A diagnostic toolkit for evaluating and selecting language-model embedding spaces.
Project description
embedprobe
A diagnostic toolkit for evaluating and selecting language-model embedding spaces.
Status: early development. This is a placeholder release to reserve the name. The first working version is coming soon.
What it will do
embedprobe helps you choose the right embedding model for your downstream task
by diagnosing why models succeed or fail on your data and language pairs, going
beyond aggregate leaderboard scores. It probes an embedding space across four levels:
- Signal-to-noise separability — how cleanly true pairs separate from noise.
- Retrieval performance — Recall@K, MRR, and cumulative-match analysis.
- Topic-level structure — UMAP projections and topic-cohesion heatmaps.
- Error categorisation — a taxonomy of retrieval misses (lexical / semantic / topic-boundary).
Install
pip install embedprobe
License
MIT
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