How to properly finetune t5 model

Question

I'm finetuning a t5-base model following this notebook. However, the loss of both validation set and training set decreases very slowly. I changed the learning_rate to a larger number, but it did not help. Eventually, the bleu score on the validation set was low (around 13.7), and the translation quality was low as well.

***** Running Evaluation *****
  Num examples = 1000
  Batch size = 32
{'eval_loss': 1.06500244140625, 'eval_bleu': 13.7229, 'eval_gen_len': 17.564, 'eval_runtime': 16.7915, 'eval_samples_per_second': 59.554, 'eval_steps_per_second': 1.906, 'epoch': 5.0}

If I use the "Helsinki-NLP/opus-mt-en-ro" model, the loss decreases properly, and at the end, the finetuned model works pretty well.

How to fine-tune t5-base properly? Did I miss something?

Answer 1

I think the metrics shown in the tutorial are for the already trained EN>RO opus-mt model which was then fine-tuned. I don't see the before and after comparison of the metrics for it, so it is hard to tell how much of a difference that fine-tuning really made.

You generally shouldn't expect the same results from fine-tuning T5 which is not a (pure) machine translation model. More important is the difference in metrics before and after the fine-tuning.

Two things I could imagine having gone wrong with your training:

1. Did you add the proper T5 prefix to the input sequences ("translate English to Romanian: ") for both your training and your evaluation? If you did not you might have been training a new task from scratch and not use the bit of pre-training the model did on MT to Romanian (and German and perhaps some other ones). You can see how that affects the model behavior for example in this inference demo: Language used during pretraining and Language not used during pretraining.
2. If you chose a relatively small model like t5-base but you stuck with the num_train_epochs=1 in the tutorial your train epoch number is probably a lot too low to make a noticable difference. Try increasing the epochs for as long as you get significant performance boosts from it, in the example this is probably the case for at least the first 5 to 10 epochs.

I actually did something very similar to what you are doing before for EN>DE (German). I fine-tuned both opus-mt-en-de and t5-base on a custom dataset of 30.000 samples for 10 epochs. opus-mt-en-de BLEU increased from 0.256 to 0.388 and t5-base from 0.166 to 0.340, just to give you an idea of what to expect. Romanian/the dataset you use might be more of a challenge for the model and result in different scores though.