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metadata
license: apache-2.0
language:
  - en
tags:
  - text-generation
  - text2text-generation
pipeline_tag: text2text-generation
widget:
  - text: >-
      Generate the question based on the answer: boxing [X_SEP] A bolo punch is
      a punch used in martial arts . A hook is a punch in boxing .
    example_title: Example1
  - text: >-
      Generate the question based on the answer: Arthur 's Magazine [X_SEP]
      Arthur 's Magazine ( 1844–1846 ) was an American literary periodical
      published in Philadelphia in the 19th century . First for Women is a woman
      's magazine published by Bauer Media Group in the USA .
    example_title: Example2

MVP-question-generation

The MVP-question-generation model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.

The detailed information and instructions can be found https://github.com/RUCAIBox/MVP.

Model Description

MVP-question-generation is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled question generation datasets. It is a variant (MVP+S) of our main MVP model. It follows a Transformer encoder-decoder architecture with layer-wise prompts.

MVP-question-generation is specially designed for question generation tasks, such as SQuAD and CoQA.

Example 

>>> from transformers import MvpTokenizer, MvpForConditionalGeneration

>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-question-generation")

>>> inputs = tokenizer(
...     "Generate the question based on the answer: boxing [X_SEP] A bolo punch is a punch used in martial arts . A hook is a punch in boxing .",
...     return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['A bolo punch and a hook are both punches used in what sport?']

Related Models

MVP: https://huggingface.co/RUCAIBox/mvp.

Prompt-based models:

Multi-task models:

Citation 

@article{tang2022mvp,
  title={MVP: Multi-task Supervised Pre-training for Natural Language Generation},
  author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong},
  journal={arXiv preprint arXiv:2206.12131},
  year={2022},
  url={https://arxiv.org/abs/2206.12131},
}