distilbertqatrain.py [179:203]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    training_args = TrainingArguments(
        output_dir=args.model_dir,
        num_train_epochs=args.epochs,
        per_device_train_batch_size=args.train_batch_size,
        per_device_eval_batch_size=args.eval_batch_size,
        warmup_steps=args.warmup_steps,
        evaluation_strategy="epoch",
        logging_dir=f"{args.output_data_dir}/logs",
        learning_rate=float(args.learning_rate),
    )

    # create Trainer instance
    trainer = Trainer(
        model=model,
        args=training_args,
        #compute_metrics=compute_metrics,
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
    )

    # train model
    trainer.train()

    # evaluate model
    eval_result = trainer.evaluate(eval_dataset=test_dataset)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



scripts/train.py [160:184]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    training_args = TrainingArguments(
        output_dir=args.model_dir,
        num_train_epochs=args.epochs,
        per_device_train_batch_size=args.train_batch_size,
        per_device_eval_batch_size=args.eval_batch_size,
        warmup_steps=args.warmup_steps,
        evaluation_strategy="epoch",
        logging_dir=f"{args.output_data_dir}/logs",
        learning_rate=float(args.learning_rate),
    )

    # create Trainer instance
    trainer = Trainer(
        model=model,
        args=training_args,
        #compute_metrics=compute_metrics,
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
    )

    # train model
    trainer.train()

    # evaluate model
    eval_result = trainer.evaluate(eval_dataset=test_dataset)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -