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Running on Zero
Running on Zero
| title: V-SPLADE Quality Document Retrieval | |
| emoji: π | |
| colorFrom: pink | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 6.19.0 | |
| app_file: app.py | |
| short_description: Visual document retrieval via sparse lexical vectors | |
| python_version: "3.12" | |
| startup_duration_timeout: 30m | |
| # V-SPLADE Quality β Visual Document Retrieval | |
| This Space demonstrates **V-SPLADE Quality** ([naver/v-splade-quality](https://huggingface.co/naver/v-splade-quality)), | |
| a 0.25B inference-free sparse retriever for visual document retrieval. | |
| Upload a document page image and enter text queries to see: | |
| - The **top activated vocabulary tokens** β the sparse lexical representation of the image | |
| - **Similarity scores** between each query and the document, with the top contributing tokens | |
| V-SPLADE encodes document pages directly into sparse vocabulary vectors without OCR or captioning, | |
| enabling retrieval 20Γ faster than caption-based pipelines. |