- I have huge respect for Cohere and this embedding model looks like it could be best-in-class, but I find it hard to commit to a proprietary embedding model that's only available via an API when there are such good open weight models available.
I really like the approach Nomic take: their most recent models are available via their API or as open weights for non-commercial use only (unless you buy a license). They later relicense their older models under Apache 2.0 licenses.
This gives me confidence that I can continue to use my calculated vectors in the future even if Nomic's model is no longer available because I can run the local one instead.
Nomic Embed Vision 1.5 for example started out as CC-BY-NC-4.0 but was later relicensed to Apache 2.0: https://www.nomic.ai/blog/posts/nomic-embed-vision
- No downloadable open weights ?
Looks like I'll stay on [bge-m3](https://huggingface.co/BAAI/bge-m3)
by lukebuehler
2 subcomments
- I just started to look into multi-modal embedding models recently, and I was surprised how few options there are.
For example, Google's model only supports 30 text tokens [1]!!
This is definitely a welcome addition.
Any pointers to similarly powerful embedding models? I'm looking specifically for text and images? I wish there'd be also one that could do audio and video, but I don't think that exists.
[1] https://cloud.google.com/vertex-ai/generative-ai/docs/embedd...
- Curious for those in the industry, is there room for Cohere? Apparently they are doing very well in the enterprise, however recently I found myself wondering what their long term value prop is.
by podgietaru
1 subcomments
- I built a little RSS Reader / Aggregator that uses Cohere in order to do some arbitrary classification into different topics. I found it incredibly cheap to work with, and pretty good overall at classifying even with very limited inputs.
I also built this into a version of an OpenSource read it later app.
You can check it out here: https://github.com/aws-samples/rss-aggregator-using-cohere-e...
- Seems to under-perform voyage-3-large on the same benchmark. At the same time, I'm unsure how useful benchmarks are for embeddings.
by pencildiver
1 subcomments
- I'm a huge fan of Cohere. We were highlighted in the launch post and use their V3 text embeddings in production: https://www.searchagora.com/
We're switching to the V4 to store unified embeddings of our products. From the early tests we ran, this should help with edge case relevancy (i.e. when a product's image and text mismatch, thus creating a greater need for multi-modal embeddings) and improve our search speed by ~100ms.
- Wondering how this compares to the Gemini (preview) embeddings as they seem to perform significantly better than OpenAI embeddings 3 large. I don't see any MTEB scores so hard to compare.
by BrandiATMuhkuh
1 subcomments
- This is really great. I'll use it asap.
I'm working with enterprise clients in the AEC space. Having a model that actually understands documents with messy data (drawings, floor plans, books, norms, ...) will be great.
The current situation of chunking and transforming is such a messy situation.
- Can someone help me understand what Cohere does.
Do they just host open source models - so you can get them up and going faster?
If so, what’s their moat?
What prevents AWS from doing the same thing?
by moralestapia
0 subcomment
- A bit expensive but the benchmarks look quite good!
by distantsounds
1 subcomments
- so which stolen properties were used to train this model?
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