Unexpected output from feature extraction pipeline
Hi everyone,

I'm not sure if it is a bug or if I simply overlooking something so I did not want to submit a bug report yet. I have the following example code:

```
from transformers import pipeline, AutoTokenizer
import numpy as np

tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased',
add_special_tokens=False)
#initialize pipeline
nlp = pipeline('feature-extraction', model='bert-base-uncased', config='bert-base-uncased', tokenizer=tokenizer, device=1)
features = nlp("Why is Howard asking questions about the food after Leonard gives him a carton ?")
features = np.squeeze(features)
print(features.shape)
```

I expect the output: (15,768)
But I receive the output: (18,768)

I think there are only 15 tokens but somehow the shape is 18. What am I missing here?

Is this output expected and am I simply missing something or is there more to it?
The tokenizer adds special tokens (here, specific to BERT) at the beginning and end of the sentence. 

You can check that with:
```python
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
len(tokenizer.encode(TEXT)) == 18
```<|||||>@julien-c is there any way to avoid the special tokens when extracting the features? I expected "add_special_tokens=False" would prevent this from happening?<|||||>I would suggest not using the Pipeline and just doing `tokenizer.encode()` then `outputs = model(input_ids)`