Loss calculation error
https://github.com/huggingface/pytorch-pretrained-BERT/blob/982339d82984466fde3b1466f657a03200aa2ffb/pytorch_pretrained_bert/modeling.py#L744

Got `ValueError: Expected target size (1, 30522), got torch.Size([1, 11])` at line 744 of `modeling.py`. I think the line should be changed to `masked_lm_loss = loss_fct(prediction_scores.view([-1, self.config.vocab_size]), masked_lm_labels.view([-1]))`.
Hi Jian, can you give me a small (self-contained) example showing how to get this error?<|||||>Hi Thomas! I modified the code in your `README.md` for an example:

```python
from pytorch_pretrained_bert.modeling import BertForMaskedLM, BertConfig
from pytorch_pretrained_bert import BertTokenizer
import torch

model = BertForMaskedLM.from_pretrained('bert-base-uncased')

# Tokenized input
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
text = "Who was Jim Henson ? Jim Henson was a puppeteer"
tokenized_text = tokenizer.tokenize(text)

# Mask a token that we will try to predict back with `BertForMaskedLM`
masked_index = 6
tokenized_text[masked_index] = '[MASK]'

# Convert token to vocabulary indices
indexed_truths = tokenizer.convert_tokens_to_ids(tokenized_text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)

# Convert inputs to PyTorch tensors
tokens_tensor = torch.tensor([indexed_tokens])
indexed_truths_tensor = torch.tensor([indexed_truths])

# Evaluate loss
model.eval()
masked_lm_logits_scores = model(tokens_tensor, masked_lm_labels=indexed_truths_tensor)
print(masked_lm_logits_scores)
```<|||||>Thank you, you are right, I fixed that on master. It will be in the next release.