mahjongmen

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by mahjongmen 1y ago
thanks for the kind words - we're always looking for ways to make our documentation more of a delight :)
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by mahjongmen 1y ago
cool project - I like the read-me but it looks like your link is down: https://djwtmt1np1xe4.cloudfront.net/
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by mahjongmen 1y ago
Thank you sir! I appreciate you.
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by mahjongmen 1y ago
That sounds really cool! Would love to better understand your use-case and make sure it works well for you! Drop me an email at elliott@cohere.ai
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by mahjongmen 1y ago
Hey Andriy! We actually did internally run benchmarks against your models since they are open-weights - however, when looking at the license on the 3bn multimodal model ( https://huggingface.co/nomic-ai/nomic-embed-multi
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by mahjongmen 1y ago
Hey Luke, Our model does exceptionally well on text and images, and in particular, when text and images are mixed together. An example of where this works well would be in E-commerce where you may have a product title, description, and mult
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by mahjongmen 1y ago
Hey Serjester Email me at elliott@cohere.ai, let's arrange time to chat. We did head to head evals with Voyage Large / Voyage Multimodal and I can share them with you if you are serious about moving your embeddings over. We tested
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by mahjongmen 1y ago
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 Cohe
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by mahjongmen 1y ago
I'll start off with, I'm not one of our founders and REALLY wouldn't want to be publicly held accountable for policies or commitments until I've been able to get internal alignment on things I say. That being said, since
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by mahjongmen 1y ago
Hey Cahaya, While we benchmarked internally, on BEIR, we opted not to report our model onto MTEB for the following reason: 1) MTEB has been gamed - if you look at this model ( https://huggingface.co/voyageai/voyage-3-m-e
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by mahjongmen 1y ago
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 par
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by mahjongmen 1y ago
Hey! Since we focus on Enterprise use-cases, we made sure to include training data from domains like you mentioned above. While in very specific use-cases finetuning may be helpful, but we also do offer that as a customization service (just
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by mahjongmen 1y ago
Hey Simon, Elliott here from Cohere. We benchmarked against Nomic's models on our consortium of datasets ranging from text-only, image-only, and mixed modalities. Without publishing additional benchmarks, I am confident in saying that