|
--- |
|
license: apache-2.0 |
|
language: |
|
- en |
|
tags: |
|
- text-generation |
|
- text2text-generation |
|
pipeline_tag: text2text-generation |
|
widget: |
|
- 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." |
|
example_title: "Example1" |
|
- 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." |
|
example_title: "Example2" |
|
- 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." |
|
example_title: "Example3" |
|
--- |
|
|
|
# MVP-task-dialog |
|
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. |
|
|
|
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP). |
|
|
|
## Model Description |
|
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. |
|
|
|
MVP-task-dialog is specially designed for task-oriented tasks, such as MultiWOZ. |
|
|
|
## Example |
|
```python |
|
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration |
|
|
|
>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") |
|
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-task-dialog") |
|
|
|
>>> inputs = tokenizer( |
|
... "Given the task dialog: System response [X_SEP] I'm looking for a affordable BBQ restaurant in Dallas for a large group of guest.", |
|
... return_tensors="pt", |
|
... ) |
|
>>> generated_ids = model.generate(**inputs) |
|
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) |
|
['What date and time would you like to go?'] |
|
``` |
|
|
|
## Citation |
|
|