YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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']

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},
}

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Paper for RUCAIBox/mtl-question-answering

MVP: Multi-task Supervised Pre-training for Natural Language Generation

Paper • 2206.12131 • Published Jun 24, 2022 • 1

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mtl-question-answering