The default behavior of Trainer(...)
when evaluating model is disabling Dropout. Concretely, y_pred
for M
runs will be exactly the same
for i in range(M):
logits, labels, metrics = trainer.predict(tokenized_datasets["eval"])
y_pred = np.argmax(logits, axis=2)
...
Now I am trying to apply Monte Carlo Dropout trick introduced this this answer. This requires to turn the Dropout on while making predictions on the validation set.
I am wondering how I achieve this goal. Any input is appreciated