] -> [orn] 1518 +[, ][but ] -> [, but ] 1519 +[u][al ] -> [ual ] 1520 +[throug][h ] -> [through ] 1521 +[ers ][from ] -> [ers from ] 1522 +[D][i] -> [Di] 1523 +[Min][ist] -> [Minist] 1524 +[�][�] -> [á] 1525 +[s, ][and ] -> [s, and ] 1526 +[ti][ve ] -> [tive ] 1527 +[a][ur] -> [aur] 1528 +[5][ km || \u000a|-id=] -> [5 km || \u000a|-id=] 1529 +[ou][t] -> [out] 1530 +[om][ar] -> [omar] 1531 +[ing ][to ] -> [ing to ] 1532 +[es ][(] -> [es (] 1533 +[iti][es in ] -> [ities in ] 1534 +[18][9] -> [189] 1535 +[gro][up] -> [group] 1536 +[a ][S] -> [a S] 1537 +[m][il] -> [mil] 1538 +[m][et] -> [met] 1539 +[m][od] -> [mod] 1540 +[oun][c] -> [ounc] 1541 +[l][ater ] -> [later ] 1542 +[id][e ] -> [ide ] 1543 +[er][\u000a] -> [er\u000a] 1544 +[||0][||] -> [||0||] 1545 +[it][t ] -> [itt ] 1546 +[Leag][ue ] -> [League ] 1547 +[mun][icipal] -> [municipal] 1548 +[Rel][ated ] -> [Related ] 1549 +[re][t] -> [ret] 1550 +[ers][on] -> [erson] 1551 +[y][.\u000a\u000a] -> [y.\u000a\u000a] 1552 +[le][ad] -> [lead] 1553 +[a][ul] -> [aul] 1554 +[it ][is ] -> [it is ] 1555 +[ion][al ] -> [ional ] 1556 +[ || ][Pal] -> [ || Pal] 1557 +[again][st ] -> [against ] 1558 +[6][ km || \u000a|-id=] -> [6 km || \u000a|-id=] 1559 +[E][x] -> [Ex] 1560 +[act][res] -> [actres] 1561 +[ro][p] -> [rop] 1562 +[L][ond] -> [Lond] 1563 +[establishment][s in ] -> [establishments in ] 1564 +[ec][t ] -> [ect ] 1565 +[Japan][ese ] -> [Japanese ] 1566 +[a][ug] -> [aug] 1567 +[mus][ic ] -> [music ] 1568 +[R][e] -> [Re] 1569 +[such ][as ] -> [such as ] 1570 +[f][il] -> [fil] 1571 +[(][d. ] -> [(d. ] 1572 +[bu][m ] -> [bum ] 1573 +[s ][for ] -> [s for ] 1574 +[ow][ev] -> [owev] 1575 +[s][. The ] -> [s. The ] 1576 +[y][n] -> [yn] 1577 +[sc][i] -> [sci] 1578 +[. ][W] -> [. W] 1579 +[Count][y, ] -> [County, ] 1580 +[S][ ] -> [S ] 1581 +[c][are] -> [care] 1582 +[e ][in the ] -> [e in the ] 1583 +[m][ember ] -> [member ] 1584 +[m][er ] -> [mer ] 1585 +[m][at] -> [mat] 1586 +[a ][l] -> [a l] 1587 +[ || Pal][omar] -> [ || Palomar] 1588 +[Related ][pag] -> [Related pag] 1589 +[ea][ch ] -> [each ] 1590 +[them][ ] -> [them ] 1591 +[t][o] -> [to] 1592 +[e][an ] -> [ean ] 1593 +[. ][D] -> [. D] 1594 +[I][r] -> [Ir] 1595 +[Pe][ak] -> [Peak] 1596 +[6][0] -> [60] 1597 +[7][ km || \u000a|-id=] -> [7 km || \u000a|-id=] 1598 +[i][o ] -> [io ] 1599 +[8][8] -> [88] 1600 +[, ][which ] -> [, which ] 1601 +[ || || — || ][March ] -> [ || || — || March ] 1602 +[e][. The ] -> [e. The ] 1603 +[d ][of ] -> [d of ] 1604 +[e][\u000a\u000a] -> [e\u000a\u000a] 1605 +[ist][ory ] -> [istory ] 1606 +[A][ct] -> [Act] 1607 +[ed ][with ] -> [ed with ] 1608 +[2][1] -> [21] 1609 +[E][ar] -> [Ear] 1610 +[ || K][itt ] -> [ || Kitt ] 1611 +[el][op] -> [elop] 1612 +[org][an] -> [organ] 1613 +[5][9] -> [59] 1614 +[str][uc] -> [struc] 1615 +[W][al] -> [Wal] 1616 +[coun][tr] -> [countr] 1617 +[t][yp] -> [typ] 1618 +[ew][atch] -> [ewatch] 1619 +[ro][c] -> [roc] 1620 +[er ][of the ] -> [er of the ] 1621 +[ || Palomar][ || ] -> [ || Palomar || ] 1622 +[mak][e ] -> [make ] 1623 +[er][y ] -> [ery ] 1624 +[er][g] -> [erg] 1625 +[ing ][a ] -> [ing a ] 1626 +[r][y ] -> [ry ] 1627 +[di][d ] -> [did ] 1628 +[ || Kitt ][Peak] -> [ || Kitt Peak] 1629 +[B][el] -> [Bel] 1630 +[ ][M] -> [ M] 1631 +[0][ km || \u000a|-id=] -> [0 km || \u000a|-id=] 1632 +[al][bum] -> [album] 1633 +[8][ km || \u000a|-id=] -> [8 km || \u000a|-id=] 1634 +[ || S][pac] -> [ || Spac] 1635 +[E][m] -> [Em] 1636 +[) ][(] -> [) (] 1637 +[ampion][ship] -> [ampionship] 1638 +[he ][was ] -> [he was ] 1639 +[play][ers\u000a] -> [players\u000a] 1640 +[d][ay] -> [day] 1641 +[ro][l] -> [rol] 1642 +[B][ra] -> [Bra] 1643 +[nam][ed ] -> [named ] 1644 +[Movi][es ] -> [Movies ] 1645 +[wi][th] -> [with] 1646 +[p][ow] -> [pow] 1647 +[par][t of the ] -> [part of the ] 1648 +[al][bum ] -> [album ] 1649 +[di][ed ] -> [died ] 1650 +[t][re] -> [tre] 1651 +[ation][s ] -> [ations ] 1652 +[NEA][T] -> [NEAT] 1653 +[st][or] -> [stor] 1654 +[, ][S] -> [, S] 1655 +[sa][id ] -> [said ] 1656 +[en]['s ] -> [en's ] 1657 +[Wh][en ] -> [When ] 1658 +[Ital][ian ] -> [Italian ] 1659 +[J][am] -> [Jam] 1660 +[ur][e ] -> [ure ] 1661 +[g][iv] -> [giv] 1662 +[d][er] -> [der] 1663 +[ || Spac][ewatch] -> [ || Spacewatch] 1664 +[h][er] -> [her] 1665 +[ing ][in ] -> [ing in ] 1666 +[m][ed] -> [med] 1667 +[c][e ] -> [ce ] 1668 +[es ][\u000a ] -> [es \u000a ] 1669 +[es ][are ] -> [es are ] 1670 +[ub][ ] -> [ub ] 1671 +[th][ese ] -> [these ] 1672 +[w][ill ] -> [will ] 1673 +[by ][the ] -> [by the ] 1674 +[7][9] -> [79] 1675 +[King][dom] -> [Kingdom] 1676 +[e]['s ] -> [e's ] 1677 +[. ][L] -> [. L] 1678 +[st][ar] -> [star] 1679 +[Off][ic] -> [Offic] 1680 +[s][on] -> [son] 1681 +[und][er ] -> [under ] 1682 +[s][ince ] -> [since ] 1683 +[as][k] -> [ask] 1684 +[it ][was ] -> [it was ] 1685 +[year][s ] -> [years ] 1686 +[y][l] -> [yl] 1687 +[7][, ] -> [7, ] 1688 +[7][3] -> [73] 1689 +[)][.\u000a\u000a] -> [).\u000a\u000a] 1690 +[has ][been ] -> [has been ] 1691 +[. H][is ] -> [. His ] 1692 +[Sc][ot] -> [Scot] 1693 +[te][am ] -> [team ] 1694 +[w][inn] -> [winn] 1695 +[G][overn] -> [Govern] 1696 +[that ][the ] -> [that the ] 1697 +[v][o] -> [vo] 1698 +[un][g] -> [ung] 1699 +[ || Kitt Peak][ || Spacewatch] -> [ || Kitt Peak || Spacewatch] 1700 +[er][t ] -> [ert ] 1701 +[is][m] -> [ism] 1702 +[T][e] -> [Te] 1703 +[9][ km || \u000a|-id=] -> [9 km || \u000a|-id=] 1704 +[u][st] -> [ust] 1705 +[uc][c] -> [ucc] 1706 +[a ][c] -> [a c] 1707 +[An][g] -> [Ang] 1708 +[ed][, ] -> [ed, ] 1709 +[ar][ch] -> [arch] 1710 +[Ar][m] -> [Arm] 1711 +[As][s] -> [Ass] 1712 +[ar][ound ] -> [around ] 1713 +[tim][es ] -> [times ] 1714 +[d][at] -> [dat] 1715 +[) ][was an ] -> [) was an ] 1716 +[7][4] -> [74] 1717 +[com][pl] -> [compl] 1718 +[�][�] -> [ö] 1719 +[W][illi] -> [Willi] 1720 +[a][ch ] -> [ach ] 1721 +[fol][low] -> [follow] 1722 +[ || || — || ][August ] -> [ || || — || August ] 1723 +[mar][ri] -> [marri] 1724 +[us][ually ] -> [usually ] 1725 +[M][ich] -> [Mich] 1726 +[on][om] -> [onom] 1727 +[7][6] -> [76] 1728 +[dur][ing the ] -> [during the ] 1729 +[was ][born in ] -> [was born in ] 1730 +[�][�] -> [’] 1731 +[us][in] -> [usin] 1732 +[le][v] -> [lev] 1733 +[ed to ][the ] -> [ed to the ] 1734 +[sh][ow] -> [show] 1735 +[. ][K] -> [. K] 1736 +[7][5] -> [75] 1737 +[ || || — || ][December ] -> [ || || — || December ] 1738 +[a][ut] -> [aut] 1739 +[, 2001][ || Socorro || LINEAR || — || align=right | ] -> [, 2001 || Socorro || LINEAR || — || align=right | ] 1740 +[ear][ly ] -> [early ] 1741 +[ig][n ] -> [ign ] 1742 +[op][er] -> [oper] 1743 +[st][udi] -> [studi] 1744 +[h][um] -> [hum] 1745 +[-][d] -> [-d] 1746 +[h][istor] -> [histor] 1747 +[t][on] -> [ton] 1748 +[are][a ] -> [area ] 1749 +[s][chool] -> [school] 1750 +[appe][ar] -> [appear] 1751 +[om][ ] -> [om ] 1752 +[ester][n ] -> [estern ] 1753 +[el][d] -> [eld] 1754 +[ing][s ] -> [ings ] 1755 +[tion][al ] -> [tional ] 1756 +[M][e] -> [Me] 1757 +[or][igin] -> [origin] 1758 +[prof][ess] -> [profess] 1759 +[im][port] -> [import] 1760 +[t][e ] -> [te ] 1761 +[our][n] -> [ourn] 1762 +[c][ould ] -> [could ] 1763 +[201][0] -> [2010] 1764 +[A][b] -> [Ab] 1765 +[Dea][th] -> [Death] 1766 +[D][is] -> [Dis] 1767 +[3][, ] -> [3, ] 1768 +[direct][ed by ] -> [directed by ] 1769 +[T][ur] -> [Tur] 1770 +[Th][is ] -> [This ] 1771 +[3][9] -> [39] 1772 +[l][ong] -> [long] 1773 +[,00][0 ] -> [,000 ] 1774 +[be][ing ] -> [being ] 1775 +[t ][in ] -> [t in ] 1776 +[Canad][ian ] -> [Canadian ] 1777 +[owev][er, ] -> [owever, ] 1778 +[\u000a][S] -> [\u000aS] 1779 +[) ][is an ] -> [) is an ] 1780 +[ers][, ] -> [ers, ] 1781 +[k][ill] -> [kill] 1782 +[. Th][ere ] -> [. There ] 1783 +[char][ac] -> [charac] 1784 +[W][ar ] -> [War ] 1785 +[id][g] -> [idg] 1786 +[bo][th ] -> [both ] 1787 +[known ][as ] -> [known as ] 1788 +[B][o] -> [Bo] 1789 +[ex][ampl] -> [exampl] 1790 +[People ][from ] -> [People from ] 1791 +[M][es] -> [Mes] 1792 +[ou][ ] -> [ou ] 1793 +[2][2] -> [22] 1794 +[�][�] -> [ü] 1795 +[ed ][and ] -> [ed and ] 1796 +[7][8] -> [78] 1797 +[|][-] -> [|-] 1798 +[b][efore ] -> [before ] 1799 +[a ][f] -> [a f] 1800 +[ing ][of ] -> [ing of ] 1801 +[th ][centur] -> [th centur] 1802 +[atic][ ] -> [atic ] 1803 +[U.S][. ] -> [U.S. ] 1804 +[r][al ] -> [ral ] 1805 +[becaus][e ] -> [because ] 1806 +[gro][up ] -> [group ] 1807 +[6][5] -> [65] 1808 +[g][ame ] -> [game ] 1809 +[. ][The ] -> [. The ] 1810 +[b][re] -> [bre] 1811 +[j][o] -> [jo] 1812 +[Au][stri] -> [Austri] 1813 +[6][4] -> [64] 1814 +[w][ar] -> [war] 1815 +[Q][u] -> [Qu] 1816 +[h][im] -> [him] 1817 +[dea][th] -> [death] 1818 +[t][t] -> [tt] 1819 +[v][ide] -> [vide] 1820 +[al][s ] -> [als ] 1821 +[ || || — || ][November ] -> [ || || — || November ] 1822 +[||][4] -> [||4] 1823 +[comp][os] -> [compos] 1824 +[ic][an ] -> [ican ] 1825 +[es ][to ] -> [es to ] 1826 +[P][er] -> [Per] 1827 +[reg][ion ] -> [region ] 1828 +[ed ][as ] -> [ed as ] 1829 +[d][e] -> [de] 1830 +[pl][e ] -> [ple ] 1831 +[a ][p] -> [a p] 1832 +[R][o] -> [Ro] 1833 +[.\u000a\u000a][H] -> [.\u000a\u000aH] 1834 +[depart][ment ] -> [department ] 1835 +[P][et] -> [Pet] 1836 +[.\u000a\u000a][S] -> [.\u000a\u000aS] 1837 +[di][ed on ] -> [died on ] 1838 +[m][ain ] -> [main ] 1839 +[known ][for ] -> [known for ] 1840 +[dr][ama ] -> [drama ] 1841 +[Sw][ed] -> [Swed] 1842 +[1][0 ] -> [10 ] 1843 +[ist][s\u000a] -> [ists\u000a] 1844 +[ad][em] -> [adem] 1845 +[a][ff] -> [aff] 1846 +[col][span="2"|] -> [colspan="2"|] 1847 +[s][l] -> [sl] 1848 +[pro][gr] -> [progr] 1849 +[cur][r] -> [curr] 1850 +[at][ing ] -> [ating ] 1851 +[6][, ] -> [6, ] 1852 +[ex][t] -> [ext] 1853 +[b][est ] -> [best ] 1854 +[V][i] -> [Vi] 1855 +[colspan="2"|][-] -> [colspan="2"|-] 1856 +[&][ ] -> [& ] 1857 +[B][e] -> [Be] 1858 +[D][em] -> [Dem] 1859 +[\u000a ][S] -> [\u000a S] 1860 +[6][9] -> [69] 1861 +[s][im] -> [sim] 1862 +[3][4] -> [34] 1863 +[if][ ] -> [if ] 1864 +[u][n ] -> [un ] 1865 +[tion ][of ] -> [tion of ] 1866 +[uc][k] -> [uck] 1867 +[li][m] -> [lim] 1868 +[Ph][il] -> [Phil] 1869 +[sev][eral ] -> [several ] 1870 +[at the ][age of ] -> [at the age of ] 1871 +[un][n] -> [unn] 1872 +[bu][il] -> [buil] 1873 +[H][er] -> [Her] 1874 +[3][5] -> [35] 1875 +[ch][il] -> [chil] 1876 +[\u000a ]["] -> [\u000a "] 1877 +[20][ ] -> [20 ] 1878 +[And][erson ] -> [Anderson ] 1879 +[h][y] -> [hy] 1880 +[D][av] -> [Dav] 1881 +[, 199][8] -> [, 1998] 1882 +[\u000a|-\u000a|][199] -> [\u000a|-\u000a|199] 1883 +[Indi][an ] -> [Indian ] 1884 +[Inter][national ] -> [International ] 1885 +[id][ent] -> [ident] 1886 +[ir][d ] -> [ird ] 1887 +[\u000a\u000aReferences\u000a\u000a][Other websit] -> [\u000a\u000aReferences\u000a\u000aOther websit] 1888 +[ati][v] -> [ativ] 1889 +[us][e ] -> [use ] 1890 +[||row][span="] -> [||rowspan="] 1891 +[E][v] -> [Ev] 1892 +[ar][ti] -> [arti] 1893 +[e, ][and ] -> [e, and ] 1894 +[it][z] -> [itz] 1895 +[l][ef] -> [lef] 1896 +[min][ist] -> [minist] 1897 +[�][�] -> [í] 1898 +[tr][ans] -> [trans] 1899 +[w][on ] -> [won ] 1900 +[r][e ] -> [re ] 1901 +[r][ac] -> [rac] 1902 +[m][illi] -> [milli] 1903 +[i][ev] -> [iev] 1904 +[um][b] -> [umb] 1905 +[n][ow ] -> [now ] 1906 +["][, ] -> [", ] 1907 +[R][ec] -> [Rec] 1908 +[wor][ld] -> [world] 1909 +[B][ro] -> [Bro] 1910 +[e][k ] -> [ek ] 1911 +[2020][ ] -> [2020 ] 1912 +[G][l] -> [Gl] 1913 +[m][u] -> [mu] 1914 +[w][ent ] -> [went ] 1915 +[3][8] -> [38] 1916 +[dev][elop] -> [develop] 1917 +[el][ect] -> [elect] 1918 +[d][ef] -> [def] 1919 +[p][ri] -> [pri] 1920 +[. ][T] -> [. T] 1921 +[a ][b] -> [a b] 1922 +[4][, ] -> [4, ] 1923 +[t ][the ] -> [t the ] 1924 +[Austral][ian ] -> [Australian ] 1925 +[f][ic] -> [fic] 1926 +[6][.] -> [6.] 1927 +[re][as] -> [reas] 1928 +[s][pe] -> [spe] 1929 +[g][et ] -> [get ] 1930 +[v][is] -> [vis] 1931 +[ || align=right | ][2.] -> [ || align=right | 2.] 1932 +[un][d ] -> [und ] 1933 +[H][ol] -> [Hol] 1934 +[m][o] -> [mo] 1935 +[Count][y ] -> [County ] 1936 +[Aw][ard ] -> [Award ] 1937 +[care][er ] -> [career ] 1938 +[ed ][for ] -> [ed for ] 1939 +[S][ec] -> [Sec] 1940 +[s][tic] -> [stic] 1941 +[Sp][ort] -> [Sport] 1942 +[H][ous] -> [Hous] 1943 +[st][ud] -> [stud] 1944 +[s][ucc] -> [succ] 1945 +[, and ][the ] -> [, and the ] 1946 +[al][, ] -> [al, ] 1947 +[for][m ] -> [form ] 1948 +[member ][of the ] -> [member of the ] 1949 +[c][r] -> [cr] 1950 +[per][i] -> [peri] 1951 +[ch][n] -> [chn] 1952 +[18][8] -> [188] 1953 +[6][8] -> [68] 1954 +[o][x] -> [ox] 1955 +[5][, ] -> [5, ] 1956 +[e][. It is ] -> [e. It is ] 1957 +[Sp][an] -> [Span] 1958 +[f][a] -> [fa] 1959 +[ed][y ] -> [edy ] 1960 +[c][as] -> [cas] 1961 +[k][et] -> [ket] 1962 +[w][ ] -> [w ] 1963 +[ad][d] -> [add] 1964 +[D][ist] -> [Dist] 1965 +[e ][is ] -> [e is ] 1966 +[H][ar] -> [Har] 1967 +[f][our ] -> [four ] 1968 +[Bra][z] -> [Braz] 1969 +[me][an] -> [mean] 1970 +[ || || — || ][January ] -> [ || || — || January ] 1971 +[ || ][Anderson ] -> [ || Anderson ] 1972 +[om][in] -> [omin] 1973 +[ist ][of ] -> [ist of ] 1974 +[people ][from ] -> [people from ] 1975 +[ac][tiv] -> [activ] 1976 +[F][ootb] -> [Footb] 1977 +[w][ell ] -> [well ] 1978 +[bur][g] -> [burg] 1979 +[is a ][commun] -> [is a commun] 1980 +[w][est ] -> [west ] 1981 +[T][V] -> [TV] 1982 +[ing ][and ] -> [ing and ] 1983 +[p][er ] -> [per ] 1984 +[ic][k ] -> [ick ] 1985 +[id][d] -> [idd] 1986 +[it][ary ] -> [itary ] 1987 +[�][�] -> [ó] 1988 +[tur][n] -> [turn] 1989 +[P][aul] -> [Paul] 1990 +[us][ed to ] -> [used to ] 1991 +[y][-] -> [y-] 1992 +[res][s] -> [ress] 1993 +[ || || — || ][February ] -> [ || || — || February ] 1994 +[pl][ay ] -> [play ] 1995 +[count][ri] -> [countri] 1996 +[r][ul] -> [rul] 1997 +[H][e] -> [He] 1998 +[M][c] -> [Mc] 1999 +[ || Anderson ][Mes] -> [ || Anderson Mes] 2000 +[i][m ] -> [im ] 2001 +[in][a ] -> [ina ] 2002 +[a][-] -> [a-] 2003 +[import][ant ] -> [important ] 2004 +[om][b] -> [omb] 2005 +[C][or] -> [Cor] 2006 +[Ch][ampionship] -> [Championship] 2007 +[Offic][ial ] -> [Official ] 2008 +[NE][OS] -> [NEOS] 2009 +[. A][f] -> [. Af] 2010 +[Commun][es in ] -> [Communes in ] 2011 +[i][th] -> [ith] 2012 +[ev][ent] -> [event] 2013 +[l][l ] -> [ll ] 2014 +[includ][ing ] -> [including ] 2015 +[st][e] -> [ste] 2016 +[l][et] -> [let] 2017 +[er][-] -> [er-] 2018 +[S][e] -> [Se] 2019 +[Russ][ian ] -> [Russian ] 2020 +[h][ear] -> [hear] 2021 +[as][s ] -> [ass ] 2022 +[t][, ] -> [t, ] 2023 +[K][or] -> [Kor] 2024 +[ak][e ] -> [ake ] 2025 +[es ][in the ] -> [es in the ] 2026 +[is][ti] -> [isti] 2027 +[people ][liv] -> [people liv] 2028 +[rec][ord] -> [record] 2029 +[8][, ] -> [8, ] 2030 +[, 200][3] -> [, 2003] 2031 +[United ][Kingdom] -> [United Kingdom] 2032 +[C][o] -> [Co] 2033 +[, 2000][ || Socorro || LINEAR || ] -> [, 2000 || Socorro || LINEAR || ] 2034 +[|-][\u000a|] -> [|-\u000a|] 2035 +[B][u] -> [Bu] 2036 +[ig][ ] -> [ig ] 2037 +[in][to the ] -> [into the ] 2038 +[9][0] -> [90] 2039 +[larg][e ] -> [large ] 2040 +[in ][a ] -> [in a ] 2041 +[L][ou] -> [Lou] 2042 +[man][ag] -> [manag] 2043 +[4][5] -> [45] 2044 +[R][eg] -> [Reg] 2045 +[w][ater ] -> [water ] 2046 +[govern][ment ] -> [government ] 2047 +[D][r] -> [Dr] 2048 +[c][ul] -> [cul] 2049 +[c][anc] -> [canc] 2050 +[b][ack ] -> [back ] 2051 +[oc][r] -> [ocr] 2052 +[c][an] -> [can] 2053 +[C][r] -> [Cr] 2054 +[des][ign] -> [design] 2055 +[a][ || L] -> [a || L] 2056 +[is][ion] -> [ision] 2057 +[st][ate ] -> [state ] 2058 +[or][d ] -> [ord ] 2059 +[Ang][el] -> [Angel] 2060 +[F][ir] -> [Fir] 2061 +[b][i] -> [bi] 2062 +[is][, ] -> [is, ] 2063 +[O][NEOS] -> [ONEOS] 2064 +[par][t of ] -> [part of ] 2065 +[ip][p] -> [ipp] 2066 +[s][ign] -> [sign] 2067 +[A][ir] -> [Air] 2068 +[writ][ten ] -> [written ] 2069 +[ev][er ] -> [ever ] 2070 +[n][e ] -> [ne ] 2071 +[Univers][ity of ] -> [University of ] 2072 +[langu][ag] -> [languag] 2073 +[A][g] -> [Ag] 2074 +[act][or ] -> [actor ] 2075 +[bu][ild] -> [build] 2076 +[in][k] -> [ink] 2077 +[at][er] -> [ater] 2078 +[. Af][ter ] -> [. After ] 2079 +[f][ac] -> [fac] 2080 +[publ][ish] -> [publish] 2081 +[count][y ] -> [county ] 2082 +[thoug][h ] -> [though ] 2083 +[19][ ] -> [19 ] 2084 +[ev][en ] -> [even ] 2085 +[v][ol] -> [vol] 2086 +[langu][age ] -> [language ] 2087 +[ers ][of ] -> [ers of ] 2088 +[m][emb] -> [memb] 2089 +[e][di] -> [edi] 2090 +[y]['s ] -> [y's ] 2091 +[J][os] -> [Jos] 2092 +[Aw][ard] -> [Award] 2093 +[Rom][an ] -> [Roman ] 2094 +[ur][ ] -> [ur ] 2095 +[n][ear ] -> [near ] 2096 +[S][up] -> [Sup] 2097 +[l][ov] -> [lov] 2098 +[sp][ort] -> [sport] 2099 +[dis][c] -> [disc] 2100 +[r][ight] -> [right] 2101 +[ || Anderson Mes][a || L] -> [ || Anderson Mesa || L] 2102 +[cont][in] -> [contin] 2103 +[Act][or] -> [Actor] 2104 +[Jap][an ] -> [Japan ] 2105 +[er][. He ] -> [er. He ] 2106 +[. ][A ] -> [. A ] 2107 +[ra][f] -> [raf] 2108 +[a][k ] -> [ak ] 2109 +[Pol][itic] -> [Politic] 2110 +[with ][a ] -> [with a ] 2111 +[M][ex] -> [Mex] 2112 +[an][e ] -> [ane ] 2113 +[l][ast ] -> [last ] 2114 +[y][\u000a ] -> [y\u000a ] 2115 +[/][0] -> [/0] 2116 +[C][ity] -> [City] 2117 +[did ][not ] -> [did not ] 2118 +[4][8] -> [48] 2119 +[v][ill] -> [vill] 2120 +[T][im] -> [Tim] 2121 +[Engl][and] -> [England] 2122 +[has ][a ] -> [has a ] 2123 +[n][ew] -> [new] 2124 +[C][ities in ] -> [Cities in ] 2125 +[a][.\u000a\u000a] -> [a.\u000a\u000a] 2126 +[have ][been ] -> [have been ] 2127 +[v][al] -> [val] 2128 +[aj][or ] -> [ajor ] 2129 +[beg][an ] -> [began ] 2130 +[m][ag] -> [mag] 2131 +[op][h] -> [oph] 2132 +[is][od] -> [isod] 2133 +[is][m ] -> [ism ] 2134 +[b][ook] -> [book] 2135 +[famil][y ] -> [family ] 2136 +[p][ort ] -> [port ] 2137 +[F][L] -> [FL] 2138 +[it][y, ] -> [ity, ] 2139 +[b][ir] -> [bir] 2140 +[s][ong ] -> [song ] 2141 +[k][e] -> [ke] 2142 +[ || || — || ][April ] -> [ || || — || April ] 2143 +[T][ex] -> [Tex] 2144 +[stit][u] -> [stitu] 2145 +[S][ch] -> [Sch] 2146 +[vide][o ] -> [video ] 2147 +[�][�] -> [ä] 2148 +[s][ch] -> [sch] 2149 +[1][2 ] -> [12 ] 2150 +[ock][ey ] -> [ockey ] 2151 +[Fl][or] -> [Flor] 2152 +[i][ef] -> [ief] 2153 +[ || Anderson Mesa || L][ONEOS] -> [ || Anderson Mesa || LONEOS] 2154 +[for][c] -> [forc] 2155 +[M][ed] -> [Med] 2156 +[U][S] -> [US] 2157 +[h][al] -> [hal] 2158 +[Pro][v] -> [Prov] 2159 +[pro][t] -> [prot] 2160 +[A][le] -> [Ale] 2161 +[Dist][ric] -> [Distric] 2162 +[o][-] -> [o-] 2163 +[h][av] -> [hav] 2164 +[d][er ] -> [der ] 2165 +[W][est ] -> [West ] 2166 +[sh][ow ] -> [show ] 2167 +[per][form] -> [perform] 2168 +[b][ut] -> [but] 2169 +[�][�] -> [è] 2170 +[||||][||||] -> [||||||||] 2171 +[an][k ] -> [ank ] 2172 +[res][tl] -> [restl] 2173 +[G][am] -> [Gam] 2174 +[N][Y] -> [NY] 2175 +[Chr][isti] -> [Christi] 2176 +[bl][em] -> [blem] 2177 +[b][all ] -> [ball ] 2178 +[3][3] -> [33] 2179 +[4][9] -> [49] 2180 +[is ][also ] -> [is also ] 2181 +[.\u000a\u000aReferences\u000a\u000aOther websit][es\u000a\u000a] -> [.\u000a\u000aReferences\u000a\u000aOther websites\u000a\u000a] 2182 +[larg][est ] -> [largest ] 2183 +[on][s] -> [ons] 2184 +[m][a] -> [ma] 2185 +[f][ri] -> [fri] 2186 +[. 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These ] 2194 +[stat][e of ] -> [state of ] 2195 +[wh][ile ] -> [while ] 2196 +[(][200] -> [(200] 2197 +[es ][of the ] -> [es of the ] 2198 +[B][er] -> [Ber] 2199 +[e, ][the ] -> [e, the ] 2200 +[year][ ] -> [year ] 2201 +[2][)\u000a ] -> [2)\u000a ] 2202 +[i][th ] -> [ith ] 2203 +[ed ][that ] -> [ed that ] 2204 +[Th][ere ] -> [There ] 2205 +[B][ur] -> [Bur] 2206 +[S][y] -> [Sy] 2207 +[le][, ] -> [le, ] 2208 +[M][un] -> [Mun] 2209 +[M][il] -> [Mil] 2210 +[201][4] -> [2014] 2211 +[rec][e] -> [rece] 2212 +[ || Palomar || ][NEAT] -> [ || Palomar || NEAT] 2213 +[I][l] -> [Il] 2214 +[an][i] -> [ani] 2215 +[Europe][an ] -> [European ] 2216 +[1][)\u000a ] -> [1)\u000a ] 2217 +[w][ro] -> [wro] 2218 +[um][mer ] -> [ummer ] 2219 +[p][erson] -> [person] 2220 +[le][y ] -> [ley ] 2221 +[it][ar] -> [itar] 2222 +[n][ov] -> [nov] 2223 +[t][em] -> [tem] 2224 +[th][-] -> [th-] 2225 +[col][l] -> [coll] 2226 +[7][)\u000a ] -> [7)\u000a ] 2227 +[h][ow ] -> [how ] 2228 +[con][s] -> [cons] 2229 +[ph][ys] -> [phys] 2230 +[after ][the ] -> [after the ] 2231 +[wh][at ] -> [what ] 2232 +[n][ot] -> [not] 2233 +[R][ich] -> [Rich] 2234 +[J][o] -> [Jo] 2235 +[a][h ] -> [ah ] 2236 +[ati][stic] -> [atistic] 2237 +[e ][to ] -> [e to ] 2238 +[V][ir] -> [Vir] 2239 +[ex][t ] -> [ext ] 2240 +[m][iss] -> [miss] 2241 +[ian][s from ] -> [ians from ] 2242 +[re][ad] -> [read] 2243 +[gen][er] -> [gener] 2244 +[b][od] -> [bod] 2245 +[a][i ] -> [ai ] 2246 +[Pri][me ] -> [Prime ] 2247 +[C][ap] -> [Cap] 2248 +[ea][st] -> [east] 2249 +[og][e] -> [oge] 2250 +[C][ath] -> [Cath] 2251 +[ea][ch] -> [each] 2252 +[F][il] -> [Fil] 2253 +[a ][L] -> [a L] 2254 +[at][ure] -> [ature] 2255 +[y][\u000a\u000a] -> [y\u000a\u000a] 2256 +[M][al] -> [Mal] 2257 +[ti][t] -> [tit] 2258 +[aug][h] -> [augh] 2259 +[M][o] -> [Mo] 2260 +[y][r] -> [yr] 2261 +[ births\u000a][201] -> [ births\u000a201] 2262 +[i][x ] -> [ix ] 2263 +[G][ro] -> [Gro] 2264 +[a][ir ] -> [air ] 2265 +[Chic][ag] -> [Chicag] 2266 +[po][int] -> [point] 2267 +[N][ew] -> [New] 2268 +[tion][s ] -> [tions ] 2269 +[ea][d ] -> [ead ] 2270 +[D][ut] -> [Dut] 2271 +[Jam][es ] -> [James ] 2272 +[er][ed ] -> [ered ] 2273 +[Death][s from ] -> [Deaths from ] 2274 +[th ][of ] -> [th of ] 2275 +[actors\u000a][American ] -> [actors\u000aAmerican ] 2276 +[: ][The ] -> [: The ] 2277 +[milli][on ] -> [million ] 2278 +[St][ate ] -> [State ] 2279 +[w][ell] -> [well] 2280 +[au][th] -> [auth] 2281 +[1][1 ] -> [11 ] 2282 +[P][ak] -> [Pak] 2283 +[Span][ish ] -> [Spanish ] 2284 +[d][ay ] -> [day ] 2285 +[3][6] -> [36] 2286 +[op][en] -> [open] 2287 +[f][re] -> [fre] 2288 +[ent][s ] -> [ents ] 2289 +[Com][p] -> [Comp] 2290 +[er ][in ] -> [er in ] 2291 +[l][ine ] -> [line ] 2292 +[5][5] -> [55] 2293 +[b][and ] -> [band ] 2294 +[9][9] -> [99] 2295 +[fam][ous ] -> [famous ] 2296 +[on][-] -> [on-] 2297 +[C][at] -> [Cat] 2298 +[o ][(] -> [o (] 2299 +[l][ist] -> [list] 2300 +[k][, ] -> [k, ] 2301 +[y][oun] -> [youn] 2302 +[prov][inc] -> [provinc] 2303 +[D][ ] -> [D ] 2304 +[h][app] -> [happ] 2305 +[h][ous] -> [hous] 2306 +[t][ook ] -> [took ] 2307 +[ag][ed ] -> [aged ] 2308 +[pos][i] -> [posi] 2309 +[o][d ] -> [od ] 2310 +[r][ain] -> [rain] 2311 +[D][on] -> [Don] 2312 +[Dem][ocr] -> [Democr] 2313 +[d][eb] -> [deb] 2314 +[hel][d ] -> [held ] 2315 +[1][5 ] -> [15 ] 2316 +[an][ti] -> [anti] 2317 +[ovi][et ] -> [oviet ] 2318 +[st][and] -> [stand] 2319 +[pro][blem] -> [problem] 2320 +[sing][le ] -> [single ] 2321 +[NY][S] -> [NYS] 2322 +[et][tl] -> [ettl] 2323 +[’][s ] -> [’s ] 2324 +[ens][us] -> [ensus] 2325 +[sti][ll ] -> [still ] 2326 +[c][iti] -> [citi] 2327 +[||0][\u000a|-\u000a|200] -> [||0\u000a|-\u000a|200] 2328 +[ b][ec] -> [ bec] 2329 +[ers ][of the ] -> [ers of the ] 2330 +[es][. ] -> [es. ] 2331 +[Prime ][Minist] -> [Prime Minist] 2332 +[in][s] -> [ins] 2333 +[Willi][am ] -> [William ] 2334 +[oc][k ] -> [ock ] 2335 +[ber][g] -> [berg] 2336 +[pr][is] -> [pris] 2337 +[c][el] -> [cel] 2338 +[3][)\u000a ] -> [3)\u000a ] 2339 +[201][5] -> [2015] 2340 +[usin][ess] -> [usiness] 2341 +[.\u000a\u000a][Other websit] -> [.\u000a\u000aOther websit] 2342 +[at][or ] -> [ator ] 2343 +[cap][ital ] -> [capital ] 2344 +[ib][le ] -> [ible ] 2345 +[giv][en ] -> [given ] 2346 +[Al][l ] -> [All ] 2347 +[mu][ch ] -> [much ] 2348 +[profess][ional ] -> [professional ] 2349 +[y ][in ] -> [y in ] 2350 +[6][)\u000a ] -> [6)\u000a ] 2351 +[. 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It ] 2362 +[P][ort] -> [Port] 2363 +[municipal][ity ] -> [municipality ] 2364 +[an][ch] -> [anch] 2365 +[200][9] -> [2009] 2366 +[popul][ation ] -> [population ] 2367 +[s][il] -> [sil] 2368 +[ul][ar ] -> [ular ] 2369 +[F][in] -> [Fin] 2370 +[rece][iv] -> [receiv] 2371 +[ || — || align=right | ][1.] -> [ || — || align=right | 1.] 2372 +[dur][ing ] -> [during ] 2373 +[Georg][e ] -> [George ] 2374 +[id][enti] -> [identi] 2375 +[el][d ] -> [eld ] 2376 +[D][ep] -> [Dep] 2377 +[201][0 ] -> [2010 ] 2378 +[198][0] -> [1980] 2379 +[Engl][ish] -> [English] 2380 +[jo][in] -> [join] 2381 +[v][ari] -> [vari] 2382 +[Univers][ity ] -> [University ] 2383 +[H][igh] -> [High] 2384 +[4][4] -> [44] 2385 +[ro][m ] -> [rom ] 2386 +[song][s\u000a] -> [songs\u000a] 2387 +[R][es] -> [Res] 2388 +[L][u] -> [Lu] 2389 +[a]['s ] -> [a's ] 2390 +[201][7] -> [2017] 2391 +[ent][ly ] -> [ently ] 2392 +[200][0] -> [2000] 2393 +[A][tl] -> [Atl] 2394 +[ ][ ] -> [ ] 2395 +[popul][ar ] -> [popular ] 2396 +[e][h] -> [eh] 2397 +[.\u000a\u000a][B] -> [.\u000a\u000aB] 2398 +[hel][p] -> [help] 2399 +[c][y] -> [cy] 2400 +[ed ][on the ] -> [ed on the ] 2401 +[c][ame ] -> [came ] 2402 +[D][iv] -> [Div] 2403 +[in][, ] -> [in, ] 2404 +[ep][isod] -> [episod] 2405 +[ch][ild] -> [child] 2406 +[18][ ] -> [18 ] 2407 +[par][tic] -> [partic] 2408 +[J][ew] -> [Jew] 2409 +[17][ ] -> [17 ] 2410 +[j][ect] -> [ject] 2411 +[K][ar] -> [Kar] 2412 +[7][7] -> [77] 2413 +[n][ey ] -> [ney ] 2414 +[politic][al ] -> [political ] 2415 +[L][os ] -> [Los ] 2416 +[er (][b. ] -> [er (b. ] 2417 +[United Stat][es\u000a] -> [United States\u000a] 2418 +[es ][from ] -> [es from ] 2419 +[K][ing ] -> [King ] 2420 +[v][ir] -> [vir] 2421 +[L][ib] -> [Lib] 2422 +[18][6] -> [186] 2423 +[ || align=right | ][3.] -> [ || align=right | 3.] 2424 +[on][y ] -> [ony ] 2425 +[C][am] -> [Cam] 2426 +[T][ ] -> [T ] 2427 +[vi][ew] -> [view] 2428 +[sm][all ] -> [small ] 2429 +[:][\u000a ] -> [:\u000a ] 2430 +[ef][f] -> [eff] 2431 +[if][ic] -> [ific] 2432 +[com][edy ] -> [comedy ] 2433 +[er ][to ] -> [er to ] 2434 +[Actor][s from ] -> [Actors from ] 2435 +[h][ar] -> [har] 2436 +[or][, ] -> [or, ] 2437 +[e][z] -> [ez] 2438 +[e][qu] -> [equ] 2439 +[B][est ] -> [Best ] 2440 +[p][erson ] -> [person ] 2441 +[7][.] -> [7.] 2442 +[seas][on] -> [season] 2443 +[ful][ ] -> [ful ] 2444 +[Sc][i] -> [Sci] 2445 +[ed ][his ] -> [ed his ] 2446 +[ch][em] -> [chem] 2447 +[3][0 ] -> [30 ] 2448 +[1][4 ] -> [14 ] 2449 +[Rob][ert ] -> [Robert ] 2450 +[l][es] -> [les] 2451 +[S][an ] -> [San ] 2452 +[an][other ] -> [another ] 2453 +[an][y] -> [any] 2454 +[b][and] -> [band] 2455 +[||][5] -> [||5] 2456 +[S][al] -> [Sal] 2457 +[the ][first ] -> [the first ] 2458 +[|| ][— || ] -> [|| — || ] 2459 +[city ][in ] -> [city in ] 2460 +[wor][d ] -> [word ] 2461 +[199][9] -> [1999] 2462 +[th][ird ] -> [third ] 2463 +[elec][tr] -> [electr] 2464 +[Col][leg] -> [Colleg] 2465 +[ir][e ] -> [ire ] 2466 +[Q][ue] -> [Que] 2467 +[World ][War ] -> [World War ] 2468 +[S][ou] -> [Sou] 2469 +[C][ity ] -> [City ] 2470 +[T][own] -> [Town] 2471 +[s ][on ] -> [s on ] 2472 +[comm][on ] -> [common ] 2473 +[ex][pl] -> [expl] 2474 +[W][ash] -> [Wash] 2475 +[S][oviet ] -> [Soviet ] 2476 +[M][or] -> [Mor] 2477 +[h][ome ] -> [home ] 2478 +[e][: ] -> [e: ] 2479 +[U][K] -> [UK] 2480 +[i][ (] -> [i (] 2481 +[ ][is ] -> [ is ] 2482 +[\u000a\u000a][The ] -> [\u000a\u000aThe ] 2483 +[T][w] -> [Tw] 2484 +[S][ing] -> [Sing] 2485 +[Al][l] -> [All] 2486 +[Ac][adem] -> [Academ] 2487 +[201][8] -> [2018] 2488 +[F][A ] -> [FA ] 2489 +[Olymp][ic] -> [Olympic] 2490 +[R][os] -> [Ros] 2491 +[s ][a ] -> [s a ] 2492 +[r][adi] -> [radi] 2493 +[re][n ] -> [ren ] 2494 +[me][di] -> [medi] 2495 +[Pak][ist] -> [Pakist] 2496 +[C][O] -> [CO] 2497 +[und][er] -> [under] 2498 +[un][ivers] -> [univers] 2499 +[re][present] -> [represent] 2500 +[t][oge] -> [toge] 2501 +[|| — || ][align=right | ] -> [|| — || align=right | ] 2502 +[offic][ial ] -> [official ] 2503 +[politic][ian ] -> [politician ] 2504 +[r][ed ] -> [red ] 2505 +[198][9] -> [1989] 2506 +[g][row] -> [grow] 2507 +[1][3 ] -> [13 ] 2508 +[they ][are ] -> [they are ] 2509 +[\u000a|][}] -> [\u000a|}] 2510 +[at][es ] -> [ates ] 2511 +[200][5] -> [2005] 2512 +[, 1999][ || Socorro || LINEAR || — || align=right | ] -> [, 1999 || Socorro || LINEAR || — || align=right | ] 2513 +[att][ack] -> [attack] 2514 +[Is][land] -> [Island] 2515 +[iv][il ] -> [ivil ] 2516 +[199][0] -> [1990] 2517 +[h][or] -> [hor] 2518 +[c][or] -> [cor] 2519 +[M][a] -> [Ma] 2520 +[g][e] -> [ge] 2521 +[M][iss] -> [Miss] 2522 +[Franc][e] -> [France] 2523 +[D][e] -> [De] 2524 +[ser][i] -> [seri] 2525 +[ep][ ] -> [ep ] 2526 +[ang][ ] -> [ang ] 2527 +[8][9] -> [89] 2528 +[.\u000a\u000a][C] -> [.\u000a\u000aC] 2529 +[A][S] -> [AS] 2530 +[E][r] -> [Er] 2531 +[all][ow] -> [allow] 2532 +[b][ack] -> [back] 2533 +[, 200][4] -> [, 2004] 2534 +[do][es ] -> [does ] 2535 +[. 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He was born in ] 2536 +[H][al] -> [Hal] 2537 +[seas][on ] -> [season ] 2538 +[m][ul] -> [mul] 2539 +[N][ot] -> [Not] 2540 +[f][em] -> [fem] 2541 +[1][6 ] -> [16 ] 2542 +[H][en] -> [Hen] 2543 +[.][.] -> [..] 2544 +[t][y ] -> [ty ] 2545 +[Fir][st ] -> [First ] 2546 +[Wash][ing] -> [Washing] 2547 +[s ][with ] -> [s with ] 2548 +[v][en] -> [ven] 2549 +[wor][ld ] -> [world ] 2550 +[j][ourn] -> [journ] 2551 +[E][ast ] -> [East ] 2552 +[is][e ] -> [ise ] 2553 +[c][our] -> [cour] 2554 +[8][5] -> [85] 2555 +[fl][u] -> [flu] 2556 +[a ][B] -> [a B] 2557 +[, 2001][ || Socorro || LINEAR || ] -> [, 2001 || Socorro || LINEAR || ] 2558 +[se][t ] -> [set ] 2559 +[spec][ies ] -> [species ] 2560 +[On][e ] -> [One ] 2561 +[g][o ] -> [go ] 2562 +[us][h] -> [ush] 2563 +[s][o] -> [so] 2564 +[R][h] -> [Rh] 2565 +[t][or] -> [tor] 2566 +[K][h] -> [Kh] 2567 +[a][el ] -> [ael ] 2568 +[i][ent ] -> [ient ] 2569 +[am][b] -> [amb] 2570 +[s][: ] -> [s: ] 2571 +[s][ex] -> [sex] 2572 +[e][an] -> [ean] 2573 +[i][ally ] -> [ially ] 2574 +[op][p] -> [opp] 2575 +[number ][of ] -> [number of ] 2576 +[dis][eas] -> [diseas] 2577 +[S][en] -> [Sen] 2578 +[196][0] -> [1960] 2579 +[||row][span="2"|] -> [||rowspan="2"|] 2580 +[c][ast] -> [cast] 2581 +[) was an ][American ] -> [) was an American ] 2582 +[chil][dr] -> [childr] 2583 +[Ch][ur] -> [Chur] 2584 +[ch][es] -> [ches] 2585 +[. 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Its ] 2586 +[O][b] -> [Ob] 2587 +[1 ][– ] -> [1 – ] 2588 +[ann][ounc] -> [announc] 2589 +[Vir][g] -> [Virg] 2590 +[a ][d] -> [a d] 2591 +[3][1] -> [31] 2592 +[div][id] -> [divid] 2593 +[ed ][at ] -> [ed at ] 2594 +[ol][ ] -> [ol ] 2595 +[200][8] -> [2008] 2596 +[Joh][n] -> [John] 2597 +[%][ ] -> [% ] 2598 +[some][times ] -> [sometimes ] 2599 +[18][5] -> [185] 2600 +[w][ant] -> [want] 2601 +[as][, ] -> [as, ] 2602 +[ev][er] -> [ever] 2603 +[c][amp] -> [camp] 2604 +[Los ][Angel] -> [Los Angel] 2605 +[W][rit] -> [Writ] 2606 +[more ][than ] -> [more than ] 2607 +[l][ess ] -> [less ] 2608 +[ation][, ] -> [ation, ] 2609 +[organ][iz] -> [organiz] 2610 +[plac][e ] -> [place ] 2611 +[o][\u000a] -> [o\u000a] 2612 +[P][i] -> [Pi] 2613 +[s][ay] -> [say] 2614 +[E][g] -> [Eg] 2615 +[our][c] -> [ourc] 2616 +[ac][t ] -> [act ] 2617 +[cl][ass] -> [class] 2618 +[n][et] -> [net] 2619 +[ist ][and ] -> [ist and ] 2620 +[Is][ra] -> [Isra] 2621 +[enti][st] -> [entist] 2622 +[football ][play] -> [football play] 2623 +[ep][h] -> [eph] 2624 +[al][d ] -> [ald ] 2625 +[. S][ome ] -> [. Some ] 2626 +[\u000a ][L] -> [\u000a L] 2627 +[ol][u] -> [olu] 2628 +[. It ][is the ] -> [. It is the ] 2629 +[ed][uc] -> [educ] 2630 +[. ][On ] -> [. On ] 2631 +[ig][g] -> [igg] 2632 +[E][mp] -> [Emp] 2633 +[Dut][ch ] -> [Dutch ] 2634 +[D][es] -> [Des] 2635 +[In][d] -> [Ind] 2636 +[200][7] -> [2007] 2637 +[Dav][id ] -> [David ] 2638 +[con][c] -> [conc] 2639 +[itt][le ] -> [ittle ] 2640 +[W][il] -> [Wil] 2641 +[em][p] -> [emp] 2642 +[v][er ] -> [ver ] 2643 +[ || || — || ][May ] -> [ || || — || May ] 2644 +[es ][that ] -> [es that ] 2645 +[includ][e ] -> [include ] 2646 +[201][3] -> [2013] 2647 +[pro][c] -> [proc] 2648 +[os][p] -> [osp] 2649 +[C][up ] -> [Cup ] 2650 +[ec][onom] -> [econom] 2651 +[f][ive ] -> [five ] 2652 +[ || — || align=right | ][2.] -> [ || — || align=right | 2.] 2653 +[202][1] -> [2021] 2654 +[. They ][are ] -> [. 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The ] 2705 +[he][ad] -> [head] 2706 +[succ][ess] -> [success] 2707 +[�][�] -> [ō] 2708 +[)][.\u000a] -> [).\u000a] 2709 +[j][ust ] -> [just ] 2710 +[M][ad] -> [Mad] 2711 +[e][i] -> [ei] 2712 +[\u000a][B] -> [\u000aB] 2713 +[ol][v] -> [olv] 2714 +[was ][releas] -> [was releas] 2715 +[l][a] -> [la] 2716 +[Ar][ch] -> [Arch] 2717 +[sid][e ] -> [side ] 2718 +['][t ] -> ['t ] 2719 +[t][own] -> [town] 2720 +[movies\u000a][American ] -> [movies\u000aAmerican ] 2721 +[Gen][eral ] -> [General ] 2722 +[song][writ] -> [songwrit] 2723 +[m][y] -> [my] 2724 +[2][5 ] -> [25 ] 2725 +[Franc][e.\u000a\u000a] -> [France.\u000a\u000a] 2726 +[:][\u000a\u000a] -> [:\u000a\u000a] 2727 +[Ar][g] -> [Arg] 2728 +[a ][in ] -> [a in ] 2729 +[a ][P] -> [a P] 2730 +[d][em] -> [dem] 2731 +[aw][ard] -> [award] 2732 +[Sport][s] -> [Sports] 2733 +[th][, ] -> [th, ] 2734 +[mil][itary ] -> [military ] 2735 +[200][6] -> [2006] 2736 +[t][ro] -> [tro] 2737 +[s, ][the ] -> [s, the ] 2738 +[2 ][– ] -> [2 – ] 2739 +[mar][k] -> [mark] 2740 +[ter][m ] -> [term ] 2741 +[wro][te ] -> [wrote ] 2742 +[n][atur] -> [natur] 2743 +[st][atistic] -> [statistic] 2744 +[w][ard ] -> [ward ] 2745 +[L][i] -> [Li] 2746 +[des][cri] -> [descri] 2747 +[201][1] -> [2011] 2748 +[es][h] -> [esh] 2749 +[Af][ter ] -> [After ] 2750 +[aint][-] -> [aint-] 2751 +[d][own ] -> [down ] 2752 +[il][y ] -> [ily ] 2753 +[M][ay] -> [May] 2754 +[B][C] -> [BC] 2755 +[v][an] -> [van] 2756 +[Th][e] -> [The] 2757 +[t ][and ] -> [t and ] 2758 +[ol][l] -> [oll] 2759 +[al][ong ] -> [along ] 2760 +[t][on, ] -> [ton, ] 2761 +[in][st] -> [inst] 2762 +[high][ ] -> [high ] 2763 +[c][ensus] -> [census] 2764 +[J][. ] -> [J. ] 2765 +[Char][les ] -> [Charles ] 2766 +[rel][ig] -> [relig] 2767 +[FL][O] -> [FLO] 2768 +[ ][k] -> [ k] 2769 +[tion][, ] -> [tion, ] 2770 +[. ][He ] -> [. He ] 2771 +[Rec][ord] -> [Record] 2772 +[ul][l] -> [ull] 2773 +[ref][ect] -> [refect] 2774 +[movies\u000a][Movies ] -> [movies\u000aMovies ] 2775 +[Footb][all ] -> [Football ] 2776 +[Flor][id] -> [Florid] 2777 +[h][am] -> [ham] 2778 +[é][ ] -> [é ] 2779 +[i][er ] -> [ier ] 2780 +[p][ut ] -> [put ] 2781 +[201][6] -> [2016] 2782 +[Chin][ese ] -> [Chinese ] 2783 +[n][ec] -> [nec] 2784 +[E][ast] -> [East] 2785 +[198][8] -> [1988] 2786 +[L][aw] -> [Law] 2787 +[P][op] -> [Pop] 2788 +[S][ummer ] -> [Summer ] 2789 +[bel][iev] -> [believ] 2790 +[es ][were ] -> [es were ] 2791 +[ro][y] -> [roy] 2792 +[e ][was ] -> [e was ] 2793 +[er]['s ] -> [er's ] 2794 +[ab][il] -> [abil] 2795 +[r][on] -> [ron] 2796 +[p][ur] -> [pur] 2797 +[S][chool] -> [School] 2798 +[li][am] -> [liam] 2799 +[pl][an] -> [plan] 2800 +[4 ][– ] -> [4 – ] 2801 +[w][estern ] -> [western ] 2802 +[fa][ther ] -> [father ] 2803 +[C][up] -> [Cup] 2804 +[ad][v] -> [adv] 2805 +[Mon][t] -> [Mont] 2806 +[V][al] -> [Val] 2807 +[Car][ol] -> [Carol] 2808 +[st][y] -> [sty] 2809 +[\u000a ][M] -> [\u000a M] 2810 +[B][al] -> [Bal] 2811 +[oun][t ] -> [ount ] 2812 +[a ][M] -> [a M] 2813 +[9][5] -> [95] 2814 +[ed][er] -> [eder] 2815 +[V][ill] -> [Vill] 2816 +[ain][s ] -> [ains ] 2817 +[minist][r] -> [ministr] 2818 +[2019][ ] -> [2019 ] 2819 +[is a commun][e. It is ] -> [is a commune. It is ] 2820 +[||0||0][||0||0] -> [||0||0||0||0] 2821 +[oc][ial ] -> [ocial ] 2822 +[7][2] -> [72] 2823 +[on][t] -> [ont] 2824 +[pl][ant] -> [plant] 2825 +["][The ] -> ["The ] 2826 +[roc][k] -> [rock] 2827 +[4][6] -> [46] 2828 +[6 ][– ] -> [6 – ] 2829 +[n][ext ] -> [next ] 2830 +[ar][k] -> [ark] 2831 +[P][o] -> [Po] 2832 +[TV][ ] -> [TV ] 2833 +[i][um ] -> [ium ] 2834 +[ex][p] -> [exp] 2835 +[er (][d. ] -> [er (d. ] 2836 +[�][�] -> [“] 2837 +[3 ][– ] -> [3 – ] 2838 +[countr][y ] -> [country ] 2839 +[8 ][– ] -> [8 – ] 2840 +[z][e ] -> [ze ] 2841 +[cl][ub ] -> [club ] 2842 +[V][I] -> [VI] 2843 +[distric][t] -> [district] 2844 +[Pr][inc] -> [Princ] 2845 +[F][or ] -> [For ] 2846 +[ip][ ] -> [ip ] 2847 +[comp][any ] -> [company ] 2848 +[journ][al] -> [journal] 2849 +[R][ev] -> [Rev] 2850 +[ph][il] -> [phil] 2851 +[Mun][icipal] -> [Municipal] 2852 +[se][at ] -> [seat ] 2853 +[. She ][was ] -> [. She was ] 2854 +[gu][itar] -> [guitar] 2855 +[phys][ic] -> [physic] 2856 +[it][ch] -> [itch] 2857 +[lin][o] -> [lino] 2858 +[y][. ] -> [y. ] 2859 +[er][.\u000a\u000a] -> [er.\u000a\u000a] 2860 +[arti][st] -> [artist] 2861 +[Lond][on] -> [London] 2862 +[A][z] -> [Az] 2863 +[197][9] -> [1979] 2864 +[D][ur] -> [Dur] 2865 +[S][S] -> [SS] 2866 +[arr][y ] -> [arry ] 2867 +[ || || ][ || ] -> [ || || || ] 2868 +[Th][ey ] -> [They ] 2869 +[ag][n] -> [agn] 2870 +[lev][el] -> [level] 2871 +[Democr][atic ] -> [Democratic ] 2872 +[K][ans] -> [Kans] 2873 +[h][and] -> [hand] 2874 +[�][�] -> [”] 2875 +[Il][lino] -> [Illino] 2876 +[as ][well ] -> [as well ] 2877 +[18][0] -> [180] 2878 +[ti][n ] -> [tin ] 2879 +[l][e of ] -> [le of ] 2880 +[ep][end] -> [epend] 2881 +[201][2] -> [2012] 2882 +[t][ot] -> [tot] 2883 +[found ][in the ] -> [found in the ] 2884 +[F][I] -> [FI] 2885 +[d][, ] -> [d, ] 2886 +[rop][ical ] -> [ropical ] 2887 +[ed ][as a ] -> [ed as a ] 2888 +[7 ][– ] -> [7 – ] 2889 +[Z][eal] -> [Zeal] 2890 +[di][ed in ] -> [died in ] 2891 +[d][oc] -> [doc] 2892 +[M][et] -> [Met] 2893 +[pos][s] -> [poss] 2894 +[r][ang] -> [rang] 2895 +[9 ][– ] -> [9 – ] 2896 +[v][iv] -> [viv] 2897 +[lif][e ] -> [life ] 2898 +[th][ing ] -> [thing ] 2899 +[ed ][into ] -> [ed into ] 2900 +[Mex][ic] -> [Mexic] 2901 +[W][e] -> [We] 2902 +[a ][R] -> [a R] 2903 +[199][8] -> [1998] 2904 +[con][sid] -> [consid] 2905 +[g][ar] -> [gar] 2906 +[buil][t ] -> [built ] 2907 +[, 200][5] -> [, 2005] 2908 +[Republ][ic ] -> [Republic ] 2909 +[publ][ic ] -> [public ] 2910 +[itz][er] -> [itzer] 2911 +[res][t] -> [rest] 2912 +[9][8] -> [98] 2913 +[re][ti] -> [reti] 2914 +[n][omin] -> [nomin] 2915 +[av][er] -> [aver] 2916 +[on][e of ] -> [one of ] 2917 +[New ][Zeal] -> [New Zeal] 2918 +[)][. ] -> [). ] 2919 +[lef][t ] -> [left ] 2920 +[ev][ery ] -> [every ] 2921 +[s][on, ] -> [son, ] 2922 +[f][ail] -> [fail] 2923 +[compl][et] -> [complet] 2924 +[Gre][at ] -> [Great ] 2925 +[T][or] -> [Tor] 2926 +[New York ][City] -> [New York City] 2927 +[h][ol] -> [hol] 2928 +[d][am] -> [dam] 2929 +[ind][epend] -> [independ] 2930 +[C][our] -> [Cour] 2931 +[Ale][x] -> [Alex] 2932 +[c][al] -> [cal] 2933 +[gr][ad] -> [grad] 2934 +[\u000a \u000a \u000a \u000a ][\u000a \u000a \u000a \u000a ] -> [\u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a ] 2935 +[s][e ] -> [se ] 2936 +[ and ][the ] -> [ and the ] 2937 +[199][5] -> [1995] 2938 +[F][am] -> [Fam] 2939 +[m][y ] -> [my ] 2940 +[p][i] -> [pi] 2941 +[c][ell] -> [cell] 2942 +[4][)\u000a ] -> [4)\u000a ] 2943 +[l][ad] -> [lad] 2944 +[0][.] -> [0.] 2945 +[United Stat][es.\u000a\u000a] -> [United States.\u000a\u000a] 2946 +[P][at] -> [Pat] 2947 +[a][im] -> [aim] 2948 +[Austri][an ] -> [Austrian ] 2949 +[bec][om] -> [becom] 2950 +[about ][the ] -> [about the ] 2951 +[hear][t ] -> [heart ] 2952 +[, 2002][ || Socorro || LINEAR || — || align=right | ] -> [, 2002 || Socorro || LINEAR || — || align=right | ] 2953 +[th][ing] -> [thing] 2954 +[\u000a ][B] -> [\u000a B] 2955 +[Virg][in] -> [Virgin] 2956 +[Un][ion ] -> [Union ] 2957 +[m][b] -> [mb] 2958 +[en][s ] -> [ens ] 2959 +[J][ack] -> [Jack] 2960 +[hum][an ] -> [human ] 2961 +[ || — || align=right | ][3.] -> [ || — || align=right | 3.] 2962 +[inter][national ] -> [international ] 2963 +[ist][, ] -> [ist, ] 2964 +[il][ar ] -> [ilar ] 2965 +[||][6] -> [||6] 2966 +[Sw][itzer] -> [Switzer] 2967 +[wi][f] -> [wif] 2968 +[ter][n] -> [tern] 2969 +[.][com] -> [.com] 2970 +[in][t ] -> [int ] 2971 +[o ][and ] -> [o and ] 2972 +[U][r] -> [Ur] 2973 +[202][2 ] -> [2022 ] 2974 +[Leag][ue] -> [League] 2975 +[Bel][g] -> [Belg] 2976 +[D][el] -> [Del] 2977 +[198][7] -> [1987] 2978 +[m][en ] -> [men ] 2979 +[reg][ion] -> [region] 2980 +[vers][ion ] -> [version ] 2981 +[me][ans ] -> [means ] 2982 +[S][aint-] -> [Saint-] 2983 +[i][or ] -> [ior ] 2984 +[-d][e-] -> [-de-] 2985 +[w][ill] -> [will] 2986 +[w][id] -> [wid] 2987 +[ia ][and ] -> [ia and ] 2988 +[) ][– ] -> [) – ] 2989 +[Afric][an ] -> [African ] 2990 +[5 ][– ] -> [5 – ] 2991 +[with][out ] -> [without ] 2992 +[an][a ] -> [ana ] 2993 +[se][en ] -> [seen ] 2994 +[people liv][ed ] -> [people lived ] 2995 +[e]["] -> [e"] 2996 +[is ][not ] -> [is not ] 2997 +[cri][tic] -> [critic] 2998 +[progr][am] -> [program] 2999 +[sou][th] -> [south] 3000 +[contin][u] -> [continu] 3001 +[ac][tion ] -> [action ] 3002 +[Hous][e of ] -> [House of ] 3003 +[1][,] -> [1,] 3004 +[\u000a ][The ] -> [\u000a The ] 3005 +[H][istory ] -> [History ] 3006 +[es][, and ] -> [es, and ] 3007 +[for][d ] -> [ford ] 3008 +[O][c] -> [Oc] 3009 +[200][3] -> [2003] 3010 +[. There ][are ] -> [. There are ] 3011 +[olog][y ] -> [ology ] 3012 +[B][ ] -> [B ] 3013 +[2][1 ] -> [21 ] 3014 +[nor][th] -> [north] 3015 +[Mich][ael ] -> [Michael ] 3016 +[all ][of ] -> [all of ] 3017 +[L][if] -> [Lif] 3018 +[K][n] -> [Kn] 3019 +[Lond][on ] -> [London ] 3020 +[Californ][ia] -> [California] 3021 +[10][0] -> [100] 3022 +[Sports][people from ] -> [Sportspeople from ] 3023 +[f][our] -> [four] 3024 +[Com][m] -> [Comm] 3025 +[u][z] -> [uz] 3026 +[ed ][at the ] -> [ed at the ] 3027 +[||][colspan="2"|-] -> [||colspan="2"|-] 3028 +[C][ong] -> [Cong] 3029 +[. ][N] -> [. N] 3030 +[Isra][el] -> [Israel] 3031 +[Ar][ab] -> [Arab] 3032 +[d][ue ] -> [due ] 3033 +[se][e ] -> [see ] 3034 +[in][d ] -> [ind ] 3035 +[sc][re] -> [scre] 3036 +[um][p] -> [ump] 3037 +[G][old] -> [Gold] 3038 +[it][al] -> [ital] 3039 +[b][ook ] -> [book ] 3040 +[Eg][yp] -> [Egyp] 3041 +[c][op] -> [cop] 3042 +[does ][not ] -> [does not ] 3043 +[oci][et] -> [ociet] 3044 +[ ][was ] -> [ was ] 3045 +[s][w] -> [sw] 3046 +[cont][ro] -> [contro] 3047 +[ar][t ] -> [art ] 3048 +[Rich][ard ] -> [Richard ] 3049 +[In ][the ] -> [In the ] 3050 +[city ][of ] -> [city of ] 3051 +[M][ac] -> [Mac] 3052 +[row][n ] -> [rown ] 3053 +[ear][ch ] -> [earch ] 3054 +[Rom][an] -> [Roman] 3055 +[Austral][ia] -> [Australia] 3056 +[cl][os] -> [clos] 3057 +[d][augh] -> [daugh] 3058 +[Politic][ians from ] -> [Politicians from ] 3059 +[able ][to ] -> [able to ] 3060 +[i][ous ] -> [ious ] 3061 +[tak][e ] -> [take ] 3062 +[cre][ated ] -> [created ] 3063 +[B][C ] -> [BC ] 3064 +[C][ounc] -> [Counc] 3065 +[sh][ort ] -> [short ] 3066 +[h][ab] -> [hab] 3067 +[198][5] -> [1985] 3068 +[. 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He is ] 3069 +[qu][e ] -> [que ] 3070 +[origin][al ] -> [original ] 3071 +[W][estern ] -> [Western ] 3072 +[s][outh ] -> [south ] 3073 +[fe][atur] -> [featur] 3074 +[s][. ] -> [s. ] 3075 +[play][er ] -> [player ] 3076 +[stat][e] -> [state] 3077 +[m][ain] -> [main] 3078 +[w][ar ] -> [war ] 3079 +[c][u] -> [cu] 3080 +[p][ain] -> [pain] 3081 +[m][en] -> [men] 3082 +[m][ost] -> [most] 3083 +[z][e] -> [ze] 3084 +[m][ount] -> [mount] 3085 +[Arg][ent] -> [Argent] 3086 +[t][op ] -> [top ] 3087 +[G][o] -> [Go] 3088 +[3][7] -> [37] 3089 +[\u000a\u000aReferenc][es \u000a\u000a] -> [\u000a\u000aReferences \u000a\u000a] 3090 +[ || align=right | ][4.] -> [ || align=right | 4.] 3091 +[ ][L] -> [ L] 3092 +[B][i] -> [Bi] 3093 +[fl][ow] -> [flow] 3094 +[them][atic] -> [thematic] 3095 +[fil][m ] -> [film ] 3096 +[follow][ing ] -> [following ] 3097 +[h][om] -> [hom] 3098 +[fi][eld] -> [field] 3099 +[T][er] -> [Ter] 3100 +[198][1] -> [1981] 3101 +[n][ation] -> [nation] 3102 +[sm][all] -> [small] 3103 +[C][re] -> [Cre] 3104 +[hel][p ] -> [help ] 3105 +[is][s ] -> [iss ] 3106 +[bas][ed on ] -> [based on ] 3107 +[10][0 ] -> [100 ] 3108 +[m][ajor ] -> [major ] 3109 +[es ][\u000a] -> [es \u000a] 3110 +[Brazil][ian ] -> [Brazilian ] 3111 +[w][ard] -> [ward] 3112 +[, ][197] -> [, 197] 3113 +[M][at] -> [Mat] 3114 +[or][s ] -> [ors ] 3115 +[Gre][ek ] -> [Greek ] 3116 +[elec][tion] -> [election] 3117 +[R][ed ] -> [Red ] 3118 +[ag][u] -> [agu] 3119 +[||0||][colspan="2"|-] -> [||0||colspan="2"|-] 3120 +[7 ][- ] -> [7 - ] 3121 +[T][ri] -> [Tri] 3122 +[tr][av] -> [trav] 3123 +[. He was ][the ] -> [. He was the ] 3124 +[ul][t ] -> [ult ] 3125 +[ut][er ] -> [uter ] 3126 +[b][ar] -> [bar] 3127 +[us][ed in ] -> [used in ] 3128 +[Roy][al ] -> [Royal ] 3129 +[2][4 ] -> [24 ] 3130 +[roc][k ] -> [rock ] 3131 +[sel][f] -> [self] 3132 +[U][S ] -> [US ] 3133 +[og][n] -> [ogn] 3134 +[e][" ] -> [e" ] 3135 +[An][n] -> [Ann] 3136 +[c][at] -> [cat] 3137 +[V][ict] -> [Vict] 3138 +[thoug][ht ] -> [thought ] 3139 +[ro][le ] -> [role ] 3140 +[D][-] -> [D-] 3141 +[H][istor] -> [Histor] 3142 +[ur][ric] -> [urric] 3143 +[ind][u] -> [indu] 3144 +[ther][land] -> [therland] 3145 +[. He ][also ] -> [. He also ] 3146 +[ro][n ] -> [ron ] 3147 +[y][. The ] -> [y. The ] 3148 +[199][7] -> [1997] 3149 +[L][ ] -> [L ] 3150 +[start][ed ] -> [started ] 3151 +[comp][et] -> [compet] 3152 +[M][er] -> [Mer] 3153 +[198][6] -> [1986] 3154 +[, ][B] -> [, B] 3155 +[m][ot] -> [mot] 3156 +[te][chn] -> [techn] 3157 +[in][form] -> [inform] 3158 +[us][ing ] -> [using ] 3159 +[200][1] -> [2001] 3160 +[F][F] -> [FF] 3161 +[ra][w] -> [raw] 3162 +[200][0 ] -> [2000 ] 3163 +[.\u000a\u000a][Related pag] -> [.\u000a\u000aRelated pag] 3164 +[Cl][ub ] -> [Club ] 3165 +[199][6] -> [1996] 3166 +[Gr][and ] -> [Grand ] 3167 +[a ][is ] -> [a is ] 3168 +[S][im] -> [Sim] 3169 +[H][am] -> [Ham] 3170 +[G][ir] -> [Gir] 3171 +[Writ][ers from ] -> [Writers from ] 3172 +[re][plac] -> [replac] 3173 +[mak][ing ] -> [making ] 3174 +[M][i] -> [Mi] 3175 +[over ][the ] -> [over the ] 3176 +[or ][and ] -> [or and ] 3177 +[l][ong ] -> [long ] 3178 +[ed to ][be ] -> [ed to be ] 3179 +[li][k] -> [lik] 3180 +[s][al] -> [sal] 3181 +[in][j] -> [inj] 3182 +[\u000a\u000a][|-\u000a|] -> [\u000a\u000a|-\u000a|] 3183 +[depart][ment] -> [department] 3184 +[Olymp][ic ] -> [Olympic ] 3185 +[vo][ice ] -> [voice ] 3186 +[ch][ur] -> [chur] 3187 +[st][re] -> [stre] 3188 +[B][att] -> [Batt] 3189 +[C][ur] -> [Cur] 3190 +[w][restl] -> [wrestl] 3191 +[cl][ub] -> [club] 3192 +[s][en] -> [sen] 3193 +[a][e] -> [ae] 3194 +[Cat][al] -> [Catal] 3195 +[ud][d] -> [udd] 3196 +[There ][are ] -> [There are ] 3197 +[eng][ine] -> [engine] 3198 +[y][ou ] -> [you ] 3199 +[202][2] -> [2022] 3200 +[ia ][(] -> [ia (] 3201 +[ (][born ] -> [ (born ] 3202 +[World War ][II] -> [World War II] 3203 +[r][at] -> [rat] 3204 +[el][, ] -> [el, ] 3205 +[ay][, ] -> [ay, ] 3206 +[201][8 ] -> [2018 ] 3207 +[fri][end] -> [friend] 3208 +[y ][to ] -> [y to ] 3209 +[Christi][an ] -> [Christian ] 3210 +[al ][of ] -> [al of ] 3211 +[mon][th] -> [month] 3212 +[tur][e ] -> [ture ] 3213 +[v][ic] -> [vic] 3214 +[198][3] -> [1983] 3215 +[8][6] -> [86] 3216 +[an][-] -> [an-] 3217 +[es][p] -> [esp] 3218 +[O][p] -> [Op] 3219 +[Ne][therland] -> [Netherland] 3220 +[ec][tion ] -> [ection ] 3221 +[l][at] -> [lat] 3222 +[t][ure] -> [ture] 3223 +[vo][ic] -> [voic] 3224 +[Cent][ral ] -> [Central ] 3225 +[sc][or] -> [scor] 3226 +[tr][adi] -> [tradi] 3227 +[di][ ] -> [di ] 3228 +[is a ][city in ] -> [is a city in ] 3229 +[us][, ] -> [us, ] 3230 +[i][-] -> [i-] 3231 +[most][ly ] -> [mostly ] 3232 +[m][ad] -> [mad] 3233 +[l][ate ] -> [late ] 3234 +[.\u000a\u000aReferences\u000a\u000aOther websit][es\u000a ] -> [.\u000a\u000aReferences\u000a\u000aOther websites\u000a ] 3235 +[Switzer][land] -> [Switzerland] 3236 +[D][ev] -> [Dev] 3237 +[fi][eld ] -> [field ] 3238 +[d][et] -> [det] 3239 +[w][w] -> [ww] 3240 +[d][efe] -> [defe] 3241 +[re][ach] -> [reach] 3242 +[2][3 ] -> [23 ] 3243 +[an ][(] -> [an (] 3244 +[os][s] -> [oss] 3245 +[e][th] -> [eth] 3246 +[fil][m] -> [film] 3247 +[pres][ident ] -> [president ] 3248 +[loc][al ] -> [local ] 3249 +[ou][se] -> [ouse] 3250 +[J][on] -> [Jon] 3251 +[there ][are ] -> [there are ] 3252 +[198][4] -> [1984] 3253 +[Ge][ograph] -> [Geograph] 3254 +[sh][ould ] -> [should ] 3255 +[h][istory ] -> [history ] 3256 +[.]["] -> [."] 3257 +[ar][k ] -> [ark ] 3258 +[c][er] -> [cer] 3259 +[. ][G] -> [. G] 3260 +[gen][eral ] -> [general ] 3261 +[ep][t ] -> [ept ] 3262 +[Engl][and ] -> [England ] 3263 +[g][ave ] -> [gave ] 3264 +[h][ockey ] -> [hockey ] 3265 +[Pet][er ] -> [Peter ] 3266 +[as][h ] -> [ash ] 3267 +[an ][and ] -> [an and ] 3268 +[sp][ir] -> [spir] 3269 +[L][ist of ] -> [List of ] 3270 +[do ][not ] -> [do not ] 3271 +[S][k] -> [Sk] 3272 +[. ][R] -> [. R] 3273 +[. ][When ] -> [. When ] 3274 +[I][m] -> [Im] 3275 +[N][o] -> [No] 3276 +[.\u000a\u000a][M] -> [.\u000a\u000aM] 3277 +[people lived ][ther] -> [people lived ther] 3278 +[east][ern ] -> [eastern ] 3279 +[h][on] -> [hon] 3280 +[y][m] -> [ym] 3281 +[e][ast ] -> [east ] 3282 +[department ][in the ] -> [department in the ] 3283 +[Y][ear] -> [Year] 3284 +[6 ][- ] -> [6 - ] 3285 +[es ][for ] -> [es for ] 3286 +[sy][l] -> [syl] 3287 +[re][turn] -> [return] 3288 +[Mus][ic] -> [Music] 3289 +[mod][ern ] -> [modern ] 3290 +[V][ol] -> [Vol] 3291 +[||0][||1] -> [||0||1] 3292 +[al][ity ] -> [ality ] 3293 +[Port][ug] -> [Portug] 3294 +[4][3] -> [43] 3295 +[2][2 ] -> [22 ] 3296 +[Y][ou] -> [You] 3297 +[k][ing ] -> [king ] 3298 +[m][ust ] -> [must ] 3299 +[B][en] -> [Ben] 3300 +[that ][is ] -> [that is ] 3301 +[a ][was ] -> [a was ] 3302 +[)][: ] -> [): ] 3303 +[rec][ord ] -> [record ] 3304 +[Riv][er ] -> [River ] 3305 +[Con][f] -> [Conf] 3306 +[establishment][s in the ] -> [establishments in the ] 3307 +[\u000a ][T] -> [\u000a T] 3308 +[)][. The ] -> [). The ] 3309 +[s][The ] -> [sThe ] 3310 +[stit][ut] -> [stitut] 3311 +[under ][the ] -> [under the ] 3312 +[st][arr] -> [starr] 3313 +[le][d] -> [led] 3314 +[. He ][was a ] -> [. He was a ] 3315 +[in][o ] -> [ino ] 3316 +[had ][been ] -> [had been ] 3317 +[t][each] -> [teach] 3318 +[s ][have ] -> [s have ] 3319 +[ettl][ement] -> [ettlement] 3320 +[ea][u] -> [eau] 3321 +[y][ing ] -> [ying ] 3322 +[H][ung] -> [Hung] 3323 +[th][ose ] -> [those ] 3324 +[spec][ial ] -> [special ] 3325 +[l][ight ] -> [light ] 3326 +[charac][ter ] -> [character ] 3327 +[bo][ard ] -> [board ] 3328 +[C][ast] -> [Cast] 3329 +[Ch][il] -> [Chil] 3330 +[vi][ol] -> [viol] 3331 +[v][oc] -> [voc] 3332 +[9][6] -> [96] 3333 +[le][-] -> [le-] 3334 +[pe][ti] -> [peti] 3335 +[Tex][as] -> [Texas] 3336 +[iz][ed ] -> [ized ] 3337 +[br][idg] -> [bridg] 3338 +[idd][le ] -> [iddle ] 3339 +[disc][over] -> [discover] 3340 +[P][enn] -> [Penn] 3341 +[nor][th ] -> [north ] 3342 +[y, ][the ] -> [y, the ] 3343 +[Ear][ly ] -> [Early ] 3344 +[L][o] -> [Lo] 3345 +[s ][or ] -> [s or ] 3346 +[Alex][and] -> [Alexand] 3347 +[200][2] -> [2002] 3348 +[O][k] -> [Ok] 3349 +[199][4] -> [1994] 3350 +[of ][a ] -> [of a ] 3351 +[Is][l] -> [Isl] 3352 +[199][3] -> [1993] 3353 +[had ][a ] -> [had a ] 3354 +[U][N] -> [UN] 3355 +[ex][ec] -> [exec] 3356 +[t][ain] -> [tain] 3357 +[. F][or ] -> [. For ] 3358 +[ation ][of the ] -> [ation of the ] 3359 +[toge][ther ] -> [together ] 3360 +[ic][ation] -> [ication] 3361 +[in][a] -> [ina] 3362 +[res][ult] -> [result] 3363 +[Official ][websit] -> [Official websit] 3364 +[K][r] -> [Kr] 3365 +[dis][s] -> [diss] 3366 +[liv][e ] -> [live ] 3367 +[Mus][e] -> [Muse] 3368 +[Swed][ish ] -> [Swedish ] 3369 +[es][tiv] -> [estiv] 3370 +[W][eb] -> [Web] 3371 +[B][rit] -> [Brit] 3372 +[ship][ ] -> [ship ] 3373 +[German][y] -> [Germany] 3374 +[mak][es ] -> [makes ] 3375 +[. H][er ] -> [. Her ] 3376 +[m][atch] -> [match] 3377 +[) ][- ] -> [) - ] 3378 +[5 ][- ] -> [5 - ] 3379 +[studi][o ] -> [studio ] 3380 +[V][en] -> [Ven] 3381 +[ep][ar] -> [epar] 3382 +[en ][(] -> [en (] 3383 +[tim][e] -> [time] 3384 +[Leag][u] -> [Leagu] 3385 +[st][er] -> [ster] 3386 +[D][en] -> [Den] 3387 +[S][ur] -> [Sur] 3388 +[gro][und] -> [ground] 3389 +[.][C] -> [.C] 3390 +[5][6] -> [56] 3391 +[18][7] -> [187] 3392 +[ne][ed] -> [need] 3393 +[ro][und] -> [round] 3394 +[govern][ment] -> [government] 3395 +[st][ory ] -> [story ] 3396 +[child][ren ] -> [children ] 3397 +[W][ind] -> [Wind] 3398 +[A][n ] -> [An ] 3399 +[ || — || align=right | ][4.] -> [ || — || align=right | 4.] 3400 +[c][le] -> [cle] 3401 +[l][av] -> [lav] 3402 +[serv][ic] -> [servic] 3403 +[4][7] -> [47] 3404 +[s][ix ] -> [six ] 3405 +[L][in] -> [Lin] 3406 +[l][ab] -> [lab] 3407 +[ment ][of ] -> [ment of ] 3408 +[S][am] -> [Sam] 3409 +[U][k] -> [Uk] 3410 +[p][aint] -> [paint] 3411 +[4 ][- ] -> [4 - ] 3412 +[, 1999][ || Socorro || LINEAR || ] -> [, 1999 || Socorro || LINEAR || ] 3413 +[sy][ch] -> [sych] 3414 +[d][est] -> [dest] 3415 +[t][en] -> [ten] 3416 +[c][ant] -> [cant] 3417 +[ri][but] -> [ribut] 3418 +[i][ed ] -> [ied ] 3419 +[Em][per] -> [Emper] 3420 +[199][2] -> [1992] 3421 +[||0][\u000a|-\u000a|] -> [||0\u000a|-\u000a|] 3422 +[os][aur] -> [osaur] 3423 +[Paul][ ] -> [Paul ] 3424 +[aw][ ] -> [aw ] 3425 +[an][i ] -> [ani ] 3426 +[5][8] -> [58] 3427 +[th-][century ] -> [th-century ] 3428 +[W][W] -> [WW] 3429 +[at][ory ] -> [atory ] 3430 +[through ][the ] -> [through the ] 3431 +[se][qu] -> [sequ] 3432 +[.][" ] -> [." ] 3433 +[l][ost ] -> [lost ] 3434 +[sh][ire] -> [shire] 3435 +[M][ass] -> [Mass] 3436 +[cy][cl] -> [cycl] 3437 +[P][ ] -> [P ] 3438 +[when ][the ] -> [when the ] 3439 +[O][hi] -> [Ohi] 3440 +[ti][g] -> [tig] 3441 +[dep][end] -> [depend] 3442 +[201][1 ] -> [2011 ] 3443 +[vi][ew ] -> [view ] 3444 +[V][ || align=right | 1.] -> [V || align=right | 1.] 3445 +[sim][ilar ] -> [similar ] 3446 +[ph][ot] -> [phot] 3447 +[President ][of ] -> [President of ] 3448 +[L][ab] -> [Lab] 3449 +[ed][eral ] -> [ederal ] 3450 +[196][9] -> [1969] 3451 +[on ][(] -> [on (] 3452 +[Bl][ack ] -> [Black ] 3453 +[vill][age ] -> [village ] 3454 +[Part][y (] -> [Party (] 3455 +[\u000a ][D] -> [\u000a D] 3456 +[nov][el] -> [novel] 3457 +[gro][und ] -> [ground ] 3458 +[pres][s] -> [press] 3459 +[ar][c] -> [arc] 3460 +[ch][arg] -> [charg] 3461 +[ass][oci] -> [associ] 3462 +[H][on] -> [Hon] 3463 +[NYS][ || align=right | 1.] -> [NYS || align=right | 1.] 3464 +[tak][en ] -> [taken ] 3465 +[/][ ] -> [/ ] 3466 +[3][2] -> [32] 3467 +[they ][were ] -> [they were ] 3468 +[E][li] -> [Eli] 3469 +[198][2] -> [1982] 3470 +[h][ost] -> [host] 3471 +[L][eg] -> [Leg] 3472 +[G][B] -> [GB] 3473 +[St][re] -> [Stre] 3474 +[career ][statistic] -> [career statistic] 3475 +[ed][.\u000a\u000a] -> [ed.\u000a\u000a] 3476 +[comp][an] -> [compan] 3477 +[ur][b] -> [urb] 3478 +[rol][l] -> [roll] 3479 +[co][-] -> [co-] 3480 +[ag][ain ] -> [again ] 3481 +[ti][l] -> [til] 3482 +[h][ead ] -> [head ] 3483 +[, ][or ] -> [, or ] 3484 +[distric][t ] -> [district ] 3485 +[on ][of ] -> [on of ] 3486 +[A][R] -> [AR] 3487 +[. This ][is ] -> [. This is ] 3488 +[d][ro] -> [dro] 3489 +[=][ ] -> [= ] 3490 +[C][as] -> [Cas] 3491 +[known ][as the ] -> [known as the ] 3492 +[m][or] -> [mor] 3493 +[em][or] -> [emor] 3494 +[curr][ent ] -> [current ] 3495 +[j][ect ] -> [ject ] 3496 +[r][en] -> [ren] 3497 +[o][x ] -> [ox ] 3498 +[r][is] -> [ris] 3499 +[w][e ] -> [we ] 3500 +[star][s ] -> [stars ] 3501 +[S][pr] -> [Spr] 3502 +[Uk][rain] -> [Ukrain] 3503 +[refect][ure] -> [refecture] 3504 +[tr][ain] -> [train] 3505 +[aw][ay ] -> [away ] 3506 +[may ][be ] -> [may be ] 3507 +[went ][to ] -> [went to ] 3508 +[\u000a][T] -> [\u000aT] 3509 +[a][il ] -> [ail ] 3510 +[||][7] -> [||7] 3511 +[Nor][thern ] -> [Northern ] 3512 +[u][el ] -> [uel ] 3513 +[1][–] -> [1–] 3514 +[197][8] -> [1978] 3515 +[dec][id] -> [decid] 3516 +[s][qu] -> [squ] 3517 +[b][ab] -> [bab] 3518 +[m][ur] -> [mur] 3519 +[on][s ] -> [ons ] 3520 +[N][av] -> [Nav] 3521 +[c][raf] -> [craf] 3522 +[en][-] -> [en-] 3523 +[J][ers] -> [Jers] 3524 +[—][ ] -> [— ] 3525 +[B][or] -> [Bor] 3526 +[h][am ] -> [ham ] 3527 +[way][s ] -> [ways ] 3528 +[Hen][ry ] -> [Henry ] 3529 +[area ][of ] -> [area of ] 3530 +[ief][ ] -> [ief ] 3531 +[re][d] -> [red] 3532 +[u][el] -> [uel] 3533 +[ect][ed ] -> [ected ] 3534 +[ro][duc] -> [roduc] 3535 +[Thom][as ] -> [Thomas ] 3536 +[, ][in ] -> [, in ] 3537 +[0 ][– ] -> [0 – ] 3538 +[U][p] -> [Up] 3539 +[go][al] -> [goal] 3540 +[em][ ] -> [em ] 3541 +[g][ir] -> [gir] 3542 +[Cathol][ic ] -> [Catholic ] 3543 +[riv][er ] -> [river ] 3544 +[television ][series ] -> [television series ] 3545 +[||0][||2] -> [||0||2] 3546 +[c][o ] -> [co ] 3547 +[gam][es\u000a] -> [games\u000a] 3548 +[8][.] -> [8.] 3549 +[. ][(] -> [. (] 3550 +[\u000a]["] -> [\u000a"] 3551 +[on][es] -> [ones] 3552 +[�][�] -> [�] 3553 +[ || Kitt Peak || Spacewatch][ || ] -> [ || Kitt Peak || Spacewatch || ] 3554 +[releas][ed ] -> [released ] 3555 +[loc][ated ] -> [located ] 3556 +[Town][s in ] -> [Towns in ] 3557 +[around ][the ] -> [around the ] 3558 +[which ][is ] -> [which is ] 3559 +[r][unn] -> [runn] 3560 +[she ][was ] -> [she was ] 3561 +[or ][of the ] -> [or of the ] 3562 +[e][e ] -> [ee ] 3563 +[re][-] -> [re-] 3564 +[ou][b] -> [oub] 3565 +[S][u] -> [Su] 3566 +[ex][c] -> [exc] 3567 +[n][, ] -> [n, ] 3568 +[ug][g] -> [ugg] 3569 +[al][y] -> [aly] 3570 +[. ][J] -> [. J] 3571 +[F][rom ] -> [From ] 3572 +[h][old] -> [hold] 3573 +[res][ent] -> [resent] 3574 +[a ][of ] -> [a of ] 3575 +[F][C ] -> [FC ] 3576 +[oug][ht ] -> [ought ] 3577 +[Sp][ain] -> [Spain] 3578 +[is][su] -> [issu] 3579 +[CO][VI] -> [COVI] 3580 +[ou][ri] -> [ouri] 3581 +[t][ow] -> [tow] 3582 +[200][1 ] -> [2001 ] 3583 +[. It ][has ] -> [. It has ] 3584 +[ob][ ] -> [ob ] 3585 +[i][y] -> [iy] 3586 +[ic][s ] -> [ics ] 3587 +[would ][be ] -> [would be ] 3588 +[is a ][municipality ] -> [is a municipality ] 3589 +[l][ook] -> [look] 3590 +[deaths\u000a][Deaths from ] -> [deaths\u000aDeaths from ] 3591 +[ation ][and ] -> [ation and ] 3592 +[olog][ical ] -> [ological ] 3593 +[195][0] -> [1950] 3594 +[known for ][his ] -> [known for his ] 3595 +[ation ][(] -> [ation (] 3596 +[tion ][of the ] -> [tion of the ] 3597 +[e][th ] -> [eth ] 3598 +[typ][e of ] -> [type of ] 3599 +[M][ur] -> [Mur] 3600 +[er][v] -> [erv] 3601 +[3][:] -> [3:] 3602 +[exampl][e, ] -> [example, ] 3603 +[th][re] -> [thre] 3604 +[And][re] -> [Andre] 3605 +[b][usiness] -> [business] 3606 +[to ][a ] -> [to a ] 3607 +[c][ord] -> [cord] 3608 +[g][od] -> [god] 3609 +[N][ob] -> [Nob] 3610 +[politician][, ] -> [politician, ] 3611 +[ra][is] -> [rais] 3612 +[c][over] -> [cover] 3613 +[on][n] -> [onn] 3614 +[ri][p] -> [rip] 3615 +[un][t] -> [unt] 3616 +[land][, ] -> [land, ] 3617 +[u][al] -> [ual] 3618 +[COVI][D-] -> [COVID-] 3619 +[Mus][ic ] -> [Music ] 3620 +[Republ][ican ] -> [Republican ] 3621 +[C][a] -> [Ca] 3622 +[\u000a ][R] -> [\u000a R] 3623 +[ b][e ] -> [ be ] 3624 +[M][ag] -> [Mag] 3625 +[se][at] -> [seat] 3626 +[Rep][resent] -> [Represent] 3627 +[M][os] -> [Mos] 3628 +[identi][al ] -> [idential ] 3629 +[3 ][- ] -> [3 - ] 3630 +[m][other ] -> [mother ] 3631 +[)][, and ] -> [), and ] 3632 +[ || || — || ][July ] -> [ || || — || July ] 3633 +[T][re] -> [Tre] 3634 +[if][ic ] -> [ific ] 3635 +[between ][the ] -> [between the ] 3636 +[2 ][- ] -> [2 - ] 3637 +[com][e ] -> [come ] 3638 +[le][ad ] -> [lead ] 3639 +[5][)\u000a ] -> [5)\u000a ] 3640 +[syl][van] -> [sylvan] 3641 +[. ][E] -> [. E] 3642 +[l][ing] -> [ling] 3643 +[ap][pl] -> [appl] 3644 +[ist][s ] -> [ists ] 3645 +[eb][all ] -> [eball ] 3646 +[elec][tion ] -> [election ] 3647 +[happ][en] -> [happen] 3648 +[B][ir] -> [Bir] 3649 +[un][g ] -> [ung ] 3650 +[i][es, ] -> [ies, ] 3651 +[e][ak] -> [eak] 3652 +[\u000a][In ] -> [\u000aIn ] 3653 +[II][I] -> [III] 3654 +[France.\u000a\u000a][Communes in ] -> [France.\u000a\u000aCommunes in ] 3655 +[er][b] -> [erb] 3656 +[t][ell] -> [tell] 3657 +[, ][K] -> [, K] 3658 +[n][on-] -> [non-] 3659 +[. ][P] -> [. P] 3660 +[en][erg] -> [energ] 3661 +[is][land ] -> [island ] 3662 +[\u000a ][K] -> [\u000a K] 3663 +[cent][ral ] -> [central ] 3664 +[2][8 ] -> [28 ] 3665 +["][ and ] -> [" and ] 3666 +[d][el] -> [del] 3667 +[) ][in ] -> [) in ] 3668 +[Ger][many ] -> [Germany ] 3669 +[n][er ] -> [ner ] 3670 +[ing][s and ] -> [ings and ] 3671 +[movie ][directed by ] -> [movie directed by ] 3672 +[f][ur] -> [fur] 3673 +[liv][ing ] -> [living ] 3674 +[ro][pol] -> [ropol] 3675 +[ar][y] -> [ary] 3676 +[ud][g] -> [udg] 3677 +[W][est] -> [West] 3678 +[T][ok] -> [Tok] 3679 +[S][ol] -> [Sol] 3680 +[et][ween ] -> [etween ] 3681 +[||][8] -> [||8] 3682 +[int][roduc] -> [introduc] 3683 +[tim][e, ] -> [time, ] 3684 +[is a ][town ] -> [is a town ] 3685 +[mul][ti] -> [multi] 3686 +[m][ix] -> [mix] 3687 +[Scot][tish ] -> [Scottish ] 3688 +[bod][y ] -> [body ] 3689 +[Americ][a] -> [America] 3690 +[phil][os] -> [philos] 3691 +[o][z] -> [oz] 3692 +[direct][or] -> [director] 3693 +[as well ][as ] -> [as well as ] 3694 +[all ][the ] -> [all the ] 3695 +[9 ][- ] -> [9 - ] 3696 +[wom][en ] -> [women ] 3697 +[ma][thematic] -> [mathematic] 3698 +[us][es ] -> [uses ] 3699 +[e ][for ] -> [e for ] 3700 +[r][o ] -> [ro ] 3701 +[mov][ed to ] -> [moved to ] 3702 +[he][al] -> [heal] 3703 +[.\u000a\u000a][On ] -> [.\u000a\u000aOn ] 3704 +[s\u000a\u000a][|-\u000a|] -> [s\u000a\u000a|-\u000a|] 3705 +[ot ][of ] -> [ot of ] 3706 +[e ][B] -> [e B] 3707 +[Par][liam] -> [Parliam] 3708 +[in][flu] -> [influ] 3709 +[er][ing ] -> [ering ] 3710 +[fin][ish] -> [finish] 3711 +[inv][olv] -> [involv] 3712 +[sup][port] -> [support] 3713 +[auth][or] -> [author] 3714 +[m][id] -> [mid] 3715 +[t][est] -> [test] 3716 +[r][un ] -> [run ] 3717 +[on ][a ] -> [on a ] 3718 +[Col][umb] -> [Columb] 3719 +[18][4] -> [184] 3720 +[c][od] -> [cod] 3721 +[O][ ] -> [O ] 3722 +[n][ow] -> [now] 3723 +[enc][y ] -> [ency ] 3724 +[an][th] -> [anth] 3725 +[ach][us] -> [achus] 3726 +[en][z] -> [enz] 3727 +[he][av] -> [heav] 3728 +[L][a ] -> [La ] 3729 +[9][4] -> [94] 3730 +[le][av] -> [leav] 3731 +[G][all] -> [Gall] 3732 +[fro][g] -> [frog] 3733 +[coun][ti] -> [counti] 3734 +[E][OS] -> [EOS] 3735 +[2][6 ] -> [26 ] 3736 +[at ][a ] -> [at a ] 3737 +[P][rem] -> [Prem] 3738 +[197][4] -> [1974] 3739 +[al][most ] -> [almost ] 3740 +[)][.\u000a ] -> [).\u000a ] 3741 +[F][ri] -> [Fri] 3742 +[P][as] -> [Pas] 3743 +[w][ood] -> [wood] 3744 +[a S][ill] -> [a Sill] 3745 +[sou][thern ] -> [southern ] 3746 +[Lou][is] -> [Louis] 3747 +[196][8] -> [1968] 3748 +[d][anc] -> [danc] 3749 +[inc][reas] -> [increas] 3750 +[ers][h] -> [ersh] 3751 +[wh][e] -> [whe] 3752 +[New ][Jers] -> [New Jers] 3753 +[|| ][ || || ] -> [|| || || ] 3754 +[b][in] -> [bin] 3755 +[play][ing ] -> [playing ] 3756 +[o][on ] -> [oon ] 3757 +[achus][ett] -> [achusett] 3758 +[winn][ing ] -> [winning ] 3759 +[ || L][a Sill] -> [ || La Sill] 3760 +[and][, ] -> [and, ] 3761 +[po][et] -> [poet] 3762 +[sel][f ] -> [self ] 3763 +[\u000a ][H] -> [\u000a H] 3764 +[ag][e] -> [age] 3765 +[New Yor][k] -> [New York] 3766 +[ers ][(] -> [ers (] 3767 +[fin][al ] -> [final ] 3768 +[Distric][t] -> [District] 3769 +[said ][that ] -> [said that ] 3770 +[n][ever ] -> [never ] 3771 +[national ][team] -> [national team] 3772 +[is ][in ] -> [is in ] 3773 +[s\u000a][American ] -> [s\u000aAmerican ] 3774 +[197][6] -> [1976] 3775 +[contro][l ] -> [control ] 3776 +[west ][of ] -> [west of ] 3777 +[people ][who ] -> [people who ] 3778 +[ec][aus] -> [ecaus] 3779 +[Jew][ish ] -> [Jewish ] 3780 +[l][ar] -> [lar] 3781 +[ag][re] -> [agre] 3782 +[St][or] -> [Stor] 3783 +[we][ek] -> [week] 3784 +[c][ir] -> [cir] 3785 +[rem][ain] -> [remain] 3786 +[G][al] -> [Gal] 3787 +[system][ ] -> [system ] 3788 +[Penn][sylvan] -> [Pennsylvan] 3789 +[ra][in ] -> [rain ] 3790 +[200][8 ] -> [2008 ] 3791 +[end][ed ] -> [ended ] 3792 +[at][e] -> [ate] 3793 +[M][el] -> [Mel] 3794 +[st][on] -> [ston] 3795 +[gre][at ] -> [great ] 3796 +[ || align=right | ][5.] -> [ || align=right | 5.] 3797 +[Summer ][Olympic] -> [Summer Olympic] 3798 +[ber][t ] -> [bert ] 3799 +[i][um] -> [ium] 3800 +[w][al] -> [wal] 3801 +[ra][il] -> [rail] 3802 +[\u000a ][ ] -> [\u000a ] 3803 +[eak][al] -> [eakal] 3804 +[b][all] -> [ball] 3805 +[b][att] -> [batt] 3806 +[in][-] -> [in-] 3807 +[ || La Sill][a || ] -> [ || La Silla || ] 3808 +[ing ][(] -> [ing (] 3809 +[k][s ] -> [ks ] 3810 +[D][u] -> [Du] 3811 +[er ][than ] -> [er than ] 3812 +[Hal][eakal] -> [Haleakal] 3813 +[tic][al ] -> [tical ] 3814 +[a][is] -> [ais] 3815 +[on][ic ] -> [onic ] 3816 +[t][op] -> [top] 3817 +[S][ant] -> [Sant] 3818 +[. It ][is a ] -> [. It is a ] 3819 +[ing ][to the ] -> [ing to the ] 3820 +[ap][ ] -> [ap ] 3821 +[w][ater] -> [water] 3822 +[c][ity] -> [city] 3823 +[a ][K] -> [a K] 3824 +[group ][of ] -> [group of ] 3825 +[school][ ] -> [school ] 3826 +[re][qu] -> [requ] 3827 +[youn][g ] -> [young ] 3828 +[re][port] -> [report] 3829 +[s][ent ] -> [sent ] 3830 +[d][own] -> [down] 3831 +[s][epar] -> [separ] 3832 +[U.S. ][state of ] -> [U.S. state of ] 3833 +[scre][en] -> [screen] 3834 +[l][in ] -> [lin ] 3835 +[Republ][ic] -> [Republic] 3836 +[am][ong ] -> [among ] 3837 +[es][-] -> [es-] 3838 +[, ][L] -> [, L] 3839 +[G][od] -> [God] 3840 +[wh][ite ] -> [white ] 3841 +[3]["|] -> [3"|] 3842 +[marri][ed ] -> [married ] 3843 +[Ir][an] -> [Iran] 3844 +[t][ed ] -> [ted ] 3845 +[atur][e ] -> [ature ] 3846 +[197][7] -> [1977] 3847 +[. ][C] -> [. C] 3848 +[5][4] -> [54] 3849 +[hab][it] -> [habit] 3850 +[ea][d of ] -> [ead of ] 3851 +[sol][di] -> [soldi] 3852 +[uc][le] -> [ucle] 3853 +[w][ood ] -> [wood ] 3854 +[l][ight] -> [light] 3855 +[mon][ey ] -> [money ] 3856 +[born ][on ] -> [born on ] 3857 +[w][at] -> [wat] 3858 +[rit][or] -> [ritor] 3859 +[w][in] -> [win] 3860 +[descri][b] -> [describ] 3861 +[ || ][Haleakal] -> [ || Haleakal] 3862 +[197][3] -> [1973] 3863 +[, ][D] -> [, D] 3864 +[became ][the ] -> [became the ] 3865 +[ad][ministr] -> [administr] 3866 +[\u000a][L] -> [\u000aL] 3867 +[con][duc] -> [conduc] 3868 +[natur][al ] -> [natural ] 3869 +[ac][ros] -> [acros] 3870 +[s][iz] -> [siz] 3871 +[ro][m] -> [rom] 3872 +[om][et] -> [omet] 3873 +[nor][thern ] -> [northern ] 3874 +[ult][ure] -> [ulture] 3875 +[J][r] -> [Jr] 3876 +[B][ul] -> [Bul] 3877 +[n][i] -> [ni] 3878 +[th ][and ] -> [th and ] 3879 +[h][an] -> [han] 3880 +[s][old ] -> [sold ] 3881 +[Y][oun] -> [Youn] 3882 +[197][5] -> [1975] 3883 +[and][id] -> [andid] 3884 +[||rowspan="][3"|] -> [||rowspan="3"|] 3885 +[ || ][T] -> [ || T] 3886 +[s][ay ] -> [say ] 3887 +[politician][s\u000a] -> [politicians\u000a] 3888 +[le][ast ] -> [least ] 3889 +[. ][O] -> [. O] 3890 +[Univers][ity] -> [University] 3891 +[. A][t ] -> [. At ] 3892 +[FI][FA ] -> [FIFA ] 3893 +[ar][ea] -> [area] 3894 +[M][ount ] -> [Mount ] 3895 +[err][y ] -> [erry ] 3896 +[C][ze] -> [Cze] 3897 +[y][, and ] -> [y, and ] 3898 +[youn][g] -> [young] 3899 +[the][ast ] -> [theast ] 3900 +[movie ][actors\u000a] -> [movie actors\u000a] 3901 +[f][act] -> [fact] 3902 +[com][peti] -> [competi] 3903 +[childr][en] -> [children] 3904 +[tem][per] -> [temper] 3905 +[1 ][- ] -> [1 - ] 3906 +[H][um] -> [Hum] 3907 +[county ][seat ] -> [county seat ] 3908 +[ettlement][s in ] -> [ettlements in ] 3909 +[2][7 ] -> [27 ] 3910 +[ex][ ] -> [ex ] 3911 +[s]["] -> [s"] 3912 +[ain][t ] -> [aint ] 3913 +[st][ation ] -> [station ] 3914 +[P][ic] -> [Pic] 3915 +[se][t in ] -> [set in ] 3916 +[fin][d ] -> [find ] 3917 +[ || Haleakal][a || ] -> [ || Haleakala || ] 3918 +[D][ay ] -> [Day ] 3919 +[r][un] -> [run] 3920 +[T][om] -> [Tom] 3921 +[8][4] -> [84] 3922 +[sh][ort] -> [short] 3923 +[V][all] -> [Vall] 3924 +[Sec][ret] -> [Secret] 3925 +[ers ][in ] -> [ers in ] 3926 +[s][. He ] -> [s. He ] 3927 +[S][ar] -> [Sar] 3928 +[" ][– ] -> [" – ] 3929 +[e][u] -> [eu] 3930 +[||0][\u000a|-\u000a|199] -> [||0\u000a|-\u000a|199] 3931 +[also ][known as ] -> [also known as ] 3932 +[ov][ ] -> [ov ] 3933 +[movi][es] -> [movies] 3934 +[s][.\u000a ] -> [s.\u000a ] 3935 +[ent][al ] -> [ental ] 3936 +[ers ][are ] -> [ers are ] 3937 +[f][ish] -> [fish] 3938 +[de ][l] -> [de l] 3939 +[t][or ] -> [tor ] 3940 +[St][ar ] -> [Star ] 3941 +[is][land] -> [island] 3942 +[s and ][the ] -> [s and the ] 3943 +[C][ivil ] -> [Civil ] 3944 +[8][7] -> [87] 3945 +[as][hi] -> [ashi] 3946 +[nor][m] -> [norm] 3947 +[, 1999][ || ] -> [, 1999 || ] 3948 +[o][ther] -> [other] 3949 +[M][y ] -> [My ] 3950 +[used ][for ] -> [used for ] 3951 +[ing ][of the ] -> [ing of the ] 3952 +[9][7] -> [97] 3953 +[ser][ies] -> [series] 3954 +[Ir][ish ] -> [Irish ] 3955 +[is ][in the ] -> [is in the ] 3956 +[bro][ad] -> [broad] 3957 +[Indi][a] -> [India] 3958 +[po][st] -> [post] 3959 +[where ][the ] -> [where the ] 3960 +[U][E] -> [UE] 3961 +[radi][o ] -> [radio ] 3962 +[En][ter] -> [Enter] 3963 +[200][6 ] -> [2006 ] 3964 +[f][ood ] -> [food ] 3965 +[pe][di] -> [pedi] 3966 +[197][2] -> [1972] 3967 +[M][ ] -> [M ] 3968 +[J. ][League ] -> [J. League ] 3969 +[called ]["] -> [called "] 3970 +[Isl][am] -> [Islam] 3971 +[out ][of ] -> [out of ] 3972 +[ow][er ] -> [ower ] 3973 +[. \u000a\u000a][The ] -> [. \u000a\u000aThe ] 3974 +[d]['] -> [d'] 3975 +[�][�] -> [ô] 3976 +[ || Haleakala || ][NEAT] -> [ || Haleakala || NEAT] 3977 +[l][iter] -> [liter] 3978 +[ar][ter] -> [arter] 3979 +[ish][op] -> [ishop] 3980 +[law][y] -> [lawy] 3981 +[P][L] -> [PL] 3982 +[t][od] -> [tod] 3983 +[con][stitu] -> [constitu] 3984 +[Pol][ish ] -> [Polish ] 3985 +[W][in] -> [Win] 3986 +[num][b] -> [numb] 3987 +[Pr][of] -> [Prof] 3988 +[p][at] -> [pat] 3989 +[d][one ] -> [done ] 3990 +[fem][ale ] -> [female ] 3991 +[high][est ] -> [highest ] 3992 +[.\u000a\u000aReferences\u000a\u000a][193] -> [.\u000a\u000aReferences\u000a\u000a193] 3993 +[ag][ ] -> [ag ] 3994 +[Chur][ch ] -> [Church ] 3995 +[P][ac] -> [Pac] 3996 +[O][R] -> [OR] 3997 +[med][al] -> [medal] 3998 +[vir][on] -> [viron] 3999 +[z][en] -> [zen] 4000 +[ch][o] -> [cho] 4001 +[arr][on] -> [arron] 4002 +[a ][in the ] -> [a in the ] 4003 +[main][ly ] -> [mainly ] 4004 +[vers][ion] -> [version] 4005 +[Vict][or] -> [Victor] 4006 +[Par][k] -> [Park] 4007 +[curr][ently ] -> [currently ] 4008 +[ing ][on ] -> [ing on ] 4009 +[.\u000a\u000a][He ] -> [.\u000a\u000aHe ] 4010 +[. ][Al] -> [. Al] 4011 +[. M][any ] -> [. Many ] 4012 +[pi][ec] -> [piec] 4013 +[Kor][ean ] -> [Korean ] 4014 +[An][im] -> [Anim] 4015 +[er-][songwrit] -> [er-songwrit] 4016 +[i][er] -> [ier] 4017 +[Fr][ank] -> [Frank] 4018 +[sh][ap] -> [shap] 4019 +[vari][ous ] -> [various ] 4020 +[sy][mb] -> [symb] 4021 +[rec][ogn] -> [recogn] 4022 +[grad][u] -> [gradu] 4023 +[tit][le ] -> [title ] 4024 +[Part][y ] -> [Party ] 4025 +[St][udi] -> [Studi] 4026 +[w][in ] -> [win ] 4027 +[com][mon] -> [common] 4028 +[Y][ou ] -> [You ] 4029 +[es\u000a\u000a][Referenc] -> [es\u000a\u000aReferenc] 4030 +[em][pl] -> [empl] 4031 +[is][l] -> [isl] 4032 +[av][ar] -> [avar] 4033 +[L][ov] -> [Lov] 4034 +[ul][ar] -> [ular] 4035 +[or][n ] -> [orn ] 4036 +[Euro][p] -> [Europ] 4037 +[ec][tion] -> [ection] 4038 +[me][as] -> [meas] 4039 +[P][ubl] -> [Publ] 4040 +[ad][di] -> [addi] 4041 +[C][amp] -> [Camp] 4042 +[includ][es ] -> [includes ] 4043 +[\u000a\u000a][S] -> [\u000a\u000aS] 4044 +[Californ][ia\u000a] -> [California\u000a] 4045 +[Chicag][o ] -> [Chicago ] 4046 +[t][ropical ] -> [tropical ] 4047 +[oug][h] -> [ough] 4048 +[mus][ical ] -> [musical ] 4049 +[arch][it] -> [archit] 4050 +[bur][g ] -> [burg ] 4051 +[Republic ][of ] -> [Republic of ] 4052 +[arron][diss] -> [arrondiss] 4053 +[ert][ain ] -> [ertain ] 4054 +[there ][were ] -> [there were ] 4055 +[anim][al] -> [animal] 4056 +[\u000a][R] -> [\u000aR] 4057 +[, ][H] -> [, H] 4058 +[Riv][er\u000a ] -> [River\u000a ] 4059 +[ births\u000a][2020 ] -> [ births\u000a2020 ] 4060 +[E][duc] -> [Educ] 4061 +[was ][also ] -> [was also ] 4062 +[For][mer ] -> [Former ] 4063 +[e][y, ] -> [ey, ] 4064 +[in][a || ] -> [ina || ] 4065 +[ograph][y ] -> [ography ] 4066 +[at][ed] -> [ated] 4067 +[st][ation] -> [station] 4068 +[||0][||3] -> [||0||3] 4069 +[att][ack ] -> [attack ] 4070 +[As][ian ] -> [Asian ] 4071 +[a ][F] -> [a F] 4072 +[Phil][ipp] -> [Philipp] 4073 +[en ][and ] -> [en and ] 4074 +[anim][ated ] -> [animated ] 4075 +[ter][m] -> [term] 4076 +[Ed][ward ] -> [Edward ] 4077 +[D][ay] -> [Day] 4078 +[O][s] -> [Os] 4079 +[s ][by ] -> [s by ] 4080 +[television ][actors\u000a] -> [television actors\u000a] 4081 +[con][tr] -> [contr] 4082 +[against ][the ] -> [against the ] 4083 +[2][9 ] -> [29 ] 4084 +[p][ut] -> [put] 4085 +[ar][di] -> [ardi] 4086 +[Illino][is] -> [Illinois] 4087 +[e][ign ] -> [eign ] 4088 +[y ][the ] -> [y the ] 4089 +[. I][f ] -> [. If ] 4090 +[s][um] -> [sum] 4091 +[H][ill] -> [Hill] 4092 +[v][ent] -> [vent] 4093 +[became ][a ] -> [became a ] 4094 +[tur][n ] -> [turn ] 4095 +[re][ad ] -> [read ] 4096 +[ric][h ] -> [rich ] 4097 +[bas][ed on the ] -> [based on the ] 4098 +[h][osp] -> [hosp] 4099 +[s][ist] -> [sist] 4100 +[posi][tion ] -> [position ] 4101 +[ac][tive ] -> [active ] 4102 +[col][leg] -> [colleg] 4103 +[G][i] -> [Gi] 4104 +[s][. She ] -> [s. She ] 4105 +[Par][k ] -> [Park ] 4106 +[M][od] -> [Mod] 4107 +[s][ocial ] -> [social ] 4108 +[s][' ] -> [s' ] 4109 +[Wh][ite ] -> [White ] 4110 +[194][0] -> [1940] 4111 +[A][D] -> [AD] 4112 +[at][ed in ] -> [ated in ] 4113 +[ati][n ] -> [atin ] 4114 +[A][ir ] -> [Air ] 4115 +[c][le ] -> [cle ] 4116 +[g][an] -> [gan] 4117 +[iti][es ] -> [ities ] 4118 +[bo][ard] -> [board] 4119 +[199][1] -> [1991] 4120 +[have ][a ] -> [have a ] 4121 +[C][SS] -> [CSS] 4122 +[\u000a ][P] -> [\u000a P] 4123 +[H][ockey ] -> [Hockey ] 4124 +[pow][er] -> [power] 4125 +[St][an] -> [Stan] 4126 +[at][, ] -> [at, ] 4127 +[r][a ] -> [ra ] 4128 +[bl][ack ] -> [black ] 4129 +[or][b] -> [orb] 4130 +[r][ ] -> [r ] 4131 +[. D][ur] -> [. Dur] 4132 +[th][od] -> [thod] 4133 +[ab][le] -> [able] 4134 +[Washing][ton ] -> [Washington ] 4135 +[sci][entist] -> [scientist] 4136 +[!][ ] -> [! ] 4137 +[all][y, ] -> [ally, ] 4138 +[Catal][ina || ] -> [Catalina || ] 4139 +[ti][tl] -> [titl] 4140 +[k][ing] -> [king] 4141 +[peri][od] -> [period] 4142 +[Swed][en] -> [Sweden] 4143 +[mat][er] -> [mater] 4144 +[are ][the ] -> [are the ] 4145 +[K][e] -> [Ke] 4146 +[ch][e] -> [che] 4147 +[spe][ak] -> [speak] 4148 +[196][7] -> [1967] 4149 +[ow][s ] -> [ows ] 4150 +[Catalina || ][CSS] -> [Catalina || CSS] 4151 +[s][. In ] -> [s. In ] 4152 +[Academ][y ] -> [Academy ] 4153 +[con][f] -> [conf] 4154 +[Atl][an] -> [Atlan] 4155 +[L][a] -> [La] 4156 +[K][ent] -> [Kent] 4157 +[ed][it] -> [edit] 4158 +[comp][uter ] -> [computer ] 4159 +[201][4 ] -> [2014 ] 4160 +[al][-] -> [al-] 4161 +[cl][aim] -> [claim] 4162 +[sec][ond] -> [second] 4163 +[tur][al ] -> [tural ] 4164 +[l][os] -> [los] 4165 +[p][ian] -> [pian] 4166 +[\u000a][H] -> [\u000aH] 4167 +[i][tion ] -> [ition ] 4168 +[l][ow ] -> [low ] 4169 +[se][a ] -> [sea ] 4170 +[r][y] -> [ry] 4171 +[A][ri] -> [Ari] 4172 +[Franc][is] -> [Francis] 4173 +[" ][| ] -> [" | ] 4174 +[end ][of the ] -> [end of the ] 4175 +[con][n] -> [conn] 4176 +[,00][0] -> [,000] 4177 +[esp][ec] -> [espec] 4178 +[d][eg] -> [deg] 4179 +[9][, ] -> [9, ] 4180 +[bas][ket] -> [basket] 4181 +[struc][ture] -> [structure] 4182 +[N][ic] -> [Nic] 4183 +[urric][ane ] -> [urricane ] 4184 +[Footb][all] -> [Football] 4185 +[i ][and ] -> [i and ] 4186 +[C][ub] -> [Cub] 4187 +[or][e] -> [ore] 4188 +[ol][e ] -> [ole ] 4189 +[iti][es of ] -> [ities of ] 4190 +[201][2 ] -> [2012 ] 4191 +[h][ard ] -> [hard ] 4192 +[), ][a ] -> [), a ] 4193 +[sy][n] -> [syn] 4194 +[s][) ] -> [s) ] 4195 +[�][�] -> [°] 4196 +[will][ be ] -> [will be ] 4197 +[pow][er ] -> [power ] 4198 +[c][lim] -> [clim] 4199 +[le][y] -> [ley] 4200 +[s][ort] -> [sort] 4201 +[ch][, ] -> [ch, ] 4202 +[I][ow] -> [Iow] 4203 +[ol][f] -> [olf] 4204 +[s][of] -> [sof] 4205 +[ac][tion] -> [action] 4206 +[S][om] -> [Som] 4207 +[qu][al] -> [qual] 4208 +[E][p] -> [Ep] 4209 +[h][it ] -> [hit ] 4210 +[ep][h ] -> [eph ] 4211 +[ec][k] -> [eck] 4212 +[||][9] -> [||9] 4213 +[, 199][7] -> [, 1997] 4214 +[a][str] -> [astr] 4215 +[201][6 ] -> [2016 ] 4216 +[ei][ther ] -> [either ] 4217 +[in][s ] -> [ins ] 4218 +[ul][ ] -> [ul ] 4219 +[201][7 ] -> [2017 ] 4220 +[bet][ter ] -> [better ] 4221 +[career statistic][s\u000a\u000a|-\u000a|] -> [career statistics\u000a\u000a|-\u000a|] 4222 +[ur][y ] -> [ury ] 4223 +[, 2000 || Socorro || LINEAR || — || align=right | ][2.] -> [, 2000 || Socorro || LINEAR || — || align=right | 2.] 4224 +[espec][ially ] -> [especially ] 4225 +[St][ev] -> [Stev] 4226 +[for ][his ] -> [for his ] 4227 +[�][�] -> [ć] 4228 +[in][e, ] -> [ine, ] 4229 +[sing][er-songwrit] -> [singer-songwrit] 4230 +[pres][ent ] -> [present ] 4231 +[A][k] -> [Ak] 4232 +[mod][el] -> [model] 4233 +[due ][to ] -> [due to ] 4234 +[th][r] -> [thr] 4235 +[ar][tic] -> [artic] 4236 +[tr][an] -> [tran] 4237 +[ary ][of ] -> [ary of ] 4238 +[In][stitut] -> [Institut] 4239 +[200][7 ] -> [2007 ] 4240 +[it][te] -> [itte] 4241 +[ies ][of ] -> [ies of ] 4242 +[pr][inc] -> [princ] 4243 +[g][old ] -> [gold ] 4244 +[g][er] -> [ger] 4245 +[pre][vi] -> [previ] 4246 +[Dep][art] -> [Depart] 4247 +[Count][y] -> [County] 4248 +[n][et ] -> [net ] 4249 +[d][riv] -> [driv] 4250 +[pris][on] -> [prison] 4251 +[hav][ing ] -> [having ] 4252 +[W][restl] -> [Wrestl] 4253 +[n][o] -> [no] 4254 +[f][unc] -> [func] 4255 +[H][a] -> [Ha] 4256 +[r][id] -> [rid] 4257 +[Jos][eph ] -> [Joseph ] 4258 +[ro][und ] -> [round ] 4259 +[V][ ] -> [V ] 4260 +[sp][ap] -> [spap] 4261 +[9][3] -> [93] 4262 +[196][4] -> [1964] 4263 +[un][ch] -> [unch] 4264 +[R][ock] -> [Rock] 4265 +[man][, ] -> [man, ] 4266 +[au][di] -> [audi] 4267 +[res][s ] -> [ress ] 4268 +[s.\u000a\u000a][The ] -> [s.\u000a\u000aThe ] 4269 +[S][he ] -> [She ] 4270 +[es ][with ] -> [es with ] 4271 +[ard][in] -> [ardin] 4272 +[President ][of the ] -> [President of the ] 4273 +[L][uc] -> [Luc] 4274 +[are][as ] -> [areas ] 4275 +[N][ ] -> [N ] 4276 +[found in the ][region ] -> [found in the region ] 4277 +[||][10] -> [||10] 4278 +[B][ang] -> [Bang] 4279 +[s][-] -> [s-] 4280 +[5][7] -> [57] 4281 +[Pres][ident] -> [President] 4282 +[L][ad] -> [Lad] 4283 +[D][an] -> [Dan] 4284 +[19][20] -> [1920] 4285 +[Californ][ia ] -> [California ] 4286 +[a ][is a ] -> [a is a ] 4287 +[resp][ons] -> [respons] 4288 +[e ][that ] -> [e that ] 4289 +[drama ][movies\u000a] -> [drama movies\u000a] 4290 +[N][et] -> [Net] 4291 +[c][ast ] -> [cast ] 4292 +[sup][er] -> [super] 4293 +[ac][e] -> [ace] 4294 +[g][ive ] -> [give ] 4295 +[caus][ed by ] -> [caused by ] 4296 +[F][ ] -> [F ] 4297 +[.\u000a\u000a][P] -> [.\u000a\u000aP] 4298 +[m][ight ] -> [might ] 4299 +[m][ir] -> [mir] 4300 +[Nob][el ] -> [Nobel ] 4301 +[St][ar] -> [Star] 4302 +[8 ][- ] -> [8 - ] 4303 +[olog][y] -> [ology] 4304 +[prov][id] -> [provid] 4305 +[was the ][first ] -> [was the first ] 4306 +[ers ][to ] -> [ers to ] 4307 +[�][�] -> [š] 4308 +[a][. The ] -> [a. The ] 4309 +[N][H] -> [NH] 4310 +[row][n] -> [rown] 4311 +[o][\u000a ] -> [o\u000a ] 4312 +[ap][pro] -> [appro] 4313 +[ch][art] -> [chart] 4314 +[r][ank] -> [rank] 4315 +[pr][ac] -> [prac] 4316 +[st][ro] -> [stro] 4317 +[Willi][am] -> [William] 4318 +[is ][one of the ] -> [is one of the ] 4319 +[T][al] -> [Tal] 4320 +[int][end] -> [intend] 4321 +[. A][t the ] -> [. At the ] 4322 +[8][)\u000a ] -> [8)\u000a ] 4323 +[sev][en ] -> [seven ] 4324 +[Que][en ] -> [Queen ] 4325 +[distric][t of ] -> [district of ] 4326 +[Represent][ativ] -> [Representativ] 4327 +[ex][ist] -> [exist] 4328 +[ bec][ame ] -> [ became ] 4329 +[B][ill] -> [Bill] 4330 +[, 2002][ || Socorro || LINEAR || ] -> [, 2002 || Socorro || LINEAR || ] 4331 +[Wh][it] -> [Whit] 4332 +[sur][viv] -> [surviv] 4333 +[er ][for ] -> [er for ] 4334 +[S][il] -> [Sil] 4335 +[as ][an ] -> [as an ] 4336 +[E][s] -> [Es] 4337 +[ab][b] -> [abb] 4338 +[c][ros] -> [cros] 4339 +[er][, and ] -> [er, and ] 4340 +[can ][also ] -> [can also ] 4341 +[daugh][ter ] -> [daughter ] 4342 +[Vi][et] -> [Viet] 4343 +[sty][le] -> [style] 4344 +[s from ][the ] -> [s from the ] 4345 +[a s][m] -> [a sm] 4346 +[196][6] -> [1966] 4347 +[inn][es] -> [innes] 4348 +[yn][ast] -> [ynast] 4349 +[con][di] -> [condi] 4350 +[M][ost ] -> [Most ] 4351 +[col][on] -> [colon] 4352 +[en][e ] -> [ene ] 4353 +[ter][s ] -> [ters ] 4354 +[c][andid] -> [candid] 4355 +[sh][ar] -> [shar] 4356 +[at][om] -> [atom] 4357 +[i][us ] -> [ius ] 4358 +[Mass][achusett] -> [Massachusett] 4359 +[N][az] -> [Naz] 4360 +[sp][ort ] -> [sport ] 4361 +[n][es] -> [nes] 4362 +[H][aw] -> [Haw] 4363 +[ro][ad] -> [road] 4364 +[M][r] -> [Mr] 4365 +[it][an ] -> [itan ] 4366 +[b][igg] -> [bigg] 4367 +[St][r] -> [Str] 4368 +[ac][ ] -> [ac ] 4369 +[m][is] -> [mis] 4370 +[end ][of ] -> [end of ] 4371 +[rem][e ] -> [reme ] 4372 +[u][h] -> [uh] 4373 +[M][ount] -> [Mount] 4374 +[wom][an ] -> [woman ] 4375 +[at][ely ] -> [ately ] 4376 +[ever][y] -> [every] 4377 +[par][ti] -> [parti] 4378 +[o][ch] -> [och] 4379 +[m][ay] -> [may] 4380 +[.][, ] -> [., ] 4381 +[e][. ] -> [e. ] 4382 +[tot][al ] -> [total ] 4383 +[f][all] -> [fall] 4384 +[Bu][ild] -> [Build] 4385 +[-][language ] -> [-language ] 4386 +[di][rec] -> [direc] 4387 +[al][ist ] -> [alist ] 4388 +[to ][make ] -> [to make ] 4389 +[Govern][or of ] -> [Governor of ] 4390 +[writ][ers\u000a] -> [writers\u000a] 4391 +[B][ern] -> [Bern] 4392 +[S][a] -> [Sa] 4393 +[6][7] -> [67] 4394 +[.\u000a\u000a][T] -> [.\u000a\u000aT] 4395 +[ bgcolor=#fefefe\u000a| 1][0] -> [ bgcolor=#fefefe\u000a| 10] 4396 +[us][tic] -> [ustic] 4397 +[population ][was ] -> [population was ] 4398 +[inc][e the ] -> [ince the ] 4399 +[ag][e, ] -> [age, ] 4400 +[Arm][en] -> [Armen] 4401 +[�][�] -> [ç] 4402 +[olu][tion] -> [olution] 4403 +[�][�] -> [å] 4404 +[tr][a] -> [tra] 4405 +[origin][ally ] -> [originally ] 4406 +[Hung][ar] -> [Hungar] 4407 +[v][ict] -> [vict] 4408 +[com][edi] -> [comedi] 4409 +[ro][ad ] -> [road ] 4410 +[anc][ient ] -> [ancient ] 4411 +[r][an ] -> [ran ] 4412 +[is][on ] -> [ison ] 4413 +[ k][m] -> [ km] 4414 +[es][: ] -> [es: ] 4415 +[pl][at] -> [plat] 4416 +[exec][u] -> [execu] 4417 +[. Th][e] -> [. The] 4418 +[d][on] -> [don] 4419 +[z][on] -> [zon] 4420 +[Gold][en ] -> [Golden ] 4421 +[ou][l] -> [oul] 4422 +[ice ][hockey ] -> [ice hockey ] 4423 +[J][ac] -> [Jac] 4424 +[rel][ated ] -> [related ] 4425 +[ev][ent ] -> [event ] 4426 +[C][oun] -> [Coun] 4427 +[innes][ot] -> [innesot] 4428 +[mer][c] -> [merc] 4429 +[sh][own ] -> [shown ] 4430 +[al ][and ] -> [al and ] 4431 +[J][er] -> [Jer] 4432 +[th][ink ] -> [think ] 4433 +[f][if] -> [fif] 4434 +[eg][g] -> [egg] 4435 +[up ][to ] -> [up to ] 4436 +[s][. They ] -> [s. They ] 4437 +[A][v] -> [Av] 4438 +[. ][El] -> [. El] 4439 +[Pakist][an] -> [Pakistan] 4440 +[to][o ] -> [too ] 4441 +[e.\u000a\u000a][The ] -> [e.\u000a\u000aThe ] 4442 +[becaus][e of ] -> [because of ] 4443 +[T][enn] -> [Tenn] 4444 +[200][9 ] -> [2009 ] 4445 +[200][4 ] -> [2004 ] 4446 +[sup][port ] -> [support ] 4447 +[dr][ug] -> [drug] 4448 +[popul][ation of ] -> [population of ] 4449 +[o][on] -> [oon] 4450 +[Miss][ouri] -> [Missouri] 4451 +[mer][g] -> [merg] 4452 +[ed ][as the ] -> [ed as the ] 4453 +[d][or] -> [dor] 4454 +[9][)\u000a ] -> [9)\u000a ] 4455 +[in][str] -> [instr] 4456 +[mat][ch ] -> [match ] 4457 +[American ][movie ] -> [American movie ] 4458 +[tak][es ] -> [takes ] 4459 +[Wom][en's ] -> [Women's ] 4460 +[found ][in ] -> [found in ] 4461 +[countri][es ] -> [countries ] 4462 +[n][d ] -> [nd ] 4463 +[Spr][ing] -> [Spring] 4464 +[air][craf] -> [aircraf] 4465 +[G][ar] -> [Gar] 4466 +[trans][l] -> [transl] 4467 +[ b][ut ] -> [ but ] 4468 +[es, ][the ] -> [es, the ] 4469 +[Riv][er\u000a] -> [River\u000a] 4470 +[e][.\u000a ] -> [e.\u000a ] 4471 +[m][ach] -> [mach] 4472 +[ear][li] -> [earli] 4473 +[f][ig] -> [fig] 4474 +[H][el] -> [Hel] 4475 +[e and ][the ] -> [e and the ] 4476 +[b][on] -> [bon] 4477 +[\u000a][C] -> [\u000aC] 4478 +[play][ers] -> [players] 4479 +[ous][ly ] -> [ously ] 4480 +[l][ah] -> [lah] 4481 +[inform][ation ] -> [information ] 4482 +[s ][at ] -> [s at ] 4483 +[Or][gan] -> [Organ] 4484 +[\u000a|}][\u000a\u000a] -> [\u000a|}\u000a\u000a] 4485 +[3][1 ] -> [31 ] 4486 +[d][en] -> [den] 4487 +[ || — || align=right | ][5.] -> [ || — || align=right | 5.] 4488 +[ ][– ] -> [ – ] 4489 +[196][5] -> [1965] 4490 +[iti][ve ] -> [itive ] 4491 +[ter][, ] -> [ter, ] 4492 +[work][ing ] -> [working ] 4493 +[, 2002][ || Palomar || NEAT] -> [, 2002 || Palomar || NEAT] 4494 +[) ][is the ] -> [) is the ] 4495 +[M][any ] -> [Many ] 4496 +[Sp][ec] -> [Spec] 4497 +[m][on ] -> [mon ] 4498 +[L][at] -> [Lat] 4499 +[p][sych] -> [psych] 4500 +[FLO][ || align=right | 1.] -> [FLO || align=right | 1.] 4501 +[, 200][6] -> [, 2006] 4502 +[193][0] -> [1930] 4503 +[�][�] -> [�] 4504 +[s, ][but ] -> [s, but ] 4505 +[:][\u000a\u000a ] -> [:\u000a\u000a ] 4506 +[A][th] -> [Ath] 4507 +[department ][in ] -> [department in ] 4508 +[New Zeal][and ] -> [New Zealand ] 4509 +[made ][by ] -> [made by ] 4510 +[cl][ose ] -> [close ] 4511 +[ric][k] -> [rick] 4512 +[2][,] -> [2,] 4513 +[, 1998][ || Socorro || LINEAR || — || align=right | ] -> [, 1998 || Socorro || LINEAR || — || align=right | ] 4514 +[I][re] -> [Ire] 4515 +[d][u] -> [du] 4516 +[Bl][ack] -> [Black] 4517 +[l][ig] -> [lig] 4518 +[Tok][y] -> [Toky] 4519 +[P][u] -> [Pu] 4520 +[v][an ] -> [van ] 4521 +[N][ig] -> [Nig] 4522 +[0 ][- ] -> [0 - ] 4523 +[St][e] -> [Ste] 4524 +[ing ][his ] -> [ing his ] 4525 +[A][t] -> [At] 4526 +[Al][p] -> [Alp] 4527 +[196][3] -> [1963] 4528 +[for][d] -> [ford] 4529 +[2020][, ] -> [2020, ] 4530 +[200][5 ] -> [2005 ] 4531 +[Enter][tain] -> [Entertain] 4532 +[deb][ut] -> [debut] 4533 +[S][ociet] -> [Societ] 4534 +[re][v] -> [rev] 4535 +[ ][\u000a\u000a] -> [ \u000a\u000a] 4536 +[en ][in ] -> [en in ] 4537 +[n][ic] -> [nic] 4538 +[1][st ] -> [1st ] 4539 +[Al][b] -> [Alb] 4540 +[car][ri] -> [carri] 4541 +[in the ][U.S. state of ] -> [in the U.S. state of ] 4542 +[L][and] -> [Land] 4543 +[like ][the ] -> [like the ] 4544 +[can][not ] -> [cannot ] 4545 +[part][y ] -> [party ] 4546 +[T][am] -> [Tam] 4547 +[ict][ure] -> [icture] 4548 +[\u000a ][N] -> [\u000a N] 4549 +[East][ern ] -> [Eastern ] 4550 +[was ][not ] -> [was not ] 4551 +[Prime Minist][er of ] -> [Prime Minister of ] 4552 +[Louis][ian] -> [Louisian] 4553 +[Or][d] -> [Ord] 4554 +[pro][per] -> [proper] 4555 +[near ][the ] -> [near the ] 4556 +[Sup][er ] -> [Super ] 4557 +[Batt][le of ] -> [Battle of ] 4558 +[Mar][tin ] -> [Martin ] 4559 +[Arm][y ] -> [Army ] 4560 +[where ][he ] -> [where he ] 4561 +[album][s\u000a] -> [albums\u000a] 4562 +[ch][ann] -> [chann] 4563 +[is ][(] -> [is (] 4564 +[val][ue] -> [value] 4565 +[from ][a ] -> [from a ] 4566 +[Counc][il ] -> [Council ] 4567 +[on ][and ] -> [on and ] 4568 +[L][atin ] -> [Latin ] 4569 +[dist][ribut] -> [distribut] 4570 +[M][ah] -> [Mah] 4571 +[ion ][of ] -> [ion of ] 4572 +[al][ways ] -> [always ] 4573 +[av][ail] -> [avail] 4574 +[D][ar] -> [Dar] 4575 +[Mex][ican ] -> [Mexican ] 4576 +[C][A] -> [CA] 4577 +[p][ap] -> [pap] 4578 +[law][ ] -> [law ] 4579 +[an][a] -> [ana] 4580 +[t][y] -> [ty] 4581 +[et][, ] -> [et, ] 4582 +[m][al] -> [mal] 4583 +[in][es ] -> [ines ] 4584 +[201][3 ] -> [2013 ] 4585 +[s ][on the ] -> [s on the ] 4586 +[miss][ion] -> [mission] 4587 +[vil][le ] -> [ville ] 4588 +[rel][ation] -> [relation] 4589 +[ || Anderson Mesa || LONEOS][ || ] -> [ || Anderson Mesa || LONEOS || ] 4590 +[(][state] -> [(state] 4591 +[receiv][ed ] -> [received ] 4592 +[H][y] -> [Hy] 4593 +[ement ][of ] -> [ement of ] 4594 +[C][ro] -> [Cro] 4595 +[O][ld ] -> [Old ] 4596 +[s][ound] -> [sound] 4597 +[dest][roy] -> [destroy] 4598 +[PL][S] -> [PLS] 4599 +[It ][is ] -> [It is ] 4600 +[T][ro] -> [Tro] 4601 +[P][ers] -> [Pers] 4602 +[canc][er] -> [cancer] 4603 +[m][a ] -> [ma ] 4604 +[Div][ision ] -> [Division ] 4605 +[e][i ] -> [ei ] 4606 +[written ][by ] -> [written by ] 4607 +[ill][er ] -> [iller ] 4608 +[football][ers\u000a] -> [footballers\u000a] 4609 +[son ][of ] -> [son of ] 4610 +[Related pag][es\u000a ] -> [Related pages\u000a ] 4611 +[th][ings ] -> [things ] 4612 +[co][ach] -> [coach] 4613 +[6][3] -> [63] 4614 +[\u000a\u000aReferences\u000a\u000aOther websit][es\u000a\u000a] -> [\u000a\u000aReferences\u000a\u000aOther websites\u000a\u000a] 4615 +[death][s in ] -> [deaths in ] 4616 +[1][1, ] -> [11, ] 4617 +[. I][n] -> [. In] 4618 +[lah][om] -> [lahom] 4619 +[d][og] -> [dog] 4620 +[le][arn] -> [learn] 4621 +[sci][ence ] -> [science ] 4622 +[, he ][was ] -> [, he was ] 4623 +[Car][l] -> [Carl] 4624 +[un][k] -> [unk] 4625 +[chang][e ] -> [change ] 4626 +[f][ight] -> [fight] 4627 +[su][ff] -> [suff] 4628 +[ast][er] -> [aster] 4629 +[e][) ] -> [e) ] 4630 +[ap][h] -> [aph] 4631 +[ || Palomar || ][PLS] -> [ || Palomar || PLS] 4632 +[ap][point] -> [appoint] 4633 +[play][s ] -> [plays ] 4634 +[Ok][lahom] -> [Oklahom] 4635 +[ic][ b] -> [ic b] 4636 +[ful][l ] -> [full ] 4637 +[mag][az] -> [magaz] 4638 +[H][aut] -> [Haut] 4639 +[en][a ] -> [ena ] 4640 +[ing ][in the ] -> [ing in the ] 4641 +[seri][es\u000a] -> [series\u000a] 4642 +[Br][idg] -> [Bridg] 4643 +[movies\u000aMovies ][directed by ] -> [movies\u000aMovies directed by ] 4644 +[ation ][in ] -> [ation in ] 4645 +[Di][rect] -> [Direct] 4646 +[ || S][eptember ] -> [ || September ] 4647 +[Riv][er] -> [River] 4648 +[old][est ] -> [oldest ] 4649 +[F][ar] -> [Far] 4650 +[el][s ] -> [els ] 4651 +[.\u000a\u000a][A] -> [.\u000a\u000aA] 4652 +[f][ood] -> [food] 4653 +[Atlan][tic ] -> [Atlantic ] 4654 +[a][\u000a\u000a] -> [a\u000a\u000a] 4655 +[Gro][up ] -> [Group ] 4656 +[con][struc] -> [construc] 4657 +[gr][and] -> [grand] 4658 +[ro][man] -> [roman] 4659 +[H][or] -> [Hor] 4660 +[l][or] -> [lor] 4661 +[Eli][z] -> [Eliz] 4662 +[", ]["] -> [", "] 4663 +[th centur][y ] -> [th century ] 4664 +[Emp][ire] -> [Empire] 4665 +[pop][ ] -> [pop ] 4666 +[L][ake ] -> [Lake ] 4667 +[9][.] -> [9.] 4668 +[pol][ice ] -> [police ] 4669 +[B][re] -> [Bre] 4670 +[us][h ] -> [ush ] 4671 +[5][0 ] -> [50 ] 4672 +[provinc][e of ] -> [province of ] 4673 +[J][u] -> [Ju] 4674 +[a f][ew ] -> [a few ] 4675 +[ing][\u000a] -> [ing\u000a] 4676 +[\u000a ][C] -> [\u000a C] 4677 +[E][l ] -> [El ] 4678 +[ab][eth ] -> [abeth ] 4679 +[ch][ampionship] -> [championship] 4680 +[o]['s ] -> [o's ] 4681 +[g][en ] -> [gen ] 4682 +[basket][ball ] -> [basketball ] 4683 +[ bec][aus] -> [ becaus] 4684 +[tre][at] -> [treat] 4685 +[the ][same ] -> [the same ] 4686 +[/][/] -> [//] 4687 +[st][op ] -> [stop ] 4688 +[pol][ic] -> [polic] 4689 +[h][our] -> [hour] 4690 +[h][ard] -> [hard] 4691 +[4][:] -> [4:] 4692 +[Ear][th] -> [Earth] 4693 +[H][all of ] -> [Hall of ] 4694 +[ian ][and ] -> [ian and ] 4695 +[ti][an ] -> [tian ] 4696 +[K][y] -> [Ky] 4697 +[ig][r] -> [igr] 4698 +[bir][d] -> [bird] 4699 +[chil][d ] -> [child ] 4700 +[V][o] -> [Vo] 4701 +[ut][e ] -> [ute ] 4702 +[197][1] -> [1971] 4703 +[played ][for ] -> [played for ] 4704 +[typ][es of ] -> [types of ] 4705 +[sel][v] -> [selv] 4706 +[M][y] -> [My] 4707 +[new][spap] -> [newspap] 4708 +[R][am] -> [Ram] 4709 +[ k][n] -> [ kn] 4710 +[ud][e ] -> [ude ] 4711 +[North ][Carol] -> [North Carol] 4712 +[h][tt] -> [htt] 4713 +[ch][an] -> [chan] 4714 +[24][, ] -> [24, ] 4715 +[194][5] -> [1945] 4716 +[u][str] -> [ustr] 4717 +[.\u000a\u000a][L] -> [.\u000a\u000aL] 4718 +[W][i] -> [Wi] 4719 +[An][ton] -> [Anton] 4720 +[u][, ] -> [u, ] 4721 +[l][y, ] -> [ly, ] 4722 +[ant][as] -> [antas] 4723 +[ai][j] -> [aij] 4724 +[Franc][e ] -> [France ] 4725 +[ad][el] -> [adel] 4726 +[D][et] -> [Det] 4727 +[found][ed in ] -> [founded in ] 4728 +[ac][adem] -> [academ] 4729 +[cent][r] -> [centr] 4730 +[Ar][th] -> [Arth] 4731 +[op][her ] -> [opher ] 4732 +[. The][ir ] -> [. Their ] 4733 +[es ][on ] -> [es on ] 4734 +[e of ][a ] -> [e of a ] 4735 +[||1][\u000a|-\u000a|200] -> [||1\u000a|-\u000a|200] 4736 +[mean][ing ] -> [meaning ] 4737 +[s][. It ] -> [s. It ] 4738 +[fr][on] -> [fron] 4739 +[St][eph] -> [Steph] 4740 +[. M][ost ] -> [. Most ] 4741 +[l][eng] -> [leng] 4742 +[201][5 ] -> [2015 ] 4743 +[ic][ally ] -> [ically ] 4744 +[d][ig] -> [dig] 4745 +[htt][p] -> [http] 4746 +[:][//] -> [://] 4747 +[n][ucle] -> [nucle] 4748 +[str][ong] -> [strong] 4749 +[ear][ch] -> [earch] 4750 +[str][ong ] -> [strong ] 4751 +[ag][ue ] -> [ague ] 4752 +[tr][y ] -> [try ] 4753 +[rel][at] -> [relat] 4754 +[cant][on of ] -> [canton of ] 4755 +[poss][ible ] -> [possible ] 4756 +[M][art] -> [Mart] 4757 +[R][ang] -> [Rang] 4758 +[fro][g ] -> [frog ] 4759 +[D][ef] -> [Def] 4760 +[chem][ical ] -> [chemical ] 4761 +[sim][pl] -> [simpl] 4762 +[dat][a-] -> [data-] 4763 +[c][ivil ] -> [civil ] 4764 +[T][ot] -> [Tot] 4765 +[195][6] -> [1956] 4766 +[h][ib] -> [hib] 4767 +[aj][or] -> [ajor] 4768 +[e][ight ] -> [eight ] 4769 +[sort][-] -> [sort-] 4770 +[ian][-] -> [ian-] 4771 +[marri][ed to ] -> [married to ] 4772 +[proc][ess] -> [process] 4773 +[ || align=right | ][6.] -> [ || align=right | 6.] 4774 +[value][="] -> [value="] 4775 +[5][3] -> [53] 4776 +[tr][ack ] -> [track ] 4777 +[a ][C] -> [a C] 4778 +[gre][at] -> [great] 4779 +[ex][per] -> [exper] 4780 +[l][ook ] -> [look ] 4781 +[k][ey] -> [key] 4782 +[D][re] -> [Dre] 4783 +[fi][re] -> [fire] 4784 +[. B][ut ] -> [. But ] 4785 +[independ][ent ] -> [independent ] 4786 +[nomin][ated ] -> [nominated ] 4787 +[Dis][ney ] -> [Disney ] 4788 +[leg][al ] -> [legal ] 4789 +[B][ab] -> [Bab] 4790 +[studio ][album ] -> [studio album ] 4791 +[. F][rom ] -> [. From ] 4792 +[Le][on] -> [Leon] 4793 +[aug][ht ] -> [aught ] 4794 +[wom][en's ] -> [women's ] 4795 +[\u000a\u000a][Other websit] -> [\u000a\u000aOther websit] 4796 +[uc][k ] -> [uck ] 4797 +[W][ood] -> [Wood] 4798 +[a][, and ] -> [a, and ] 4799 +[bre][ak] -> [break] 4800 +[H][is ] -> [His ] 4801 +[to ][re] -> [to re] 4802 +[par][t ] -> [part ] 4803 +[data-][sort-] -> [data-sort-] 4804 +[-][L] -> [-L] 4805 +[s ][as ] -> [s as ] 4806 +[Al][li] -> [Alli] 4807 +[et][er] -> [eter] 4808 +[Lou][is ] -> [Louis ] 4809 +[200][2 ] -> [2002 ] 4810 +[p][en] -> [pen] 4811 +[con][om] -> [conom] 4812 +[an][o ] -> [ano ] 4813 +[s ][\u000a] -> [s \u000a] 4814 +[ed by ][a ] -> [ed by a ] 4815 +[s][ix] -> [six] 4816 +[. ][Y] -> [. Y] 4817 +[ing ][for ] -> [ing for ] 4818 +[in ][(] -> [in (] 4819 +[Muse][um ] -> [Museum ] 4820 +[Emper][or ] -> [Emperor ] 4821 +[re][al ] -> [real ] 4822 +[bro][ther ] -> [brother ] 4823 +[Al][ab] -> [Alab] 4824 +[l][er ] -> [ler ] 4825 +[f][ar] -> [far] 4826 +[ed ][of ] -> [ed of ] 4827 +[Fr][ank ] -> [Frank ] 4828 +[e][ch] -> [ech] 4829 +[cons][ist] -> [consist] 4830 +[sp][ac] -> [spac] 4831 +[camp][a] -> [campa] 4832 +[:][\u000a] -> [:\u000a] 4833 +[Ar][k] -> [Ark] 4834 +[�][�] -> [ā] 4835 +[Sec][ond ] -> [Second ] 4836 +[four][th ] -> [fourth ] 4837 +[that ][he ] -> [that he ] 4838 +[ed][. The ] -> [ed. The ] 4839 +[wh][il] -> [whil] 4840 +[G][ra] -> [Gra] 4841 +[17][9] -> [179] 4842 +[E][. W] -> [E. W] 4843 +[aver][age ] -> [average ] 4844 +[Fil][m] -> [Film] 4845 +[lev][el ] -> [level ] 4846 +[d][in] -> [din] 4847 +[throug][h] -> [through] 4848 +[roug][ht ] -> [rought ] 4849 +[actres][s (] -> [actress (] 4850 +[deb][ut ] -> [debut ] 4851 +[ut][y ] -> [uty ] 4852 +[t][ari] -> [tari] 4853 +[t][ext] -> [text] 4854 +[em][ber] -> [ember] 4855 +[J][ack ] -> [Jack ] 4856 +[Award ][for ] -> [Award for ] 4857 +[Is][land ] -> [Island ] 4858 +[data-sort-][value="] -> [data-sort-value="] 4859 +[P][an] -> [Pan] 4860 +[St][. ] -> [St. ] 4861 +[U][l] -> [Ul] 4862 +[inv][ent] -> [invent] 4863 +[D][oc] -> [Doc] 4864 +[W][is] -> [Wis] 4865 +[Parliam][ent ] -> [Parliament ] 4866 +[in][divid] -> [individ] 4867 +[en][viron] -> [environ] 4868 +[E][UN] -> [EUN] 4869 +[ ][B] -> [ B] 4870 +[u][j] -> [uj] 4871 +[d][ri] -> [dri] 4872 +[elect][ed ] -> [elected ] 4873 +[I][S] -> [IS] 4874 +[R][ail] -> [Rail] 4875 +[As][ia] -> [Asia] 4876 +[�][�] -> [ñ] 4877 +[8][3] -> [83] 4878 +[in][i] -> [ini] 4879 +[E][conom] -> [Econom] 4880 +[T][our] -> [Tour] 4881 +[es ][from the ] -> [es from the ] 4882 +[, 2001 || Socorro || LINEAR || — || align=right | ][1.] -> [, 2001 || Socorro || LINEAR || — || align=right | 1.] 4883 +[F][i] -> [Fi] 4884 +[met][al ] -> [metal ] 4885 +[p][al] -> [pal] 4886 +[bas][ed in ] -> [based in ] 4887 +[s][ourc] -> [sourc] 4888 +[h][ost ] -> [host ] 4889 +[195][9] -> [1959] 4890 +[.\u000a\u000aH][istor] -> [.\u000a\u000aHistor] 4891 +[D][ani] -> [Dani] 4892 +[bas][eball ] -> [baseball ] 4893 +[qu][arter] -> [quarter] 4894 +[. ][This ] -> [. This ] 4895 +[with ][his ] -> [with his ] 4896 +[P][un] -> [Pun] 4897 +[in ][S] -> [in S] 4898 +[nor][th of ] -> [north of ] 4899 +[N][i] -> [Ni] 4900 +[e][ing ] -> [eing ] 4901 +[f][ew ] -> [few ] 4902 +[pres][idential ] -> [presidential ] 4903 +[h][t] -> [ht] 4904 +[C][ri] -> [Cri] 4905 +[N][S] -> [NS] 4906 +[publ][ic] -> [public] 4907 +[is ][called ] -> [is called ] 4908 +[ births\u000a2020 ][deaths\u000a] -> [ births\u000a2020 deaths\u000a] 4909 +[Belg][ian ] -> [Belgian ] 4910 +[enc][e, ] -> [ence, ] 4911 +[consid][er] -> [consider] 4912 +[c][li] -> [cli] 4913 +[Tr][an] -> [Tran] 4914 +[B][ig ] -> [Big ] 4915 +[align=right ][data-sort-value="] -> [align=right data-sort-value="] 4916 +[sp][r] -> [spr] 4917 +[. ]["] -> [. "] 4918 +[song][s] -> [songs] 4919 +[po][int ] -> [point ] 4920 +[ell][ow ] -> [ellow ] 4921 +[posi][tion] -> [position] 4922 +[h][us] -> [hus] 4923 +[ropol][itan ] -> [ropolitan ] 4924 +[R][en] -> [Ren] 4925 +[ul][p] -> [ulp] 4926 +[h][ouse] -> [house] 4927 +[Party (][United States] -> [Party (United States] 4928 +[beg][inn] -> [beginn] 4929 +[c][ar ] -> [car ] 4930 +[le][t ] -> [let ] 4931 +[Kans][as ] -> [Kansas ] 4932 +[t][old ] -> [told ] 4933 +[ by ][the ] -> [ by the ] 4934 +[re][view] -> [review] 4935 +[ ][is a ] -> [ is a ] 4936 +[usin][ess ] -> [usiness ] 4937 +[ian][s ] -> [ians ] 4938 +[a l][ot of ] -> [a lot of ] 4939 +[. They ][were ] -> [. They were ] 4940 +[cri][me ] -> [crime ] 4941 +[h][old ] -> [hold ] 4942 +[World ][Cup ] -> [World Cup ] 4943 +[f][ree ] -> [free ] 4944 +[Par][is] -> [Paris] 4945 +[w][ant ] -> [want ] 4946 +[tour][nam] -> [tournam] 4947 +[rem][ov] -> [remov] 4948 +[.\u000a\u000aReferences\u000a\u000aOther websit][es\u000a] -> [.\u000a\u000aReferences\u000a\u000aOther websites\u000a] 4949 +[Pro][duc] -> [Produc] 4950 +[199][6 ] -> [1996 ] 4951 +[Ital][y] -> [Italy] 4952 +[aut][om] -> [autom] 4953 +[e][at] -> [eat] 4954 +[l][og] -> [log] 4955 +[Con][stitu] -> [Constitu] 4956 +[el][ement] -> [element] 4957 +[n][ear] -> [near] 4958 +[L][ittle ] -> [Little ] 4959 +[Ind][ones] -> [Indones] 4960 +[before ][the ] -> [before the ] 4961 +[o][to ] -> [oto ] 4962 +[ens][ive ] -> [ensive ] 4963 +[GB][T ] -> [GBT ] 4964 +[�][�] -> [â] 4965 +[football play][er. He ] -> [football player. He ] 4966 +[H][M] -> [HM] 4967 +[align=right data-sort-value="][0.] -> [align=right data-sort-value="0.] 4968 +[p][riv] -> [priv] 4969 +[Ar][t] -> [Art] 4970 +[r][u] -> [ru] 4971 +[b][ul] -> [bul] 4972 +[s ][to the ] -> [s to the ] 4973 +[en][ough ] -> [enough ] 4974 +[m][os] -> [mos] 4975 +[196][2] -> [1962] 4976 +[iss][ipp] -> [issipp] 4977 +[ ][- ] -> [ - ] 4978 +[e ][\u000a\u000a] -> [e \u000a\u000a] 4979 +[partic][ip] -> [particip] 4980 +[: ]["] -> [: "] 4981 +[Ph][ys] -> [Phys] 4982 +[T][elevision ] -> [Television ] 4983 +[ast][er ] -> [aster ] 4984 +[sci][enti] -> [scienti] 4985 +[J][es] -> [Jes] 4986 +[a ][G] -> [a G] 4987 +[voc][al] -> [vocal] 4988 +[ac][ter] -> [acter] 4989 +[C][ros] -> [Cros] 4990 +[" ][is a ] -> [" is a ] 4991 +[Sw][iss ] -> [Swiss ] 4992 +[establishments in the ][United States\u000a] -> [establishments in the United States\u000a] 4993 +[Mich][ig] -> [Michig] 4994 +[N][A] -> [NA] 4995 +[them][selv] -> [themselv] 4996 +[E. W][. El] -> [E. W. El] 4997 +[Party (United States][) ] -> [Party (United States) ] 4998 +[which ][was ] -> [which was ] 4999 +[Ob][serv] -> [Observ] 5000 +[M][innesot] -> [Minnesot] 5001 +[sing][ers\u000a] -> [singers\u000a] 5002 +[s][n] -> [sn] 5003 +[symb][ol] -> [symbol] 5004 +[c][ou] -> [cou] 5005 +[common][ly ] -> [commonly ] 5006 +[ne][-] -> [ne-] 5007 +[cont][ain] -> [contain] 5008 +[Pri][z] -> [Priz] 5009 +[W][at] -> [Wat] 5010 +[el][le ] -> [elle ] 5011 +[S][i] -> [Si] 5012 +[fic][tion ] -> [fiction ] 5013 +[Tr][ans] -> [Trans] 5014 +[or ][(] -> [or (] 5015 +[B][ost] -> [Bost] 5016 +[18][3] -> [183] 5017 +[�][�] -> [ã] 5018 +[\u000a][A] -> [\u000aA] 5019 +[w][ind] -> [wind] 5020 +[194][7] -> [1947] 5021 +[t][ain ] -> [tain ] 5022 +[t][ing ] -> [ting ] 5023 +[ay][s ] -> [ays ] 5024 +[l][ast] -> [last] 5025 +[n]['t ] -> [n't ] 5026 +[ch][os] -> [chos] 5027 +[O][x] -> [Ox] 5028 +[Mar][k ] -> [Mark ] 5029 +[Cze][ch ] -> [Czech ] 5030 +[y][an ] -> [yan ] 5031 +[att][emp] -> [attemp] 5032 +[f][oc] -> [foc] 5033 +[En][g] -> [Eng] 5034 +[sur][-] -> [sur-] 5035 +[di][e ] -> [die ] 5036 +[popul][ar] -> [popular] 5037 +[E. W. El][st ] -> [E. W. Elst ] 5038 +[st][er ] -> [ster ] 5039 +[195][7] -> [1957] 5040 +[s][oci] -> [soci] 5041 +[On][tari] -> [Ontari] 5042 +[, 2000 || Socorro || LINEAR || — || align=right | ][1.] -> [, 2000 || Socorro || LINEAR || — || align=right | 1.] 5043 +[||1][1] -> [||11] 5044 +[New Jers][ey] -> [New Jersey] 5045 +[p][a] -> [pa] 5046 +[ag][g] -> [agg] 5047 +[sp][ok] -> [spok] 5048 +[ b][etween ] -> [ between ] 5049 +[House of ][Representativ] -> [House of Representativ] 5050 +[ur][al ] -> [ural ] 5051 +[R][adi] -> [Radi] 5052 +[Wor][ld] -> [World] 5053 +[an][z] -> [anz] 5054 +[Un][ion] -> [Union] 5055 +[ec][tiv] -> [ectiv] 5056 +[lif][e\u000a] -> [life\u000a] 5057 +[com][bin] -> [combin] 5058 +[er][al] -> [eral] 5059 +[Fil][m ] -> [Film ] 5060 +[D][or] -> [Dor] 5061 +[Football][ers from ] -> [Footballers from ] 5062 +[and ][was ] -> [and was ] 5063 +[ap][ol] -> [apol] 5064 +[w][est] -> [west] 5065 +[the][at] -> [theat] 5066 +[H][arr] -> [Harr] 5067 +[195][8] -> [1958] 5068 +[ad][or] -> [ador] 5069 +[e][.] -> [e.] 5070 +[e, ][but ] -> [e, but ] 5071 +[199][9 ] -> [1999 ] 5072 +[wi][m] -> [wim] 5073 +[Gr][amm] -> [Gramm] 5074 +[Con][t] -> [Cont] 5075 +[ear][n] -> [earn] 5076 +[e ][on ] -> [e on ] 5077 +[or][al ] -> [oral ] 5078 +[Nor][w] -> [Norw] 5079 +[or ][the ] -> [or the ] 5080 +[st][ar ] -> [star ] 5081 +[, 2001 || Socorro || LINEAR || — || align=right | ][2.] -> [, 2001 || Socorro || LINEAR || — || align=right | 2.] 5082 +[is a commune. It is ][found in the region ] -> [is a commune. It is found in the region ] 5083 +[fe][ature] -> [feature] 5084 +[ab][ove ] -> [above ] 5085 +[, ][193] -> [, 193] 5086 +[ar][a ] -> [ara ] 5087 +[Kent][uck] -> [Kentuck] 5088 +[200][3 ] -> [2003 ] 5089 +[ bgcolor=#E9E9E9\u000a| 1][0] -> [ bgcolor=#E9E9E9\u000a| 10] 5090 +[O]['] -> [O'] 5091 +[l][ist of ] -> [list of ] 5092 +[football][er\u000a ] -> [footballer\u000a ] 5093 +[2][2, ] -> [22, ] 5094 +[ S][chool] -> [ School] 5095 +[in][habit] -> [inhabit] 5096 +[we][ight ] -> [weight ] 5097 +[, ][who ] -> [, who ] 5098 +[es\u000a\u000aReferenc][es\u000a\u000a] -> [es\u000a\u000aReferences\u000a\u000a] 5099 +[2][nd ] -> [2nd ] 5100 +[al][though ] -> [although ] 5101 +[op][en ] -> [open ] 5102 +[hal][f ] -> [half ] 5103 +[An][other ] -> [Another ] 5104 +[of ][S] -> [of S] 5105 +[qu][ick] -> [quick] 5106 +[which ][are ] -> [which are ] 5107 +[that ][was ] -> [that was ] 5108 +[p][p] -> [pp] 5109 +[elev][ision] -> [elevision] 5110 +[N][am] -> [Nam] 5111 +[rock][ b] -> [rock b] 5112 +[2][1, ] -> [21, ] 5113 +[abil][ity ] -> [ability ] 5114 +[Tenn][es] -> [Tennes] 5115 +[prac][tic] -> [practic] 5116 +[on][ce ] -> [once ] 5117 +[Te][chn] -> [Techn] 5118 +[sh][oot] -> [shoot] 5119 +[te][en ] -> [teen ] 5120 +[) ][is ] -> [) is ] 5121 +[co][ach ] -> [coach ] 5122 +[in ][his ] -> [in his ] 5123 +[�][�] -> [É] 5124 +[t]['s ] -> [t's ] 5125 +[M][as] -> [Mas] 5126 +[w][ide ] -> [wide ] 5127 +[H][ear] -> [Hear] 5128 +[Le][e ] -> [Lee ] 5129 +[n][eg] -> [neg] 5130 +[u][ch] -> [uch] 5131 +[ers ][were ] -> [ers were ] 5132 +[American movie ][actors\u000aAmerican ] -> [American movie actors\u000aAmerican ] 5133 +[television ][series\u000a] -> [television series\u000a] 5134 +[A][sh] -> [Ash] 5135 +[becom][es ] -> [becomes ] 5136 +[a sm][all ] -> [a small ] 5137 +[sport][s ] -> [sports ] 5138 +[arti][st ] -> [artist ] 5139 +[\u000a ][J] -> [\u000a J] 5140 +[in][es] -> [ines] 5141 +[194][9] -> [1949] 5142 +[;][ b] -> [; b] 5143 +[archit][ect] -> [architect] 5144 +[sc][rip] -> [scrip] 5145 +[starr][ing ] -> [starring ] 5146 +[build][ing ] -> [building ] 5147 +[V][ide] -> [Vide] 5148 +[it][, ] -> [it, ] 5149 +[O][tt] -> [Ott] 5150 +[Pr][ince ] -> [Prince ] 5151 +[An][c] -> [Anc] 5152 +[United Stat][es, ] -> [United States, ] 5153 +[T][-] -> [T-] 5154 +[to ][have ] -> [to have ] 5155 +[qu][es] -> [ques] 5156 +[ia ][in ] -> [ia in ] 5157 +[relig][ious ] -> [religious ] 5158 +[Prem][ier ] -> [Premier ] 5159 +[I][f ] -> [If ] 5160 +[d][raw] -> [draw] 5161 +[Nor][th] -> [North] 5162 +[m][et ] -> [met ] 5163 +[. He ][played ] -> [. He played ] 5164 +[diff][ic] -> [diffic] 5165 +[our][g] -> [ourg] 5166 +[M][iddle ] -> [Middle ] 5167 +[A][li] -> [Ali] 5168 +[Mus][ical ] -> [Musical ] 5169 +[E][th] -> [Eth] 5170 +[is ][about ] -> [is about ] 5171 +[Pac][ific ] -> [Pacific ] 5172 +[ti][st] -> [tist] 5173 +[ob][il] -> [obil] 5174 +[\u000a|-][\u000a| ] -> [\u000a|-\u000a| ] 5175 +[Minist][er of ] -> [Minister of ] 5176 +[miss][ion ] -> [mission ] 5177 +[ ][ ] -> [ ] 5178 +[195][4] -> [1954] 5179 +[s][em] -> [sem] 5180 +[195][5] -> [1955] 5181 +[m][ol] -> [mol] 5182 +[as][e ] -> [ase ] 5183 +[t][el] -> [tel] 5184 +[Bl][u] -> [Blu] 5185 +[I][d] -> [Id] 5186 +[cel][ebr] -> [celebr] 5187 +[T][ak] -> [Tak] 5188 +[f][ace ] -> [face ] 5189 +[gir][l] -> [girl] 5190 +[c][ulture] -> [culture] 5191 +[produc][tion ] -> [production ] 5192 +[s][ettl] -> [settl] 5193 +[in the ][United States] -> [in the United States] 5194 +[the][or] -> [theor] 5195 +[progr][amm] -> [programm] 5196 +[ed ][him ] -> [ed him ] 5197 +[w][all] -> [wall] 5198 +[�][�] -> [�] 5199 +[out ][the ] -> [out the ] 5200 +[Vill][ag] -> [Villag] 5201 +[194][8] -> [1948] 5202 +[ti][ ] -> [ti ] 5203 +[r][d ] -> [rd ] 5204 +[ri][bu] -> [ribu] 5205 +[Te][am ] -> [Team ] 5206 +[ass][ist] -> [assist] 5207 +[Europe][\u000a] -> [Europe\u000a] 5208 +[tr][ad] -> [trad] 5209 +[C][ity, ] -> [City, ] 5210 +[ kn][own ] -> [ known ] 5211 +[erb][aij] -> [erbaij] 5212 +[M][ain] -> [Main] 5213 +[Gl][ob] -> [Glob] 5214 +[ec][tive ] -> [ective ] 5215 +[occ][up] -> [occup] 5216 +[ent][, ] -> [ent, ] 5217 +[K][ur] -> [Kur] 5218 +[ber][g ] -> [berg ] 5219 +[199][8 ] -> [1998 ] 5220 +[Mar][y] -> [Mary] 5221 +[tradi][tional ] -> [traditional ] 5222 +[9][2] -> [92] 5223 +[S][ettlements in ] -> [Settlements in ] 5224 +[an][\u000a] -> [an\u000a] 5225 +[tr][ack] -> [track] 5226 +[es][\u000a \u000a ] -> [es\u000a \u000a ] 5227 +[we][ap] -> [weap] 5228 +[build][ing] -> [building] 5229 +[for][mer] -> [former] 5230 +[os][h] -> [osh] 5231 +[UE][FA ] -> [UEFA ] 5232 +[actres][s\u000a ] -> [actress\u000a ] 5233 +[L][em] -> [Lem] 5234 +[�][�] -> [²] 5235 +[Gre][en] -> [Green] 5236 +[progr][am ] -> [program ] 5237 +[a p][erson ] -> [a person ] 5238 +[||1][2] -> [||12] 5239 +[adel][ph] -> [adelph] 5240 +[for][t] -> [fort] 5241 +[T][o ] -> [To ] 5242 +[d][ic] -> [dic] 5243 +[includ][ed ] -> [included ] 5244 +[m][ary ] -> [mary ] 5245 +[roman][tic ] -> [romantic ] 5246 +[s][af] -> [saf] 5247 +[Sci][enc] -> [Scienc] 5248 +[ke][ep] -> [keep] 5249 +[50][0 ] -> [500 ] 5250 +[cre][ate ] -> [create ] 5251 +[th][ir] -> [thir] 5252 +[row][ ] -> [row ] 5253 +[a ][as ] -> [a as ] 5254 +[ett][e ] -> [ette ] 5255 +[C][D] -> [CD] 5256 +[R][og] -> [Rog] 5257 +[ian][s\u000a] -> [ians\u000a] 5258 +[Az][erbaij] -> [Azerbaij] 5259 +[ric][k ] -> [rick ] 5260 +[j][ob] -> [job] 5261 +[2019][, ] -> [2019, ] 5262 +[Th][ese ] -> [These ] 5263 +[Tex][as ] -> [Texas ] 5264 +[\u000a ][G] -> [\u000a G] 5265 +[ath][let] -> [athlet] 5266 +[ag][on] -> [agon] 5267 +[. Th][at ] -> [. That ] 5268 +[Alexand][er ] -> [Alexander ] 5269 +[el][li] -> [elli] 5270 +[Canad][a] -> [Canada] 5271 +[univers][ity ] -> [university ] 5272 +[Phil][adelph] -> [Philadelph] 5273 +[hum][an] -> [human] 5274 +[ian][, ] -> [ian, ] 5275 +[politician ][and ] -> [politician and ] 5276 +[Argent][ine ] -> [Argentine ] 5277 +[amp][ion ] -> [ampion ] 5278 +[O][per] -> [Oper] 5279 +[a ][D] -> [a D] 5280 +[actor][, ] -> [actor, ] 5281 +[ch][ampion] -> [champion] 5282 +[g][e ] -> [ge ] 5283 +[English][-language ] -> [English-language ] 5284 +[ens][e ] -> [ense ] 5285 +[Sou][thern ] -> [Southern ] 5286 +[l][ot] -> [lot] 5287 +[ers][.\u000a\u000a] -> [ers.\u000a\u000a] 5288 +[R][ ] -> [R ] 5289 +[w][.] -> [w.] 5290 +[\u000a\u000aReferences\u000a\u000aOther websit][es\u000a ] -> [\u000a\u000aReferences\u000a\u000aOther websites\u000a ] 5291 +[1][-] -> [1-] 5292 +[Miss][issipp] -> [Mississipp] 5293 +[pro][mot] -> [promot] 5294 +[C][art] -> [Cart] 5295 +[re][s ] -> [res ] 5296 +[on][e] -> [one] 5297 +[ear][th] -> [earth] 5298 +[M][ary ] -> [Mary ] 5299 +[id][ing ] -> [iding ] 5300 +[s][aw ] -> [saw ] 5301 +[T][ai] -> [Tai] 5302 +[kill][ed ] -> [killed ] 5303 +["][S] -> ["S] 5304 +[W][all] -> [Wall] 5305 +[id][enc] -> [idenc] 5306 +[Provinc][e of ] -> [Province of ] 5307 +[t][a] -> [ta] 5308 +[emb][ly ] -> [embly ] 5309 +[enn][is ] -> [ennis ] 5310 +[o][o ] -> [oo ] 5311 +[b][y] -> [by] 5312 +[ke][ep ] -> [keep ] 5313 +[ment][al ] -> [mental ] 5314 +[peri][od ] -> [period ] 5315 +[\u000a\u000a][In ] -> [\u000a\u000aIn ] 5316 +[B][oy] -> [Boy] 5317 +[appear][anc] -> [appearanc] 5318 +[p][il] -> [pil] 5319 +[er ][was ] -> [er was ] 5320 +[Mar][io ] -> [Mario ] 5321 +[st][opp] -> [stopp] 5322 +[1 ][ ] -> [1 ] 5323 +[s][ell] -> [sell] 5324 +[K][ ] -> [K ] 5325 +[chur][ch ] -> [church ] 5326 +[F][ro] -> [Fro] 5327 +[j][udg] -> [judg] 5328 +[u][x] -> [ux] 5329 +[w][a] -> [wa] 5330 +[om][e of ] -> [ome of ] 5331 +[kn][ow ] -> [know ] 5332 +[cri][min] -> [crimin] 5333 +[liv][e in ] -> [live in ] 5334 +[N][atur] -> [Natur] 5335 +[want][ed to ] -> [wanted to ] 5336 +[201][0s ] -> [2010s ] 5337 +[at ][least ] -> [at least ] 5338 +[Prof][ess] -> [Profess] 5339 +[er ][is ] -> [er is ] 5340 +[mov][ement] -> [movement] 5341 +[commun][ity ] -> [community ] 5342 +[fail][ure] -> [failure] 5343 +["][.\u000a\u000a] -> [".\u000a\u000a] 5344 +[is a town ][in ] -> [is a town in ] 5345 +[O][l] -> [Ol] 5346 +[g][a] -> [ga] 5347 +[199][2 ] -> [1992 ] 5348 +[ad][, ] -> [ad, ] 5349 +[A][C] -> [AC] 5350 +[ex][peri] -> [experi] 5351 +[ed ][for the ] -> [ed for the ] 5352 +[ed ][with the ] -> [ed with the ] 5353 +[Portug][u] -> [Portugu] 5354 +[th][ous] -> [thous] 5355 +[s ][\u000a ] -> [s \u000a ] 5356 +[. It ][stars ] -> [. It stars ] 5357 +[N][intend] -> [Nintend] 5358 +[, 2000 || Socorro || LINEAR || — || align=right | ][3.] -> [, 2000 || Socorro || LINEAR || — || align=right | 3.] 5359 +[e][ed] -> [eed] 5360 +[li][fe] -> [life] 5361 +[R][u] -> [Ru] 5362 +[named ][after ] -> [named after ] 5363 +[k][il] -> [kil] 5364 +[produc][ed by ] -> [produced by ] 5365 +[UK][ ] -> [UK ] 5366 +[a ][T] -> [a T] 5367 +[m][el] -> [mel] 5368 +[A][h] -> [Ah] 5369 +[B][ook] -> [Book] 5370 +[some][thing ] -> [something ] 5371 +[t][s] -> [ts] 5372 +[hous][e ] -> [house ] 5373 +[res][earch ] -> [research ] 5374 +[. ][They ] -> [. They ] 5375 +[inc][or] -> [incor] 5376 +[pr][int] -> [print] 5377 +[ac][y ] -> [acy ] 5378 +[ar][, ] -> [ar, ] 5379 +[aly][mp] -> [alymp] 5380 +[F][estiv] -> [Festiv] 5381 +[a, ][the ] -> [a, the ] 5382 +[l][ay] -> [lay] 5383 +[194][6] -> [1946] 5384 +[an][j] -> [anj] 5385 +[Russ][ia] -> [Russia] 5386 +[ww][w.] -> [www.] 5387 +[au][ ] -> [au ] 5388 +[St][ation ] -> [Station ] 5389 +[ü][r] -> [ür] 5390 +[influ][enc] -> [influenc] 5391 +[l][ittle ] -> [little ] 5392 +[op][pos] -> [oppos] 5393 +[), ][the ] -> [), the ] 5394 +[ec][tr] -> [ectr] 5395 +[t][ree ] -> [tree ] 5396 +[when ][he ] -> [when he ] 5397 +[Music][ians from ] -> [Musicians from ] 5398 +[fir][st] -> [first] 5399 +[a][id] -> [aid] 5400 +[dr][um] -> [drum] 5401 +[, 199][6] -> [, 1996] 5402 +[par][ent] -> [parent] 5403 +[bo][x] -> [box] 5404 +[B][ol] -> [Bol] 5405 +[eh][ic] -> [ehic] 5406 +[z][z] -> [zz] 5407 +[C][y] -> [Cy] 5408 +[right][s ] -> [rights ] 5409 +[problem][s ] -> [problems ] 5410 +[en][k] -> [enk] 5411 +[or][e, ] -> [ore, ] 5412 +[Par][alymp] -> [Paralymp] 5413 +[Comp][an] -> [Compan] 5414 +[C][orn] -> [Corn] 5415 +[S][and] -> [Sand] 5416 +[I][V] -> [IV] 5417 +[po][et ] -> [poet ] 5418 +[sid][e the ] -> [side the ] 5419 +[Wind][ows ] -> [Windows ] 5420 +[th][es] -> [thes] 5421 +[K][ir] -> [Kir] 5422 +[% ][of the ] -> [% of the ] 5423 +[Gre][en ] -> [Green ] 5424 +[la][unch] -> [launch] 5425 +[sk][i] -> [ski] 5426 +[ed ][from the ] -> [ed from the ] 5427 +[L][GBT ] -> [LGBT ] 5428 +[r][ace ] -> [race ] 5429 +[song][ by ] -> [song by ] 5430 +[ad][o ] -> [ado ] 5431 +[res][ul] -> [resul] 5432 +[eff][ect] -> [effect] 5433 +[of ][his ] -> [of his ] 5434 +[e][. He ] -> [e. He ] 5435 +[offic][ially ] -> [officially ] 5436 +[e][)\u000a ] -> [e)\u000a ] 5437 +[ó][n ] -> [ón ] 5438 +[inhabit][ant] -> [inhabitant] 5439 +[G][od ] -> [God ] 5440 +[B][und] -> [Bund] 5441 +[enti][al ] -> [ential ] 5442 +[Israel][i ] -> [Israeli ] 5443 +[c][ertain ] -> [certain ] 5444 +[Ch][ampion] -> [Champion] 5445 +[back ][to ] -> [back to ] 5446 +[195][3] -> [1953] 5447 +[Norw][eg] -> [Norweg] 5448 +[ches][tr] -> [chestr] 5449 +[ard][s ] -> [ards ] 5450 +[ || La Silla || ][E. W. Elst ] -> [ || La Silla || E. W. Elst ] 5451 +[day][s ] -> [days ] 5452 +[oc][cur] -> [occur] 5453 +[ref][err] -> [referr] 5454 +[Gre][ec] -> [Greec] 5455 +[em][per] -> [emper] 5456 +[ar][ies ] -> [aries ] 5457 +[ak][h] -> [akh] 5458 +[ed][d] -> [edd] 5459 +[ ][ ] -> [ ] 5460 +[end][er ] -> [ender ] 5461 +[group][s ] -> [groups ] 5462 +[div][ision] -> [division] 5463 +[Ser][b] -> [Serb] 5464 +[Chur][ch] -> [Church] 5465 +[N][o ] -> [No ] 5466 +[pl][ant ] -> [plant ] 5467 +[Ire][land] -> [Ireland] 5468 +[Kans][as] -> [Kansas] 5469 +[Govern][or] -> [Governor] 5470 +[Nig][er] -> [Niger] 5471 +[New York City][\u000a] -> [New York City\u000a] 5472 +[Comm][itte] -> [Committe] 5473 +[200][0s ] -> [2000s ] 5474 +[S][el] -> [Sel] 5475 +[i][ograph] -> [iograph] 5476 +[r][or ] -> [ror ] 5477 +[e][at ] -> [eat ] 5478 +[the][ir] -> [their] 5479 +[o][id ] -> [oid ] 5480 +[Dis][c] -> [Disc] 5481 +[m][ic] -> [mic] 5482 +[ett][ing ] -> [etting ] 5483 +[f][ish ] -> [fish ] 5484 +[serv][ice ] -> [service ] 5485 +[low][er ] -> [lower ] 5486 +[Wh][ile ] -> [While ] 5487 +[\u000a ][\u000a\u000a] -> [\u000a \u000a\u000a] 5488 +[at][ed by ] -> [ated by ] 5489 +[am][a] -> [ama] 5490 +[S][ir ] -> [Sir ] 5491 +[form ][of ] -> [form of ] 5492 +[F][ur] -> [Fur] 5493 +[199][0 ] -> [1990 ] 5494 +[In][depend] -> [Independ] 5495 +[S][pe] -> [Spe] 5496 +[H][om] -> [Hom] 5497 +[j][az] -> [jaz] 5498 +[od][y ] -> [ody ] 5499 +[re][al] -> [real] 5500 +[b][ank] -> [bank] 5501 +[l][l] -> [ll] 5502 +[W][ik] -> [Wik] 5503 +[f][all ] -> [fall ] 5504 +[ist ][(b. ] -> [ist (b. ] 5505 +[S][aint ] -> [Saint ] 5506 +[s][. S] -> [s. S] 5507 +[North ][Americ] -> [North Americ] 5508 +[kill][ing ] -> [killing ] 5509 +[g][un] -> [gun] 5510 +[energ][y ] -> [energy ] 5511 +[ar][rest] -> [arrest] 5512 +[conn][ect] -> [connect] 5513 +[N][ep] -> [Nep] 5514 +[some][one ] -> [someone ] 5515 +[cl][oth] -> [cloth] 5516 +[a ][new ] -> [a new ] 5517 +[an]['s ] -> [an's ] 5518 +[winn][ers\u000a] -> [winners\u000a] 5519 +[min][ut] -> [minut] 5520 +[Col][or] -> [Color] 5521 +[�][�] -> [ú] 5522 +[N][ation] -> [Nation] 5523 +[includ][ing the ] -> [including the ] 5524 +[COVID-][19 ] -> [COVID-19 ] 5525 +[. ][V] -> [. V] 5526 +[M][ic] -> [Mic] 5527 +[stor][m ] -> [storm ] 5528 +[er][e] -> [ere] 5529 +[Lib][r] -> [Libr] 5530 +[B][at] -> [Bat] 5531 +[er][\u000a\u000a] -> [er\u000a\u000a] 5532 +[An][th] -> [Anth] 5533 +[deg][re] -> [degre] 5534 +[v][on ] -> [von ] 5535 +[M][ir] -> [Mir] 5536 +[iv][il] -> [ivil] 5537 +[gam][es ] -> [games ] 5538 +[Club ][career statistics\u000a\u000a|-\u000a|] -> [Club career statistics\u000a\u000a|-\u000a|] 5539 +[i][ro] -> [iro] 5540 +[II][ ] -> [II ] 5541 +[b][ass] -> [bass] 5542 +[Sci][entist] -> [Scientist] 5543 +[inter][est] -> [interest] 5544 +[B][ay] -> [Bay] 5545 +[4][2] -> [42] 5546 +[f][ight ] -> [fight ] 5547 +[U][s] -> [Us] 5548 +[K][en] -> [Ken] 5549 +[dec][lar] -> [declar] 5550 +[R][A] -> [RA] 5551 +[F][eder] -> [Feder] 5552 +[or ]["] -> [or "] 5553 +[Mar][ia ] -> [Maria ] 5554 +[R][ock ] -> [Rock ] 5555 +[L][e ] -> [Le ] 5556 +[b][rought ] -> [brought ] 5557 +[sci][enc] -> [scienc] 5558 +[G][ard] -> [Gard] 5559 +[o][id] -> [oid] 5560 +[k][ey ] -> [key ] 5561 +[196][1] -> [1961] 5562 +[is][tic] -> [istic] 5563 +[am][oun] -> [amoun] 5564 +[.\u000a\u000aReferences\u000a\u000a][192] -> [.\u000a\u000aReferences\u000a\u000a192] 5565 +[\u000a][P] -> [\u000aP] 5566 +[ob][ject] -> [object] 5567 +[. This ][was ] -> [. This was ] 5568 +[Govern][ment ] -> [Government ] 5569 +[w][ea] -> [wea] 5570 +[S][ax] -> [Sax] 5571 +[L][y] -> [Ly] 5572 +[Ark][ans] -> [Arkans] 5573 +[end][ing ] -> [ending ] 5574 +[202][1, ] -> [2021, ] 5575 +[South ][Afric] -> [South Afric] 5576 +[work][s ] -> [works ] 5577 +[kin][d of ] -> [kind of ] 5578 +[k][h] -> [kh] 5579 +[television ][actors\u000aAmerican ] -> [television actors\u000aAmerican ] 5580 +[off][ice ] -> [office ] 5581 +[l][ist ] -> [list ] 5582 +[H][ow] -> [How] 5583 +[199][4 ] -> [1994 ] 5584 +[politician][s] -> [politicians] 5585 +[adv][ent] -> [advent] 5586 +[e ][from ] -> [e from ] 5587 +[Gall][er] -> [Galler] 5588 +[ric][ket] -> [ricket] 5589 +[Vi][enn] -> [Vienn] 5590 +[195][2] -> [1952] 5591 +[b][ig ] -> [big ] 5592 +[s][ound ] -> [sound ] 5593 +[pl][om] -> [plom] 5594 +[p][un] -> [pun] 5595 +[is an ][American ] -> [is an American ] 5596 +[R][em] -> [Rem] 5597 +[ol][f ] -> [olf ] 5598 +[Pri][ze ] -> [Prize ] 5599 +[al ][(] -> [al (] 5600 +[Ch][ann] -> [Chann] 5601 +[ne][igh] -> [neigh] 5602 +[liv][es in ] -> [lives in ] 5603 +[W][ith ] -> [With ] 5604 +[Austral][ia ] -> [Australia ] 5605 +[im][m] -> [imm] 5606 +[ch][air] -> [chair] 5607 +[my][th] -> [myth] 5608 +[um][on] -> [umon] 5609 +[h][ur] -> [hur] 5610 +[Cl][ub] -> [Club] 5611 +[f][ar ] -> [far ] 5612 +[Oc][ean] -> [Ocean] 5613 +[es][h ] -> [esh ] 5614 +[Bro][ok] -> [Brook] 5615 +[ || Kitt Peak || Spacewatch][ || — || align=right | 1.] -> [ || Kitt Peak || Spacewatch || — || align=right | 1.] 5616 +[im][ag] -> [imag] 5617 +[Chr][is ] -> [Chris ] 5618 +[, 199][5] -> [, 1995] 5619 +[W][inn] -> [Winn] 5620 +[Eliz][abeth ] -> [Elizabeth ] 5621 +[Cl][ass] -> [Class] 5622 +[Wis][cons] -> [Wiscons] 5623 +[l][ak] -> [lak] 5624 +[mus][e] -> [muse] 5625 +[creat][ed in ] -> [created in ] 5626 +[Don][ald ] -> [Donald ] 5627 +[ut][en] -> [uten] 5628 +[o][th ] -> [oth ] 5629 +[until ][his ] -> [until his ] 5630 +[style][="] -> [style="] 5631 +[194][4] -> [1944] 5632 +[S][un] -> [Sun] 5633 +[Bang][lad] -> [Banglad] 5634 +[was releas][ed on ] -> [was released on ] 5635 +[such ][as the ] -> [such as the ] 5636 +[bigg][est ] -> [biggest ] 5637 +[D][uk] -> [Duk] 5638 +[H][ot ] -> [Hot ] 5639 +[, ][with ] -> [, with ] 5640 +[comp][ound] -> [compound] 5641 +[along ][with ] -> [along with ] 5642 +[sh][ot ] -> [shot ] 5643 +[p][ot] -> [pot] 5644 +[er][. She ] -> [er. She ] 5645 +[k][s] -> [ks] 5646 +[erson][al ] -> [ersonal ] 5647 +[ul][f] -> [ulf] 5648 +[stud][ent] -> [student] 5649 +[World ][Cup] -> [World Cup] 5650 +[and ][a ] -> [and a ] 5651 +[te][en] -> [teen] 5652 +[or][c] -> [orc] 5653 +[fir][m] -> [firm] 5654 +[Living ][people] -> [Living people] 5655 +[P][ap] -> [Pap] 5656 +[ord][er ] -> [order ] 5657 +[read][y ] -> [ready ] 5658 +[Anc][ient ] -> [Ancient ] 5659 +[val][u] -> [valu] 5660 +[The ][L] -> [The L] 5661 +[al][i] -> [ali] 5662 +[As][ia\u000a] -> [Asia\u000a] 5663 +[P][en] -> [Pen] 5664 +[series ][of ] -> [series of ] 5665 +[D][ou] -> [Dou] 5666 +[pl][ann] -> [plann] 5667 +[ag][o] -> [ago] 5668 +[Web][sit] -> [Websit] 5669 +[%][ of ] -> [% of ] 5670 +[deaths\u000a][American ] -> [deaths\u000aAmerican ] 5671 +[199][0s ] -> [1990s ] 5672 +[Associ][ation ] -> [Association ] 5673 +[into ][a ] -> [into a ] 5674 +[tr][y] -> [try] 5675 +[.\u000a\u000aReferences\u000a\u000aOther websit][es \u000a ] -> [.\u000a\u000aReferences\u000a\u000aOther websites \u000a ] 5676 +[United States.\u000a\u000a][Cities in ] -> [United States.\u000a\u000aCities in ] 5677 +[philos][oph] -> [philosoph] 5678 +[Pas][-de-] -> [Pas-de-] 5679 +[gre][w ] -> [grew ] 5680 +[F][A] -> [FA] 5681 +[r][am] -> [ram] 5682 +[Kor][ea] -> [Korea] 5683 +[bab][ly ] -> [bably ] 5684 +[ed][. ] -> [ed. ] 5685 +[r][up] -> [rup] 5686 +[qu][e] -> [que] 5687 +[k][ind] -> [kind] 5688 +[199][5 ] -> [1995 ] 5689 +[comm][and] -> [command] 5690 +[Af][g] -> [Afg] 5691 +[El][ectr] -> [Electr] 5692 +[Mos][c] -> [Mosc] 5693 +[say][s that ] -> [says that ] 5694 +[T][el] -> [Tel] 5695 +[h][ot] -> [hot] 5696 +[seat][s in ] -> [seats in ] 5697 +[t][om] -> [tom] 5698 +[Tr][ack ] -> [Track ] 5699 +[part][s of ] -> [parts of ] 5700 +[4]["|] -> [4"|] 5701 +[B][ob] -> [Bob] 5702 +[s][ugg] -> [sugg] 5703 +[Pas-de-][Cal] -> [Pas-de-Cal] 5704 +[en][em] -> [enem] 5705 +[N][ord] -> [Nord] 5706 +[N][ag] -> [Nag] 5707 +[Fr][an] -> [Fran] 5708 +[leg][isl] -> [legisl] 5709 +[B][ill ] -> [Bill ] 5710 +[New York ][(state] -> [New York (state] 5711 +[Mar][i] -> [Mari] 5712 +[O][ut] -> [Out] 5713 +[ || || — || ][June ] -> [ || || — || June ] 5714 +[N][a] -> [Na] 5715 +[until ][the ] -> [until the ] 5716 +[sing][er and ] -> [singer and ] 5717 +[m][as] -> [mas] 5718 +[ed in ][a ] -> [ed in a ] 5719 +[He][av] -> [Heav] 5720 +[O][liv] -> [Oliv] 5721 +[is][on] -> [ison] 5722 +[who ][was ] -> [who was ] 5723 +[Pop][e ] -> [Pope ] 5724 +[For][c] -> [Forc] 5725 +[ell][ow] -> [ellow] 5726 +[their ][own ] -> [their own ] 5727 +[med][ical ] -> [medical ] 5728 +[Indi][a ] -> [India ] 5729 +[so][on ] -> [soon ] 5730 +[bl][ood ] -> [blood ] 5731 +[16][0] -> [160] 5732 +[Pl][at] -> [Plat] 5733 +[movi][es ] -> [movies ] 5734 +[A][re] -> [Are] 5735 +[Col][omb] -> [Colomb] 5736 +[hal][f] -> [half] 5737 +[.][ ] -> [. ] 5738 +[for][est] -> [forest] 5739 +[199][1 ] -> [1991 ] 5740 +[ births\u000a][2021 ] -> [ births\u000a2021 ] 5741 +[play][er\u000a ] -> [player\u000a ] 5742 +[me][et] -> [meet] 5743 +[. ][In ] -> [. In ] 5744 +[; b][orn ] -> [; born ] 5745 +[inj][ur] -> [injur] 5746 +[||rowspan="][4"|] -> [||rowspan="4"|] 5747 +[h][ind ] -> [hind ] 5748 +[F][e] -> [Fe] 5749 +[p][and] -> [pand] 5750 +[acc][ord] -> [accord] 5751 +[cr][ash] -> [crash] 5752 +[multi][ple ] -> [multiple ] 5753 +[mount][ain] -> [mountain] 5754 +[R][od] -> [Rod] 5755 +[nov][el ] -> [novel ] 5756 +[History ][of ] -> [History of ] 5757 +[.\u000a\u000aReferences \u000a\u000a][Communes in ] -> [.\u000a\u000aReferences \u000a\u000aCommunes in ] 5758 +[S][ome ] -> [Some ] 5759 +[play][ers ] -> [players ] 5760 +[amoun][t of ] -> [amount of ] 5761 +[a][: ] -> [a: ] 5762 +[f][ut] -> [fut] 5763 +[a ][H] -> [a H] 5764 +[Reg][ion] -> [Region] 5765 +[b][le ] -> [ble ] 5766 +[4][0 ] -> [40 ] 5767 +[y\u000a][The ] -> [y\u000aThe ] 5768 +[Part][y] -> [Party] 5769 +[died ][of ] -> [died of ] 5770 +[th centur][y] -> [th century] 5771 +[n][ess ] -> [ness ] 5772 +[com][par] -> [compar] 5773 +[as][p] -> [asp] 5774 +[a m][ember ] -> [a member ] 5775 +[a (][born ] -> [a (born ] 5776 +[ed ][her ] -> [ed her ] 5777 +[17][0] -> [170] 5778 +[L][ak] -> [Lak] 5779 +[n][ess] -> [ness] 5780 +[ic][s\u000a] -> [ics\u000a] 5781 +[f][ast] -> [fast] 5782 +[us ][(] -> [us (] 5783 +[caus][e ] -> [cause ] 5784 +[v][ey ] -> [vey ] 5785 +[m][ale ] -> [male ] 5786 +[Christi][an] -> [Christian] 5787 +[es ][or ] -> [es or ] 5788 +[s][burg] -> [sburg] 5789 +[com][merc] -> [commerc] 5790 +[him][self ] -> [himself ] 5791 +[San ][Francis] -> [San Francis] 5792 +[arm][y ] -> [army ] 5793 +[Tw][o ] -> [Two ] 5794 +[eb][ec] -> [ebec] 5795 +[there ][is ] -> [there is ] 5796 +[F][ound] -> [Found] 5797 +[le][y, ] -> [ley, ] 5798 +[K][at] -> [Kat] 5799 +[.\u000a ][The ] -> [.\u000a The ] 5800 +[k][ill ] -> [kill ] 5801 +[S][av] -> [Sav] 5802 +[s][ince the ] -> [since the ] 5803 +[\u000a][!] -> [\u000a!] 5804 +[."][\u000a\u000a] -> [."\u000a\u000a] 5805 +[ || ][N] -> [ || N] 5806 +[1 ][January ] -> [1 January ] 5807 +[. A][c] -> [. Ac] 5808 +[mount][ain ] -> [mountain ] 5809 +[, ][it ] -> [, it ] 5810 +[memb][ers of the ] -> [members of the ] 5811 +[er ][from ] -> [er from ] 5812 +[sh][r] -> [shr] 5813 +[W][ay] -> [Way] 5814 +[F][ederal ] -> [Federal ] 5815 +[K][al] -> [Kal] 5816 +[mad][e of ] -> [made of ] 5817 +[er][a ] -> [era ] 5818 +[bas][ed ] -> [based ] 5819 +[9][1] -> [91] 5820 +[sof][tw] -> [softw] 5821 +[ti][sh] -> [tish] 5822 +[Aug][ust] -> [August] 5823 +[i][pl] -> [ipl] 5824 +[writ][er and ] -> [writer and ] 5825 +[Stre][et ] -> [Street ] 5826 +[f][le] -> [fle] 5827 +[mov][e ] -> [move ] 5828 +[||1][4] -> [||14] 5829 +[H][ug] -> [Hug] 5830 +[or ][to ] -> [or to ] 5831 +[year][s, ] -> [years, ] 5832 +[Com][mon] -> [Common] 5833 +[in ][and ] -> [in and ] 5834 +[Championship][ ] -> [Championship ] 5835 +[Con][nec] -> [Connec] 5836 +[go][es ] -> [goes ] 5837 +[||1][3] -> [||13] 5838 +[is a ][county ] -> [is a county ] 5839 +[ist][s] -> [ists] 5840 +[B][udd] -> [Budd] 5841 +[N][ik] -> [Nik] 5842 +[High][ School] -> [High School] 5843 +[ord][in] -> [ordin] 5844 +[Mar][c] -> [Marc] 5845 +[as ][of ] -> [as of ] 5846 +[G][ri] -> [Gri] 5847 +[Win][ter ] -> [Winter ] 5848 +[Youn][g ] -> [Young ] 5849 +[erenc][e ] -> [erence ] 5850 +[G][reg] -> [Greg] 5851 +[Islam][ic ] -> [Islamic ] 5852 +[countr][y] -> [country] 5853 +[fl][o] -> [flo] 5854 +[bir][th] -> [birth] 5855 +[citi][zen] -> [citizen] 5856 +[Sy][stem] -> [System] 5857 +[fr][u] -> [fru] 5858 +[actor ][and ] -> [actor and ] 5859 +[pro][bably ] -> [probably ] 5860 +[T][a] -> [Ta] 5861 +[Norweg][ian ] -> [Norwegian ] 5862 +[198][0 ] -> [1980 ] 5863 +[Con][serv] -> [Conserv] 5864 +[.\u000a\u000aH][istory ] -> [.\u000a\u000aHistory ] 5865 +[g][ets ] -> [gets ] 5866 +[J][ustic] -> [Justic] 5867 +[b][omb] -> [bomb] 5868 +[Me][di] -> [Medi] 5869 +[S][ub] -> [Sub] 5870 +[, 199][3] -> [, 1993] 5871 +[Distric][t ] -> [District ] 5872 +[re][duc] -> [reduc] 5873 +[Sen][ate ] -> [Senate ] 5874 +[in the ][United Kingdom] -> [in the United Kingdom] 5875 +[could ][be ] -> [could be ] 5876 +[M][id] -> [Mid] 5877 +[s][ent] -> [sent] 5878 +[ed ][into the ] -> [ed into the ] 5879 +[b][on ] -> [bon ] 5880 +[. A][s ] -> [. As ] 5881 +[�][�] -> [а] 5882 +[Fren][ch] -> [French] 5883 +[h][u] -> [hu] 5884 +[all][ow ] -> [allow ] 5885 +[was ][first ] -> [was first ] 5886 +[pass][eng] -> [passeng] 5887 +[indu][str] -> [industr] 5888 +[es][tig] -> [estig] 5889 +[er, ][the ] -> [er, the ] 5890 +[ter][ritor] -> [territor] 5891 +[l][ing ] -> [ling ] 5892 +[er][m] -> [erm] 5893 +[X][ ] -> [X ] 5894 +[he ][had ] -> [he had ] 5895 +[establish][ed in ] -> [established in ] 5896 +[fa][ther] -> [father] 5897 +[199][7 ] -> [1997 ] 5898 +[oth][ers ] -> [others ] 5899 +[v][ehic] -> [vehic] 5900 +[" ][in ] -> [" in ] 5901 +[ic][a ] -> [ica ] 5902 +[J][ud] -> [Jud] 5903 +[doc][ument] -> [document] 5904 +[tion ][to ] -> [tion to ] 5905 +[D][ak] -> [Dak] 5906 +[empl][oy] -> [employ] 5907 +[id][a] -> [ida] 5908 +[Los Angel][es ] -> [Los Angeles ] 5909 +[202][3] -> [2023] 5910 +[.\u000a\u000aIn ][the ] -> [.\u000a\u000aIn the ] 5911 +[th][is] -> [this] 5912 +[reg][ular ] -> [regular ] 5913 +[st][one ] -> [stone ] 5914 +[, ][192] -> [, 192] 5915 +[b][at] -> [bat] 5916 +[n][av] -> [nav] 5917 +[WW][E ] -> [WWE ] 5918 +[F][inn] -> [Finn] 5919 +[stud][y ] -> [study ] 5920 +[il][ly ] -> [illy ] 5921 +[17][8] -> [178] 5922 +[we][al] -> [weal] 5923 +[qu][es ] -> [ques ] 5924 +[video ][game ] -> [video game ] 5925 +[incor][por] -> [incorpor] 5926 +[194][3] -> [1943] 5927 +[Nintend][o ] -> [Nintendo ] 5928 +[heav][y ] -> [heavy ] 5929 +[becaus][e the ] -> [because the ] 5930 +[join][ed ] -> [joined ] 5931 +[ain][ed ] -> [ained ] 5932 +[ing ][from ] -> [ing from ] 5933 +[Gir][l] -> [Girl] 5934 +[ay][s] -> [ays] 5935 +[m][ing] -> [ming] 5936 +[s ][like ] -> [s like ] 5937 +[A][qu] -> [Aqu] 5938 +[an][o] -> [ano] 5939 +[um][ent ] -> [ument ] 5940 +[n][u] -> [nu] 5941 +[ory ][of ] -> [ory of ] 5942 +[T][od] -> [Tod] 5943 +[Le][ad] -> [Lead] 5944 +[M][an ] -> [Man ] 5945 +[\u000a][D] -> [\u000aD] 5946 +[Or][igin] -> [Origin] 5947 +[has ][also ] -> [has also ] 5948 +[Municipal][ities in ] -> [Municipalities in ] 5949 +[.\u000a\u000aReferences\u000a\u000aOther websites\u000a\u000a][ ] -> [.\u000a\u000aReferences\u000a\u000aOther websites\u000a\u000a ] 5950 +[cont][roll] -> [controll] 5951 +[J. League ][1] -> [J. League 1] 5952 +[al][c] -> [alc] 5953 +[R][aj] -> [Raj] 5954 +[es][tim] -> [estim] 5955 +[at][tr] -> [attr] 5956 +[ro][om] -> [room] 5957 +[Cur][r] -> [Curr] 5958 +[ b][eg] -> [ beg] 5959 +[f][ind] -> [find] 5960 +[col][or ] -> [color ] 5961 +[Villag][es in ] -> [Villages in ] 5962 +[M][ill] -> [Mill] 5963 +[In][c] -> [Inc] 5964 +[or][s] -> [ors] 5965 +[me][an ] -> [mean ] 5966 +[C][B] -> [CB] 5967 +[K][am] -> [Kam] 5968 +[ay][ashi] -> [ayashi] 5969 +[194][2] -> [1942] 5970 +[Gramm][y ] -> [Grammy ] 5971 +[e][. In ] -> [e. In ] 5972 +[years ][old] -> [years old] 5973 +[gener][ally ] -> [generally ] 5974 +[G][ ] -> [G ] 5975 +[le][ague ] -> [league ] 5976 +[2010 ][census] -> [2010 census] 5977 +[ed][ic] -> [edic] 5978 +[if][ied ] -> [ified ] 5979 +[tri][ed to ] -> [tried to ] 5980 +[County ][seats in ] -> [County seats in ] 5981 +[e ][is a ] -> [e is a ] 5982 +[ep][p] -> [epp] 5983 +[n][ight ] -> [night ] 5984 +[sur][round] -> [surround] 5985 +[C][el] -> [Cel] 5986 +[Washing][ton, ] -> [Washington, ] 5987 +[Indi][an] -> [Indian] 5988 +[ant][s ] -> [ants ] 5989 +[E][R] -> [ER] 5990 +[avail][able ] -> [available ] 5991 +[join][ed the ] -> [joined the ] 5992 +[s][wim] -> [swim] 5993 +[um][i] -> [umi] 5994 +[ad][ap] -> [adap] 5995 +[et][-] -> [et-] 5996 +[Sp][ain ] -> [Spain ] 5997 +[at][e, ] -> [ate, ] 5998 +[distribut][ed by ] -> [distributed by ] 5999 +[ad][op] -> [adop] 6000 +[ar][riv] -> [arriv] 6001 +[ã][o ] -> [ão ] 6002 +[sc][ulp] -> [sculp] 6003 +[er][to ] -> [erto ] 6004 +[k ][(] -> [k (] 6005 +[am][ed ] -> [amed ] 6006 +[15][0] -> [150] 6007 +[I][ra] -> [Ira] 6008 +[P][ow] -> [Pow] 6009 +[200][ ] -> [200 ] 6010 +[s][low] -> [slow] 6011 +[es ][a ] -> [es a ] 6012 +[b][our] -> [bour] 6013 +[C][ir] -> [Cir] 6014 +[s][pl] -> [spl] 6015 +[start][ed in ] -> [started in ] 6016 +[co][ast] -> [coast] 6017 +[" and ]["] -> [" and "] 6018 +[f][ought ] -> [fought ] 6019 +[Turk][ish ] -> [Turkish ] 6020 +[. A][s of the ] -> [. As of the ] 6021 +[ || ][align=right data-sort-value="0.] -> [ || align=right data-sort-value="0.] 6022 +[. It is ][also ] -> [. It is also ] 6023 +[b][ ] -> [b ] 6024 +[G][il] -> [Gil] 6025 +[or][i] -> [ori] 6026 +[sl][av] -> [slav] 6027 +[W][ ] -> [W ] 6028 +[er][c] -> [erc] 6029 +[Ohi][o] -> [Ohio] 6030 +[n][ight] -> [night] 6031 +[est][-] -> [est-] 6032 +[Sh][ow] -> [Show] 6033 +[tro][op] -> [troop] 6034 +[st][ag] -> [stag] 6035 +[it][age ] -> [itage ] 6036 +[star][t ] -> [start ] 6037 +[.\u000a\u000aC][are] -> [.\u000a\u000aCare] 6038 +[er ][in the ] -> [er in the ] 6039 +[un][i] -> [uni] 6040 +[2020 ][census] -> [2020 census] 6041 +[a][ud] -> [aud] 6042 +[u][ter] -> [uter] 6043 +[thr][iller ] -> [thriller ] 6044 +[0 ][m || \u000a|-id=] -> [0 m || \u000a|-id=] 6045 +[perform][anc] -> [performanc] 6046 +[county seat ][is ] -> [county seat is ] 6047 +[, 2003][ || Socorro || LINEAR || — || align=right | ] -> [, 2003 || Socorro || LINEAR || — || align=right | ] 6048 +[u][ff] -> [uff] 6049 +[H][all] -> [Hall] 6050 +[s ][such as ] -> [s such as ] 6051 +[des][ign ] -> [design ] 6052 +[ang][er] -> [anger] 6053 +[ings and ][structure] -> [ings and structure] 6054 +[s. ][\u000a\u000a] -> [s. \u000a\u000a] 6055 +[. A][b] -> [. Ab] 6056 +[5][2] -> [52] 6057 +[ert][ain] -> [ertain] 6058 +[ births\u000aLiving people\u000a][Footballers from ] -> [ births\u000aLiving people\u000aFootballers from ] 6059 +[Peopl][e's ] -> [People's ] 6060 +[Conf][eder] -> [Confeder] 6061 +[c][ong] -> [cong] 6062 +[us][p] -> [usp] 6063 +[h][, ] -> [h, ] 6064 +[r][or] -> [ror] 6065 +[Roman ][Catholic ] -> [Roman Catholic ] 6066 +[played ][for the ] -> [played for the ] 6067 +[ef][ ] -> [ef ] 6068 +[sp][ent ] -> [spent ] 6069 +[An][ti] -> [Anti] 6070 +[Ser][ie ] -> [Serie ] 6071 +[ur][ity ] -> [urity ] 6072 +[, 2001][ || Palomar || NEAT] -> [, 2001 || Palomar || NEAT] 6073 +[histor][ical ] -> [historical ] 6074 +[Tor][on] -> [Toron] 6075 +[help][ed ] -> [helped ] 6076 +[. After ][the ] -> [. After the ] 6077 +[hy][dro] -> [hydro] 6078 +[Dur][ing the ] -> [During the ] 6079 +[success][ful ] -> [successful ] 6080 +[ing][.\u000a\u000a] -> [ing.\u000a\u000a] 6081 +[a l][ong ] -> [a long ] 6082 +[t][-] -> [t-] 6083 +[a][. ] -> [a. ] 6084 +[Bill][board ] -> [Billboard ] 6085 +[Sing][ap] -> [Singap] 6086 +[div][ision ] -> [division ] 6087 +[Movies ][set in ] -> [Movies set in ] 6088 +[B][all] -> [Ball] 6089 +[ation][s] -> [ations] 6090 +[County ][is a county ] -> [County is a county ] 6091 +[il][le ] -> [ille ] 6092 +[part][s of the ] -> [parts of the ] 6093 +[ob][ile ] -> [obile ] 6094 +[Bro][ad] -> [Broad] 6095 +[sty][le ] -> [style ] 6096 +[z][, ] -> [z, ] 6097 +[ra][ther ] -> [rather ] 6098 +[y][a ] -> [ya ] 6099 +[professional ][wrestl] -> [professional wrestl] 6100 +[ed][g] -> [edg] 6101 +[D][omin] -> [Domin] 6102 +[bo][x ] -> [box ] 6103 +[J][im] -> [Jim] 6104 +[L][im] -> [Lim] 6105 +[Air][lin] -> [Airlin] 6106 +[k][id] -> [kid] 6107 +[pur][pos] -> [purpos] 6108 +[bur][n] -> [burn] 6109 +[Bl][ue ] -> [Blue ] 6110 +[it][a ] -> [ita ] 6111 +[kn][ow] -> [know] 6112 +[b][ad] -> [bad] 6113 +[per][c] -> [perc] 6114 +[H][eal] -> [Heal] 6115 +[y][st] -> [yst] 6116 +[re][vol] -> [revol] 6117 +[k ][and ] -> [k and ] 6118 +[ation][\u000a] -> [ation\u000a] 6119 +[Cal][v] -> [Calv] 6120 +[Mil][itary ] -> [Military ] 6121 +[ul][l ] -> [ull ] 6122 +[Ch][ief ] -> [Chief ] 6123 +[E][ver] -> [Ever] 6124 +[Viet][nam] -> [Vietnam] 6125 +[ist ][(d. ] -> [ist (d. ] 6126 +[s][ens] -> [sens] 6127 +[Sy][d] -> [Syd] 6128 +[indu][stri] -> [industri] 6129 +[n][it] -> [nit] 6130 +[Tot][al] -> [Total] 6131 +[was ][made ] -> [was made ] 6132 +[||0][\u000a] -> [||0\u000a] 6133 +[||3][0] -> [||30] 6134 +[\u000a ][F] -> [\u000a F] 6135 +[adi][um ] -> [adium ] 6136 +[tim][es] -> [times] 6137 +[an][al] -> [anal] 6138 +[Jack][son ] -> [Jackson ] 6139 +[Ass][embly ] -> [Assembly ] 6140 +[instr][ument] -> [instrument] 6141 +[reti][red ] -> [retired ] 6142 +[. S][ince ] -> [. Since ] 6143 +[e][. It ] -> [e. It ] 6144 +[Ear][th ] -> [Earth ] 6145 +[198][8 ] -> [1988 ] 6146 +[\u000a|-][\u000a!] -> [\u000a|-\u000a!] 6147 +[in][iti] -> [initi] 6148 +[Movi][e ] -> [Movie ] 6149 +[T][it] -> [Tit] 6150 +[L][ove ] -> [Love ] 6151 +[wh][ose ] -> [whose ] 6152 +[Cro][ati] -> [Croati] 6153 +[res][ign] -> [resign] 6154 +[cord][ing to ] -> [cording to ] 6155 +[C][anc] -> [Canc] 6156 +[gr][av] -> [grav] 6157 +[L][or] -> [Lor] 6158 +[, ][The ] -> [, The ] 6159 +[has ][the ] -> [has the ] 6160 +[end][s ] -> [ends ] 6161 +[f][ederal ] -> [federal ] 6162 +[fin][al] -> [final] 6163 +[childr][en's ] -> [children's ] 6164 +[R][ed] -> [Red] 6165 +[o][di] -> [odi] 6166 +[an ][area of ] -> [an area of ] 6167 +[, 1998][ || Socorro || LINEAR || ] -> [, 1998 || Socorro || LINEAR || ] 6168 +[be][hav] -> [behav] 6169 +[c][ricket] -> [cricket] 6170 +[man ][and ] -> [man and ] 6171 +[es][cap] -> [escap] 6172 +[c][am] -> [cam] 6173 +[d][é] -> [dé] 6174 +[; ][the ] -> [; the ] 6175 +[reach][ed ] -> [reached ] 6176 +[. B][ecaus] -> [. Becaus] 6177 +[Div][ision] -> [Division] 6178 +[along ][the ] -> [along the ] 6179 +[med][ic] -> [medic] 6180 +[species ][of ] -> [species of ] 6181 +[best ][known for ] -> [best known for ] 6182 +[nation][al] -> [national] 6183 +[ob][serv] -> [observ] 6184 +[ful][ly ] -> [fully ] 6185 +[T][ay] -> [Tay] 6186 +[Par][is ] -> [Paris ] 6187 +[writ][ing ] -> [writing ] 6188 +[ou][ch] -> [ouch] 6189 +[ang][e ] -> [ange ] 6190 +[sp][ace ] -> [space ] 6191 +[s, ][which ] -> [s, which ] 6192 +[U][t] -> [Ut] 6193 +[. A][n ] -> [. An ] 6194 +[M][P] -> [MP] 6195 +[sing][er ] -> [singer ] 6196 +[es][lig] -> [eslig] 6197 +[chur][ch] -> [church] 6198 +[s ][is ] -> [s is ] 6199 +[with][in the ] -> [within the ] 6200 +[comedy ][movies\u000a] -> [comedy movies\u000a] 6201 +[R][&] -> [R&] 6202 +[consid][ered ] -> [considered ] 6203 +[f][arm] -> [farm] 6204 +[Iran][ian ] -> [Iranian ] 6205 +[ist ][(] -> [ist (] 6206 +[p][et] -> [pet] 6207 +[milli][on] -> [million] 6208 +[C][ity of ] -> [City of ] 6209 +[199][3 ] -> [1993 ] 6210 +[as ]["] -> [as "] 6211 +[memb][ers ] -> [members ] 6212 +[e][. It was ] -> [e. It was ] 6213 +[b][ach] -> [bach] 6214 +[cont][ains ] -> [contains ] 6215 +[co][ast ] -> [coast ] 6216 +[ia][\u000a ] -> [ia\u000a ] 6217 +[ || align=right | ][7.] -> [ || align=right | 7.] 6218 +[di][plom] -> [diplom] 6219 +[music][ian ] -> [musician ] 6220 +[divid][ed into ] -> [divided into ] 6221 +[||1][5] -> [||15] 6222 +[wh][it] -> [whit] 6223 +[g][ood] -> [good] 6224 +[ö][r] -> [ör] 6225 +[os][, ] -> [os, ] 6226 +[could ][not ] -> [could not ] 6227 +[B][ook ] -> [Book ] 6228 +[to ][get ] -> [to get ] 6229 +[in][i ] -> [ini ] 6230 +[ies ][and ] -> [ies and ] 6231 +[t][ax] -> [tax] 6232 +[US][A] -> [USA] 6233 +[Connec][tic] -> [Connectic] 6234 +[most ][of the ] -> [most of the ] 6235 +[a ]["] -> [a "] 6236 +[Ira][q] -> [Iraq] 6237 +[Portugu][ese ] -> [Portuguese ] 6238 +[appear][ed in ] -> [appeared in ] 6239 +[el][f] -> [elf] 6240 +[li][e ] -> [lie ] 6241 +[S][qu] -> [Squ] 6242 +[Ad][am] -> [Adam] 6243 +[ || ][Catalina || CSS] -> [ || Catalina || CSS] 6244 +[ord][er] -> [order] 6245 +[r][ad] -> [rad] 6246 +[their][ b] -> [their b] 6247 +[Scot][land] -> [Scotland] 6248 +[N][ev] -> [Nev] 6249 +[c][art] -> [cart] 6250 +[bir][th ] -> [birth ] 6251 +[mov][ement ] -> [movement ] 6252 +[ar][i ] -> [ari ] 6253 +[Dak][ot] -> [Dakot] 6254 +[\u000a][F] -> [\u000aF] 6255 +[�][�] -> [�] 6256 +[for][ce ] -> [force ] 6257 +[ed][, and ] -> [ed, and ] 6258 +[Hungar][ian ] -> [Hungarian ] 6259 +[person][al] -> [personal] 6260 +[http][://] -> [http://] 6261 +[h][a] -> [ha] 6262 +[of][t ] -> [oft ] 6263 +[n][é] -> [né] 6264 +[ong ][K] -> [ong K] 6265 +[h][o] -> [ho] 6266 +[publish][ed in ] -> [published in ] 6267 +[most ][important ] -> [most important ] 6268 +[ar][y, ] -> [ary, ] 6269 +[er (][born ] -> [er (born ] 6270 +[sex][ual ] -> [sexual ] 6271 +[ens][us ] -> [ensus ] 6272 +[After ][the ] -> [After the ] 6273 +[releas][ed on ] -> [released on ] 6274 +[is][i] -> [isi] 6275 +[pand][em] -> [pandem] 6276 +[”][ ] -> [” ] 6277 +[5][00] -> [500] 6278 +[town ][of ] -> [town of ] 6279 +[Fam][ily ] -> [Family ] 6280 +[rec][ip] -> [recip] 6281 +[pres][ident] -> [president] 6282 +[m ][(] -> [m (] 6283 +[ri][an ] -> [rian ] 6284 +[le][ave ] -> [leave ] 6285 +[bl][u] -> [blu] 6286 +[H][arry ] -> [Harry ] 6287 +[Tennes][se] -> [Tennesse] 6288 +[,][" ] -> [," ] 6289 +[f][estiv] -> [festiv] 6290 +[e, ][a ] -> [e, a ] 6291 +[bro][th] -> [broth] 6292 +[B][BC ] -> [BBC ] 6293 +[e][uten] -> [euten] 6294 +[releas][ed in ] -> [released in ] 6295 +[A][ff] -> [Aff] 6296 +[it][or] -> [itor] 6297 +[um][p ] -> [ump ] 6298 +[v][ice ] -> [vice ] 6299 +[b][ad ] -> [bad ] 6300 +[es][. ] -> [es. ] 6301 +[u][fact] -> [ufact] 6302 +[s][le] -> [sle] 6303 +[Un][der] -> [Under] 6304 +[had ][to ] -> [had to ] 6305 +[Det][ro] -> [Detro] 6306 +[Col][l] -> [Coll] 6307 +[ut][t] -> [utt] 6308 +[ó][n] -> [ón] 6309 +[and ][his ] -> [and his ] 6310 +[an][ch ] -> [anch ] 6311 +[Counc][il] -> [Council] 6312 +[region ][of ] -> [region of ] 6313 +[as ][(] -> [as (] 6314 +[\u000a|-\u000a!][Total] -> [\u000a|-\u000a!Total] 6315 +[Ari][zon] -> [Arizon] 6316 +[Ch][em] -> [Chem] 6317 +[||][16] -> [||16] 6318 +[202][2, ] -> [2022, ] 6319 +[B][ib] -> [Bib] 6320 +[B][ah] -> [Bah] 6321 +[B][ell] -> [Bell] 6322 +[) ][was ] -> [) was ] 6323 +[coun][c] -> [counc] 6324 +[. The ][first ] -> [. The first ] 6325 +[sup][pl] -> [suppl] 6326 +[C][E] -> [CE] 6327 +[is][ch] -> [isch] 6328 +[R][ap] -> [Rap] 6329 +[establishment][s\u000a] -> [establishments\u000a] 6330 +[ne][umon] -> [neumon] 6331 +[Organ][iz] -> [Organiz] 6332 +[P][ur] -> [Pur] 6333 +[ed ][that the ] -> [ed that the ] 6334 +[ri][ ] -> [ri ] 6335 +[ex][tr] -> [extr] 6336 +[P][or] -> [Por] 6337 +[administr][ative ] -> [administrative ] 6338 +[Gr][and] -> [Grand] 6339 +[. He was ][also ] -> [. He was also ] 6340 +[f][ic ] -> [fic ] 6341 +[B][os] -> [Bos] 6342 +[R][i] -> [Ri] 6343 +[Publ][ic ] -> [Public ] 6344 +[mon][d ] -> [mond ] 6345 +[ins][ul] -> [insul] 6346 +[ || — || align=right | ][6.] -> [ || — || align=right | 6.] 6347 +[clim][ate ] -> [climate ] 6348 +[counti][es] -> [counties] 6349 +[\u000a ][A] -> [\u000a A] 6350 +[p][an] -> [pan] 6351 +[\u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a ][\u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a ] -> [\u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a \u000a ] 6352 +[c][are ] -> [care ] 6353 +[relig][ion] -> [religion] 6354 +[ab][in] -> [abin] 6355 +[sh][e] -> [she] 6356 +[mar][ket] -> [market] 6357 +[aw][ard ] -> [award ] 6358 +[Columb][ia ] -> [Columbia ] 6359 +[19][18] -> [1918] 6360 +[Related pag][es\u000a] -> [Related pages\u000a] 6361 +[sch][ol] -> [schol] 6362 +[me][chan] -> [mechan] 6363 +[al][ready ] -> [already ] 6364 +[il][i] -> [ili] 6365 +[mean][s that ] -> [means that ] 6366 +[reti][r] -> [retir] 6367 +[all][eng] -> [alleng] 6368 +[St][atistic] -> [Statistic] 6369 +[peopl][e, ] -> [people, ] 6370 +[f][er] -> [fer] 6371 +[ed ][(] -> [ed (] 6372 +[bo][y] -> [boy] 6373 +[astr][onom] -> [astronom] 6374 +[10][, ] -> [10, ] 6375 +[T][om ] -> [Tom ] 6376 +[roug][h] -> [rough] 6377 +[Politic][al ] -> [Political ] 6378 +[um][n] -> [umn] 6379 +[M][ajor ] -> [Major ] 6380 +[decid][ed to ] -> [decided to ] 6381 +[15][, ] -> [15, ] 6382 +[U][ ] -> [U ] 6383 +[su][ic] -> [suic] 6384 +[ b][as] -> [ bas] 6385 +[nam][e of ] -> [name of ] 6386 +[M][AS] -> [MAS] 6387 +[T][un] -> [Tun] 6388 +[194][1] -> [1941] 6389 +[T][ub] -> [Tub] 6390 +[3][,] -> [3,] 6391 +[ob][ayashi] -> [obayashi] 6392 +[us][e the ] -> [use the ] 6393 +[og][ ] -> [og ] 6394 +[Gro][up] -> [Group] 6395 +[, 199][4] -> [, 1994] 6396 +[us][ed by ] -> [used by ] 6397 +[Build][ings and structure] -> [Buildings and structure] 6398 +[long][er ] -> [longer ] 6399 +[pol][l] -> [poll] 6400 +[illi][on ] -> [illion ] 6401 +[in ][H] -> [in H] 6402 +[Stan][ley ] -> [Stanley ] 6403 +[Ro][ad ] -> [Road ] 6404 +[c][ov] -> [cov] 6405 +[in][em] -> [inem] 6406 +[r][ic ] -> [ric ] 6407 +[h][und] -> [hund] 6408 +[17][, ] -> [17, ] 6409 +[h][owever, ] -> [however, ] 6410 +[c][i] -> [ci] 6411 +[ing][s] -> [ings] 6412 +[am][i] -> [ami] 6413 +[J][ourn] -> [Journ] 6414 +[serv][ed as ] -> [served as ] 6415 +[sp][read ] -> [spread ] 6416 +[acros][s the ] -> [across the ] 6417 +[Jo][e ] -> [Joe ] 6418 +[ne][y] -> [ney] 6419 +[195][1] -> [1951] 6420 +[al][d] -> [ald] 6421 +[8][2] -> [82] 6422 +[o][ot ] -> [oot ] 6423 +[design][ated ] -> [designated ] 6424 +[tion][\u000a] -> [tion\u000a] 6425 +[Hous][e ] -> [House ] 6426 +[Franc][e, ] -> [France, ] 6427 +[The][at] -> [Theat] 6428 +[ births\u000aLiving people\u000a][American ] -> [ births\u000aLiving people\u000aAmerican ] 6429 +[med][al ] -> [medal ] 6430 +[-][Rh] -> [-Rh] 6431 +[sk][y ] -> [sky ] 6432 +[Wh][at ] -> [What ] 6433 +[Al][ber] -> [Alber] 6434 +[show][s ] -> [shows ] 6435 +[n ][(] -> [n (] 6436 +[v][ia ] -> [via ] 6437 +[C][2] -> [C2] 6438 +[8][1] -> [81] 6439 +[19][, ] -> [19, ] 6440 +[Med][ic] -> [Medic] 6441 +[Con][st] -> [Const] 6442 +[E][ag] -> [Eag] 6443 +[is ][now ] -> [is now ] 6444 +[.\u000a\u000aReferences\u000a\u000aOther websit][es \u000a\u000a ] -> [.\u000a\u000aReferences\u000a\u000aOther websites \u000a\u000a ] 6445 +[Ser][ies ] -> [Series ] 6446 +[K][enn] -> [Kenn] 6447 +[ || T][. K] -> [ || T. K] 6448 +[th][ink] -> [think] 6449 +[.\u000a\u000a][It ] -> [.\u000a\u000aIt ] 6450 +[198][9 ] -> [1989 ] 6451 +[B][ow] -> [Bow] 6452 +[re][, ] -> [re, ] 6453 +[Virgin][ia] -> [Virginia] 6454 +[act][ing ] -> [acting ] 6455 +[a ][from ] -> [a from ] 6456 +[An][d ] -> [And ] 6457 +[ ][\u000a] -> [ \u000a] 6458 +[\u000a|}][\u000a\u000aReferences\u000a\u000a] -> [\u000a|}\u000a\u000aReferences\u000a\u000a] 6459 +[F][ame ] -> [Fame ] 6460 +[B][avar] -> [Bavar] 6461 +[us][e of ] -> [use of ] 6462 +[phot][ograph] -> [photograph] 6463 +[s that ][are ] -> [s that are ] 6464 +[O][ver] -> [Over] 6465 +[Mon][tre] -> [Montre] 6466 +[D][o] -> [Do] 6467 +[well][-] -> [well-] 6468 +[5][1] -> [51] 6469 +[expl][or] -> [explor] 6470 +[event][ually ] -> [eventually ] 6471 +[) ][of ] -> [) of ] 6472 +[tic][s ] -> [tics ] 6473 +[ü][n] -> [ün] 6474 +[p][ers] -> [pers] 6475 +[ord][er to ] -> [order to ] 6476 +[voice ][actors\u000a] -> [voice actors\u000a] 6477 +[m][ass] -> [mass] 6478 +[ab][or] -> [abor] 6479 +[famil][y] -> [family] 6480 +[C][ul] -> [Cul] 6481 +[un][it] -> [unit] 6482 +[Cl][ar] -> [Clar] 6483 +[in][a, ] -> [ina, ] 6484 +[2010][, ] -> [2010, ] 6485 +[electr][ic ] -> [electric ] 6486 +[. A][s of ] -> [. As of ] 6487 +[heal][th ] -> [health ] 6488 +[sid][e of the ] -> [side of the ] 6489 +[ro][b] -> [rob] 6490 +[sc][en] -> [scen] 6491 +[south ][of ] -> [south of ] 6492 +[Mexic][o ] -> [Mexico ] 6493 +[. ][On] -> [. On] 6494 +[occ][er ] -> [occer ] 6495 +[al][s] -> [als] 6496 +[was a ][member of the ] -> [was a member of the ] 6497 +[sex][ ] -> [sex ] 6498 +[||2][9] -> [||29] 6499 +[R][a] -> [Ra] 6500 +[that ][they ] -> [that they ] 6501 +[. It ][also ] -> [. It also ] 6502 +[Chin][a] -> [China] 6503 +[||][18] -> [||18] 6504 +[A][tt] -> [Att] 6505 +[gam][es] -> [games] 6506 +[bo][di] -> [bodi] 6507 +[6][2] -> [62] 6508 +[12][, ] -> [12, ] 6509 +[ic][, ] -> [ic, ] 6510 +[organ][is] -> [organis] 6511 +[of the ][same ] -> [of the same ] 6512 +[hosp][ital ] -> [hospital ] 6513 +[.\u000a\u000a][Early ] -> [.\u000a\u000aEarly ] 6514 +[old][er ] -> [older ] 6515 +[m][ajor] -> [major] 6516 +[st][on ] -> [ston ] 6517 +[m][ph] -> [mph] 6518 +[Y][our ] -> [Your ] 6519 +[Wal][ter ] -> [Walter ] 6520 +[For][mul] -> [Formul] 6521 +[R][y] -> [Ry] 6522 +[stage ][actors\u000a] -> [stage actors\u000a] 6523 +[e][on ] -> [eon ] 6524 +[broad][cast] -> [broadcast] 6525 +[antas][y ] -> [antasy ] 6526 +[poss][ib] -> [possib] 6527 +[s][ociet] -> [societ] 6528 +[n][ine ] -> [nine ] 6529 +[(b. ][192] -> [(b. 192] 6530 +[acc][ept] -> [accept] 6531 +[P][refecture] -> [Prefecture] 6532 +[Ar][t ] -> [Art ] 6533 +[fre][qu] -> [frequ] 6534 +[f][av] -> [fav] 6535 +[Cam][bridg] -> [Cambridg] 6536 +[Observ][atory ] -> [Observatory ] 6537 +[E][st] -> [Est] 6538 +[li][m ] -> [lim ] 6539 +[rail][way ] -> [railway ] 6540 +[e][j] -> [ej] 6541 +[F][ox ] -> [Fox ] 6542 +[ bgcolor=#d6d6d6\u000a| 1][0] -> [ bgcolor=#d6d6d6\u000a| 10] 6543 +[ver][t] -> [vert] 6544 +[g][al] -> [gal] 6545 +[olu][tion ] -> [olution ] 6546 +[T][y] -> [Ty] 6547 +[Ad][ministr] -> [Administr] 6548 +[con][st] -> [const] 6549 +[fir][e ] -> [fire ] 6550 +[Sup][er] -> [Super] 6551 +[ask][et] -> [asket] 6552 +[Track ][list] -> [Track list] 6553 +[�][�] -> [о] 6554 +[–][present] -> [–present] 6555 +[def][in] -> [defin] 6556 +[e][)] -> [e)] 6557 +[u][s of ] -> [us of ] 6558 +[ed ][it ] -> [ed it ] 6559 +[at the ][University of ] -> [at the University of ] 6560 +[member ][of ] -> [member of ] 6561 +[D][e ] -> [De ] 6562 +[20][th ] -> [20th ] 6563 +[. Th][en ] -> [. Then ] 6564 +[S][ea] -> [Sea] 6565 +[: ][A ] -> [: A ] 6566 +[with][in ] -> [within ] 6567 +[dea][d ] -> [dead ] 6568 +[J][ur] -> [Jur] 6569 +[cap][tur] -> [captur] 6570 +[7][1] -> [71] 6571 +[pain][ter] -> [painter] 6572 +[D][al] -> [Dal] 6573 +[people ][were ] -> [people were ] 6574 +[Cour][t ] -> [Court ] 6575 +[s ][was ] -> [s was ] 6576 +[16][, ] -> [16, ] 6577 +[. He was a ][member of the ] -> [. He was a member of the ] 6578 +[S][it] -> [Sit] 6579 +[me][th] -> [meth] 6580 +[J][1 ] -> [J1 ] 6581 +[w][av] -> [wav] 6582 +[198][4 ] -> [1984 ] 6583 +[p][neumon] -> [pneumon] 6584 +[L][ev] -> [Lev] 6585 +[Vide][o ] -> [Video ] 6586 +[S][ong] -> [Song] 6587 +[associ][ation ] -> [association ] 6588 +[al][t] -> [alt] 6589 +[198][6 ] -> [1986 ] 6590 +[ches][ter ] -> [chester ] 6591 +[r][ig] -> [rig] 6592 +[p][rem] -> [prem] 6593 +[Steph][en ] -> [Stephen ] 6594 +[actor][\u000a ] -> [actor\u000a ] 6595 +[er][o ] -> [ero ] 6596 +[s ][who ] -> [s who ] 6597 +[198][1 ] -> [1981 ] 6598 +[tri][b] -> [trib] 6599 +[on][e, ] -> [one, ] 6600 +[k][en ] -> [ken ] 6601 +[tri][but] -> [tribut] 6602 +[n][ative ] -> [native ] 6603 +[Virgin][ia ] -> [Virginia ] 6604 +[death ][in ] -> [death in ] 6605 +[iz][umi] -> [izumi] 6606 +[ref][er ] -> [refer ] 6607 +[Egyp][tian ] -> [Egyptian ] 6608 +[.\u000a][The ] -> [.\u000aThe ] 6609 +[3][00] -> [300] 6610 +[New Zeal][and] -> [New Zealand] 6611 +[ou][th] -> [outh] 6612 +[am][i ] -> [ami ] 6613 +[vil][le] -> [ville] 6614 +[W][ill] -> [Will] 6615 +[ish][, ] -> [ish, ] 6616 +[P][icture] -> [Picture] 6617 +[mol][ec] -> [molec] 6618 +[ag][o ] -> [ago ] 6619 +[V][an ] -> [Van ] 6620 +[c][ot] -> [cot] 6621 +[be][hind ] -> [behind ] 6622 +[science ][fiction ] -> [science fiction ] 6623 +[ing][ton ] -> [ington ] 6624 +[anim][al ] -> [animal ] 6625 +[No][. ] -> [No. ] 6626 +[197][6 ] -> [1976 ] 6627 +[me][ant ] -> [meant ] 6628 +[. \u000a\u000a][In ] -> [. \u000a\u000aIn ] 6629 +[France][.\u000a\u000aReferences \u000a\u000aCommunes in ] -> [France.\u000a\u000aReferences \u000a\u000aCommunes in ] 6630 +[st][yl] -> [styl] 6631 +[mur][der] -> [murder] 6632 +[R][ac] -> [Rac] 6633 +[I][rel] -> [Irel] 6634 +[s][.\u000a\u000aReferences\u000a\u000a] -> [s.\u000a\u000aReferences\u000a\u000a] 6635 +[R][ic] -> [Ric] 6636 +[par][liam] -> [parliam] 6637 +[n][y ] -> [ny ] 6638 +[wro][t] -> [wrot] 6639 +[N][A ] -> [NA ] 6640 +[ed ][out ] -> [ed out ] 6641 +[lik][ely ] -> [likely ] 6642 +[direct][or ] -> [director ] 6643 +[R][on] -> [Ron] 6644 +[b][gcolor=#] -> [bgcolor=#] 6645 +[P][ot] -> [Pot] 6646 +[F][er] -> [Fer] 6647 +[pres][s ] -> [press ] 6648 +[, ]["] -> [, "] 6649 +[Ex][ampl] -> [Exampl] 6650 +[Wor][k] -> [Work] 6651 +[struc][tur] -> [structur] 6652 +[tr][ain ] -> [train ] 6653 +[izumi][ || T. K] -> [izumi || T. K] 6654 +[et ][(] -> [et (] 6655 +[Ven][ez] -> [Venez] 6656 +[193][9] -> [1939] 6657 +[S][l] -> [Sl] 6658 +[. ][Ch] -> [. Ch] 6659 +[es][.\u000a ] -> [es.\u000a ] 6660 +[creat][ed by ] -> [created by ] 6661 +[s in ][a ] -> [s in a ] 6662 +[Wrestl][ing ] -> [Wrestling ] 6663 +[Pol][and] -> [Poland] 6664 +[n][ic ] -> [nic ] 6665 +[return][ed to ] -> [returned to ] 6666 +[Lem][mon ] -> [Lemmon ] 6667 +[G][ab] -> [Gab] 6668 +[Stev][e ] -> [Steve ] 6669 +[eas][on ] -> [eason ] 6670 +[than ][the ] -> [than the ] 6671 +[int][ell] -> [intell] 6672 +[ak][i ] -> [aki ] 6673 +[s, ][including ] -> [s, including ] 6674 +[B][ad] -> [Bad] 6675 +[ti][es ] -> [ties ] 6676 +[198][0s ] -> [1980s ] 6677 +[v][e] -> [ve] 6678 +[d][oub] -> [doub] 6679 +[tod][ay] -> [today] 6680 +[s][un] -> [sun] 6681 +[gre][en ] -> [green ] 6682 +[S][lov] -> [Slov] 6683 +[p][ast] -> [past] 6684 +[B][A] -> [BA] 6685 +[re][t ] -> [ret ] 6686 +[K][o] -> [Ko] 6687 +[similar ][to ] -> [similar to ] 6688 +[L][ind] -> [Lind] 6689 +[nucle][ar ] -> [nuclear ] 6690 +[m][em] -> [mem] 6691 +[An][t] -> [Ant] 6692 +[Tai][w] -> [Taiw] 6693 +[bu][s ] -> [bus ] 6694 +[ishop][ of ] -> [ishop of ] 6695 +[n][early ] -> [nearly ] 6696 +[�][�] -> [�] 6697 +[||2][8] -> [||28] 6698 +[with ][an ] -> [with an ] 6699 +[izumi || T. K][obayashi] -> [izumi || T. Kobayashi] 6700 +[13][, ] -> [13, ] 6701 +[a ][- ] -> [a - ] 6702 +[l][em] -> [lem] 6703 +[Fre][der] -> [Freder] 6704 +[music ][group] -> [music group] 6705 +[ley][ball ] -> [leyball ] 6706 +[et][c] -> [etc] 6707 +[it][ch ] -> [itch ] 6708 +[ric][h] -> [rich] 6709 +[N][AS] -> [NAS] 6710 +[ufact][ur] -> [ufactur] 6711 +[it ][the ] -> [it the ] 6712 +[his ][first ] -> [his first ] 6713 +[T][ony ] -> [Tony ] 6714 +[um][e ] -> [ume ] 6715 +[n][er] -> [ner] 6716 +[, 2000 || Socorro || LINEAR || — || align=right | ][4.] -> [, 2000 || Socorro || LINEAR || — || align=right | 4.] 6717 +[emor][ial ] -> [emorial ] 6718 +[Mexic][o] -> [Mexico] 6719 +[ births\u000a][2022 ] -> [ births\u000a2022 ] 6720 +[econom][ic ] -> [economic ] 6721 +[in ][which ] -> [in which ] 6722 +[Mar][sh] -> [Marsh] 6723 +[ug][by ] -> [ugby ] 6724 +[K][az] -> [Kaz] 6725 +[M][ot] -> [Mot] 6726 +[priv][ate ] -> [private ] 6727 +[2020][) was a ] -> [2020) was a ] 6728 +[ri][e ] -> [rie ] 6729 +[it ][of ] -> [it of ] 6730 +[net][work ] -> [network ] 6731 +[cap][ital] -> [capital] 6732 +[who ][is ] -> [who is ] 6733 +[B][ut] -> [But] 6734 +[com][un] -> [comun] 6735 +[y][our ] -> [your ] 6736 +[Mount ][Lemmon ] -> [Mount Lemmon ] 6737 +[e. ][\u000a\u000a] -> [e. \u000a\u000a] 6738 +[pos][itive ] -> [positive ] 6739 +[squ][are ] -> [square ] 6740 +[y][n ] -> [yn ] 6741 +[||][17] -> [||17] 6742 +[should ][be ] -> [should be ] 6743 +[num][ber] -> [number] 6744 +[cent][er] -> [center] 6745 +[drama ][movie directed by ] -> [drama movie directed by ] 6746 +[cri][p] -> [crip] 6747 +[nic][kn] -> [nickn] 6748 +[ed ][an ] -> [ed an ] 6749 +[O][reg] -> [Oreg] 6750 +[trav][el] -> [travel] 6751 +[H][amp] -> [Hamp] 6752 +[condi][tion] -> [condition] 6753 +[x][im] -> [xim] 6754 +[oth][ers] -> [others] 6755 +[execu][tive ] -> [executive ] 6756 +[al][tern] -> [altern] 6757 +[lin][k] -> [link] 6758 +[, ][P] -> [, P] 6759 +[at][a ] -> [ata ] 6760 +[n][a ] -> [na ] 6761 +[usiness][people from ] -> [usinesspeople from ] 6762 +[H][Y] -> [HY] 6763 +[jaz][z ] -> [jazz ] 6764 +[pr][on] -> [pron] 6765 +[ births\u000a][2019 ] -> [ births\u000a2019 ] 6766 +[inst][ead of ] -> [instead of ] 6767 +[writ][er, ] -> [writer, ] 6768 +[es][s of ] -> [ess of ] 6769 +[s][av] -> [sav] 6770 +['s ][first ] -> ['s first ] 6771 +[201][1, ] -> [2011, ] 6772 +[2][ bgcolor=#fefefe\u000a| ] -> [2 bgcolor=#fefefe\u000a| ] 6773 +[u][v] -> [uv] 6774 +[s][)] -> [s)] 6775 +[d][el ] -> [del ] 6776 +[�][�] -> [ū] 6777 +[Ser][vic] -> [Servic] 6778 +[T][o] -> [To] 6779 +[websit][e ] -> [website ] 6780 +[vil][le, ] -> [ville, ] 6781 +[c][ivil] -> [civil] 6782 +[Dou][gl] -> [Dougl] 6783 +[land ][and ] -> [land and ] 6784 +[ach][iev] -> [achiev] 6785 +[le][ar] -> [lear] 6786 +[ad][ult ] -> [adult ] 6787 +[exc][ept ] -> [except ] 6788 +[ing ][that ] -> [ing that ] 6789 +[means ]["] -> [means "] 6790 +[Arth][ur ] -> [Arthur ] 6791 +[Cor][por] -> [Corpor] 6792 +[U][p ] -> [Up ] 6793 +[norm][al ] -> [normal ] 6794 +[Radi][o ] -> [Radio ] 6795 +[national ][football ] -> [national football ] 6796 +[g][as ] -> [gas ] 6797 +[ation ][for ] -> [ation for ] 6798 +[St][and] -> [Stand] 6799 +[ight][s ] -> [ights ] 6800 +[T][ar] -> [Tar] 6801 +[exper][im] -> [experim] 6802 +[Lif][e ] -> [Life ] 6803 +[L][ew] -> [Lew] 6804 +[W][e ] -> [We ] 6805 +[acc][ident] -> [accident] 6806 +[Award ][winning ] -> [Award winning ] 6807 +[an][n ] -> [ann ] 6808 +[, 2001 || Socorro || LINEAR || — || align=right | ][3.] -> [, 2001 || Socorro || LINEAR || — || align=right | 3.] 6809 +[Irel][and ] -> [Ireland ] 6810 +[r][h] -> [rh] 6811 +[14][, ] -> [14, ] 6812 +[20][, ] -> [20, ] 6813 +[cre][d] -> [cred] 6814 +[lead][ing ] -> [leading ] 6815 +[k][a ] -> [ka ] 6816 +[as][c] -> [asc] 6817 +[M][ik] -> [Mik] 6818 +[ || ][ || September ] -> [ || || September ] 6819 +[rel][ativ] -> [relativ] 6820 +[on][ic] -> [onic] 6821 +[D][.C] -> [D.C] 6822 +[Cong][res] -> [Congres] 6823 +[A][ut] -> [Aut] 6824 +[ograph][y\u000a\u000a] -> [ography\u000a\u000a] 6825 +[vill][ag] -> [villag] 6826 +[Be][at] -> [Beat] 6827 +[L][ord ] -> [Lord ] 6828 +[kn][own] -> [known] 6829 +[D][oub] -> [Doub] 6830 +[) is a ][former ] -> [) is a former ] 6831 +[histor][y] -> [history] 6832 +[wh][ole ] -> [whole ] 6833 +[h][ood ] -> [hood ] 6834 +[ent ][of ] -> [ent of ] 6835 +[7][-] -> [7-] 6836 +[Formul][a ] -> [Formula ] 6837 +[Sou][th] -> [South] 6838 +[John][son ] -> [Johnson ] 6839 +[if ][the ] -> [if the ] 6840 +[ind][ic] -> [indic] 6841 +[4][1] -> [41] 6842 +[spec][ial] -> [special] 6843 +[s][. This ] -> [s. This ] 6844 +[if][i] -> [ifi] 6845 +[g][ard] -> [gard] 6846 +[J][ean] -> [Jean] 6847 +[Hall of ][Fame ] -> [Hall of Fame ] 6848 +[b][ott] -> [bott] 6849 +[b][en] -> [ben] 6850 +[N][ight ] -> [Night ] 6851 +[In][f] -> [Inf] 6852 +[Fl][ight ] -> [Flight ] 6853 +[Br][uc] -> [Bruc] 6854 +[p][ay ] -> [pay ] 6855 +[e (][born ] -> [e (born ] 6856 +[ b][l] -> [ bl] 6857 +[hor][ror ] -> [horror ] 6858 +[Dev][elop] -> [Develop] 6859 +[chang][ed ] -> [changed ] 6860 +[lead][er of the ] -> [leader of the ] 6861 +[Law][r] -> [Lawr] 6862 +[G][ood] -> [Good] 6863 +[wif][e ] -> [wife ] 6864 +[pro][pos] -> [propos] 6865 +[made ][up ] -> [made up ] 6866 +[s][et] -> [set] 6867 +[ births\u000a201][7 ] -> [ births\u000a2017 ] 6868 +[South ][Wal] -> [South Wal] 6869 +[organiz][ation] -> [organization] 6870 +[d][rop] -> [drop] 6871 +[ing ][is ] -> [ing is ] 6872 +[B][on] -> [Bon] 6873 +[at][o ] -> [ato ] 6874 +[el][ (] -> [el (] 6875 +[L][aur] -> [Laur] 6876 +[.\u000a\u000aRelated pag][es\u000a ] -> [.\u000a\u000aRelated pages\u000a ] 6877 +[most ][famous ] -> [most famous ] 6878 +[giv][es ] -> [gives ] 6879 +[people ][in ] -> [people in ] 6880 +[Im][per] -> [Imper] 6881 +[i][o] -> [io] 6882 +[buil][d ] -> [build ] 6883 +[197][2 ] -> [1972 ] 6884 +[193][4] -> [1934] 6885 +[ch][ampion ] -> [champion ] 6886 +[fin][anc] -> [financ] 6887 +[Leagu][e (] -> [League (] 6888 +[mak][e the ] -> [make the ] 6889 +[a][qu] -> [aqu] 6890 +[l][es, ] -> [les, ] 6891 +[ian][t ] -> [iant ] 6892 +[al][ph] -> [alph] 6893 +[high][er ] -> [higher ] 6894 +[Er][n] -> [Ern] 6895 +[An][gl] -> [Angl] 6896 +[201][6, ] -> [2016, ] 6897 +[u][an] -> [uan] 6898 +[K][ash] -> [Kash] 6899 +[J][im ] -> [Jim ] 6900 +[HY][G] -> [HYG] 6901 +[. He ][is the ] -> [. He is the ] 6902 +[also ][called ] -> [also called ] 6903 +[mo][ther] -> [mother] 6904 +[198][2 ] -> [1982 ] 6905 +[Pl][ac] -> [Plac] 6906 +[N][ative ] -> [Native ] 6907 +[\u000a][American ] -> [\u000aAmerican ] 6908 +[�][�] -> [и] 6909 +[st][ay] -> [stay] 6910 +[mar][k ] -> [mark ] 6911 +[Lond][on, ] -> [London, ] 6912 +[fif][th ] -> [fifth ] 6913 +[Scot][t ] -> [Scott ] 6914 +[Inter][net ] -> [Internet ] 6915 +[quick][ly ] -> [quickly ] 6916 +[Kor][e] -> [Kore] 6917 +[com][ment] -> [comment] 6918 +[cl][ass ] -> [class ] 6919 +[Sm][ith] -> [Smith] 6920 +[prov][ince ] -> [province ] 6921 +[st][and ] -> [stand ] 6922 +[Lin][col] -> [Lincol] 6923 +[D][un] -> [Dun] 6924 +[in the ][canton of ] -> [in the canton of ] 6925 +[�][�] -> [�] 6926 +[iti][es] -> [ities] 6927 +[Y][am] -> [Yam] 6928 +[stand][ing ] -> [standing ] 6929 +[es ][have ] -> [es have ] 6930 +[sit][u] -> [situ] 6931 +[Afg][han] -> [Afghan] 6932 +[B][ay ] -> [Bay ] 6933 +[K][ing of ] -> [King of ] 6934 +[Ch][ap] -> [Chap] 6935 +[Christ][m] -> [Christm] 6936 +[runn][ing ] -> [running ] 6937 +[Sup][reme ] -> [Supreme ] 6938 +[Dani][el ] -> [Daniel ] 6939 +[G][ame ] -> [Game ] 6940 +[, 1998][ || ] -> [, 1998 || ] 6941 +[con][firm] -> [confirm] 6942 +[enti][re ] -> [entire ] 6943 +[N][ad] -> [Nad] 6944 +[actres][s and ] -> [actress and ] 6945 +[Sp][ace ] -> [Space ] 6946 +[i][or] -> [ior] 6947 +[on][y] -> [ony] 6948 +[tion ][and ] -> [tion and ] 6949 +[Bri][tish] -> [British] 6950 +[actor ][(] -> [actor (] 6951 +[H][un] -> [Hun] 6952 +[s][" ] -> [s" ] 6953 +[]][ ] -> [] ] 6954 +[Hum][an ] -> [Human ] 6955 +[c][all ] -> [call ] 6956 +[N][ations ] -> [Nations ] 6957 +[D][am] -> [Dam] 6958 +[||2][\u000a|-\u000a|200] -> [||2\u000a|-\u000a|200] 6959 +[Sport][s ] -> [Sports ] 6960 +[Her][itage ] -> [Heritage ] 6961 +[gen][e] -> [gene] 6962 +[es ][as ] -> [es as ] 6963 +[st][ra] -> [stra] 6964 +[Emp][ire ] -> [Empire ] 6965 +[B][ak] -> [Bak] 6966 +[rit][ory ] -> [ritory ] 6967 +[en][e] -> [ene] 6968 +[previ][ous ] -> [previous ] 6969 +[con][serv] -> [conserv] 6970 +[d][id] -> [did] 6971 +[compet][ed at the ] -> [competed at the ] 6972 +[Qu][ebec] -> [Quebec] 6973 +[Buildings and structure][s in ] -> [Buildings and structures in ] 6974 +[recogn][iz] -> [recogniz] 6975 +[D][own] -> [Down] 6976 +[they ][have ] -> [they have ] 6977 +[o][il ] -> [oil ] 6978 +[defe][ated ] -> [defeated ] 6979 +[. It is ][in the ] -> [. It is in the ] 6980 +[liv][ed in ] -> [lived in ] 6981 +[v][ar] -> [var] 6982 +[by ][a ] -> [by a ] 6983 +[s][), ] -> [s), ] 6984 +[d][re] -> [dre] 6985 +[.\u000a\u000aReferences\u000a\u000a][Cities in ] -> [.\u000a\u000aReferences\u000a\u000aCities in ] 6986 +[li][br] -> [libr] 6987 +[wom][en] -> [women] 6988 +[u][y] -> [uy] 6989 +[Afghan][ist] -> [Afghanist] 6990 +[T][op ] -> [Top ] 6991 +[�][�] -> [ł] 6992 +[di][al] -> [dial] 6993 +[incorpor][ated ] -> [incorporated ] 6994 +[Vall][ey ] -> [Valley ] 6995 +[on][c] -> [onc] 6996 +[t][ool] -> [tool] 6997 +[experi][enc] -> [experienc] 6998 +[ox][id] -> [oxid] 6999 +[1][ bgcolor=#fefefe\u000a| ] -> [1 bgcolor=#fefefe\u000a| ] 7000 +[s\u000a][The ] -> [s\u000aThe ] 7001 +[S][atur] -> [Satur] 7002 +[||2][7] -> [||27] 7003 +[ur][g] -> [urg] 7004 +[son]['s ] -> [son's ] 7005 +[earth][qu] -> [earthqu] 7006 +[Ukrain][ian ] -> [Ukrainian ] 7007 +[est][in] -> [estin] 7008 +[dom][in] -> [domin] 7009 +[m][iddle ] -> [middle ] 7010 +[M][aur] -> [Maur] 7011 +[v][all] -> [vall] 7012 +[bor][d] -> [bord] 7013 +[O][d] -> [Od] 7014 +[u][tion] -> [ution] 7015 +[phys][ical ] -> [physical ] 7016 +[Met][ropolitan ] -> [Metropolitan ] 7017 +[on][z] -> [onz] 7018 +[some ][of the ] -> [some of the ] 7019 +[3][rd ] -> [3rd ] 7020 +[ers][\u000a\u000a] -> [ers\u000a\u000a] 7021 +[J][ord] -> [Jord] 7022 +[N][FL] -> [NFL] 7023 +[t][a ] -> [ta ] 7024 +[193][6] -> [1936] 7025 +[Ar][n] -> [Arn] 7026 +[re][turn ] -> [return ] 7027 +[arr][ang] -> [arrang] 7028 +[Hol][y ] -> [Holy ] 7029 +[Nor][way] -> [Norway] 7030 +[Cent][ury ] -> [Century ] 7031 +[ing][\u000a ] -> [ing\u000a ] 7032 +[char][act] -> [charact] 7033 +[\u000a][K] -> [\u000aK] 7034 +[par][k] -> [park] 7035 +[ab][ ] -> [ab ] 7036 +[con][fl] -> [confl] 7037 +[-][B] -> [-B] 7038 +[193][8] -> [1938] 7039 +[compl][ication] -> [complication] 7040 +[Chin][a ] -> [China ] 7041 +[ers ][in the ] -> [ers in the ] 7042 +[t][elevision] -> [television] 7043 +[c][ust] -> [cust] 7044 +[al][p] -> [alp] 7045 +[al][um] -> [alum] 7046 +[Sc][re] -> [Scre] 7047 +[cri][m] -> [crim] 7048 +[An][dr] -> [Andr] 7049 +[sur][fac] -> [surfac] 7050 +[ers][' ] -> [ers' ] 7051 +[L][angu] -> [Langu] 7052 +[V][ice ] -> [Vice ] 7053 +[people lived ther][e.\u000a\u000a] -> [people lived there.\u000a\u000a] 7054 +[Sant][a ] -> [Santa ] 7055 +[á][n] -> [án] 7056 +[.\u000a\u000aReferences\u000a\u000aOther websit][es \u000a\u000a] -> [.\u000a\u000aReferences\u000a\u000aOther websites \u000a\u000a] 7057 +[act][ually ] -> [actually ] 7058 +[stand][ard ] -> [standard ] 7059 +[, ][G] -> [, G] 7060 +[death][s] -> [deaths] 7061 +[d][u ] -> [du ] 7062 +[19][19] -> [1919] 7063 +[stor][m] -> [storm] 7064 +[em][erg] -> [emerg] 7065 +[cal][end] -> [calend] 7066 +[par][k ] -> [park ] 7067 +[es][ter ] -> [ester ] 7068 +[h][ere ] -> [here ] 7069 +[city ][in the ] -> [city in the ] 7070 +[is ][often ] -> [is often ] 7071 +[ir][o ] -> [iro ] 7072 +[Rev][olution] -> [Revolution] 7073 +[up ][in ] -> [up in ] 7074 +[er][o] -> [ero] 7075 +[sil][ver ] -> [silver ] 7076 +[ b][efore ] -> [ before ] 7077 +[li][qu] -> [liqu] 7078 +[go][ing ] -> [going ] 7079 +[Br][ad] -> [Brad] 7080 +[municipal][ity of ] -> [municipality of ] 7081 +[ular][ly ] -> [ularly ] 7082 +[C][op] -> [Cop] 7083 +[ate ][the ] -> [ate the ] 7084 +[toge][ther] -> [together] 7085 +[agn][os] -> [agnos] 7086 +[develop][ment ] -> [development ] 7087 +[E][P] -> [EP] 7088 +[M][oh] -> [Moh] 7089 +[ births\u000a2021 ][deaths\u000a] -> [ births\u000a2021 deaths\u000a] 7090 +[made ][the ] -> [made the ] 7091 +[ers][. 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L] -> [St. L] 7117 +[Play][Station ] -> [PlayStation ] 7118 +[P][itt] -> [Pitt] 7119 +[od][e ] -> [ode ] 7120 +[P][re] -> [Pre] 7121 +[\u000a ][V] -> [\u000a V] 7122 +[an-][American ] -> [an-American ] 7123 +[compan][ies ] -> [companies ] 7124 +[d][en ] -> [den ] 7125 +[larg][er ] -> [larger ] 7126 +[||3][4] -> [||34] 7127 +[cont][rol] -> [control] 7128 +[bl][ack] -> [black] 7129 +[M][ak] -> [Mak] 7130 +[. They ][have ] -> [. They have ] 7131 +[)\u000a ][The ] -> [)\u000a The ] 7132 +[tak][ing ] -> [taking ] 7133 +[193][5] -> [1935] 7134 +[a P][refecture] -> [a Prefecture] 7135 +[c][ut ] -> [cut ] 7136 +[||3][2] -> [||32] 7137 +[national ][team ] -> [national team ] 7138 +[s to ][be ] -> [s to be ] 7139 +[J][enn] -> [Jenn] 7140 +[sing][er, ] -> [singer, ] 7141 +[o][en] -> [oen] 7142 +[f][li] -> [fli] 7143 +[Russ][ia ] -> [Russia ] 7144 +[18][, ] -> [18, ] 7145 +[euten][ant ] -> [eutenant ] 7146 +[R][ol] -> [Rol] 7147 +[, 2000][ || Anderson Mesa || LONEOS] -> [, 2000 || Anderson Mesa || LONEOS] 7148 +[p][ay] -> [pay] 7149 +[to ][become ] -> [to become ] 7150 +[anti][-] -> [anti-] 7151 +[was ][born on ] -> [was born on ] 7152 +[N][ar] -> [Nar] 7153 +[great][est ] -> [greatest ] 7154 +[t][aught ] -> [taught ] 7155 +[Mus][lim ] -> [Muslim ] 7156 +[Californ][ia, ] -> [California, ] 7157 +[ent][ertain] -> [entertain] 7158 +[Entertain][ment ] -> [Entertainment ] 7159 +[il][d ] -> [ild ] 7160 +[Sm][ith ] -> [Smith ] 7161 +[k][er ] -> [ker ] 7162 +[k][i] -> [ki] 7163 +[ k][m ] -> [ km ] 7164 +[I][s ] -> [Is ] 7165 +[th-century ][establishments in ] -> [th-century establishments in ] 7166 +[s, ][a ] -> [s, a ] 7167 +[y][g] -> [yg] 7168 +[193][3] -> [1933] 7169 +[||][20] -> [||20] 7170 +[lat][er, ] -> [later, ] 7171 +[publish][ed ] -> [published ] 7172 +[V][ic] -> [Vic] 7173 +[ing][u] -> [ingu] 7174 +[||2][2] -> [||22] 7175 +[St][ra] -> [Stra] 7176 +[�][�] -> [ı] 7177 +[Venez][uel] -> [Venezuel] 7178 +[S][t ] -> [St ] 7179 +[. 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She also ] 7180 +[b][ri] -> [bri] 7181 +[C][er] -> [Cer] 7182 +[is ][and ] -> [is and ] 7183 +[Ber][lin] -> [Berlin] 7184 +[ing][, and ] -> [ing, and ] 7185 +[eg][r] -> [egr] 7186 +[G][h] -> [Gh] 7187 +[fl][ag] -> [flag] 7188 +[mov][ed ] -> [moved ] 7189 +[Bund][eslig] -> [Bundeslig] 7190 +[..][.] -> [...] 7191 +[f][ast ] -> [fast ] 7192 +[prot][ect ] -> [protect ] 7193 +[Br][un] -> [Brun] 7194 +[Canc][er ] -> [Cancer ] 7195 +[sugg][est] -> [suggest] 7196 +[Ch][am] -> [Cham] 7197 +[mark][et ] -> [market ] 7198 +[not ][be ] -> [not be ] 7199 +[t][ell ] -> [tell ] 7200 +[um][, ] -> [um, ] 7201 +[J][as] -> [Jas] 7202 +[colleg][e ] -> [college ] 7203 +[.\u000a\u000aReferences\u000a\u000aOther websites\u000a ][\u000a\u000a] -> [.\u000a\u000aReferences\u000a\u000aOther websites\u000a \u000a\u000a] 7204 +[small][er ] -> [smaller ] 7205 +[Spec][ial ] -> [Special ] 7206 +[Barcel][on] -> [Barcelon] 7207 +[ip][ur] -> [ipur] 7208 +[Z][h] -> [Zh] 7209 +[s, ][or ] -> [s, or ] 7210 +[�][�] -> [ß] 7211 +[s][\u000a\u000aReferences\u000a\u000a] -> [s\u000a\u000aReferences\u000a\u000a] 7212 +[simpl][y ] -> [simply ] 7213 +[t][ub] -> [tub] 7214 +[l][ic] -> [lic] 7215 +[asket][ball ] -> [asketball ] 7216 +[s ][for the ] -> [s for the ] 7217 +[i][tion] -> [ition] 7218 +[u][s and ] -> [us and ] 7219 +[he ][is ] -> [he is ] 7220 +[ag][on ] -> [agon ] 7221 +[S][um] -> [Sum] 7222 +[y ][of the ] -> [y of the ] 7223 +[h][urricane ] -> [hurricane ] 7224 +[w][r] -> [wr] 7225 +[produc][e ] -> [produce ] 7226 +[th][en] -> [then] 7227 +[. W][ith ] -> [. With ] 7228 +[19][00] -> [1900] 7229 +[referr][ed to ] -> [referred to ] 7230 +[F][ol] -> [Fol] 7231 +[began ][to ] -> [began to ] 7232 +[7][ bgcolor=#fefefe\u000a| ] -> [7 bgcolor=#fefefe\u000a| ] 7233 +[ing ][with ] -> [ing with ] 7234 +[net][work] -> [network] 7235 +[Tim][e ] -> [Time ] 7236 +[on]['s ] -> [on's ] 7237 +[H][ay] -> [Hay] 7238 +[-d][ay ] -> [-day ] 7239 +[tal][k ] -> [talk ] 7240 +[Ad][vent] -> [Advent] 7241 +[O][izumi || T. Kobayashi] -> [Oizumi || T. Kobayashi] 7242 +[Al][bert ] -> [Albert ] 7243 +[at ][his ] -> [at his ] 7244 +[K][a] -> [Ka] 7245 +[J][ean ] -> [Jean ] 7246 +[W][ild] -> [Wild] 7247 +[can also ][be ] -> [can also be ] 7248 +[pro][ject] -> [project] 7249 +[former][ly ] -> [formerly ] 7250 +[\u000a][People from ] -> [\u000aPeople from ] 7251 +[caus][ed ] -> [caused ] 7252 +[I][ce ] -> [Ice ] 7253 +[B][a] -> [Ba] 7254 +[cros][s] -> [cross] 7255 +[it ][to ] -> [it to ] 7256 +[ ][in the ] -> [ in the ] 7257 +[at][o] -> [ato] 7258 +[a member ][of the ] -> [a member of the ] 7259 +[Pl][an] -> [Plan] 7260 +[eau][ti] -> [eauti] 7261 +[, 2002 || Socorro || LINEAR || — || align=right | ][1.] -> [, 2002 || Socorro || LINEAR || — || align=right | 1.] 7262 +[S][an] -> [San] 7263 +[res][earch] -> [research] 7264 +[finish][ed ] -> [finished ] 7265 +[Ter][ritor] -> [Territor] 7266 +[ly][ric] -> [lyric] 7267 +[197][5 ] -> [1975 ] 7268 +[B][ob ] -> [Bob ] 7269 +[in ][L] -> [in L] 7270 +[whe][ther ] -> [whether ] 7271 +[Other websit][es\u000a\u000a] -> [Other websites\u000a\u000a] 7272 +[j][an ] -> [jan ] 7273 +[li][ber] -> [liber] 7274 +[appe][ar ] -> [appear ] 7275 +[COVID-][19] -> [COVID-19] 7276 +[ion][s ] -> [ions ] 7277 +[is ][to ] -> [is to ] 7278 +[Colleg][e ] -> [College ] 7279 +[197][0 ] -> [1970 ] 7280 +[sol][v] -> [solv] 7281 +[V][is] -> [Vis] 7282 +[anc][e of ] -> [ance of ] 7283 +[chem][ist] -> [chemist] 7284 +[Mus][lim] -> [Muslim] 7285 +[on ][September ] -> [on September ] 7286 +[p][ic] -> [pic] 7287 +[Sh][ar] -> [Shar] 7288 +[Observatory ][|| ] -> [Observatory || ] 7289 +[anim][als ] -> [animals ] 7290 +[c][it] -> [cit] 7291 +[in][spir] -> [inspir] 7292 +[cat][eg] -> [categ] 7293 +[, ][R] -> [, R] 7294 +[al][g] -> [alg] 7295 +[ug][u] -> [ugu] 7296 +[d][ynast] -> [dynast] 7297 +[t][ennis ] -> [tennis ] 7298 +[mid][-] -> [mid-] 7299 +[ent][er] -> [enter] 7300 +[meas][ur] -> [measur] 7301 +[198][3 ] -> [1983 ] 7302 +[m][ess] -> [mess] 7303 +[forc][es ] -> [forces ] 7304 +[w][ing] -> [wing] 7305 +[||1][\u000a|-\u000a|] -> [||1\u000a|-\u000a|] 7306 +[C][level] -> [Clevel] 7307 +[Christ][opher ] -> [Christopher ] 7308 +[y][z] -> [yz] 7309 +[pain][ter ] -> [painter ] 7310 +[All][-] -> [All-] 7311 +[\u000a\u000a][|-] -> [\u000a\u000a|-] 7312 +[o][il] -> [oil] 7313 +[d][es ] -> [des ] 7314 +[s][i] -> [si] 7315 +[le ][and ] -> [le and ] 7316 +[ra][z] -> [raz] 7317 +[Award for ][Best ] -> [Award for Best ] 7318 +[for][eign ] -> [foreign ] 7319 +[.\u000a\u000a][Geograph] -> [.\u000a\u000aGeograph] 7320 +[wid][ely ] -> [widely ] 7321 +[inv][estig] -> [investig] 7322 +[e][en ] -> [een ] 7323 +[198][7 ] -> [1987 ] 7324 +[s][; ] -> [s; ] 7325 +[3][ bgcolor=#fefefe\u000a| ] -> [3 bgcolor=#fefefe\u000a| ] 7326 +[h][r] -> [hr] 7327 +[H][ome ] -> [Home ] 7328 +[Mar][k] -> [Mark] 7329 +[19][10] -> [1910] 7330 +[28][, ] -> [28, ] 7331 +[Bost][on ] -> [Boston ] 7332 +[Ev][ent] -> [Event] 7333 +[D][V] -> [DV] 7334 +[. ][\u000a\u000a] -> [. \u000a\u000a] 7335 +[Chicag][o] -> [Chicago] 7336 +[u][ (] -> [u (] 7337 +[most ][of ] -> [most of ] 7338 +[ec][tion of ] -> [ection of ] 7339 +[Sh][er] -> [Sher] 7340 +[25][, ] -> [25, ] 7341 +[at][s ] -> [ats ] 7342 +[Bri][an ] -> [Brian ] 7343 +[e][), ] -> [e), ] 7344 +[ || — || ][September ] -> [ || — || September ] 7345 +[K][an] -> [Kan] 7346 +[s][. 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It was ] 7347 +[Toky][o ] -> [Tokyo ] 7348 +[daughter ][of ] -> [daughter of ] 7349 +[||3][1] -> [||31] 7350 +[Championship][ (] -> [Championship (] 7351 +[s.\u000a\u000a][In ] -> [s.\u000a\u000aIn ] 7352 +[K][id] -> [Kid] 7353 +[it ][in ] -> [it in ] 7354 +[O][w] -> [Ow] 7355 +[�][�] -> [č] 7356 +[H][ong K] -> [Hong K] 7357 +[ated ][with ] -> [ated with ] 7358 +[W][y] -> [Wy] 7359 +[advent][ure ] -> [adventure ] 7360 +[Hol][ly] -> [Holly] 7361 +[S][oci] -> [Soci] 7362 +[Americ][an] -> [American] 7363 +[, 199][2] -> [, 1992] 7364 +[C][ost] -> [Cost] 7365 +[plac][es ] -> [places ] 7366 +[many ][different ] -> [many different ] 7367 +[Mi][ke ] -> [Mike ] 7368 +[lov][e ] -> [love ] 7369 +[way ][to ] -> [way to ] 7370 +[h][op] -> [hop] 7371 +[tr][ue ] -> [true ] 7372 +[H][ou] -> [Hou] 7373 +[os][lav] -> [oslav] 7374 +[S][é] -> [Sé] 7375 +[up ][the ] -> [up the ] 7376 +[er, ][American ] -> [er, American ] 7377 +[3][1, ] -> [31, ] 7378 +[al][i ] -> [ali ] 7379 +[K][el] -> [Kel] 7380 +[bre][ak ] -> [break ] 7381 +[world]['s ] -> [world's ] 7382 +[Comp][any ] -> [Company ] 7383 +[Arab][ ] -> [Arab ] 7384 +[St][ock] -> [Stock] 7385 +[k][ept ] -> [kept ] 7386 +[people lived ther][e] -> [people lived there] 7387 +[, ][T] -> [, T] 7388 +[-][b] -> [-b] 7389 +[V][eg] -> [Veg] 7390 +[H][ind] -> [Hind] 7391 +[nor][theast ] -> [northeast ] 7392 +[owev][er ] -> [owever ] 7393 +[ch][ief ] -> [chief ] 7394 +[olog][ist] -> [ologist] 7395 +[Mar][ie ] -> [Marie ] 7396 +[con][qu] -> [conqu] 7397 +[sign][ific] -> [signific] 7398 +[at][ell] -> [atell] 7399 +[ary ][of the ] -> [ary of the ] 7400 +[ill][ustr] -> [illustr] 7401 +[. She ][is ] -> [. She is ] 7402 +[ro][les ] -> [roles ] 7403 +[cour][t ] -> [court ] 7404 +[previ][ously ] -> [previously ] 7405 +[was ][in ] -> [was in ] 7406 +[201][2, ] -> [2012, ] 7407 +[enc][e of ] -> [ence of ] 7408 +[S][und] -> [Sund] 7409 +[ar ][and ] -> [ar and ] 7410 +[Har][v] -> [Harv] 7411 +[, which ][is ] -> [, which is ] 7412 +[N][ick] -> [Nick] 7413 +[||2][5] -> [||25] 7414 +[arrondiss][ement of ] -> [arrondissement of ] 7415 +[Pakist][an ] -> [Pakistan ] 7416 +[193][7] -> [1937] 7417 +[School][ ] -> [School ] 7418 +[em][bl] -> [embl] 7419 +[spe][ak ] -> [speak ] 7420 +[ro][ut] -> [rout] 7421 +[s][.\u000a\u000aReferences \u000a\u000a] -> [s.\u000a\u000aReferences \u000a\u000a] 7422 +[Brit][ain] -> [Britain] 7423 +[ation ][is ] -> [ation is ] 7424 +[f][ash] -> [fash] 7425 +[ax][im] -> [axim] 7426 +[is a ][former ] -> [is a former ] 7427 +[M][and] -> [Mand] 7428 +[spok][en ] -> [spoken ] 7429 +[You][Tub] -> [YouTub] 7430 +[writ][e ] -> [write ] 7431 +[typ][e ] -> [type ] 7432 +[Ital][y ] -> [Italy ] 7433 +[wh][ich] -> [which] 7434 +[�][�] -> [е] 7435 +[ress][ion ] -> [ression ] 7436 +[L][am] -> [Lam] 7437 +[g][etting ] -> [getting ] 7438 +[0][0 ] -> [00 ] 7439 +[Mount][ain] -> [Mountain] 7440 +[F][all] -> [Fall] 7441 +[s. ][B] -> [s. B] 7442 +[T][HM] -> [THM] 7443 +[2][-] -> [2-] 7444 +[oper][a ] -> [opera ] 7445 +[e][.\u000a] -> [e.\u000a] 7446 +[ion][, ] -> [ion, ] 7447 +[V][ers] -> [Vers] 7448 +[S][ocial ] -> [Social ] 7449 +[Res][earch ] -> [Research ] 7450 +[k][i ] -> [ki ] 7451 +[A][is] -> [Ais] 7452 +[version ][of ] -> [version of ] 7453 +[6][0 ] -> [60 ] 7454 +[aircraf][t ] -> [aircraft ] 7455 +[u][ch ] -> [uch ] 7456 +[sur][e ] -> [sure ] 7457 +[E][ach ] -> [Each ] 7458 +[Aff][air] -> [Affair] 7459 +[go][al ] -> [goal ] 7460 +[Haw][ai] -> [Hawai] 7461 +[s ][which ] -> [s which ] 7462 +[)\u000a][The ] -> [)\u000aThe ] 7463 +[bel][ong] -> [belong] 7464 +[ard][o ] -> [ardo ] 7465 +[spec][ies] -> [species] 7466 +[NH][L ] -> [NHL ] 7467 +[en][th ] -> [enth ] 7468 +[Ev][en ] -> [Even ] 7469 +[for ][exampl] -> [for exampl] 7470 +[w][ear] -> [wear] 7471 +[r][y, ] -> [ry, ] 7472 +[Fro][g] -> [Frog] 7473 +[ab][ov] -> [abov] 7474 +[Dis][eas] -> [Diseas] 7475 +[O][f ] -> [Of ] 7476 +[s ][|| ] -> [s || ] 7477 +[ass][in] -> [assin] 7478 +[em][on] -> [emon] 7479 +[er ][who ] -> [er who ] 7480 +[t][all] -> [tall] 7481 +[org][an ] -> [organ ] 7482 +[im][prov] -> [improv] 7483 +[whil][e the ] -> [while the ] 7484 +[pl][ane ] -> [plane ] 7485 +[Andre][w ] -> [Andrew ] 7486 +[ed][.\u000a ] -> [ed.\u000a ] 7487 +[iding ][Spring] -> [iding Spring] 7488 +[s, ][such as ] -> [s, such as ] 7489 +[sing][er\u000a ] -> [singer\u000a ] 7490 +[L][is] -> [Lis] 7491 +[e, ][which ] -> [e, which ] 7492 +[fur][ther ] -> [further ] 7493 +[are ][not ] -> [are not ] 7494 +[me][thod] -> [method] 7495 +[.\u000a\u000aOther websit][es\u000a\u000a] -> [.\u000a\u000aOther websites\u000a\u000a] 7496 +[V][ik] -> [Vik] 7497 +[diffic][ult ] -> [difficult ] 7498 +[Pennsylvan][ia] -> [Pennsylvania] 7499 +[I][T] -> [IT] 7500 +[26][, ] -> [26, ] 7501 +[canc][er ] -> [cancer ] 7502 +[in][ste] -> [inste] 7503 +[a ][de ] -> [a de ] 7504 +[ || S][iding Spring] -> [ || Siding Spring] 7505 +[s][ac] -> [sac] 7506 +[tim][es, ] -> [times, ] 7507 +[y][" ] -> [y" ] 7508 +[. It ][is ] -> [. It is ] 7509 +[ric][ul] -> [ricul] 7510 +[D][om] -> [Dom] 7511 +[New York ][City ] -> [New York City ] 7512 +[Christm][as ] -> [Christmas ] 7513 +["][. The ] -> [". The ] 7514 +[||2][1] -> [||21] 7515 +[popul][ation] -> [population] 7516 +[Li][ke ] -> [Like ] 7517 +[s][o, ] -> [so, ] 7518 +[ed][ul] -> [edul] 7519 +[es][.\u000a\u000aThe ] -> [es.\u000a\u000aThe ] 7520 +[, 199][1] -> [, 1991] 7521 +[192][9] -> [1929] 7522 +[o][ir] -> [oir] 7523 +[||2][4] -> [||24] 7524 +[liv][ed ] -> [lived ] 7525 +[F][ic] -> [Fic] 7526 +[off ][the ] -> [off the ] 7527 +[fri][end ] -> [friend ] 7528 +[O][t] -> [Ot] 7529 +[id][ence ] -> [idence ] 7530 +[hon][or] -> [honor] 7531 +[S][ong ] -> [Song ] 7532 +[ists\u000a][American ] -> [ists\u000aAmerican ] 7533 +[Al][though ] -> [Although ] 7534 +[studi][ed ] -> [studied ] 7535 +[b][acter] -> [bacter] 7536 +[d][ate ] -> [date ] 7537 +[j][am] -> [jam] 7538 +[w][atch] -> [watch] 7539 +[ou][ld] -> [ould] 7540 +[kind][s of ] -> [kinds of ] 7541 +[batt][le ] -> [battle ] 7542 +[cord][ing to the ] -> [cording to the ] 7543 +[back][ground] -> [background] 7544 +[ births\u000a201][8 ] -> [ births\u000a2018 ] 7545 +[. For ][example, ] -> [. For example, ] 7546 +[re][p] -> [rep] 7547 +[f][und] -> [fund] 7548 +[Depart][ment of ] -> [Department of ] 7549 +[J. League ][2] -> [J. League 2] 7550 +[him][self] -> [himself] 7551 +[el][t ] -> [elt ] 7552 +[, ][she ] -> [, she ] 7553 +[ide][a ] -> [idea ] 7554 +[Canad][a ] -> [Canada ] 7555 +[H][O] -> [HO] 7556 +[com][ed] -> [comed] 7557 +[t][est ] -> [test ] 7558 +[A][y] -> [Ay] 7559 +[ersh][ip ] -> [ership ] 7560 +[. ][All ] -> [. All ] 7561 +[Col][on] -> [Colon] 7562 +[. The ][movie ] -> [. The movie ] 7563 +[king][dom] -> [kingdom] 7564 +[E][ur] -> [Eur] 7565 +[ex][press] -> [express] 7566 +[on ][January ] -> [on January ] 7567 +[f][actor] -> [factor] 7568 +[e, ][American ] -> [e, American ] 7569 +[power][ful ] -> [powerful ] 7570 +[4][00] -> [400] 7571 +[V][ar] -> [Var] 7572 +[gr][aph] -> [graph] 7573 +[+][ ] -> [+ ] 7574 +[att][en] -> [atten] 7575 +[�][�] -> [•] 7576 +[campa][ign ] -> [campaign ] 7577 +[them ][to ] -> [them to ] 7578 +[is][tic ] -> [istic ] 7579 +[d][y] -> [dy] 7580 +[Banglad][esh] -> [Bangladesh] 7581 +[person][al ] -> [personal ] 7582 +[, 1999 || Socorro || LINEAR || — || align=right | ][2.] -> [, 1999 || Socorro || LINEAR || — || align=right | 2.] 7583 +[Pun][j] -> [Punj] 7584 +[19][17] -> [1917] 7585 +[I]['] -> [I'] 7586 +[ail][y ] -> [aily ] 7587 +[ide][as ] -> [ideas ] 7588 +[Washington, ][D.C] -> [Washington, D.C] 7589 +[) ][in the ] -> [) in the ] 7590 +[ō][ ] -> [ō ] 7591 +[Ar][c] -> [Arc] 7592 +[urric][an] -> [urrican] 7593 +[must ][be ] -> [must be ] 7594 +[Em][my ] -> [Emmy ] 7595 +[ch][i] -> [chi] 7596 +[ing][\u000a\u000a] -> [ing\u000a\u000a] 7597 +[ers][, and ] -> [ers, and ] 7598 +[Bul][gar] -> [Bulgar] 7599 +[Al][an ] -> [Alan ] 7600 +[p][ast ] -> [past ] 7601 +[b][an] -> [ban] 7602 +[-][-] -> [--] 7603 +[th][at] -> [that] 7604 +[por][ary ] -> [porary ] 7605 +[Youn][g] -> [Young] 7606 +[relation][ship] -> [relationship] 7607 +[a][||] -> [a||] 7608 +[�][�] -> [à] 7609 +[High][ ] -> [High ] 7610 +[sec][ret] -> [secret] 7611 +[stor][ies ] -> [stories ] 7612 +[ell][, ] -> [ell, ] 7613 +[trans][f] -> [transf] 7614 +[2][ bgcolor=#E9E9E9\u000a| ] -> [2 bgcolor=#E9E9E9\u000a| ] 7615 +[C][are] -> [Care] 7616 +[ and ][was ] -> [ and was ] 7617 +[S][ab] -> [Sab] 7618 +[was releas][ed in ] -> [was released in ] 7619 +[17][7] -> [177] 7620 +[F][ox] -> [Fox] 7621 +[, 200][7] -> [, 2007] 7622 +[Up][per ] -> [Upper ] 7623 +[home ][in ] -> [home in ] 7624 +[']['] -> [''] 7625 +[Priz][e in ] -> [Prize in ] 7626 +[neigh][bor] -> [neighbor] 7627 +[G][E] -> [GE] 7628 +[th][is, ] -> [this, ] 7629 +[Pas-de-Cal][a] -> [Pas-de-Cala] 7630 +[. ][Other ] -> [. Other ] 7631 +[0s ][establishments in ] -> [0s establishments in ] 7632 +[the ][most ] -> [the most ] 7633 +[rec][ent ] -> [recent ] 7634 +[es\u000a][The ] -> [es\u000aThe ] 7635 +[li][p] -> [lip] 7636 +[t][oo] -> [too] 7637 +[wor][th ] -> [worth ] 7638 +[Sh][ak] -> [Shak] 7639 +[||2][3] -> [||23] 7640 +[ ][The ] -> [ The ] 7641 +[ext][inc] -> [extinc] 7642 +[Air][port ] -> [Airport ] 7643 +[bl][ock] -> [block] 7644 +[that ][it ] -> [that it ] 7645 +[b][rain ] -> [brain ] 7646 +[Pic][tur] -> [Pictur] 7647 +[video ][games\u000a] -> [video games\u000a] 7648 +[is a municipality ][in the ] -> [is a municipality in the ] 7649 +[man][ufactur] -> [manufactur] 7650 +[P][a] -> [Pa] 7651 +[C][ant] -> [Cant] 7652 +[Dre][am] -> [Dream] 7653 +[s ][about ] -> [s about ] 7654 +[M][e ] -> [Me ] 7655 +[ig][en] -> [igen] 7656 +[Duk][e of ] -> [Duke of ] 7657 +[cul][tural ] -> [cultural ] 7658 +[for the ][first ] -> [for the first ] 7659 +[, 2001 || Socorro || LINEAR || — || align=right | ][4.] -> [, 2001 || Socorro || LINEAR || — || align=right | 4.] 7660 +[musician][s\u000a] -> [musicians\u000a] 7661 +[6][1] -> [61] 7662 +[Naz][i ] -> [Nazi ] 7663 +[201][7, ] -> [2017, ] 7664 +[Mar][v] -> [Marv] 7665 +[W][els] -> [Wels] 7666 +[o][ke ] -> [oke ] 7667 +[Lu][x] -> [Lux] 7668 +[amm][ad ] -> [ammad ] 7669 +[||0||][4] -> [||0||4] 7670 +[lef][t the ] -> [left the ] 7671 +[well][ known ] -> [well known ] 7672 +[ard][, ] -> [ard, ] 7673 +[When ][the ] -> [When the ] 7674 +[d][ens] -> [dens] 7675 +[ser][ve ] -> [serve ] 7676 +[func][tion] -> [function] 7677 +[trav][el ] -> [travel ] 7678 +[best ][known ] -> [best known ] 7679 +[or ][a ] -> [or a ] 7680 +[e][. This ] -> [e. This ] 7681 +[6][ bgcolor=#fefefe\u000a| ] -> [6 bgcolor=#fefefe\u000a| ] 7682 +[Br][and] -> [Brand] 7683 +[V][inc] -> [Vinc] 7684 +[200][7, ] -> [2007, ] 7685 +[head][quarter] -> [headquarter] 7686 +[dam][age ] -> [damage ] 7687 +[c][ell ] -> [cell ] 7688 +[ bgcolor=#fefefe\u000a| 1][5] -> [ bgcolor=#fefefe\u000a| 15] 7689 +[A][M] -> [AM] 7690 +[ || || — || September ][24] -> [ || || — || September 24] 7691 +[)\u000a ]["] -> [)\u000a "] 7692 +[mater][ial] -> [material] 7693 +[Jos][é ] -> [José ] 7694 +[Pat][rick ] -> [Patrick ] 7695 +[mod][el ] -> [model ] 7696 +[COVID-19 ][pandem] -> [COVID-19 pandem] 7697 +[st][ay ] -> [stay ] 7698 +[h][i ] -> [hi ] 7699 +[b][usiness ] -> [business ] 7700 +[E][cu] -> [Ecu] 7701 +[South ][Carol] -> [South Carol] 7702 +[ b][u] -> [ bu] 7703 +[Mar][gar] -> [Margar] 7704 +[k][o ] -> [ko ] 7705 +[aw][ay] -> [away] 7706 +[canc][er\u000a] -> [cancer\u000a] 7707 +[or ][in ] -> [or in ] 7708 +[. Dur][ing the ] -> [. During the ] 7709 +[Wels][h ] -> [Welsh ] 7710 +[F][ac] -> [Fac] 7711 +[b][ot] -> [bot] 7712 +[ bgcolor=#fefefe\u000a| 1][3] -> [ bgcolor=#fefefe\u000a| 13] 7713 +[Al][-] -> [Al-] 7714 +[prot][est] -> [protest] 7715 +[col][our] -> [colour] 7716 +[An][ne ] -> [Anne ] 7717 +[Franc][is ] -> [Francis ] 7718 +[Connectic][ut] -> [Connecticut] 7719 +[F][.C] -> [F.C] 7720 +[A][l ] -> [Al ] 7721 +[fr][ed ] -> [fred ] 7722 +[dr][ink] -> [drink] 7723 +[us][tin ] -> [ustin ] 7724 +[when ][they ] -> [when they ] 7725 +[Pu][erto ] -> [Puerto ] 7726 +[ine ][(] -> [ine (] 7727 +[Th][ree ] -> [Three ] 7728 +[city ][is ] -> [city is ] 7729 +[m][amm] -> [mamm] 7730 +[For][eign ] -> [Foreign ] 7731 +[hus][band ] -> [husband ] 7732 +[M][emb] -> [Memb] 7733 +[, ][an ] -> [, an ] 7734 +[M][ul] -> [Mul] 7735 +[O][m] -> [Om] 7736 +[o][d of ] -> [od of ] 7737 +[ri][ L] -> [ri L] 7738 +[, 1999 || ][Catalina || CSS] -> [, 1999 || Catalina || CSS] 7739 +[ers\u000a][American ] -> [ers\u000aAmerican ] 7740 +[M][AR] -> [MAR] 7741 +[20][th centur] -> [20th centur] 7742 +[Al][bum] -> [Album] 7743 +[A][ub] -> [Aub] 7744 +[president ][of the ] -> [president of the ] 7745 +[, 199][0] -> [, 1990] 7746 +[Comm][and] -> [Command] 7747 +[Egyp][t] -> [Egypt] 7748 +[urb][an ] -> [urban ] 7749 +[Civil ][War] -> [Civil War] 7750 +[self][-] -> [self-] 7751 +[C][iti] -> [Citi] 7752 +[.\u000a\u000a][A ] -> [.\u000a\u000aA ] 7753 +[C][I] -> [CI] 7754 +[Academ][y of ] -> [Academy of ] 7755 +[Ott][om] -> [Ottom] 7756 +[doc][tor] -> [doctor] 7757 +[201][8, ] -> [2018, ] 7758 +[ic][al] -> [ical] 7759 +[alc][oh] -> [alcoh] 7760 +[R][ay] -> [Ray] 7761 +[po][em] -> [poem] 7762 +[ab][ol] -> [abol] 7763 +[municipal][ities of ] -> [municipalities of ] 7764 +[D][S] -> [DS] 7765 +[y][: ] -> [y: ] 7766 +[at ][and ] -> [at and ] 7767 +[rev][eal] -> [reveal] 7768 +[ro][t] -> [rot] 7769 +[Man][ag] -> [Manag] 7770 +[1 ][and ] -> [1 and ] 7771 +[V][lad] -> [Vlad] 7772 +[succ][eed] -> [succeed] 7773 +[South ][African ] -> [South African ] 7774 +[197][9 ] -> [1979 ] 7775 +[b][al] -> [bal] 7776 +[bl][ue ] -> [blue ] 7777 +[on][line ] -> [online ] 7778 +[fol][k ] -> [folk ] 7779 +[pris][on ] -> [prison ] 7780 +[rick][et ] -> [ricket ] 7781 +[F][a] -> [Fa] 7782 +[Kingdom][ of ] -> [Kingdom of ] 7783 +[nominated ][for ] -> [nominated for ] 7784 +[H][ot] -> [Hot] 7785 +[Dan][ish ] -> [Danish ] 7786 +[complet][e ] -> [complete ] 7787 +[gu][n ] -> [gun ] 7788 +[Finn][ish ] -> [Finnish ] 7789 +[adap][t] -> [adapt] 7790 +[many ][other ] -> [many other ] 7791 +[olog][y, ] -> [ology, ] 7792 +[episod][e ] -> [episode ] 7793 +[m][ap] -> [map] 7794 +[27][, ] -> [27, ] 7795 +[30][, ] -> [30, ] 7796 +[ou][d] -> [oud] 7797 +[F][air] -> [Fair] 7798 +[German][y, ] -> [Germany, ] 7799 +[arrondiss][ement] -> [arrondissement] 7800 +[G][ord] -> [Gord] 7801 +[us][ing the ] -> [using the ] 7802 +[ac][id] -> [acid] 7803 +[in][o] -> [ino] 7804 +[m][it] -> [mit] 7805 +[appro][xim] -> [approxim] 7806 +[�][�] -> [\u200e] 7807 +[div][orc] -> [divorc] 7808 +[Air][port] -> [Airport] 7809 +[Atl][ant] -> [Atlant] 7810 +[b][ourn] -> [bourn] 7811 +[, 2001][ || Anderson Mesa || LONEOS] -> [, 2001 || Anderson Mesa || LONEOS] 7812 +[p][ati] -> [pati] 7813 +[announc][ed that ] -> [announced that ] 7814 +[st][ep] -> [step] 7815 +[des][c] -> [desc] 7816 +[res][t ] -> [rest ] 7817 +[V][as] -> [Vas] 7818 +[e.\u000a\u000a][In ] -> [e.\u000a\u000aIn ] 7819 +[er][. The ] -> [er. The ] 7820 +[1][ bgcolor=#E9E9E9\u000a| ] -> [1 bgcolor=#E9E9E9\u000a| ] 7821 +[year][-] -> [year-] 7822 +[es][. In ] -> [es. In ] 7823 +[Franc][e\u000a] -> [France\u000a] 7824 +[Wik][i] -> [Wiki] 7825 +[�][�] -> [н] 7826 +[p][age ] -> [page ] 7827 +[dor][f] -> [dorf] 7828 +[environ][ment] -> [environment] 7829 +[Mar][ch] -> [March] 7830 +[Anth][ony ] -> [Anthony ] 7831 +[year][s of ] -> [years of ] 7832 +[H][it] -> [Hit] 7833 +[oc][ol] -> [ocol] 7834 +[k][y ] -> [ky ] 7835 +[ai][m ] -> [aim ] 7836 +[B][eng] -> [Beng] 7837 +[to ][s] -> [to s] 7838 +[let][ter ] -> [letter ] 7839 +[l][ed by ] -> [led by ] 7840 +[N][ight] -> [Night] 7841 +[ö][n] -> [ön] 7842 +[dist][ance ] -> [distance ] 7843 +[196][8 ] -> [1968 ] 7844 +[pre][v] -> [prev] 7845 +[mon][t] -> [mont] 7846 +[h][att] -> [hatt] 7847 +[Ch][art] -> [Chart] 7848 +[New York (state][)\u000a] -> [New York (state)\u000a] 7849 +[North ][America] -> [North America] 7850 +[cul][tur] -> [cultur] 7851 +[in ][18] -> [in 18] 7852 +[a][el] -> [ael] 7853 +[co][st] -> [cost] 7854 +[, ][they ] -> [, they ] 7855 +[Cam][er] -> [Camer] 7856 +[ub][b] -> [ubb] 7857 +[out ][of the ] -> [out of the ] 7858 +[L][yn] -> [Lyn] 7859 +[capital ][of the ] -> [capital of the ] 7860 +[tr][y to ] -> [try to ] 7861 +[l][a ] -> [la ] 7862 +[M][ol] -> [Mol] 7863 +[fi][ed ] -> [fied ] 7864 +[att][le ] -> [attle ] 7865 +[S][un ] -> [Sun ] 7866 +[district][)] -> [district)] 7867 +[I][cel] -> [Icel] 7868 +[Sing][ers from ] -> [Singers from ] 7869 +[medi][a ] -> [media ] 7870 +[s][.\u000a] -> [s.\u000a] 7871 +[raf][t] -> [raft] 7872 +[actres][s. She ] -> [actress. She ] 7873 +[websit][e\u000a\u000a] -> [website\u000a\u000a] 7874 +[reg][ard] -> [regard] 7875 +[e][ti] -> [eti] 7876 +[meas][ure] -> [measure] 7877 +[m][aint] -> [maint] 7878 +[constitu][enc] -> [constituenc] 7879 +[ing][s, ] -> [ings, ] 7880 +[e][igh] -> [eigh] 7881 +[D][ynast] -> [Dynast] 7882 +[mon][th ] -> [month ] 7883 +[2][ (] -> [2 (] 7884 +[D][all] -> [Dall] 7885 +[H][av] -> [Hav] 7886 +[separ][ate ] -> [separate ] 7887 +[\u000a][A ] -> [\u000aA ] 7888 +[2][:] -> [2:] 7889 +[v][eg] -> [veg] 7890 +[H][o] -> [Ho] 7891 +[no ][longer ] -> [no longer ] 7892 +[aff][ect] -> [affect] 7893 +[un][ion ] -> [union ] 7894 +[i][ke ] -> [ike ] 7895 +[sy][mp] -> [symp] 7896 +[202][3 ] -> [2023 ] 7897 +[anc][e, ] -> [ance, ] 7898 +[for][d, ] -> [ford, ] 7899 +[30][0 ] -> [300 ] 7900 +[try][ing to ] -> [trying to ] 7901 +[new][s ] -> [news ] 7902 +[Stor][m ] -> [Storm ] 7903 +[sent][enc] -> [sentenc] 7904 +[an][ese ] -> [anese ] 7905 +[A][t ] -> [At ] 7906 +[po][ol] -> [pool] 7907 +[m][itt] -> [mitt] 7908 +[L][oc] -> [Loc] 7909 +[r][ing ] -> [ring ] 7910 +[�][�] -> [ş] 7911 +[st][aff] -> [staff] 7912 +[Sh][e] -> [She] 7913 +[h][an ] -> [han ] 7914 +[23][, ] -> [23, ] 7915 +[Car][l ] -> [Carl ] 7916 +[ bgcolor=#E9E9E9\u000a| 1][5] -> [ bgcolor=#E9E9E9\u000a| 15] 7917 +[album][s] -> [albums] 7918 +[2020][ in ] -> [2020 in ] 7919 +[to][-] -> [to-] 7920 +[Cap][tain ] -> [Captain ] 7921 +[fl][ight ] -> [flight ] 7922 +[O][ri] -> [Ori] 7923 +[represent][ativ] -> [representativ] 7924 +[best ][known for his ] -> [best known for his ] 7925 +[e. ][B] -> [e. B] 7926 +[.][A] -> [.A] 7927 +[respons][ible ] -> [responsible ] 7928 +[St. L][ou] -> [St. Lou] 7929 +[M][iss ] -> [Miss ] 7930 +[music group][s\u000a] -> [music groups\u000a] 7931 +[E][di] -> [Edi] 7932 +[s ][players\u000a] -> [s players\u000a] 7933 +[ang][el] -> [angel] 7934 +[ births\u000a2022 ][deaths\u000a] -> [ births\u000a2022 deaths\u000a] 7935 +[Democratic ][Party (United States) ] -> [Democratic Party (United States) ] 7936 +[h][ot ] -> [hot ] 7937 +[) ][and the ] -> [) and the ] 7938 +[f][ath] -> [fath] 7939 +[Other websit][es \u000a ] -> [Other websites \u000a ] 7940 +[it ][has ] -> [it has ] 7941 +[. He ][has ] -> [. He has ] 7942 +[ || || — || August ][24] -> [ || || — || August 24] 7943 +[comp][ut] -> [comput] 7944 +[||][19] -> [||19] 7945 +[ti][fic] -> [tific] 7946 +[F][ort ] -> [Fort ] 7947 +[alph][ab] -> [alphab] 7948 +[grow][ ] -> [grow ] 7949 +[tod][ay ] -> [today ] 7950 +[c][. ] -> [c. ] 7951 +[g][old] -> [gold] 7952 +[D][og] -> [Dog] 7953 +[ebr][ask] -> [ebrask] 7954 +[ed ]["] -> [ed "] 7955 +[ov][, ] -> [ov, ] 7956 +[on ][May ] -> [on May ] 7957 +[gl][ob] -> [glob] 7958 +[. He ][is a ] -> [. He is a ] 7959 +[scienti][fic ] -> [scientific ] 7960 +[l][ock] -> [lock] 7961 +[Municipal][ities of ] -> [Municipalities of ] 7962 +[Ham][il] -> [Hamil] 7963 +[ag][es ] -> [ages ] 7964 +[h][aw] -> [haw] 7965 +[held ][in ] -> [held in ] 7966 +[)][; ] -> [); ] 7967 +[l][ung ] -> [lung ] 7968 +[For][ce ] -> [Force ] 7969 +[from ][their ] -> [from their ] 7970 +[fe][el] -> [feel] 7971 +[Yor][k] -> [York] 7972 +[-][designated ] -> [-designated ] 7973 +[s][. It is ] -> [s. It is ] 7974 +[g][ri] -> [gri] 7975 +[In][t] -> [Int] 7976 +[tr][ade ] -> [trade ] 7977 +[\u000a][N] -> [\u000aN] 7978 +[rac][ing ] -> [racing ] 7979 +[competi][tion] -> [competition] 7980 +[pe][ak] -> [peak] 7981 +[sea ][level] -> [sea level] 7982 +[di][agnos] -> [diagnos] 7983 +[it][ing ] -> [iting ] 7984 +[reas][on] -> [reason] 7985 +[s\u000a][Movies ] -> [s\u000aMovies ] 7986 +[D][on ] -> [Don ] 7987 +[buil][t in ] -> [built in ] 7988 +[Ch][air] -> [Chair] 7989 +[am][in] -> [amin] 7990 +[tell][s ] -> [tells ] 7991 +[h][ang] -> [hang] 7992 +[R][ad] -> [Rad] 7993 +[mater][ial ] -> [material ] 7994 +[at][ed to ] -> [ated to ] 7995 +[ || Kitt Peak || Spacewatch][ || — || align=right | 2.] -> [ || Kitt Peak || Spacewatch || — || align=right | 2.] 7996 +[S][ul] -> [Sul] 7997 +[capital ][of ] -> [capital of ] 7998 +[re][ally ] -> [really ] 7999 +[;][ and ] -> [; and ] 8000 +[went to ][number ] -> [went to number ] 8001 +[. S][ome] -> [. Some] 8002 +[sc][ap] -> [scap] 8003 +[spec][ific ] -> [specific ] 8004 +[g][as] -> [gas] 8005 +[ing][. The ] -> [ing. The ] 8006 +[f][ill] -> [fill] 8007 +[screen][writ] -> [screenwrit] 8008 +[es][s, ] -> [ess, ] 8009 +[start][ed to ] -> [started to ] 8010 +[For ][example, ] -> [For example, ] 8011 +[War][ner ] -> [Warner ] 8012 +[5][:] -> [5:] 8013 +[es][The ] -> [esThe ] 8014 +[Y][ear ] -> [Year ] 8015 +[m][ast] -> [mast] 8016 +[–][0] -> [–0] 8017 +[is the ][first ] -> [is the first ] 8018 +[commun][ic] -> [communic] 8019 +[M][ov] -> [Mov] 8020 +[M][eg] -> [Meg] 8021 +[ed][om] -> [edom] 8022 +[Re][ag] -> [Reag] 8023 +[dev][ic] -> [devic] 8024 +[version ][of the ] -> [version of the ] 8025 +[Pr][ad] -> [Prad] 8026 +[eng][ine ] -> [engine ] 8027 +[Eng][ine] -> [Engine] 8028 +[i][op] -> [iop] 8029 +[chos][en ] -> [chosen ] 8030 +[FIFA ][World Cup ] -> [FIFA World Cup ] 8031 +[ri][ver] -> [river] 8032 +[N][ebrask] -> [Nebrask] 8033 +[th][or] -> [thor] 8034 +[plac][e in ] -> [place in ] 8035 +[gir][l ] -> [girl ] 8036 +[in][a\u000a] -> [ina\u000a] 8037 +[m][outh ] -> [mouth ] 8038 +[it][self ] -> [itself ] 8039 +[D][ol] -> [Dol] 8040 +[on ][October ] -> [on October ] 8041 +[ri L][ank] -> [ri Lank] 8042 +[W][ell] -> [Well] 8043 +[rest][aur] -> [restaur] 8044 +[sel][ect] -> [select] 8045 +[tim][e in ] -> [time in ] 8046 +[R][et] -> [Ret] 8047 +[football ][manag] -> [football manag] 8048 +[W][olf] -> [Wolf] 8049 +[197][4 ] -> [1974 ] 8050 +[i][es\u000a] -> [ies\u000a] 8051 +[ b][ook] -> [ book] 8052 +[. T][o ] -> [. To ] 8053 +[Liv][e ] -> [Live ] 8054 +[ter][ror] -> [terror] 8055 +[en][s, ] -> [ens, ] 8056 +[is ][of ] -> [is of ] 8057 +[A][T] -> [AT] 8058 +[Austral][ia, ] -> [Australia, ] 8059 +[Other websit][es\u000a ] -> [Other websites\u000a ] 8060 +[Di][eg] -> [Dieg] 8061 +[qu][ite ] -> [quite ] 8062 +[3][-] -> [3-] 8063 +[I][NS] -> [INS] 8064 +[l][oy] -> [loy] 8065 +[O][ber] -> [Ober] 8066 +[Toron][to ] -> [Toronto ] 8067 +[pa][id ] -> [paid ] 8068 +[com][es from the ] -> [comes from the ] 8069 +[ic ][and ] -> [ic and ] 8070 +[young][er ] -> [younger ] 8071 +[s][/] -> [s/] 8072 +[the][ory ] -> [theory ] 8073 +[def][end] -> [defend] 8074 +[D][eb] -> [Deb] 8075 +[individ][ual ] -> [individual ] 8076 +[Des][ert ] -> [Desert ] 8077 +[19][14] -> [1914] 8078 +[artic][le ] -> [article ] 8079 +[I][t] -> [It] 8080 +[is a ][list of ] -> [is a list of ] 8081 +[it][o ] -> [ito ] 8082 +[in][e-] -> [ine-] 8083 +[h][air] -> [hair] 8084 +[commerc][ial ] -> [commercial ] 8085 +[pri][me ] -> [prime ] 8086 +[||0][\u000a|-\u000a!Total] -> [||0\u000a|-\u000a!Total] 8087 +[sk][i ] -> [ski ] 8088 +[196][0 ] -> [1960 ] 8089 +[R&][B ] -> [R&B ] 8090 +[ac][ea] -> [acea] 8091 +[ing ][at ] -> [ing at ] 8092 +[et][ter ] -> [etter ] 8093 +[techn][i] -> [techni] 8094 +[.\u000a\u000aRelated pag][es\u000a] -> [.\u000a\u000aRelated pages\u000a] 8095 +[J][eff] -> [Jeff] 8096 +[192][8] -> [1928] 8097 +[candid][ate ] -> [candidate ] 8098 +[all ][of the ] -> [all of the ] 8099 +[P][yr] -> [Pyr] 8100 +[er][.\u000a ] -> [er.\u000a ] 8101 +[tim][e of ] -> [time of ] 8102 +[b][ought ] -> [bought ] 8103 +[On ][the ] -> [On the ] 8104 +[I][ts ] -> [Its ] 8105 +[Per][c] -> [Perc] 8106 +[m][ass ] -> [mass ] 8107 +[H][ill ] -> [Hill ] 8108 +[Brit][ain ] -> [Britain ] 8109 +[bod][y] -> [body] 8110 +[s ][- ] -> [s - ] 8111 +[Jr][. ] -> [Jr. ] 8112 +[ro][om ] -> [room ] 8113 +[t][ail] -> [tail] 8114 +[Europ][e ] -> [Europe ] 8115 +[9][/] -> [9/] 8116 +[3][ bgcolor=#E9E9E9\u000a| ] -> [3 bgcolor=#E9E9E9\u000a| ] 8117 +[d ][(] -> [d (] 8118 +[m][, ] -> [m, ] 8119 +[South ][Korean ] -> [South Korean ] 8120 +[E][ston] -> [Eston] 8121 +[E][S] -> [ES] 8122 +[\u000a|}\u000a\u000a][International ] -> [\u000a|}\u000a\u000aInternational ] 8123 +[Wiscons][in] -> [Wisconsin] 8124 +[ex][hib] -> [exhib] 8125 +[people ][to ] -> [people to ] 8126 +[u][-] -> [u-] 8127 +[ k][il] -> [ kil] 8128 +[play][s for ] -> [plays for ] 8129 +[ia][.\u000a\u000a] -> [ia.\u000a\u000a] 8130 +[ km][²] -> [ km²] 8131 +[prot][ect] -> [protect] 8132 +[it][em] -> [item] 8133 +[é][r] -> [ér] 8134 +[y ][is ] -> [y is ] 8135 +[direct][ly ] -> [directly ] 8136 +[Tay][l] -> [Tayl] 8137 +[Los Angel][es, ] -> [Los Angeles, ] 8138 +[television ][program] -> [television program] 8139 +[s][. He was ] -> [s. He was ] 8140 +[song ]["] -> [song "] 8141 +[B][ure] -> [Bure] 8142 +[north][west of ] -> [northwest of ] 8143 +[Cancer ][deaths in ] -> [Cancer deaths in ] 8144 +[192][6] -> [1926] 8145 +[s][ug] -> [sug] 8146 +[cent][er of ] -> [center of ] 8147 +[Y][ug] -> [Yug] 8148 +[J][ef] -> [Jef] 8149 +[there ][is a ] -> [there is a ] 8150 +[in ][T] -> [in T] 8151 +[c][ook] -> [cook] 8152 +[c][over ] -> [cover ] 8153 +[29][, ] -> [29, ] 8154 +[s][.] -> [s.] 8155 +[B][rown ] -> [Brown ] 8156 +[, 2000 || Socorro || LINEAR || — || align=right | ][5.] -> [, 2000 || Socorro || LINEAR || — || align=right | 5.] 8157 +[�][�] -> [ø] 8158 +[http://][www.] -> [http://www.] 8159 +[tot][al of ] -> [total of ] 8160 +[diseas][e ] -> [disease ] 8161 +[El][l] -> [Ell] 8162 +[on ][August ] -> [on August ] 8163 +[month][s ] -> [months ] 8164 +[s of the ][United States\u000a] -> [s of the United States\u000a] 8165 +[s][ea] -> [sea] 8166 +[wh][y ] -> [why ] 8167 +[an][\u000a ] -> [an\u000a ] 8168 +[e][.\u000a\u000aReferences\u000a\u000a] -> [e.\u000a\u000aReferences\u000a\u000a] 8169 +[H][urricane ] -> [Hurricane ] 8170 +[Chann][el ] -> [Channel ] 8171 +[Mosc][ow] -> [Moscow] 8172 +[ar][a] -> [ara] 8173 +[magaz][ine ] -> [magazine ] 8174 +[�][�] -> [ا] 8175 +[county seat ][of ] -> [county seat of ] 8176 +[Armen][ian ] -> [Armenian ] 8177 +[ k][m || ] -> [ km || ] 8178 +[197][8 ] -> [1978 ] 8179 +[oth][er, ] -> [other, ] 8180 +[oper][ating ] -> [operating ] 8181 +[sk][in ] -> [skin ] 8182 +[h][ug] -> [hug] 8183 +[ig][a] -> [iga] 8184 +[E][sp] -> [Esp] 8185 +[scor][ed ] -> [scored ] 8186 +[broad][cast ] -> [broadcast ] 8187 +[L][ist] -> [List] 8188 +[history ][of ] -> [history of ] 8189 +[diff][erenc] -> [differenc] 8190 +[is a commune. It is ][in ] -> [is a commune. It is in ] 8191 +[13][0] -> [130] 8192 +[Ex][t] -> [Ext] 8193 +[al][k] -> [alk] 8194 +[Secret][ary of ] -> [Secretary of ] 8195 +[se][t of ] -> [set of ] 8196 +[anth][rop] -> [anthrop] 8197 +[Pitt][sburg] -> [Pittsburg] 8198 +[may ][have ] -> [may have ] 8199 +[on ][June ] -> [on June ] 8200 +[Gam][es ] -> [Games ] 8201 +[Que][ens] -> [Queens] 8202 +[4][ bgcolor=#fefefe\u000a| ] -> [4 bgcolor=#fefefe\u000a| ] 8203 +[ef][ul ] -> [eful ] 8204 +[r][ay ] -> [ray ] 8205 +[II][I ] -> [III ] 8206 +[was ][born ] -> [was born ] 8207 +[H][u] -> [Hu] 8208 +[stre][am] -> [stream] 8209 +[7][ bgcolor=#E9E9E9\u000a| ] -> [7 bgcolor=#E9E9E9\u000a| ] 8210 +[football ][club] -> [football club] 8211 +[In][v] -> [Inv] 8212 +[II][ of ] -> [II of ] 8213 +[, 2001][ || ] -> [, 2001 || ] 8214 +[, ][English ] -> [, English ] 8215 +[U][-] -> [U-] 8216 +[Re][al ] -> [Real ] 8217 +[we][ak] -> [weak] 8218 +[ || Kitt Peak || Spacewatch][ || — || align=right | 3.] -> [ || Kitt Peak || Spacewatch || — || align=right | 3.] 8219 +[politician ][(b. ] -> [politician (b. ] 8220 +[4][,] -> [4,] 8221 +[h][and ] -> [hand ] 8222 +[P][ok] -> [Pok] 8223 +[, 2004][ || Socorro || LINEAR || — || align=right | ] -> [, 2004 || Socorro || LINEAR || — || align=right | ] 8224 +[Un][der ] -> [Under ] 8225 +[F][ern] -> [Fern] 8226 +[es]["] -> [es"] 8227 +[tw][o-] -> [two-] 8228 +[R][ot] -> [Rot] 8229 +[195][0 ] -> [1950 ] 8230 +[it][ed] -> [ited] 8231 +[sc][al] -> [scal] 8232 +[actor][s] -> [actors] 8233 +[l][ater] -> [later] 8234 +[or][chestr] -> [orchestr] 8235 +[ beg][an ] -> [ began ] 8236 +[||||||||][||||||||] -> [||||||||||||||||] 8237 +[ar][s ] -> [ars ] 8238 +[sing][le] -> [single] 8239 +[League ][players\u000a] -> [League players\u000a] 8240 +[Americ][a ] -> [America ] 8241 +[in ][Switzerland] -> [in Switzerland] 8242 +[on ][his ] -> [on his ] 8243 +[met][ers ] -> [meters ] 8244 +[roy][al ] -> [royal ] 8245 +[ar][ (] -> [ar (] 8246 +[E][y] -> [Ey] 8247 +[ent][r] -> [entr] 8248 +[S][id] -> [Sid] 8249 +[vari][et] -> [variet] 8250 +[Mic][ros] -> [Micros] 8251 +[. B][y ] -> [. By ] 8252 +[Mer][c] -> [Merc] 8253 +[ ][�] -> [ �] 8254 +[G][ood ] -> [Good ] 8255 +[S][au] -> [Sau] 8256 +[softw][are ] -> [software ] 8257 +[St][ud] -> [Stud] 8258 +[Th][at ] -> [That ] 8259 +[Te][am] -> [Team] 8260 +[st][op] -> [stop] 8261 +[lear][n ] -> [learn ] 8262 +[man ][of the ] -> [man of the ] 8263 +[contro][vers] -> [controvers] 8264 +[people ][are ] -> [people are ] 8265 +[: ][the ] -> [: the ] 8266 +[) ][or ] -> [) or ] 8267 +[.\u000a\u000a][Not] -> [.\u000a\u000aNot] 8268 +[Ar][d] -> [Ard] 8269 +[men][tion] -> [mention] 8270 +[contr][act ] -> [contract ] 8271 +[n][am ] -> [nam ] 8272 +[om][er] -> [omer] 8273 +[s][ail] -> [sail] 8274 +[tw][ent] -> [twent] 8275 +[equ][al ] -> [equal ] 8276 +[de][ad] -> [dead] 8277 +[||1][\u000a|-\u000a|199] -> [||1\u000a|-\u000a|199] 8278 +[sing][les ] -> [singles ] 8279 +[im][ir ] -> [imir ] 8280 +[Tre][at] -> [Treat] 8281 +[ation][\u000a\u000a] -> [ation\u000a\u000a] 8282 +[dé][part] -> [départ] 8283 +[igh][ter ] -> [ighter ] 8284 +[INS][E] -> [INSE] 8285 +[Florid][a] -> [Florida] 8286 +[man][n ] -> [mann ] 8287 +[ated ][from ] -> [ated from ] 8288 +[M][en] -> [Men] 8289 +[le ][(] -> [le (] 8290 +[of ][these ] -> [of these ] 8291 +[g][ress] -> [gress] 8292 +[United States][. The ] -> [United States. The ] 8293 +[o][is ] -> [ois ] 8294 +[chann][el ] -> [channel ] 8295 +[ox][y] -> [oxy] 8296 +[ch][lor] -> [chlor] 8297 +[made ][a ] -> [made a ] 8298 +[C][ensus] -> [Census] 8299 +[Pro][t] -> [Prot] 8300 +[P][ost] -> [Post] 8301 +[l][ed ] -> [led ] 8302 +[ersh][ip] -> [ership] 8303 +[MAS][ || align=right | 1.] -> [MAS || align=right | 1.] 8304 +[ich][ol] -> [ichol] 8305 +[cancer][.\u000a] -> [cancer.\u000a] 8306 +[record][ed ] -> [recorded ] 8307 +[tradi][tion] -> [tradition] 8308 +[as ][and ] -> [as and ] 8309 +[192][7] -> [1927] 8310 +[M][ast] -> [Mast] 8311 +[d][al] -> [dal] 8312 +[stud][ents ] -> [students ] 8313 +[l][ess] -> [less] 8314 +[°][ ] -> [° ] 8315 +[ch][ess ] -> [chess ] 8316 +[f][antasy ] -> [fantasy ] 8317 +[. \u000a\u000a][He ] -> [. \u000a\u000aHe ] 8318 +[Univers][al ] -> [Universal ] 8319 +[j][ur] -> [jur] 8320 +[, ][German ] -> [, German ] 8321 +[pl][an ] -> [plan ] 8322 +[el][y] -> [ely] 8323 +[Pro][gr] -> [Progr] 8324 +[heart ][attack] -> [heart attack] 8325 +[Cre][ek] -> [Creek] 8326 +[their ][first ] -> [their first ] 8327 +[: ][#] -> [: #] 8328 +[S][ev] -> [Sev] 8329 +[Nic][ol] -> [Nicol] 8330 +[Ph][oen] -> [Phoen] 8331 +[17][5] -> [175] 8332 +[e][ch ] -> [ech ] 8333 +[Muse][um] -> [Museum] 8334 +[role ][as ] -> [role as ] 8335 +[I][g] -> [Ig] 8336 +[ ][the ] -> [ the ] 8337 +[United States.\u000a\u000a][Towns in ] -> [United States.\u000a\u000aTowns in ] 8338 +[y ][was ] -> [y was ] 8339 +[cont][est] -> [contest] 8340 +[our][ce ] -> [ource ] 8341 +[ ][D] -> [ D] 8342 +[h][ir] -> [hir] 8343 +[o][ti] -> [oti] 8344 +[o ][in ] -> [o in ] 8345 +[rock b][and ] -> [rock band ] 8346 +[its ][own ] -> [its own ] 8347 +[sit][e ] -> [site ] 8348 +[ist][\u000a ] -> [ist\u000a ] 8349 +[196][4 ] -> [1964 ] 8350 +[form][ed in ] -> [formed in ] 8351 +[acros][s ] -> [across ] 8352 +[B][usinesspeople from ] -> [Businesspeople from ] 8353 +[on ][July ] -> [on July ] 8354 +[n][om] -> [nom] 8355 +[or]['s ] -> [or's ] 8356 +[L][ower ] -> [Lower ] 8357 +[T][ropical ] -> [Tropical ] 8358 +[er ][for the ] -> [er for the ] 8359 +[inv][as] -> [invas] 8360 +[I][ of ] -> [I of ] 8361 +[Dea][th ] -> [Death ] 8362 +[A][P] -> [AP] 8363 +[s (][born ] -> [s (born ] 8364 +[ap][art] -> [apart] 8365 +[h ][(] -> [h (] 8366 +["][the ] -> ["the ] 8367 +[Rob][ert] -> [Robert] 8368 +[id][ay ] -> [iday ] 8369 +[u][tr] -> [utr] 8370 +[ || La Silla || E. W. Elst ][|| — || align=right | ] -> [ || La Silla || E. W. Elst || — || align=right | ] 8371 +[won ][a ] -> [won a ] 8372 +[os][sil] -> [ossil] 8373 +[relat][ed to ] -> [related to ] 8374 +[ity ][and ] -> [ity and ] 8375 +[po][in] -> [poin] 8376 +[want ][to ] -> [want to ] 8377 +[car][di] -> [cardi] 8378 +[gen][us ] -> [genus ] 8379 +[play][w] -> [playw] 8380 +[s\u000a][People from ] -> [s\u000aPeople from ] 8381 +[F][ree ] -> [Free ] 8382 +[on ][November ] -> [on November ] 8383 +[d][ict] -> [dict] 8384 +[al][ized ] -> [alized ] 8385 +[Part][y of ] -> [Party of ] 8386 +[B][ank ] -> [Bank ] 8387 +[Turk][ey] -> [Turkey] 8388 +[wh][om ] -> [whom ] 8389 +[which ][the ] -> [which the ] 8390 +[, ][French ] -> [, French ] 8391 +[Roman][ian ] -> [Romanian ] 8392 +[competi][tion ] -> [competition ] 8393 +[ib][er] -> [iber] 8394 +[part][n] -> [partn] 8395 +[s ][establishments in ] -> [s establishments in ] 8396 +[us][al] -> [usal] 8397 +[led][g] -> [ledg] 8398 +[-designated ][plac] -> [-designated plac] 8399 +[actres][s, ] -> [actress, ] 8400 +[ect][or ] -> [ector ] 8401 +[comp][uter] -> [computer] 8402 +[sp][ell] -> [spell] 8403 +[ || Haleakala || NEAT][ || ] -> [ || Haleakala || NEAT || ] 8404 +[en][h] -> [enh] 8405 +[ch][alleng] -> [challeng] 8406 +[ation][.\u000a\u000a] -> [ation.\u000a\u000a] 8407 +[Pop][ul] -> [Popul] 8408 +[i][es.\u000a\u000a] -> [ies.\u000a\u000a] 8409 +[193][2] -> [1932] 8410 +[fic][tional ] -> [fictional ] 8411 +[heal][th] -> [health] 8412 +[at ][(] -> [at (] 8413 +[died ][from ] -> [died from ] 8414 +[20th ][Century ] -> [20th Century ] 8415 +[at][su] -> [atsu] 8416 +[im][p] -> [imp] 8417 +[es ][by ] -> [es by ] 8418 +[Er][ic ] -> [Eric ] 8419 +[H][ard] -> [Hard] 8420 +[ation][s of ] -> [ations of ] 8421 +[B][erg] -> [Berg] 8422 +[Cor][p] -> [Corp] 8423 +[American movie actors\u000aAmerican ][television actors\u000aAmerican ] -> [American movie actors\u000aAmerican television actors\u000aAmerican ] 8424 +[.\u000a\u000aOther websit][es\u000a ] -> [.\u000a\u000aOther websites\u000a ] 8425 +[anal][ys] -> [analys] 8426 +[s][am] -> [sam] 8427 +[O][sc] -> [Osc] 8428 +[when ][he was ] -> [when he was ] 8429 +[son ][(] -> [son (] 8430 +[In][form] -> [Inform] 8431 +[Lab][our ] -> [Labour ] 8432 +[hy][th] -> [hyth] 8433 +[loc][ated in ] -> [located in ] 8434 +[Con][c] -> [Conc] 8435 +[et][t ] -> [ett ] 8436 +[due ][to the ] -> [due to the ] 8437 +[40][0 ] -> [400 ] 8438 +[Ju][an ] -> [Juan ] 8439 +[Bar][bar] -> [Barbar] 8440 +[Mat][th] -> [Matth] 8441 +[Tr][in] -> [Trin] 8442 +[pl][as] -> [plas] 8443 +[Tam][il ] -> [Tamil ] 8444 +[island ][of ] -> [island of ] 8445 +[H][osp] -> [Hosp] 8446 +[dam][ag] -> [damag] 8447 +[Anton][io ] -> [Antonio ] 8448 +[Ab][d] -> [Abd] 8449 +[es][c] -> [esc] 8450 +[gal][ax] -> [galax] 8451 +[H][er ] -> [Her ] 8452 +[R][io ] -> [Rio ] 8453 +[A][s ] -> [As ] 8454 +[bur][i] -> [buri] 8455 +[ian][s] -> [ians] 8456 +[is ][very ] -> [is very ] 8457 +[Tr][av] -> [Trav] 8458 +[mag][ne] -> [magne] 8459 +[do][ing ] -> [doing ] 8460 +[cl][us] -> [clus] 8461 +[v][el] -> [vel] 8462 +[cov][ers ] -> [covers ] 8463 +[C][-] -> [C-] 8464 +[K][OR] -> [KOR] 8465 +[im][pl] -> [impl] 8466 +[C][ret] -> [Cret] 8467 +[H][op] -> [Hop] 8468 +[ b][r] -> [ br] 8469 +[P][ul] -> [Pul] 8470 +[J][e] -> [Je] 8471 +[play][ed by ] -> [played by ] 8472 +[P][s ] -> [Ps ] 8473 +[ || || — || September ][16] -> [ || || — || September 16] 8474 +[196][7 ] -> [1967 ] 8475 +[ti][al ] -> [tial ] 8476 +[k][el] -> [kel] 8477 +[ath][an ] -> [athan ] 8478 +[Jim][my ] -> [Jimmy ] 8479 +[sh][op] -> [shop] 8480 +[Conf][erenc] -> [Conferenc] 8481 +[, 200][8] -> [, 2008] 8482 +[ens][ion ] -> [ension ] 8483 +[Bal][tim] -> [Baltim] 8484 +[giv][ing ] -> [giving ] 8485 +[3][–] -> [3–] 8486 +[K][on] -> [Kon] 8487 +[res][ist] -> [resist] 8488 +[ev][in ] -> [evin ] 8489 +[each ][other] -> [each other] 8490 +[mon][arch] -> [monarch] 8491 +[Fri][ed] -> [Fried] 8492 +[Austral][ia\u000a] -> [Australia\u000a] 8493 +[Ear][l ] -> [Earl ] 8494 +[nu][mer] -> [numer] 8495 +[through][out the ] -> [throughout the ] 8496 +[5]["|] -> [5"|] 8497 +[Jer][usal] -> [Jerusal] 8498 +[an][e] -> [ane] 8499 +[S][em] -> [Sem] 8500 +[ic][s, ] -> [ics, ] 8501 +[) ][are ] -> [) are ] 8502 +[ || align=right | ][8.] -> [ || align=right | 8.] 8503 +[id][el] -> [idel] 8504 +[H][art] -> [Hart] 8505 +[il][ar] -> [ilar] 8506 +[molec][ul] -> [molecul] 8507 +[Ch][ild] -> [Child] 8508 +[ch][as] -> [chas] 8509 +[L][iter] -> [Liter] 8510 +[s][ome of ] -> [some of ] 8511 +[is ][usually ] -> [is usually ] 8512 +[Span][ish] -> [Spanish] 8513 +[ed to ][a ] -> [ed to a ] 8514 +[G][old ] -> [Gold ] 8515 +[19][15] -> [1915] 8516 +[Geograph][y of ] -> [Geography of ] 8517 +[Mal][ays] -> [Malays] 8518 +[conduc][t] -> [conduct] 8519 +[illi][on] -> [illion] 8520 +[an][s, ] -> [ans, ] 8521 +[Man][chester ] -> [Manchester ] 8522 +[V][ || align=right | 2.] -> [V || align=right | 2.] 8523 +[like ][a ] -> [like a ] 8524 +[p][rec] -> [prec] 8525 +[in the ][United States ] -> [in the United States ] 8526 +[192][5] -> [1925] 8527 +[Ar][tic] -> [Artic] 8528 +[John][ny ] -> [Johnny ] 8529 +[ad][o] -> [ado] 8530 +[T][i] -> [Ti] 8531 +[department in the ][north of ] -> [department in the north of ] 8532 +[Serie ][A] -> [Serie A] 8533 +[television series ][debut] -> [television series debut] 8534 +[a][.] -> [a.] 8535 +[Lux][emb] -> [Luxemb] 8536 +[up][per ] -> [upper ] 8537 +[b][us] -> [bus] 8538 +[ter][min] -> [termin] 8539 +[becaus][e of the ] -> [because of the ] 8540 +[ac][qu] -> [acqu] 8541 +[Emp][ir] -> [Empir] 8542 +[may ][also ] -> [may also ] 8543 +[M][uh] -> [Muh] 8544 +[, 201][7] -> [, 2017] 8545 +[Coun][try ] -> [Country ] 8546 +[a s][ub] -> [a sub] 8547 +[c][ut] -> [cut] 8548 +[e][" (] -> [e" (] 8549 +[both ][the ] -> [both the ] 8550 +[d][eal] -> [deal] 8551 +[intell][ig] -> [intellig] 8552 +[Nav][y ] -> [Navy ] 8553 +[sell][ing ] -> [selling ] 8554 +["][\u000a "] -> ["\u000a "] 8555 +[Scientist][s from ] -> [Scientists from ] 8556 +[United Stat][es and ] -> [United States and ] 8557 +[Ur][ugu] -> [Urugu] 8558 +[n][ar] -> [nar] 8559 +[r][ugby ] -> [rugby ] 8560 +[par][ish ] -> [parish ] 8561 +[Am][bass] -> [Ambass] 8562 +[, ][England] -> [, England] 8563 +[ur][s] -> [urs] 8564 +[t][ap] -> [tap] 8565 +[t][end] -> [tend] 8566 +[b][a] -> [ba] 8567 +[i][\u000a ] -> [i\u000a ] 8568 +[on][\u000a] -> [on\u000a] 8569 +[produc][ed ] -> [produced ] 8570 +[south][western ] -> [southwestern ] 8571 +[br][anch ] -> [branch ] 8572 +[Man][ipur] -> [Manipur] 8573 +[Ar][tist] -> [Artist] 8574 +[play][er] -> [player] 8575 +[mach][ine ] -> [machine ] 8576 +[es ][at ] -> [es at ] 8577 +[F][ly] -> [Fly] 8578 +[Americ][ans ] -> [Americans ] 8579 +[H][ell] -> [Hell] 8580 +[refer ][to] -> [refer to] 8581 +[R][S] -> [RS] 8582 +[Sim][p] -> [Simp] 8583 +[ä][r] -> [är] 8584 +[pro][te] -> [prote] 8585 +[2 km || \u000a|-id=][0] -> [2 km || \u000a|-id=0] 8586 +[near][by ] -> [nearby ] 8587 +[Bu][ff] -> [Buff] 8588 +[sou][theast ] -> [southeast ] 8589 +[dist][ribu] -> [distribu] 8590 +[aw][a ] -> [awa ] 8591 +[J][ess] -> [Jess] 8592 +[\u000a\u000aReferences\u000a\u000aOther websit][es \u000a ] -> [\u000a\u000aReferences\u000a\u000aOther websites \u000a ] 8593 +[al][ong] -> [along] 8594 +[ment][, ] -> [ment, ] 8595 +[es ][on the ] -> [es on the ] 8596 +[Calv][ad] -> [Calvad] 8597 +[||rowspan="][5"|] -> [||rowspan="5"|] 8598 +[Imper][ial ] -> [Imperial ] 8599 +[on][ze ] -> [onze ] 8600 +[after ][a ] -> [after a ] 8601 +[. B][efore ] -> [. Before ] 8602 +[Com][mission] -> [Commission] 8603 +[us][ed] -> [used] 8604 +[Canad][a, ] -> [Canada, ] 8605 +[are][as] -> [areas] 8606 +[on ][December ] -> [on December ] 8607 +[s ][can ] -> [s can ] 8608 +[Dep][uty ] -> [Deputy ] 8609 +[veg][et] -> [veget] 8610 +[for ][their ] -> [for their ] 8611 +[Rog][er ] -> [Roger ] 8612 +[Premier ][League] -> [Premier League] 8613 +[s and ][other ] -> [s and other ] 8614 +[class][ical ] -> [classical ] 8615 +[Car][ib] -> [Carib] 8616 +[e][)\u000a] -> [e)\u000a] 8617 +[) ][was the ] -> [) was the ] 8618 +[-][born ] -> [-born ] 8619 +[L][eb] -> [Leb] 8620 +[ab][s] -> [abs] 8621 +[produc][t] -> [product] 8622 +[r][ay] -> [ray] 8623 +[Pi][err] -> [Pierr] 8624 +[b][it] -> [bit] 8625 +[F][el] -> [Fel] 8626 +[H][owever, ] -> [However, ] 8627 +[, 1997][ || ] -> [, 1997 || ] 8628 +[album][, ] -> [album, ] 8629 +[Commun][ist ] -> [Communist ] 8630 +[h][ood] -> [hood] 8631 +[fr][anch] -> [franch] 8632 +[det][ail] -> [detail] 8633 +[p][ick] -> [pick] 8634 +[R][av] -> [Rav] 8635 +[om][o] -> [omo] 8636 +[N][ot ] -> [Not ] 8637 +[one of the ][most ] -> [one of the most ] 8638 +[. ][U] -> [. U] 8639 +[B][A ] -> [BA ] 8640 +[. There ][were ] -> [. There were ] 8641 +[County is a county ][in the U.S. state of ] -> [County is a county in the U.S. state of ] 8642 +[sh][or] -> [shor] 8643 +[on ][March ] -> [on March ] 8644 +[) ][\u000a ] -> [) \u000a ] 8645 +[Yug][oslav] -> [Yugoslav] 8646 +[u][th] -> [uth] 8647 +[say][s ] -> [says ] 8648 +[an ][important ] -> [an important ] 8649 +[n][ob] -> [nob] 8650 +[s of ][a ] -> [s of a ] 8651 +[Rail][way ] -> [Railway ] 8652 +[\u000a\u000a|-][bgcolor=#] -> [\u000a\u000a|-bgcolor=#] 8653 +[Ph][illi] -> [Philli] 8654 +[ ][\u000a ] -> [ \u000a ] 8655 +[u][tive ] -> [utive ] 8656 +[prov][ide ] -> [provide ] 8657 +[me][et ] -> [meet ] 8658 +[17][6] -> [176] 8659 +[not][able ] -> [notable ] 8660 +[. They ][also ] -> [. They also ] 8661 +[-][0] -> [-0] 8662 +[ea][u ] -> [eau ] 8663 +[Fin][land] -> [Finland] 8664 +[tim][e. ] -> [time. ] 8665 +[cas][e ] -> [case ] 8666 +[ad][or ] -> [ador ] 8667 +[fi][x] -> [fix] 8668 +[among ][the ] -> [among the ] 8669 +[14][0] -> [140] 8670 +[ births\u000a201][4 ] -> [ births\u000a2014 ] 8671 +[less ][than ] -> [less than ] 8672 +[direc][t ] -> [direct ] 8673 +[Bruc][e ] -> [Bruce ] 8674 +[Ep][isod] -> [Episod] 8675 +[World ][Heritage ] -> [World Heritage ] 8676 +[s][)\u000a ] -> [s)\u000a ] 8677 +[z][h] -> [zh] 8678 +[rap][p] -> [rapp] 8679 +[or][ph] -> [orph] 8680 +[reg][n] -> [regn] 8681 +[š][n] -> [šn] 8682 +[used ][as a ] -> [used as a ] 8683 +[pro][ject ] -> [project ] 8684 +[Bel][ar] -> [Belar] 8685 +[Vo][ic] -> [Voic] 8686 +[Th][ail] -> [Thail] 8687 +[J][av] -> [Jav] 8688 +[con][ven] -> [conven] 8689 +[á][n ] -> [án ] 8690 +[oc][rac] -> [ocrac] 8691 +[197][3 ] -> [1973 ] 8692 +[. ][People ] -> [. People ] 8693 +[Ber][lin ] -> [Berlin ] 8694 +[dis][ord] -> [disord] 8695 +[plan][et] -> [planet] 8696 +[UK][ M] -> [UK M] 8697 +[Soviet ][Union] -> [Soviet Union] 8698 +[mic][ro] -> [micro] 8699 +[po][or ] -> [poor ] 8700 +[word][s ] -> [words ] 8701 +[on][es ] -> [ones ] 8702 +[FLO][ || align=right | 2.] -> [FLO || align=right | 2.] 8703 +[R][id] -> [Rid] 8704 +[201][7 - ] -> [2017 - ] 8705 +[.][D] -> [.D] 8706 +[Gen][er] -> [Gener] 8707 +[ births\u000a][Living people] -> [ births\u000aLiving people] 8708 +[inter][est ] -> [interest ] 8709 +[thous][and] -> [thousand] 8710 +[Man][hatt] -> [Manhatt] 8711 +[�][�] -> [ă] 8712 +[exec][ut] -> [execut] 8713 +[i][als ] -> [ials ] 8714 +[Pakist][ani ] -> [Pakistani ] 8715 +[stat][es ] -> [states ] 8716 +[con][centr] -> [concentr] 8717 +[pri][mar] -> [primar] 8718 +[Z][e] -> [Ze] 8719 +[T][own ] -> [Town ] 8720 +[allow][ed to ] -> [allowed to ] 8721 +[flow][s ] -> [flows ] 8722 +["][B] -> ["B] 8723 +[L][a L] -> [La L] 8724 +[east ][of ] -> [east of ] 8725 +[P][-L] -> [P-L] 8726 +[reas][on ] -> [reason ] 8727 +[N][at] -> [Nat] 8728 +[Alb][an] -> [Alban] 8729 +[medal][ists\u000a] -> [medalists\u000a] 8730 +[19][12] -> [1912] 8731 +[3 km || \u000a|-id=][0] -> [3 km || \u000a|-id=0] 8732 +[cour][s] -> [cours] 8733 +[UK M][Ps ] -> [UK MPs ] 8734 +[D][ig] -> [Dig] 8735 +[ed][on] -> [edon] 8736 +[n][in] -> [nin] 8737 +[television ][present] -> [television present] 8738 +[bel][ow ] -> [below ] 8739 +[1960][ || Palomar || PLS] -> [1960 || Palomar || PLS] 8740 +[song][s ] -> [songs ] 8741 +[t][arg] -> [targ] 8742 +[Jes][us ] -> [Jesus ] 8743 +[Nep][al] -> [Nepal] 8744 +[ed ][or ] -> [ed or ] 8745 +[il][le] -> [ille] 8746 +[soldi][ers ] -> [soldiers ] 8747 +[.\u000a\u000a][ ] -> [.\u000a\u000a ] 8748 +[Th][ird ] -> [Third ] 8749 +[complet][ely ] -> [completely ] 8750 +[L][ine ] -> [Line ] 8751 +[serv][ed as the ] -> [served as the ] 8752 +[fin][ally ] -> [finally ] 8753 +[p][ack] -> [pack] 8754 +[and ][is ] -> [and is ] 8755 +[Ab][out ] -> [About ] 8756 +[expl][os] -> [explos] 8757 +[com][ing ] -> [coming ] 8758 +[min][or ] -> [minor ] 8759 +[descri][be ] -> [describe ] 8760 +[d][ance ] -> [dance ] 8761 +[, 2003][ || Socorro || LINEAR || ] -> [, 2003 || Socorro || LINEAR || ] 8762 +[)][. He ] -> [). He ] 8763 +[197][7 ] -> [1977 ] 8764 +[D][a] -> [Da] 8765 +[name ][of the ] -> [name of the ] 8766 +[M][ap] -> [Map] 8767 +[S][in] -> [Sin] 8768 +[rather ][than ] -> [rather than ] 8769 +[on ][April ] -> [on April ] 8770 +[o][j] -> [oj] 8771 +[m][m ] -> [mm ] 8772 +[ bgcolor=#fefefe\u000a| ][5] -> [ bgcolor=#fefefe\u000a| 5] 8773 +[name ][was ] -> [name was ] 8774 +[str][at] -> [strat] 8775 +[Ottom][an ] -> [Ottoman ] 8776 +[25][0] -> [250] 8777 +[ro][v] -> [rov] 8778 +[sequ][el ] -> [sequel ] 8779 +[Victor][ia ] -> [Victoria ] 8780 +[Rel][ig] -> [Relig] 8781 +[electr][ic] -> [electric] 8782 +[1][ (] -> [1 (] 8783 +[s][. These ] -> [s. These ] 8784 +[s][we] -> [swe] 8785 +[cour][t] -> [court] 8786 +[on][e-] -> [one-] 8787 +[S][n] -> [Sn] 8788 +[Con][n] -> [Conn] 8789 +[f][t] -> [ft] 8790 +[I][v] -> [Iv] 8791 +[award][ed the ] -> [awarded the ] 8792 +[2][ bgcolor=#fefefe\u000a| 1] -> [2 bgcolor=#fefefe\u000a| 1] 8793 +[eb][re] -> [ebre] 8794 +[day][, ] -> [day, ] 8795 +[run][s ] -> [runs ] 8796 +[her][b] -> [herb] 8797 +[in][ary ] -> [inary ] 8798 +[, 201][4] -> [, 2014] 8799 +[, 200][9] -> [, 2009] 8800 +[ne][y, ] -> [ney, ] 8801 +[Florid][a ] -> [Florida ] 8802 +[or][m] -> [orm] 8803 +[19][16] -> [1916] 8804 +[.\u000a\u000a][Club career statistics\u000a\u000a|-\u000a|] -> [.\u000a\u000aClub career statistics\u000a\u000a|-\u000a|] 8805 +[Or][thod] -> [Orthod] 8806 +[form][ed ] -> [formed ] 8807 +[, which ][was ] -> [, which was ] 8808 +[norm][ally ] -> [normally ] 8809 +[go ][to ] -> [go to ] 8810 +[hosp][ital] -> [hospital] 8811 +[seri][es, ] -> [series, ] 8812 +[Jam][a] -> [Jama] 8813 +[Fil][ip] -> [Filip] 8814 +[campa][ign] -> [campaign] 8815 +[Los Angel][es\u000a] -> [Los Angeles\u000a] 8816 +[ || — || align=right | ][7.] -> [ || — || align=right | 7.] 8817 +[til][l] -> [till] 8818 +[, but ][the ] -> [, but the ] 8819 +[r][as] -> [ras] 8820 +[190][0 ] -> [1900 ] 8821 +[d][a ] -> [da ] 8822 +[comp][any] -> [company] 8823 +[ ][K] -> [ K] 8824 +[(][) is a ] -> [() is a ] 8825 +[El][ect] -> [Elect] 8826 +[e][; ] -> [e; ] 8827 +[gener][al] -> [general] 8828 +[Afric][a] -> [Africa] 8829 +[th][ers ] -> [thers ] 8830 +[U][T] -> [UT] 8831 +[n][ut] -> [nut] 8832 +[m][ill] -> [mill] 8833 +[re][y ] -> [rey ] 8834 +[s ][at the ] -> [s at the ] 8835 +[v][ision ] -> [vision ] 8836 +[berg][, ] -> [berg, ] 8837 +[mot][or] -> [motor] 8838 +[came ][to ] -> [came to ] 8839 +[Philipp][in] -> [Philippin] 8840 +[m][orn] -> [morn] 8841 +[stri][p] -> [strip] 8842 +[ti][st ] -> [tist ] 8843 +[ig][in ] -> [igin ] 8844 +[16][6] -> [166] 8845 +[af][ter] -> [after] 8846 +[192][4] -> [1924] 8847 +[ith][uan] -> [ithuan] 8848 +["][ by ] -> [" by ] 8849 +[C][av] -> [Cav] 8850 +[Gre][ek] -> [Greek] 8851 +[y][t] -> [yt] 8852 +[Formula ][One ] -> [Formula One ] 8853 +[ km || ][\u000a|}\u000a\u000a] -> [ km || \u000a|}\u000a\u000a] 8854 +[2][ bgcolor=#E9E9E9\u000a| 1] -> [2 bgcolor=#E9E9E9\u000a| 1] 8855 +[adv][anc] -> [advanc] 8856 +[hor][s] -> [hors] 8857 +[Sci][ence ] -> [Science ] 8858 +[Canadian ][ice hockey ] -> [Canadian ice hockey ] 8859 +[11][ km || \u000a|-id=] -> [11 km || \u000a|-id=] 8860 +[El][ection ] -> [Election ] 8861 +[.\u000a\u000aS][he ] -> [.\u000a\u000aShe ] 8862 +[ ][from ] -> [ from ] 8863 +[tal][k] -> [talk] 8864 +[South ][Korea] -> [South Korea] 8865 +[Ron][ald ] -> [Ronald ] 8866 +[Ac][c] -> [Acc] 8867 +[chann][el] -> [channel] 8868 +[. A][s a ] -> [. As a ] 8869 +[Th][er] -> [Ther] 8870 +[marri][age ] -> [marriage ] 8871 +[bo][y ] -> [boy ] 8872 +[4 km || \u000a|-id=][0] -> [4 km || \u000a|-id=0] 8873 +[ref][erenc] -> [referenc] 8874 +[found][er of ] -> [founder of ] 8875 +[6 km || \u000a|-id=][0] -> [6 km || \u000a|-id=0] 8876 +[I][mp] -> [Imp] 8877 +[196][9 ] -> [1969 ] 8878 +[fl][ood] -> [flood] 8879 +[din][osaur] -> [dinosaur] 8880 +[receiv][ed the ] -> [received the ] 8881 +[st][an] -> [stan] 8882 +[d][anger] -> [danger] 8883 +[Mod][ern ] -> [Modern ] 8884 +[L][es] -> [Les] 8885 +[year-][old ] -> [year-old ] 8886 +[b][ound] -> [bound] 8887 +[||1][||1] -> [||1||1] 8888 +[T][on] -> [Ton] 8889 +[feature][s ] -> [features ] 8890 +[to ][do ] -> [to do ] 8891 +[Bur][n] -> [Burn] 8892 +[C][C] -> [CC] 8893 +[az][z] -> [azz] 8894 +[h][ill] -> [hill] 8895 +[ing][s of ] -> [ings of ] 8896 +[Carib][be] -> [Caribbe] 8897 +[. However, ][the ] -> [. However, the ] 8898 +[Golden ][Glob] -> [Golden Glob] 8899 +[Georg][ia] -> [Georgia] 8900 +[i]['s ] -> [i's ] 8901 +[cal][cul] -> [calcul] 8902 +[con][nec] -> [connec] 8903 +[fem][in] -> [femin] 8904 +[ity ][in ] -> [ity in ] 8905 +[ism][, ] -> [ism, ] 8906 +[man][n] -> [mann] 8907 +[proc][ess ] -> [process ] 8908 +[Jan][e ] -> [Jane ] 8909 +[st][adium ] -> [stadium ] 8910 +[or ][more ] -> [or more ] 8911 +[anc][ell] -> [ancell] 8912 +[f][ly ] -> [fly ] 8913 +[us][ed the ] -> [used the ] 8914 +[Ma][x ] -> [Max ] 8915 +[L][ong] -> [Long] 8916 +[muse][um ] -> [museum ] 8917 +[peopl][e of ] -> [people of ] 8918 +[d ][and ] -> [d and ] 8919 +[pro][ve ] -> [prove ] 8920 +[event][s\u000a] -> [events\u000a] 8921 +[Cl][int] -> [Clint] 8922 +[partic][ular ] -> [particular ] 8923 +[her][o] -> [hero] 8924 +[B][and] -> [Band] 8925 +[Fam][il] -> [Famil] 8926 +[com][es from ] -> [comes from ] 8927 +[car][bon ] -> [carbon ] 8928 +[ins][ect] -> [insect] 8929 +[Institut][e of ] -> [Institute of ] 8930 +[problems ][caused by ] -> [problems caused by ] 8931 +[used ][in the ] -> [used in the ] 8932 +[organiz][ation ] -> [organization ] 8933 +[way ][of ] -> [way of ] 8934 +[dig][ital ] -> [digital ] 8935 +[B][ank] -> [Bank] 8936 +[character][s ] -> [characters ] 8937 +[Alab][am] -> [Alabam] 8938 +[tournam][ent] -> [tournament] 8939 +[sur][face ] -> [surface ] 8940 +[thre][at] -> [threat] 8941 +[Grand ][Pri] -> [Grand Pri] 8942 +[en][burg] -> [enburg] 8943 +[Freder][ick ] -> [Frederick ] 8944 +[o][is] -> [ois] 8945 +[D][anc] -> [Danc] 8946 +[C][all] -> [Call] 8947 +[M][L] -> [ML] 8948 +[f][ing] -> [fing] 8949 +[R][ud] -> [Rud] 8950 +[Bo][y ] -> [Boy ] 8951 +[or][i ] -> [ori ] 8952 +[made up ][of ] -> [made up of ] 8953 +[c][arry ] -> [carry ] 8954 +[D][er] -> [Der] 8955 +[bas][ic ] -> [basic ] 8956 +[Common][weal] -> [Commonweal] 8957 +[F][ut] -> [Fut] 8958 +[includ][e the ] -> [include the ] 8959 +[del][iv] -> [deliv] 8960 +[stopp][ed ] -> [stopped ] 8961 +[Dur][ing ] -> [During ] 8962 +[N][apol] -> [Napol] 8963 +[K][im ] -> [Kim ] 8964 +[B][h] -> [Bh] 8965 +[. Dur][ing ] -> [. During ] 8966 +[M][emorial ] -> [Memorial ] 8967 +[with ][her ] -> [with her ] 8968 +[w][er] -> [wer] 8969 +[W][ill ] -> [Will ] 8970 +[197][1 ] -> [1971 ] 8971 +[ress][ional ] -> [ressional ] 8972 +[ b][el] -> [ bel] 8973 +[O][ak] -> [Oak] 8974 +[each ][other ] -> [each other ] 8975 +[above ][sea level] -> [above sea level] 8976 +[raf][t ] -> [raft ] 8977 +[nor][th-] -> [north-] 8978 +[b][ow] -> [bow] 8979 +[Phil][ip ] -> [Philip ] 8980 +[oc][cas] -> [occas] 8981 +[der][iv] -> [deriv] 8982 +[Bo][ard ] -> [Board ] 8983 +[P][ed] -> [Ped] 8984 +[Mississipp][i] -> [Mississippi] 8985 +[e, ][or ] -> [e, or ] 8986 +[requ][ir] -> [requir] 8987 +[Record][s\u000a ] -> [Records\u000a ] 8988 +[ann][y ] -> [anny ] 8989 +[19][13] -> [1913] 8990 +[5 km || \u000a|-id=][0] -> [5 km || \u000a|-id=0] 8991 +[. J][. B] -> [. J. B] 8992 +[pre][par] -> [prepar] 8993 +[con][vict] -> [convict] 8994 +[st][anc] -> [stanc] 8995 +[gu][il] -> [guil] 8996 +[Award ][winners\u000a] -> [Award winners\u000a] 8997 +[tim][e.\u000a\u000a] -> [time.\u000a\u000a] 8998 +[s. ][A ] -> [s. A ] 8999 +[Frog][s of ] -> [Frogs of ] 9000 +[ing][s\u000a] -> [ings\u000a] 9001 +[er][. He was ] -> [er. He was ] 9002 +[E][N] -> [EN] 9003 +[. S][h] -> [. Sh] 9004 +[C][D ] -> [CD ] 9005 +[sil][ent ] -> [silent ] 9006 +[M][ore ] -> [More ] 9007 +[vol][leyball ] -> [volleyball ] 9008 +[ated ][the ] -> [ated the ] 9009 +[Ad][am ] -> [Adam ] 9010 +[f][air] -> [fair] 9011 +[s ][is a ] -> [s is a ] 9012 +[ap][prov] -> [approv] 9013 +[ant][ine ] -> [antine ] 9014 +[for ]["] -> [for "] 9015 +[caus][es ] -> [causes ] 9016 +[cust][om] -> [custom] 9017 +[exp][ect] -> [expect] 9018 +[per][man] -> [perman] 9019 +[�][�] -> [р] 9020 +[193][1] -> [1931] 9021 +[because ][they ] -> [because they ] 9022 +[tr][aff] -> [traff] 9023 +[1 km || \u000a|-id=][0] -> [1 km || \u000a|-id=0] 9024 +[M][ach] -> [Mach] 9025 +[t][able ] -> [table ] 9026 +[weap][on] -> [weapon] 9027 +[.\u000a\u000aHistor][y\u000a] -> [.\u000a\u000aHistory\u000a] 9028 +[ter][ritory ] -> [territory ] 9029 +[develop][ed ] -> [developed ] 9030 +[a][o ] -> [ao ] 9031 +[stat][es] -> [states] 9032 +[B][rown] -> [Brown] 9033 +[le][agu] -> [leagu] 9034 +[resp][ond] -> [respond] 9035 +[y][l ] -> [yl ] 9036 +[re][ach ] -> [reach ] 9037 +[ul][a ] -> [ula ] 9038 +[�][�] -> [‘] 9039 +[organ][ism] -> [organism] 9040 +[r][al] -> [ral] 9041 +[long][-] -> [long-] 9042 +[Atl][anti] -> [Atlanti] 9043 +[sk][ill] -> [skill] 9044 +[eff][ect ] -> [effect ] 9045 +[heart ][failure] -> [heart failure] 9046 +[19][th centur] -> [19th centur] 9047 +[thoug][h] -> [though] 9048 +[Univers][ity, ] -> [University, ] 9049 +[Soviet ][Union ] -> [Soviet Union ] 9050 +[tem][pl] -> [templ] 9051 +[for ][its ] -> [for its ] 9052 +[N][ichol] -> [Nichol] 9053 +[es ][to the ] -> [es to the ] 9054 +[Liv][er] -> [Liver] 9055 +[1][10] -> [110] 9056 +[, ][C] -> [, C] 9057 +[1][20] -> [120] 9058 +[ph][er] -> [pher] 9059 +[-][sur-] -> [-sur-] 9060 +[N][el] -> [Nel] 9061 +[, M][ich] -> [, Mich] 9062 +[rais][ed in ] -> [raised in ] 9063 +[in][side ] -> [inside ] 9064 +[P][S] -> [PS] 9065 +[Pr][o ] -> [Pro ] 9066 +[Fur][ther ] -> [Further ] 9067 +[Ch][ang] -> [Chang] 9068 +[i][k ] -> [ik ] 9069 +[Commun][es of the ] -> [Communes of the ] 9070 +[d][edic] -> [dedic] 9071 +[temper][atur] -> [temperatur] 9072 +[Me][it] -> [Meit] 9073 +[movies\u000aMovies ][based on ] -> [movies\u000aMovies based on ] 9074 +[oc][curr] -> [occurr] 9075 +[Bro][s. ] -> [Bros. ] 9076 +[p][up] -> [pup] 9077 +[Bu][en] -> [Buen] 9078 +[people ][(] -> [people (] 9079 +[man ][(] -> [man (] 9080 +[2][ bgcolor=#d6d6d6\u000a| ] -> [2 bgcolor=#d6d6d6\u000a| ] 9081 +[im][a ] -> [ima ] 9082 +[D][at] -> [Dat] 9083 +[Ord][er of ] -> [Order of ] 9084 +[seas][on, ] -> [season, ] 9085 +[is a commun][e in the ] -> [is a commune in the ] 9086 +[who ][were ] -> [who were ] 9087 +[5][ bgcolor=#fefefe\u000a| ] -> [5 bgcolor=#fefefe\u000a| ] 9088 +[G][or] -> [Gor] 9089 +[web][ ] -> [web ] 9090 +[– ][The ] -> [– The ] 9091 +[th][at, ] -> [that, ] 9092 +[m][ang] -> [mang] 9093 +[4][ bgcolor=#E9E9E9\u000a| ] -> [4 bgcolor=#E9E9E9\u000a| ] 9094 +[od][g] -> [odg] 9095 +[Pro][test] -> [Protest] 9096 +[c][orn] -> [corn] 9097 +[ell][a ] -> [ella ] 9098 +[featur][ing ] -> [featuring ] 9099 +[T][ol] -> [Tol] 9100 +[mov][ing ] -> [moving ] 9101 +[H][em] -> [Hem] 9102 +[football][ers] -> [footballers] 9103 +[re][li] -> [reli] 9104 +[play][ed in ] -> [played in ] 9105 +[dom][es] -> [domes] 9106 +[2][–] -> [2–] 9107 +[icip][al ] -> [icipal ] 9108 +[el][ and ] -> [el and ] 9109 +[i][mer] -> [imer] 9110 +[L][ar] -> [Lar] 9111 +[drama ][movies\u000aAmerican ] -> [drama movies\u000aAmerican ] 9112 +[sid][e of ] -> [side of ] 9113 +[L][es ] -> [Les ] 9114 +[and][o ] -> [ando ] 9115 +[)][.\u000a\u000aThe ] -> [).\u000a\u000aThe ] 9116 +[Roman ][Cathol] -> [Roman Cathol] 9117 +[Cap][e ] -> [Cape ] 9118 +[, 2000][ || ] -> [, 2000 || ] 9119 +[fath][er, ] -> [father, ] 9120 +[requ][ire] -> [require] 9121 +[er][ve ] -> [erve ] 9122 +[Wiki][pedi] -> [Wikipedi] 9123 +[am][ount ] -> [amount ] 9124 +[Co][ast ] -> [Coast ] 9125 +[tournam][ent ] -> [tournament ] 9126 +[us][ual ] -> [usual ] 9127 +[h][un] -> [hun] 9128 +[movi][e, ] -> [movie, ] 9129 +[Pol][and ] -> [Poland ] 9130 +[h][ead of ] -> [head of ] 9131 +[after ][his ] -> [after his ] 9132 +[ext][rem] -> [extrem] 9133 +[fut][ure ] -> [future ] 9134 +[Dougl][as ] -> [Douglas ] 9135 +[Wal][t ] -> [Walt ] 9136 +[iv][al] -> [ival] 9137 +[out][side ] -> [outside ] 9138 +[�][�] -> [ë] 9139 +[j][ob ] -> [job ] 9140 +[em][o] -> [emo] 9141 +[ant][, ] -> [ant, ] 9142 +[ap][s] -> [aps] 9143 +[. ][St] -> [. St] 9144 +[Med][ical ] -> [Medical ] 9145 +[es, ][but ] -> [es, but ] 9146 +[in the ][world] -> [in the world] 9147 +[Id][ah] -> [Idah] 9148 +[reg][ional ] -> [regional ] 9149 +[e-][related ] -> [e-related ] 9150 +[Republican ][Party (United States) ] -> [Republican Party (United States) ] 9151 +[ific][ation ] -> [ification ] 9152 +[. J. B][us ] -> [. J. Bus ] 9153 +[6][ bgcolor=#E9E9E9\u000a| ] -> [6 bgcolor=#E9E9E9\u000a| ] 9154 +[W][ater] -> [Water] 9155 +[dr][y ] -> [dry ] 9156 +[\u000a ][E] -> [\u000a E] 9157 +[becom][ing ] -> [becoming ] 9158 +[Med][it] -> [Medit] 9159 +[b][ank ] -> [bank ] 9160 +[we][ek ] -> [week ] 9161 +[open][ed in ] -> [opened in ] 9162 +[5][,] -> [5,] 9163 +[Os][ak] -> [Osak] 9164 +[at][es] -> [ates] 9165 +[only ][one ] -> [only one ] 9166 +[team][s ] -> [teams ] 9167 +[bor][der] -> [border] 9168 +[d][ark ] -> [dark ] 9169 +[2021][) was a ] -> [2021) was a ] 9170 +[ing ][on the ] -> [ing on the ] 9171 +[Cent][er ] -> [Center ] 9172 +[Ch][e] -> [Che] 9173 +[Chicag][o\u000a] -> [Chicago\u000a] 9174 +[a][uc] -> [auc] 9175 +[Am][az] -> [Amaz] 9176 +[M][ong] -> [Mong] 9177 +[201][3, ] -> [2013, ] 9178 +[an ][un] -> [an un] 9179 +[On][ly ] -> [Only ] 9180 +[. T][w] -> [. Tw] 9181 +[is][ed ] -> [ised ] 9182 +[�][�] -> [�] 9183 +[philos][opher ] -> [philosopher ] 9184 +[mur][d] -> [murd] 9185 +[it]['s ] -> [it's ] 9186 +[reg][ist] -> [regist] 9187 +[av][i] -> [avi] 9188 +[earli][er ] -> [earlier ] 9189 +[Fr][on] -> [Fron] 9190 +[Cong][ress ] -> [Congress ] 9191 +[B][en ] -> [Ben ] 9192 +[South ][America] -> [South America] 9193 +[\u000a][On ] -> [\u000aOn ] 9194 +[in][stitu] -> [institu] 9195 +[Viet][nam ] -> [Vietnam ] 9196 +[accord][ing to ] -> [according to ] 9197 +[se][g] -> [seg] 9198 +[m][ut] -> [mut] 9199 +[, 2003][ || Palomar || NEAT] -> [, 2003 || Palomar || NEAT] 9200 +[years ][ago] -> [years ago] 9201 +[earli][est ] -> [earliest ] 9202 +[symp][tom] -> [symptom] 9203 +[Sh][r] -> [Shr] 9204 +[au][x] -> [aux] 9205 +[ed][ral ] -> [edral ] 9206 +[, 1999 || Socorro || LINEAR || — || align=right | ][3.] -> [, 1999 || Socorro || LINEAR || — || align=right | 3.] 9207 +[y][ellow ] -> [yellow ] 9208 +[spl][it ] -> [split ] 9209 +[NYS][ || align=right | 2.] -> [NYS || align=right | 2.] 9210 +[in ][K] -> [in K] 9211 +[Ro][ad] -> [Road] 9212 +[h][unt] -> [hunt] 9213 +[m][om] -> [mom] 9214 +[4][th ] -> [4th ] 9215 +[eng][u] -> [engu] 9216 +[anc][y ] -> [ancy ] 9217 +[ob][ject ] -> [object ] 9218 +[Philadelph][ia ] -> [Philadelphia ] 9219 +[C][os] -> [Cos] 9220 +[f][res] -> [fres] 9221 +[opp][on] -> [oppon] 9222 +[attemp][t] -> [attempt] 9223 +[Sen][ator ] -> [Senator ] 9224 +[Rel][eas] -> [Releas] 9225 +[W][ond] -> [Wond] 9226 +[ay ][(] -> [ay (] 9227 +[C][S] -> [CS] 9228 +[Scot][land ] -> [Scotland ] 9229 +[spir][it] -> [spirit] 9230 +[w][as] -> [was] 9231 +[P][in] -> [Pin] 9232 +[L][ast ] -> [Last ] 9233 +[ch][ester] -> [chester] 9234 +[had ][the ] -> [had the ] 9235 +[w][in the ] -> [win the ] 9236 +[Official websit][e\u000a\u000a] -> [Official website\u000a\u000a] 9237 +[stud][ent ] -> [student ] 9238 +[iz][z] -> [izz] 9239 +[on ][to ] -> [on to ] 9240 +[tri][es to ] -> [tries to ] 9241 +[\u000a][G] -> [\u000aG] 9242 +[Pers][ian ] -> [Persian ] 9243 +[Al][fred ] -> [Alfred ] 9244 +[Pol][ice ] -> [Police ] 9245 +[Brook][ly] -> [Brookly] 9246 +[V][anc] -> [Vanc] 9247 +[activ][ist] -> [activist] 9248 +[B][illy ] -> [Billy ] 9249 +[5 ][deaths\u000a] -> [5 deaths\u000a] 9250 +[ births\u000a2019 ][deaths\u000a] -> [ births\u000a2019 deaths\u000a] 9251 +[assist][ant ] -> [assistant ] 9252 +[H][an] -> [Han] 9253 +[sub][ject] -> [subject] 9254 +[Afric][an-American ] -> [African-American ] 9255 +[it ][(] -> [it (] 9256 +[L][ong ] -> [Long ] 9257 +[Go][og] -> [Goog] 9258 +[Rob][in] -> [Robin] 9259 +[N][um] -> [Num] 9260 +[charact][ers] -> [characters] 9261 +[Lad][y ] -> [Lady ] 9262 +[high][ly ] -> [highly ] 9263 +[is the ][second ] -> [is the second ] 9264 +[rol][es in ] -> [roles in ] 9265 +[w][ind ] -> [wind ] 9266 +[3][ bgcolor=#E9E9E9\u000a| 1] -> [3 bgcolor=#E9E9E9\u000a| 1] 9267 +[ri][b] -> [rib] 9268 +[direct][or, ] -> [director, ] 9269 +[H][ow ] -> [How ] 9270 +[197][0s ] -> [1970s ] 9271 +[\u000a\u000aReferences\u000a\u000aOther websites\u000a ][\u000a\u000a] -> [\u000a\u000aReferences\u000a\u000aOther websites\u000a \u000a\u000a] 9272 +[ism ][and ] -> [ism and ] 9273 +[god][des] -> [goddes] 9274 +[ti][s ] -> [tis ] 9275 +[1][ bgcolor=#fefefe\u000a| 1] -> [1 bgcolor=#fefefe\u000a| 1] 9276 +[for ][her ] -> [for her ] 9277 +[Ph][ot] -> [Phot] 9278 +[most ][common ] -> [most common ] 9279 +[Ad][v] -> [Adv] 9280 +[P][ad] -> [Pad] 9281 +[A][G] -> [AG] 9282 +[f][un] -> [fun] 9283 +[vic][tim] -> [victim] 9284 +[Bas][eball ] -> [Baseball ] 9285 +[addi][tional ] -> [additional ] 9286 +[en][\u000a ] -> [en\u000a ] 9287 +[newspap][er ] -> [newspaper ] 9288 +[writ][er (d. ] -> [writer (d. ] 9289 +[D][y] -> [Dy] 9290 +[it][ar ] -> [itar ] 9291 +[z][en ] -> [zen ] 9292 +[ag][h] -> [agh] 9293 +[a ][large ] -> [a large ] 9294 +[i][ent] -> [ient] 9295 +[qu][art] -> [quart] 9296 +[1][ bgcolor=#E9E9E9\u000a| 1] -> [1 bgcolor=#E9E9E9\u000a| 1] 9297 +[.com][/] -> [.com/] 9298 +[Lib][eral ] -> [Liberal ] 9299 +[in ][The ] -> [in The ] 9300 +[196][6 ] -> [1966 ] 9301 +[in ][this ] -> [in this ] 9302 +[or][f] -> [orf] 9303 +[lo][ad] -> [load] 9304 +[Carl][os ] -> [Carlos ] 9305 +[r][ati] -> [rati] 9306 +[famil][ies ] -> [families ] 9307 +[–][18] -> [–18] 9308 +[equ][ip] -> [equip] 9309 +[P][ass] -> [Pass] 9310 +[leng][th ] -> [length ] 9311 +[pol][o ] -> [polo ] 9312 +[En][d] -> [End] 9313 +[Cret][ace] -> [Cretace] 9314 +[, ][British ] -> [, British ] 9315 +[mon][ey] -> [money] 9316 +[lef][t] -> [left] 9317 +[dea][th of ] -> [death of ] 9318 +[F][le] -> [Fle] 9319 +[tri][al ] -> [trial ] 9320 +[inst][all] -> [install] 9321 +[k][ov] -> [kov] 9322 +[ro][d] -> [rod] 9323 +[air][port ] -> [airport ] 9324 +[u][ and ] -> [u and ] 9325 +[c][um] -> [cum] 9326 +[text][-] -> [text-] 9327 +[O][ver ] -> [Over ] 9328 +[Iow][a] -> [Iowa] 9329 +[Prime Minist][er ] -> [Prime Minister ] 9330 +[re][pe] -> [repe] 9331 +[O][bl] -> [Obl] 9332 +[ion][e] -> [ione] 9333 +[ey ][(] -> [ey (] 9334 +[football][er (] -> [footballer (] 9335 +[educ][ation ] -> [education ] 9336 +[T][ig] -> [Tig] 9337 +[er][: ] -> [er: ] 9338 +[. As of the ][2020 census] -> [. As of the 2020 census] 9339 +[allow][s ] -> [allows ] 9340 +[ic][s] -> [ics] 9341 +[long][est ] -> [longest ] 9342 +[Pres][s, ] -> [Press, ] 9343 +[, 201][5] -> [, 2015] 9344 +[ff][ ] -> [ff ] 9345 +[Kar][l ] -> [Karl ] 9346 +[, 201][0] -> [, 2010] 9347 +[because ][it ] -> [because it ] 9348 +[numb][ers ] -> [numbers ] 9349 +[histor][ic ] -> [historic ] 9350 +[T][u] -> [Tu] 9351 +[people ][and ] -> [people and ] 9352 +[igen][ous ] -> [igenous ] 9353 +[8][0 ] -> [80 ] 9354 +[writ][er (b. ] -> [writer (b. ] 9355 +[Lib][er] -> [Liber] 9356 +[) ][to ] -> [) to ] 9357 +[. ][One ] -> [. One ] 9358 +[includ][es the ] -> [includes the ] 9359 +[Eth][iop] -> [Ethiop] 9360 +[St][ory ] -> [Story ] 9361 +[-][S] -> [-S] 9362 +[GE][F] -> [GEF] 9363 +[)][. ] -> [). ] 9364 +[Prime Minist][ers of ] -> [Prime Ministers of ] 9365 +[t][ext ] -> [text ] 9366 +[In][n] -> [Inn] 9367 +[�][�] -> [Ö] 9368 +[�][�] -> [�] 9369 +[United Kingdom][\u000a] -> [United Kingdom\u000a] 9370 +[ul][s] -> [uls] 9371 +[h][op ] -> [hop ] 9372 +[six][th ] -> [sixth ] 9373 +[se][em] -> [seem] 9374 +[c][ost ] -> [cost ] 9375 +[Net][work ] -> [Network ] 9376 +[prev][ent ] -> [prevent ] 9377 +[tion ][in ] -> [tion in ] 9378 +[Georg][ia (] -> [Georgia (] 9379 +[W][el] -> [Wel] 9380 +[financ][ial ] -> [financial ] 9381 +[en][ing ] -> [ening ] 9382 +[in][duc] -> [induc] 9383 +[y][weight ] -> [yweight ] 9384 +[establishments in ][Europe\u000a] -> [establishments in Europe\u000a] 9385 +[politician ][(d. ] -> [politician (d. ] 9386 +[t][un] -> [tun] 9387 +[Fin][al ] -> [Final ] 9388 +[New ][Hamp] -> [New Hamp] 9389 +[stage ][actors\u000aAmerican ] -> [stage actors\u000aAmerican ] 9390 +[Mount][ain ] -> [Mountain ] 9391 +[Jun][ior ] -> [Junior ] 9392 +[hug][e ] -> [huge ] 9393 +[Gab][ri] -> [Gabri] 9394 +[liv][ing in ] -> [living in ] 9395 +[m][and] -> [mand] 9396 +[in][side the ] -> [inside the ] 9397 +[contin][ue ] -> [continue ] 9398 +[Mad][rid] -> [Madrid] 9399 +[sist][er ] -> [sister ] 9400 +[ed][, the ] -> [ed, the ] 9401 +[ill][a ] -> [illa ] 9402 +[ain][e ] -> [aine ] 9403 +[o][pedi] -> [opedi] 9404 +[drug][ ] -> [drug ] 9405 +[e][. S] -> [e. S] 9406 +[Char][l] -> [Charl] 9407 +[fash][ion ] -> [fashion ] 9408 +[er][min] -> [ermin] 9409 +[Jean][-] -> [Jean-] 9410 +[S][us] -> [Sus] 9411 +[Law][y] -> [Lawy] 9412 +[F][em] -> [Fem] 9413 +[German][y\u000a] -> [Germany\u000a] 9414 +[tran][sport] -> [transport] 9415 +[this ][is ] -> [this is ] 9416 +[Nobel ][Prize in ] -> [Nobel Prize in ] 9417 +[un][ch ] -> [unch ] 9418 +[s][ess] -> [sess] 9419 +[17][4] -> [174] 9420 +[autom][obil] -> [automobil] 9421 +[Frank][lin ] -> [Franklin ] 9422 +[H][ ] -> [H ] 9423 +[\u000a ][195] -> [\u000a 195] 9424 +[201][5, ] -> [2015, ] 9425 +[ kil][omet] -> [ kilomet] 9426 +[E][ug] -> [Eug] 9427 +[R][ay ] -> [Ray ] 9428 +[so ][that ] -> [so that ] 9429 +[fi][el] -> [fiel] 9430 +[World War ][I] -> [World War I] 9431 +[ful][l] -> [full] 9432 +[j][ump] -> [jump] 9433 +[ || || — || January ][5] -> [ || || — || January 5] 9434 +[ograph][ical ] -> [ographical ] 9435 +[||1][||0||0] -> [||1||0||0] 9436 +[Op][en ] -> [Open ] 9437 +[C][aus] -> [Caus] 9438 +[h][at] -> [hat] 9439 +[Ap][pl] -> [Appl] 9440 +[from ][his ] -> [from his ] 9441 +[att][end] -> [attend] 9442 +[Official websit][e \u000a\u000a] -> [Official website \u000a\u000a] 9443 +[- ][The ] -> [- The ] 9444 +[ bgcolor=#d6d6d6\u000a| ][5] -> [ bgcolor=#d6d6d6\u000a| 5] 9445 +[Con][ven] -> [Conven] 9446 +[alum][ni] -> [alumni] 9447 +[sk][y] -> [sky] 9448 +[Ob][s. ] -> [Obs. ] 9449 +[in]['s ] -> [in's ] 9450 +[g][all] -> [gall] 9451 +[Ut][ah] -> [Utah] 9452 +[7][ bgcolor=#fefefe\u000a| 1] -> [7 bgcolor=#fefefe\u000a| 1] 9453 +[an ][American ] -> [an American ] 9454 +[Del][aw] -> [Delaw] 9455 +[Sal][z] -> [Salz] 9456 +[comedy ][movies\u000aAmerican ] -> [comedy movies\u000aAmerican ] 9457 +[err][an] -> [erran] 9458 +[S][-] -> [S-] 9459 +[ || || ][ || September ] -> [ || || || September ] 9460 +[ian][o ] -> [iano ] 9461 +[i][ć] -> [ić] 9462 +[dist][inc] -> [distinc] 9463 +[-d]['] -> [-d'] 9464 +[her][o ] -> [hero ] 9465 +[al][s, ] -> [als, ] 9466 +[comm][itt] -> [committ] 9467 +[Ch][er] -> [Cher] 9468 +[success][ful] -> [successful] 9469 +[7 km || \u000a|-id=][0] -> [7 km || \u000a|-id=0] 9470 +[, 201][8] -> [, 2018] 9471 +[ig][u] -> [igu] 9472 +[publish][ed by ] -> [published by ] 9473 +[201][4, ] -> [2014, ] 9474 +[im][medi] -> [immedi] 9475 +[||0||0][\u000a|-\u000a|] -> [||0||0\u000a|-\u000a|] 9476 +[fol][low ] -> [follow ] 9477 +[S][eg] -> [Seg] 9478 +[. It was ][the ] -> [. It was the ] 9479 +[S][ão ] -> [São ] 9480 +[par][ents ] -> [parents ] 9481 +[c][ard] -> [card] 9482 +[) ][of the ] -> [) of the ] 9483 +[London][\u000a] -> [London\u000a] 9484 +[ab][ly ] -> [ably ] 9485 +[ann][ual ] -> [annual ] 9486 +[y][ou] -> [you] 9487 +[partic][ularly ] -> [particularly ] 9488 +[fron][t ] -> [front ] 9489 +[set ][in the ] -> [set in the ] 9490 +[Ch][ief] -> [Chief] 9491 +[ib][il] -> [ibil] 9492 +[in B][avar] -> [in Bavar] 9493 +[ ][– ] -> [ – ] 9494 +[he][at ] -> [heat ] 9495 +[k ][of ] -> [k of ] 9496 +[||0][\u000a\u000a|-\u000a|] -> [||0\u000a\u000a|-\u000a|] 9497 +[f][oot] -> [foot] 9498 +[Ac][t ] -> [Act ] 9499 +[Harv][ard ] -> [Harvard ] 9500 +[in ][an ] -> [in an ] 9501 +[bur][y ] -> [bury ] 9502 +[Gir][ond] -> [Girond] 9503 +[F][err] -> [Ferr] 9504 +[serv][ing ] -> [serving ] 9505 +[a][x ] -> [ax ] 9506 +[lif][e and ] -> [life and ] 9507 +[i ][K] -> [i K] 9508 +[voic][ed by ] -> [voiced by ] 9509 +[Detro][it ] -> [Detroit ] 9510 +[bi][o] -> [bio] 9511 +[Conserv][ative ] -> [Conservative ] 9512 +[New][s ] -> [News ] 9513 +[Vanc][ou] -> [Vancou] 9514 +[ar][m ] -> [arm ] 9515 +[ph][ere ] -> [phere ] 9516 +[it][self] -> [itself] 9517 +[ent][er ] -> [enter ] 9518 +[F][T] -> [FT] 9519 +[ate ][and ] -> [ate and ] 9520 +[movie ][director] -> [movie director] 9521 +[continu][ed to ] -> [continued to ] 9522 +[COVID-19 pandem][ic ] -> [COVID-19 pandemic ] 9523 +[in][struc] -> [instruc] 9524 +[og][g] -> [ogg] 9525 +[word ]["] -> [word "] 9526 +[neg][ative ] -> [negative ] 9527 +[. M][c] -> [. Mc] 9528 +[8][ bgcolor=#fefefe\u000a| ] -> [8 bgcolor=#fefefe\u000a| ] 9529 +[ing ][or ] -> [ing or ] 9530 +[ic][tion] -> [iction] 9531 +[sur][g] -> [surg] 9532 +[own][ed by ] -> [owned by ] 9533 +[countri][es] -> [countries] 9534 +[Oc][ean ] -> [Ocean ] 9535 +[-][American ] -> [-American ] 9536 +[Di][ff] -> [Diff] 9537 +[Pal][ac] -> [Palac] 9538 +[sub][urb] -> [suburb] 9539 +[. B][oth ] -> [. Both ] 9540 +[Stre][et] -> [Street] 9541 +[Don]['t ] -> [Don't ] 9542 +[H][ouse] -> [House] 9543 +[per][f] -> [perf] 9544 +[C][ab] -> [Cab] 9545 +[m][i] -> [mi] 9546 +[sit][com] -> [sitcom] 9547 +[aul][t ] -> [ault ] 9548 +[F][our] -> [Four] 9549 +[direct][or and ] -> [director and ] 9550 +[B][ach] -> [Bach] 9551 +[3][ bgcolor=#fefefe\u000a| 1] -> [3 bgcolor=#fefefe\u000a| 1] 9552 +[, 1999 || Socorro || LINEAR || — || align=right | ][1.] -> [, 1999 || Socorro || LINEAR || — || align=right | 1.] 9553 +[year][, ] -> [year, ] 9554 +[bro][ke ] -> [broke ] 9555 +[||3][\u000a|-\u000a|200] -> [||3\u000a|-\u000a|200] 9556 +[ine ][and ] -> [ine and ] 9557 +[wrot][e the ] -> [wrote the ] 9558 +[. The ][song ] -> [. The song ] 9559 +[Church ][of ] -> [Church of ] 9560 +[m][en's ] -> [men's ] 9561 +[196][2 ] -> [1962 ] 9562 +[, and ][was ] -> [, and was ] 9563 +[Nik][ol] -> [Nikol] 9564 +[)][. In ] -> [). In ] 9565 +[ev][al ] -> [eval ] 9566 +[Other websit][es \u000a\u000a ] -> [Other websites \u000a\u000a ] 9567 +[at the ][2010 census] -> [at the 2010 census] 9568 +[r][y of ] -> [ry of ] 9569 +[in][ch] -> [inch] 9570 +[Mary][land] -> [Maryland] 9571 +[1 ][in ] -> [1 in ] 9572 +[s, ][American ] -> [s, American ] 9573 +[he][im ] -> [heim ] 9574 +[ograp][her ] -> [ographer ] 9575 +[h][ers] -> [hers] 9576 +[0 km || \u000a|-id=][0] -> [0 km || \u000a|-id=0] 9577 +[z][ (] -> [z (] 9578 +[ex][ual ] -> [exual ] 9579 +[con][sol] -> [consol] 9580 +[aircraf][t] -> [aircraft] 9581 +[ bgcolor=#][C2] -> [ bgcolor=#C2] 9582 +[di][am] -> [diam] 9583 +[up][on ] -> [upon ] 9584 +[Ex][ec] -> [Exec] 9585 +[name ][is ] -> [name is ] 9586 +[es][tr] -> [estr] 9587 +[et][y ] -> [ety ] 9588 +[com][es ] -> [comes ] 9589 +[on ][February ] -> [on February ] 9590 +[a][e ] -> [ae ] 9591 +[For][d ] -> [Ford ] 9592 +[sum][m] -> [summ] 9593 +[speak][ing ] -> [speaking ] 9594 +[for][est ] -> [forest ] 9595 +[New Yor][k, ] -> [New York, ] 9596 +[P][y] -> [Py] 9597 +[eth][nic ] -> [ethnic ] 9598 +[19][11] -> [1911] 9599 +[R][oll] -> [Roll] 9600 +[began ][in ] -> [began in ] 9601 +[Ath][le] -> [Athle] 9602 +[pun][ish] -> [punish] 9603 +[F][ort] -> [Fort] 9604 +[h][p] -> [hp] 9605 +[En][cycl] -> [Encycl] 9606 +[||0||][5] -> [||0||5] 9607 +[a][)\u000a ] -> [a)\u000a ] 9608 +[7][ bgcolor=#E9E9E9\u000a| 1] -> [7 bgcolor=#E9E9E9\u000a| 1] 9609 +[inter][view ] -> [interview ] 9610 +[st][ore ] -> [store ] 9611 +[Champion][s ] -> [Champions ] 9612 +[S][ri Lank] -> [Sri Lank] 9613 +[V][an] -> [Van] 9614 +[Studi][o ] -> [Studio ] 9615 +[Parliam][ent] -> [Parliament] 9616 +[ation][s, ] -> [ations, ] 9617 +[R][ub] -> [Rub] 9618 +[pro][mis] -> [promis] 9619 +[Pr][uss] -> [Pruss] 9620 +[.\u000a\u000aB][iograph] -> [.\u000a\u000aBiograph] 9621 +[Museum ][of ] -> [Museum of ] 9622 +[imm][igr] -> [immigr] 9623 +[television series debut][s\u000a] -> [television series debuts\u000a] 9624 +[pron][ounc] -> [pronounc] 9625 +[) ][as ] -> [) as ] 9626 +[T][ow] -> [Tow] 9627 +[8 km || \u000a|-id=][0] -> [8 km || \u000a|-id=0] 9628 +[Eag][le ] -> [Eagle ] 9629 +[Academy ][Award] -> [Academy Award] 9630 +[dr][am] -> [dram] 9631 +[Princ][e of ] -> [Prince of ] 9632 +[ad][ver] -> [adver] 9633 +[fe][et ] -> [feet ] 9634 +[Comp][uter ] -> [Computer ] 9635 +[at][e of ] -> [ate of ] 9636 +[ bgcolor=#E9E9E9\u000a| ][9] -> [ bgcolor=#E9E9E9\u000a| 9] 9637 +[World ][Championship] -> [World Championship] 9638 +[ou][d ] -> [oud ] 9639 +[con][v] -> [conv] 9640 +[A][-] -> [A-] 9641 +[E][mil] -> [Emil] 9642 +[sim][ple ] -> [simple ] 9643 +[sch][edul] -> [schedul] 9644 +[to ][help ] -> [to help ] 9645 +[text-][align] -> [text-align] 9646 +[Margar][et ] -> [Margaret ] 9647 +[pol][y] -> [poly] 9648 +[es ][for the ] -> [es for the ] 9649 +[p][itch] -> [pitch] 9650 +[Fri][end] -> [Friend] 9651 +[B][ou] -> [Bou] 9652 +[St][ew] -> [Stew] 9653 +[V][el] -> [Vel] 9654 +[e. ][A ] -> [e. A ] 9655 +[to ][his ] -> [to his ] 9656 +[would ][have ] -> [would have ] 9657 +[e.][g] -> [e.g] 9658 +[Ch][i] -> [Chi] 9659 +[S][or] -> [Sor] 9660 +[tow][ards ] -> [towards ] 9661 +[Sp][ort ] -> [Sport ] 9662 +[se][ed] -> [seed] 9663 +[Buen][os ] -> [Buenos ] 9664 +[ph][as] -> [phas] 9665 +[adi][um] -> [adium] 9666 +[L][ang] -> [Lang] 9667 +[y]["] -> [y"] 9668 +[person][n] -> [personn] 9669 +[tit][ud] -> [titud] 9670 +[dis][put] -> [disput] 9671 +[Li][eutenant ] -> [Lieutenant ] 9672 +[comp][ani] -> [compani] 9673 +[Pet][ers] -> [Peters] 9674 +[Vi][šn] -> [Višn] 9675 +[Wh][o ] -> [Who ] 9676 +[S][ag] -> [Sag] 9677 +[L][ight] -> [Light] 9678 +[Y][ok] -> [Yok] 9679 +[electr][onic ] -> [electronic ] 9680 +[, 2001 || Socorro || LINEAR || — || align=right | ][5.] -> [, 2001 || Socorro || LINEAR || — || align=right | 5.] 9681 +[of ][them ] -> [of them ] 9682 +[P][op ] -> [Pop ] 9683 +[A][N] -> [AN] 9684 +[S][on] -> [Son] 9685 +[, 2002 || Socorro || LINEAR || — || align=right | ][2.] -> [, 2002 || Socorro || LINEAR || — || align=right | 2.] 9686 +[" ][- ] -> [" - ] 9687 +[Sur][vey ] -> [Survey ] 9688 +[perform][ance ] -> [performance ] 9689 +[people ][with ] -> [people with ] 9690 +[Sh][ad] -> [Shad] 9691 +[languag][es] -> [languages] 9692 +[G][oth] -> [Goth] 9693 +[M][ess] -> [Mess] 9694 +[fac][il] -> [facil] 9695 +[Per][form] -> [Perform] 9696 +[Isra][el ] -> [Israel ] 9697 +[c][os] -> [cos] 9698 +[ing ][was ] -> [ing was ] 9699 +[sa][id] -> [said] 9700 +[Heal][th ] -> [Health ] 9701 +[,][0] -> [,0] 9702 +[sc][ore ] -> [score ] 9703 +[B][ack] -> [Back] 9704 +[text-align][:] -> [text-align:] 9705 +[ed ][about ] -> [ed about ] 9706 +[. They ][had ] -> [. They had ] 9707 +[be][ing the ] -> [being the ] 9708 +[el][ve ] -> [elve ] 9709 +[Višn][jan ] -> [Višnjan ] 9710 +[NH][L] -> [NHL] 9711 +[M][ain ] -> [Main ] 9712 +[made ][his ] -> [made his ] 9713 +[drop][p] -> [dropp] 9714 +[" ][or "] -> [" or "] 9715 +[. Ac][cording to ] -> [. According to ] 9716 +[Picture][s ] -> [Pictures ] 9717 +[F][C] -> [FC] 9718 +[department in the north of ][France.\u000a\u000aCommunes in ] -> [department in the north of France.\u000a\u000aCommunes in ] 9719 +[he ][became ] -> [he became ] 9720 +[Golden Glob][e ] -> [Golden Globe ] 9721 +[Sy][ri] -> [Syri] 9722 +[World War II][ ] -> [World War II ] 9723 +[.\u000a\u000aOther websit][es \u000a ] -> [.\u000a\u000aOther websites \u000a ] 9724 +[stre][am ] -> [stream ] 9725 +[s][ummer ] -> [summer ] 9726 +[ati][ ] -> [ati ] 9727 +[o][.\u000a\u000a] -> [o.\u000a\u000a] 9728 +[ || || — || April ][5] -> [ || || — || April 5] 9729 +[t][ouch] -> [touch] 9730 +[N][ick ] -> [Nick ] 9731 +[inter][n] -> [intern] 9732 +[A][ch] -> [Ach] 9733 +[W][ater ] -> [Water ] 9734 +[M][ember ] -> [Member ] 9735 +[Major ][League ] -> [Major League ] 9736 +[S][ud] -> [Sud] 9737 +[sol][o ] -> [solo ] 9738 +[Summer ][Paralymp] -> [Summer Paralymp] 9739 +[Bo][eing ] -> [Boeing ] 9740 +[Chil][ean ] -> [Chilean ] 9741 +[||0||0][\u000a|-\u000a|200] -> [||0||0\u000a|-\u000a|200] 9742 +[reti][re] -> [retire] 9743 +[number ][one ] -> [number one ] 9744 +[�][�] -> [£] 9745 +[.][org] -> [.org] 9746 +[studi][ed at ] -> [studied at ] 9747 +[,][\u000a ] -> [,\u000a ] 9748 +[orn][ey ] -> [orney ] 9749 +[pe][t ] -> [pet ] 9750 +[is also ][the ] -> [is also the ] 9751 +[li][z] -> [liz] 9752 +[an][a, ] -> [ana, ] 9753 +[Vlad][imir ] -> [Vladimir ] 9754 +[releas][e ] -> [release ] 9755 +[Arch][iv] -> [Archiv] 9756 +[T][empl] -> [Templ] 9757 +[3][5 ] -> [35 ] 9758 +[so ][the ] -> [so the ] 9759 +[they ][can ] -> [they can ] 9760 +[b][an ] -> [ban ] 9761 +[7][0 ] -> [70 ] 9762 +[conc][ert ] -> [concert ] 9763 +[al ][inf] -> [al inf] 9764 +[San Francis][co] -> [San Francisco] 9765 +[. ][She ] -> [. She ] 9766 +[H][ann] -> [Hann] 9767 +[May][or of ] -> [Mayor of ] 9768 +[exc][ep] -> [excep] 9769 +[p][icture] -> [picture] 9770 +[n][a] -> [na] 9771 +[inc][umb] -> [incumb] 9772 +[comun][e in the ] -> [comune in the ] 9773 +[200][6, ] -> [2006, ] 9774 +[dec][or] -> [decor] 9775 +[L][anc] -> [Lanc] 9776 +[y][.\u000a ] -> [y.\u000a ] 9777 +[am][, ] -> [am, ] 9778 +[Al][ger] -> [Alger] 9779 +[ || || — || September ][26] -> [ || || — || September 26] 9780 +[ev][ ] -> [ev ] 9781 +[ot][s ] -> [ots ] 9782 +[c][av] -> [cav] 9783 +[he][im] -> [heim] 9784 +[T][err] -> [Terr] 9785 +[As][ia ] -> [Asia ] 9786 +[al ][in ] -> [al in ] 9787 +[T][ra] -> [Tra] 9788 +[W][ith] -> [With] 9789 +[with ][their ] -> [with their ] 9790 +[n][iv] -> [niv] 9791 +[eas][ily ] -> [easily ] 9792 +[200][8, ] -> [2008, ] 9793 +[res][t of the ] -> [rest of the ] 9794 +[\u000a][UK MPs ] -> [\u000aUK MPs ] 9795 +[st][ated ] -> [stated ] 9796 +[Al][z] -> [Alz] 9797 +[G][ian] -> [Gian] 9798 +[K][l] -> [Kl] 9799 +[countri][es, ] -> [countries, ] 9800 +[, and ][a ] -> [, and a ] 9801 +[mix][ed ] -> [mixed ] 9802 +[how ][to ] -> [how to ] 9803 +[ab][as] -> [abas] 9804 +[of the ][Year] -> [of the Year] 9805 +[H][ex] -> [Hex] 9806 +[S][eason ] -> [Season ] 9807 +[Ad][el] -> [Adel] 9808 +[J][ ] -> [J ] 9809 +[.\u000a\u000a][Track list] -> [.\u000a\u000aTrack list] 9810 +[res][ident] -> [resident] 9811 +[Ad][di] -> [Addi] 9812 +[ol][a ] -> [ola ] 9813 +[he][imer] -> [heimer] 9814 +[sport][s] -> [sports] 9815 +[br][anch] -> [branch] 9816 +[people liv][ing in ] -> [people living in ] 9817 +[Washing][ton] -> [Washington] 9818 +[R][ain] -> [Rain] 9819 +[rank][ed ] -> [ranked ] 9820 +[sup][por] -> [suppor] 9821 +[c][k] -> [ck] 9822 +[An][n ] -> [Ann ] 9823 +[p][ath] -> [path] 9824 +[�][�] -> [ê] 9825 +[Meit][ei ] -> [Meitei ] 9826 +[it][z ] -> [itz ] 9827 +[is also ][a ] -> [is also a ] 9828 +[a][er] -> [aer] 9829 +[en][\u000a] -> [en\u000a] 9830 +[18][00] -> [1800] 9831 +[as well ][as the ] -> [as well as the ] 9832 +[Austri][a ] -> [Austria ] 9833 +[re][s (] -> [res (] 9834 +[spe][ed ] -> [speed ] 9835 +[per][form ] -> [perform ] 9836 +[m][emor] -> [memor] 9837 +[compl][ex ] -> [complex ] 9838 +[am][ount] -> [amount] 9839 +[Gre][ater ] -> [Greater ] 9840 +[ver][g] -> [verg] 9841 +[Y][on] -> [Yon] 9842 +[ || Siding Spring][ || S] -> [ || Siding Spring || S] 9843 +[ bgcolor=#E9E9E9\u000a| ][8] -> [ bgcolor=#E9E9E9\u000a| 8] 9844 +[de l][a L] -> [de la L] 9845 +[H][iro] -> [Hiro] 9846 +[a][.\u000a ] -> [a.\u000a ] 9847 +[in][burg] -> [inburg] 9848 +[ births\u000a2017 ][deaths\u000a] -> [ births\u000a2017 deaths\u000a] 9849 +[hus][band] -> [husband] 9850 +[FF][FF] -> [FFFF] 9851 +[ch][ap] -> [chap] 9852 +[24, ][1960 || Palomar || PLS] -> [24, 1960 || Palomar || PLS] 9853 +[Develop][ment ] -> [Development ] 9854 +[h][a ] -> [ha ] 9855 +[St][one ] -> [Stone ] 9856 +[Al][t] -> [Alt] 9857 +[liter][ature] -> [literature] 9858 +[w][ick] -> [wick] 9859 +[4][-] -> [4-] 9860 +[it ][and ] -> [it and ] 9861 +[s][s] -> [ss] 9862 +[on ][their ] -> [on their ] 9863 +[Hall of ][Fam] -> [Hall of Fam] 9864 +[comed][y-] -> [comedy-] 9865 +[Tran][sport ] -> [Transport ] 9866 +[Se][a ] -> [Sea ] 9867 +[al][ist] -> [alist] 9868 +[Nev][ad] -> [Nevad] 9869 +[ator][s from ] -> [ators from ] 9870 +[D][ark ] -> [Dark ] 9871 +[o][k ] -> [ok ] 9872 +[its ][first ] -> [its first ] 9873 +[Micros][oft ] -> [Microsoft ] 9874 +[about ][ ] -> [about ] 9875 +[Chicag][o, ] -> [Chicago, ] 9876 +[I][R] -> [IR] 9877 +[em][an ] -> [eman ] 9878 +[2014][)\u000a ] -> [2014)\u000a ] 9879 +[actor ][(b. ] -> [actor (b. ] 9880 +[L][av] -> [Lav] 9881 +[Punj][ab] -> [Punjab] 9882 +[Wom][en ] -> [Women ] 9883 +[prime ][minist] -> [prime minist] 9884 +[t ][|| ] -> [t || ] 9885 +[F][ish] -> [Fish] 9886 +[9 km || \u000a|-id=][0] -> [9 km || \u000a|-id=0] 9887 +[P][ays ] -> [Pays ] 9888 +[er][. ] -> [er. ] 9889 +[s][ign ] -> [sign ] 9890 +[h][em] -> [hem] 9891 +[4][5 ] -> [45 ] 9892 +[sh][ire ] -> [shire ] 9893 +[Com][pl] -> [Compl] 9894 +[acc][ident ] -> [accident ] 9895 +[let][ter] -> [letter] 9896 +[Sal][v] -> [Salv] 9897 +[Stat][e of ] -> [State of ] 9898 +[J][udg] -> [Judg] 9899 +[ k][m] -> [ km] 9900 +[th][er, ] -> [ther, ] 9901 +[ator][, ] -> [ator, ] 9902 +[G][ib] -> [Gib] 9903 +[ec][t of ] -> [ect of ] 9904 +[de][al ] -> [deal ] 9905 +[doub][le ] -> [double ] 9906 +[lot][s of ] -> [lots of ] 9907 +[Pierr][e ] -> [Pierre ] 9908 +[Council ][of ] -> [Council of ] 9909 +[. Al][though ] -> [. Although ] 9910 +[u][d ] -> [ud ] 9911 +[we][ight] -> [weight] 9912 +[Jon][es ] -> [Jones ] 9913 +[Or][le] -> [Orle] 9914 +[d][-] -> [d-] 9915 +[at][a] -> [ata] 9916 +[P][on] -> [Pon] 9917 +[. L][ater ] -> [. Later ] 9918 +[Bar][n] -> [Barn] 9919 +[go][ing to ] -> [going to ] 9920 +[How][ard ] -> [Howard ] 9921 +[him ][to ] -> [him to ] 9922 +[Sh][ow ] -> [Show ] 9923 +[ (][b. ] -> [ (b. ] 9924 +[produc][tion] -> [production] 9925 +[pe][ac] -> [peac] 9926 +[5][-] -> [5-] 9927 +[res][ourc] -> [resourc] 9928 +[help][s ] -> [helps ] 9929 +[e. ][On ] -> [e. On ] 9930 +[E][C] -> [EC] 9931 +[j][ud] -> [jud] 9932 +[.\u000a\u000aP][ersonal ] -> [.\u000a\u000aPersonal ] 9933 +[vol][um] -> [volum] 9934 +[Bul][l ] -> [Bull ] 9935 +[School][ of ] -> [School of ] 9936 +[ste][in ] -> [stein ] 9937 +[3][2 ] -> [32 ] 9938 +[ity ][of the ] -> [ity of the ] 9939 +[Ed][g] -> [Edg] 9940 +[ || || — || September ][9] -> [ || || — || September 9] 9941 +[and][a ] -> [anda ] 9942 +[, 201][6] -> [, 2016] 9943 +[iti][es\u000a] -> [ities\u000a] 9944 +[Reg][ional ] -> [Regional ] 9945 +[disc][uss] -> [discuss] 9946 +[his ][own ] -> [his own ] 9947 +[appoint][ed ] -> [appointed ] 9948 +[m][ist] -> [mist] 9949 +[. Th][er] -> [. Ther] 9950 +[all][, ] -> [all, ] 9951 +[fe][el ] -> [feel ] 9952 +[Portug][al] -> [Portugal] 9953 +[used to ][be ] -> [used to be ] 9954 +[us][\u000a ] -> [us\u000a ] 9955 +[ect][or] -> [ector] 9956 +[ar][ian ] -> [arian ] 9957 +[Doc][tor ] -> [Doctor ] 9958 +[United ][Nations ] -> [United Nations ] 9959 +[Di][am] -> [Diam] 9960 +[s are ][not ] -> [s are not ] 9961 +[I][M] -> [IM] 9962 +[inst][ead ] -> [instead ] 9963 +[thre][e-] -> [three-] 9964 +[Stock][hol] -> [Stockhol] 9965 +[called ][a ] -> [called a ] 9966 +[Further ][read] -> [Further read] 9967 +[which ][he ] -> [which he ] 9968 +[b][ach ] -> [bach ] 9969 +[Day ][(] -> [Day (] 9970 +[iti][es, ] -> [ities, ] 9971 +[Braz][il ] -> [Brazil ] 9972 +[at the ][same ] -> [at the same ] 9973 +[dem][on] -> [demon] 9974 +[y][cl] -> [ycl] 9975 +[H][and] -> [Hand] 9976 +[there ][was ] -> [there was ] 9977 +[det][ermin] -> [determin] 9978 +[e][ung] -> [eung] 9979 +[ bgcolor=#d6d6d6\u000a| ][9] -> [ bgcolor=#d6d6d6\u000a| 9] 9980 +[temper][ature] -> [temperature] 9981 +[memb][ers] -> [members] 9982 +[G][un] -> [Gun] 9983 +[P][ower ] -> [Power ] 9984 +[cir][cu] -> [circu] 9985 +[record][ing ] -> [recording ] 9986 +[who ][are ] -> [who are ] 9987 +[�][�] -> [�] 9988 +[e][ir] -> [eir] 9989 +[well-][known ] -> [well-known ] 9990 +[Bir][d] -> [Bird] 9991 +[ies ][in ] -> [ies in ] 9992 +[, ][is a ] -> [, is a ] 9993 +[as][k ] -> [ask ] 9994 +[Sé][rie ] -> [Série ] 9995 +[Joh][ann ] -> [Johann ] 9996 +[England][, ] -> [England, ] 9997 +[<|pad|>] 9998 +[<|endoftext|>] 9999 diff --git a/models/components/tokenizers/tokenizer_models/bpe_simple_en_wiki_258.model b/models/components/tokenizers/tokenizer_models/bpe_simple_en_wiki_258.model deleted file mode 100644 index e69de29b..00000000 diff --git a/models/components/tokenizers/tokenizer_models/bpe_simple_en_wiki_258.vocab b/models/components/tokenizers/tokenizer_models/bpe_simple_en_wiki_258.vocab deleted file mode 100644 index 89cacc74..00000000 --- a/models/components/tokenizers/tokenizer_models/bpe_simple_en_wiki_258.vocab +++ /dev/null @@ -1,258 +0,0 @@ -[\u0000] 0 -[\u0001] 1 -[\u0002] 2 -[\u0003] 3 -[\u0004] 4 -[\u0005] 5 -[\u0006] 6 -[\u0007] 7 -[\u0008] 8 -[\u0009] 9 -[\u000a] 10 -[\u000b] 11 -[\u000c] 12 -[\u000d] 13 -[\u000e] 14 -[\u000f] 15 -[\u0010] 16 -[\u0011] 17 -[\u0012] 18 -[\u0013] 19 -[\u0014] 20 -[\u0015] 21 -[\u0016] 22 -[\u0017] 23 -[\u0018] 24 -[\u0019] 25 -[\u001a] 26 -[\u001b] 27 -[\u001c] 28 -[\u001d] 29 -[\u001e] 30 -[\u001f] 31 -[ ] 32 -[!] 33 -["] 34 -[#] 35 -[$] 36 -[%] 37 -[&] 38 -['] 39 -[(] 40 -[)] 41 -[*] 42 -[+] 43 -[,] 44 -[-] 45 -[.] 46 -[/] 47 -[0] 48 -[1] 49 -[2] 50 -[3] 51 -[4] 52 -[5] 53 -[6] 54 -[7] 55 -[8] 56 -[9] 57 -[:] 58 -[;] 59 -[<] 60 -[=] 61 -[>] 62 -[?] 63 -[@] 64 -[A] 65 -[B] 66 -[C] 67 -[D] 68 -[E] 69 -[F] 70 -[G] 71 -[H] 72 -[I] 73 -[J] 74 -[K] 75 -[L] 76 -[M] 77 -[N] 78 -[O] 79 -[P] 80 -[Q] 81 -[R] 82 -[S] 83 -[T] 84 -[U] 85 -[V] 86 -[W] 87 -[X] 88 -[Y] 89 -[Z] 90 -[[] 91 -[\] 92 -[]] 93 -[^] 94 -[_] 95 -[`] 96 -[a] 97 -[b] 98 -[c] 99 -[d] 100 -[e] 101 -[f] 102 -[g] 103 -[h] 104 -[i] 105 -[j] 106 -[k] 107 -[l] 108 -[m] 109 -[n] 110 -[o] 111 -[p] 112 -[q] 113 -[r] 114 -[s] 115 -[t] 116 -[u] 117 -[v] 118 -[w] 119 -[x] 120 -[y] 121 -[z] 122 -[{] 123 -[|] 124 -[}] 125 -[~] 126 -[\u007f] 127 -[�] 128 -[�] 129 -[�] 130 -[�] 131 -[�] 132 -[�] 133 -[�] 134 -[�] 135 -[�] 136 -[�] 137 -[�] 138 -[�] 139 -[�] 140 -[�] 141 -[�] 142 -[�] 143 -[�] 144 -[�] 145 -[�] 146 -[�] 147 -[�] 148 -[�] 149 -[�] 150 -[�] 151 -[�] 152 -[�] 153 -[�] 154 -[�] 155 -[�] 156 -[�] 157 -[�] 158 -[�] 159 -[�] 160 -[�] 161 -[�] 162 -[�] 163 -[�] 164 -[�] 165 -[�] 166 -[�] 167 -[�] 168 -[�] 169 -[�] 170 -[�] 171 -[�] 172 -[�] 173 -[�] 174 -[�] 175 -[�] 176 -[�] 177 -[�] 178 -[�] 179 -[�] 180 -[�] 181 -[�] 182 -[�] 183 -[�] 184 -[�] 185 -[�] 186 -[�] 187 -[�] 188 -[�] 189 -[�] 190 -[�] 191 -[�] 192 -[�] 193 -[�] 194 -[�] 195 -[�] 196 -[�] 197 -[�] 198 -[�] 199 -[�] 200 -[�] 201 -[�] 202 -[�] 203 -[�] 204 -[�] 205 -[�] 206 -[�] 207 -[�] 208 -[�] 209 -[�] 210 -[�] 211 -[�] 212 -[�] 213 -[�] 214 -[�] 215 -[�] 216 -[�] 217 -[�] 218 -[�] 219 -[�] 220 -[�] 221 -[�] 222 -[�] 223 -[�] 224 -[�] 225 -[�] 226 -[�] 227 -[�] 228 -[�] 229 -[�] 230 -[�] 231 -[�] 232 -[�] 233 -[�] 234 -[�] 235 -[�] 236 -[�] 237 -[�] 238 -[�] 239 -[�] 240 -[�] 241 -[�] 242 -[�] 243 -[�] 244 -[�] 245 -[�] 246 -[�] 247 -[�] 248 -[�] 249 -[�] 250 -[�] 251 -[�] 252 -[�] 253 -[�] 254 -[�] 255 -[<|pad|>] 256 -[<|endoftext|>] 257 diff --git a/models/components/tokenizers/tokenizer_models/bpe_simple_en_wiki_259.vocab b/models/components/tokenizers/tokenizer_models/bpe_simple_en_wiki_259.vocab deleted file mode 100644 index a55ce0d5..00000000 --- a/models/components/tokenizers/tokenizer_models/bpe_simple_en_wiki_259.vocab +++ /dev/null @@ -1,259 +0,0 @@ -[\u0000] 0 -[\u0001] 1 -[\u0002] 2 -[\u0003] 3 -[\u0004] 4 -[\u0005] 5 -[\u0006] 6 -[\u0007] 7 -[\u0008] 8 -[\u0009] 9 -[\u000a] 10 -[\u000b] 11 -[\u000c] 12 -[\u000d] 13 -[\u000e] 14 -[\u000f] 15 -[\u0010] 16 -[\u0011] 17 -[\u0012] 18 -[\u0013] 19 -[\u0014] 20 -[\u0015] 21 -[\u0016] 22 -[\u0017] 23 -[\u0018] 24 -[\u0019] 25 -[\u001a] 26 -[\u001b] 27 -[\u001c] 28 -[\u001d] 29 -[\u001e] 30 -[\u001f] 31 -[ ] 32 -[!] 33 -["] 34 -[#] 35 -[$] 36 -[%] 37 -[&] 38 -['] 39 -[(] 40 -[)] 41 -[*] 42 -[+] 43 -[,] 44 -[-] 45 -[.] 46 -[/] 47 -[0] 48 -[1] 49 -[2] 50 -[3] 51 -[4] 52 -[5] 53 -[6] 54 -[7] 55 -[8] 56 -[9] 57 -[:] 58 -[;] 59 -[<] 60 -[=] 61 -[>] 62 -[?] 63 -[@] 64 -[A] 65 -[B] 66 -[C] 67 -[D] 68 -[E] 69 -[F] 70 -[G] 71 -[H] 72 -[I] 73 -[J] 74 -[K] 75 -[L] 76 -[M] 77 -[N] 78 -[O] 79 -[P] 80 -[Q] 81 -[R] 82 -[S] 83 -[T] 84 -[U] 85 -[V] 86 -[W] 87 -[X] 88 -[Y] 89 -[Z] 90 -[[] 91 -[\] 92 -[]] 93 -[^] 94 -[_] 95 -[`] 96 -[a] 97 -[b] 98 -[c] 99 -[d] 100 -[e] 101 -[f] 102 -[g] 103 -[h] 104 -[i] 105 -[j] 106 -[k] 107 -[l] 108 -[m] 109 -[n] 110 -[o] 111 -[p] 112 -[q] 113 -[r] 114 -[s] 115 -[t] 116 -[u] 117 -[v] 118 -[w] 119 -[x] 120 -[y] 121 -[z] 122 -[{] 123 -[|] 124 -[}] 125 -[~] 126 -[\u007f] 127 -[�] 128 -[�] 129 -[�] 130 -[�] 131 -[�] 132 -[�] 133 -[�] 134 -[�] 135 -[�] 136 -[�] 137 -[�] 138 -[�] 139 -[�] 140 -[�] 141 -[�] 142 -[�] 143 -[�] 144 -[�] 145 -[�] 146 -[�] 147 -[�] 148 -[�] 149 -[�] 150 -[�] 151 -[�] 152 -[�] 153 -[�] 154 -[�] 155 -[�] 156 -[�] 157 -[�] 158 -[�] 159 -[�] 160 -[�] 161 -[�] 162 -[�] 163 -[�] 164 -[�] 165 -[�] 166 -[�] 167 -[�] 168 -[�] 169 -[�] 170 -[�] 171 -[�] 172 -[�] 173 -[�] 174 -[�] 175 -[�] 176 -[�] 177 -[�] 178 -[�] 179 -[�] 180 -[�] 181 -[�] 182 -[�] 183 -[�] 184 -[�] 185 -[�] 186 -[�] 187 -[�] 188 -[�] 189 -[�] 190 -[�] 191 -[�] 192 -[�] 193 -[�] 194 -[�] 195 -[�] 196 -[�] 197 -[�] 198 -[�] 199 -[�] 200 -[�] 201 -[�] 202 -[�] 203 -[�] 204 -[�] 205 -[�] 206 -[�] 207 -[�] 208 -[�] 209 -[�] 210 -[�] 211 -[�] 212 -[�] 213 -[�] 214 -[�] 215 -[�] 216 -[�] 217 -[�] 218 -[�] 219 -[�] 220 -[�] 221 -[�] 222 -[�] 223 -[�] 224 -[�] 225 -[�] 226 -[�] 227 -[�] 228 -[�] 229 -[�] 230 -[�] 231 -[�] 232 -[�] 233 -[�] 234 -[�] 235 -[�] 236 -[�] 237 -[�] 238 -[�] 239 -[�] 240 -[�] 241 -[�] 242 -[�] 243 -[�] 244 -[�] 245 -[�] 246 -[�] 247 -[�] 248 -[�] 249 -[�] 250 -[�] 251 -[�] 252 -[�] 253 -[�] 254 -[�] 255 -[e][ ] -> [e ] 256 -[<|pad|>] 257 -[<|endoftext|>] 258 diff --git a/models/components/tokenizers/utils.py b/models/components/tokenizers/utils.py deleted file mode 100644 index 946dba6f..00000000 --- a/models/components/tokenizers/utils.py +++ /dev/null @@ -1,95 +0,0 @@ -""" -A collection of utils for the tokenizers. -""" - -import os -import unicodedata -from collections import Counter - -import hydra # to get the absolute path to the tokenizer - - -def get_tokenizer_path(tokenizer_type, vocab_size, dataset_name): - """ - Get the path to the tokenizer. - """ - tokenizer_folder = os.path.join( - "models", "components", "tokenizers", "tokenizer_models" - ) - tokenizer_folder = hydra.utils.to_absolute_path(tokenizer_folder) - tokenizer_full_path = os.path.join( - tokenizer_folder, f"{tokenizer_type}_{dataset_name}_{vocab_size}.model" - ) - return tokenizer_folder, tokenizer_full_path - - -def check_if_tokenizer_exists(tokenizer_type, vocab_size, dataset_name): - """ - Check if the tokenizer already exists. - """ - _, tokenizer_path = get_tokenizer_path(tokenizer_type, vocab_size, dataset_name) - return os.path.exists(tokenizer_path) - - -def get_stats(ids): - """Return a Counter object of the token pairs.""" - return Counter(zip(ids, ids[1:])) - - -def multi_merge(ids, pairs): - """Merge multiple pairs of tokens in a list of token ids.""" - skip = False - newids = [ - ( - pairs[(ids[i], ids[i + 1])] - if (ids[i], ids[i + 1]) in pairs and (skip := True) - else ids[i] - ) - for i in range(len(ids) - 1) - if not skip or (skip := False) - ] - if not skip: # if the last pair was not replaced, append the last token - newids.append(ids[-1]) - return newids - - -def merge(ids, pair, idx): - """Merge a pair of tokens in a list of token ids.""" - skip = False - newids = [ - ( - idx - if (ids[i] == pair[0] and ids[i + 1] == pair[1] and (skip := True)) - else ids[i] - ) - for i in range(len(ids) - 1) - if not skip or (skip := False) - ] - if not skip: # if the last pair was not replaced, append the last token - newids.append(ids[-1]) - return newids - - -def replace_control_characters(s: str) -> str: - """Replace control characters with their unicode escape sequence. - - This is useful when printing tokens, as - we don't want to print control characters - which distort the output (e.g. \n or much worse) - https://stackoverflow.com/questions/4324790/removing-control-characters-from-a-string-in-python/19016117#19016117 - http://www.unicode.org/reports/tr44/#GC_Values_Table - """ - chars = [] - for ch in s: - if unicodedata.category(ch)[0] != "C": - chars.append(ch) # this character is ok - else: - chars.append(f"\\u{ord(ch):04x}") # escape - return "".join(chars) - - -def render_token(t: bytes) -> str: - """Pretty print a token, escaping control characters.""" - s = t.decode("utf-8", errors="replace") - s = replace_control_characters(s) - return s diff --git a/models/components/layers/transformer_blocks.py b/models/components/transformer_blocks.py similarity index 77% rename from models/components/layers/transformer_blocks.py rename to models/components/transformer_blocks.py index 879ff744..f79e17a7 100644 --- a/models/components/layers/transformer_blocks.py +++ b/models/components/transformer_blocks.py @@ -5,9 +5,9 @@ import torch -from models.components.layers.attention import build_attention -from models.components.layers.feedforward import build_ffn -from models.components.layers.normalization import build_normalization +from models.components.attention import build_attention +from models.components.feedforward import build_ffn +from models.components.normalization import build_normalization class GenericTransformerBlock(torch.nn.Module): @@ -16,7 +16,7 @@ class GenericTransformerBlock(torch.nn.Module): FFN, Attn and normalization. """ - def __init__(self, hidden_dim, context_window, use_rope, ffn_cfg, attn_cfg): + def __init__(self, attention_type, hidden_dim, context_window, use_rope, ffn_cfg, attn_cfg): super().__init__() # build the attn norm @@ -28,6 +28,7 @@ def __init__(self, hidden_dim, context_window, use_rope, ffn_cfg, attn_cfg): # build the attention self.attn = build_attention( + attention_type=attention_type, hidden_dim=hidden_dim, context_window=context_window, use_rope=use_rope, @@ -36,7 +37,7 @@ def __init__(self, hidden_dim, context_window, use_rope, ffn_cfg, attn_cfg): # build the ffn norm self.ffn_norm = build_normalization( - normalization_name=ffn_cfg["normalization"], + normalization_name=ffn_cfg.get("normalization", "rms_norm"), # Default: rms_norm dim=hidden_dim, bias=ffn_cfg["bias"], ) diff --git a/models/components/utils/__init__.py b/models/components/utils/__init__.py new file mode 100644 index 00000000..8b04df61 --- /dev/null +++ b/models/components/utils/__init__.py @@ -0,0 +1,2 @@ +from models.components.utils.tokenizer_utils import * +from models.components.utils.attention_utils import * \ No newline at end of file diff --git a/models/components/utils/attention_utils.py b/models/components/utils/attention_utils.py new file mode 100644 index 00000000..fb3dad79 --- /dev/null +++ b/models/components/utils/attention_utils.py @@ -0,0 +1,99 @@ +""" +Builds the attention functions specified in the config. + +According to the Pytorch documentation, the `torch.nn.functional.scaled_dot_product_attention` function +is efficiently implemented with flash attention. Hence, using the standard function is recommended during training. + +However, where users may want the components of the attention values, the `detailed_scaled_dot_product_attention` function +uses the raw calculations to provide intermediate attention values and the query, key, and value matrices. This might be useful +for inferencing. For example, the adaptive sampler in the `generate.py` script uses the detailed attention function to extract +the scaled attention values (before softmax) for the model's attention heads. +""" + +import torch +import torch.nn.functional as F +from typing import Callable + + +def detailed_scaled_dot_product_attention(query, key, value, attn_mask, dropout_p, is_causal) -> torch.Tensor: + """ + Initializes a detailed attention module that grants users the extraction of intermediate attention values and + the query, key, and value matrices. + + Args: + query (torch.Tensor): Query tensor of shape (..., seq_len_q, dim_k) + key (torch.Tensor): Key tensor of shape (..., seq_len_k, dim_k) + value (torch.Tensor): Value tensor of shape (..., seq_len_v, dim_v) + attn_mask (torch.Tensor, optional): Attention mask. + dropout_p (float, optional): Dropout probability. + is_causal (bool): If True, applies a causal mask. + """ + + # Compute QK^T + prenorm_scores = torch.matmul(query, key.transpose(-2, -1)) + # Scale by sqrt(dim_k) + scaling_factor = key.size(-1) ** 0.5 + scaled_scores = prenorm_scores / scaling_factor + + # Apply attention mask if provided + if attn_mask is not None: + scaled_scores = scaled_scores.masked_fill(attn_mask == 0, float('-inf')) + + # Apply causal mask if required + if is_causal: + seq_len = query.size(-2) + causal_mask = torch.tril(torch.ones((seq_len, seq_len), device=query.device)).bool() + scaled_scores = scaled_scores.masked_fill(~causal_mask, float('-inf')) + + # Compute attention probabilities + attn_probs = F.softmax(scaled_scores, dim=-1) + + # Apply dropout if specified + if dropout_p is not None: + attn_probs = F.dropout(attn_probs, p=dropout_p) + + # Compute the attention output + output = torch.matmul(attn_probs, value) + + return output, {"query": query, "key": key, "value": value, "scaled_scores": scaled_scores} + +def standard_scaled_dot_product_attention(query, key, value, attn_mask = None, dropout_p = None, is_causal = False) -> torch.Tensor: + """ + Standard scaled dot-product attention using PyTorch's built-in function. + + Args: + query (torch.Tensor): Query tensor. + key (torch.Tensor): Key tensor. + value (torch.Tensor): Value tensor. + attn_mask (torch.Tensor, optional): Attention mask. + dropout_p (float, optional): Dropout probability. + is_causal (bool): If True, applies a causal mask. + + Returns: + torch.Tensor: The attention output. + """ + return torch.nn.functional.scaled_dot_product_attention( + query=query, + key=key, + value=value, + attn_mask=attn_mask, + dropout_p=dropout_p, + is_causal=is_causal + ), None + +ATTENTION_DICT = { + "standard": lambda query, key, value, attn_mask, dropout_p, is_causal: standard_scaled_dot_product_attention(query, key, value, attn_mask, dropout_p, is_causal), + "standard_detailed": lambda query, key, value, attn_mask, dropout_p, is_causal: detailed_scaled_dot_product_attention(query, key, value, attn_mask, dropout_p, is_causal) +} + +def use_attention_type(attention_type: str, query, key, value, attn_mask, dropout_p, is_causal) -> Callable: + """ + Returns the attention function corresponding to the specified type. + + Args: + attention_type (str): The type of attention to use. + + Returns: + Callable: The attention function. + """ + return ATTENTION_DICT[attention_type](query, key, value, attn_mask, dropout_p, is_causal) \ No newline at end of file diff --git a/models/components/utils/generation_utils.py b/models/components/utils/generation_utils.py new file mode 100644 index 00000000..3309ff63 --- /dev/null +++ b/models/components/utils/generation_utils.py @@ -0,0 +1,174 @@ +""" +Build the data generation types specified in the given config. + +Two methods are used. + +The first method is to use the given text generation strategy (e.g. standard, beam search, etc) +to generate N data responses for a given input text, up till length L. The value model is then used +to evaluate the data responses generated by giving it a score. Sorting them by rank, the top R data +responses are then saved and extracted. + +The second method is to use the given text generation strategy (e.g. standard, beam search, etc) +and use the given input text to build a Monte-Carlo Tree Search (MCTS) tree up to depth K. At +each step, the value model is used to evaluate the data responses generated by giving it a score. +The best child node response is then selected and the MCTS tree is updated. This process is repeated +till the end of the tree. Based on the tree, the top R data responses are then saved and extracted. +""" + +from models.generators import build_generator + +class StandardDataGenerator: + """ + Standard data generation strategy + """ + def __init__(self, model, value_model, generate_cfg, strategy_cfg, device): + self.model = model + self.value_model = value_model + self.generate_cfg = generate_cfg + self.strategy_cfg = strategy_cfg + self.device = device + + def generate_data(self, input_text): + """ + Generate data using the standard generation strategy + """ + generator = build_generator( + model=self.model, + generate_cfg=self.generate_cfg, + device=self.device + ) + + # generate the text + generated_text = generator.default_generate( + input_text=input_text ## TODO - any supporting prompts + ) + + # step value evaluation + text = "".join(generated_text) + value = self.value_model.evaluate( + model_input=text, + token=self.strategy_cfg["step_token"] + ) + value = 1 + for v in value: + value *= v + + return generated_text, value + +class Node: + """ + Node class for MCTS + """ + def __init__(self, parent, value, text): + self.parent = parent + self.children = [] + self.value = value + self.text = text + +class MCTSDataGenerator: + """ + Monte-Carlo Tree Search data generation strategy + """ + def __init__(self, model, value_model, generate_cfg, strategy_cfg, device): + self.model = model + self.value_model = value_model + self.generate_cfg = generate_cfg + self.strategy_cfg = strategy_cfg + self.device = device + + def generate_data(self, input_text): + """ + Generate data using the MCTS generation strategy. Basically build a tree and select the best child node + at each step. + """ + root = Node(None, 0, input_text) + nodes = [root] + + for _ in range(self.strategy_cfg["max_tree_depth"]): + new_nodes = [] + for node in nodes: + # expand the node + child_prompts = self._generate_child_prompts(node.prompt, num_samples=self.strategy_cfg['max_child_nodes']) + for prompt in child_prompts: + child = Node(node, 0, prompt) + node.children.append(child) + new_nodes.append(child) + # evaluate the nodes by assigning them a value + self._score_nodes(new_nodes) + # select the best child node + best_child = self._select_best_child(node) + nodes = [best_child] + + return self._stitch_responses(best_child) + + + def _generate_child_prompts(self, prompt, num_samples): + """ + Generate child prompts + """ + generator = build_generator( + model=self.model, + generate_cfg=self.generate_cfg, + device=self.device + ) + + child_prompts = [] + for _ in range(num_samples): + generated_text = generator.default_generate( + input_text=self.strategy_cfg["expansion_prompt"].format(prompt) ## !! TODO - check if this is buggy + ) + generated_text = "".join(generated_text) + child_prompts.append(generated_text) + return child_prompts + + def _score_nodes(self, nodes): + """ + Score the nodes + """ + for node in nodes: + node.value = self.value_model.evaluate( + generated_text=node.text + ) + + def _select_best_child(self, node): + """ + Select the best child node + """ + best_child = None + best_score = -1 + for child in node.children: + if child.value > best_score: + best_child = child + best_score = child.value + return best_child + + def _stitch_responses(self, node): + """ + Stitch the responses together + """ + responses = [] + value = 1 + while node is not None: + responses.append(node.text) + value *= node.value + node = node.parent + return responses[::-1], value ## reverse the responses + + + +GENERATION_STRATEGIES = { + "standard": lambda model, value_model, generate_cfg, strategy_cfg, device: StandardDataGenerator(model, value_model, generate_cfg, strategy_cfg, device), + "mcts": lambda model, value_model, generate_cfg, strategy_cfg, device: MCTSDataGenerator(model, value_model, generate_cfg, strategy_cfg, device) +} + +def get_generator_type(model, value_model, generate_cfg, strategy_cfg, device): + """ + Get the generator type based on the strategy + """ + return GENERATION_STRATEGIES[strategy_cfg['name']]( + model=model, + value_model=value_model, + generate_cfg=generate_cfg, + strategy_cfg=strategy_cfg, + device=device + ) \ No newline at end of file diff --git a/models/components/utils/tokenizer_utils.py b/models/components/utils/tokenizer_utils.py new file mode 100644 index 00000000..ea2b43cc --- /dev/null +++ b/models/components/utils/tokenizer_utils.py @@ -0,0 +1,52 @@ +""" +A collection of util functions +""" +import os +import hydra + + +### Tokenizer Utils +def get_tokenizer_path(tokenizer_type, vocab_size, dataset_names, simplify, num_reserved_tokens): + """ + Get the path to the tokenizer. + """ + # Get the project root directory + project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) + ## Hot-fix because the hydra.utils.get_original_cwd() is not working + ## TODO: find better solution + + tokenizer_folder = os.path.join( + project_root, "components", "tokenizer_models" + ) + + # create folder if not exists + if not os.path.exists(tokenizer_folder): + os.mkdir(tokenizer_folder) + + # Ensure dataset_names is a list + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + + # Create a unique identifier for the combined dataset + combined_dataset_name = '_'.join(dataset_names) + + tokenizer_full_path = os.path.join( + tokenizer_folder, f"{tokenizer_type}_{combined_dataset_name}_{vocab_size}_{num_reserved_tokens}.model" + ) + if simplify: + tokenizer_full_path = tokenizer_full_path.replace(".model", "_simplified.model") + return tokenizer_folder, tokenizer_full_path + +def check_if_tokenizer_exists(tokenizer_type, vocab_size, dataset_names, simplify, num_reserved_tokens): + """ + Check if the tokenizer already exists. + """ + _, tokenizer_path = get_tokenizer_path( + tokenizer_type=tokenizer_type, + vocab_size=vocab_size, + dataset_names=dataset_names, + simplify=simplify, + num_reserved_tokens=num_reserved_tokens, + ) + return os.path.exists(tokenizer_path) + diff --git a/models/core_models.py b/models/core_models.py index 96ec2b34..9f136289 100644 --- a/models/core_models.py +++ b/models/core_models.py @@ -4,7 +4,7 @@ import torch -from models.components.layers.transformer_blocks import GenericTransformerBlock +from models.components.transformer_blocks import GenericTransformerBlock class GenericTransformer(torch.nn.Module): @@ -23,18 +23,40 @@ def __init__(self, model_cfg): "h": torch.nn.ModuleList( [ GenericTransformerBlock( + attention_type=model_cfg.get("attention_type", "standard"), hidden_dim=model_cfg["hidden_dim"], context_window=model_cfg["context_window"], use_rope=model_cfg["positional_encoding_type"] == "rope", - ffn_cfg=model_cfg["core_model"]["ffn"], - attn_cfg=model_cfg["core_model"]["attn"], + ffn_cfg=model_cfg["ffn"], + attn_cfg=model_cfg["attn"], ) - for _ in range(model_cfg["core_model"]["num_layers"]) + for _ in range(model_cfg["num_layers"]) ] ), } ) + if model_cfg.get("ffn_weight_tying", False): # Default: False + # Share the weights between all FFN blocks, similar to: + # https://arxiv.org/abs/2402.16840 + ffn_0 = self.transformer.h[0].ffn + for i in range(1, len(self.transformer.h)): + for name, module in ffn_0.named_modules(): + if isinstance(module, torch.nn.Linear): + target_module = dict(self.transformer.h[i].ffn.named_modules())[name] + target_module.weight = module.weight + target_module.bias = module.bias + + if model_cfg.get("cproj_weight_tying", False): # Default: False + # Share the weights between all CProj blocks + cproj_0 = self.transformer.h[0].attn.c_proj + for i in range(1, len(self.transformer.h)): + for name, module in cproj_0.named_modules(): + if isinstance(module, torch.nn.Linear): + target_module = dict(self.transformer.h[i].attn.c_proj.named_modules())[name] + target_module.weight = module.weight + target_module.bias = module.bias + def forward(self, x): """ Pass an input through the model @@ -47,32 +69,16 @@ def forward(self, x): # apply dropout x = self.transformer.drop(x) + # init matrices to get attention dict + self.attn_components = [] + # pass through the transformer blocks for block in self.transformer.h: x = block(x) + + # append the attention components + self.attn_components.append(block.attn.attn_components) return x -class GenericFFNSharedTransfomer(GenericTransformer): - """ - Generic Transformer Class that shares the weights - between all FFN blocks (similar to - https://arxiv.org/abs/2402.16840). - """ - - def __init__(self, model_cfg): - super().__init__(model_cfg=model_cfg) - - # share the weights between transformer blocks - ffn_0 = self.transformer.h[0].ffn - - for i in range(1, len(self.transformer.h)): - # find all linear layers in the ffn subnets and tie them to the first layer - for name, module in ffn_0.named_modules(): - if isinstance(module, torch.nn.Linear): - target_module = dict(self.transformer.h[i].ffn.named_modules())[ - name - ] - target_module.weight = module.weight - target_module.bias = module.bias diff --git a/models/embedding_models.py b/models/embedding_models.py index c7892203..1b51f579 100644 --- a/models/embedding_models.py +++ b/models/embedding_models.py @@ -5,6 +5,7 @@ """ import torch +import numpy as np from models.components.positional_encoding import build_positional_encodings from models.components.tokenizers import build_tokenizer @@ -99,9 +100,11 @@ def __init__(self, model_cfg): super().__init__() # build the tokenizer self.tokenizer = build_tokenizer( - tokenizer_type=model_cfg["embedder"]["tokenizer_type"], - vocab_size=model_cfg["vocab_size"], - dataset_name=model_cfg["embedder"]["dataset_name"], + tokenizer_type=model_cfg["tokenizer_type"], + vocab_size=model_cfg.get("vocab_size", None), + dataset_names=model_cfg.get("tokenizer_dataset_names", None), + simplify=model_cfg.get("tokenizer_simplify", True), # Default True + num_reserved_tokens=model_cfg.get("tokenizer_num_reserved_tokens", 0), # By Default, no spaces are reserved ) # build the token embeddings @@ -115,6 +118,30 @@ def __init__(self, model_cfg): self.eot_token = self.tokenizer.eot_token self.model_cfg = model_cfg + self.dropout = torch.nn.Dropout(p=model_cfg.get("embedding_dropout", 0.0)) + + self.token_byte_length_cache = self._precompute_byte_lengths() + + def _precompute_byte_lengths(self): + """ + Precompute byte lengths for all tokens in the vocabulary. + """ + vocab_size = self.tokenizer.vocab_size + token_byte_lengths = np.zeros(vocab_size, dtype=np.int32) + for token in range(vocab_size): + token_str = self.tokenizer.decode([token]) + token_bytes = token_str.encode('utf-8') + token_byte_lengths[token] = len(token_bytes) + return token_byte_lengths + + def get_byte_lengths(self, tokens): + """ + Given a list/array of tokens, return a NumPy array of byte lengths using the cache. + """ + tokens = np.array(tokens) + byte_lengths = self.token_byte_length_cache[tokens] + return byte_lengths + def forward(self, token_ids): """ Takes the token_ids as input diff --git a/models/experimental/byte_level/layers.py b/models/experimental/byte_level/layers.py index bc2e700b..1948f444 100644 --- a/models/experimental/byte_level/layers.py +++ b/models/experimental/byte_level/layers.py @@ -4,9 +4,9 @@ import torch -from models.components.layers.activations import build_activation -from models.components.layers.attention import Attention -from models.components.layers.normalization import build_normalization +from models.components.activations import build_activation +from models.components.attention import Attention +from models.components.normalization import build_normalization class ProjectingFFN(torch.nn.Module): diff --git a/models/experimental/hugging_face.py b/models/experimental/hugging_face.py index b6b4b70a..cac8def0 100644 --- a/models/experimental/hugging_face.py +++ b/models/experimental/hugging_face.py @@ -4,11 +4,41 @@ import torch from transformers import AutoModelForCausalLM, AutoTokenizer -from models.components.tokenizers.base_class import Tokenizer +from models.components.tokenizers import TokenizerClass from models.embedding_models import EmbedderInterface from models.model_shell import ModelShell from trainers.base_trainer import BaseTrainer +import dotenv +dotenv.load_dotenv() +import os + +import torch.nn as nn + +class LoRALinear(nn.Module): + def __init__(self, original_layer: nn.Linear, r=8, lora_alpha=32, lora_dropout=0.1): + super(LoRALinear, self).__init__() + self.original_layer = original_layer # Store the original nn.Linear layer + self.r = r # Rank of the LoRA approximation + self.lora_alpha = lora_alpha # Scaling factor for LoRA + self.scaling = lora_alpha / r # Scale factor + self.lora_dropout = nn.Dropout(p=lora_dropout) # Dropout for LoRA + + # Low-rank matrices + in_features, out_features = original_layer.weight.T.shape + self.lora_A = nn.Parameter(torch.randn(in_features, r) * 0.01) + self.lora_B = nn.Parameter(torch.randn(r, out_features) * 0.01) + + def forward(self, x): + # Perform the forward pass of the original linear layer + original_output = self.original_layer(x) + + # Compute the LoRA output + lora_out = self.lora_dropout(x @ self.lora_A) @ self.lora_B + + # Add the LoRA output to the original output, scaled by self.scaling + return original_output + lora_out * self.scaling + def build_model(model_cfg): ''' Helper function to build a model from the huggingface model hub. @@ -27,16 +57,41 @@ def build_model(model_cfg): model = AutoModelForCausalLM.from_pretrained( model_str, trust_remote_code=True, - attn_implementation=attn_impl, - torch_dtype=torch.float16, + # attn_implementation=attn_impl, + # torch_dtype=torch.float16, + token=os.getenv("HF_TOKEN") ) - + model.half() + use_lora = model_cfg.get("use_lora", False) + if use_lora: + targets = model_cfg.get("lora_targets", []) + # Freeze original model parameters except LoRA layers + for name, param in model.named_parameters(): + if "lora" not in name: + param.requires_grad = False + + # Iterate over the model modules to find the target layers and replace them with LoRA layers + for target in targets: + for name, module in model.named_modules(): + if target in name and isinstance(module, nn.Linear): + + # Replace the module with a LoRALayer (wrap the original Linear layer) + lora_layer = LoRALinear(module) + + # We need to set the new LoRA layer into the model hierarchy + parent_name, attr_name = name.rsplit('.', 1) # Split the module name to get parent and attribute + parent_module = dict(model.named_modules())[parent_name] # Access the parent module + + setattr(parent_module, attr_name, lora_layer) + + # quantize = model_cfg.get("quantize", False) + # if quantize: return model -class HFTokenizerWrapper(Tokenizer): +class HFTokenizerWrapper(TokenizerClass): def __init__(self, hf_tokenizer_name): - self.hf_tokenizer = AutoTokenizer.from_pretrained(hf_tokenizer_name) + self.hf_tokenizer = AutoTokenizer.from_pretrained(hf_tokenizer_name, token=os.getenv("HF_TOKEN")) self.eot_token = self.hf_tokenizer.eos_token_id self.pad_token = self.hf_tokenizer.pad_token_id self.vocab_size = self.hf_tokenizer.vocab_size @@ -79,6 +134,8 @@ def __init__(self, model_cfg): model_string = model_cfg["model_string"] self.tokenizer = HFTokenizerWrapper(model_string) self.embeddings = build_model(model_cfg).get_input_embeddings() + # clear torch memory + torch.cuda.empty_cache() def decode(self, token_ids): """ @@ -128,22 +185,21 @@ class HFTransformerCore(torch.nn.Module): def __init__(self, model_cfg): super().__init__() self.model = build_model(model_cfg = model_cfg) - - ## freeze the parameters - print("Note: Freezing the parameters of the hf_core model.") - for param in self.model.parameters(): - param.requires_grad = False + # del the models' embeddings and head + del self.model.model.embed_tokens + # del self.model.lm_head def forward(self, x): """ Calls the huggingface model in question, and returns the last hidden state. """ ## get the hidden states - hidden_states = self.model(inputs_embeds = x, output_hidden_states = True).hidden_states + output = self.model(inputs_embeds = x) + return output.logits - ## return the last hidden state - if isinstance(hidden_states, tuple): - return hidden_states[-1] + # ## return the last hidden state + # if isinstance(hidden_states, tuple): + # return hidden_states[-1] @@ -154,7 +210,11 @@ class HFLMHead(torch.nn.Module): def __init__(self, model_cfg): super().__init__() - self.lm_head = build_model(model_cfg = model_cfg).get_output_embeddings() + # self.lm_head = build_model(model_cfg = model_cfg).get_output_embeddings() + self.lm_head = nn.Identity() + # clear torch memory + torch.cuda.empty_cache() + def forward(self, x): """ @@ -192,3 +252,38 @@ def _run_step(self, *args, **kwargs): def _save_model(self, iter_num=0): """We don't want to save the model in this case...""" pass + + +# def get_qk_scores_during_forward_pass(model): +# """ +# Set up hooks to extract Q and K projections from the model during its forward pass. +# """ +# raw_attentions = [] +# hooks = [] + +# def attention_hook_qwen(module, input, output): +# """Hook to capture the attention for Qwen 2 models.""" +# qk = output # Raw attention logits (QK^T) +# raw_attentions.append(qk.detach()) + +# def attention_hook_gpt2(module, input, output): +# """Hook to capture the attention for GPT-2 models.""" +# qkv = output # shape: (batch_size, seq_len, 3 * hidden_size) +# hidden_size = qkv.shape[-1] // 3 +# q, k, v = qkv.split(hidden_size, dim=-1) +# raw_attentions.append(q.detach()) +# raw_attentions.append(k.detach()) + +# # Register hooks based on the model type +# if 'Qwen' in model.core_model.model.__class__.__name__: +# for name, module in model.named_modules(): +# if 'q_proj' in name or 'k_proj' in name: +# hook = module.register_forward_hook(attention_hook_qwen) +# hooks.append(hook) +# elif 'GPT2' in model.core_model.model.__class__.__name__: +# for name, module in model.named_modules(): +# if 'c_attn' in name: +# hook = module.register_forward_hook(attention_hook_gpt2) +# hooks.append(hook) +# else: +# raise ValueError("Unsupported model type: {}".format(model.core_model.model.__class__.__name__)) diff --git a/models/experimental/moe_weight_sharing.py b/models/experimental/moe_weight_sharing.py new file mode 100644 index 00000000..bc53c648 --- /dev/null +++ b/models/experimental/moe_weight_sharing.py @@ -0,0 +1,304 @@ +""" +Simple LoRA FFN weight sharing but with softmax-weighted experts. +""" +import torch +import math +from models.core_models import GenericTransformer + +from models.components.attention import build_attention +from models.components.feedforward import build_ffn +from models.components.normalization import build_normalization + +from models.components.activations import build_activation + + +class MoELoRA(torch.nn.Module): + def __init__( + self, + in_features: int, + out_features: int, + lora_rank: int, + n_experts: int, + lora_alpha: float = 1.0, + global_gating: bool = False + ): + """ + LoRA MoE implementation + + Args: + in_features (int): Number of input features. + out_features (int): Number of output features. + lora_rank (int): The rank of the LoRA matrices. + n_experts (int): Number of experts. + lora_alpha (float): Scaling factor for the LoRA update. + global_gating (bool): If True, use the same expert for all sequence items. + """ + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.lora_rank = lora_rank + self.n_experts = n_experts + self.lora_alpha = lora_alpha + self.global_gating = global_gating + self.scaling = self.lora_alpha / self.lora_rank + + # Initialize main weight matrix + self.weight = torch.nn.Parameter(torch.empty((out_features, in_features))) + + # Initialize gate linear + self.gate_linear = torch.nn.Linear(in_features, n_experts, bias=False) + + # Initialize LoRA matrices + self.lora_experts_U = torch.nn.Parameter( + torch.empty((n_experts, lora_rank, in_features)) + ) + self.lora_experts_V = torch.nn.Parameter( + torch.zeros((n_experts, out_features, lora_rank)) + ) + + self.reset_parameters() + + def reset_parameters(self): + torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + self.gate_linear.reset_parameters() + + for i in range(self.n_experts): + torch.nn.init.kaiming_uniform_(self.lora_experts_U[i], a=math.sqrt(5)) + # V is already initialized to zeros + torch.nn.init.kaiming_uniform_(self.lora_experts_V[i], a=math.sqrt(5)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass where the gating is applied to each element in the + sequence independently + """ + if self.global_gating: + gate = torch.nn.functional.softmax(self.gate_linear(x[:,-1]), dim=-1) # torch.Size([2, 8]) torch.Size([8, 32, 416]) torch.Size([8, 1072, 32]) + + lora_weights = self.lora_experts_V @ self.lora_experts_U + updated_weight = self.weight + self.scaling * lora_weights + #print(x.size(), updated_weight.size()) # torch.Size([2, 512, 416]) torch.Size([8, 1072, 416]) + #input() + output = torch.einsum('bsh,efh->besf', x, updated_weight) # torch.Size([2, 8, 1072]) + output = torch.sum(gate.unsqueeze(2).unsqueeze(3) * output, dim=1) + return output + input(output.size()) + + lora_weights_U = torch.einsum('bi,ijk->bjk', gate, self.lora_experts_U) # resulting shape: [2, 32, 416] + lora_weights_V = torch.einsum('bi,ijk->bjk', gate, self.lora_experts_V) # resulting shape: [2, 1072, 32] + # combine + lora_weights = torch.einsum('bij,bjk->bik', lora_weights_V, lora_weights_U) # resulting shape: [2, 1072, 416] + # apply + updated_weight = self.weight + self.scaling * lora_weights + #print(x.size(), updated_weight.size()) # torch.Size([2, 512, 416]) torch.Size([2, 1072, 416]) + #input() + output = torch.einsum("bsh,bfh->bsf", x, updated_weight) + return output + else: + gate = torch.nn.functional.softmax(self.gate_linear(x), dim=-1) #torch.Size([24, 512, 8]) + B, S, E = gate.size() + + # flatten gate along the B & S dim + gate = gate.view(-1, E) # torch.Size([12288, 8]) + x = x.view(-1, self.in_features) # torch.Size([12288, 416]) + + lora_weights = self.lora_experts_V @ self.lora_experts_U # torch.Size([8, 1072, 416]) + # Add LoRA update to the main weight + updated_weight = self.weight + self.scaling * lora_weights # torch.Size([8, 1072, 416]) + # apply weights + #print(updated_weight.size(), x.size()) # torch.Size([8, 1072, 416]) torch.Size([1024, 416]) + output = torch.einsum('ijk,bk->bij', updated_weight, x) # torch.Size([1024, 8, 1072]) + # apply the gates across the second dim and pool + #print(gate.size(), output.size()) # torch.Size([1024, 8]) torch.Size([1024, 8, 1072]) + output = torch.sum(gate.unsqueeze(2) * output, dim=1) + #output = torch.einsum('ij,ikj->ik', gate, output) # torch.Size([12288, 1072]) + return output.view(B, S, self.out_features) + + input(output.size()) + output = torch.einsum('ij,ikj->ik', x, updated_weight) # torch.Size([12288, 1072]) + input(updated_weight.size()) + lora_weights_U = gate.unsqueeze(2).unsqueeze(3) * self.lora_experts_U # torch.Size([12288, 8, 32, 416]) + # now average over experts + lora_weights_U = lora_weights_U.mean(dim=1) # torch.Size([12288, 1072, 416]) + + lora_weights_V = gate.unsqueeze(2).unsqueeze(3) * self.lora_experts_V # torch.Size([12288, 8, 1072, 32]) + # now average over experts + lora_weights_V = lora_weights_V.mean(dim=1) # torch.Size([12288, 1072, 32]) + + lora_weights = lora_weights_V @ lora_weights_U # torch.Size([12288, 1072, 416]) + # Add LoRA update to the main weight + updated_weight = self.weight + self.scaling * lora_weights + + + # apply weights + output = torch.einsum('ij,ikj->ik', x, updated_weight) + return output.view(B, S, self.out_features) + + +class SharedMoEFFN(torch.nn.Module): + """ """ + def __init__(self, hidden_dim, ffn_dim, lora_rank, n_experts, lora_alpha): + super().__init__() + self.linear_1 = MoELoRA( + in_features=hidden_dim, + out_features=ffn_dim, + lora_rank=lora_rank, + n_experts=n_experts, + lora_alpha=lora_alpha + ) + + self.linear_2 = MoELoRA( + in_features=ffn_dim, + out_features=hidden_dim, + lora_rank=lora_rank, + n_experts=n_experts, + lora_alpha=lora_alpha + ) + self.linear_3 = MoELoRA( + in_features=hidden_dim, + out_features=ffn_dim, + lora_rank=lora_rank, + n_experts=n_experts, + lora_alpha=lora_alpha + ) + + + def forward(self, x): + """ + A simple forward pass through the FFN + """ + return self.linear_2(torch.nn.functional.silu(self.linear_1(x)) * self.linear_3(x)) + + +class SharedTransformerBlock(torch.nn.Module): + """ + LoRA shared transformer block + """ + def __init__(self, hidden_dim, context_window, use_rope, ffn_cfg, attn_cfg): + super().__init__() + + # build the attn norm + self.attn_norm = build_normalization( + normalization_name=attn_cfg["normalization"], + dim=hidden_dim, + bias=attn_cfg["bias"], + ) + + # build the attention + self.attn = build_attention( + hidden_dim=hidden_dim, + context_window=context_window, + use_rope=use_rope, + attn_cfg=attn_cfg, + ) + + # build the ffn norm + self.ffn_norm = build_normalization( + normalization_name=ffn_cfg["normalization"], + dim=hidden_dim, + bias=ffn_cfg["bias"], + ) + + # build the ffn block + self.ffn = SharedMoEFFN( + hidden_dim=hidden_dim, + ffn_dim=ffn_cfg["ffn_dim"], + lora_rank=ffn_cfg["lora_rank"], + n_experts=ffn_cfg["n_experts"], + lora_alpha=ffn_cfg["lora_alpha"], + ) + + def forward(self, x, attention_mask=None): + """ + A simple, residual forward + pass through the GPT block. + Args: + x: the input tensor (b, s, h) + attention_mask: the attention mask + Returns: + x: the output tensor (b, s, h) + """ + x = x + self.attn(self.attn_norm(x), attention_mask) + x = x + self.ffn(self.ffn_norm(x)) + return x + + +class SharedMoE(torch.nn.Module): + """ + core model class with shared MoE weights + """ + def __init__(self, model_cfg): + super().__init__() + + # build the transformer + self.transformer = torch.nn.ModuleDict( + { + "drop": torch.nn.Dropout(), + "h": torch.nn.ModuleList( + [ + SharedTransformerBlock( + hidden_dim=model_cfg["hidden_dim"], + context_window=model_cfg["context_window"], + use_rope=model_cfg["positional_encoding_type"] == "rope", + ffn_cfg=model_cfg["core_model"]["ffn"], + attn_cfg=model_cfg["core_model"]["attn"], + ) + for _ in range(model_cfg["core_model"]["num_layers"]) + ] + ), + } + ) + + # share the weights between all ffn blocks + ffn_0 = self.transformer.h[0].ffn + """for i in range(1, len(self.transformer.h)): + self.transformer.h[i].ffn.linear_1.weight = ffn_0.linear_1.weight + self.transformer.h[i].ffn.linear_2.gate_linear.weight = ffn_0.linear_2.gate_linear.weight + for j in range(ffn_0.linear_1.n_experts): + self.transformer.h[i].ffn.linear_1.lora_experts_U[j] = ffn_0.linear_1.lora_experts_U[j] + self.transformer.h[i].ffn.linear_1.lora_experts_V[j] = ffn_0.linear_1.lora_experts_V[j] + + self.transformer.h[i].ffn.linear_2.weight = ffn_0.linear_2.weight + self.transformer.h[i].ffn.linear_2.gate_linear.weight = ffn_0.linear_2.gate_linear.weight + for j in range(ffn_0.linear_2.n_experts): + self.transformer.h[i].ffn.linear_2.lora_experts_U[j] = ffn_0.linear_2.lora_experts_U[j] + self.transformer.h[i].ffn.linear_2.lora_experts_V[j] = ffn_0.linear_2.lora_experts_V[j] + + self.transformer.h[i].ffn.linear_3.weight = ffn_0.linear_3.weight + self.transformer.h[i].ffn.linear_3.gate_linear.weight = ffn_0.linear_3.gate_linear.weight + for j in range(ffn_0.linear_3.n_experts): + self.transformer.h[i].ffn.linear_3.lora_experts_U[j] = ffn_0.linear_3.lora_experts_U[j] + self.transformer.h[i].ffn.linear_3.lora_experts_V[j] = ffn_0.linear_3.lora_experts_V[j]""" + for i in range(1, len(self.transformer.h)): + self.transformer.h[i].ffn.linear_1.weight = ffn_0.linear_1.weight + self.transformer.h[i].ffn.linear_1.gate_linear.weight = ffn_0.linear_1.gate_linear.weight + self.transformer.h[i].ffn.linear_1.lora_experts_U = ffn_0.linear_1.lora_experts_U + self.transformer.h[i].ffn.linear_1.lora_experts_V = ffn_0.linear_1.lora_experts_V + + self.transformer.h[i].ffn.linear_2.weight = ffn_0.linear_2.weight + self.transformer.h[i].ffn.linear_2.gate_linear.weight = ffn_0.linear_2.gate_linear.weight + self.transformer.h[i].ffn.linear_2.lora_experts_U = ffn_0.linear_2.lora_experts_U + self.transformer.h[i].ffn.linear_2.lora_experts_V = ffn_0.linear_2.lora_experts_V + + self.transformer.h[i].ffn.linear_3.weight = ffn_0.linear_3.weight + self.transformer.h[i].ffn.linear_3.gate_linear.weight = ffn_0.linear_3.gate_linear.weight + self.transformer.h[i].ffn.linear_3.lora_experts_U = ffn_0.linear_3.lora_experts_U + self.transformer.h[i].ffn.linear_3.lora_experts_V = ffn_0.linear_3.lora_experts_V + + def forward(self, x): + """ + Pass an input through the model + Args: + x: torch.tensor(B, S, H) + Returns: + x: torch.tensor(B, S, H) + """ + + # apply dropout + x = self.transformer.drop(x) + + # pass through the transformer blocks + for block in self.transformer.h: + x = block(x) + + return x diff --git a/models/experimental/next_thought/core_models.py b/models/experimental/next_thought/core_models.py deleted file mode 100644 index de760aa6..00000000 --- a/models/experimental/next_thought/core_models.py +++ /dev/null @@ -1,76 +0,0 @@ -""" -The core next-thought model. -""" -import torch - - - -class BaselineCoreModel(torch.nn.Module): - """ - An extremely simplistic core model for - next thought prediction. - """ - def __init__(self, model_cfg): - super().__init__() - - self.model = torch.nn.Sequential( - torch.nn.Linear( - in_features=model_cfg["latent_dim"], - out_features=model_cfg["latent_dim"], - ), - torch.nn.ReLU(), - torch.nn.Linear( - in_features=model_cfg["latent_dim"], - out_features=model_cfg["latent_dim"], - ), - ) - - def forward(self, x): - """ - Pass an input through the model - Args: - x: torch.tensor(B, S, H) - Returns: - x: torch.tensor(B, S, H) - """ - return self.model(x) - - -class Conv1dCoreModel(torch.nn.Module): - """ - A core model for next thought prediction using Conv1d layers. - """ - def __init__(self, model_cfg): - super().__init__() - - # 4800 - self.conv1 = torch.nn.Linear(30, 30) - self.conv2 = torch.nn.Linear(300, 300) - self.conv3 = torch.nn.Linear(3, 3) - - - - - def forward(self, x): - """ - Pass an input through the model - Args: - x: torch.tensor(B, S, H) - Returns: - x: torch.tensor(B, S, H) - """ - # B, 4800 -> B, 4800/30, 30 - x = x.view(x.size(0), 160, 30) - x = self.conv1(x) - x = x.view(x.size(0), 4800) - - # B, 4800 -> B, 4800/300, 300 - x = x.view(x.size(0), 16, 300) - x = self.conv2(x) - x = x.view(x.size(0), 4800) - - # B, 4800 -> B, 4800/3, 3 - x = x.view(x.size(0), 1600, 3) - x = self.conv3(x) - x = x.view(x.size(0), 4800) - return x \ No newline at end of file diff --git a/models/experimental/next_thought/embedding_models.py b/models/experimental/next_thought/embedding_models.py deleted file mode 100644 index c958fd54..00000000 --- a/models/experimental/next_thought/embedding_models.py +++ /dev/null @@ -1,87 +0,0 @@ -""" -The Embedding model for a VAE style sequence to sequence model. -""" -import torch - -from models.embedding_models import GenericEmbedder -from models.components.layers.transformer_blocks import GenericTransformerBlock - -from models.components.positional_encoding import build_positional_encodings -from models.components.tokenizers import build_tokenizer - - -# import local components -from models.experimental.next_thought.layers import AttentionPoolingRemoval - - - -class HierarchicalEncoder(GenericEmbedder): - """ - Accepts an arbitrary length sequence as input, - uses the QK^T matrix to, at every layer, - pick the top n-percent of nodes to pool into - a single token (the one paying most attention - to the other should be pooled into the other token). - """ - def __init__(self, model_cfg): - super().__init__(model_cfg=model_cfg) - # build the tokenizer - self.tokenizer = build_tokenizer( - tokenizer_type=model_cfg["embedder"]["tokenizer_type"], - vocab_size=model_cfg["vocab_size"], - dataset_name=model_cfg["embedder"]["dataset_name"], - ) - - # build the token embeddings - self.token_embedder = torch.nn.Embedding( - num_embeddings=model_cfg["vocab_size"], - embedding_dim=model_cfg["embedder"]["pooling_dims"][0], - ) - - # build the positional encodings - self.positional_encodings = build_positional_encodings(model_cfg=model_cfg) - - - self.standard_transformer = torch.nn.ModuleList( - [ - GenericTransformerBlock( - hidden_dim=model_cfg["embedder"]["pooling_dims"][0], - context_window=model_cfg["embedder"]["context_window"], - use_rope=False, - ffn_cfg=model_cfg["embedder"]["standard_ffn_block"], - attn_cfg=model_cfg["embedder"]["standard_attn_block"], - ) - ] - ) - - self.pooling_transformer = torch.nn.ModuleList( - [ - - AttentionPoolingRemoval( - hidden_size_in=model_cfg["embedder"]["pooling_dims"][i], - hidden_size_out=model_cfg["embedder"]["pooling_dims"][i+1], - num_attention_heads=12, - pct_pool_per_layer=model_cfg["embedder"]["pct_pool_per_layer"][i], - ) for i in range(len(model_cfg["embedder"]["pooling_dims"]) - 1) - ] - ) - - - def forward(self, token_ids): - # embed the input - x = self.embedding(token_ids) - - # apply positional encoding - x = x + self.positional_encoding(x) - - - # first pass through normal attention blocks - for layer in self.standard: - x = layer(x) - - # then pass through pooling attention blocks - for layer in self.pooling_attention: - x = layer(x) - # mean pool final representation - x = x.mean(dim=-2) - return x \ No newline at end of file diff --git a/models/experimental/next_thought/layers.py b/models/experimental/next_thought/layers.py deleted file mode 100644 index 9dd84fa2..00000000 --- a/models/experimental/next_thought/layers.py +++ /dev/null @@ -1,203 +0,0 @@ -""" -Layers that are specific to the next thought models -""" -import torch -import math - - -class AttentionPoolingRemoval(torch.nn.Module): - """ - Transformer block that removes the top-k - least paid-attention to tokens. - """ - def __init__(self, hidden_size_in, hidden_size_out, num_attention_heads, pct_pool_per_layer): - super().__init__() - self.pct_pool = pct_pool_per_layer - self.hidden_size_in = hidden_size_in - - self.attention = CustomMultiHeadAttention(hidden_size_in, num_attention_heads) - - self.ffn = torch.nn.Sequential( - torch.nn.Linear(hidden_size_in, 4 * hidden_size_in), - torch.nn.GELU(), - torch.nn.Linear(4 * hidden_size_in, hidden_size_out), - torch.nn.Dropout(), - ) - - self.norm1 = torch.nn.LayerNorm(hidden_size_in) - self.norm2 = torch.nn.LayerNorm(hidden_size_out) - - def forward(self, x): - # Apply multi-head attention - attn_output, attn_output_weights = self.attention(x, x, x) - - # average the attention weights across heads - attn_output_weights = attn_output_weights.mean(dim=1) - - # find how much each token was attended to on average - attn_output_weights = attn_output_weights.mean(dim=-2) - - - # Normalize and add residual connection - x = self.norm1(x + attn_output) - - # Calculate the top-k indices to keep based on the attention scores - seq_len = x.shape[1] - top_k = int(seq_len * (1 - self.pct_pool)) # Keeping the top 60% - - # Get the indices for the top-k tokens based on the highest attention scores - _, top_k_indices = torch.topk(attn_output_weights, top_k, dim=-1) - - # Reshape idx tensor to match weights - idx_expanded = top_k_indices.unsqueeze(-1).expand(-1, -1, x.size(-1)) - - # Use torch.gather to gather values from weights tensor based on indices - reduced_x = torch.gather(x, 1, idx_expanded) - - # Apply feedforward network and normalization - reduced_x = self.norm2(self.ffn(reduced_x)) - - return reduced_x - -# Scaled Dot-Product Attention -def scaled_dot_product_attention(query, key, value, mask=None): - """ - Compute scaled dot-product attention. - """ - # Q * K^T - scores = torch.matmul(query, key.transpose(-2, -1)) # (batch_size, num_heads, seq_len, seq_len) - - # Scale by the square root of the key dimension - d_k = query.size(-1) - scores = scores / math.sqrt(d_k) - - # Apply mask if provided (optional, for example, in Transformer Decoders) - #if mask is not None: - # scores = scores.masked_fill(mask == 0, -1e9) - - # Softmax to get attention weights - attention_weights = torch.nn.functional.softmax(scores, dim=-1) - - # Multiply by the value to get the final attention output - output = torch.matmul(attention_weights, value) # (batch_size, num_heads, seq_len, depth_per_head) - #input(attention_weights.size()) - - return output, attention_weights - - -class CustomMultiHeadAttention(torch.nn.Module): - """ - Custom implementation of multi-head attention from scratch. - """ - def __init__(self, hidden_size, num_heads): - super().__init__() - assert hidden_size % num_heads == 0, "Hidden size must be evenly divisible by number of heads." - - self.hidden_size = hidden_size - self.num_heads = num_heads - self.depth_per_head = hidden_size // num_heads - - # Linear layers for projecting into multiple heads - self.query_proj = torch.nn.Linear(hidden_size, hidden_size) - self.key_proj = torch.nn.Linear(hidden_size, hidden_size) - self.value_proj = torch.nn.Linear(hidden_size, hidden_size) - - # Final linear layer for output projection - self.out_proj = torch.nn.Linear(hidden_size, hidden_size) - - def split_into_heads(self, x): - """ - Split into multiple heads, reshaping accordingly. - """ - batch_size = x.size(0) - seq_len = x.size(1) - - # Reshape and split into heads - x = x.view(batch_size, seq_len, self.num_heads, self.depth_per_head) - x = x.permute(0, 2, 1, 3) # (batch_size, num_heads, seq_len, depth_per_head) - - return x - - def forward(self, q, k, v): - """ - x: (batch_size, seq_len, hidden_size) - """ - # Project into queries, keys, and values - query = self.split_into_heads(self.query_proj(q)) - key = self.split_into_heads(self.key_proj(k)) - value = self.split_into_heads(self.value_proj(v)) - - # Apply scaled dot-product attention - attention_output, attention_weights = scaled_dot_product_attention(query, key, value) - - # Concatenate the heads - attention_output = attention_output.permute(0, 2, 1, 3).reshape(q.size(0), q.size(1), self.hidden_size) - - # Final projection to maintain consistent output - output = self.out_proj(attention_output) - - return output, attention_weights - - - -class LatentSpaceDecoder(torch.nn.Module): - """ - Uses a fixed number of heads to decode - the latent space into the same hidden dim - as the sequence - """ - def __init__(self, hidden_dim, decoding_length, latent_dim): - super().__init__() - self.hidden_dim = hidden_dim - self.decoding_length = decoding_length - self.latent_dim = latent_dim - - self.decoding_layer = torch.nn.Linear( - in_features=latent_dim, - out_features=hidden_dim*decoding_length - ) - - def forward(self, x): - """ - x: (batch_size, latent_dim) - """ - # TODO, this only needs to be computed once - batch_size = x.size(0) - - # Project the latent space into the hidden dimension - x = self.decoding_layer(x) - x = x.view(batch_size, self.decoding_length, self.hidden_dim) - - return x - -class LatentSpaceQuery(torch.nn.Module): - """ - Lets the decoder query the latent space - """ - def __init__(self, hidden_dim, latent_decoded_length, latent_dim): - super().__init__() - self.hidden_dim = hidden_dim - self.latent_decoded_length = latent_decoded_length - self.latent_dim = latent_dim - - # k,v come from latent space - # q comes from the sequence - self.attention = CustomMultiHeadAttention( - hidden_size=hidden_dim, - num_heads=12 - ) - - def forward(self, x, latent_space): - """ - x: (batch_size, seq_len, hidden_dim) - latent_space: (batch_size, latent_decoded_length, hidden_dim) - """ - - # Query the latent space - x, _ = self.attention( - q=x, - k=latent_space, - v=latent_space - ) - - return x \ No newline at end of file diff --git a/models/experimental/next_thought/model_heads.py b/models/experimental/next_thought/model_heads.py deleted file mode 100644 index 00238115..00000000 --- a/models/experimental/next_thought/model_heads.py +++ /dev/null @@ -1,80 +0,0 @@ -""" -The latent to variable length sequence decoder. -""" -import torch - -from models.experimental.next_thought.layers import ( - LatentSpaceDecoder, - LatentSpaceQuery -) - -from models.embedding_models import GenericEmbedder -from models.components.layers.transformer_blocks import GenericTransformerBlock -from models.components.positional_encoding import build_positional_encodings - - - -class VariableLengthLatentDecoder(torch.nn.Module): - """ - Given a latent space representation, decode it into a sequence. - This should be similar to how VLMs work (i.e. have an encoder - for the latent space and query it at each step to generate the - next token). - """ - def __init__(self, model_cfg, embedding_model): - super().__init__() - self.model_cfg = model_cfg - self.latent_decoder = torch.nn.Linear( - in_features=model_cfg["latent_dim"], - out_features=model_cfg["embedding_dim"] * model_cfg["lm_head"]["latent_decoded_into"], - bias=False - ) - - self.token_embedder = embedding_model.token_embedder - self.positional_encodings = embedding_model.positional_encodings - - self.autoregressive_transformer = torch.nn.ModuleList( - [ - GenericTransformerBlock( - hidden_dim=model_cfg["embedding_dim"], - context_window=model_cfg["context_window"], - use_rope=False, - ffn_cfg=model_cfg["lm_head"]["standard_ffn_block"], - attn_cfg=model_cfg["lm_head"]["standard_attn_block"], - ) for _ in range(model_cfg["lm_head"]["num_layers"]) - ] - ) - - self.lm_head = torch.nn.Linear( - in_features=model_cfg["embedding_dim"], - out_features=model_cfg["vocab_size"], - bias=False - ) - - - def forward(self, x, x_raw=None): - """ - forward - """ - # decode latent into tokens - x = self.latent_decoder(x) - # reshape - x = x.view(x.size(0), self.model_cfg["lm_head"]["latent_decoded_into"], self.model_cfg["embedding_dim"]) - - # encode the target tokens with the embedder (w/o gradient) - y = self.token_embedder(x_raw) - - # add positional encoding - y = y + self.positional_encodings(y) - - # concat with the latent tokens - x = torch.cat([x, y], dim=1) - - # pass through autoregressive transformer blocks - for layer in self.autoregressive_transformer: - x = layer(x) - - # pass through lm head - x = self.lm_head(x[:, self.model_cfg["lm_head"]["latent_decoded_into"]:]) - - return x, None \ No newline at end of file diff --git a/models/experimental/weight_sharing-old.py b/models/experimental/weight_sharing-old.py new file mode 100644 index 00000000..19a777c6 --- /dev/null +++ b/models/experimental/weight_sharing-old.py @@ -0,0 +1,74 @@ +from models.core_models import GenericTransformer +import torch +from torch import nn +# params: k_interior_layers, lora_rank + +class LoRA(nn.Module): + def __init__(self, linear_layer, lora_rank): + """Wraps the linear layer with LoRA""" + super().__init__() + self.linear_layer = linear_layer + self.lora_rank = lora_rank + self.U = nn.Linear(linear_layer.in_features, lora_rank) + self.V = nn.Linear(lora_rank, linear_layer.out_features) + + def forward(self, x): + """Forward pass through the linear layer with LoRA""" + # compute the LoRA weight matrix + return self.linear_layer(x) + self.V(self.U(x)) + +class SharedInteriorFFNLora(GenericTransformer): + def __init__(self, model_cfg): + super().__init__(model_cfg) + self.k_interior_layers = model_cfg["k_interior_layers"] + self.lora_rank = model_cfg["lora_rank"] + # share the weights between transformer blocks in layers 1+k_interior_layers to D-k_interior_layers + base_layer = 1 + self.k_interior_layers + ffn_0 = self.transformer.h[base_layer].ffn + shared_weights = {} + for name, module in ffn_0.named_modules(): + if isinstance(module, torch.nn.Linear): + shared_weights[name] = module.weight + for i in range(1 + self.k_interior_layers, len(self.transformer.h) - self.k_interior_layers): + for name, module in self.transformer.h[i].ffn.named_modules(): + if isinstance(module, torch.nn.Linear): + module.weight = shared_weights[name] + # wrap the linear layer with LoRA + if self.lora_rank is not None: + setattr(self.transformer.h[i].ffn, name, LoRA(module, self.lora_rank)) + +class SharedInteriorFFNLoraAndCProj(GenericTransformer): + def __init__(self, model_cfg): + super().__init__(model_cfg) + self.k_interior_layers = model_cfg["k_interior_layers"] + self.lora_rank = model_cfg["lora_rank"] + # share the weights between transformer blocks in layers 1+k_interior_layers to D-k_interior_layers + base_layer = 1 + self.k_interior_layers + ffn_0 = self.transformer.h[base_layer].ffn + shared_weights = {} + for name, module in ffn_0.named_modules(): + if isinstance(module, torch.nn.Linear): + shared_weights[name] = module.weight + for i in range(1 + self.k_interior_layers, len(self.transformer.h) - self.k_interior_layers): + for name, module in self.transformer.h[i].ffn.named_modules(): + if isinstance(module, torch.nn.Linear): + module.weight = shared_weights[name] + # wrap the linear layer with LoRA + if self.lora_rank is not None: + setattr(self.transformer.h[i].ffn, name, LoRA(module, self.lora_rank)) + + # share the c_proj for interior layers + cproj_0 = self.transformer.h[base_layer].attn.c_proj + shared_weights = {} + for name, module in cproj_0.named_modules(): + if isinstance(module, torch.nn.Linear): + shared_weights[name] = module.weight + for i in range(1 + self.k_interior_layers, len(self.transformer.h) - self.k_interior_layers): + for name, module in self.transformer.h[i].attn.c_proj.named_modules(): + if isinstance(module, torch.nn.Linear): + module.weight = shared_weights[name] + # wrap the linear layer with LoRA + if self.lora_rank is not None: + setattr(self.transformer.h[i].attn, name, LoRA(module, self.lora_rank)) + + \ No newline at end of file diff --git a/models/experimental/weight_sharing.py b/models/experimental/weight_sharing.py new file mode 100644 index 00000000..ae339c40 --- /dev/null +++ b/models/experimental/weight_sharing.py @@ -0,0 +1,81 @@ +import torch +import torch.nn as nn +import math +from models.core_models import GenericTransformer + +class LoRA(nn.Module): + def __init__(self, linear_layer: nn.Linear, lora_rank: int, lora_alpha: float = 1.0): + """ + Wraps the linear layer with LoRA (Low-Rank Adaptation) + + Args: + linear_layer (nn.Linear): The linear layer to be wrapped + lora_rank (int): The rank of the LoRA matrices + lora_alpha (float): Scaling factor for the LoRA update + """ + super().__init__() + self.linear_layer = linear_layer + self.lora_rank = lora_rank + self.lora_alpha = lora_alpha + self.scaling = self.lora_alpha / self.lora_rank + + self.lora_A = nn.Parameter(torch.empty((lora_rank, linear_layer.in_features))) + self.lora_B = nn.Parameter(torch.zeros((linear_layer.out_features, lora_rank))) + + print("LoRA A shape:", self.lora_A.shape) + print("LoRA B shape:", self.lora_B.shape) + print("Input feature size:", linear_layer.in_features) + print("Output feature size:", linear_layer.out_features) + + + self.reset_parameters() + + def reset_parameters(self): + nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass through the linear layer with LoRA""" + return self.linear_layer(x) + (self.lora_B @ self.lora_A @ x.transpose(1,2)).transpose(1,2) * self.scaling + +class SharedInteriorFFNLora(GenericTransformer): + def __init__(self, model_cfg): + super().__init__(model_cfg) + self.k_interior_layers = model_cfg["k_interior_layers"] + self.lora_rank = model_cfg["lora_rank"] + self.lora_alpha = model_cfg.get("lora_alpha", 1.0) + + self._apply_weight_sharing_and_lora( + start_layer=1 + self.k_interior_layers, + end_layer=len(self.transformer.h) - self.k_interior_layers, + module_name='ffn' + ) + + def _apply_weight_sharing_and_lora(self, start_layer: int, end_layer: int, module_name: str): + base_module = getattr(self.transformer.h[start_layer], module_name) + shared_weights = {name: module.weight for name, module in base_module.named_modules() if isinstance(module, nn.Linear)} + + for i in range(start_layer, end_layer): + target_module = getattr(self.transformer.h[i], module_name) + for name, module in target_module.named_modules(): + if isinstance(module, nn.Linear): + module.weight = shared_weights[name] + if self.lora_rank is not None: + lora_module = LoRA(module, self.lora_rank, self.lora_alpha) + setattr(target_module, name, lora_module) + + + +class SharedInteriorFFNLoraAndCProj(SharedInteriorFFNLora): + def __init__(self, model_cfg): + super().__init__(model_cfg) + + # now strictly share the c_proj weights w/o lora + for i in range(1 + self.k_interior_layers, len(self.transformer.h) - self.k_interior_layers): + base_cproj = self.transformer.h[1 + self.k_interior_layers].attn.c_proj + shared_cproj_weights = {name: module.weight for name, module in base_cproj.named_modules() if isinstance(module, nn.Linear)} + target_cproj = self.transformer.h[i].attn.c_proj + for name, module in target_cproj.named_modules(): + if isinstance(module, nn.Linear): + module.weight = shared_cproj_weights[name] + diff --git a/models/generator.py b/models/generator.py deleted file mode 100644 index aed6c5bf..00000000 --- a/models/generator.py +++ /dev/null @@ -1,93 +0,0 @@ -""" -Generator Base Wrapper -""" - -import torch - - -class StandardGenerator(torch.nn.Module): - """Standard Generator Wrapper for GPT models""" - - def __init__(self, model, generate_cfg): - """Initialize the model and the configuration""" - super().__init__() - self.model = model - self.model = self.model.to(torch.device("cuda")) - self.generate_config = generate_cfg - - def default_generate(self, input_text): - """ - Generate text using the default generation method - """ - return self.generate( - input_text, - self.generate_config["max_new_tokens"], - self.generate_config["temperature"], - self.generate_config["top_k"], - ) - - @torch.no_grad() - def generate(self, input_text, max_new_tokens, temperature=1.0, top_k=None): - """ - Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete - the sequence max_new_tokens times, feeding the predictions back into the model each time. - Most likely you'll want to make sure to be in model.eval() mode of operation for this. - """ - idx = self.model.embedding_model.tokenize_input(input_string=input_text, - add_eot=False, - truncate=True) - # push to device - idx = torch.tensor(idx).unsqueeze(0).to(torch.device("cuda")) - for _ in range(max_new_tokens): - # forward the model to get the logits for the index in the sequence - logits = self.model.inference(idx) - # pluck the logits at the final step and scale by desired temperature - logits = logits / temperature - # logits have shape (b,t,v) - # optionally crop the logits to only the top k options - if top_k is not None: - v, _ = torch.topk(logits, min(top_k, logits.size(-1))) - # check for dim - if len(v.size()) == 3: - logits[logits < v[:, :, [-1]]] = -float("Inf") - else: - logits[logits < v[:, [-1]]] = -float("Inf") - # apply softmax to convert logits to (normalized) probabilities - probs = torch.nn.functional.softmax(logits, dim=-1) - # sample from the distribution - # check if byte-level and if so, flatten - if len(probs.size()) == 4: - B, S, S_c, H = probs.size() - probs = probs.view(B* S * S_c, H) - flattened = True - else: - flattened = False - - - - idx_next = torch.multinomial(probs, num_samples=1) - - # check if byte-level and if so, unflatten - if flattened: - idx_next = idx_next.view(B, S) - elif idx_next == self.model.embedding_model.eot_token: - break - - if flattened: - idx_next = idx_next.unsqueeze(0) - idx = torch.cat((idx, idx_next), dim=1) - - return self.model.embedding_model.decode(idx.tolist()) - - def forward(self, x): - """Call the underlying model""" - return self.model(x) - - def embed(self, x): - """Embed the input""" - return self.model.embed(x) - - -def build_generator(model, generate_cfg): - """Build the generator""" - return StandardGenerator(model, generate_cfg) diff --git a/models/generators.py b/models/generators.py new file mode 100644 index 00000000..1b1a0fac --- /dev/null +++ b/models/generators.py @@ -0,0 +1,740 @@ +""" +Generator Base Wrapper +""" + +import torch +import torch.nn.functional as F + +## libraries for entropy calculation +import math +from typing import Dict, Optional + +class AdaptiveGenerator(torch.nn.Module): + """ + Adaptive Generator with adaptive sampling strategies based on entropy metrics. + Reference: https://github.com/xjdr-alt/entropix/tree/main + """ + + LN_2 = math.log(2) # ln(2) for entropy calculations + + def __init__(self, model, generate_cfg: Dict, device: str = "cuda"): + """ + Initialize the generator with the model and sampling configuration. + + Args: + model: The language model to use for generation. + generate_cfg (Dict): Configuration dictionary for sampler hyperparameters. + device (str): Device to run the generator on ('cuda' or 'cpu'). + """ + super().__init__() + self.model = model.to(device) + self.device = device + self.generate_config = generate_cfg + + def default_generate(self, input_text: str) -> str: + """ + Generate text using the default generation method. + + Args: + input_text (str): The initial text prompt. + + Returns: + str: Generated text. + """ + return self.generate( + input_text, + max_new_tokens=self.generate_config.max_new_tokens, # Adjust as needed + temperature=self.generate_config.temperature, + top_k=self.generate_config.top_k, + top_p=self.generate_config.top_p, + min_p=self.generate_config.min_p + ) + + @torch.no_grad() + def generate( + self, + input_text: str, + max_new_tokens: int, + temperature: float = 1.0, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + min_p: Optional[float] = None, + clarifying_question_token: int = 2564 + ) -> str: + """ + Generate text with advanced sampling strategies. + + Args: + input_text (str): The initial text prompt. + max_new_tokens (int): Number of tokens to generate. + temperature (float, optional): Temperature for scaling logits. + top_k (int, optional): Top-K sampling parameter. + top_p (float, optional): Top-P (nucleus) sampling parameter. + min_p (float, optional): Minimum probability threshold. + clarifying_question_token (int, optional): Token ID for clarifying questions. + + Returns: + str: Generated text. + """ + # Tokenize input + idx = self.model.embedding_model.tokenize_input( + input_string=input_text, + add_eot=False, + truncate=True + ) + # Convert to tensor and move to device + idx = torch.tensor(idx, dtype=torch.long).unsqueeze(0).to(self.device) # Shape: (batch_size=1, sequence_length) + + for _ in range(max_new_tokens): + # Forward pass to get logits and attention scores + logits, attention_scores = self.model.inference(idx) + # Logits shape: (batch_size, sequence_length, vocab_size) + # attention_scores shape: list of tensors, each of shape (batch_size, num_heads, sequence_length, sequence_length) + + # Debugging: Print shapes (optional, remove in production) + # print(f"logits shape: {logits.shape}") + # print(f"attention_scores length: {len(attention_scores)}") + # for i, m in enumerate(self.model.core_model.matrices): + # print(f"matrix {i} scaled_scores shape: {m['scaled_scores'].shape}") + + # Take logits of the last token + if logits.dim() == 3: + logits = logits[:, -1, :] # Shape: (batch_size, vocab_size) + elif logits.dim() == 2: + logits = logits # Shape: (batch_size, vocab_size) + elif logits.dim() == 1: + logits = logits.unsqueeze(0) # Shape: (1, vocab_size) + else: + raise ValueError(f"Unexpected logits shape: {logits.shape}") + + # Ensure logits is 2D + if logits.dim() != 2: + raise ValueError(f"Logits should be 2D after processing, got shape: {logits.shape}") + + # Scale logits by temperature + scaled_logits = logits / temperature # Shape: (batch_size, vocab_size) + + # Retrieve and concatenate attention scores + # Ensure that self.model.core_model.matrices is correctly structured + attention_scores = torch.cat([attn_component['scaled_scores'] for attn_component in self.model.core_model.attn_components], dim=1) + # attention_scores shape: (batch_size, total_heads, seq_length, seq_length) + + # Calculate metrics + metrics = self._calculate_metrics(scaled_logits, attention_scores) + + # Decide on sampling strategy based on metrics + next_token = self._decide_next_token(logits, metrics, clarifying_question_token) + + # Check if the next token is the end-of-text token + if next_token.item() == self.model.embedding_model.eot_token: + print("EOT token found, stopping generation.") + break + + # Debugging: Print shapes (optional, remove in production) + # print("next token shape!!!", next_token.shape) # Should be [batch_size, 1] + # print("idx shape!!!", idx.shape) # Should be [batch_size, current_length] + + # Append the next token to the sequence + idx = torch.cat((idx, next_token), dim=1) # Shape: (batch_size, current_length + 1) + # print(idx.ty) + # Decode the generated tokens to string + return self.model.embedding_model.decode(idx.tolist()) + + def _calculate_metrics(self, logits: torch.Tensor, attention_scores: torch.Tensor) -> Dict[str, torch.Tensor]: + """ + Calculate entropy, varentropy, and other metrics from logits and attention scores. + + Args: + logits (torch.Tensor): Logits from the model. Shape: (batch_size, vocab_size) + attention_scores (torch.Tensor): Attention scores from the model. Shape: (batch_size, total_heads, seq_length, seq_length) + + Returns: + Dict[str, torch.Tensor]: Dictionary of calculated metrics. + """ + # Calculate log probabilities + log_probs = F.log_softmax(logits, dim=-1) # Shape: (batch_size, vocab_size) + probs = torch.exp(log_probs) # Shape: (batch_size, vocab_size) + + # Entropy: -sum(p * log2(p)) + entropy = -torch.sum(probs * log_probs, dim=-1) / self.LN_2 # Shape: (batch_size,) + + # Varentropy: sum(p * (log2(p) + entropy)^2) + entropy_expanded = entropy.unsqueeze(-1) # Shape: (batch_size, 1) + varentropy = torch.sum(probs * (log_probs / self.LN_2 + entropy_expanded) ** 2, dim=-1) # Shape: (batch_size,) + + # Attention probabilities + attention_probs = F.softmax(attention_scores, dim=-1) # Shape: (batch_size, total_heads, seq_length, seq_length) + + # Attention entropy: -sum(p * log2(p)) + attn_entropy = -torch.sum( + attention_probs * torch.log2(torch.clamp(attention_probs, min=1e-10)), + dim=-1 + ) # Shape: (batch_size, total_heads, seq_length) + + # Aggregate attention entropy by averaging over sequence length + attn_entropy = torch.mean(attn_entropy, dim=-1) # Shape: (batch_size, total_heads) + + # Attention varentropy: variance of attention entropy across heads + attn_varentropy = torch.var(attn_entropy, dim=1) # Shape: (batch_size,) + + # Mean attention + mean_attention = torch.mean(attention_probs, dim=1) # Shape: (batch_size, seq_length, seq_length) + + # Agreement: mean absolute difference from mean attention + agreement = torch.mean(torch.abs(attention_probs - mean_attention.unsqueeze(1)), dim=(1, 2, 3)) # Shape: (batch_size,) + + # Interaction strength: mean absolute attention scores + interaction_strength = torch.mean(torch.abs(attention_scores), dim=(1, 2, 3)) # Shape: (batch_size,) + + return { + "logits_entropy": entropy.mean(), + "logits_varentropy": varentropy.mean(), + "attn_entropy": attn_entropy.mean(), + "attn_varentropy": attn_varentropy.mean(), + "agreement": agreement.mean(), + "interaction_strength": interaction_strength.mean() + } + + def _decide_next_token( + self, + logits: torch.Tensor, + metrics: Dict[str, torch.Tensor], + clarifying_question_token: int + ) -> torch.Tensor: + """ + Decide the next token to generate based on the calculated metrics. + + Args: + logits (torch.Tensor): Logits from the model. Shape: (batch_size, vocab_size) + metrics (Dict[str, torch.Tensor]): Calculated metrics. + clarifying_question_token (int): Token ID for clarifying questions. + + Returns: + torch.Tensor: Next token ID. Shape: (batch_size, 1) + """ + cfg = self.generate_config + ent = metrics["logits_entropy"].item() + vent = metrics["logits_varentropy"].item() + attn_ent = metrics["attn_entropy"].item() + attn_vent = metrics["attn_varentropy"].item() + agreement = metrics["agreement"].item() + interaction_strength = metrics["interaction_strength"].item() + + # Low Entropy, Low Varentropy: Greedy decoding + if ent < cfg.low_ent_thresh and vent < cfg.low_vent_thresh: + next_token = torch.argmax(logits, dim=-1, keepdim=True) # Shape: (batch_size, 1) + return next_token + + # High Entropy, Low Varentropy: Insert clarifying question + elif ent > cfg.high_ent_thresh and vent < cfg.low_vent_thresh: + # Insert a clarifying question token for each sequence in the batch + batch_size = logits.shape[0] + next_token = torch.full((batch_size, 1), clarifying_question_token, device=self.device) # Shape: (batch_size, 1) + return next_token + + # Low Entropy, High Varentropy: Adjust temperature and top_k + elif ent < cfg.high_ent_thresh and vent > cfg.high_vent_thresh: + temp_adj = cfg.lehv_interaction_strength_offset + cfg.lehv_interaction_strength_coef * interaction_strength + adjusted_temp = min(1.5, cfg.temperature * temp_adj) + adjusted_top_k = max(5, int(cfg.top_k * (1 + 0.5 * (1 - agreement)))) + return self._sample(logits, temperature=adjusted_temp, top_p=cfg.top_p, top_k=adjusted_top_k, min_p=cfg.min_p) + + # High Entropy, High Varentropy: High temperature and adjusted top_p + elif ent > cfg.med_ent_thresh and vent > cfg.high_vent_thresh: + temp_adj = cfg.hehv_attn_vent_offset + cfg.hehv_attn_vent_coef * attn_vent + adjusted_temp = max(2.0, cfg.temperature * temp_adj) + top_p_adj = max(0.5, cfg.top_p - cfg.hehv_attn_ent_coef * attn_ent) + return self._sample(logits, temperature=adjusted_temp, top_p=top_p_adj, top_k=cfg.top_k, min_p=cfg.min_p) + + # Middle ground: Adaptive sampling + else: + logits_uncertainty = metrics["logits_entropy"] + metrics["logits_varentropy"] + attn_uncertainty = metrics["attn_entropy"] + metrics["attn_varentropy"] + + temperature = cfg.temperature * ( + 1 + + cfg.ada_temp_logits * logits_uncertainty + + cfg.ada_temp_attn * attn_uncertainty + - cfg.ada_temp_agree * agreement + ) + top_p = torch.clamp(cfg.top_p * (1 + cfg.ada_top_p * metrics["attn_varentropy"]), 0.1, 1.0) + top_k = int( + torch.clamp( + torch.round( + torch.tensor(cfg.top_k) * ( + 1 + + cfg.ada_top_k_int * interaction_strength + - cfg.ada_top_k_agree * agreement + ) + ), + min=1, + max=100 + ).item() + ) + min_p = torch.clamp(cfg.min_p * (1 - cfg.ada_min_p * logits_uncertainty), 0.01, 0.5) + + # Perform multiple adaptive samples and select the best one + samples = [] + sample_scores = [] + for _ in range(cfg.n_adaptive_samples): + sample = self._sample( + logits, + temperature=temperature.item(), + top_p=top_p.item(), + top_k=top_k, + min_p=min_p.item() + ) + score = self._score_sample(sample, logits, metrics) + samples.append(sample) + sample_scores.append(score) + + # Select the sample with the highest score + sample_scores = torch.tensor(sample_scores) + best_sample_idx = torch.argmax(sample_scores).item() + best_sample = samples[best_sample_idx] # Shape: (batch_size, 1) + return best_sample # Shape: (batch_size, 1) + + def _sample(self, logits: torch.Tensor, temperature: float, top_p: float, top_k: int, min_p: float) -> torch.Tensor: + """ + Sample a token based on adjusted logits with temperature, top_p, top_k, and min_p. + + Args: + logits (torch.Tensor): Logits from the model. Shape: (batch_size, vocab_size) + temperature (float): Temperature scaling factor. + top_p (float): Cumulative probability threshold for nucleus sampling. + top_k (int): Number of top tokens to consider for top-k sampling. + min_p (float): Minimum probability threshold. + + Returns: + torch.Tensor: Sampled token IDs. Shape: (batch_size, 1) + """ + # Apply temperature + scaled_logits = logits / temperature # Shape: (batch_size, vocab_size) + + # Apply min_p sampling + if min_p > 0.0: + probs = F.softmax(scaled_logits, dim=-1) # Shape: (batch_size, vocab_size) + p_max, _ = torch.max(probs, dim=-1, keepdim=True) # Shape: (batch_size, 1) + indices_to_remove = probs < (min_p * p_max) # Shape: (batch_size, vocab_size) + scaled_logits = torch.where(indices_to_remove, torch.full_like(scaled_logits, -float('Inf')), scaled_logits) + + # Apply top_k sampling + if top_k is not None and top_k > 0: + topk_logits, topk_indices = torch.topk(scaled_logits, top_k, dim=-1) + scaled_logits = torch.full_like(scaled_logits, -float('Inf')).scatter_(-1, topk_indices, topk_logits) + + # Apply top_p (nucleus) sampling + if top_p is not None and top_p < 1.0: + sorted_probs, sorted_indices = torch.sort(F.softmax(scaled_logits, dim=-1), descending=True, dim=-1) # [batch_size, vocab_size] + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) # [batch_size, vocab_size] + sorted_indices_to_remove = cumulative_probs > top_p # [batch_size, vocab_size] + # Shift the mask right to keep the first token above the threshold + sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone() + sorted_indices_to_remove[:, 0] = 0 + # Scatter back to original ordering + indices_to_remove = sorted_indices_to_remove.scatter(-1, sorted_indices, sorted_indices_to_remove) # [batch_size, vocab_size] + scaled_logits = torch.where(indices_to_remove, torch.full_like(scaled_logits, -float('Inf')), scaled_logits) + + # Re-normalize the logits + probs = F.softmax(scaled_logits, dim=-1) # Shape: (batch_size, vocab_size) + + # Multinomial sampling + next_token = torch.multinomial(probs, num_samples=1) # Shape: (batch_size, 1) + return next_token + + def _score_sample(self, sample: torch.Tensor, logits: torch.Tensor, metrics: Dict[str, torch.Tensor]) -> float: + """ + Score a sampled token based on various confidence metrics. + + Args: + sample (torch.Tensor): Sampled token ID. Shape: (batch_size, 1) + logits (torch.Tensor): Logits from the model. Shape: (batch_size, vocab_size) + metrics (Dict[str, torch.Tensor]): Calculated metrics. + + Returns: + float: Confidence score for the sampled token. + """ + # Calculate log probability of the sampled token + log_probs = F.log_softmax(logits, dim=-1) # Shape: (batch_size, vocab_size) + sample_log_prob = log_probs.gather(-1, sample).squeeze(-1).item() + + # Confidence score based on metrics and configuration + cfg = self.generate_config + confidence_score = ( + (1 - metrics["logits_entropy"].item()) * cfg.ada_score_logits_ent + + (1 - metrics["attn_entropy"].item()) * cfg.ada_score_attn_ent + + (1 - metrics["logits_varentropy"].item()) * cfg.ada_score_logits_vent + + (1 - metrics["attn_varentropy"].item()) * cfg.ada_score_attn_vent + + metrics["agreement"].item() * cfg.ada_score_agree + + metrics["interaction_strength"].item() * cfg.ada_score_int + ) + + return sample_log_prob + confidence_score + + def forward(self, x): + """Call the underlying model's forward method.""" + return self.model(x) + + def embed(self, x): + """Embed the input using the underlying model's embedding.""" + return self.model.embed(x) + +class BaseGenerator(torch.nn.Module): + """ TODO """ + def __init__(self, model): + super().__init__() + """ TODOD """ + self.model = model + self.device = self.model.device + + @torch.no_grad() + def generate(self, input_text): + """ TODO """ + raise NotImplementedError("Each Generator needs to implement the generate function") + +class EntropyTemperatureGenerator(BaseGenerator): + ''' + From: https://arxiv.org/pdf/2403.14541 + Entropy based temepraure adjusts the temperature based on the entropy of the logits. + If logits highly uncertain, entropy is high, temperature is increased. + If logits are certain, entropy is low, temperature is decreased. + ''' + + def __init__(self, model, generate_cfg, device="cuda"): + super().__init__() + self.model = model + self.device = device + self.model = self.model.to(torch.device(self.device)) + self.generate_config = generate_cfg + + def default_generate(self, input_text): + ''' + Generate text using the default generation method + ''' + return self.generate( + input_text, + self.generate_config["max_new_tokens"], + self.generate_config["temperature"], + self.generate_config["top_k"], + self.generate_config.get("temperature_scaling_factor", 0.1) + ) + + @torch.no_grad() + def generate(self, input_text, max_new_tokens, temperature_ceiling, top_k, temperature_scaling_factor): + """ + Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete + the sequence max_new_tokens times, feeding the predictions back into the model each time. + Most likely you'll want to make sure to be in model.eval() mode of operation for this. + """ + idx = self.model.embedding_model.tokenize_input( + input_string=input_text, + add_eot=False, + truncate=True + ) + # push to device + idx = torch.tensor(idx).unsqueeze(0).to(torch.device(self.device)) + for _ in range(max_new_tokens): + # forward the model to get the logits for the index in the sequence + logits, _ = self.model.inference(idx) + # calculate the entropy of the logits + entropy = self.calculate_entropy(F.softmax(logits[-1], dim=-1)) + # calculate the temperatures + temperature = temperature_ceiling * (0.8 ** (temperature_scaling_factor / entropy)) # 0.8 is fixed based on the paper + # pluck the logits at the final step and scale by desired temperature + logits = logits / temperature + # logits might have shape (b,t,v) or (t,v) + # optionally crop the logits to only the top k options + if top_k is not None: + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + ## for batched logits + if len(logits.shape) == 3: + logits[logits < v[:, :, [-1]]] = -float("Inf") + ## for single logits + else: + logits[logits < v[:, [-1]]] = -float("Inf") + + # apply softmax to convert logits to (normalized) probabilities + probs = torch.nn.functional.softmax(logits, dim=-1) + + idx_next = torch.multinomial(probs, num_samples=1) + + # check if done + if idx_next == self.model.embedding_model.eot_token: + break + + idx = torch.cat((idx, idx_next), dim=1) + + return self.model.embedding_model.decode(idx.tolist()) + + def calculate_entropy(self, probabilities): + """ + Calculate the entropy of a probability distribution from softmaxed logits. + + :param probabilities: A PyTorch tensor of softmaxed logits + :return: The entropy value as a PyTorch tensor + """ + + # Ensure the input is a PyTorch tensor + if not isinstance(probabilities, torch.Tensor): + probabilities = torch.tensor(probabilities) + + # Ensure the tensor is float for numerical stability + probabilities = probabilities.float() + + return -torch.sum(probabilities * torch.log(probabilities), dim=-1) + + + def forward(self, x): + """Call the underlying model""" + return self.model(x) + + def embed(self, x): + """Embed the input""" + return self.model.embed(x) + + + +class BeamSearchGenerator(BaseGenerator): + def __init__(self, model, generate_cfg, device="cuda"): + super().__init__() + self.model = model + self.device = device + self.model = self.model.to(torch.device(self.device)) + self.generate_config = generate_cfg + + def default_generate(self, input_text): + return self.generate( + input_text, + self.generate_config["max_new_tokens"], + self.generate_config["temperature"], + self.generate_config["top_k"], + self.generate_config.get("beam_width", 5), + self.generate_config.get("use_sampling", False), + self.generate_config.get("repetition_penalty", 1.2), + self.generate_config.get("repetition_window", 32) + ) + + @torch.no_grad() + def generate(self, input_text, max_new_tokens, temperature=1.0, top_k=None, beam_width=5, + use_sampling=False, repetition_penalty=1.2, repetition_window=32): + idx = self.model.embedding_model.tokenize_input(input_string=input_text, add_eot=False, truncate=True) + idx = torch.tensor(idx).unsqueeze(0).to(torch.device(self.device)) + + # Initialize beam with the input sequence + beam = [(idx, 0.0)] + + for _ in range(max_new_tokens): + all_candidates = [] + for seq, score in beam: + # Get logits for the entire sequence + logits = self.model.inference(seq)[0] + # Consider only the last token's logits + last_token_logits = logits / temperature + + # Apply repetition penalty + if repetition_penalty > 1.0: + self.apply_repetition_penalty(last_token_logits, seq, repetition_penalty, repetition_window) + + if top_k is not None: + v, _ = torch.topk(last_token_logits, min(top_k, last_token_logits.size(-1))) + last_token_logits[last_token_logits < v[:, [-1]]] = -float("Inf") + + probs = F.softmax(last_token_logits, dim=-1) + + if use_sampling: + # Sampling + sampled_indices = torch.multinomial(probs, num_samples=beam_width) + top_indices = sampled_indices[0] + top_probs = probs[0, top_indices] + else: + # Greedy selection + top_probs, top_indices = torch.topk(probs, k=beam_width) + top_probs = top_probs[0] + top_indices = top_indices[0] + + for prob, idx_next in zip(top_probs, top_indices): + new_seq = torch.cat([seq, idx_next.unsqueeze(0).unsqueeze(0)], dim=1) + new_score = score - torch.log(prob).item() + all_candidates.append((new_seq, new_score)) + + # Select top beam_width candidates + beam = sorted(all_candidates, key=lambda x: x[1])[:beam_width] + + # Check if any beam has generated EOT token + #if any(seq[0, -1] == self.model.embedding_model.eot_token for seq, _ in beam): + # break + + # Return the sequence with the best score + best_seq, _ = min(beam, key=lambda x: x[1]) + return self.model.embedding_model.decode(best_seq.tolist()) + + def apply_repetition_penalty(self, logits, sequence, penalty, window): + # Get the most recent tokens within the window + recent_tokens = sequence[0, -window:] + + # Count the occurrences of each token + unique_tokens, counts = torch.unique(recent_tokens, return_counts=True) + + # Apply penalty to the logits of repeated tokens + logits[0, unique_tokens] /= penalty ** counts.float() + + +class StandardGenerator(BaseGenerator): + """Standard Generator Wrapper for GPT models""" + + def __init__(self, model, generate_cfg, device="cuda"): + """Initialize the model and the configuration""" + super().__init__(model) + # self.model = model + self.device = device + self.model = self.model.to(torch.device(device)) + self.generate_config = generate_cfg + + def default_generate(self, input_text): + """ + Generate text using the default generation method + """ + return self.generate( + input_text, + self.generate_config["max_new_tokens"], + self.generate_config["temperature"], + self.generate_config["top_k"], + ) + + @torch.no_grad() + def generate(self, input_text, max_new_tokens, temperature=1.0, top_k=None): + """ + Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete + the sequence max_new_tokens times, feeding the predictions back into the model each time. + Most likely you'll want to make sure to be in model.eval() mode of operation for this. + """ + idx = self.model.embedding_model.tokenize_input( + input_string=input_text, + add_eot=False, + truncate=True + ) + # push to device + idx = torch.tensor(idx).unsqueeze(0).to(torch.device(self.device)) + for _ in range(max_new_tokens): + # forward the model to get the logits for the index in the sequence + logits, _ = self.model.inference(idx) + # pluck the logits at the final step and scale by desired temperature + logits = logits / temperature + # logits might have shape (b,t,v) or (t,v) + # optionally crop the logits to only the top k options + if top_k is not None: + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + ## for batched logits + if len(logits.shape) == 3: + logits[logits < v[:, :, [-1]]] = -float("Inf") + ## for single logits + else: + logits[logits < v[:, [-1]]] = -float("Inf") + + # apply softmax to convert logits to (normalized) probabilities + probs = torch.nn.functional.softmax(logits, dim=-1) + + idx_next = torch.multinomial(probs, num_samples=1) + + # check if done + if idx_next == self.model.embedding_model.eot_token: + break + + idx = torch.cat((idx, idx_next), dim=1) + print(idx) + return self.model.embedding_model.decode(idx.tolist()) + + def forward(self, x): + """Call the underlying model""" + return self.model(x) + + def embed(self, x): + """Embed the input""" + return self.model.embed(x) + + + +class StandardGenerator(BaseGenerator): + """Standard Generator Wrapper for GPT models""" + + def __init__(self, model, generate_cfg, device="cuda"): + """Initialize the model and the configuration""" + super().__init__(model) + self.device = device + self.model = self.model.to(torch.device(device)) + self.generate_config = generate_cfg + + def default_generate(self, input_text): + """ + Generate text using the default generation method + """ + return self.generate( + input_text, + self.generate_config["max_new_tokens"], + self.generate_config["temperature"], + self.generate_config["top_k"], + ) + + @torch.no_grad() + def generate(self, input_text, max_new_tokens=100, temperature=1.0, top_k=None): + """ + Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete + the sequence max_new_tokens times, feeding the predictions back into the model each time. + Most likely you'll want to make sure to be in model.eval() mode of operation for this. + """ + idx = self.model.embedding_model.tokenize_input( + input_string=input_text, + add_eot=False, + truncate=True + ) + # push to device + idx = torch.tensor(idx).unsqueeze(0).to(torch.device(self.device)) + for _ in range(max_new_tokens): + # forward the model to get the logits for the index in the sequence + logits, _ = self.model.inference(idx) + # pluck the logits at the final step and scale by desired temperature + logits = logits / temperature + # logits might have shape (b,t,v) or (t,v) + # optionally crop the logits to only the top k options + if top_k is not None: + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + ## for batched logits + if len(logits.shape) == 3: + logits[logits < v[:, :, [-1]]] = -float("Inf") + ## for single logits + else: + logits[logits < v[:, [-1]]] = -float("Inf") + + # apply softmax to convert logits to (normalized) probabilities + probs = torch.nn.functional.softmax(logits, dim=-1) + + idx_next = torch.multinomial(probs, num_samples=1) + + # check if done + if idx_next == self.model.embedding_model.eot_token: + break + + idx = torch.cat((idx, idx_next), dim=1) + + return self.model.embedding_model.decode(idx.tolist()) + + def forward(self, x): + """Call the underlying model""" + return self.model(x) + + def embed(self, x): + """Embed the input""" + return self.model.embed(x) + +GENERATOR_DICT = { + "standard": lambda model, generate_cfg, device: StandardGenerator(model=model, generate_cfg=generate_cfg, device=device), + "beam_search": lambda model, generate_cfg, device: BeamSearchGenerator(model=model, generate_cfg=generate_cfg, device=device), + "entropy_temperature": lambda model, generate_cfg, device: EntropyTemperatureGenerator(model=model, generate_cfg=generate_cfg, device=device), + "adaptive_sampler": lambda model, generate_cfg, device: AdaptiveGenerator(model=model, generate_cfg=generate_cfg, device=device) + } + +def build_generator(model, generate_cfg, device): + """ + Build the generator + """ + return GENERATOR_DICT[generate_cfg['generator_type']](model, generate_cfg, device) \ No newline at end of file diff --git a/models/model_heads.py b/models/model_heads.py index ab1eb107..98e5eeef 100644 --- a/models/model_heads.py +++ b/models/model_heads.py @@ -4,7 +4,7 @@ import torch -from models.components.layers.normalization import build_normalization +from models.components.normalization import build_normalization class AutoregressiveLMHead(torch.nn.Module): @@ -15,14 +15,17 @@ class AutoregressiveLMHead(torch.nn.Module): def __init__(self, model_cfg): super().__init__() self.layer_norm = build_normalization( - normalization_name=model_cfg["lm_head"]["normalization"], + normalization_name=model_cfg["lm_head_normalization"], dim=model_cfg["hidden_dim"], - bias=model_cfg["lm_head"]["bias"], + bias=model_cfg["lm_head_bias"], ) self.linear = torch.nn.Linear( in_features=model_cfg["hidden_dim"], out_features=model_cfg["vocab_size"], - bias=model_cfg["lm_head"]["bias"], + bias=model_cfg["lm_head_bias"], + ) + self.dropout = torch.nn.Dropout( + p=model_cfg.get("lm_head_dropout", 0.0) # Default is no Dropout ) def forward(self, x): @@ -37,6 +40,9 @@ def forward(self, x): # apply layer norm x = self.layer_norm(x) + # apply dropout if necessary + x = self.dropout(x) + # pass through the linear layer x = self.linear(x) diff --git a/models/model_shell.py b/models/model_shell.py index 330f6de3..500464b2 100644 --- a/models/model_shell.py +++ b/models/model_shell.py @@ -11,26 +11,32 @@ class ModelShell(torch.nn.Module): """ - Unify the embedding model, core model and LM head + Unify the embedding model, core model and LM head into a single object; initializes the weights and prints basic model statistics. """ def __init__( self, + model_cfg, embedding_model: embedding_models.EmbedderInterface, core_model: core_models.GenericTransformer, model_head: model_heads.AutoregressiveLMHead, - weight_init_func=None, ): super().__init__() self.embedding_model = embedding_model self.core_model = core_model self.model_head = model_head - # initialize model weights - if weight_init_func is not None: - self.apply(weight_init_func) + + # check if embedding model weights are to be shared with the model head + if model_cfg.get("embedding_weight_tying", True): + # share the weights between the token embeddings and the final + # logit layer, following: https://paperswithcode.com/method/weight-tying + assert model_head.linear.weight.shape == embedding_model.token_embedder.weight.shape, \ + "The embedding model and the model head should have the same output dimension." + embedding_model.token_embedder.weight = model_head.linear.weight + self.device = ... # override to device to set the attribute @@ -97,7 +103,14 @@ def loglikelihood(self, prefixes, continuations): total_strings = [f"{prefix} {cont}" for prefix, cont in zip(prefixes, continuations)] input_tokens = [self.embedding_model.tokenize_input(string, truncate=True) for string in total_strings] padded_batch, mask = self.embedding_model.pad_batch(input_tokens, direction="right") - input_tensor = torch.tensor(padded_batch, device=self.device, dtype=torch.long) + # Check if padded_batch is already a tensor + if isinstance(padded_batch, torch.Tensor): + input_tensor = padded_batch.to(device=self.device, dtype=torch.long) + else: + # If padded_batch is a list or another type, convert it to a tensor + input_tensor = torch.tensor(padded_batch, device=self.device, dtype=torch.long) + + # input_tensor = torch.tensor(padded_batch, device=self.device, dtype=torch.long) logits, _ = self.forward(input_tensor) logits = logits[:, :-1].reshape(-1, logits.size(-1)) target_tensor = input_tensor[:, 1:].reshape(-1) @@ -105,4 +118,99 @@ def loglikelihood(self, prefixes, continuations): mask = mask[:, 1:].reshape(-1).to(ll.device) ll = ll * mask ll = ll.view(input_tensor.size(0), -1).sum(dim=1) - return -ll \ No newline at end of file + return -ll + + @torch.no_grad() + def evaluate(self, model_input, token=None): + """ + Evaluate the model on the input text. Then, fetch the log likelihood of the specified token_ids + Args: + input_text: str + fetch_token_id: int + + Returns: + ll: torch.tensor(B) + """ + # check if input is string + if isinstance(model_input, str): + # use inference function of the embedding model + model_input = self.embedding_model.tokenize_input(model_input, truncate=True, add_eot=False) # tokenize input, shape (S,) + x = torch.tensor(model_input, device=self.device, dtype=torch.long).unsqueeze(0) # convert to tensor, shape (B, S) + x = self.embedding_model(model_input) # pass through the embedding model + x = self.core_model(x) # pass through the core model + + if token is None: + # if token is None, return the final token logits + values = [self.model_head.inference(x)] + return values + else: + # if token is not None, return the logits at the given token's indexes + x = self.model_head(x) # pass through the model head + token_id = self.embedding_model.tokenizer.token_to_id(token) # get the token_id + step_indices = [i for i, token in enumerate(x[0]) if token == token_id] # get the indices of the token_id + values = x[:, step_indices] # get the values of the token_id + values = values.squeeze(0) # remove the batch dimension + return values + + + @torch.no_grad() + def generate(self, + prompt: str, + max_new_tokens: int=100, + temperature: float=1.0, + top_k: int=None, + repetition_penalty: float=None, + repetition_window: int=None + ): + """ Basic text generation function. """ + + # tokenize input tokens + idx = self.embedding_model.tokenize_input( + input_string=prompt, + add_eot=False, + truncate=True + ) + + # push to device + idx = torch.tensor(idx).unsqueeze(0).to(torch.device(self.device)) + for _ in range(max_new_tokens): + # forward the model to get the logits for th index in the sequence + logits, _ = self.inference(idx) + + # scale by temp + logits = logits / temperature + + + # apply repetition penalty + if repetition_penalty is not None: + # Get the most recent tokens within the window + recent_tokens = idx[0, -repetition_window:] + # Count the occurrences of each token + unique_tokens, counts = torch.unique( + recent_tokens, + return_counts=True + ) + # Apply penalty to the logits of repeated tokens + logits[0, unique_tokens] /= repetition_penalty ** counts.float() + + + # crop logits to top_k + if top_k is not None: + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + logits[logits < v[:, [-1]]] = -float("Inf") + + + # apply softmax to convert logits to (normalized) probabilities + probs = torch.nn.functional.softmax(logits, dim=-1) + # sample from the disstribution + idx_next = torch.multinomial(probs, num_samples=1) + + # check if end of text + if idx_next == self.embedding_model.eot_token: + break + + # otherwaise append + idx = torch.cat((idx, idx_next), dim=1) + + # return back as string + return self.embedding_model.decode(idx.tolist()) diff --git a/models/utils.py b/models/utils.py index 57cbf3dd..1f08f014 100644 --- a/models/utils.py +++ b/models/utils.py @@ -64,3 +64,5 @@ def format_number(n): # Print the table print(df.to_string(index=False)) + + return format_number(total_params) diff --git a/pytest.ini b/pytest.ini deleted file mode 100644 index e69de29b..00000000 diff --git a/requirements.txt b/requirements.txt index df8e9571..52c3051b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,4 +4,10 @@ datasets wandb mteb pre-commit -prettytable \ No newline at end of file +prettytable +levenshtein +sentencepiece +textstat +language-tool-python +nltk +python-dotenv \ No newline at end of file diff --git a/train.py b/train.py index 6e474567..a37eac01 100644 --- a/train.py +++ b/train.py @@ -9,13 +9,14 @@ from trainers.build_trainers import build_trainer, ddp_setup from trainers import base_trainer from trainers.utils import create_folder_structure, init_print_override, restore_print_override -from models.utils import print_model_stats + import torch from torch.distributed import destroy_process_group import torch.multiprocessing as mp from trainers.prepare import prepare_data + def ddp_main(rank, world_size, cfg): """ Main function for distributed training @@ -28,16 +29,20 @@ def ddp_main(rank, world_size, cfg): print("Rank: ", rank, "World Size: ", world_size) ddp_setup(rank=rank, world_size=world_size) - model = build_model(model_cfg=cfg["model"]) + model, loaded_train_config = build_model( # train_config is not None when loading checkpoints + model_cfg=cfg["model"], + checkpoint_path=cfg["model"].get("checkpoint_path", None), + device=cfg["general"]["device"] + ) model.to(cfg["general"]["device"]) model.train() print(f"Rank{rank} Model built") - print_model_stats(model) # load the relevant trainer trainer: base_trainer.BaseTrainer = build_trainer( cfg=cfg, model=model, - gpu_id=rank + gpu_id=rank, + loaded_train_config=loaded_train_config ) print(f"Rank{rank} Trainer built") # train the model @@ -54,7 +59,11 @@ def basic_main(cfg): """ Main function for single GPU training """ - model = build_model(model_cfg=cfg["model"]) + model, loaded_train_config = build_model( + model_cfg=cfg["model"], + checkpoint_path=cfg["model"].get("checkpoint_path", None), + device=cfg["general"]["device"] + ) model.to(cfg["general"]["device"]) model.train() print("Model built") @@ -62,22 +71,33 @@ def basic_main(cfg): trainer = build_trainer( cfg=cfg, model=model, - gpu_id=None # disables DDP + gpu_id=None, # disables DDP + loaded_train_config=loaded_train_config ) # train the model trainer.train() -@hydra.main(config_path="configs", config_name="train") +@hydra.main(config_path="configs/train", config_name="baseline-10m") def main(cfg): world_size = torch.cuda.device_count() - + if len(cfg) == 1: + # TODO: this is a hot-fix for sub-folder configs. Fix later + cfg = cfg[list(cfg.keys())[0]] if "full_configs" in cfg: cfg = cfg["full_configs"] cfg["general"]["paths"]["data_dir"] = hydra.utils.to_absolute_path( cfg["general"]["paths"]["data_dir"] ) # must be done before multiprocessing or else the path is wrong? + cfg["general"]["paths"]["eval_dir"] = hydra.utils.to_absolute_path( + cfg["general"]["paths"]["eval_dir"] + ) + + # get absolute path for checkpoint + if "checkpoint_path" in cfg["model"]: + cfg["model"]["checkpoint_path"] = hydra.utils.to_absolute_path(cfg["model"]["checkpoint_path"]) + create_folder_structure(path_config=cfg["general"]["paths"]) diff --git a/trainers/base_trainer.py b/trainers/base_trainer.py index 4b956b84..8387f136 100644 --- a/trainers/base_trainer.py +++ b/trainers/base_trainer.py @@ -1,26 +1,26 @@ """Trainer class for training models with Next Token Prediction""" import time - -import torch import wandb from omegaconf import OmegaConf -from torch.profiler import ProfilerActivity, profile, record_function -from copy import deepcopy from contextlib import nullcontext -from models import model_shell -from trainers import datasets as train_dataloader +# local imports from trainers import utils - -from trainers.evaluator import train_eval +from trainers.evaluator import intra_training_evaluation +# from trainers.evaluator import ( +# train_eval_mcq, +# train_eval_text_modeling, +# train_eval_text_generation, +# train_free_form, +# ) +from trainers.utils import aggregate_value, print_evaluation_results +from models.utils import print_model_stats import numpy as np from itertools import islice +import torch from torch.nn.parallel import DistributedDataParallel as DDP -from torch.utils.data.distributed import DistributedSampler -from torch.utils.data import SequentialSampler -from trainers.utils import aggregate_value, print_evaluation_results # pylint: disable invalid-name @@ -33,66 +33,119 @@ class BaseTrainer: def __init__( self, cfg, - model: model_shell.ModelShell, + model, optimizer, train_dataloader, val_dataloader, loss_fn, gpu_id=None, lr_scheduler=None, - dropout_scheduler=None, + loaded_train_config=None, ) -> None: self.model = model + # print model stats and save them + total_params_formated = print_model_stats(model) + if gpu_id is not None: # using ddp self.dist = True self.DDP_model = DDP(self.model, device_ids=[gpu_id]) else: self.dist = False self.DDP_model = model + self.gpu_id = gpu_id self.optimizer = optimizer self.lr_scheduler = lr_scheduler - self.dropout_scheduler = dropout_scheduler self.train_dataloader_iter = iter(train_dataloader) self.val_dataloader = val_dataloader self.loss_fn = loss_fn self.cfg = cfg - #assert self.cfg["trainer"]["training"]["gradient_accumulation_steps"] % torch.cuda.device_count() == 0, "Gradient Accumulation Steps must be divisible by the number of GPUs" - self.gradient_accumulation_steps = cfg["trainer"]["training"][ + + # Load prev training parameters as necessary + if loaded_train_config is not None: + self.current_iter = loaded_train_config["iter_num"] + + if self.cfg["trainer"].get("load_prev_optimizer_state", False): + print("Loading the previous optimizer state") + self.optimizer.load_state_dict(loaded_train_config["optimizer"]) + + else: + self.current_iter = 0 + + + # adjusting the correct batch-size accumulation step ratio for each node + self.gradient_accumulation_steps = cfg["trainer"][ "gradient_accumulation_steps" - ] // torch.cuda.device_count() if torch.cuda.is_available() else cfg["trainer"]["training"][ + ] // torch.cuda.device_count() if torch.cuda.is_available() else cfg["trainer"][ "gradient_accumulation_steps" ]## divide by number of GPUs to maximise throughput + + self.scaler = None + self.ctx = self._setup_ctx() + self.use_wandb = cfg["general"]["logging"]["wandb_log"] self.checkpoint_dir = cfg["general"]["paths"]["checkpoint_dir"] - self.cached_sets = {"train": {}, "val": {}} - self.batch_size = cfg["trainer"]["training"]["batch_size"] ## new + self.batch_size = cfg["trainer"]["batch_size"] + self.evaluate_byte_metrics = self.cfg["trainer"]["eval"].get("eval_byte_metrics", False) + + + # print training statistics + train_token_count = f"{len(train_dataloader.dataset)/1e9:.2f}B" + val_token_count = f"{len(val_dataloader.dataset)/1e9:.2f}B" + + print(f"Training the model on {self.cfg.model.get('dataset', None)} with {train_token_count} tokens.") - # For training, always force the device to be cuda - #assert torch.cuda.is_available(), "CUDA must be available for training" - self.ctx = self._setup_ctx() if self.use_wandb and (self.gpu_id == 0 or not self.dist): ## ensures that only the first GPU logs to wandb - self._setup_logging() - if cfg.trainer.training.run_profiler and (self.gpu_id == 0 or not self.dist): ## ensures that only the first GPU runs the profiler - self.run_profile() - raise SystemExit - - def _setup_logging(self): - # set run name - run_name = ( - f"{self.cfg.model['model_shell_type']}" - f"_{self.cfg.model['core_model']['core_model_type']}" - f"_{self.cfg.trainer['dataset']}_{self.cfg.model['embedder']['embedding_model_type']}" - f"_{self.cfg.model['vocab_size']}" - ) + self._setup_logging( + total_parameter_count_str=total_params_formated, + train_token_count=train_token_count, + val_token_count=val_token_count + ) + + + def _setup_logging( + self, + total_parameter_count_str=None, + train_token_count=None, + val_token_count=None + ): + # check if run_name was provided + if self.cfg["general"]["logging"].get("run_name", None) is not None: + run_name = self.cfg["general"]["logging"]["run_name"] + \ + f" (Size: {total_parameter_count_str})" + else: + # provide a generic (hopefully descriptive) run name if none was provided + run_name = ( + f"Unname_Model_{self.cfg.trainer['dataset']}" + f"_{self.cfg.model['vocab_size']}" + f"_Parameters_{total_parameter_count_str}" + f"_TrainTokens_{train_token_count}" + ) + + + # Specific the tags + tags = [ + f"Core-{self.cfg.model.get('core_model_type', None)}", + f"Shell-{self.cfg.model.get('model_shell_type', None)}", + f"Embebdding-{self.cfg.model.get('embedding_model_type', None)}", + f"LM_Head-{self.cfg.model.get('lm_head_type', None)}", + f"Dataset-{self.cfg.trainer.get('dataset', None)}", + f"Vocab_size-{self.cfg.model.get('vocab_size', None)}", + f"Parameters-{total_parameter_count_str.split('.')[0]}", + f"TrainTokens-{train_token_count}", + f"ValTokens-{val_token_count}" + ] + + wandb.init( - project=self.cfg.general.logging.wandb_project, + project=self.cfg["general"]["logging"].get("wandb_project", "SuperTinyLanguageModels"), config=OmegaConf.to_container(self.cfg), name=run_name, + tags=tags, + group=self.cfg["general"]["logging"].get("group_name", "General") ) - wandb.init(project=self.cfg.general.logging.wandb_project) - print("wand_b_initted") + print("Weight and Biases Initialized") def _setup_ctx(self): """Get the context manager""" @@ -109,58 +162,78 @@ def _setup_ctx(self): def _setup_scaler(self, dtype=torch.float16): """Setup the scaler""" - self.scaler = torch.cuda.amp.GradScaler(enabled=dtype == torch.float16) - + # self.scaler = torch.cuda.amp.GradScaler(enabled=dtype == torch.float16) + self.scaler = torch.amp.GradScaler(self.model.device, enabled=dtype == torch.float16) @torch.no_grad() - def estimate_performance(self, eval_iters=None): - """Estimate the loss""" - if eval_iters is None: - eval_iters = self.cfg.trainer.training.eval_iters - eval_results = {} - self.model.eval() + def _get_validation_loss(self, eval_iters): + """ Estimate performance on validation set """ + accumulated_loss = 0 + accumulated_aux_loss = 0 + accumulated_total_loss = 0 - # eval on val set - losses = [] - perplexities = [] for i, (x, y) in enumerate(self.val_dataloader): x = x.to(self.gpu_id if self.gpu_id is not None else self.model.device) y = y.to(self.gpu_id if self.gpu_id is not None else self.model.device) - with self.ctx: - output, _ = self.model(x) - # compute loss + with self.ctx: + output, aux_loss = self.model(x) loss = self.loss_fn(output, y) - losses.append(loss.item()) - # compute perplexity - perplexity = torch.exp(loss) # since seq len is always the same during training anyway - perplexities.append(perplexity.item()) + accumulated_loss += loss.item() + if aux_loss is not None: + total_loss = aux_loss + loss + accumulated_aux_loss += aux_loss.item() + accumulated_total_loss += total_loss.item() + if eval_iters is not None and i>= eval_iters: + break + return { + "Validation/loss": accumulated_loss/eval_iters, + "Validation/aux_loss": accumulated_aux_loss/eval_iters, + "Validation/total_loss": accumulated_total_loss/eval_iters + } - if i >= eval_iters: - break - - avg_loss = aggregate_value(np.mean(losses), self.cfg.general.device) - eval_results["Loss"] = avg_loss - avg_perplexity = aggregate_value(np.mean(perplexities), self.cfg.general.device) - eval_results["Perplexity"] = avg_perplexity + @torch.no_grad() + def estimate_performance(self, iter_num, eval_iters=None): + """Estimate the model performance""" + # Initialize eval results + eval_results = { + "iter": iter_num, + "token_num": ( + self.batch_size + * self.gradient_accumulation_steps + * iter_num + * self.cfg.model["context_window"] + * (torch.cuda.device_count() if torch.cuda.is_available() else 1) # To account for the divided accumulation steps + ), + } + # Make sure the model is in eval mode + self.model.eval() - evaluator_results = {} - for evaluator in self.cfg.trainer["eval"]: - evaluator_results[evaluator["evaluator"]] = train_eval(evaluator, self.model) - # recurse over metrics to prepend the evaluator name as a prefix - relabeled_results = {} - for metric in evaluator_results[evaluator["evaluator"]]: - relabeled_results[f"{evaluator['evaluator']}/{metric}"] = evaluator_results[evaluator["evaluator"]][metric] - evaluator_results[evaluator["evaluator"]] = relabeled_results - self.model.train() - return eval_results, evaluator_results + # run the model on external eval sets + eval_results.update(intra_training_evaluation( + model=self.model, + benchmarks=self.cfg["trainer"]["eval"].get("benchmarks", []), + )) + + # get validation loss + eval_results.update(self._get_validation_loss(eval_iters=eval_iters)) + # print the eval results + print_evaluation_results(iter_num=iter_num, eval_results=eval_results) + + # log the evaluation results + if (self.gpu_id==0 or self.gpu_id is None) and self.use_wandb: # ensure only the first GPU logs + wandb.log(eval_results, step=eval_results["token_num"]) + + # set model back into train mode + self.model.train() + return eval_results def _run_step(self): @@ -206,43 +279,6 @@ def _run_step(self): return accumulated_loss - def run_profile(self): - """Run the profiler""" - utils.profilize(self.model) - with profile( - activities=[ - ProfilerActivity.CPU, - ProfilerActivity.CUDA, - ], - record_shapes=True, - profile_memory=True, - with_stack=True, - ) as prof: - for i in range(10): - if i <= 3: - self._run_step() ## set the 'epoch' to ensure shuffle - else: - with record_function("_run_step"): - self._run_step() ## set the 'epoch' to ensure shuffle - # place profile in dictionary - backwards_prof = prof.key_averages().table(sort_by="self_cpu_time_total") - print(backwards_prof) - with profile( - activities=[ - ProfilerActivity.CPU, - ProfilerActivity.CUDA, - ], - record_shapes=True, - profile_memory=True, - with_stack=True, - ) as prof: - self.estimate_performance(eval_iters=1) - with record_function("estimate_performance"): - self.estimate_performance(eval_iters=10) - # place profile in dictionary - forwards_prof = prof.key_averages().table(sort_by="self_cpu_time_total") - print(forwards_prof) - def _save_model(self, iter_num=0): """ store the current model checkpoint. @@ -259,38 +295,25 @@ def _save_model(self, iter_num=0): def run_training_loop(self): """Run the training loop""" - for iter_num in range(self.cfg.trainer.training.max_iters): + print("Training loop is starting") + for iter_num in range(self.current_iter, self.cfg["trainer"]["max_iters"]): start_time = time.time() if self.lr_scheduler is not None: lr = self.lr_scheduler.step(self.optimizer, iter_num) else: lr = self.optimizer.param_groups[0]["lr"] - dropout = self.dropout_scheduler.step(self.model, iter_num) - # estimate the loss on the train/val sets - if ( - not iter_num % self.cfg.trainer.training.eval_interval - ): # run on first iter to prevent bugs causing it to crash - eval_results, benchmark_results = self.estimate_performance() - - # print the evals as table - # evals format is d1: type d2: train/val - print_evaluation_results( - iter_num=iter_num, - eval_results=eval_results, - benchmark_results=benchmark_results - ) - # Log to wandb - if (self.gpu_id == 0 or self.gpu_id is None) and self.use_wandb: # ensure only the first GPU logs - log_dict = {"iter": iter_num, "lr": lr, "dropout": dropout} - log_dict.update(eval_results) # Directly add evals to the log dictionary - log_dict.update({k:v for k,v in benchmark_results.items()}) # Add benchmark results to the log dictionary - - wandb.log(log_dict) + # estimate model performance + # run on first iter to prevent bugs causing it to crash + if (not iter_num % self.cfg["trainer"]["eval_interval"]): + self.estimate_performance( + iter_num=iter_num, + eval_iters=self.cfg["trainer"].get("val_loss_iters", 100) + ) # save checkpoints if ( - not iter_num % self.cfg.trainer.training.checkpoint_interval + not iter_num % self.cfg["trainer"]["checkpoint_interval"] and iter_num > 0 and ( self.gpu_id == 0 @@ -300,31 +323,39 @@ def run_training_loop(self): self._save_model(iter_num) - lossf = self._run_step() ## set the 'epoch' to ensure shuffle + lossf = self._run_step() end_time = time.time() - if not iter_num % self.cfg.trainer.training.log_interval and iter_num > 0: + if not iter_num % self.cfg["trainer"]["log_interval"] and iter_num > 0: ## uncomment the following line to print the loss on all GPUs # print(f"GPU {self.gpu_id}: step {iter_num}: loss {lossf:.4f}, lr {lr:.1e}, dt {end_time-start_time:.1f}s") ## aggregate the loss across all GPUs - lossf = aggregate_value(lossf, self.cfg.general.device) + lossf = aggregate_value(lossf, self.cfg["general"]["device"]) ## print and log the result only on the first GPU after aggregation print(f"All GPU(s): step {iter_num}: loss {lossf:.4f}, lr {lr:.1e}, dt {end_time-start_time:.1f}s") if (self.gpu_id == 0 or self.gpu_id is None) and self.use_wandb: + token_num = ( + self.batch_size + *self.gradient_accumulation_steps + *iter_num + *self.cfg["model"]["context_window"] + * torch.cuda.device_count() if torch.cuda.is_available() else 1 # To account for the divided accumulation steps + ) wandb.log( { "iter": iter_num, "loss": lossf, "lr": lr, - "dropout": dropout, - } + "token_num": token_num, + }, + step=token_num ) # save the final model if self.gpu_id == 0 or self.gpu_id is None: ## ensure only the first GPU saves the model - self._save_model(iter_num) + self._save_model(iter_num+1) # just so it looks nicer def train(self, seed=42): """Train the model""" utils.set_seed(seed) - self.run_training_loop() + self.run_training_loop() \ No newline at end of file diff --git a/trainers/build_trainers.py b/trainers/build_trainers.py index 1f213a61..0e8b5a27 100644 --- a/trainers/build_trainers.py +++ b/trainers/build_trainers.py @@ -18,16 +18,12 @@ ) from trainers.loss_fn import ( cross_entropy_loss_fn, - masked_cross_entropy_loss_fn, next_token_mlm_loss_fn, ) from trainers.optimizer import configure_nanoGPT_optimizer from trainers.scheduler import ( CosineLRScheduler, - DropoutScheduler, - LinearDropoutScheduler, LRScheduler, - TriangleDropoutScheduler, ) @@ -68,15 +64,18 @@ def build_optimizer(model, optimizer_config): """ Given the optimizer config, build the optimizer """ - return OPTIMIZER_DICT[optimizer_config["name"]]( + return OPTIMIZER_DICT[optimizer_config["optimizer_name"]]( model=model, trainer_cfg=optimizer_config ) SCHEDULER_DICT = { "cosine": lambda trainer_cfg: CosineLRScheduler( - warmup_iters=trainer_cfg["training"]["warmup_iters"], - decay_iters=trainer_cfg["training"]["lr_decay_iters"], + warmup_iters=trainer_cfg["lr_scheduler"]["warmup_iters"], + decay_iters=trainer_cfg["lr_scheduler"].get( + "lr_decay_iters", + trainer_cfg["max_iters"] + ), lr=trainer_cfg["optimizer"]["lr"], min_lr=trainer_cfg["optimizer"]["min_lr"], ), @@ -93,29 +92,6 @@ def build_lr_scheduler(trainer_cfg): return SCHEDULER_DICT[trainer_cfg["lr_scheduler"]["name"]](trainer_cfg=trainer_cfg) -def build_dropout_scheduler(trainer_cfg): - """ - Given the trainer config, build the dropout scheduler. - """ - if trainer_cfg["dropout_scheduler"]["dropout_type"] == "constant": - return DropoutScheduler(trainer_cfg["dropout_scheduler"]["dropout"]) - if trainer_cfg["dropout_scheduler"]["dropout_type"] == "linear": - return LinearDropoutScheduler( - start_dropout_p=trainer_cfg["dropout_scheduler"]["start_dropout_p"], - end_dropout_p=trainer_cfg["dropout_scheduler"]["end_dropout_p"], - start_iter=trainer_cfg["dropout_scheduler"]["start_iter"], - end_iter=trainer_cfg["dropout_scheduler"]["end_iter"], - ) - if trainer_cfg["dropout_scheduler"]["dropout_type"] == "triangle": - return TriangleDropoutScheduler( - dropout_trough=trainer_cfg["dropout_scheduler"]["dropout_trough"], - dropout_peak=trainer_cfg["dropout_scheduler"]["dropout_peak"], - num_iterations=trainer_cfg["dropout_scheduler"]["num_iterations"], - num_cycles=trainer_cfg["dropout_scheduler"]["num_cycles"], - ) - raise NotImplementedError( - f"dropout scheduler {trainer_cfg['dropout_scheduler']['dropout_type']} not implemented." - ) DATASET_DICT: dict[str, DatasetInterface] = { @@ -136,7 +112,6 @@ def build_dataset(cfg, split): LOSS_FN_DICT = { "cross_entropy": cross_entropy_loss_fn, "next_token_mlm": next_token_mlm_loss_fn, - "masked_cross_entropy": masked_cross_entropy_loss_fn, } @@ -153,7 +128,7 @@ def build_loss_fn(loss_fn_name): } -def build_trainer(cfg, model, gpu_id): +def build_trainer(cfg, model, gpu_id, loaded_train_config): """ Given a config, this function builds a trainer and all relevant components of it. @@ -165,58 +140,38 @@ def build_trainer(cfg, model, gpu_id): # build LR scheduler lr_scheduler = build_lr_scheduler(trainer_cfg=cfg.trainer) - # build dropout scheduler - dropout_scheduler = build_dropout_scheduler(trainer_cfg=cfg.trainer) - # build dataloder train_dataset = build_dataset(cfg=cfg, split="train") val_dataset = build_dataset(cfg=cfg, split="val") - # Determine if DistributedSampler is necessary - world_size = torch.cuda.device_count() - if world_size > 1: - train_sampler = torch.utils.data.distributed.DistributedSampler( - train_dataset, num_replicas=world_size, rank=gpu_id, shuffle=False - ) - val_sampler = torch.utils.data.distributed.DistributedSampler( - val_dataset, num_replicas=world_size, rank=gpu_id, shuffle=False - ) - else: - train_sampler = None - val_sampler = None - train_sampler = torch.utils.data.SequentialSampler(train_dataset) - val_sampler = torch.utils.data.SequentialSampler(val_dataset) - # wrap in dataloaders train_dataloader = torch.utils.data.DataLoader( dataset=train_dataset, - batch_size=cfg["trainer"]["training"]["batch_size"], + batch_size=cfg["trainer"]["batch_size"], shuffle=False, - sampler=train_sampler, ) val_dataloader = torch.utils.data.DataLoader( dataset=val_dataset, - batch_size=cfg["trainer"]["training"]["batch_size"], + batch_size=cfg["trainer"]["batch_size"], shuffle=False, - sampler=val_sampler, ) # build loss function loss_fn = build_loss_fn(loss_fn_name=cfg.trainer["loss_fn"]["name"]) # build the trainer - print(cfg.trainer["training"]["trainer_type"]) - trainer = TRAINER_DICT[cfg.trainer["training"]["trainer_type"]]( + print(cfg.trainer["trainer_type"]) + trainer = TRAINER_DICT[cfg.trainer["trainer_type"]]( cfg=cfg, model=model, optimizer=optimizer, lr_scheduler=lr_scheduler, - dropout_scheduler=dropout_scheduler, train_dataloader=train_dataloader, val_dataloader=val_dataloader, loss_fn=loss_fn, gpu_id=gpu_id, + loaded_train_config=loaded_train_config, ) return trainer diff --git a/trainers/data_utils.py b/trainers/data_utils.py new file mode 100644 index 00000000..b987303d --- /dev/null +++ b/trainers/data_utils.py @@ -0,0 +1,165 @@ +"""Utilities for data""" + +import numpy as np +from datasets import load_dataset, DatasetDict, concatenate_datasets +from datasets import Features, Value + + +DATASET_DICT = { + "simple_en_wiki": lambda: load_dataset("wikimedia/wikipedia", "20231101.simple"), + "en_wiki": lambda: load_dataset("wikimedia/wikipedia", "20231101.en"), + "babylm_100m": lambda: load_dataset("Sree1994/babylm_100M"), + "tinystories": lambda: load_dataset("roneneldan/TinyStories"), + "openwebtext": lambda: load_dataset("Skylion007/openwebtext", trust_remote_code=True), + "pints": lambda: load_dataset("pints-ai/Expository-Prose-V1"), + "the_pile": lambda: load_dataset("The Pile", "pile-cc"), + "tiny_textbooks": lambda: load_dataset("nampdn-ai/tiny-textbooks"), + "tiny_webtext": lambda: load_dataset("nampdn-ai/tiny-webtext"), + "tiny_bridgedict": lambda: load_dataset("nampdn-ai/tiny-bridgedict"), + "mini_fineweb": lambda: load_dataset("nampdn-ai/mini-fineweb"), + "openhermes-2.5": lambda: load_general_dataset( + dataset_name="teknium/OpenHermes-2.5", + lambda_fn=lambda x: {"text": f"Question: {x['conversations'][0]['value']}\nAnswers: {x['conversations'][1]['value']}"}, + ), + "github-code": lambda: load_general_dataset( + dataset_name="codeparrot/github-code", + lambda_fn=lambda x: {"text": x["code"]} + ), + "competition_math": lambda: load_general_dataset( + dataset_name="hendrycks/competition_math", + lambda_fn=lambda x: {"text": f"Problem: {x['problem']}\nSolution: {x['solution']}"} + ), + "super_natural_instructions": lambda: load_general_dataset( + dataset_name="andersonbcdefg/supernatural-instructions-2m", + lambda_fn=lambda x: {"text": f"Question: {x['prompt']}\nAnswer: {x['response']}"} + ), + "tiny_codes": lambda: load_general_dataset( + dataset_name="nampdn-ai/tiny-codes", + lambda_fn=lambda x: {"text": f"Question: {x['prompt']}\nAnswer: {x['response']}"} + ), + "tiny_orca_textbooks": lambda: load_general_dataset( + dataset_name="nampdn-ai/tiny-orca-textbooks", + lambda_fn=lambda x: {"text": f"{x['textbook']}\n Question: {x['question']}\nAnswer: {x['response']}"} + ), + "tiny_lessons": lambda: load_general_dataset( + dataset_name="nampdn-ai/tiny-lessons", + lambda_fn=lambda x: {"text": x['textbook']} + ), + "mini_cot": lambda: load_general_dataset( + dataset_name="nampdn-ai/mini-CoT-Collection", + lambda_fn=lambda x: {"text": f"Question: {x['source']}\nAnswer: {x['rationale']} - {x['target']}"} + ), + "mini_ultrachat": lambda: load_general_dataset( + dataset_name="nampdn-ai/mini-ultrachat", + lambda_fn=lambda x: { + "text": "".join( + [ + f"Question: {t}" + if i % 2 == 0 else f"Answer: {t}" + for i, t in enumerate(x['data']) + ] + ) + } + ), + "textbooks_are_all_you_need_lite": lambda: load_general_dataset( + dataset_name="SciPhi/textbooks-are-all-you-need-lite", + lambda_fn=lambda x: {"text": x["completion"]} + ), + "openphi_textbooks": lambda: load_general_dataset( + dataset_name="open-phi/textbooks", + lambda_fn=lambda x: {"text": x["markdown"]} + ), + "openphi_programming_books": lambda: load_general_dataset( + dataset_name="open-phi/programming_books_llama", + lambda_fn=lambda x: {"text": x["markdown"]} + ), + "natural_instructions": lambda: load_general_dataset( + dataset_name="Muennighoff/natural-instructions", + lambda_fn=lambda x: {"text": f"Task: Definition: {x['definition']}\nQuestion: {x['inputs']}\nAnswer: {x['targets']}"} + ), + "fineweb_edu_100B": lambda: load_dataset("HuggingFaceFW/fineweb-edu", "sample-100BT"), + "fineweb_edu_10B": lambda: load_dataset("HuggingFaceFW/fineweb-edu", "sample-10BT"), + "prm800k": lambda: load_dataset("tasksource/PRM800K"), + "MATH": lambda: load_general_dataset( + dataset_name="lighteval/MATH", + lambda_fn=lambda x: {"text": f"{x['problem']}\n{x['solution']}"} + ) + +} + + +def load_general_dataset(dataset_name, lambda_fn): + """ + load and re-format a huggingface dataset + """ + dataset = load_dataset(dataset_name) + dataset = dataset.map(lambda_fn) + return dataset + +def get_dataset_byte_size(dataset): + """ + Get the byte size of a dataset + """ + return sum([len(item["text"]) for item in dataset]) + + +def load_data(dataset_names, shuffle=True): + """Load the data""" + # Check if only a single dataset name was provided + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + + datasets_list = [] + for dataset_name in dataset_names: + assert dataset_name in DATASET_DICT, f"Dataset {dataset_name} not found!" + dataset = DATASET_DICT[dataset_name]() + datasets_list.append(dataset["train"]) + + # Concatenate datasets if there are multiple datasets + if len(datasets_list) > 1: + combined_dataset = concatenate_datasets(datasets_list) + else: + combined_dataset = datasets_list[0] + + # Create dataset split + split_dataset = combined_dataset.train_test_split( + test_size=0.01, seed=489, shuffle=shuffle + ) + + # Rename test split to val + split_dataset["val"] = split_dataset.pop("test") + + # Return the training and validation datasets + return split_dataset + +# def load_data(dataset_names, shuffle=True): +# """Load the data""" +# # check if only a single dataset name was provided +# if isinstance(dataset_names, str): +# dataset_names = [dataset_names] + + +# for dataset_name in dataset_names: +# assert dataset_name in DATASET_DICT, f"Dataset {dataset_name} not found!" +# dataset = DATASET_DICT[dataset_name]() + + +# # create dataset split +# split_dataset = dataset["train"].train_test_split( +# test_size=0.01, seed=489, shuffle=shuffle +# ) + +# # rename test split to val +# split_dataset["val"] = split_dataset.pop("test") + +# # return the training and validation datasets +# return split_dataset + + +def load_prm800k_dataset(): + """ + Load the PRM800k dataset + https://arxiv.org/abs/2305.20050 + https://huggingface.co/datasets/tasksource/PRM800K + """ + pass \ No newline at end of file diff --git a/trainers/datasets.py b/trainers/datasets.py index 905d59d0..c104a225 100644 --- a/trainers/datasets.py +++ b/trainers/datasets.py @@ -10,11 +10,11 @@ import random from models.embedding_models import GenericEmbedder -from trainers.utils import load_data +from trainers.data_utils import load_data -class DatasetInterface(torch.utils.data.Dataset): +class DatasetInterface(torch.utils.data.IterableDataset): """ A basic interface to be used by the remaining datasets """ @@ -25,12 +25,18 @@ def __init__(self, split, cfg): """ super().__init__() self.cfg = cfg - self.dataset_name = self.cfg["trainer"]["dataset"] + dataset_names = self.cfg["trainer"]["dataset"] self.context_window = self.cfg["model"]["context_window"] + # Ensure dataset_names is a list + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + + # Create a unique identifier for the combined datasets + combined_dataset_name = '_'.join(dataset_names) self.data_path = os.path.join( self.cfg["general"]["paths"]["data_dir"], - self.dataset_name, - f'{self.cfg["model"]["embedder"]["tokenizer_type"]}-{self.cfg["model"]["vocab_size"]}-{self.cfg["trainer"]["dataloader"]["name"]}', + combined_dataset_name, + f'{self.cfg["model"]["tokenizer_type"]}-{self.cfg["model"]["vocab_size"]}-{self.cfg["trainer"]["dataloader"]["name"]}', f"{split}.bin" ) @@ -56,9 +62,10 @@ def __len__(self): """ return self.dataset_len - def __getitem__(self, idx): + def __iter__(self, idx): raise NotImplementedError + class BaseDatasetRandom(DatasetInterface): """ Simple base dataloader for standard gpt-2'esk architectures and training. @@ -67,15 +74,20 @@ def __init__(self, split, cfg): super().__init__(split, cfg) - def __getitem__(self, idx): + def __iter__(self): """ - Get a batch of data + Get a batch of random data points in an infinite loop. """ - # get a random data sample - idx = random.randint(0, self.dataset_len - 1) - x = torch.from_numpy((self.data[idx: idx + self.context_window]).astype(np.int64)) - y = torch.from_numpy((self.data[idx + 1: idx + 1 + self.context_window]).astype(np.int64)) - return x, y + while True: + # Get a random index + idx = random.randint(0, self.dataset_len - 1) + + # Extract a slice of data for x and y + x = torch.from_numpy((self.data[idx: idx + self.context_window]).astype(np.int64)) + y = torch.from_numpy((self.data[idx + 1: idx + 1 + self.context_window]).astype(np.int64)) + + # Yield the data points + yield x, y class BytePoolingDataset(DatasetInterface): @@ -107,13 +119,15 @@ def _load_data(self): shape=self.loading_shape, ) - def __getitem__(self, idx): + def __iter__(self): """ Get a batch of data """ - x = torch.from_numpy((self.data[idx: idx + self.context_window]).astype(np.int64)) - y = torch.from_numpy((self.data[idx + 1: idx + 1 + self.context_window]).astype(np.int64)) - return x, y + while True: + idx = random.randint(0, self.dataset_len - 1) + x = torch.from_numpy((self.data[idx: idx + self.context_window]).astype(np.int64)) + y = torch.from_numpy((self.data[idx + 1: idx + 1 + self.context_window]).astype(np.int64)) + yield x, y class DualBytePooling(DatasetInterface): @@ -159,16 +173,18 @@ def _load_data(self): mode="r", ) - def __getitem__(self, idx): + def __iter__(self): """ Get a batch of data from both the byte and higher token level """ - # get byte level batch - x_byte = torch.from_numpy((self.data_byte[idx: idx + self.context_window]).astype(np.int64)) - #y_byte = torch.from_numpy((self.data_byte[idx + 1: idx + 1 + self.context_window]).astype(np.int64)) + while True: + idx = random.randint(0, self.dataset_len - 1) + # get byte level batch + x_byte = torch.from_numpy((self.data_byte[idx: idx + self.context_window]).astype(np.int64)) + #y_byte = torch.from_numpy((self.data_byte[idx + 1: idx + 1 + self.context_window]).astype(np.int64)) - # get token level batch - #x_token = torch.from_numpy((self.data_token[idx: idx + self.context_window]).astype(np.int64)) - y_token = torch.from_numpy((self.data[idx + 1: idx + 1 + self.context_window]).astype(np.int64)) - return x_byte, y_token + # get token level batch + #x_token = torch.from_numpy((self.data_token[idx: idx + self.context_window]).astype(np.int64)) + y_token = torch.from_numpy((self.data[idx + 1: idx + 1 + self.context_window]).astype(np.int64)) + yield x_byte, y_token diff --git a/trainers/evaluator.py b/trainers/evaluator.py index 510a3671..99516e25 100644 --- a/trainers/evaluator.py +++ b/trainers/evaluator.py @@ -1,13 +1,25 @@ """Code for running samples from the evaluation benchmarks""" +import evals +from tqdm import tqdm -from evals.load_evaluators import load_evaluator - -def train_eval(eval_cfg, model): - """Train the model""" - evaluator_name = eval_cfg["evaluator"] - kwargs = { - key: value for key, value in eval_cfg.items() if key != "evaluator" - } - evaluator = load_evaluator(evaluator_name, model, **kwargs) - results = evaluator.evaluate() - return results +def intra_training_evaluation(model, benchmarks): + """ + Evaluates the model on multiple benchmarks during training. + + Args: + model: The model to evaluate. + benchmarks List[str]: A list of benchmark names to evaluate the model on. + """ + results_dict = {} + + # Outer progress bar for benchmarks + with tqdm(benchmarks, desc="Evaluating benchmarks", position=0, leave=True) as benchmark_bar: + for benchmark in benchmark_bar: + # Create the benchmark evaluator + benchmark_evaluator = evals.make(benchmark) + + # Evaluating within the benchmark, tqdm already exists in the yield function + results = benchmark_evaluator.evaluate(model=model) + results_dict.update(results) + + return results_dict \ No newline at end of file diff --git a/trainers/loss_fn.py b/trainers/loss_fn.py index 694b61de..0a2a44dd 100644 --- a/trainers/loss_fn.py +++ b/trainers/loss_fn.py @@ -8,16 +8,8 @@ import torch -def masked_cross_entropy_loss_fn(logits, y, mask=None): - """Cross Entropy Loss Function""" - # mask the pad token from y - pad_token_id = 257 - logits = logits.view(-1, logits.size(-1)) - y = y.view(-1) - #return torch.nn.functional.cross_entropy(logits, y, weight=mask, ignore_index=-1) - return torch.nn.functional.cross_entropy(logits, y, ignore_index=pad_token_id) -def cross_entropy_loss_fn(logits, y, mask=None): +def cross_entropy_loss_fn(logits, y): """Cross Entropy Loss Function""" logits = logits.view(-1, logits.size(-1)) y = y.view(-1) @@ -38,49 +30,14 @@ def next_token_mlm_loss_fn(logits, y_mask, masked_loss=True): return cross_entropy_loss_fn(logits, y) -def compute_perplexity(logits, y, char_lengths, mask=None): - """ - Compute perplexity - Args: - logits: torch.tensor(B, S, H) or torch.tensor(B, S, S_c, H_c) - y: torch.tensor(B, S) or torch.tensor(B, S, S_c) - char_lengths: List[int] - Returns: - perplexity: torch.tensor(1) - """ - - # pull everything onto cpu - logits = logits.cpu() - y = y.cpu() - if mask is not None: - mask = mask.cpu() - - # check if logits is byte-level - if len(logits.size()) > 3: - B, S, S_c = y.size() - seq_len = S * S_c - logits = logits.view(B, seq_len, -1) - y = y.view(B, seq_len) - else: - B, seq_len = y.size() - - # B, S, H / B, S, 1 - # calculate non-reduced loss - # flatten both - logits = logits.view(-1, logits.size(-1)) - y = y.view(-1) - loss = torch.nn.functional.cross_entropy(logits, y, reduction="none") - # B, S, 1 - # unflatten - loss = loss.view(B, seq_len) - loss = loss * mask / torch.tensor(char_lengths).view(-1, 1) - loss = loss.sum(dim=-1) - - return (torch.exp(loss)).mean().item() - +LOSS_FN_DICT = { + "cross_entropy": cross_entropy_loss_fn, + "next_token_mlm": next_token_mlm_loss_fn, +} def build_loss_fn(loss_fn_type: str): """Build the loss function""" - if loss_fn_type == "cross_entropy": - return cross_entropy_loss_fn - raise ValueError(f"Loss function {loss_fn_type} not supported.") + assert loss_fn_type in LOSS_FN_DICT, \ + f"Loss function {loss_fn_type} not found! Available options: {LOSS_FN_DICT.keys()}" + + return LOSS_FN_DICT[loss_fn_type] \ No newline at end of file diff --git a/trainers/prepare.py b/trainers/prepare.py index 9f1df57a..25d3fe92 100644 --- a/trainers/prepare.py +++ b/trainers/prepare.py @@ -5,7 +5,7 @@ import torch import numpy as np from tqdm import tqdm -from trainers.utils import load_data +from trainers.data_utils import load_data from models.build_models import build_embedding_model @@ -146,20 +146,26 @@ def write_tokenized_data(self, tokenized, tokenized_data_folder): - - def prepare_data(cfg): """ Split the data, process & tokenize it, and store it as memmap bin files """ - # check if the data is already preprocessed + # Check if the data is already preprocessed dataloader_name = cfg["trainer"]["dataloader"]["name"] - dataset_name = cfg["trainer"]["dataset"] + dataset_names = cfg["trainer"]["dataset"] + + # Ensure dataset_names is a list + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + + # Create a unique identifier for the combined datasets + combined_dataset_name = '_'.join(dataset_names) + tokenized_data_folder = os.path.join( cfg["general"]["paths"]["data_dir"], - dataset_name, - f'{cfg["model"]["embedder"]["tokenizer_type"]}-{cfg["model"]["vocab_size"]}-{cfg["trainer"]["dataloader"]["name"]}', + combined_dataset_name, + f'{cfg["model"]["tokenizer_type"]}-{cfg["model"]["vocab_size"]}-{dataloader_name}', ) # check if already exists (check len because some datasets use differen filenames @@ -178,7 +184,7 @@ def prepare_data(cfg): # load the dataset split_dataset = load_data( - dataset_name=dataset_name, + dataset_names=dataset_names, ) processor_object = DATALOADER_PROCESSORS[dataloader_name]( @@ -190,7 +196,7 @@ def prepare_data(cfg): # Get the maximum number of processors max_procs = os.cpu_count() # cap at 12 to reduce memory usage - max_procs = 1 #min(max_procs, 12) # TODO properly fix this + max_procs = min(max_procs, 12) # TODO properly fix this print(f"Using {max_procs} processors") # tokenize the dataset diff --git a/trainers/scheduler.py b/trainers/scheduler.py index 431cfca2..f2a43359 100644 --- a/trainers/scheduler.py +++ b/trainers/scheduler.py @@ -45,87 +45,3 @@ def get_lr(self, iter_num): return self.min_lr + 0.5 * (self.lr - self.min_lr) * ( 1 + math.cos((iter_num - self.warmup_iters) / self.decay_iters * math.pi) ) - - -class DropoutScheduler: - """Constant Dropout Scheduler""" - - def __init__(self, dropout_p=0.1): - self.dropout_p = dropout_p - - def get_dropout(self, _): - """Return Constant Dropout""" - return self.dropout_p - - def set_dropout(self, model, dropout_p): - """Set the dropout probability for the model""" - for module in model.modules(): - if isinstance(module, nn.Dropout): - module.p = dropout_p - - def step(self, model, iter_num): - """Step the scheduler""" - dropout_p = self.get_dropout(iter_num) - self.set_dropout(model, dropout_p) - return dropout_p - - -class LinearDropoutScheduler(DropoutScheduler): - """Dropout Scheduler""" - - def __init__(self, start_iter, end_iter, start_dropout_p, end_dropout_p): - """Initialize the dropout schedule""" - super().__init__(start_dropout_p) - self.start_iter = start_iter - self.end_iter = end_iter - self.start_dropout_p = start_dropout_p - self.end_dropout_p = end_dropout_p - - def get_dropout(self, iter_num): - """Return Constant Dropout""" - if iter_num < self.start_iter: - return self.start_dropout_p - if iter_num >= self.end_iter: - return self.end_dropout_p - return self.start_dropout_p + (iter_num - self.start_iter) * ( - self.end_dropout_p - self.start_dropout_p - ) / (self.end_iter - self.start_iter) - - -class TriangleDropoutScheduler(DropoutScheduler): - """Triangle Dropout Scheduler. Ref: https://arxiv.org/pdf/1506.01186""" - - def __init__( - self, - dropout_trough, - dropout_peak, - num_iterations, - num_cycles=4, - ): - """Initialize the dropout schedule - Args: - dropout_trough: The minimum dropout probability - dropout_peak: The maximum dropout probability - num_iterations: The total number of iterations - num_cycles: The number of cycles""" - super().__init__(dropout_trough) - self.dropout_trough = dropout_trough - self.dropout_peak = dropout_peak - self.total_iterations = num_iterations - self.cycle_length = self.total_iterations // num_cycles - - def get_dropout(self, iter_num): - cycle_position = iter_num % self.cycle_length - half_cycle = self.cycle_length / 2 - if cycle_position < half_cycle: - return self.dropout_trough + (self.dropout_peak - self.dropout_trough) * ( - cycle_position / half_cycle - ) - return self.dropout_peak - (self.dropout_peak - self.dropout_trough) * ( - (cycle_position - half_cycle) / half_cycle - ) - - def step(self, model, iter_num): - dropout_p = self.get_dropout(iter_num) - self.set_dropout(model, dropout_p) - return dropout_p diff --git a/trainers/utils.py b/trainers/utils.py index e9983802..a5e86b3c 100644 --- a/trainers/utils.py +++ b/trainers/utils.py @@ -1,15 +1,13 @@ -"""Utilities for the trainer""" +""" General trainer utils """ import importlib -from prettytable import PrettyTable import inspect -import os +import os, re import pkgutil - import numpy as np -import torch -from datasets import load_dataset, DatasetDict, concatenate_datasets +from prettytable import PrettyTable +import torch import torch.distributed as dist def set_seed(seed): @@ -29,127 +27,6 @@ def create_folder_structure(path_config): if not os.path.exists(path_config["checkpoint_dir"]): os.makedirs(path_config["checkpoint_dir"]) -def create_stlm_data_mix(): - """ - A small custom datamix for STLM models containing: - - simple English Wikipedia - - Python Code (Deepmind Code Contest) - sampled for easy questions - - technical QA style (StackExchange) - """ - # Load simple English Wikipedia - wiki = load_dataset("wikimedia/wikipedia", "20231101.simple")["train"] - - # Add a "text" column for simple English Wikipedia - wiki = wiki.map(lambda x: {"text": x["text"]}) - - # Load Python code from DeepMind Code Contests - code_dataset = load_dataset("jtatman/python-code-dataset-500k")["train"] - code_dataset = code_dataset.map(lambda x: {"text": f"Instruction: {x['instruction']}\nOutput: {x['output']}"}) - - - # Load technical QA style data from StackExchange - openhermes = load_dataset("teknium/OpenHermes-2.5")["train"] - - # Transform to have a "text" column with both question and answers - openhermes = openhermes.map(lambda x: {"text": f"Question: {x['conversations'][0]['value']}\nAnswers: {x['conversations'][1]['value']}"}) - - # Add tiny stories - tiny_stories = load_dataset("roneneldan/TinyStories")["train"] - - - # Calculate and print the distribution of string lengths - def calculate_length_distribution(dataset): - lengths = [len(item["text"]) for item in dataset] - return sum(lengths), lengths - - wiki_length, wiki_lengths = calculate_length_distribution(wiki) - python3_code_length, python3_code_lengths = calculate_length_distribution(code_dataset) - openhermes_length, openhermes_lengths = calculate_length_distribution(openhermes) - tiny_stories_length, tiny_stories_lengths = calculate_length_distribution(tiny_stories) - - total_length = wiki_length + python3_code_length + openhermes_length + tiny_stories_length - - print(f"Wiki Text Length: {wiki_length} ({wiki_length/total_length*100:.2f}%)") - print(f"Python Code Text Length: {python3_code_length} ({python3_code_length/total_length*100:.2f}%)") - print(f"openhermes Text Length: {openhermes_length} ({openhermes_length/total_length*100:.2f}%)") - - # Concatenate datasets - combined_dataset = concatenate_datasets([wiki, code_dataset, openhermes, tiny_stories]) - - combined_dataset = DatasetDict({ - "train": combined_dataset, - }) - - return combined_dataset - - -def load_github_code_dataset(): - """ - load and re-format the github code dataset - https://huggingface.co/datasets/codeparrot/github-code - """ - dataset = load_dataset("codeparrot/github-code") - - # rename "code" column to "text" column - dataset = dataset.map(lambda x: {"text": x["code"]})["train"] - - #dataset = DatasetDict({ - # "train": dataset, - #}) - - - return dataset - -def load_competition_math_dataset(): - """ - load and re-format the competition math dataset - https://huggingface.co/datasets/hendrycks/competition_math - """ - dataset = load_dataset("hendrycks/competition_math") - - # format the problem and solution into a single "text" column - dataset = dataset.map(lambda x: {"text": f"Problem: {x['problem']}\nSolution: {x['solution']}"}) - - dataset = DatasetDict({ - "train": dataset, - }) - - return dataset - - - -DATASET_DICT = { - "debug": lambda: load_dataset("wikimedia/wikipedia", "20231101.simple"), - "en_wiki": lambda: load_dataset("wikimedia/wikipedia", "20231101.en"), - "simple_en_wiki": lambda: load_dataset("wikimedia/wikipedia", "20231101.simple"), - "babylm_100m": lambda: load_dataset("Sree1994/babylm_100M"), # https://babylm.github.io/ - "tinystories": lambda: load_dataset("roneneldan/TinyStories"), # https://huggingface.co/datasets/roneneldan/TinyStories - "stlm": create_stlm_data_mix, - "openhermes-2.5": lambda: load_dataset("teknium/OpenHermes-2.5"), - "openwebtext": lambda: load_dataset("Skylion007/openwebtext"), - "github-code": lambda: load_github_code_dataset(), - "competition_math": lambda: load_competition_math_dataset(), -} - - -def load_data(dataset_name, shuffle=True): - """Load the data""" - assert dataset_name in DATASET_DICT, f"Dataset {dataset_name} not found!" - dataset = DATASET_DICT[dataset_name]() - - # create dataset split - split_dataset = dataset["train"].train_test_split( - test_size=0.01, seed=489, shuffle=shuffle - ) - - # rename test split to val - split_dataset["val"] = split_dataset.pop("test") - - if dataset_name == "debug": - split_dataset["train"] = split_dataset["train"].select(range(2048)) - - # return the training and validation datasets - return split_dataset def get_classes_from_module(module_name): @@ -204,45 +81,8 @@ def backward_hook(grad): tensor.register_hook(backward_hook) -def profilize(model, classes=None): - """Recursively add hooks to the model for recording PyTorch profiler traces with module names""" - if classes is None: - classes = get_classes_from_package("models") - classes += get_classes_from_package("models.components.layers") - print(f"Found classes for profiling: {classes}") - - for module in model.children(): - if isinstance(module, torch.nn.Module): - profilize(module, classes=classes) - if isinstance(module, torch.nn.ModuleDict): - for sub_module in module.values(): - profilize(sub_module, classes=classes) - if isinstance(module, torch.nn.ModuleList): - for sub_module in module: - profilize(sub_module, classes=classes) - - if ( - hasattr(model, "forward") - and any(isinstance(model, cls) for cls in classes) - and not hasattr(model, "old_forward") - ): - model.old_forward = model.forward - print(f"added forward profiling wrapper for {model.__class__.__name__}") - - def forward_wrapper(*args, **kwargs): - nested_module_name = model.__class__.__name__ - with torch.autograd.profiler.record_function( - f"{nested_module_name}.forward" - ): - outputs = model.old_forward(*args, **kwargs) - if isinstance(outputs, (list, tuple)): - for output in outputs: - register_backward_hooks(output, nested_module_name) - else: - register_backward_hooks(outputs, nested_module_name) - return outputs - - model.forward = forward_wrapper + + def is_dist(): """ @@ -289,33 +129,46 @@ def restore_print_override(original_print): -# Function to print evaluation results and benchmark results -def print_evaluation_results(iter_num, eval_results, benchmark_results): - headers = ['Metric', 'Value'] - table = PrettyTable(headers) - - # Adding eval_results rows - for metric, value in eval_results.items(): - row = [metric, value] - table.add_row(row) - - print(f"Iteration {iter_num}") - print(table) - +def print_evaluation_results(iter_num, eval_results): + """ + This function processes and visualizes the evaluation results. + The input format is a dictionary where each key has a logging path/metric_name format. + Keys without the '/' should be ignored. + The function prints tables for each unique logging path. + """ - benchmark_table = PrettyTable(['Benchmark', 'Accuracy', "Path Conf.", "Ground Conf."]) - for eval_method in benchmark_results.keys(): - if eval_method == "ft_qa": - continue - for benchmark, value in benchmark_results[eval_method].items(): - benchmark_table.add_row([ - f"{benchmark}", - value['accuracy'], - value['path_confidence'], - value['ground_confidence'] - ]) - - print("Benchmark Results") - print(benchmark_table) - - + # Keys to be ignored (e.g., 'token_num', 'iter') + ignore_keys = set(["token_num", "iter"]) + + # Filter out keys that don't have a "/" + valid_keys = {k: v for k, v in eval_results.items() if "/" in k and k.split("/")[0] not in ignore_keys} + + # Identify unique logging paths (the part before "/") + logging_paths = set([k.split("/")[0] for k in valid_keys]) + + # Dictionary to store tables for each logging path + tables = {} + + # Loop through each logging path and generate a table + for table_name in logging_paths: + # Collect columns for the table (the part after "/") + columns = sorted(set([k.split("/")[1] for k in valid_keys if table_name == k.split("/")[0]])) + + # Initialize a table with the logging path as the category and columns for the metrics + table = PrettyTable(["Evaluation"] + columns) + + # Collect values for the current logging path + row_values = {col: "" for col in columns} + for k, v in valid_keys.items(): + logging_path, col_name = k.split("/") + if logging_path == table_name: + row_values[col_name] = v + + # Add the row to the table + table.add_row([table_name] + [row_values[col] for col in columns]) + tables[table_name] = table + + # Print all tables + for table_name, table in tables.items(): + print(f"\nResults for {table_name}:") + print(table)