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ReStructuredText
27 lines
1.5 KiB
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BERTology
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There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT
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(that some call "BERTology"). Some good examples of this field are:
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* BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick:
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https://arxiv.org/abs/1905.05950
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* Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
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* What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D.
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Manning: https://arxiv.org/abs/1906.04341
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In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to
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help people access the inner representations, mainly adapted from the great work of Paul Michel
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(https://arxiv.org/abs/1905.10650):
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* accessing all the hidden-states of BERT/GPT/GPT-2,
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* accessing all the attention weights for each head of BERT/GPT/GPT-2,
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* retrieving heads output values and gradients to be able to compute head importance score and prune head as explained
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in https://arxiv.org/abs/1905.10650.
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To help you understand and use these features, we have added a specific example script: `bertology.py
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<https://github.com/huggingface/transformers/blob/master/examples/bertology/run_bertology.py>`_ while extract
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information and prune a model pre-trained on GLUE.
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