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Jina AI launches open-source 8k text embedding

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Re: Jina AI launches open-source 8k text embedding

#11

Post by [deleted] »

sroussey wrote: post #7
omneity wrote: post #4

Impressive work. I wonder what would be the best way to use 8k embeddings. It’s a lot of information to keep in a vector, so things like “precision” of the embedding space and its ability to distinguish very similar large documents will be key. Maybe it can be useful for coarse similarity matching, for example to detect plagiarism?

8K is the context length. Their vector dimension size is actual much smaller, which is great for a number of use cases, though maybe not the ones you are thinking about.

[deleted]

Re: Jina AI launches open-source 8k text embedding

#13

Post by sroussey »

sroussey wrote: post #10
sroussey wrote: post #8

Does anyone know what they are using for this comparison and ranking? And where does instruct-xl stand in the mix?

Oh duh, it’s right in the post and instructor-xl is number 9. And so many new participants now!

The ranking are here:

https://huggingface.co/spaces/mteb/leaderboard

It’s amazing how many new and better ones there are since I last looked a few months ago. Instructor-xl was number 1, now it is number 9, and its size is more than 10x the number 2 ranked!

Things move fast!

Re: Jina AI launches open-source 8k text embedding

#14

Post by jncraton »

This is great to see. It looks like the size of the embedding vector is half the size of text-embedding-ada-002 (768 vs 1536) while providing competitive performance. This will save space in databases and make lookups somewhat faster.

For those unaware, if 512 tokens of context is sufficient for your use case, there are already many options that outperform text-embedding-ada-002 on common benchmarks:

https://huggingface.co/spaces/mteb/leaderboard

Re: Jina AI launches open-source 8k text embedding

#15

Post by BoorishBears »

andrewstuart wrote: post #5

Anyone got links to examples of text embedding?

Easiest example is taking three words: Universe, University, College.

- University and Universe are similar alphabetically.

- University and College are similar in meaning.

Take embeddings for those three words and `University` will be near `College`, while `Universe` will be further away, because embeddings capture meaning:

UniversityCollegeUniverse

_

With old school search you'd need to handle the special case of treating University and College as similar, but embeddings already handle it.

With embeddings you can do math to find how similar two results are, based on how close their vectors are. The closer the embeddings, the closer the meaning.

Re: Jina AI launches open-source 8k text embedding

#20

Post by omneity »

sroussey wrote: post #7
omneity wrote: post #4

Impressive work. I wonder what would be the best way to use 8k embeddings. It’s a lot of information to keep in a vector, so things like “precision” of the embedding space and its ability to distinguish very similar large documents will be key. Maybe it can be useful for coarse similarity matching, for example to detect plagiarism?

8K is the context length. Their vector dimension size is actual much smaller, which is great for a number of use cases, though maybe not the ones you are thinking about.

Yes that’s also how I understood it. Maybe it was ambiguously expressed, but I mean “8k tokens as input is a lot of information to encode”
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