Getting an unexpected EOF when trying to download 'bert-large-uncased-whole-word-masking-finetuned-squad' model.
Author: agrawalarpan
Pull request: no
Labels: name: wontfix

## 🐛 Bug

<!-- Important information -->

Model I am using (Bert, XLNet....): Bert

Language I am using the model on (English, Chinese....): English

The problem arise when using:
* [ ] the official example scripts: (give details): BertForQuestionAnswering
* [ ] my own modified scripts: (give details)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name): SQuaD
* [ ] my own task or dataset: (give details)

## To Reproduce

Steps to reproduce the behavior:

1. from pytorch_transformers import BertForQuestionAnswering
2. model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')

<!-- If you have a code sample, error messages, stack traces, please provide it here as well. -->

## Expected behavior

<!-- A clear and concise description of what you expected to happen. -->

## Environment

* OS:
* Python version:
* PyTorch version:
* PyTorch Transformers version (or branch):
* Using GPU ?
* Distributed of parallel setup ?
* Any other relevant information:

## Additional context

<!-- Add any other context about the problem here. -->

Comments:
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.


In my environment, **it works as expected**!

_Environment_:

- **Python**: 3.6.9
- **O.S.** : Linux-4.15.0-70-generic-x86_64-with-debian-buster-sid
- **Transformers**: 2.1.1 (installed from source with `pip install git+https://github.com/huggingface/transformers.git`)
- **Torch**: 1.3.1

_Example code_:
```
>>> import transformers
>>> from transformers import BertForQuestionAnswering
>>> model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 341/341 [00:00<00:00, 152821.63B/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1340675298/1340675298 [02:04<00:00, 10793596.06B/s]
>>> ...
```

The same correct behavior occurs with TensorFlow 2.0:
```
>>> import transformers
>>> from transformers import TFBertForQuestionAnswering
>>> model = TFBertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1340675298/1340675298 [02:30<00:00, 8890808.45B/s]
>>> ...
```

Now, you can close this issue!

> ##  Bug
> Model I am using (Bert, XLNet....): Bert
> 
> Language I am using the model on (English, Chinese....): English
> 
> The problem arise when using:
> 
> * [ ]  the official example scripts: (give details): BertForQuestionAnswering
> * [ ]  my own modified scripts: (give details)
> 
> The tasks I am working on is:
> 
> * [ ]  an official GLUE/SQUaD task: (give the name): SQuaD
> * [ ]  my own task or dataset: (give details)
> 
> ## To Reproduce
> Steps to reproduce the behavior:
> 
> 1. from pytorch_transformers import BertForQuestionAnswering
> 2. model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
> 
> ## Expected behavior
> ## Environment
> * OS:
> * Python version:
> * PyTorch version:
> * PyTorch Transformers version (or branch):
> * Using GPU ?
> * Distributed of parallel setup ?
> * Any other relevant information:
> 
> ## Additional context

This is usually because of 
- a network error or
- not enough space on the disk

while downloading the file. To make sure it isn't the first, you can try running the `from_pretrained` method with the `force_download` option set to `True`:

```py
model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad', force_download=True)
```

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.