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The sentence embedding model powering workout retrieval in the
CoachTwin app. It is
BAAI/bge-small-en-v1.5, chosen by evaluating three encoders on the
CoachTwin Workouts
dataset (10,393 workouts).
Leave-one-out retrieval. Strict relevance requires a match on
both goal and body_focus; loose requires body_focus.
| model | params | dim | strict P@3 | loose P@3 | MRR@10 | corpus encode |
|---|---|---|---|---|---|---|
| bge-small-en-v1.5 | 33M | 384 | 0.6687 | 0.8273 | 0.8020 | 10.4s |
| all-mpnet-base-v2 | 110M | 768 | 0.5047 | 0.7453 | 0.6866 | 31.2s |
| all-MiniLM-L6-v2 | 22M | 384 | 0.4733 | 0.6667 | 0.6686 | 7.0s |
Random-retrieval baseline: strict P@3 0.0220, loose 0.1200, MRR@10 0.0685. A precision number without its baseline is not interpretable.
Selected: BAAI/bge-small-en-v1.5 - strict P@3 0.669, about 30x random.
The middle row is the interesting one: all-mpnet-base-v2 is 3.3x the parameters,
3x slower, and scores worse. The bigger encoder is not the better one here.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("OrDora/coachtwin-embedder")
emb = model.encode([text], normalize_embeddings=True)
Documents and queries use different templates, both recorded in
embedding_info.json. This is a BGE model, so queries - not documents - take the
prefix Represent this sentence for searching relevant passages:
(needs_query_prefix: true).
Saved in the sentence-transformers 3.x module format. A repo saved by 5.x fails
on 3.x with No module named 'sentence_transformers.base', and a client with a
try/except fallback then silently swaps in a different encoder - no error, wrong
neighbours, because several candidates share 384 dimensions.
Base checkpoint, not fine-tuned. Evaluated only on synthetic English workout descriptions. Not fitness or medical advice.
BAAI/bge-small-en-v1.5