README.md

metadata

language: en
tags:
  - counterfactual generation
widget:
  - text: >-
      It is great for kids. <perturb> [negation] It [BLANK] great for kids.
      [SEP]

Polyjuice

Model description

This is a ported version of Polyjuice, the general-purpose counterfactual generator.

How to use

from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("uw-hai/polyjuice")
model = AutoModelWithLMHead.from_pretrained("uw-hai/polyjuice")


prompt_text = "A dog is embraced by the woman. <perturb> [negation] A dog is [BLANK] the woman."
# or try: "A dog is embraced by the woman. <perturb> [restructure] A dog is [BLANK] the woman."
perturb_tok, end_tok = "<|perturb|>", "<|endoftext|>"
encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")
input_ids = encoded_prompt
stop_token= '\n'
repetition_penalty=1
output_sequences = model.generate(
    input_ids=input_ids,
    max_length=100 + len(encoded_prompt[0]),
    temperature=0.1,
    num_beams=10,
    num_return_sequences=3)

if len(output_sequences.shape) > 2:
    output_sequences.squeeze_()

for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
    generated_sequence = generated_sequence.tolist()
    # Decode text
    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
    # Remove all text after the stop token
    text = text[: text.find(stop_token) if stop_token and text.find(stop_token)>-1 else None]
    text = text[: text.find(end_tok) if end_tok and text.find(end_tok)>-1 else None]
    print(text)

BibTeX entry and citation info

@article{wu2021polyjuice,
  title={Polyjuice: Automated, General-purpose Counterfactual Generation},
  author = {Wu, Tongshuang and Ribeiro, Marco Tulio and Heer, Jeffrey and Weld Daniel S.},
  journal={arXiv preprint},
  year={2021}
}