microsoft-dialogrpt-depth
Version: 3
Demo
Please try this ➤➤➤ Colab Notebook Demo (click me!)| Context | Response | depth score |
|---|---|---|
| I love NLP! | Can anyone recommend a nice review paper? | 0.724 |
| I love NLP! | Me too! | 0.032 |
depth score predicts how likely the response is getting a long follow-up discussion thread.
DialogRPT-depth
Dialog Ranking Pretrained Transformers
How likely a dialog response is upvoted 👍 and/or gets replied 💬?This is what DialogRPT is learned to predict.
It is a set of dialog response ranking models proposed by Microsoft Research NLP Group trained on 100 + millions of human feedback data.
It can be used to improve existing dialog generation model (e.g., DialoGPT ) by re-ranking the generated response candidates. Quick Links: We considered the following tasks and provided corresponding pretrained models.
| Task | Description | Pretrained model |
|---|---|---|
| Human feedback | given a context and its two human responses, predict... | |
updown | ... which gets more upvotes? | model card |
width | ... which gets more direct replies? | model card |
depth | ... which gets longer follow-up thread? | this model |
| Human-like (human vs fake) | given a context and one human response, distinguish it with... | |
human_vs_rand | ... a random human response | model card |
human_vs_machine | ... a machine generated response | model card |
Contact:
Please create an issue on our repoCitation:
@inproceedings{gao2020dialogrpt,
title={Dialogue Response RankingTraining with Large-Scale Human Feedback Data},
author={Xiang Gao and Yizhe Zhang and Michel Galley and Chris Brockett and Bill Dolan},
year={2020},
booktitle={EMNLP}
}
microsoft/DialogRPT-depth is a pre-trained language model available on the Hugging Face Hub. It's specifically designed for the text-classification task in the transformers library. If you want to learn more about the model's architecture, hyperparameters, limitations, and biases, you can find this information on the model's dedicated Model Card on the Hugging Face Hub .
Here's an example API request payload that you can use to obtain predictions from the model:
{
"inputs": "I like you. I love you"
}
Model Specifications
Last UpdatedJuly 2025
ProviderHuggingFace