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navar | 1 month ago
https://huggingface.co/MongoDB/mdbr-leaf-ir
It ranks #1 on a bunch of leaderboards for models of its size. It can be used interchangeably with the model it has been distilled from (https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1...).
You can see an example comparing semantic (i.e., embeddings-based) search vs bm25 vs hybrid here: http://search-sensei.s3-website-us-east-1.amazonaws.com (warning! It will download ~50MB of data for the model weights and onnx runtime on first load, but should otherwise run smoothly even on a phone)
This mini app illustrates the advantage of semantic vs bm25 search. For instance, embedding models "know" that j lo refers to jennifer lopez.
We have also published the recipe to train this type of models if you were interested in doing so; we show that it can be done on relatively modest hardware and training data is very easy to obtain: https://arxiv.org/abs/2509.12539
HanClinto|1 month ago
I don't know if this is too much to ask, but something that would really help me adopt your model is to include a fine-tuning setup. The BGE series of embeddings-models has been my go-to for a couple of years now -- not because it's the best-performing in the leaderboards, but because they make it so incredibly easy to fine-tune the model [0]. Give it a JSONL file of a bunch of training triplets, and you can fine-tune the base models on your own dataset. I appreciate you linking to the paper on the recipe for training this type of model -- how close to turnkey is your model to helping me do transfer learning with my own dataset? I looked around for a fine-tuning example of this model, and didn't happen to see anything, but I would be very interested in trying this one out.
Does support for fine-tuning already exist? If so, then I would be able to switch to this model away from BGE immediately.
* [0] - https://github.com/FlagOpen/FlagEmbedding/tree/master/exampl...
navar|1 month ago
Note that bge-base-en-v1.5 is a 110M params model - our is 23M. * BEIR performance is bge=53.23 vs ours=53.55 * RTEB performance is bge=43.75 vs ours=44.82 -> overall they should be very similar, except ours is 5x smaller and hence that much faster.
rcarmo|1 month ago
jasonjmcghee|1 month ago
navar|1 month ago
These are very interesting models.
The tradeoff here is that you get even faster inference, but lose on retrieval accuracy [0].
Specifically, inference will be faster because essentially you are only doing tokenization + a lookup table + an average. So despite the fact that their largest model is 32M params, you can expect inference speeds to be higher than ours, which 23M params but it is transformer-based.
I am not sure about typical inference speeds on a CPU for their models, but with ours you can expect to do ~22 docs per second, and ~120 queries per second on a standard 2vCPU server.
As far as retrieval accuracy goes, on BEIR we score 53.55, all-MiniLM-L12-v2 (a widely adopted compact text embedding model) scores 42.69, while potion-8M scores 30.43.
I can't find their larger models but you can generally get an idea of the power level of different embedding models here: https://huggingface.co/spaces/mteb/leaderboard
If you want to run them on a CPU it may make sense to filter for smaller models (e.g., <100M params). On the other side our models achieve higher retrieval scores.
[0] "accuracy" in layman terms, not in accuracy vs recall terms. The correct word here would be "effectiveness".
3abiton|1 month ago