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Update model instantiation #31

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7 changes: 3 additions & 4 deletions submissions/client_encode_encrypt_input.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,12 +4,11 @@
import numpy as np
import torch
from desilofhe import Engine
from transformers import BertForNextSentencePrediction
from transformers import BertForSequenceClassification

from params import InstanceParams

# For encoding, base model is used.
EMBED_MODEL_ID = "google-bert/bert-base-cased"
EMBED_MODEL_ID = "google-bert/bert-base-cased-finetuned-mrpc"
EMBED_LEVEL = 9


Expand Down Expand Up @@ -82,7 +81,7 @@ def main():
)
secret_key = engine.read_secret_key(io_dir / "secret_key")

embedding_model = BertForNextSentencePrediction.from_pretrained(EMBED_MODEL_ID).bert.embeddings
embedding_model = BertForSequenceClassification.from_pretrained(EMBED_MODEL_ID).bert.embeddings
embedding_model.eval()

records = []
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3 changes: 1 addition & 2 deletions submissions/client_preprocess_input.py
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Expand Up @@ -4,8 +4,7 @@
from params import InstanceParams
from transformers import AutoTokenizer

# For encoding, base model is used.
MODEL_ID = "google-bert/bert-base-cased"
MODEL_ID = "google-bert/bert-base-cased-finetuned-mrpc"
MAX_LENGTH = 128


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4 changes: 2 additions & 2 deletions submissions/server_preprocess_model.py
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Expand Up @@ -4,7 +4,7 @@
from pathlib import Path

from desilofhe import Engine
from transformers import BertForNextSentencePrediction
from transformers import BertForSequenceClassification

from params import InstanceParams
from encode_weights import (
Expand Down Expand Up @@ -77,7 +77,7 @@ def main():
warm_cache(lp_path)
return

model = BertForNextSentencePrediction.from_pretrained(MODEL_ID)
model = BertForSequenceClassification.from_pretrained(MODEL_ID, output_hidden_states=True)
model.eval()

weights = {k: v.detach().cpu().numpy() for k, v in model.state_dict().items()}
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