
# MVP-question-answering
The MVP-question-answering model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) 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-question-answering is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled question answering datasets. It is a variant (MVP+S) of our [MVP](https://huggingface.co/RUCAIBox/mvp) [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a Transformer encoder-decoder architecture with layer-wise prompts.

MVP-question-answering is specially designed for question answering tasks, such as reading comprehension (SQuAD), conversational question answering (CoQA) and closed-book question-answering (Natural Questions).

## Example
```python
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration

>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-question-answering")

>>> inputs = tokenizer(
...     "Answer the following question: From which country did Angola achieve independence in 1975?",
...     return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Portugal']
```

## Related Models
**MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp).

**Prompt-based models**:

- MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task).
- MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization).
- MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog).
- MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text).
- MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story).
- MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering).
- MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation).
- MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog).

**Multi-task models**:
- MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization).
- MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog).
- MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text).
- MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story).
- MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering).
- MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation).
- MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog).

## Citation
```bibtex
@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},
}
```

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Answer the following question: From which country did Angola achieve independence in 1975?", "example_title": "Example1"}, {"text": "Answer the following question: what is ce certified [X_SEP] The CE marking is the manufacturer's declaration that the product meets the requirements of the applicable EC directives. Officially, CE is an abbreviation of Conformite Conformit\u00e9, europeenne Europ\u00e9enne Meaning. european conformity", "example_title": "Example2"}]}
transformers pytorch mvp text-generation text2text-generation en arxiv:2206.12131 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us 


# MVP-question-answering
The MVP-question-answering 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 URL

## Model Description
MVP-question-answering is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled question answering datasets. It is a variant (MVP+S) of our MVP MVP model. It follows a Transformer encoder-decoder architecture with layer-wise prompts.

MVP-question-answering is specially designed for question answering tasks, such as reading comprehension (SQuAD), conversational question answering (CoQA) and closed-book question-answering (Natural Questions).

## Example


## Related Models
MVP: URL

Prompt-based models:

- MVP-multi-task: URL
- MVP-summarization: URL
- MVP-open-dialog: URL
- MVP-data-to-text: URL
- MVP-story: URL
- MVP-question-answering: URL
- MVP-question-generation: URL
- MVP-task-dialog: URL

Multi-task models:
- MTL-summarization: URL
- MTL-open-dialog: URL
- MTL-data-to-text: URL
- MTL-story: URL
- MTL-question-answering: URL
- MTL-question-generation: URL
- MTL-task-dialog: URL


# MVP-question-answering
The MVP-question-answering 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 URL ## Model Description
MVP-question-answering is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled question answering datasets. It is a variant (MVP+S) of our MVP MVP model. It follows a Transformer encoder-decoder architecture with layer-wise prompts.

MVP-question-answering is specially designed for question answering tasks, such as reading comprehension (SQuAD), conversational question answering (CoQA) and closed-book question-answering (Natural Questions). ## Example ## Related Models
MVP: URL

Prompt-based models:

- MVP-multi-task: URL
- MVP-summarization: URL
- MVP-open-dialog: URL
- MVP-data-to-text: URL
- MVP-story: URL
- MVP-question-answering: URL
- MVP-question-generation: URL
- MVP-task-dialog: URL

Multi-task models:
- MTL-summarization: URL
- MTL-open-dialog: URL
- MTL-data-to-text: URL
- MTL-story: URL
- MTL-question-answering: URL
- MTL-question-generation: URL
- MTL-task-dialog: URL
TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us 
 # MVP-question-answering
The MVP-question-answering 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 URL ## Model Description
MVP-question-answering is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled question answering datasets. It is a variant (MVP+S) of our MVP MVP model. It follows a Transformer encoder-decoder architecture with layer-wise prompts.

MVP-question-answering is specially designed for question answering tasks, such as reading comprehension (SQuAD), conversational question answering (CoQA) and closed-book question-answering (Natural Questions). ## Example ## Related Models
MVP: URL

Prompt-based models:

- MVP-multi-task: URL
- MVP-summarization: URL
- MVP-open-dialog: URL
- MVP-data-to-text: URL
- MVP-story: URL
- MVP-question-answering: URL
- MVP-question-generation: URL
- MVP-task-dialog: URL

Multi-task models:
- MTL-summarization: URL
- MTL-open-dialog: URL
- MTL-data-to-text: URL
- MTL-story: URL
- MTL-question-answering: URL
- MTL-question-generation: URL
- MTL-task-dialog: URL
passage: TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us 
# MVP-question-answering
The MVP-question-answering 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 URL## Model Description
MVP-question-answering is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled question answering datasets. It is a variant (MVP+S) of our MVP MVP model. It follows a Transformer encoder-decoder architecture with layer-wise prompts.

MVP-question-answering is specially designed for question answering tasks, such as reading comprehension (SQuAD), conversational question answering (CoQA) and closed-book question-answering (Natural Questions).## Example## Related Models
MVP: URL

Prompt-based models:

- MVP-multi-task: URL
- MVP-summarization: URL
- MVP-open-dialog: URL
- MVP-data-to-text: URL
- MVP-story: URL
- MVP-question-answering: URL
- MVP-question-generation: URL
- MVP-task-dialog: URL

Multi-task models:
- MTL-summarization: URL
- MTL-open-dialog: URL
- MTL-data-to-text: URL
- MTL-story: URL
- MTL-question-answering: URL
- MTL-question-generation: URL
- MTL-task-dialog: URL