Implement >512 max_seq_len for Google BERT (pytorch-pretrained-bert) for long articles

Problem description: I want to use this multi-label classifier for Google BERT: https://medium.com/huggingface/multi-label-text-classification-using-bert-the-mighty-transformer-69714fa3fb3d

However, by default, when Google BERT converts a document to features, it has a max sequence length of up to 512 WordPiece tokens. It will truncate text from articles which are longer than that.

The SQuAD classifier for BERT actually implements a sliding window solution for longer articles

I tried to splice it into the multi-label classifier but didnt get it right


Deliverable: I want a solution to this problem of ingesting long articles (>512 wordpiece tokens) into Google BERT with code in a Jupyter notebook. So perhaps the article is 1024 words long, using the doc_stride solution, it would perhaaps be ingested as 2x512 sequences, then the classification will be done across both of the articles and the arg_max of the predictions is provided.

Comments and documentation of how you created the solution would also be appreciated.
About the recuiter
Member since Mar 14, 2020
Beypeople
from Assam, India

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