41–50 of 51 posts
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…
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.
Which benchmark are you referring to? Voyage-3-large is a text-only and much larger model than Embed-v4. If you want to unlock multimodality with Voyage-3-large, you'd have to either OCR (really bad results usually) or use a VLM to parse your data into textual descriptions (this works alright, but the cost of using a VLM will jack-up your data-pre-processing costs).
I looked at the NDCG and thought that was the dataset.since voyage and cohere both used NDCG. I now realize it was separate benchmarks with the same evaluation metric.
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?
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?
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...
cool project - I like the read-me but it looks like your link is down: https://djwtmt1np1xe4.cloudfront.net/
Earlier quoted context omitted.
cool project - I like the read-me but it looks like your link is down: https://djwtmt1np1xe4.cloudfront.net/
Still down—behold, the vibe coding is upon us
It literally has the entire IaC stack for you to deploy it yourself.
Earlier quoted context omitted.
I had the same thought, although voyage is 32k vs 128k for cohere 4. Anecdotal evidence points to benchmarks correlating with result quality for data I've dealt with. I haven't spent a lot of time comparing results between models, because we were happy with the results after trying a few and tuning some settings. Unless my dataset lines up really well with a benchmark's dataset, creating my own benchmark is probably…
Are people using 32k embeddings and no longer chunking? It feels like embedding content that large -- especially in dense texts -- will lead to loss of fidelity/signal in the output vector.
Earlier quoted context omitted.
To someone building a long term dataset, I’m not sure what assurances would help. Certainly a personal assurance doesn’t (though you’re kind to offer), and even a corporate statement doesn’t (new owners or C-suite could walk that back anytime). It might take a formal third-party “model escrow” arrangement to be really convincing.
Hey All, Thanks for engaging! Apologies for the delay but HN seems to have throttled my account from posting so I'm answering as fast as I can (or they will let me). You're right in the sense that I could wake up tomorrow and Cohere could lay me off, fire me, or I could quit! All of these are possible statements, but the reason I don't want to publicly commit particularly on our policy on Open Sourcing our models if…
I believe the term "going concern" means exactly the opposite of what you were trying to say here. Generally, comments about pedantry aren't helpful or uninteresting. This case was amusing to me in the context of assuring people Cohere is likely to stay around by boldly stating Cohere is at risk of being insolvent or ceasing operations ("Cohere is not a going concern"). Beyond that, I think it's pretty interesting how understandable it is to look at the term without knowing its meaning and assume the presence of the word "concern" must mean people are concerned about it going [bankrupt?].
I'm sure given the context nobody got the wrong impression. If anything, it makes me wonder if the term could, at least in informal contexts, reach a point of semantic inversion.
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?
they develop models around a very defined set of used cases, and they are very good at it. Look through their documentation and throughout their API. It’s very opinionated and quite a delight, honestly.