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pipeline_tag: feature-extraction
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#
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With MiniCPM-Visual-Embedding, it is possible to directly build knowledge base with raw PDF/Book/Document without any OCR technique nor OCR pipeline. The model only takes images as document-side inputs and produce vectors representing document pages.
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[Github Repo](https://github.com/bokesyo/minicpm-visual-embedding)
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# tensor([[0.6506, 4.9630, 3.8614]], device='cuda:0')
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```
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pipeline_tag: feature-extraction
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# An OCR-free Visual-Based Document Embedding Model Based on MiniCPM-V-2.0 as Your Personal Librarian
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With MiniCPM-Visual-Embedding, it is possible to directly build knowledge base with raw PDF/Book/Document without any OCR technique nor OCR pipeline. The model only takes images as document-side inputs and produce vectors representing document pages. minicpm-visual-embedding-v0 is trained with over 30k paired query - visual document pages, including textual document, visual document, arxiv figures, industry documents, textbooks, ebooks, etc. The performance of minicpm-visual-embedding-v0 is on a par with a text embedding on text-oriented documents, and an advantages on visually-intensive documents.
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[Github Repo](https://github.com/bokesyo/minicpm-visual-embedding)
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# tensor([[0.6506, 4.9630, 3.8614]], device='cuda:0')
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```
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# Limitations
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Currently, please ensure that dpi of input images be a high value like `300` dpi, a lower dpi like `100` may cause the model performance degrade. We will augment data and fix this in our latest version.
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