Hey HN, which embedding models are people using? There has been so much development around foundational LLMs, but haven't seen much news about embedding models.
I've liked qwen and embeddinggemma for local search. Qwen because 32K is enough to basically fit a whole page into the context window and embeddiggemma because it's crazy efficient.
Cohere's embed-v4.0 is my daily driver as far as a high performance model is concerned. I do a lot of cluster analysis and data visualization and I like that there's an `input_type="clustering"` mode in addition to the standard `input_type="search"` mode.
I’ve been using MixedBread, which is a pretty old model at this point. Recently, I tried comparing it to some newer models and was disappointed that the results weren’t dramatically and uniformly better.
You probably can’t go wrong if you pick a recent one that scores decently well on benchmarks and is at the right price point (or memory requirement) for whatever you’re trying to do.
At a minimum, you increase top-k to cast a wider net, then after reranking, take the N you really want. You have to play around with it a bit, but that’s the idea.
https://huggingface.co/spaces/mteb/leaderboard
best in what?
For a fast, open, and local model, I've found it hard to beat https://huggingface.co/sentence-transformers/all-MiniLM-L6-v...
You probably can’t go wrong if you pick a recent one that scores decently well on benchmarks and is at the right price point (or memory requirement) for whatever you’re trying to do.