# Source: https://huggingface.co/docs/transformers/v5.0.0/model_doc/dialogpt.md

# DialoGPT

## Overview

DialoGPT was proposed in [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://huggingface.co/papers/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.*

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

## Usage 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, refer to [GPT2's documentation page](gpt2) for API reference and examples.