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mvp

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The MVP 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. MVP is supervised pre-trained using a mixture of labeled datasets. It follows a standard Transformer encoder-decoder architecture. MVP is specially designed for natural language generation and can be adapted to a wide range of generation tasks, including but not limited to summarization, data-to-text generation, open-ended dialogue system, story generation, question answering, question generation, task-oriented dialogue system, commonsense generation, paraphrase generation, text style transfer, and text simplification. Our model can also be adapted to natural language understanding tasks such as sequence classification and (extractive) question answering.

Text generation

Input Text→ Output Text

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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mvp

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