Special tokens / XLNet
it is necessary to add [CLS], [SEP] tokens in case of XLNet transformers? 

Thanks!


*I used only the tokenizer.encode() function even if the sample had several sentences and I didn't set any special tokens. I think it was not the right way, isn't? It was done for a classification task. 
Hi, in the case of sequence classification, XLNet does indeed use special tokens. For sentence pairs, it looks like this:

```
A [SEP] B [SEP][CLS]
```

You can either create those yourself or use the flag `add_special_tokens` from the `encode` function as follows:

```
tokenizer.encode(a, b, add_special_tokens=True)
```

which will return the correct list of tokens according to the tokenizer you used (which should be the `XLNetTokenizer` in your case)<|||||>@LysandreJik how to deal with more than two sentences? In the same way?<|||||>I'm not a dev of this lib but just stumbling upon this whilst searching from something else so I'll reply ;)
I think for more than 2 sentences you can use A [SEP] B [SEP] C [SEP] [CLS] for the encoding, and then specify token_type_ids as explained [there](https://github.com/huggingface/pytorch-transformers/blob/32e1332acf6fd1ad372b81c296d43be441d3b0b1/pytorch_transformers/modeling_xlnet.py#L505) to tell the model which token belongs to which segment. <|||||>regarding token_type_ids:

@LysandreJik wrote here about two sentences, 
 
https://github.com/huggingface/pytorch-transformers/issues/1208#issuecomment-528515647

> If I recall correctly the XLNet model has 0 for the first sequence token_type_ids, 1 for the second sequence, and 2 for the last (cls) token.

what is to do for the third, fourth, fifth ... sentences ? 0 and 1 alternating?<|||||>I think you can put 0 for first sentence, 1 for second, 2 for third etc..
but the actual indices do not matter because the encoding is relative (see
XLNet paper section 2.5), the only important thing is that tokens from a
same sentence have the same token_type_ids. XLnet was made this way in
order to handle an arbitrary number of sentences at finetuning. At least
that is the way I understand it.

Le ven. 13 sept. 2019 à 15:55, cherepanovic <notifications@github.com> a
écrit :

> regarding token_type_ids:
>
> @LysandreJik <https://github.com/LysandreJik> wrote here about two
> sentences,
>
> #1208 (comment)
> <https://github.com/huggingface/pytorch-transformers/issues/1208#issuecomment-528515647>
>
> If I recall correctly the XLNet model has 0 for the first sequence
> token_type_ids, 1 for the second sequence, and 2 for the last (cls) token.
>
> what is to do for the third, fourth, fifth ... sentences ?
>
> —
> You are receiving this because you commented.
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/pytorch-transformers/issues/1242?email_source=notifications&email_token=AD6A5ZI5KDS2BLIQLBJB5QLQJOLVJA5CNFSM4IVKBXC2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOD6VCZMA#issuecomment-531246256>,
> or mute the thread
> <https://github.com/notifications/unsubscribe-auth/AD6A5ZNAFKI6SQPLVVSKYLLQJOLVJANCNFSM4IVKBXCQ>
> .
>
<|||||>> Hi, in the case of sequence classification, XLNet does indeed use special tokens. For sentence pairs, it looks like this:

@LysandreJik 
you are speaking about sentence pairs, what is to do with several sentences, could you please give an advice

thanks a lot<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>> > Hi, in the case of sequence classification, XLNet does indeed use special tokens. For sentence pairs, it looks like this:
> 
> @LysandreJik
> you are speaking about sentence pairs, what is to do with several sentences, could you please give an advice
> 
> thanks a lot

so have you got the answer about  how to deal with more than two sentences?<|||||>@sloth2012 

 >>so have you got the answer about how to deal with more than two sentences?

no, but I did it in this way [SEP] A.B.C [CLS], in this way