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rafmacalaba/datause-extraction-webgpu

sourceHugging Faceapache-2.0updated 1mo agoView on Hugging Face

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App README

Data-use Extraction — WebGPU

Fully client-side demo that extracts data mentions from research text and attributes each one with provenance and usage/impact — all running locally in your browser on WebGPU. No server, no uploads; nothing leaves your machine.

Pipeline

text
  → span model (GLiNER)          → mention spans + specificity
  → LFM2.5-350M multitask        → provenance {producer, year, geography, acronym}
                                   + usage/impact {data_type, usage_action, impact_label, usage_summary}
  → verbatim filter → render

Models

stage	model	HF repo
span detection	GLiNER	`rafmacalaba/gliner_datause_extended-onnx`
attribution	LFM2.5-350M multitask	`rafmacalaba/lfm2.5-350M-datause-multitask-onnx`

Models download from the Hub on first load and are cached in your browser's IndexedDB. Requires a WebGPU-capable browser (Chrome/Edge with WebGPU enabled).

Run locally

bash

git clone https://github.com/rafmacalaba/ai4data-playground.git
cd ai4data-playground/web && python3 -m http.server 8000
# open http://localhost:8000