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microsoft/DialogRPT-human-vs-rand microsoft/DialogRPT-human-vs-rand
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Microsoft company
15 team members · 31 models

How to use this model directly from the 🤗/transformers library:

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from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/DialogRPT-human-vs-rand") model = AutoModel.from_pretrained("microsoft/DialogRPT-human-vs-rand")
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Please try this ➤➤➤ Colab Notebook Demo (click me!)

Context Response human_vs_rand score
I love NLP! He is a great basketball player. 0.027
I love NLP! Can you tell me how it works? 0.754
I love NLP! Me too! 0.631

The human_vs_rand score predicts how likely the response is corresponding to the given context, rather than a random response.


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.

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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? model card
Human-like (human vs fake) given a context and one human response, distinguish it with...
human_vs_rand ... a random human response this model
human_vs_machine ... a machine generated response model card


Please create an issue on our repo


    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},