scripts/transformers/run_fewshot.py [92:128]:
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            overwrite_output_dir=True,
            learning_rate=learning_rate,
            per_device_train_batch_size=batch_size,
            per_device_eval_batch_size=batch_size,
            weight_decay=0.01,
            eval_strategy="epoch",
            logging_steps=100,
            save_strategy="no",
            fp16=True,
            report_to="none",
        )

        if push_to_hub:
            training_args.push_to_hub = True
            training_args.hub_strategy = ("end",)
            training_args.hub_model_id = f"SetFit/{ckpt_name}"

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

        def hp_space(trial):
            return {
                "num_train_epochs": trial.suggest_int("num_train_epochs", num_train_epochs_min, num_train_epochs_max)
            }

        best_run = trainer.hyperparameter_search(n_trials=10, direction="maximize", hp_space=hp_space)

        for k, v in best_run.hyperparameters.items():
            setattr(trainer.args, k, v)

        trainer.train()
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scripts/transformers/run_fewshot_multilingual.py [117:153]:
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            overwrite_output_dir=True,
            learning_rate=learning_rate,
            per_device_train_batch_size=batch_size,
            per_device_eval_batch_size=batch_size,
            weight_decay=0.01,
            eval_strategy="epoch",
            logging_steps=100,
            save_strategy="no",
            fp16=True,
            report_to="none",
        )

        if push_to_hub:
            training_args.push_to_hub = True
            training_args.hub_strategy = ("end",)
            training_args.hub_model_id = f"SetFit/{ckpt_name}"

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

        def hp_space(trial):
            return {
                "num_train_epochs": trial.suggest_int("num_train_epochs", num_train_epochs_min, num_train_epochs_max)
            }

        best_run = trainer.hyperparameter_search(n_trials=10, direction="maximize", hp_space=hp_space)

        for k, v in best_run.hyperparameters.items():
            setattr(trainer.args, k, v)

        trainer.train()
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