Error(s) in loading state_dict for BertForTokenClassification
### Who can help
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## Information

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

The problem arises when using:
* [ ] the official example scripts: (give details below)
* my own modified scripts: (give details below):

I am trying to run the following script
```
from transformers import BertTokenizer, BertConfig
from transformers import BertForTokenClassification, AdamW
import torch
import argparse
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
parser = argparse.ArgumentParser(description='BERT Keyword Extractor')
parser.add_argument('--sentence', type=str, default=' ',
                    help='sentence to get keywords')
parser.add_argument('--path', type=str, default='model.pt',
                    help='path to load model')
args = parser.parse_args()
tag2idx = {'B': 0, 'I': 1, 'O': 2}
tags_vals = ['B', 'I', 'O']
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
model = BertForTokenClassification.from_pretrained("bert-base-uncased", num_labels=len(tag2idx))

def keywordextract(sentence, path):
    text = sentence
    tkns = tokenizer.tokenize(text)
    indexed_tokens = tokenizer.convert_tokens_to_ids(tkns)
    segments_ids = [0] * len(tkns)
    tokens_tensor = torch.tensor([indexed_tokens]).to(device)
    segments_tensors = torch.tensor([segments_ids]).to(device)
    model.load_state_dict(torch.load(path))
    model.eval()
    prediction = []
    logit = model(tokens_tensor, token_type_ids=None,
                                  attention_mask=segments_tensors)
    logit = logit.detach().cpu().numpy()
    prediction.extend([list(p) for p in np.argmax(logit, axis=2)])
    for k, j in enumerate(prediction[0]):
        if j==1 or j==0:
            print(tokenizer.convert_ids_to_tokens(tokens_tensor[0].to('cpu').numpy())[k], j)
keywordextract(args.sentence, args.path)
```
The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* my own task or dataset: (give details below)
SemEval2017 task 10

## To reproduce

Steps to reproduce the behavior:

1. run this script with any string ("Huggingface is a blessing for transfer learning")


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The error is 
```
Traceback (most recent call last):
  File "keyword-extractor.py", line 40, in <module>
    keywordextract(args.sentence, args.path)
  File "keyword-extractor.py", line 28, in keywordextract
    model.load_state_dict(torch.load(path))
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 1045, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for BertForTokenClassification:
	size mismatch for classifier.weight: copying a param with shape torch.Size([2, 768]) from checkpoint, the shape in current model is torch.Size([3, 768]).
	size mismatch for classifier.bias: copying a param with shape torch.Size([2]) from checkpoint, the shape in current model is torch.Size([3]).
```
If I put in a model directly downloaded from huggingface archive, the error changes to
```
Traceback (most recent call last):
  File "keyword-extractor.py", line 40, in <module>
    keywordextract(args.sentence, args.path)
  File "keyword-extractor.py", line 28, in keywordextract
    model.load_state_dict(torch.load(path))
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 1045, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for BertForTokenClassification:
	Missing key(s) in state_dict: "bert.embeddings.position_ids", "bert.embeddings.LayerNorm.weight", "bert.embeddings.LayerNorm.bias", "bert.encoder.layer.0.attention.output.LayerNorm.weight", "bert.encoder.layer.0.attention.output.LayerNorm.bias", "bert.encoder.layer.0.output.LayerNorm.weight", "bert.encoder.layer.0.output.LayerNorm.bias", "bert.encoder.layer.1.attention.output.LayerNorm.weight", "bert.encoder.layer.1.attention.output.LayerNorm.bias", "bert.encoder.layer.1.output.LayerNorm.weight", "bert.encoder.layer.1.output.LayerNorm.bias", "bert.encoder.layer.2.attention.output.LayerNorm.weight", "bert.encoder.layer.2.attention.output.LayerNorm.bias", "bert.encoder.layer.2.output.LayerNorm.weight", "bert.encoder.layer.2.output.LayerNorm.bias", "bert.encoder.layer.3.attention.output.LayerNorm.weight", "bert.encoder.layer.3.attention.output.LayerNorm.bias", "bert.encoder.layer.3.output.LayerNorm.weight", "bert.encoder.layer.3.output.LayerNorm.bias", "bert.encoder.layer.4.attention.output.LayerNorm.weight", "bert.encoder.layer.4.attention.output.LayerNorm.bias", "bert.encoder.layer.4.output.LayerNorm.weight", "bert.encoder.layer.4.output.LayerNorm.bias", "bert.encoder.layer.5.attention.output.LayerNorm.weight", "bert.encoder.layer.5.attention.output.LayerNorm.bias", "bert.encoder.layer.5.output.LayerNorm.weight", "bert.encoder.layer.5.output.LayerNorm.bias", "bert.encoder.layer.6.attention.output.LayerNorm.weight", "bert.encoder.layer.6.attention.output.LayerNorm.bias", "bert.encoder.layer.6.output.LayerNorm.weight", "bert.encoder.layer.6.output.LayerNorm.bias", "bert.encoder.layer.7.attention.output.LayerNorm.weight", "bert.encoder.layer.7.attention.output.LayerNorm.bias", "bert.encoder.layer.7.output.LayerNorm.weight", "bert.encoder.layer.7.output.LayerNorm.bias", "bert.encoder.layer.8.attention.output.LayerNorm.weight", "bert.encoder.layer.8.attention.output.LayerNorm.bias", "bert.encoder.layer.8.output.LayerNorm.weight", "bert.encoder.layer.8.output.LayerNorm.bias", "bert.encoder.layer.9.attention.output.LayerNorm.weight", "bert.encoder.layer.9.attention.output.LayerNorm.bias", "bert.encoder.layer.9.output.LayerNorm.weight", "bert.encoder.layer.9.output.LayerNorm.bias", "bert.encoder.layer.10.attention.output.LayerNorm.weight", "bert.encoder.layer.10.attention.output.LayerNorm.bias", "bert.encoder.layer.10.output.LayerNorm.weight", "bert.encoder.layer.10.output.LayerNorm.bias", "bert.encoder.layer.11.attention.output.LayerNorm.weight", "bert.encoder.layer.11.attention.output.LayerNorm.bias", "bert.encoder.layer.11.output.LayerNorm.weight", "bert.encoder.layer.11.output.LayerNorm.bias", "classifier.weight", "classifier.bias". 
	Unexpected key(s) in state_dict: "cls.predictions.bias", "cls.predictions.transform.dense.weight", "cls.predictions.transform.dense.bias", "cls.predictions.transform.LayerNorm.gamma", "cls.predictions.transform.LayerNorm.beta", "cls.predictions.decoder.weight", "cls.seq_relationship.weight", "cls.seq_relationship.bias", "bert.pooler.dense.weight", "bert.pooler.dense.bias", "bert.embeddings.LayerNorm.gamma", "bert.embeddings.LayerNorm.beta", "bert.encoder.layer.0.attention.output.LayerNorm.gamma", "bert.encoder.layer.0.attention.output.LayerNorm.beta", "bert.encoder.layer.0.output.LayerNorm.gamma", "bert.encoder.layer.0.output.LayerNorm.beta", "bert.encoder.layer.1.attention.output.LayerNorm.gamma", "bert.encoder.layer.1.attention.output.LayerNorm.beta", "bert.encoder.layer.1.output.LayerNorm.gamma", "bert.encoder.layer.1.output.LayerNorm.beta", "bert.encoder.layer.2.attention.output.LayerNorm.gamma", "bert.encoder.layer.2.attention.output.LayerNorm.beta", "bert.encoder.layer.2.output.LayerNorm.gamma", "bert.encoder.layer.2.output.LayerNorm.beta", "bert.encoder.layer.3.attention.output.LayerNorm.gamma", "bert.encoder.layer.3.attention.output.LayerNorm.beta", "bert.encoder.layer.3.output.LayerNorm.gamma", "bert.encoder.layer.3.output.LayerNorm.beta", "bert.encoder.layer.4.attention.output.LayerNorm.gamma", "bert.encoder.layer.4.attention.output.LayerNorm.beta", "bert.encoder.layer.4.output.LayerNorm.gamma", "bert.encoder.layer.4.output.LayerNorm.beta", "bert.encoder.layer.5.attention.output.LayerNorm.gamma", "bert.encoder.layer.5.attention.output.LayerNorm.beta", "bert.encoder.layer.5.output.LayerNorm.gamma", "bert.encoder.layer.5.output.LayerNorm.beta", "bert.encoder.layer.6.attention.output.LayerNorm.gamma", "bert.encoder.layer.6.attention.output.LayerNorm.beta", "bert.encoder.layer.6.output.LayerNorm.gamma", "bert.encoder.layer.6.output.LayerNorm.beta", "bert.encoder.layer.7.attention.output.LayerNorm.gamma", "bert.encoder.layer.7.attention.output.LayerNorm.beta", "bert.encoder.layer.7.output.LayerNorm.gamma", "bert.encoder.layer.7.output.LayerNorm.beta", "bert.encoder.layer.8.attention.output.LayerNorm.gamma", "bert.encoder.layer.8.attention.output.LayerNorm.beta", "bert.encoder.layer.8.output.LayerNorm.gamma", "bert.encoder.layer.8.output.LayerNorm.beta", "bert.encoder.layer.9.attention.output.LayerNorm.gamma", "bert.encoder.layer.9.attention.output.LayerNorm.beta", "bert.encoder.layer.9.output.LayerNorm.gamma", "bert.encoder.layer.9.output.LayerNorm.beta", "bert.encoder.layer.10.attention.output.LayerNorm.gamma", "bert.encoder.layer.10.attention.output.LayerNorm.beta", "bert.encoder.layer.10.output.LayerNorm.gamma", "bert.encoder.layer.10.output.LayerNorm.beta", "bert.encoder.layer.11.attention.output.LayerNorm.gamma", "bert.encoder.layer.11.attention.output.LayerNorm.beta", "bert.encoder.layer.11.output.LayerNorm.gamma", "bert.encoder.layer.11.output.LayerNorm.beta". 


```
## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->
To extract the keywords from a string
Hello, I would say that there's a mismatch between the number of labels you've provided and the number of labels supported by your `model.pt`.

How did you obtain your `model.pt`?<|||||>I downloaded it directly from the links provided for each bert models with `!wget`<|||||>Which links? Why did you switch from using the `from_pretrained` utility to the `load_state_dict` torch util?<|||||>I had to use the same model for two tasks. Since the downloaded model using `.from_pretrained` method go to root/ directory in colab, they aren't directly accessible (I actually opened an issue about that as well [here](https://github.com/huggingface/transformers/issues/7847)). Thus, to use the model for both tasks, I downloaded it manually in a model/ directory using `!wget https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json`<|||||>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.
<|||||>I had the case when we tried to provide our own tokenizer with vocab.txt file.
The mistake we did was that we update the config.json file and set vocab_size as the total line number in the new vocab.txt file.

This was the wrong step we took. config.json has to be the one exactly for model.pt we want to use. If the model.pt is from the hub, then no need to change the config.json.
