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* Clean up model documentation * Formatting * Preparation work * Long lines * Main work on rst files * Cleanup all config files * Syntax fix * Clean all tokenizers * Work on first models * Models beginning * FaluBERT * All PyTorch models * All models * Long lines again * Fixes * More fixes * Update docs/source/model_doc/bert.rst Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update docs/source/model_doc/electra.rst Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Last fixes Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
97 lines
4.5 KiB
ReStructuredText
97 lines
4.5 KiB
ReStructuredText
BertGeneration
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using
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:class:`~transformers.EncoderDecoderModel` as proposed in `Leveraging Pre-trained Checkpoints for Sequence Generation
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Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
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The abstract from the paper is the following:
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*Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By
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warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple
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benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language
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Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We
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developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT,
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GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both
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encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation,
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Text Summarization, Sentence Splitting, and Sentence Fusion.*
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Usage:
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- The model can be used in combination with the :class:`~transformers.EncoderDecoderModel` to leverage two pretrained
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BERT checkpoints for subsequent fine-tuning.
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:: code-block
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# leverage checkpoints for Bert2Bert model...
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# use BERT's cls token as BOS token and sep token as EOS token
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encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)
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# add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
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decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
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bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
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# create tokenizer...
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tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")
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input_ids = tokenizer('This is a long article to summarize', add_special_tokens=False, return_tensors="pt").input_ids
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labels = tokenizer('This is a short summary', return_tensors="pt").input_ids
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# train...
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loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels, return_dict=True).loss
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loss.backward()
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- Pretrained :class:`~transformers.EncoderDecoderModel` are also directly available in the model hub, e.g.,
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:: code-block
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# instantiate sentence fusion model
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sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
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tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
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input_ids = tokenizer('This is the first sentence. This is the second sentence.', add_special_tokens=False, return_tensors="pt").input_ids
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outputs = sentence_fuser.generate(input_ids)
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print(tokenizer.decode(outputs[0]))
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Tips:
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- :class:`~transformers.BertGenerationEncoder` and :class:`~transformers.BertGenerationDecoder` should be used in
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combination with :class:`~transformers.EncoderDecoder`.
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- For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input.
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Therefore, no EOS token should be added to the end of the input.
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The original code can be found `here <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__.
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BertGenerationConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertGenerationConfig
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:members:
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BertGenerationTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertGenerationTokenizer
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:members: save_vocabulary
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BertGenerationEncoder
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertGenerationEncoder
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:members: forward
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BertGenerationDecoder
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertGenerationDecoder
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:members: forward
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