DialoGPT

Transformers documentation

Natural Language Processing

Performance and scalability

Reinforcement learning models

## [](#overview)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.

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._

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).

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.

DialoGPT’s architecture is based on the GPT2 model, so one can refer to [GPT2’s documentation page](gpt2).

The original code can be found [here](https://github.com/microsoft/DialoGPT).