Numpy format string issue in TFTrainer

🐛 Bug

Information

Model I am using (Bert, XLNet …): bert

Language I am using the model on (English, Chinese …): English

The problem arises when using:

• the official example scripts: (give details below)
• my own modified scripts: (give details below)

The tasks I am working on is:

• an official GLUE/SQUaD task: tf_ner
• my own task or dataset: (give details below)

To reproduce

Steps to reproduce the behavior:

Running the run_tf_ner example raises the following exception:

Traceback (most recent call last): File "run_tf_ner.py", line 282, in <module> main() File "run_tf_ner.py", line 213, in main trainer.train() File "venv/lib/python3.7/site-packages/transformers/trainer_tf.py", line 308, in train logger.info("Epoch {} Step {} Train Loss {:.4f}".format(epoch, step, training_loss.numpy())) TypeError: unsupported format string passed to numpy.ndarray.__format__

This issue was reported by multiple people: https://github.com/numpy/numpy/issues/12491 https://github.com/numpy/numpy/issues/5543

I think the easiest solution is to avoid using the numpy format string this way in TFTrainer.

Environment info

• transformers version: 2.1.0
• Platform: Ubuntu-18.04
• Python version: 3.7.7
• PyTorch version (GPU?): N/A
• Tensorflow version (GPU?): 2.1.0
• Using GPU in script?: Yes
• Using distributed or parallel set-up in script?: No

[jplu]

Cool! Happy we found the problem.

When you run the TF Trainer you have to specify over which task it will be trained on, here for example it is token-classification when it on text content it will be text-classification (the default) and the same for the two other tasks QA and MC.

This behavior will be removed in the next version of the TF trainer.

[jplu]

@xl2602 Thanks for your feedback, -1 was also the default value of pad_token_label_id in the previous version of the script.

@jx669 and @xl2602 Can you try to add the --mode token-classification parameter?