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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- text-generation
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- text2text-generation
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pipeline_tag: text2text-generation
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widget:
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- 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 ."
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example_title: "Example1"
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- 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 ."
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example_title: "Example2"
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---
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# MTL-question-generation
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The MTL-question-generation model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://github.com/RUCAIBox/MVP/blob/main/paper.pdf) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
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The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
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## Model Description
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MTL-question-generation is supervised pre-trained using a mixture of labeled question generation datasets. It is a variant (Single) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a standard Transformer encoder-decoder architecture.
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MTL-question-generation is specially designed for question generation tasks, such as SQuAD and CoQA.
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## Example
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```python
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>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
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>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
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>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-question-generation")
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>>> inputs = tokenizer(
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... "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 .",
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... return_tensors="pt",
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... )
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>>> generated_ids = model.generate(**inputs)
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>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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['A bolo punch and a hook are both punches used in what sport?]
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```
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## Citation
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