Edit model card

MVP-open-dialog

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

MVP-open-dialog is specially designed for open dialogue system (conversation) tasks, such as chitchat (PersonaChat, DailyDialog), knowledge grounded conversation (Topical-Chat, Wizard of Wikipedia) and visual dialog (DSTC7-AVSD).

Example

>>> from transformers import MvpTokenizer, MvpForConditionalGeneration

>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-open-dialog")

>>> inputs = tokenizer(
...     "Given the dialog: do you like dance? [SEP] Yes I do. Did you know Bruce Lee was a cha cha dancer?",
...     return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['I did not know that. I did know that Tupac danced ballet in high school.']

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},
}
Downloads last month
15
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.