title: jina-embeddings-v3: Multilingual Embeddings With Task LoRA
summary: We introduce jina-embeddings-v3, a novel text embedding model with 570
  million parameters, achieves state-of-the-art performance on multilingual data
  and long-context retrieval tasks, supporting context lengths of up to 8192
  tokens. The model includes a set of task-specific Low-Rank Adaptation (LoRA)
  adapters to generate high-quality embeddings for query-document retrieval,
  clustering, classification, and text matching. Additionally, Matryoshka
  Representation Learning is integrated into the training process, allowing
  flexible truncation of embedding dimensions without compromising performance.
  Evaluation on the MTEB benchmark shows that jina-embeddings-v3 outperforms the
  latest proprietary embeddings from OpenAI and Cohere on English tasks, while
  achieving superior performance compared to multilingual-e5-large-instruct
  across all multilingual tasks.
paper_authors._id: 66e926caff154e75c55e597f
paper_authors.name: Saba Sturua
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T15:08:29.709Z
paper_authors._id: 64c23f6d569648a60737eddb
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64c23f6d569648a60737eddb/iZq7bp-yYaGl5VBVoN5Dg.jpeg
paper_authors.fullname: Saba Sturua
paper_authors.type: user
paper_authors.user: jupyterjazz
paper_authors._id: 66e926caff154e75c55e5980
paper_authors.name: Isabelle Mohr
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T15:08:44.633Z
paper_authors._id: 64a830cd6cc1a9a131f62619
paper_authors.avatarUrl: /avatars/e7b0ad62e8fb8c99c8d75edf45688995.svg
paper_authors.fullname: Isabelle Mohr
paper_authors.type: user
paper_authors.user: isacat
paper_authors._id: 66e926caff154e75c55e5981
paper_authors.name: Mohammad Kalim Akram
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T15:08:51.647Z
paper_authors._id: 64d22f33032a420d1863b6ea
paper_authors.avatarUrl: /avatars/ed3eaf4bab70dd6ab9a2b67b5928e4fb.svg
paper_authors.fullname: Mohammad Kalim Akram
paper_authors.type: user
paper_authors.user: makram93
paper_authors._id: 66e926caff154e75c55e5982
paper_authors.name: Michael Günther
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T15:09:15.549Z
paper_authors._id: 6476ff2699a5ce743ccea3fc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6476ff2699a5ce743ccea3fc/zmFmF8tXXDaAGcl8RYiRr.jpeg
paper_authors.fullname: Michael Günther
paper_authors.type: user
paper_authors.user: michael-guenther
paper_authors._id: 66e926caff154e75c55e5983
paper_authors.name: Bo Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-02T07:41:34.129Z
paper_authors._id: 63491dc83d8dc83a55cb749c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63491dc83d8dc83a55cb749c/IoqJrOIaEnYO_S7si4KGp.jpeg
paper_authors.fullname: Bo Wang
paper_authors.type: user
paper_authors.user: bwang0911
paper_authors._id: 66e926caff154e75c55e5984
paper_authors.name: Markus Krimmel
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T15:10:04.475Z
paper_authors._id: 651e707ca92910108b12ecf4
paper_authors.avatarUrl: /avatars/140ef2d6980aa2d7fec1550d245fc33e.svg
paper_authors.fullname: Markus Krimmel
paper_authors.type: user
paper_authors.user: Markus28
paper_authors._id: 66e926caff154e75c55e5985
paper_authors.name: Feng Wang
paper_authors._id: 66e926caff154e75c55e5986
paper_authors.name: Georgios Mastrapas
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T15:10:19.566Z
paper_authors._id: 64f8620e492828088373ddf9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64f8620e492828088373ddf9/g6XQmUzEPNMQYNc34gYpR.jpeg
paper_authors.fullname: Georgios Mastrapas
paper_authors.type: user
paper_authors.user: gmastrapas
paper_authors._id: 66e926caff154e75c55e5987
paper_authors.name: Andreas Koukounas
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T15:10:25.511Z
paper_authors._id: 651e7084570ba4662812114b
paper_authors.avatarUrl: /avatars/1438e5caa483f63dc0da5ee7508ef7eb.svg
paper_authors.fullname: Andreas Koukounas
paper_authors.type: user
paper_authors.user: koukandre
paper_authors._id: 66e926caff154e75c55e5988
paper_authors.name: Andreas Koukounas
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T15:10:31.608Z
paper_authors._id: 651e7084570ba4662812114b
paper_authors.avatarUrl: /avatars/1438e5caa483f63dc0da5ee7508ef7eb.svg
paper_authors.fullname: Andreas Koukounas
paper_authors.type: user
paper_authors.user: koukandre
paper_authors._id: 66e926caff154e75c55e5989
paper_authors.name: Nan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-22T08:03:17.814Z
paper_authors._id: 60124e957bdbda85b0874f7d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60124e957bdbda85b0874f7d/tBxDFq00g3Gz8X0Bv2bwD.jpeg
paper_authors.fullname: Nan Wang
paper_authors.type: user
paper_authors.user: nan
paper_authors._id: 66e926caff154e75c55e598a
paper_authors.name: Han Xiao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T15:14:54.542Z
paper_authors._id: 603763514de52ff951d89793
paper_authors.avatarUrl: /avatars/4484b361bb887fc73a6d38d953bfe9e0.svg
paper_authors.fullname: Han Xiao
paper_authors.type: user
paper_authors.user: hanxiao
paper_title: jina-embeddings-v3: Multilingual Embeddings With Task LoRA
paper_summary: We introduce jina-embeddings-v3, a novel text embedding model with 570
  million parameters, achieves state-of-the-art performance on multilingual data
  and long-context retrieval tasks, supporting context lengths of up to 8192
  tokens. The model includes a set of task-specific Low-Rank Adaptation (LoRA)
  adapters to generate high-quality embeddings for query-document retrieval,
  clustering, classification, and text matching. Additionally, Matryoshka
  Representation Learning is integrated into the training process, allowing
  flexible truncation of embedding dimensions without compromising performance.
  Evaluation on the MTEB benchmark shows that jina-embeddings-v3 outperforms the
  latest proprietary embeddings from OpenAI and Cohere on English tasks, while
  achieving superior performance compared to multilingual-e5-large-instruct
  across all multilingual tasks.
paper_ai_summary: jina-embeddings-v3, a large-scale text embedding model, achieves state-of-the-art performance in multilingual and long-context retrieval tasks using Low-Rank Adaptation and Matryoshka Representation Learning.
paper_ai_keywords: Low-Rank Adaptation LoRA adapters Matryoshka Representation Learning MTEB benchmark