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--- |
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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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[](https://github.com/PaddlePaddle/PaddleNLP) |
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# PaddlePaddle/unimo-text-1.0-summary |
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## Introduction |
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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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More detail: https://arxiv.org/abs/2012.15409 |
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## Available Models |
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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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## How to Use? |
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Click on the *Use in paddlenlp* button on the top right! |
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## Citation Info |
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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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