Hi, i am trying to fine tune a T5 model with C4_200m dataset, i use cuda and pytorch with my task, here is my training
batch_size = 16
args = Seq2SeqTrainingArguments(output_dir="/content/drive/MyDrive/c4_200m/weights",
evaluation_strategy="steps",
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
learning_rate=2e-5,
num_train_epochs=1,
weight_decay=0.01,
save_total_limit=2,
predict_with_generate=True,
fp16 = True,
gradient_accumulation_steps = 6,
eval_steps = 500,
save_steps = 500,
load_best_model_at_end=True,
logging_dir="/logs",
report_to="wandb")
here is my metric function:
def compute_metrics(eval_pred):
predictions, labels = eval_pred
decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
# Replace -100 in the labels as we can't decode them.
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
# Rouge expects a newline after each sentence
decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]
result = rouge_metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
# Extract a few results
result = {key: value * 100 for key, value in result.items()}
# Add mean generated length
prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]
result["gen_len"] = np.mean(prediction_lens)
return {k: round(v, 4) for k, v in result.items()}
after i run trainer.train(), it will always got stuck at step 500 which is my eval_steps setting.
what is my problem here?