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mtl-summarization

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metadata
license: apache-2.0
language:
  - en
tags:
  - text-generation
  - text2text-generation
  - summarization
pipeline_tag: text2text-generation
widget:
  - text: >-
      Summarize: You may want to stick it to your boss and leave your job, but
      don't do it if these are your reasons.
    example_title: Example1
  - text: >-
      Summarize: Jorge Alfaro drove in two runs, Aaron Nola pitched seven
      innings of two-hit ball and the Philadelphia Phillies beat the Los Angeles
      Dodgers 2-1 Thursday, spoiling Clayton Kershaw's first start in almost a
      month. Hitting out of the No. 8 spot in the ...
    example_title: Example2

MTL-summarization

The MTL-summarization 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

MTL-summarization is supervised pre-trained using a mixture of labeled summarization datasets. It is a variant (Single) of our main MVP model. It follows a standard Transformer encoder-decoder architecture.

MTL-summarization is specially designed for summarization tasks, such as new summarization (CNN/DailyMail, XSum) and dialog summarization (SAMSum).

Example 

>>> from transformers import MvpTokenizer, MvpForConditionalGeneration

>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-summarization")

>>> inputs = tokenizer(
...     "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.",
...     return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["Don't do it if these are your reasons"]

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},
}