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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>
40 lines
2.6 KiB
ReStructuredText
40 lines
2.6 KiB
ReStructuredText
DialoGPT
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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DialoGPT was proposed in
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`DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`_
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by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
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It's a GPT2 Model trained on 147M conversation-like exchanges extracted from Reddit.
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The abstract from the paper is the following:
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*We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
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Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings.
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We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems.
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The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.*
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Tips:
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- DialoGPT 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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- DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful at response generation in open-domain dialogue systems.
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- DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on `DialoGPT's model card <https://huggingface.co/microsoft/DialoGPT-medium>`_.
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Training:
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In order to train or fine-tune DialoGPT, one can use causal language modeling training.
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To cite the official paper:
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*We follow the OpenAI GPT-2 to model a multiturn dialogue session
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as a long text and frame the generation task as language modeling. We first
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concatenate all dialog turns within a dialogue session into a long text
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x_1,..., x_N (N is the sequence length), ended by the end-of-text token.*
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For more information please confer to the original paper.
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DialoGPT's architecture is based on the GPT2 model, so one can refer to GPT2's `docstring <https://huggingface.co/transformers/model_doc/gpt2.html>`_.
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The original code can be found `here <https://github.com/microsoft/DialoGPT>`_.
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