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--- |
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language: "en" |
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tags: |
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- controlled generation |
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- perturbation |
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widget: |
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- text: "[VERB+passive+past: break | PATIENT+partial: cup] <extra_id_0> <extra_id_1> <extra_id_2> ." |
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- max_length: |
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--- |
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# Tailor |
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## Model description |
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This is a ported version of [Tailor](https://homes.cs.washington.edu/~wtshuang/static/papers/2021-arxiv-tailor.pdf), the general-purpose counterfactual generator. |
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For more code release, please refer to [this github page](https://github.com/allenai/tailor). |
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#### How to use |
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```python |
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from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM |
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model_path = "allenai/tailor" |
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generator = pipeline("text2text-generation", |
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model=AutoModelForSeq2SeqLM.from_pretrained(model_path), |
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tokenizer=AutoTokenizer.from_pretrained(model_path), |
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framework="pt", device=0) |
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prompt_text = "[VERB+active+past: comfort | AGENT+complete: the doctor | PATIENT+partial: athlete | LOCATIVE+partial: in] <extra_id_0> , <extra_id_1> <extra_id_2> <extra_id_3> ." |
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generator(prompt_text, max_length=200) |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{ross2021tailor, |
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title={Tailor: Generating and Perturbing Text with Semantic Controls}, |
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author={Alexis Ross and Tongshuang Wu and Hao Peng and Matthew E. Peters and Matt Gardner}, |
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year={2021}, |
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eprint={2107.07150}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2107.07150}, |
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} |
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``` |
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