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