I did this by adding a custom callback which calls the evaluate() method with train_dataset at the end of every callback.
class CustomCallback(TrainerCallback):
def __init__(self, trainer) -> None:
super().__init__()
self._trainer = trainer
def on_epoch_end(self, args, state, control, **kwargs):
if control.should_evaluate:
control_copy = deepcopy(control)
self._trainer.evaluate(eval_dataset=self._trainer.train_dataset, metric_key_prefix="train")
return control_copy
trainer = Trainer(
model=model, # the instantiated Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=valid_dataset, # evaluation dataset
compute_metrics=compute_metrics, # the callback that computes metrics of interest
tokenizer=tokenizer
)
trainer.add_callback(CustomCallback(trainer))
train = trainer.train()
This gives the train metrics like following:
{‘train_loss’: 0.7159061431884766, ‘train_accuracy’: 0.4, ‘train_f1’: 0.5714285714285715, ‘train_runtime’: 6.2973, ‘train_samples_per_second’: 2.382, ‘train_steps_per_second’: 0.159, ‘epoch’: 1.0}
{‘eval_loss’: 0.8529007434844971, ‘eval_accuracy’: 0.0, ‘eval_f1’: 0.0, ‘eval_runtime’: 2.0739, ‘eval_samples_per_second’: 0.964, ‘eval_steps_per_second’: 0.482, ‘epoch’: 1.0}