Trouble running 'bert-base-multilingual-cased'
## ❓ Questions & Help

Hi, 
I got trouble when I apply Quickstart BERT example to korean news text.

but when I run this code using model 'bert-base-multilingual-cased',

```{.python}
# Predict hidden states features for each layer
with torch.no_grad():
    # See the models docstrings for the detail of the inputs
    outputs = model(tokens_tensor, token_type_ids=segments_tensors)
    # Transformers models always output tuples.
    # See the models docstrings for the detail of all the outputs
    # In our case, the first element is the hidden state of the last layer of the Bert model
    encoded_layers = outputs[0]
# We have encoded our input sequence in a FloatTensor of shape (batch size, sequence length, model hidden dimension)
assert tuple(encoded_layers.shape) == (1, len(indexed_tokens), model.config.hidden_size)
```

 I sometimes got error like below,

```{.python}
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-39-b8e77f8ffc14> in <module>
      2 with torch.no_grad():
      3     # See the models docstrings for the detail of the inputs
----> 4     outputs = model(tokens_tensor, token_type_ids=segments_tensors)
      5     # Transformers models always output tuples.
      6     # See the models docstrings for the detail of all the outputs

//anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--> 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

//anaconda3/lib/python3.7/site-packages/transformers/modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask)
    712             head_mask = [None] * self.config.num_hidden_layers
    713 
--> 714         embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
    715         encoder_outputs = self.encoder(embedding_output,
    716                                        attention_mask=extended_attention_mask,

//anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--> 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

//anaconda3/lib/python3.7/site-packages/transformers/modeling_bert.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds)
    176             inputs_embeds = self.word_embeddings(input_ids)
    177         position_embeddings = self.position_embeddings(position_ids)
--> 178         token_type_embeddings = self.token_type_embeddings(token_type_ids)
    179 
    180         embeddings = inputs_embeds + position_embeddings + token_type_embeddings

//anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--> 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

//anaconda3/lib/python3.7/site-packages/torch/nn/modules/sparse.py in forward(self, input)
    112         return F.embedding(
    113             input, self.weight, self.padding_idx, self.max_norm,
--> 114             self.norm_type, self.scale_grad_by_freq, self.sparse)
    115 
    116     def extra_repr(self):

//anaconda3/lib/python3.7/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
   1482         # remove once script supports set_grad_enabled
   1483         _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1484     return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
   1485 
   1486 

RuntimeError: index out of range: Tried to access index 2 out of table with 1 rows. at ../aten/src/TH/generic/THTensorEvenMoreMath.cpp:418
```

Any help will be greatly appreciated.
Thanks!


Info:
OS: MacOsX 10.14.6 (Mojave)
python : 3.7
PyTorch : 1.3.1




I think the error was because text is greater than 512 tokens.
I got no error when text is smaller than 512 tokens.<|||||>Hi! Indeed the models have a maximum input size, which is 512 for BERT. You should have received a warning when tokenizing your sequence, but unfortunately, there isn't much more we can do to clarify this error further.<|||||>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.
