How can I add a CNN layer on top of bert model?
### Information
I'm working on a **binary classification task** and used **BERT** model from transformers library to do it using the custom model below:

```python
class BERT(nn.Module):
  def __init__(self):
    super(BERT, self).__init__()
    self.bert = BertModel.from_pretrained(BERT_PATH, return_dict=False)
    self.dropout = nn.Dropout(0.2)
    self.out = nn.Linear(768, 1)

  def forward(self, ids, mask, token_type_ids):
    outputs = self.bert(ids, attention_mask=mask,token_type_ids=token_type_ids)
    
    # Use the pooled output
    output = self.dropout(outputs[1]) 
    return self.out(output)
```
### What I'm looking for?
Now I'm looking to use a `CNN` layer on top of `BERT` with the following configurations to see how my model will perform:

```
self.cnn = nn.Sequential(
        nn.Conv2d(? ? ?),
        nn.ReLU(),
        nn.MaxPool2d(? ? ?)
    )
```

### The problem encountered.
I have already tried but encountered errors regarding setting the dimensions. In your opinion what configuration should I put in the sequential model to avoid the problem of adjusting the dimensions? If you can **copy-paste** my code and offer me the final custom model with the right **Sequential model included**, I will be thankful.
Hi,

please ask this question on the [forum](https://discuss.huggingface.co/). We like to keep Github issues for bugs/feature requests.

Thanks. <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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