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* Map model_type and doc pages names * Add script * Fix typo * Quality * Manual check for Auto Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
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147 lines
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Plaintext
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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# BigBird
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## Overview
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The BigBird model was proposed in [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by
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Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
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Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention
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based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse
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attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it
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has been shown that applying sparse, global, and random attention approximates full attention, while being
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computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context,
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BigBird has shown improved performance on various long document NLP tasks, such as question answering and
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summarization, compared to BERT or RoBERTa.
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The abstract from the paper is the following:
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*Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP.
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Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence
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length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that
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reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
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is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
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theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
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sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
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8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context,
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BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also
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propose novel applications to genomics data.*
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Tips:
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- For an in-detail explanation on how BigBird's attention works, see [this blog post](https://huggingface.co/blog/big-bird).
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- BigBird comes with 2 implementations: **original_full** & **block_sparse**. For the sequence length < 1024, using
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**original_full** is advised as there is no benefit in using **block_sparse** attention.
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- The code currently uses window size of 3 blocks and 2 global blocks.
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- Sequence length must be divisible by block size.
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- Current implementation supports only **ITC**.
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- Current implementation doesn't support **num_random_blocks = 0**
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This model was contributed by [vasudevgupta](https://huggingface.co/vasudevgupta). The original code can be found
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[here](https://github.com/google-research/bigbird).
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## BigBirdConfig
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[[autodoc]] BigBirdConfig
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## BigBirdTokenizer
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[[autodoc]] BigBirdTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## BigBirdTokenizerFast
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[[autodoc]] BigBirdTokenizerFast
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## BigBird specific outputs
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[[autodoc]] models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput
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## BigBirdModel
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[[autodoc]] BigBirdModel
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- forward
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## BigBirdForPreTraining
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[[autodoc]] BigBirdForPreTraining
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- forward
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## BigBirdForCausalLM
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[[autodoc]] BigBirdForCausalLM
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- forward
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## BigBirdForMaskedLM
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[[autodoc]] BigBirdForMaskedLM
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- forward
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## BigBirdForSequenceClassification
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[[autodoc]] BigBirdForSequenceClassification
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- forward
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## BigBirdForMultipleChoice
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[[autodoc]] BigBirdForMultipleChoice
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- forward
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## BigBirdForTokenClassification
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[[autodoc]] BigBirdForTokenClassification
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- forward
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## BigBirdForQuestionAnswering
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[[autodoc]] BigBirdForQuestionAnswering
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- forward
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## FlaxBigBirdModel
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[[autodoc]] FlaxBigBirdModel
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- __call__
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## FlaxBigBirdForPreTraining
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[[autodoc]] FlaxBigBirdForPreTraining
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- __call__
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## FlaxBigBirdForMaskedLM
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[[autodoc]] FlaxBigBirdForMaskedLM
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- __call__
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## FlaxBigBirdForSequenceClassification
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[[autodoc]] FlaxBigBirdForSequenceClassification
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- __call__
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## FlaxBigBirdForMultipleChoice
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[[autodoc]] FlaxBigBirdForMultipleChoice
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- __call__
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## FlaxBigBirdForTokenClassification
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[[autodoc]] FlaxBigBirdForTokenClassification
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- __call__
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## FlaxBigBirdForQuestionAnswering
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[[autodoc]] FlaxBigBirdForQuestionAnswering
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- __call__
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