Mtl Data To Text

🚀 MTL-data-to-text

The MTL-data-to-text model is designed for data - to - text generation tasks. It offers a practical solution for converting structured data into natural language text, which is useful in various scenarios such as knowledge graph to text, table to text, and meaning representation to text generation.

🚀 Quick Start

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

✨ Features

The MTL-data-to-text 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.

It is supervised pre - trained using a mixture of labeled data - to - text datasets. It is a variant (Single) of the main MVP model and follows a standard Transformer encoder - decoder architecture. This model is specially designed for data - to - text generation tasks, including KG - to - text generation (WebNLG, DART), table - to - text generation (WikiBio, ToTTo) and MR - to - text generation (E2E).

💻 Usage Examples

Basic Usage

>>> from transformers import MvpTokenizer, MvpForConditionalGeneration

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

>>> inputs = tokenizer(
...     "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man",
...     return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Iron Man is a fictional superhero appearing in American comic books published by Marvel Comics.']

📚 Documentation

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

This project 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},
}