The corpus is mainly trained in english, unfortunately no other languages have been included in the distillation training. Yes it would work like fuse.js, but unlocks semantic search.

Source code has the entire embedding distillation pipeline includes dataset preparation. You could run the same distillation training (but not sure if teacher model used a multi-language dataset).

Open up an issue on the github repo and I can reply with details

The OSM tag wiki would probably not make for good training data because it only has a single short description for each tag (and even on the French wiki many descriptions seem to be in English?) whereas you want to map multiple descriptions to the same tag.

Ideally you would have real query data (e.g. from cartes.app telemetry), then you could get a LLM to write a bespoke Overpass query for each one and use that as the ground truth. Alternatively, start from the list of OSM tag values used in the wild and ask an LLM to list possible reasons to visit that POI.

You could then use that data to finetune an embedding model for your use case. But, you know, somewhere in that model there's going to be a token vocabulary that the model knows about and at the other end you get a similarity score for each tag value. If you don't need to support complex queries where interactions between words matter ("any restaurant that is NOT Korean"), you could get away with a simple list of words and tags that they match to. Which is right where you started, except it could be more exhaustive. Why limit yourself to two Korean dishes when you can have a LLM list many more for you?

Same! I’m trying to find small models that can embed effectively to enable BM25/hybrid search over a large number of documents for a personal information repository. Ideally, it should run on consumer hardware.

bge-small-en-v1.5 is one that is comparable and what we’re working with for now.

the base model I clocked it at 5 ms per embedded on my mac studio. There is a mini variant (the demo version) that is sub - 2 ms. It could be a SIMD issue.. I'll look into this for better runtime support (also fee free to file an issue)

It's entirely the QAT. The whole distillation process is quantization-aware from the start, so the ternary weights are learned rather than fitted after the fact.

The only post-training quantization I applied was int4 on the embedding layer, and I ran a small ablation there to find the sweet spot between size and quality.

super cool use case! Hopefully it can provide quality embeddings + retrieval. Would love to learn to results/issues or feedback. Please feel free to file for issues on github

gte-small outscores all-MiniLM-L6 on MTEB (~61 vs ~56 avg per the GTE paper). MiniLM is ternlight's teacher (ternlight holds 0.84 Spearman fidelity to teacher). I haven't run a head-to-head yet; STS-B/MTEB numbers are on the roadmap. Also on the roadmap is to distill gte-small as teacher.