Mtl Summarization

🚀 MTL-summarization

The MTL-summarization model is designed for text2text-generation tasks, such as text summarization. It offers a supervised pre-trained solution for various summarization needs, including news and dialog summarization.

🚀 Quick Start

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

✨ Features

• Proposed in Research: 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.
• Supervised Pre-training: It is supervised pre-trained using a mixture of labeled summarization datasets.
• Transformer Architecture: Follows a standard Transformer encoder - decoder architecture.
• Task - Specific Design: Specially designed for summarization tasks, such as news summarization (CNN/DailyMail, XSum) and dialog summarization (SAMSum).

📚 Documentation

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 news summarization (CNN/DailyMail, XSum) and dialog summarization (SAMSum).

💻 Usage Examples

Basic Usage

>>> 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:
  • MVP - multi - task: [https://huggingface.co/RUCAIBox/mvp - multi - task](https://huggingface.co/RUCAIBox/mvp - multi - task).
  • MVP - summarization: [https://huggingface.co/RUCAIBox/mvp - summarization](https://huggingface.co/RUCAIBox/mvp - summarization).
  • MVP - open - dialog: [https://huggingface.co/RUCAIBox/mvp - open - dialog](https://huggingface.co/RUCAIBox/mvp - open - dialog).
  • MVP - data - to - text: [https://huggingface.co/RUCAIBox/mvp - data - to - text](https://huggingface.co/RUCAIBox/mvp - data - to - text).
  • MVP - story: [https://huggingface.co/RUCAIBox/mvp - story](https://huggingface.co/RUCAIBox/mvp - story).
  • MVP - question - answering: [https://huggingface.co/RUCAIBox/mvp - question - answering](https://huggingface.co/RUCAIBox/mvp - question - answering).
  • MVP - question - generation: [https://huggingface.co/RUCAIBox/mvp - question - generation](https://huggingface.co/RUCAIBox/mvp - question - generation).
  • MVP - task - dialog: [https://huggingface.co/RUCAIBox/mvp - task - dialog](https://huggingface.co/RUCAIBox/mvp - task - dialog).
• Multi - task models:
  • MTL - summarization: [https://huggingface.co/RUCAIBox/mtl - summarization](https://huggingface.co/RUCAIBox/mtl - summarization).
  • MTL - open - dialog: [https://huggingface.co/RUCAIBox/mtl - open - dialog](https://huggingface.co/RUCAIBox/mtl - open - dialog).
  • MTL - data - to - text: [https://huggingface.co/RUCAIBox/mtl - data - to - text](https://huggingface.co/RUCAIBox/mtl - data - to - text).
  • MTL - story: [https://huggingface.co/RUCAIBox/mtl - story](https://huggingface.co/RUCAIBox/mtl - story).
  • MTL - question - answering: [https://huggingface.co/RUCAIBox/mtl - question - answering](https://huggingface.co/RUCAIBox/mtl - question - answering).
  • MTL - question - generation: [https://huggingface.co/RUCAIBox/mtl - question - generation](https://huggingface.co/RUCAIBox/mtl - question - generation).
  • MTL - task - dialog: [https://huggingface.co/RUCAIBox/mtl - task - dialog](https://huggingface.co/RUCAIBox/mtl - task - dialog).

📄 License

The model is licensed under the Apache 2.0 license.

📚 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},
}