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mvp-open-dialog

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metadata
license: apache-2.0
language:
  - en
tags:
  - text-generation
  - text2text-generation
  - conversational
pipeline_tag: text2text-generation
widget:
  - text: >-
      Given the dialog: do you like dance? [SEP] Yes I do. Did you know Bruce
      Lee was a cha cha dancer?
    example_title: Example1
  - text: >-
      Given the dialog: i used to scare for darkness [X_SEP] it feels like
      hitting to blank wall when i see the darkness [SEP] Oh ya? I don't really
      see how [SEP] dont you feel so.. its a wonder [SEP] I do actually hit
      blank walls a lot of times but i get by
    example_title: Example2

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},
}