JINA EMBEDDINGS 2: 8192-Token General-Purpose Text Embeddings: Conclusion & Referencesby@escholar

JINA EMBEDDINGS 2: 8192-Token General-Purpose Text Embeddings: Conclusion & References

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Too Long; Didn't Read

Text embedding models have emerged as powerful tools for transforming sentences into fixedsized feature vectors that encapsulate semantic information.
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(1) Michael Günther, michael.guenther;

(2) Jackmin Ong,;

(3) Isabelle Mohr, isabelle.mohr;

(4) Alaeddine Abdessalem, alaeddine.abdessalem;

(5) Tanguy Abel, tanguy.abel;

(6) Mohammad Kalim Akram, kalim.akram;

(7) Susana Guzman, susana.guzman;

(8) Georgios Mastrapas, georgios.mastrapas;

(9) Saba Sturua, saba.sturua;

(10) Bo Wang,;

(11) Maximilian Werk, maximilian.werk;

(12) Nan Wang,;

(13) Han Xiao, han.xiao}

7 Conclusion

We have introduced Jina Embeddings v2, a novel embedding model based on a modified BERT architecture. This model eschews positional embeddings and instead employs bi-directional ALiBi slopes to capture positional information. By training a series of embedding models with this innovative architecture on the Web document corpus C4 and subsequently fine-tuning them, we have enabled the encoding of the semantics of both short and long textual values into meaningful vector representations. This effort has produced a new suite of open-source embedding models capable of encoding texts containing up to 8192 tokens. These embeddings signify a 16x increase in the maximum sequence length compared to leading open-source embedding models. Additionally, our model suite exhibits competitive performance on the MTEB benchmark. We also demonstrate how utilizing extended sequence lengths can offer our models an advantage over those without such capabilities.


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