Variable num_predict in target_mapping for XLNet

Models
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I have a pretraining task for XLNet. One of the inputs for XLNetLMHeadModel is target_mapping that is of the shape (batch_size,num_predict,seq_len).

I want to predict for all tokens in an input sentence, which means num_predict will vary within a batch, for sentences of different length. This leads to error while building a data_loader in PyTorch. Can anyone suggest a workaround for this problem?

Thanks

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I might be wrong here but I would assume that in the language modeling task, num_predict is actually the size of the vocabulary because for each mask you try to predict the highest probability token in the vocab (in MLM). Seq_len is the max length of sequences you want to be able to model. If a sentence is smaller then you just pad it.

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Dear @BramVanroy, thank you for the reply, but num_predict is not the size of vocabulary. It’s the number of predictions to be made for that particular input sentence. seq_len is not the issue because as you pointed out, it can be padded. But I am not sure if the same is true for num_predict, hence this question :slight_smile:

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You are absolutely right.

But if you want to predict all tokens, can’t you just leave target_mapping to the default (None)? From the docs:

If target_mapping is None, then num_predict corresponds to sequence_length.

Or is your point that this mapping does not take into account the different sequence lengths in the batch? If that is the question, I cannot help with that. I do not have enough experience with XLNet. Perhaps someone else can chime in.

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