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* tests: add integration tests for new Bort model
* bort: add conversion script from Gluonnlp to Transformers 🚀
* bort: minor cleanup (BORT -> Bort)
* add docs
* make fix-copies
* clean doc a bit
* correct docs
* Update docs/source/model_doc/bort.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update docs/source/model_doc/bort.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* correct dialogpt doc
* correct link
* Update docs/source/model_doc/bort.rst
* Update docs/source/model_doc/dialogpt.rst
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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54 lines
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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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the License. You may obtain a copy of the License at
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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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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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DialoGPT
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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DialoGPT was proposed in `DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
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<https://arxiv.org/abs/1911.00536>`_ by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao,
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Jianfeng Gao, Jingjing Liu, Bill Dolan. It's a GPT2 Model trained on 147M conversation-like exchanges extracted from
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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
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transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning
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from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human
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both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems
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that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline
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systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response
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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 the right rather
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than the left.
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- DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful
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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
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<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. To cite the official paper: *We
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follow the OpenAI GPT-2 to model a multiturn dialogue session as a long text and frame the generation task as language
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modeling. We first concatenate all dialog turns within a dialogue session into a long text x_1,..., x_N (N is the
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sequence length), ended by the end-of-text token.* 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 :doc:`GPT2's documentation page <gpt2>`.
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The original code can be found `here <https://github.com/microsoft/DialoGPT>`_.
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