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+# DialogRPT-human-vs-rand
+
+### Dialog Ranking Pretrained Transformers
+
+> How likely a dialog response is upvoted 👍 and/or gets replied 💬? 
+
+This is what [**DialogRPT**](https://github.com/golsun/DialogRPT) is learned to predict.
+It is a set of dialog response ranking models proposed by [Microsoft Research NLP Group](https://www.microsoft.com/en-us/research/group/natural-language-processing/) trained on 100 + millions of human feedback data. 
+It can be used to improve existing dialog generation model (e.g., [DialoGPT](https://huggingface.co/microsoft/DialoGPT-medium)) by re-ranking the generated response candidates.
+
+Quick Links:
+* [EMNLP'20 Paper](https://arxiv.org/abs/2009.06978/)
+* [Dataset, training, and evaluation](https://github.com/golsun/DialogRPT)
+* [Colab Notebook Demo](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
+
+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](https://huggingface.co/microsoft/DialogRPT-updown) |
+| `width`| ... which gets more direct replies?  | [model card](https://huggingface.co/microsoft/DialogRPT-width) |
+| `depth`|  ... which gets longer follow-up thread?  | [model card](https://huggingface.co/microsoft/DialogRPT-depth) |
+|  **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](https://huggingface.co/microsoft/DialogRPT-human-vs-machine) |
+
+
+
+### Examples:
+The `human_vs_rand` score predicts how likely the response is corresponding to the given context, rather than a random response.
+Examples below can be reproduced with this [Colab Notebook](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
+
+| Context | Response | `human_vs_rand` score |
+| :------ | :------- | :------------: |
+| I love NLP! | Can anyone recommend a nice review paper? | 0.724 |
+| I love NLP! | Me too! | 0.032 |
+
+### Contact:
+Please create an issue on [our repo](https://github.com/golsun/DialogRPT)
+
+### 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}
+}
+```