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  library_name: paddlenlp
 
 
 
 
 
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  ---
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- # PaddlePaddle/unimo-text-1.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  library_name: paddlenlp
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+ license: apache-2.0
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+ tags:
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+ - summarization
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+ language:
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+ - zh
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  ---
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+
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+ [![paddlenlp-banner](https://user-images.githubusercontent.com/1371212/175816733-8ec25eb0-9af3-4380-9218-27c154518258.png)](https://github.com/PaddlePaddle/PaddleNLP)
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+
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+ # PaddlePaddle/unimo-text-1.0
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+
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+ ## Introduction
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+
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+ Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other.
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+ They can only utilize single-modal data (i.e. text or image) or limited multi-modal data (i.e. image-text pairs).
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+ In this work, we propose a unified-modal pre-training architecture, namely UNIMO, which can effectively adapt to both single-modal and multi-modal
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+ understanding and generation tasks. Large scale of free text corpus and image collections can be utilized to improve the capability of visual
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+ and textual understanding, and cross-modal contrastive learning (CMCL) is leveraged to align the textual and visual information into a unified
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+ semantic space over a corpus of image-text pairs. As the non-paired single-modal data is very rich, our model can utilize much larger scale of
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+ data to learn more generalizable representations. Moreover, the textual knowledge and visual knowledge can enhance each other in the unified semantic space.
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+ The experimental results show that UNIMO significantly improves the performance of several single-modal and multi-modal downstream tasks.
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+
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+ More detail: https://arxiv.org/abs/2012.15409
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+
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+ ## Available Models
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+
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+ - **unimo-text-1.0**, *12 layer, 12 heads, 768 hidden size, pretrained model*
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+ - **unimo-text-1.0-large**, *24 layer, 16 heads, 1024 hidden size, pretrained model*
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+ - **unimo-text-1.0-lcsts-new**, *12 layer, 12 heads, 768 hidden size, finetuned on the lcsts-new Chinese summarization dataset*
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+ - **unimo-text-1.0-summary**, *12 layer, 12 heads, 768 hidden size, finetuned on several in-house Chinese summarization datasets*
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+
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+ ## How to Use?
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+
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+ Click on the *Use in paddlenlp* button on the top right!
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+
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+ ## Citation Info
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+
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+ ```text
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+ @article{ernie2.0,
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+ title = {UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning},
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+ author = {Li, Wei and Gao, Can and Niu, Guocheng and Xiao, Xinyan and Liu, Hao and Liu, Jiachen and Wu, Hua and Wang, Haifeng},
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+ journal={arXiv preprint arXiv:2012.15409},
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+ year = {2020},
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+ }
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+ ```
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+