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model.py
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model.py
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import csv
import json
from sentence_transformers import SentenceTransformer, InputExample, losses
from torch.utils.data import DataLoader
# Example Models can be found here: https://www.sbert.net/docs/pretrained_models.html
# Model Card: https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1
model = SentenceTransformer("multi-qa-MiniLM-L6-cos-v1")
def main():
# Format csv file to prepare for model training
# Example csv file: https://github.com/dsteedRTI/csv-to-embeddings-model/blob/main/example_pairs.csv
with open("pairs.csv") as f:
pairs = [{k: str(v) for k, v in row.items()}
for row in csv.DictReader(f, skipinitialspace=True)]
for pair in pairs:
if isinstance(pair["label"], str):
pair["label"] = float(pair["label"])
with open("pairs.json", "w") as fp:
json.dump(pairs, fp)
#Define your train examples.
f = open("pairs.json")
data = json.load(f)
train_pairs = []
for pair in data:
train_pairs.append(InputExample(texts=[pair["text1"], pair["text2"]], label=pair["label"]))
#Define your train dataset, the dataloader and the train loss
train_dataloader = DataLoader(train_pairs, shuffle=True, batch_size=16)
train_loss = losses.CosineSimilarityLoss(model)
#Tune the model
model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=1, warmup_steps=100)
model.save("trained_model")
if __name__ == "__main__":
main()