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--- |
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language: "en" |
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tags: |
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- counterfactual generation |
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widget: |
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- text: "It is great for kids. <|perturb|> [negation] It [BLANK] great for kids. [SEP]" |
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--- |
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# Polyjuice |
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## Model description |
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This is a ported version of [Polyjuice](https://homes.cs.washington.edu/~wtshuang/static/papers/2021-arxiv-polyjuice.pdf), the general-purpose counterfactual generator. |
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For more code release, please refer to [this github page](https://github.com/tongshuangwu/polyjuice). |
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#### How to use |
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```python |
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from transformers import AutoTokenizer, AutoModelWithLMHead |
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM |
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model_path = "uw-hai/polyjuice" |
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generator = pipeline("text-generation", |
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model=AutoModelForCausalLM.from_pretrained(model_path), |
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tokenizer=AutoTokenizer.from_pretrained(model_path), |
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framework="pt", device=0 if is_cuda else -1) |
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prompt_text = "A dog is embraced by the woman. <|perturb|> [negation] A dog is [BLANK] the woman." |
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generator(prompt_text, num_beams=3, num_return_sequences=3) |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@inproceedings{polyjuice:acl21, |
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title = "{P}olyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models", |
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author = "Tongshuang Wu and Marco Tulio Ribeiro and Jeffrey Heer and Daniel S. Weld", |
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booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics", |
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year = "2021", |
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publisher = "Association for Computational Linguistics" |
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``` |
