---
license: apache-2.0
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- fitness
- retrieval
base_model: BAAI/bge-small-en-v1.5
---

# CoachTwin Embedder

The sentence embedding model powering workout retrieval in the
[CoachTwin](https://huggingface.co/spaces/OrDora/coachtwin) app. It is
`BAAI/bge-small-en-v1.5`, chosen by evaluating three encoders on the
[CoachTwin Workouts](https://huggingface.co/datasets/OrDora/coachtwin-workouts)
dataset (10,393 workouts).

## Why this model

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.

## Usage

    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`).

## Serialization note

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.

## Limitations

Base checkpoint, **not fine-tuned**. Evaluated only on synthetic
English workout descriptions. Not fitness or medical advice.