scripts/transformers/run_full.py [91:115]:
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        load_best_model_at_end=True,
        save_strategy="epoch",
        save_total_limit=1,
        fp16=True,
        report_to="none",
    )

    if push_to_hub:
        ckpt_name = f"{model_name}-finetuned-{dataset_id}-train-full"
        training_args.push_to_hub = True
        training_args.hub_strategy = ("end",)
        training_args.hub_model_id = f"SetFit/{ckpt_name}"

    callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]

    trainer = Trainer(
        model_init=model_init,
        args=training_args,
        compute_metrics=compute_metrics,
        train_dataset=train_eval_dataset["train"],
        eval_dataset=train_eval_dataset["test"],
        tokenizer=tokenizer,
        callbacks=callbacks,
    )
    trainer.train()
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scripts/transformers/run_full_multilingual.py [110:134]:
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        load_best_model_at_end=True,
        save_strategy="epoch",
        save_total_limit=1,
        fp16=True,
        report_to="none",
    )

    if push_to_hub:
        ckpt_name = f"{model_name}-finetuned-{dataset_id}-train-full"
        training_args.push_to_hub = True
        training_args.hub_strategy = ("end",)
        training_args.hub_model_id = f"SetFit/{ckpt_name}"

    callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]

    trainer = Trainer(
        model_init=model_init,
        args=training_args,
        compute_metrics=compute_metrics,
        train_dataset=train_eval_dataset["train"],
        eval_dataset=train_eval_dataset["test"],
        tokenizer=tokenizer,
        callbacks=callbacks,
    )
    trainer.train()
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