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* Reorganize doc for multilingual support * Fix style * Style * Toc trees * Adapt templates
118 lines
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118 lines
4.3 KiB
Plaintext
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# OpenAI GPT
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## Overview
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OpenAI GPT model was proposed in [Improving Language Understanding by Generative Pre-Training](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) transformer
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pre-trained using language modeling on a large corpus will long range dependencies, the Toronto Book 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 as textual entailment, question answering,
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semantic similarity assessment, and 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 discriminatively trained models to
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perform adequately. We demonstrate that large gains on these tasks can be realized by generative pretraining of a
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language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task. In
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contrast to previous approaches, we make use of task-aware input transformations during fine-tuning to achieve
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effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our
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approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms
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discriminatively trained models that use architectures specifically crafted for each task, significantly improving upon
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the 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 the right rather than
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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 it can be
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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 Hugging Face
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showcasing the generative capabilities of several models. GPT is one of them.
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This model was contributed by [thomwolf](https://huggingface.co/thomwolf). 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 `ftfy`
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and `SpaCy`:
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```bash
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pip install spacy ftfy==4.4.3
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python -m spacy download en
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```
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If you don't install `ftfy` and `SpaCy`, the [`OpenAIGPTTokenizer`] will default to tokenize
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using BERT's `BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
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## OpenAIGPTConfig
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[[autodoc]] OpenAIGPTConfig
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## OpenAIGPTTokenizer
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[[autodoc]] OpenAIGPTTokenizer
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- save_vocabulary
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## OpenAIGPTTokenizerFast
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[[autodoc]] OpenAIGPTTokenizerFast
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## OpenAI specific outputs
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[[autodoc]] models.openai.modeling_openai.OpenAIGPTDoubleHeadsModelOutput
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[[autodoc]] models.openai.modeling_tf_openai.TFOpenAIGPTDoubleHeadsModelOutput
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## OpenAIGPTModel
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[[autodoc]] OpenAIGPTModel
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- forward
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## OpenAIGPTLMHeadModel
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[[autodoc]] OpenAIGPTLMHeadModel
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- forward
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## OpenAIGPTDoubleHeadsModel
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[[autodoc]] OpenAIGPTDoubleHeadsModel
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- forward
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## OpenAIGPTForSequenceClassification
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[[autodoc]] OpenAIGPTForSequenceClassification
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- forward
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## TFOpenAIGPTModel
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[[autodoc]] TFOpenAIGPTModel
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- call
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## TFOpenAIGPTLMHeadModel
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[[autodoc]] TFOpenAIGPTLMHeadModel
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- call
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## TFOpenAIGPTDoubleHeadsModel
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[[autodoc]] TFOpenAIGPTDoubleHeadsModel
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- call
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## TFOpenAIGPTForSequenceClassification
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[[autodoc]] TFOpenAIGPTForSequenceClassification
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- call
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