Reduce the CamemBERT dimensions
I want to reduce the output dimension by adding a linear layer at the end of the camem model.

code :
```python
from transformers import CamembertTokenizer, CamembertModel
import torch
from torch.nn import Sequential, Linear

tokenizer = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertModel.from_pretrained('camembert-base')
input_ids = torch.tensor(tokenizer.encode("La pose d'un panneau stop.", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
# labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0)  # Batch size 1
model = Sequential(model, Linear(768, 256))

outputs = model(input_ids)

print(input_ids)
print(outputs[1].size())
print(outputs[0].size())
```
I got this : 

```shell
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in linear(input, weight, bias)
   1366         - Output: :math:`(N, *, out\_features)`
   1367     """
-> 1368     if input.dim() == 2 and bias is not None:
   1369         # fused op is marginally faster
   1370         ret = torch.addmm(bias, input, weight.t())

AttributeError: 'tuple' object has no attribute 'dim'
```

Additionally, I want to do a word-level embedding, however, the 768 dimensions is too big from my point.

Thanks for your helps.

Hi @AlafateABULIMITI sounds like a question that's better suited for Stack Overflow. Thanks!