RUCAIBox / mtl-question-answering

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Model's Last Updated: June 27 2022
text-generation

Introduction of mtl-question-answering

Model Details of mtl-question-answering

MTL-question-answering

The MTL-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 https://github.com/RUCAIBox/MVP .

Model Description

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

MTL-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
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration

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

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},
}

Runs of RUCAIBox mtl-question-answering on huggingface.co

28
Total runs
-1
24-hour runs
3
3-day runs
3
7-day runs
1
30-day runs

More Information About mtl-question-answering huggingface.co Model

More mtl-question-answering license Visit here:

https://choosealicense.com/licenses/apache-2.0

mtl-question-answering huggingface.co

mtl-question-answering huggingface.co is an AI model on huggingface.co that provides mtl-question-answering's model effect (), which can be used instantly with this RUCAIBox mtl-question-answering model. huggingface.co supports a free trial of the mtl-question-answering model, and also provides paid use of the mtl-question-answering. Support call mtl-question-answering model through api, including Node.js, Python, http.

mtl-question-answering huggingface.co Url

https://huggingface.co/RUCAIBox/mtl-question-answering

RUCAIBox mtl-question-answering online free

mtl-question-answering huggingface.co is an online trial and call api platform, which integrates mtl-question-answering's modeling effects, including api services, and provides a free online trial of mtl-question-answering, you can try mtl-question-answering online for free by clicking the link below.

RUCAIBox mtl-question-answering online free url in huggingface.co:

https://huggingface.co/RUCAIBox/mtl-question-answering

mtl-question-answering install

mtl-question-answering is an open source model from GitHub that offers a free installation service, and any user can find mtl-question-answering on GitHub to install. At the same time, huggingface.co provides the effect of mtl-question-answering install, users can directly use mtl-question-answering installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

mtl-question-answering install url in huggingface.co:

https://huggingface.co/RUCAIBox/mtl-question-answering

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