model.py

from setfit import SetFitModel, SetFitTrainer
from sentence_transformers.losses import CosineSimilarityLoss


# Function to create a pipeline for text classification using the trained model
def create_classifier(model_path):
    classifier = SetFitModel.from_pretrained(
        model_path,
        local_files_only=True,
    )
    return classifier


def run_setfit_training(
    session_id, model_id, model_name, train_dataset, batch_size, num_iterations
):

    model = SetFitModel.from_pretrained(model_id)

    # Create trainer
    trainer = SetFitTrainer(
        model=model,
        train_dataset=train_dataset,
        eval_dataset=train_dataset,
        loss_class=CosineSimilarityLoss,
        metric="accuracy",
        batch_size=batch_size,
        num_iterations=num_iterations,  # The number of text pairs to generate for contrastive learning
        num_epochs=1,  # The number of epochs to use for constrastive learning
        column_mapping={"text": "text", "label": "label"},
    )

    trainer.train()
    # metrics = trainer.evaluate()
    # accuracy = metrics["accuracy"]

    print(f"model used: {model_id}")
    print(f"train dataset: {len(train_dataset)} samples")
    # print(f"accuracy: {accuracy}")

    save_model_path = f"./models/{session_id}/{model_id}_{model_name}"

    trainer.model._save_pretrained(
        save_directory=f"./models/{session_id}/{model_id}_{model_name}"
    )

    return save_model_path