Fine tune BERT based models using Trainer fails
## Environment info
- `transformers` version: 3.1.0
- Platform: Linux-5.4.0-45-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyTorch version (GPU?): 1.5.0 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: Yes.
- Using distributed or parallel set-up in script?: No.

### Who can help
 Trainer: @sgugger 

## Information

I am using pretrained BERT 'bert-base-multilingual-uncased' model and I would like to fine tune it on Next Sentence Prediction task. I followed example given here: [https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py).

## To reproduce

Steps to reproduce the behavior:

1. python3.8 program.py

program.py:
```python
import torch
from transformers import (BertForNextSentencePrediction,
                          BertTokenizer,
                          RobertaModel, RobertaTokenizer, Trainer,
                          TrainingArguments)
from transformers.data.datasets.language_modeling import TextDatasetForNextSentencePrediction
from transformers.data.data_collator import DataCollatorForNextSentencePrediction

if __name__ == "__main__":
    model_dir = "./model/"
    result_model_dir = "./result/"
    logs_directory = './logs'
    dataset_path = 'train.txt' # file in TextDatasetForNextSentencePrediction format
    tokenizer = RobertaTokenizer.from_pretrained('bert-base-multilingual-uncased')
    finetune_model = BertForNextSentencePrediction.from_pretrained('bert-base-multilingual-uncased')

    training_args = TrainingArguments(
        output_dir=result_model_dir,
        num_train_epochs=3,
        per_device_train_batch_size=1,
        per_device_eval_batch_size=1,
        warmup_steps=500,
        weight_decay=0.01,
        logging_dir=logs_directory,
    )

    data_collator = DataCollatorForNextSentencePrediction(
        tokenizer=tokenizer,
        mlm=False,
        block_size=512,
        nsp_probability=0.5,
      )

    train_dataset = TextDatasetForNextSentencePrediction(
        tokenizer=tokenizer,
        file_path=dataset_path,
        block_size=512,
    )

    trainer = Trainer(
        model=finetune_model,
        args=training_args,
        train_dataset=train_dataset,
        data_collator=data_collator,
    )

    trainer.train()
    trainer.save_model(result_model_dir)
```
Output in terminal:
```bash
Special tokens have been added in the vocabulary, make sure the associated word emebedding are fine-tuned or trained.
Some weights of the model checkpoint at ./model/ were not used when initializing BertForNextSentencePrediction: ['roberta.embeddings.word_embeddings.weight', 'roberta.embeddings.position_embeddings.weight', 'roberta.embeddings.token_type_embeddings.weight', 'roberta.embeddings.LayerNorm.weight', 'roberta.embeddings.LayerNorm.bias', 'roberta.encoder.layer.0.attention.self.query.weight', 'roberta.encoder.layer.0.attention.self.query.bias', 'roberta.encoder.layer.0.attention.self.key.weight', 'roberta.encoder.layer.0.attention.self.key.bias', 'roberta.encoder.layer.0.attention.self.value.weight', 'roberta.encoder.layer.0.attention.self.value.bias', 'roberta.encoder.layer.0.attention.output.dense.weight', 'roberta.encoder.layer.0.attention.output.dense.bias', 'roberta.encoder.layer.0.attention.output.LayerNorm.weight', 'roberta.encoder.layer.0.attention.output.LayerNorm.bias', 'roberta.encoder.layer.0.intermediate.dense.weight', 'roberta.encoder.layer.0.intermediate.dense.bias', 'roberta.encoder.layer.0.output.dense.weight', 'roberta.encoder.layer.0.output.dense.bias', 'roberta.encoder.layer.0.output.LayerNorm.weight', 'roberta.encoder.layer.0.output.LayerNorm.bias', 'roberta.encoder.layer.1.attention.self.query.weight', 'roberta.encoder.layer.1.attention.self.query.bias', 'roberta.encoder.layer.1.attention.self.key.weight', 'roberta.encoder.layer.1.attention.self.key.bias', 'roberta.encoder.layer.1.attention.self.value.weight', 'roberta.encoder.layer.1.attention.self.value.bias', 'roberta.encoder.layer.1.attention.output.dense.weight', 'roberta.encoder.layer.1.attention.output.dense.bias', 'roberta.encoder.layer.1.attention.output.LayerNorm.weight', 'roberta.encoder.layer.1.attention.output.LayerNorm.bias', 'roberta.encoder.layer.1.intermediate.dense.weight', 'roberta.encoder.layer.1.intermediate.dense.bias', 'roberta.encoder.layer.1.output.dense.weight', 'roberta.encoder.layer.1.output.dense.bias', 'roberta.encoder.layer.1.output.LayerNorm.weight', 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'roberta.encoder.layer.23.attention.output.dense.weight', 'roberta.encoder.layer.23.attention.output.dense.bias', 'roberta.encoder.layer.23.attention.output.LayerNorm.weight', 'roberta.encoder.layer.23.attention.output.LayerNorm.bias', 'roberta.encoder.layer.23.intermediate.dense.weight', 'roberta.encoder.layer.23.intermediate.dense.bias', 'roberta.encoder.layer.23.output.dense.weight', 'roberta.encoder.layer.23.output.dense.bias', 'roberta.encoder.layer.23.output.LayerNorm.weight', 'roberta.encoder.layer.23.output.LayerNorm.bias', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.decoder.bias']
- This IS expected if you are initializing BertForNextSentencePrediction from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).
- This IS NOT expected if you are initializing BertForNextSentencePrediction from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForNextSentencePrediction were not initialized from the model checkpoint at ./model/ and are newly initialized: ['embeddings.word_embeddings.weight', 'embeddings.position_embeddings.weight', 'embeddings.token_type_embeddings.weight', 'embeddings.LayerNorm.weight', 'embeddings.LayerNorm.bias', 'encoder.layer.0.attention.self.query.weight', 'encoder.layer.0.attention.self.query.bias', 'encoder.layer.0.attention.self.key.weight', 'encoder.layer.0.attention.self.key.bias', 'encoder.layer.0.attention.self.value.weight', 'encoder.layer.0.attention.self.value.bias', 'encoder.layer.0.attention.output.dense.weight', 'encoder.layer.0.attention.output.dense.bias', 'encoder.layer.0.attention.output.LayerNorm.weight', 'encoder.layer.0.attention.output.LayerNorm.bias', 'encoder.layer.0.intermediate.dense.weight', 'encoder.layer.0.intermediate.dense.bias', 'encoder.layer.0.output.dense.weight', 'encoder.layer.0.output.dense.bias', 'encoder.layer.0.output.LayerNorm.weight', 'encoder.layer.0.output.LayerNorm.bias', 'encoder.layer.1.attention.self.query.weight', 'encoder.layer.1.attention.self.query.bias', 'encoder.layer.1.attention.self.key.weight', 'encoder.layer.1.attention.self.key.bias', 'encoder.layer.1.attention.self.value.weight', 'encoder.layer.1.attention.self.value.bias', 'encoder.layer.1.attention.output.dense.weight', 'encoder.layer.1.attention.output.dense.bias', 'encoder.layer.1.attention.output.LayerNorm.weight', 'encoder.layer.1.attention.output.LayerNorm.bias', 'encoder.layer.1.intermediate.dense.weight', 'encoder.layer.1.intermediate.dense.bias', 'encoder.layer.1.output.dense.weight', 'encoder.layer.1.output.dense.bias', 'encoder.layer.1.output.LayerNorm.weight', 'encoder.layer.1.output.LayerNorm.bias', 'encoder.layer.2.attention.self.query.weight', 'encoder.layer.2.attention.self.query.bias', 'encoder.layer.2.attention.self.key.weight', 'encoder.layer.2.attention.self.key.bias', 'encoder.layer.2.attention.self.value.weight', 'encoder.layer.2.attention.self.value.bias', 'encoder.layer.2.attention.output.dense.weight', 'encoder.layer.2.attention.output.dense.bias', 'encoder.layer.2.attention.output.LayerNorm.weight', 'encoder.layer.2.attention.output.LayerNorm.bias', 'encoder.layer.2.intermediate.dense.weight', 'encoder.layer.2.intermediate.dense.bias', 'encoder.layer.2.output.dense.weight', 'encoder.layer.2.output.dense.bias', 'encoder.layer.2.output.LayerNorm.weight', 'encoder.layer.2.output.LayerNorm.bias', 'encoder.layer.3.attention.self.query.weight', 'encoder.layer.3.attention.self.query.bias', 'encoder.layer.3.attention.self.key.weight', 'encoder.layer.3.attention.self.key.bias', 'encoder.layer.3.attention.self.value.weight', 'encoder.layer.3.attention.self.value.bias', 'encoder.layer.3.attention.output.dense.weight', 'encoder.layer.3.attention.output.dense.bias', 'encoder.layer.3.attention.output.LayerNorm.weight', 'encoder.layer.3.attention.output.LayerNorm.bias', 'encoder.layer.3.intermediate.dense.weight', 'encoder.layer.3.intermediate.dense.bias', 'encoder.layer.3.output.dense.weight', 'encoder.layer.3.output.dense.bias', 'encoder.layer.3.output.LayerNorm.weight', 'encoder.layer.3.output.LayerNorm.bias', 'encoder.layer.4.attention.self.query.weight', 'encoder.layer.4.attention.self.query.bias', 'encoder.layer.4.attention.self.key.weight', 'encoder.layer.4.attention.self.key.bias', 'encoder.layer.4.attention.self.value.weight', 'encoder.layer.4.attention.self.value.bias', 'encoder.layer.4.attention.output.dense.weight', 'encoder.layer.4.attention.output.dense.bias', 'encoder.layer.4.attention.output.LayerNorm.weight', 'encoder.layer.4.attention.output.LayerNorm.bias', 'encoder.layer.4.intermediate.dense.weight', 'encoder.layer.4.intermediate.dense.bias', 'encoder.layer.4.output.dense.weight', 'encoder.layer.4.output.dense.bias', 'encoder.layer.4.output.LayerNorm.weight', 'encoder.layer.4.output.LayerNorm.bias', 'encoder.layer.5.attention.self.query.weight', 'encoder.layer.5.attention.self.query.bias', 'encoder.layer.5.attention.self.key.weight', 'encoder.layer.5.attention.self.key.bias', 'encoder.layer.5.attention.self.value.weight', 'encoder.layer.5.attention.self.value.bias', 'encoder.layer.5.attention.output.dense.weight', 'encoder.layer.5.attention.output.dense.bias', 'encoder.layer.5.attention.output.LayerNorm.weight', 'encoder.layer.5.attention.output.LayerNorm.bias', 'encoder.layer.5.intermediate.dense.weight', 'encoder.layer.5.intermediate.dense.bias', 'encoder.layer.5.output.dense.weight', 'encoder.layer.5.output.dense.bias', 'encoder.layer.5.output.LayerNorm.weight', 'encoder.layer.5.output.LayerNorm.bias', 'encoder.layer.6.attention.self.query.weight', 'encoder.layer.6.attention.self.query.bias', 'encoder.layer.6.attention.self.key.weight', 'encoder.layer.6.attention.self.key.bias', 'encoder.layer.6.attention.self.value.weight', 'encoder.layer.6.attention.self.value.bias', 'encoder.layer.6.attention.output.dense.weight', 'encoder.layer.6.attention.output.dense.bias', 'encoder.layer.6.attention.output.LayerNorm.weight', 'encoder.layer.6.attention.output.LayerNorm.bias', 'encoder.layer.6.intermediate.dense.weight', 'encoder.layer.6.intermediate.dense.bias', 'encoder.layer.6.output.dense.weight', 'encoder.layer.6.output.dense.bias', 'encoder.layer.6.output.LayerNorm.weight', 'encoder.layer.6.output.LayerNorm.bias', 'encoder.layer.7.attention.self.query.weight', 'encoder.layer.7.attention.self.query.bias', 'encoder.layer.7.attention.self.key.weight', 'encoder.layer.7.attention.self.key.bias', 'encoder.layer.7.attention.self.value.weight', 'encoder.layer.7.attention.self.value.bias', 'encoder.layer.7.attention.output.dense.weight', 'encoder.layer.7.attention.output.dense.bias', 'encoder.layer.7.attention.output.LayerNorm.weight', 'encoder.layer.7.attention.output.LayerNorm.bias', 'encoder.layer.7.intermediate.dense.weight', 'encoder.layer.7.intermediate.dense.bias', 'encoder.layer.7.output.dense.weight', 'encoder.layer.7.output.dense.bias', 'encoder.layer.7.output.LayerNorm.weight', 'encoder.layer.7.output.LayerNorm.bias', 'encoder.layer.8.attention.self.query.weight', 'encoder.layer.8.attention.self.query.bias', 'encoder.layer.8.attention.self.key.weight', 'encoder.layer.8.attention.self.key.bias', 'encoder.layer.8.attention.self.value.weight', 'encoder.layer.8.attention.self.value.bias', 'encoder.layer.8.attention.output.dense.weight', 'encoder.layer.8.attention.output.dense.bias', 'encoder.layer.8.attention.output.LayerNorm.weight', 'encoder.layer.8.attention.output.LayerNorm.bias', 'encoder.layer.8.intermediate.dense.weight', 'encoder.layer.8.intermediate.dense.bias', 'encoder.layer.8.output.dense.weight', 'encoder.layer.8.output.dense.bias', 'encoder.layer.8.output.LayerNorm.weight', 'encoder.layer.8.output.LayerNorm.bias', 'encoder.layer.9.attention.self.query.weight', 'encoder.layer.9.attention.self.query.bias', 'encoder.layer.9.attention.self.key.weight', 'encoder.layer.9.attention.self.key.bias', 'encoder.layer.9.attention.self.value.weight', 'encoder.layer.9.attention.self.value.bias', 'encoder.layer.9.attention.output.dense.weight', 'encoder.layer.9.attention.output.dense.bias', 'encoder.layer.9.attention.output.LayerNorm.weight', 'encoder.layer.9.attention.output.LayerNorm.bias', 'encoder.layer.9.intermediate.dense.weight', 'encoder.layer.9.intermediate.dense.bias', 'encoder.layer.9.output.dense.weight', 'encoder.layer.9.output.dense.bias', 'encoder.layer.9.output.LayerNorm.weight', 'encoder.layer.9.output.LayerNorm.bias', 'encoder.layer.10.attention.self.query.weight', 'encoder.layer.10.attention.self.query.bias', 'encoder.layer.10.attention.self.key.weight', 'encoder.layer.10.attention.self.key.bias', 'encoder.layer.10.attention.self.value.weight', 'encoder.layer.10.attention.self.value.bias', 'encoder.layer.10.attention.output.dense.weight', 'encoder.layer.10.attention.output.dense.bias', 'encoder.layer.10.attention.output.LayerNorm.weight', 'encoder.layer.10.attention.output.LayerNorm.bias', 'encoder.layer.10.intermediate.dense.weight', 'encoder.layer.10.intermediate.dense.bias', 'encoder.layer.10.output.dense.weight', 'encoder.layer.10.output.dense.bias', 'encoder.layer.10.output.LayerNorm.weight', 'encoder.layer.10.output.LayerNorm.bias', 'encoder.layer.11.attention.self.query.weight', 'encoder.layer.11.attention.self.query.bias', 'encoder.layer.11.attention.self.key.weight', 'encoder.layer.11.attention.self.key.bias', 'encoder.layer.11.attention.self.value.weight', 'encoder.layer.11.attention.self.value.bias', 'encoder.layer.11.attention.output.dense.weight', 'encoder.layer.11.attention.output.dense.bias', 'encoder.layer.11.attention.output.LayerNorm.weight', 'encoder.layer.11.attention.output.LayerNorm.bias', 'encoder.layer.11.intermediate.dense.weight', 'encoder.layer.11.intermediate.dense.bias', 'encoder.layer.11.output.dense.weight', 'encoder.layer.11.output.dense.bias', 'encoder.layer.11.output.LayerNorm.weight', 'encoder.layer.11.output.LayerNorm.bias', 'encoder.layer.12.attention.self.query.weight', 'encoder.layer.12.attention.self.query.bias', 'encoder.layer.12.attention.self.key.weight', 'encoder.layer.12.attention.self.key.bias', 'encoder.layer.12.attention.self.value.weight', 'encoder.layer.12.attention.self.value.bias', 'encoder.layer.12.attention.output.dense.weight', 'encoder.layer.12.attention.output.dense.bias', 'encoder.layer.12.attention.output.LayerNorm.weight', 'encoder.layer.12.attention.output.LayerNorm.bias', 'encoder.layer.12.intermediate.dense.weight', 'encoder.layer.12.intermediate.dense.bias', 'encoder.layer.12.output.dense.weight', 'encoder.layer.12.output.dense.bias', 'encoder.layer.12.output.LayerNorm.weight', 'encoder.layer.12.output.LayerNorm.bias', 'encoder.layer.13.attention.self.query.weight', 'encoder.layer.13.attention.self.query.bias', 'encoder.layer.13.attention.self.key.weight', 'encoder.layer.13.attention.self.key.bias', 'encoder.layer.13.attention.self.value.weight', 'encoder.layer.13.attention.self.value.bias', 'encoder.layer.13.attention.output.dense.weight', 'encoder.layer.13.attention.output.dense.bias', 'encoder.layer.13.attention.output.LayerNorm.weight', 'encoder.layer.13.attention.output.LayerNorm.bias', 'encoder.layer.13.intermediate.dense.weight', 'encoder.layer.13.intermediate.dense.bias', 'encoder.layer.13.output.dense.weight', 'encoder.layer.13.output.dense.bias', 'encoder.layer.13.output.LayerNorm.weight', 'encoder.layer.13.output.LayerNorm.bias', 'encoder.layer.14.attention.self.query.weight', 'encoder.layer.14.attention.self.query.bias', 'encoder.layer.14.attention.self.key.weight', 'encoder.layer.14.attention.self.key.bias', 'encoder.layer.14.attention.self.value.weight', 'encoder.layer.14.attention.self.value.bias', 'encoder.layer.14.attention.output.dense.weight', 'encoder.layer.14.attention.output.dense.bias', 'encoder.layer.14.attention.output.LayerNorm.weight', 'encoder.layer.14.attention.output.LayerNorm.bias', 'encoder.layer.14.intermediate.dense.weight', 'encoder.layer.14.intermediate.dense.bias', 'encoder.layer.14.output.dense.weight', 'encoder.layer.14.output.dense.bias', 'encoder.layer.14.output.LayerNorm.weight', 'encoder.layer.14.output.LayerNorm.bias', 'encoder.layer.15.attention.self.query.weight', 'encoder.layer.15.attention.self.query.bias', 'encoder.layer.15.attention.self.key.weight', 'encoder.layer.15.attention.self.key.bias', 'encoder.layer.15.attention.self.value.weight', 'encoder.layer.15.attention.self.value.bias', 'encoder.layer.15.attention.output.dense.weight', 'encoder.layer.15.attention.output.dense.bias', 'encoder.layer.15.attention.output.LayerNorm.weight', 'encoder.layer.15.attention.output.LayerNorm.bias', 'encoder.layer.15.intermediate.dense.weight', 'encoder.layer.15.intermediate.dense.bias', 'encoder.layer.15.output.dense.weight', 'encoder.layer.15.output.dense.bias', 'encoder.layer.15.output.LayerNorm.weight', 'encoder.layer.15.output.LayerNorm.bias', 'encoder.layer.16.attention.self.query.weight', 'encoder.layer.16.attention.self.query.bias', 'encoder.layer.16.attention.self.key.weight', 'encoder.layer.16.attention.self.key.bias', 'encoder.layer.16.attention.self.value.weight', 'encoder.layer.16.attention.self.value.bias', 'encoder.layer.16.attention.output.dense.weight', 'encoder.layer.16.attention.output.dense.bias', 'encoder.layer.16.attention.output.LayerNorm.weight', 'encoder.layer.16.attention.output.LayerNorm.bias', 'encoder.layer.16.intermediate.dense.weight', 'encoder.layer.16.intermediate.dense.bias', 'encoder.layer.16.output.dense.weight', 'encoder.layer.16.output.dense.bias', 'encoder.layer.16.output.LayerNorm.weight', 'encoder.layer.16.output.LayerNorm.bias', 'encoder.layer.17.attention.self.query.weight', 'encoder.layer.17.attention.self.query.bias', 'encoder.layer.17.attention.self.key.weight', 'encoder.layer.17.attention.self.key.bias', 'encoder.layer.17.attention.self.value.weight', 'encoder.layer.17.attention.self.value.bias', 'encoder.layer.17.attention.output.dense.weight', 'encoder.layer.17.attention.output.dense.bias', 'encoder.layer.17.attention.output.LayerNorm.weight', 'encoder.layer.17.attention.output.LayerNorm.bias', 'encoder.layer.17.intermediate.dense.weight', 'encoder.layer.17.intermediate.dense.bias', 'encoder.layer.17.output.dense.weight', 'encoder.layer.17.output.dense.bias', 'encoder.layer.17.output.LayerNorm.weight', 'encoder.layer.17.output.LayerNorm.bias', 'encoder.layer.18.attention.self.query.weight', 'encoder.layer.18.attention.self.query.bias', 'encoder.layer.18.attention.self.key.weight', 'encoder.layer.18.attention.self.key.bias', 'encoder.layer.18.attention.self.value.weight', 'encoder.layer.18.attention.self.value.bias', 'encoder.layer.18.attention.output.dense.weight', 'encoder.layer.18.attention.output.dense.bias', 'encoder.layer.18.attention.output.LayerNorm.weight', 'encoder.layer.18.attention.output.LayerNorm.bias', 'encoder.layer.18.intermediate.dense.weight', 'encoder.layer.18.intermediate.dense.bias', 'encoder.layer.18.output.dense.weight', 'encoder.layer.18.output.dense.bias', 'encoder.layer.18.output.LayerNorm.weight', 'encoder.layer.18.output.LayerNorm.bias', 'encoder.layer.19.attention.self.query.weight', 'encoder.layer.19.attention.self.query.bias', 'encoder.layer.19.attention.self.key.weight', 'encoder.layer.19.attention.self.key.bias', 'encoder.layer.19.attention.self.value.weight', 'encoder.layer.19.attention.self.value.bias', 'encoder.layer.19.attention.output.dense.weight', 'encoder.layer.19.attention.output.dense.bias', 'encoder.layer.19.attention.output.LayerNorm.weight', 'encoder.layer.19.attention.output.LayerNorm.bias', 'encoder.layer.19.intermediate.dense.weight', 'encoder.layer.19.intermediate.dense.bias', 'encoder.layer.19.output.dense.weight', 'encoder.layer.19.output.dense.bias', 'encoder.layer.19.output.LayerNorm.weight', 'encoder.layer.19.output.LayerNorm.bias', 'encoder.layer.20.attention.self.query.weight', 'encoder.layer.20.attention.self.query.bias', 'encoder.layer.20.attention.self.key.weight', 'encoder.layer.20.attention.self.key.bias', 'encoder.layer.20.attention.self.value.weight', 'encoder.layer.20.attention.self.value.bias', 'encoder.layer.20.attention.output.dense.weight', 'encoder.layer.20.attention.output.dense.bias', 'encoder.layer.20.attention.output.LayerNorm.weight', 'encoder.layer.20.attention.output.LayerNorm.bias', 'encoder.layer.20.intermediate.dense.weight', 'encoder.layer.20.intermediate.dense.bias', 'encoder.layer.20.output.dense.weight', 'encoder.layer.20.output.dense.bias', 'encoder.layer.20.output.LayerNorm.weight', 'encoder.layer.20.output.LayerNorm.bias', 'encoder.layer.21.attention.self.query.weight', 'encoder.layer.21.attention.self.query.bias', 'encoder.layer.21.attention.self.key.weight', 'encoder.layer.21.attention.self.key.bias', 'encoder.layer.21.attention.self.value.weight', 'encoder.layer.21.attention.self.value.bias', 'encoder.layer.21.attention.output.dense.weight', 'encoder.layer.21.attention.output.dense.bias', 'encoder.layer.21.attention.output.LayerNorm.weight', 'encoder.layer.21.attention.output.LayerNorm.bias', 'encoder.layer.21.intermediate.dense.weight', 'encoder.layer.21.intermediate.dense.bias', 'encoder.layer.21.output.dense.weight', 'encoder.layer.21.output.dense.bias', 'encoder.layer.21.output.LayerNorm.weight', 'encoder.layer.21.output.LayerNorm.bias', 'encoder.layer.22.attention.self.query.weight', 'encoder.layer.22.attention.self.query.bias', 'encoder.layer.22.attention.self.key.weight', 'encoder.layer.22.attention.self.key.bias', 'encoder.layer.22.attention.self.value.weight', 'encoder.layer.22.attention.self.value.bias', 'encoder.layer.22.attention.output.dense.weight', 'encoder.layer.22.attention.output.dense.bias', 'encoder.layer.22.attention.output.LayerNorm.weight', 'encoder.layer.22.attention.output.LayerNorm.bias', 'encoder.layer.22.intermediate.dense.weight', 'encoder.layer.22.intermediate.dense.bias', 'encoder.layer.22.output.dense.weight', 'encoder.layer.22.output.dense.bias', 'encoder.layer.22.output.LayerNorm.weight', 'encoder.layer.22.output.LayerNorm.bias', 'encoder.layer.23.attention.self.query.weight', 'encoder.layer.23.attention.self.query.bias', 'encoder.layer.23.attention.self.key.weight', 'encoder.layer.23.attention.self.key.bias', 'encoder.layer.23.attention.self.value.weight', 'encoder.layer.23.attention.self.value.bias', 'encoder.layer.23.attention.output.dense.weight', 'encoder.layer.23.attention.output.dense.bias', 'encoder.layer.23.attention.output.LayerNorm.weight', 'encoder.layer.23.attention.output.LayerNorm.bias', 'encoder.layer.23.intermediate.dense.weight', 'encoder.layer.23.intermediate.dense.bias', 'encoder.layer.23.output.dense.weight', 'encoder.layer.23.output.dense.bias', 'encoder.layer.23.output.LayerNorm.weight', 'encoder.layer.23.output.LayerNorm.bias', 'pooler.dense.weight', 'pooler.dense.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Epoch:   0%|                                                                                                                                                                     | 0/3 [00:00<?, ?it/sTraceback (most recent call last):                                                                                                                                                | 0/1 [00:00<?, ?it/s]
  File "polish_roberta.py", line 77, in <module>
  File "/home/awawrzynski/miniconda3/envs/polish_roberta/lib/python3.8/site-packages/transformers/trainer.py", line 707, in train
    tr_loss += self.training_step(model, inputs)
  File "/home/awawrzynski/miniconda3/envs/polish_roberta/lib/python3.8/site-packages/transformers/trainer.py", line 995, in training_step
    outputs = model(**inputs)
  File "/home/awawrzynski/miniconda3/envs/polish_roberta/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/awawrzynski/miniconda3/envs/polish_roberta/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 155, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "/home/awawrzynski/miniconda3/envs/polish_roberta/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 165, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "/home/awawrzynski/miniconda3/envs/polish_roberta/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 85, in parallel_apply
    output.reraise()
  File "/home/awawrzynski/miniconda3/envs/polish_roberta/lib/python3.8/site-packages/torch/_utils.py", line 395, in reraise
    raise self.exc_type(msg)
TypeError: Caught TypeError in replica 0 on device 0.
Original Traceback (most recent call last):
  File "/home/awawrzynski/miniconda3/envs/polish_roberta/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 60, in _worker
    output = module(*input, **kwargs)
  File "/home/awawrzynski/miniconda3/envs/polish_roberta/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
TypeError: forward() got an unexpected keyword argument 'masked_lm_labels'

Epoch:   0%|                                                                                                                                                                     | 0/3 [00:04<?, ?it/s]
Iteration:   0%|                                                                                                                                                                 | 0/1 [00:04<?, ?it/s]
```

## Expected behavior
Model is trained and after training is finished model is saved into pointed directory. 

## Solution
I've followed errors in terminal and find out that excessive parameter is passed into Trainer's training_step function. I've added line that removes this key from dictionary passed into model and it works.

```python
def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
        """
        Perform a training step on a batch of inputs.

        Subclass and override to inject custom behavior.

        Args:
            model (:obj:`nn.Module`):
                The model to train.
            inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument :obj:`labels`. Check your model's documentation for all accepted arguments.

        Return:
            :obj:`torch.Tensor`: The tensor with training loss on this batch.
        """
        if hasattr(self, "_training_step"):
            warnings.warn(
                "The `_training_step` method is deprecated and won't be called in a future version, define `training_step` in your subclass.",
                FutureWarning,
            )
            return self._training_step(model, inputs, self.optimizer)

        model.train()
        inputs = self._prepare_inputs(inputs)
        inputs.pop("masked_lm_labels") # I added this line and it works.

        if self.args.fp16 and _use_native_amp:
            with autocast():
                outputs = model(**inputs)
                loss = outputs[0]
        else:
            outputs = model(**inputs)
            # We don't use .loss here since the model may return tuples instead of ModelOutput.
            loss = outputs[0]

        if self.args.past_index >= 0:
            self._past = outputs[self.args.past_index]

        if self.args.n_gpu > 1:
            loss = loss.mean()  # mean() to average on multi-gpu parallel training

        if self.args.gradient_accumulation_steps > 1:
            loss = loss / self.args.gradient_accumulation_steps

        if self.args.fp16 and _use_native_amp:
            self.scaler.scale(loss).backward()
        elif self.args.fp16 and _use_apex:
            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                scaled_loss.backward()
        else:
            loss.backward()

        return loss
```


Thanks for flagging, I have pushed a fix inside the data collator in the PR mentioned above.