readme: ---
  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},
  }
  ```
  