MVP-data-to-text

The MVP-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.

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

Model Description

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

MVP-data-to-text is specially designed for data-to-text generation tasks, such as KG-to-text generation (WebNLG, DART), table-to-text generation (WikiBio, ToTTo) and MR-to-text generation (E2E).

Example

>>> from transformers import MvpTokenizer, MvpForConditionalGeneration

>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-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.']

Related Models

MVP: https://huggingface.co/RUCAIBox/mvp.

Prompt-based models:

• MVP-multi-task: https://huggingface.co/RUCAIBox/mvp-multi-task.
• MVP-summarization: https://huggingface.co/RUCAIBox/mvp-summarization.
• MVP-open-dialog: https://huggingface.co/RUCAIBox/mvp-open-dialog.
• MVP-data-to-text: https://huggingface.co/RUCAIBox/mvp-data-to-text.
• MVP-story: https://huggingface.co/RUCAIBox/mvp-story.
• MVP-question-answering: https://huggingface.co/RUCAIBox/mvp-question-answering.
• MVP-question-generation: https://huggingface.co/RUCAIBox/mvp-question-generation.
• MVP-task-dialog: https://huggingface.co/RUCAIBox/mvp-task-dialog.

Multi-task models:

• MTL-summarization: https://huggingface.co/RUCAIBox/mtl-summarization.
• MTL-open-dialog: https://huggingface.co/RUCAIBox/mtl-open-dialog.
• MTL-data-to-text: https://huggingface.co/RUCAIBox/mtl-data-to-text.
• MTL-story: https://huggingface.co/RUCAIBox/mtl-story.
• MTL-question-answering: https://huggingface.co/RUCAIBox/mtl-question-answering.
• MTL-question-generation: https://huggingface.co/RUCAIBox/mtl-question-generation.
• MTL-task-dialog: https://huggingface.co/RUCAIBox/mtl-task-dialog.

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},
}