pytorch gpt2 text-classification arxiv:2009.06978 transformers
.gitattributes README.md config.json merges.txt pytorch_model.bin special_tokens_map.json vocab.json
# Demo

Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)

| Context | Response | `human_vs_machine` score |
| :------ | :------- | :------------: |
| I love NLP! | I'm not sure if it's a good idea. | 0.000 |
| I love NLP! | Me too! | 0.605 |

The `human_vs_machine` score predicts how likely the response is from a human rather than a machine.

# DialogRPT-human-vs-machine

### 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  | [model card](https://huggingface.co/microsoft/DialogRPT-human-vs-rand) |
| `human_vs_machine`| ... a machine generated response  | this model |


### 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}
}
```
