Why the 'tokenizer' will return more than one token for single word in GPT2?
I'm trying to use the GPT2 to get the feature of a single word. But i find not every single word will get a single token from the 'tokenizer'.  I don't know why it's like that. 
Here is my code:

```
def test_gpt2():
    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
    model = GPT2Model.from_pretrained('gpt2')
    model.to(torch.device("cuda"))
    # model.feature_extractor._freeze_parameters()
    text = 'irving'#"irving"
    encoded_input = tokenizer(text, return_tensors='pt')
    print(encoded_input)
    encoded_input = encoded_input.to(torch.device("cuda"))
    output = model(**encoded_input)
    print(encoded_input)
   #  print(output.last_hidden_state)
    print(output.last_hidden_state.shape)
```
and the output is:

{'input_ids': tensor([[  81, 1075]]), 'attention_mask': tensor([[1, 1]])}
{'input_ids': tensor([[  81, 1075]], device='cuda:0'), 'attention_mask': tensor([[1, 1]], device='cuda:0')}
torch.Size([1, 2, 768]

It seems like the word 'irving' is mapped to '81' and '1075'

It also happens on some other words.
Please use the [forums](https://discuss.huggingface.co/) to ask questions like this as we keep issues for bugs and feature requests only.

Most Transformer models use subword tokenizers, which mean that one word can be split into several tokens. Here the GPT2 tokenizer splits `"irving"` into `["ir", "ving"]`.<|||||>> Please use the [forums](https://discuss.huggingface.co/) to ask questions like this as we keep issues for bugs and feature requests only.
> 
> Most Transformer models use subword tokenizers, which mean that one word can be split into several tokens. Here the GPT2 tokenizer splits `"irving"` into `["ir", "ving"]`.

I am sry for putting the question in the wrong place....

and thanks for your help, I've got it!