Pegasus
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**DISCLAIMER:** If you see something strange, file a `Github Issue
<https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__
and assign @sshleifer.


Overview
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The Pegasus model was proposed in `PEGASUS: Pre-training with Extracted Gap-sentences for
Abstractive Summarization <https://arxiv.org/pdf/1912.08777.pdf>`__ by Jingqing Zhang, Yao Zhao, Mohammad Saleh and
Peter J. Liu on Dec 18, 2019.

According to the abstract,

- Pegasus' pretraining task is intentionally similar to summarization: important sentences are removed/masked from an
  input document and are generated together as one output sequence from the remaining sentences, similar to an
  extractive summary.
- Pegasus achieves SOTA summarization performance on all 12 downstream tasks, as measured by ROUGE and human eval.

The Authors' code can be found `here <https://github.com/google-research/pegasus>`__.


Checkpoints
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All the `checkpoints <https://huggingface.co/models?search=pegasus>`__ are fine-tuned for summarization, besides 
`pegasus-large`, whence the other checkpoints are fine-tuned:

- Each checkpoint is 2.2 GB on disk and 568M parameters.
- FP16 is not supported (help/ideas on this appreciated!).
- Summarizing xsum in fp32 takes about 400ms/sample, with default parameters on a v100 GPU.
- For XSUM, The paper reports rouge1,rouge2, rougeL of paper: 47.21/24.56/39.25. As of Aug 9, this port scores
  46.91/24.34/39.1.

The gap is likely because of different alpha/length_penalty implementations in beam search.


Implementation Notes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- All models are transformer encoder-decoders with 16 layers in each component.
- The implementation is completely inherited from :class:`~transformers.BartForConditionalGeneration`
- Some key configuration differences:
    - static, sinusoidal position embeddings
    - no :obj:`layernorm_embedding` (:obj`PegasusConfig.normalize_embedding=False`)
    - the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix.
    - more beams are used (:obj:`num_beams=8`)
- All pretrained pegasus checkpoints are the same besides three attributes: :obj:`tokenizer.model_max_length` (maximum
  input size), :obj:`max_length` (the maximum number of tokens to generate) and :obj:`length_penalty`.
- The code to convert checkpoints trained in the author's `repo <https://github.com/google-research/pegasus>`_ can be
  found in ``convert_pegasus_tf_to_pytorch.py``.


Usage Example
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.. code-block:: python

    from transformers import PegasusForConditionalGeneration, PegasusTokenizer
    import torch
    src_text = [
        """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."""
    ]

    model_name = 'google/pegasus-xsum'
    torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
    tokenizer = PegasusTokenizer.from_pretrained(model_name)
    model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
    batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)
    translated = model.generate(**batch)
    tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
    assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers."



PegasusConfig
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.. autoclass:: transformers.PegasusConfig


PegasusTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
warning: ``add_tokens`` does not work at the moment.

.. autoclass:: transformers.PegasusTokenizer
    :members: __call__, prepare_seq2seq_batch


PegasusForConditionalGeneration
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.. autoclass:: transformers.PegasusForConditionalGeneration
