Some question about training BERT after change the Vocab.txt size
The original bert-base-chinese-vocab.txt size is 21128.
I use my own vocab.txt size is 44900
When I try to train Fine-tune BERT Model using BertForMaskedLM it have some promble about size mismatch.
I have try to change the BertEmbeddings its self.word_embeddings config.vocab_size to 44900 like this
`self.word_embeddings = nn.Embedding(44900, config.hidden_size, padding_idx=0) `
But it still have problem like this

```
RuntimeError: Error(s) in loading state_dict for BertForMaskedLM:
        size mismatch for bert.embeddings.word_embeddings.weight: copying a param with shape torch.Size([21128, 768]) from checkpoint, the shape in current model is torch.Size([44900, 768]).
        size mismatch for cls.predictions.bias: copying a param with shape torch.Size([21128]) from checkpoint, the shape in current model is torch.Size([44900]).
        size mismatch for cls.predictions.decoder.weight: copying a param with shape torch.Size([21128, 768]) from checkpoint, the shape in current model is torch.Size([44900, 768]).
```
I do not sure how to fix it.
I have think about if I need to change the pre trained BERT config.json file? Its vocab_size from 21128 to 44900

You can't really change the vocabulary without re-training the whole model. Is there some overlap between the BERT vocabulary and your custom vocabulary? If so, you can add the 20k+ tokens using `add_tokens` (which will probably slow down things, as that's a lot of added tokens).<|||||>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.
<|||||>you can now do this. Keras (tf-nightly version) has added a new util `keras.utils.warmstart_embedding_matrix`. Using this you can continuously train your model with changing vocabulary. https://www.tensorflow.org/api_docs/python/tf/keras/utils/warmstart_embedding_matrix
