Input length is not equal to output length?
## ❓ Questions & Help

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Hi @RichardHWD, I'm afraid we'll need a bit more information than what you have given.<|||||>@LysandreJik  Sorry. In your example:
```
import torch
from transformers import *

model_class = BertModel
tokenizer_class = BertTokenizer
pretrained_weights = 'bert-base-uncased'

tokenizer = tokenizer_class.from_pretrained(pretrained_weights)
model = model_class.from_pretrained(pretrained_weights)

 # Encode text
input_ids = torch.tensor([tokenizer.encode("Here is some text to encode", add_special_tokens=False)])
with torch.no_grad():
    last_hidden_states = model(input_ids)[0]  # Models outputs are now tuples
    print(last_hidden_states.size())
    print(last_hidden_states)
```

I set add_special_tokens=False, and sentence "Here is some text to encode" has 6 words. But the output size is [1, 7, 768]. I want an equal length embedding, how to fix it?<|||||>What’s the shape of input_ids?<|||||>(I suspect it's gonna be 7, you should look into what BertTokenizer does. Thanks!). 