SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Doc styling (#8067)

Browse Source 
* Important files

* Styling them all

* Revert "Styling them all"

This reverts commit 7d029395fdae8513b8281cbc2a6c239f8093503e.

* Syling them for realsies

* Fix syntax error

* Fix benchmark_utils

* More fixes

* Fix modeling auto and script

* Remove new line

* Fixes

* More fixes

* Fix more files

* Style

* Add FSMT

* More fixes

* More fixes

* More fixes

* More fixes

* Fixes

* More fixes

* More fixes

* Last fixes

* Make sphinx happy
This commit is contained in:
Sylvain Gugger
2020-10-26 18:26:02 -04:00
committed by GitHub
parent 04a17f8550
commit 08f534d2da
271 changed files with 9726 additions and 8991 deletions
Download Patch File Download Diff File Expand all files Collapse all files

45
docs/source/model_doc/dialogpt.rst
View File

@@ -4,36 +4,39 @@ DialoGPT
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
DialoGPT was proposed in
`DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`_
by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
It's a GPT2 Model trained on 147M conversation-like exchanges extracted from Reddit.
DialoGPT was proposed in `DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
<https://arxiv.org/abs/1911.00536>`_ by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao,
Jianfeng Gao, Jingjing Liu, Bill Dolan. It's a GPT2 Model trained on 147M conversation-like exchanges extracted from
Reddit.
The abstract from the paper is the following:
*We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
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.
We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems.
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.*
*We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained
transformer). 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. We show that conversational systems
that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline
systems. 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.*
Tips:
- DialoGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
- 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.
- 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>`_.
- DialoGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
than the left.
- 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.
- 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>`_.
Training:
In order to train or fine-tune DialoGPT, one can use causal language modeling training.
To cite the official paper:
*We follow the OpenAI GPT-2 to model a multiturn dialogue session
as a long text and frame the generation task as language modeling. We first
concatenate all dialog turns within a dialogue session into a long text
x_1,..., x_N (N is the sequence length), ended by the end-of-text token.*
For more information please confer to the original paper.
In order to train or fine-tune DialoGPT, one can use causal language modeling training. To cite the official paper: *We
follow the OpenAI GPT-2 to model a multiturn dialogue session as a long text and frame the generation task as language
modeling. We first concatenate all dialog turns within a dialogue session into a long text x_1,..., x_N (N is the
sequence length), ended by the end-of-text token.* For more information please confer to the original paper.
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>`_.
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>`_.
The original code can be found `here <https://github.com/microsoft/DialoGPT>`_.