microsoft-dialogrpt-depth
Version: 3
HuggingFaceLast updated July 2025

Demo

Please try this ➤➤➤ Colab Notebook Demo (click me!)
ContextResponsedepth score
I love NLP!Can anyone recommend a nice review paper?0.724
I love NLP!Me too!0.032
The 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.
TaskDescriptionPretrained model
Human feedbackgiven 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 responsemodel card
human_vs_machine... a machine generated responsemodel card

Contact:

Please create an issue on our repo

Citation:

@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