Migrate doc files to Markdown. (#24376)
* Rename index.mdx to index.md * With saved modifs * Address review comment * Treat all files * .mdx -> .md * Remove special char * Update utils/tests_fetcher.py Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr> --------- Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
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Sylvain Gugger
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docs/source/en/model_doc/big_bird.md
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<!--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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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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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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- BigBird is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
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the left.
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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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## Documentation resources
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- [Text classification task guide](../tasks/sequence_classification)
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- [Token classification task guide](../tasks/token_classification)
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- [Question answering task guide](../tasks/question_answering)
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- [Causal language modeling task guide](../tasks/language_modeling)
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- [Masked language modeling task guide](../tasks/masked_language_modeling)
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- [Multiple choice task guide](../tasks/multiple_choice)
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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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## FlaxBigBirdForCausalLM
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[[autodoc]] FlaxBigBirdForCausalLM
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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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