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deploy: Docker Space (qdrant + FastAPI + React SPA)

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metadata

title: ReviewSearch
emoji: 🔍
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
license: mit
short_description: Semantic hybrid search over 206k ML/NLP peer reviews

ReviewSearch

Semantic search over 205,988 ML/NLP peer reviews (ICLR, NeurIPS, ICML, COLM, …). Hybrid retrieval: a fine-tuned dense encoder (embeddinggemma-300m) fused with an inference-free learned-sparse encoder (SPLADE) via reciprocal-rank fusion (k=10, 1:1), served on CPU over Qdrant.

Dense model: yjoonjang/reviewsearch-dense
Sparse model: yjoonjang/reviewsearch-sparse
Index snapshot: yjoonjang/reviewsearch-index

How it runs

On boot the container downloads the Qdrant snapshot from the dataset repo, recovers the collection, and serves the API + React SPA. The dense/sparse models are pulled from the Hub on first use.

Note: free CPU Spaces sleep when idle; the first request after a cold start takes a few minutes (snapshot download + model load), then queries run in ~tens of ms. ndcg@10 ≈ 0.258 on the judged gold (hybrid, full-fidelity index).