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* Start plumbing * Marian close * Small stubs for all children * Fixed bart * marian working * pegasus test is good, but failing * Checkin tests * More model files * Subtle marian, pegasus integration test failures * Works well * rm print * boom boom * Still failing model2doc * merge master * Equivalence test failing, all others fixed * cleanup * Fix embed_scale * Cleanup marian pipeline test * Undo extra changes * Smaller delta * Cleanup model testers * undo delta * fix tests import structure * cross test decorator * Cleaner set_weights * Respect authorized_unexpected_keys * No warnings * No warnings * style * Nest tf import * black * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * functional dropout * fixup * Fixup * style_doc * embs * shape list * delete slow force_token_id_to_be_generated func * fixup Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
104 lines
4.9 KiB
ReStructuredText
104 lines
4.9 KiB
ReStructuredText
Pegasus
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**DISCLAIMER:** If you see something strange, file a `Github Issue
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<https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__
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and assign @sshleifer.
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The Pegasus model was proposed in `PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
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<https://arxiv.org/pdf/1912.08777.pdf>`__ by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
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According to the abstract,
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- Pegasus' pretraining task is intentionally similar to summarization: important sentences are removed/masked from an
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input document and are generated together as one output sequence from the remaining sentences, similar to an
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extractive summary.
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- Pegasus achieves SOTA summarization performance on all 12 downstream tasks, as measured by ROUGE and human eval.
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The Authors' code can be found `here <https://github.com/google-research/pegasus>`__.
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Checkpoints
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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All the `checkpoints <https://huggingface.co/models?search=pegasus>`__ are fine-tuned for summarization, besides
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`pegasus-large`, whence the other checkpoints are fine-tuned:
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- Each checkpoint is 2.2 GB on disk and 568M parameters.
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- FP16 is not supported (help/ideas on this appreciated!).
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- Summarizing xsum in fp32 takes about 400ms/sample, with default parameters on a v100 GPU.
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- For XSUM, The paper reports rouge1,rouge2, rougeL of paper: 47.21/24.56/39.25. As of Aug 9, this port scores
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46.91/24.34/39.1.
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The gap is likely because of different alpha/length_penalty implementations in beam search.
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Implementation Notes
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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- All models are transformer encoder-decoders with 16 layers in each component.
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- The implementation is completely inherited from :class:`~transformers.BartForConditionalGeneration`
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- Some key configuration differences:
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- static, sinusoidal position embeddings
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- no :obj:`layernorm_embedding` (:obj`PegasusConfig.normalize_embedding=False`)
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- the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix.
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- more beams are used (:obj:`num_beams=8`)
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- All pretrained pegasus checkpoints are the same besides three attributes: :obj:`tokenizer.model_max_length` (maximum
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input size), :obj:`max_length` (the maximum number of tokens to generate) and :obj:`length_penalty`.
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- The code to convert checkpoints trained in the author's `repo <https://github.com/google-research/pegasus>`_ can be
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found in ``convert_pegasus_tf_to_pytorch.py``.
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Usage Example
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. code-block:: python
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from transformers import PegasusForConditionalGeneration, PegasusTokenizer
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import torch
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src_text = [
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""" 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."""
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]
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model_name = 'google/pegasus-xsum'
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torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
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tokenizer = PegasusTokenizer.from_pretrained(model_name)
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model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
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batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)
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translated = model.generate(**batch)
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tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
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assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers."
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PegasusConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.PegasusConfig
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PegasusTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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warning: ``add_tokens`` does not work at the moment.
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.. autoclass:: transformers.PegasusTokenizer
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:members: __call__, prepare_seq2seq_batch
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PegasusForConditionalGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.PegasusForConditionalGeneration
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TFPegasusForConditionalGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFPegasusForConditionalGeneration
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