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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>
126 lines
5.1 KiB
ReStructuredText
126 lines
5.1 KiB
ReStructuredText
OpenAI GPT
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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OpenAI GPT model was proposed in `Improving Language Understanding by Generative Pre-Training
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<https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf>`__
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by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. It's a causal (unidirectional)
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transformer pre-trained using language modeling on a large corpus will long range dependencies, the Toronto Book
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Corpus.
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The abstract from the paper is the following:
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*Natural language understanding comprises a wide range of diverse tasks such
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as textual entailment, question answering, semantic similarity assessment, and
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document classification. Although large unlabeled text corpora are abundant,
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labeled data for learning these specific tasks is scarce, making it challenging for
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discriminatively trained models to perform adequately. We demonstrate that large
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gains on these tasks can be realized by generative pre-training of a language model
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on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each
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specific task. In contrast to previous approaches, we make use of task-aware input
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transformations during fine-tuning to achieve effective transfer while requiring
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minimal changes to the model architecture. We demonstrate the effectiveness of
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our approach on a wide range of benchmarks for natural language understanding.
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Our general task-agnostic model outperforms discriminatively trained models that
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use architectures specifically crafted for each task, significantly improving upon the
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state of the art in 9 out of the 12 tasks studied.*
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Tips:
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- GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
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the right rather than the left.
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- GPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
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token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as
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it can be observed in the `run_generation.py` example script.
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`Write With Transformer <https://transformer.huggingface.co/doc/gpt>`__ is a webapp created and hosted by
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Hugging Face showcasing the generative capabilities of several models. GPT is one of them.
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The original code can be found `here <https://github.com/openai/finetune-transformer-lm>`__.
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Note:
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If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install
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``ftfy`` and ``SpaCy``::
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pip install spacy ftfy==4.4.3
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python -m spacy download en
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If you don't install ``ftfy`` and ``SpaCy``, the :class:`~transformers.OpenAIGPTTokenizer` will default to tokenize
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using BERT's :obj:`BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't
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worry).
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OpenAIGPTConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.OpenAIGPTConfig
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:members:
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OpenAIGPTTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.OpenAIGPTTokenizer
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:members: save_vocabulary
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OpenAIGPTTokenizerFast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.OpenAIGPTTokenizerFast
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:members:
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OpenAI specific outputs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_openai.OpenAIGPTDoubleHeadsModelOutput
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:members:
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.. autoclass:: transformers.modeling_tf_openai.TFOpenAIGPTDoubleHeadsModelOutput
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:members:
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OpenAIGPTModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.OpenAIGPTModel
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:members: forward
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OpenAIGPTLMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.OpenAIGPTLMHeadModel
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:members: forward
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OpenAIGPTDoubleHeadsModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.OpenAIGPTDoubleHeadsModel
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:members: forward
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TFOpenAIGPTModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFOpenAIGPTModel
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:members: call
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TFOpenAIGPTLMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFOpenAIGPTLMHeadModel
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:members: call
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TFOpenAIGPTDoubleHeadsModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFOpenAIGPTDoubleHeadsModel
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:members: call
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