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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

docs/source/en/model_doc/bigbird_pegasus.md

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+
+# BigBirdPegasus
+
+## Overview
+
+The BigBird model was proposed in [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by
+Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
+Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention
+based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse
+attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it
+has been shown that applying sparse, global, and random attention approximates full attention, while being
+computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context,
+BigBird has shown improved performance on various long document NLP tasks, such as question answering and
+summarization, compared to BERT or RoBERTa.
+
+The abstract from the paper is the following:
+
+*Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP.
+Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence
+length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that
+reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
+is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
+theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
+sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
+8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context,
+BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also
+propose novel applications to genomics data.*
+
+Tips:
+
+- For an in-detail explanation on how BigBird's attention works, see [this blog post](https://huggingface.co/blog/big-bird).
+- BigBird comes with 2 implementations: **original_full** & **block_sparse**. For the sequence length < 1024, using
+  **original_full** is advised as there is no benefit in using **block_sparse** attention.
+- The code currently uses window size of 3 blocks and 2 global blocks.
+- Sequence length must be divisible by block size.
+- Current implementation supports only **ITC**.
+- Current implementation doesn't support **num_random_blocks = 0**.
+- BigBirdPegasus uses the [PegasusTokenizer](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pegasus/tokenization_pegasus.py).
+- BigBird is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
+  the left.
+
+The original code can be found [here](https://github.com/google-research/bigbird).
+
+## Documentation resources
+
+- [Text classification task guide](../tasks/sequence_classification)
+- [Question answering task guide](../tasks/question_answering)
+- [Causal language modeling task guide](../tasks/language_modeling)
+- [Translation task guide](../tasks/translation)
+- [Summarization task guide](../tasks/summarization)
+
+## BigBirdPegasusConfig
+
+[[autodoc]] BigBirdPegasusConfig
+    - all
+
+## BigBirdPegasusModel
+
+[[autodoc]] BigBirdPegasusModel
+    - forward
+
+## BigBirdPegasusForConditionalGeneration
+
+[[autodoc]] BigBirdPegasusForConditionalGeneration
+    - forward
+
+## BigBirdPegasusForSequenceClassification
+
+[[autodoc]] BigBirdPegasusForSequenceClassification
+    - forward
+
+## BigBirdPegasusForQuestionAnswering
+
+[[autodoc]] BigBirdPegasusForQuestionAnswering
+    - forward
+
+## BigBirdPegasusForCausalLM
+
+[[autodoc]] BigBirdPegasusForCausalLM
+    - forward