Create a conda environment CASPER using the environment.yml file provided using the following command:
conda env create -f environment.yml --name CASPER
Activate the environment:
conda activate CASPER
Install PyTorch according to the CUDA version of the GPU you are using:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
Now you are ready to execute the code.
CASPER process executor is a menu-driven program that takes the input of all required variables from the user and then calls the CASPER main program separately for summarization, improvement, and evaluation.
Below is a snapshot of the file in execution:
- Events: Can take values from 1-1 to 1-15.
- n (Number of Iterations): Specifies how many iterations the planning engine should run.
- n = 0: Represents the 0-shot case, where no improvement happens.
- n > 0: The engine will iteratively improve the summaries.
-
Summarization LLM Options:
- Llama 3.1
- GPT-4o Mini
-
Improvement LLM Options:
- Phi 4
- GPT-4o Mini
- Phi 3 Mini
- Gemma 2
- Deepseek
-
Evaluation LLM:
- Fixed as Phi 4
Note: It is important to type the LLM names exactly as given in the options (case and space sensitive) to prevent exceptions.
- Summaries will be saved in:
Example:
./Generated_summaries_{sum_prefix}/{testimony_code}_{attribute_for_saving_file_name}.txt./Generated_summaries_Llama_3.1/E1T1_Event_Details.txt
- Improved summaries will be saved in:
Example:
./Generated_summaries_{sum_prefix}_sum_{improve_prefix}_eval_and_imp/{summary_path_prefix}_p{i+1}.txt./Generated_summaries_Llama_3.1_sum_GPT-4o_Mini_eval_and_imp/E1T1_Event_Details_p1.txt
- Evaluation logs will be saved in:
Example:
./evaluation_logs_{sum_prefix}_sum_{improve_prefix}_eval_and_imp/{summary_path_prefix}_eval.txt./evaluation_logs_Llama_3.1_sum_GPT-4o_Mini_eval_and_imp/E1T1_Event_Details_eval.txt
