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?']