Hugging Face's logo

RUCAIBox
/
mvp-question-answering

Text2Text Generation
Transformers
PyTorch
English
mvp
text-generation
Inference Endpoints
Model card Files Community
mvp-question-answering / README.md
StevenTang's picture
StevenTang
Update README
4c2aabe
preview code
|
raw history blame contribute delete
No virus
4.4 kB
metadata
license: apache-2.0
language:
  - en
tags:
  - text-generation
  - text2text-generation
pipeline_tag: text2text-generation
widget:
  - text: >-
      Answer the following question: From which country did Angola achieve
      independence in 1975?
    example_title: Example1
  - text: >-
      Answer the following question: what is ce certified [X_SEP] The CE marking
      is the manufacturer's declaration that the product meets the requirements
      of the applicable EC directives. Officially, CE is an abbreviation of
      Conformite Conformité, europeenne Européenne Meaning. european conformity
    example_title: Example2

MVP-question-answering

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

MVP-question-answering is specially designed for question answering tasks, such as reading comprehension (SQuAD), conversational question answering (CoQA) and closed-book question-answering (Natural Questions).

Example 

>>> from transformers import MvpTokenizer, MvpForConditionalGeneration

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

>>> inputs = tokenizer(
...     "Answer the following question: From which country did Angola achieve independence in 1975?",
...     return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Portugal']

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},
}