Overview
DialoGPT was proposed in DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation 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 . DialoGPT’s architecture is based on the GPT2 model, so one can refer to GPT2’s docstring .
The original code can be found here . Previous 链接地址： https://huggingface.co/transformers/model_doc/dialogpt.html