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

MTL-open-dialog

The MTL-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

MTL-open-dialog is supervised pre-trained using a mixture of labeled open dialogue system datasets. It is a variant (Single) of our main MVP model. It follows a standard Transformer encoder-decoder architecture.

MTL-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/mtl-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)
['Yes he won the Hong Kong Cha Cha championship in 1958']

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},
}

RUCAIBox
/

mtl-open-dialog

MTL-open-dialog

Model Description

Example

Related Models

Citation