Error while trying to run run_wwm_mlm.py using my saved model: TypeError: ‘NoneType’ object is not iterable

jungminc88 commented 3 years ago

Environment info

transformers version: 4.7.0
Platform: Linux-4.18.0-25-generic-x86_64-with-debian-buster-sid
Python version: 3.7.9
PyTorch version (GPU?): 1.8.1+cu111 (True)
Tensorflow version (GPU?): not installed (NA)
Using GPU in script?: Yes
Using distributed or parallel set-up in script?: Yes

Who can help

research_projects/bert-loses-patience: @JetRunner
research_projects/distillation: @VictorSanh @LysandreJik

Information

Model I am using (Bert, XLNet ...):

The problem arises when using:

[x] the official example scripts: (give details below)

The tasks I am working on is:

[x] my own task or dataset: (give details below)

To reproduce

Steps to reproduce the behavior:

1. I have trained a BertForSequenceClassification model, saved the model and tokenizer:

model.save_pretrained('output_mlm_cls')
tokenizer.save_pretrained('output_mlm_cls')

2. I tried to run run_mlm_wwm.py, giving the the saved model above as the input model:

python run_mlm_wwm.py \ --model_name_or_path /path/to/output_mlm_cls \ --train_file /path/to/my_data.txt \ --do_train \ --output_dir /output_dir

I got this error message:

Traceback (most recent call last): File “run_mlm_wwm.py”, line 408, in main() File “run_mlm_wwm.py”, line 367, in main train_result = trainer.train(resume_from_checkpoint=checkpoint) File “/home/cl/jungmin-c/.pyenv/versions/anaconda3-5.1.0/envs/jp/lib/python3.7/site-packages/transformers/trainer.py”, line 1066, in train self._load_state_dict_in_model(state_dict) File “/home/cl/jungmin-c/.pyenv/versions/anaconda3-5.1.0/envs/jp/lib/python3.7/site-packages/transformers/trainer.py”, line 1387, in _load_state_dict_in_model if set(load_result.missing_keys) == set(self.model._keys_to_ignore_on_save): TypeError: ‘NoneType’ object is not iterable

Expected behavior

It should run and train the input model on the whole word masking MLM task. When I run the same thing only changing --model_name_or_path to one of the HuggingFace provided pretrained models (cl-tohoku/bert-base-japanese-whole-word-masking), it runs without a problem, so it's not the problem with the dataset.

qqaatw commented 3 years ago

Hi, since your case is a mlm task, you should probably use BertForMaskedLM instead of BertForSequenceClassification to train your model first, and then feed it into run_wwm_mlm.py script.

jungminc88 commented 3 years ago

@qqaatw Thank you for your suggestion!

Hi, since your case is a mlm task, you should probably use BertForMaskedLM instead of BertForSequenceClassification to train your model first, and then feed it into run_wwm_mlm.py script.

My objective is to see the effect of training BERT on different tasks. I am wondering if training on MLM task after training on classification yields better results. Is there a way to do this using the script?

qqaatw commented 3 years ago

I got your point. You can use BertForPreTraining, which includes two prediction heads (MLM, NSP), to train a sentence classification task first, then feed the trained model into run_wwm_mlm.py to run MLM task. Because BertForPreTraining has two heads already, running mlm afterwards will no longer raise an error regarding mlm head missing.

jungminc88 commented 3 years ago

@qqaatw That's a neat solution! Thank you!

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huggingface / transformers