metadata
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
pipeline_tag: text2text-generation
widget:
- text: >-
Describe the following data: Iron Man | instance of | Superhero [SEP] Stan
Lee | creator | Iron Man
example_title: Example1
- text: >-
Describe the following data: First Clearing | LOCATION | On NYS 52 1 Mi.
Youngsville [SEP] On NYS 52 1 Mi. Youngsville | CITY_OR_TOWN | Callicoon,
New York
example_title: Example2
MTL-data-to-text
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.
The detailed information and instructions can be found https://github.com/RUCAIBox/MVP.
Model Description
MTL-data-to-text is supervised pre-trained using a mixture of labeled data-to-text datasets. It is a variant (Single) of our main MVP model. It follows a standard Transformer encoder-decoder architecture.
MTL-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/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.']
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},
}
