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Update README
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- text-generation
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- text2text-generation
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pipeline_tag: text2text-generation
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widget:
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- text: "Given the task dialog: Belief state [X_SEP] I'm looking for a affordable BBQ restaurant in Dallas for a large group of guest."
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example_title: "Example1"
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- text: "Given the task dialog: Dialogue action [X_SEP] I'm looking for a affordable BBQ restaurant in Dallas for a large group of guest."
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example_title: "Example2"
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- text: "Given the task dialog: System response [X_SEP] I'm looking for a affordable BBQ restaurant in Dallas for a large group of guest."
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example_title: "Example3"
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---
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# MVP-task-dialog
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The MVP-task-dialog model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://github.com/RUCAIBox/MVP/blob/main/paper.pdf) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
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The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
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## Model Description
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MVP-task-dialog is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled task-oriented system datasets. It is a variant (MVP+S) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a Transformer encoder-decoder architecture with layer-wise prompts.
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MVP-task-dialog is specially designed for task-oriented tasks, such as MultiWOZ.
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## Example
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```python
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>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
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>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
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>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-task-dialog")
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>>> inputs = tokenizer(
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... "Given the task dialog: System response [X_SEP] I'm looking for a affordable BBQ restaurant in Dallas for a large group of guest.",
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... return_tensors="pt",
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... )
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>>> generated_ids = model.generate(**inputs)
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>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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['What date and time would you like to go?']
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```
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## Citation
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