Open juanps90 opened 1 year ago
Currently validation is disabled during finetuning. If you need to use validation set, you can change the settings in finetune.py
training_arguments = transformers.TrainingArguments(
per_device_train_batch_size=ft_config.mbatch_size,
gradient_accumulation_steps=ft_config.gradient_accumulation_steps,
warmup_steps=ft_config.warmup_steps,
optim="adamw_torch",
num_train_epochs=ft_config.epochs,
learning_rate=ft_config.lr,
fp16=True,
logging_steps=ft_config.logging_steps,
evaluation_strategy="no",
save_strategy="steps",
eval_steps=None,
save_steps=ft_config.save_steps,
output_dir=ft_config.lora_out_dir,
save_total_limit=ft_config.save_total_limit,
load_best_model_at_end=False,
ddp_find_unused_parameters=False if ft_config.ddp else None,
)
trainer = transformers.Trainer(
model=model,
train_dataset=data.train_data,
eval_dataset=data.val_data,
args=training_arguments,
data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
)
Is there any way to see eval loss while finetuning?