Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction
  Retriever
Multi-vector dense models, such as ColBERT, have proven highly effective in
information retrieval. ColBERT's late interaction scoring approximates the
joint query-document attention seen in cross-encoders while maintaining
inference efficiency closer to traditional dense retrieval models, thanks to
its bi-encoder architecture and recent optimizations in indexing and search. In
this paper, we introduce several improvements to the ColBERT model architecture
and training pipeline, leveraging techniques successful in the more established
single-vector embedding model paradigm, particularly those suited for
heterogeneous multilingual data. Our new model, Jina-ColBERT-v2, demonstrates
strong performance across a range of English and multilingual retrieval tasks,
while also cutting storage requirements by up to 50% compared to previous
models.
66d585b7092cc13d0f961d44
Rohan Jha
66d585b7092cc13d0f961d45
Bo Wang
extracted_confirmed
2024-10-02T07:11:25.862Z
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Bo Wang
user
bwang0911
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Michael Günther
admin_assigned
2024-09-02T09:35:08.111Z
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Michael Günther
user
michael-guenther
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Saba Sturua
admin_assigned
2024-09-02T09:35:17.536Z
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Saba Sturua
user
jupyterjazz
66d585b7092cc13d0f961d48
Mohammad Kalim Akram
admin_assigned
2024-09-02T09:35:23.144Z
64d22f33032a420d1863b6ea
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Mohammad Kalim Akram
user
makram93
66d585b7092cc13d0f961d49
Han Xiao
admin_assigned
2024-09-02T09:35:38.480Z
603763514de52ff951d89793
https://cdn-avatars.huggingface.co/v1/production/uploads/603763514de52ff951d89793/n-QouGYg7oE5QeDaAb3Ns.png
Han Xiao
user
hanxiao
Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction
  Retriever
Multi-vector dense models, such as ColBERT, have proven highly effective in
information retrieval. ColBERT's late interaction scoring approximates the
joint query-document attention seen in cross-encoders while maintaining
inference efficiency closer to traditional dense retrieval models, thanks to
its bi-encoder architecture and recent optimizations in indexing and search. In
this paper, we introduce several improvements to the ColBERT model architecture
and training pipeline, leveraging techniques successful in the more established
single-vector embedding model paradigm, particularly those suited for
heterogeneous multilingual data. Our new model, Jina-ColBERT-v2, demonstrates
strong performance across a range of English and multilingual retrieval tasks,
while also cutting storage requirements by up to 50% compared to previous
models.
Jina-ColBERT-v2 enhances ColBERT's performance and reduces storage requirements through improvements to its architecture and training pipeline, benefiting multilingual retrieval tasks.
multi-vector dense models ColBERT late interaction scoring joint query-document attention cross-encoders bi-encoder architecture indexing search single-vector embedding Jina-ColBERT-v2 heterogeneous multilingual data