evum/lab-marker-e5-small
A contrastive fine-tune of multilingual-e5-small (MIT) for lab-marker naming in Evum — it maps a lab-report label (any language) to one of Evum's marker ids by embedding similarity. Runs on-device via transformers.js; the user's health data never leaves the browser.
Open weights, closed recipe: the weights are public and the eval below is reproducible on the open fixtures, but the training pipeline is proprietary.
Eval (held-out fixtures, precision-first)
| model | precision | recall | threshold | margin |
|---|---|---|---|---|
| base multilingual-e5-small | 1.0 | 0.833 | 0.89 | 0.02 |
| this model (q8 ONNX) | 1.0 | 1.0 | 0.7 | 0 |
Use
Feature-extraction; prefix report labels with query: and marker names with passage: ,
mean-pool, L2-normalize, cosine similarity. Names only — it never produces a measurement value.
Limitations
Small fine-tune; a naming fallback behind a deterministic catalog. Not medical advice.
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Model tree for evum/lab-marker-e5-small
Base model
intfloat/multilingual-e5-small
from sentence_transformers import SentenceTransformer model = SentenceTransformer("evum/lab-marker-e5-small") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3]