Hey, I have been trying to train my model on mnli and the learning rate seems to keep decreasing for no reason. Can someone help me? -
train_args = TrainingArguments(
output_dir=f'./resultsv3/output',
logging_dir=f'./resultsv3/output/logs',
learning_rate=3e-6,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
num_train_epochs=4,
load_best_model_at_end=True,
metric_for_best_model="accuracy",
fp16=True,
fp16_full_eval=True,
evaluation_strategy="epoch",
save_strategy = "epoch",
save_total_limit=5,
logging_strategy="epoch",
report_to="all")
def compute_metrics(eval_pred):
predictions, labels = eval_pred
predictions = np.argmax(predictions, axis=1)
return metric.compute(predictions=predictions, references=labels)
trainer = Trainer(
model=model,
tokenizer=tokenizer,
args=train_args,
data_collator=data_collator,
train_dataset=encoded_dataset_train,
eval_dataset=encoded_dataset_test,
compute_metrics=compute_metrics
)
which parameter is causing the decrease in Learning rate every epoch?