README.md

---
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - mteb
inference: false
license: apache-2.0
language:
- en
- zh
model-index:
- name: jina-embeddings-v2-base-zh
  results:
  - task:
      type: STS
    dataset:
      type: C-MTEB/AFQMC
      name: MTEB AFQMC
      config: default
      split: validation
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 48.51403119231363
    - type: cos_sim_spearman
      value: 50.5928547846445
    - type: euclidean_pearson
      value: 48.750436310559074
    - type: euclidean_spearman
      value: 50.50950238691385
    - type: manhattan_pearson
      value: 48.7866189440328
    - type: manhattan_spearman
      value: 50.58692402017165
  - task:
      type: STS
    dataset:
      type: C-MTEB/ATEC
      name: MTEB ATEC
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 50.25985700105725
    - type: cos_sim_spearman
      value: 51.28815934593989
    - type: euclidean_pearson
      value: 52.70329248799904
    - type: euclidean_spearman
      value: 50.94101139559258
    - type: manhattan_pearson
      value: 52.6647237400892
    - type: manhattan_spearman
      value: 50.922441325406176
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (zh)
      config: zh
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 34.944
    - type: f1
      value: 34.06478860660109
  - task:
      type: STS
    dataset:
      type: C-MTEB/BQ
      name: MTEB BQ
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 65.15667035488342
    - type: cos_sim_spearman
      value: 66.07110142081
    - type: euclidean_pearson
      value: 60.447598102249714
    - type: euclidean_spearman
      value: 61.826575796578766
    - type: manhattan_pearson
      value: 60.39364279354984
    - type: manhattan_spearman
      value: 61.78743491223281
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/CLSClusteringP2P
      name: MTEB CLSClusteringP2P
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 39.96714175391701
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/CLSClusteringS2S
      name: MTEB CLSClusteringS2S
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 38.39863566717934
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/CMedQAv1-reranking
      name: MTEB CMedQAv1
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 83.63680381780644
    - type: mrr
      value: 86.16476190476192
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/CMedQAv2-reranking
      name: MTEB CMedQAv2
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 83.74350667859487
    - type: mrr
      value: 86.10388888888889
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/CmedqaRetrieval
      name: MTEB CmedqaRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 22.072
    - type: map_at_10
      value: 32.942
    - type: map_at_100
      value: 34.768
    - type: map_at_1000
      value: 34.902
    - type: map_at_3
      value: 29.357
    - type: map_at_5
      value: 31.236000000000004
    - type: mrr_at_1
      value: 34.259
    - type: mrr_at_10
      value: 41.957
    - type: mrr_at_100
      value: 42.982
    - type: mrr_at_1000
      value: 43.042
    - type: mrr_at_3
      value: 39.722
    - type: mrr_at_5
      value: 40.898
    - type: ndcg_at_1
      value: 34.259
    - type: ndcg_at_10
      value: 39.153
    - type: ndcg_at_100
      value: 46.493
    - type: ndcg_at_1000
      value: 49.01
    - type: ndcg_at_3
      value: 34.636
    - type: ndcg_at_5
      value: 36.278
    - type: precision_at_1
      value: 34.259
    - type: precision_at_10
      value: 8.815000000000001
    - type: precision_at_100
      value: 1.474
    - type: precision_at_1000
      value: 0.179
    - type: precision_at_3
      value: 19.73
    - type: precision_at_5
      value: 14.174000000000001
    - type: recall_at_1
      value: 22.072
    - type: recall_at_10
      value: 48.484
    - type: recall_at_100
      value: 79.035
    - type: recall_at_1000
      value: 96.15
    - type: recall_at_3
      value: 34.607
    - type: recall_at_5
      value: 40.064
  - task:
      type: PairClassification
    dataset:
      type: C-MTEB/CMNLI
      name: MTEB Cmnli
      config: default
      split: validation
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 76.7047504509922
    - type: cos_sim_ap
      value: 85.26649874800871
    - type: cos_sim_f1
      value: 78.13528724646915
    - type: cos_sim_precision
      value: 71.57587548638132
    - type: cos_sim_recall
      value: 86.01823708206688
    - type: dot_accuracy
      value: 70.13830426939266
    - type: dot_ap
      value: 77.01510412382171
    - type: dot_f1
      value: 73.56710042713817
    - type: dot_precision
      value: 63.955094991364426
    - type: dot_recall
      value: 86.57937806873977
    - type: euclidean_accuracy
      value: 75.53818400481059
    - type: euclidean_ap
      value: 84.34668448241264
    - type: euclidean_f1
      value: 77.51741608613047
    - type: euclidean_precision
      value: 70.65614777756399
    - type: euclidean_recall
      value: 85.85457096095394
    - type: manhattan_accuracy
      value: 75.49007817197835
    - type: manhattan_ap
      value: 84.40297506704299
    - type: manhattan_f1
      value: 77.63185324160932
    - type: manhattan_precision
      value: 70.03949595636637
    - type: manhattan_recall
      value: 87.07037643207856
    - type: max_accuracy
      value: 76.7047504509922
    - type: max_ap
      value: 85.26649874800871
    - type: max_f1
      value: 78.13528724646915
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/CovidRetrieval
      name: MTEB CovidRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 69.178
    - type: map_at_10
      value: 77.523
    - type: map_at_100
      value: 77.793
    - type: map_at_1000
      value: 77.79899999999999
    - type: map_at_3
      value: 75.878
    - type: map_at_5
      value: 76.849
    - type: mrr_at_1
      value: 69.44200000000001
    - type: mrr_at_10
      value: 77.55
    - type: mrr_at_100
      value: 77.819
    - type: mrr_at_1000
      value: 77.826
    - type: mrr_at_3
      value: 75.957
    - type: mrr_at_5
      value: 76.916
    - type: ndcg_at_1
      value: 69.44200000000001
    - type: ndcg_at_10
      value: 81.217
    - type: ndcg_at_100
      value: 82.45
    - type: ndcg_at_1000
      value: 82.636
    - type: ndcg_at_3
      value: 77.931
    - type: ndcg_at_5
      value: 79.655
    - type: precision_at_1
      value: 69.44200000000001
    - type: precision_at_10
      value: 9.357
    - type: precision_at_100
      value: 0.993
    - type: precision_at_1000
      value: 0.101
    - type: precision_at_3
      value: 28.1
    - type: precision_at_5
      value: 17.724
    - type: recall_at_1
      value: 69.178
    - type: recall_at_10
      value: 92.624
    - type: recall_at_100
      value: 98.209
    - type: recall_at_1000
      value: 99.684
    - type: recall_at_3
      value: 83.772
    - type: recall_at_5
      value: 87.882
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/DuRetrieval
      name: MTEB DuRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 25.163999999999998
    - type: map_at_10
      value: 76.386
    - type: map_at_100
      value: 79.339
    - type: map_at_1000
      value: 79.39500000000001
    - type: map_at_3
      value: 52.959
    - type: map_at_5
      value: 66.59
    - type: mrr_at_1
      value: 87.9
    - type: mrr_at_10
      value: 91.682
    - type: mrr_at_100
      value: 91.747
    - type: mrr_at_1000
      value: 91.751
    - type: mrr_at_3
      value: 91.267
    - type: mrr_at_5
      value: 91.527
    - type: ndcg_at_1
      value: 87.9
    - type: ndcg_at_10
      value: 84.569
    - type: ndcg_at_100
      value: 87.83800000000001
    - type: ndcg_at_1000
      value: 88.322
    - type: ndcg_at_3
      value: 83.473
    - type: ndcg_at_5
      value: 82.178
    - type: precision_at_1
      value: 87.9
    - type: precision_at_10
      value: 40.605000000000004
    - type: precision_at_100
      value: 4.752
    - type: precision_at_1000
      value: 0.488
    - type: precision_at_3
      value: 74.9
    - type: precision_at_5
      value: 62.96000000000001
    - type: recall_at_1
      value: 25.163999999999998
    - type: recall_at_10
      value: 85.97399999999999
    - type: recall_at_100
      value: 96.63000000000001
    - type: recall_at_1000
      value: 99.016
    - type: recall_at_3
      value: 55.611999999999995
    - type: recall_at_5
      value: 71.936
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/EcomRetrieval
      name: MTEB EcomRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 48.6
    - type: map_at_10
      value: 58.831
    - type: map_at_100
      value: 59.427
    - type: map_at_1000
      value: 59.44199999999999
    - type: map_at_3
      value: 56.383
    - type: map_at_5
      value: 57.753
    - type: mrr_at_1
      value: 48.6
    - type: mrr_at_10
      value: 58.831
    - type: mrr_at_100
      value: 59.427
    - type: mrr_at_1000
      value: 59.44199999999999
    - type: mrr_at_3
      value: 56.383
    - type: mrr_at_5
      value: 57.753
    - type: ndcg_at_1
      value: 48.6
    - type: ndcg_at_10
      value: 63.951
    - type: ndcg_at_100
      value: 66.72200000000001
    - type: ndcg_at_1000
      value: 67.13900000000001
    - type: ndcg_at_3
      value: 58.882
    - type: ndcg_at_5
      value: 61.373
    - type: precision_at_1
      value: 48.6
    - type: precision_at_10
      value: 8.01
    - type: precision_at_100
      value: 0.928
    - type: precision_at_1000
      value: 0.096
    - type: precision_at_3
      value: 22.033
    - type: precision_at_5
      value: 14.44
    - type: recall_at_1
      value: 48.6
    - type: recall_at_10
      value: 80.10000000000001
    - type: recall_at_100
      value: 92.80000000000001
    - type: recall_at_1000
      value: 96.1
    - type: recall_at_3
      value: 66.10000000000001
    - type: recall_at_5
      value: 72.2
  - task:
      type: Classification
    dataset:
      type: C-MTEB/IFlyTek-classification
      name: MTEB IFlyTek
      config: default
      split: validation
      revision: None
    metrics:
    - type: accuracy
      value: 47.36437091188918
    - type: f1
      value: 36.60946954228577
  - task:
      type: Classification
    dataset:
      type: C-MTEB/JDReview-classification
      name: MTEB JDReview
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 79.5684803001876
    - type: ap
      value: 42.671935929201524
    - type: f1
      value: 73.31912729103752
  - task:
      type: STS
    dataset:
      type: C-MTEB/LCQMC
      name: MTEB LCQMC
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 68.62670112113864
    - type: cos_sim_spearman
      value: 75.74009123170768
    - type: euclidean_pearson
      value: 73.93002595958237
    - type: euclidean_spearman
      value: 75.35222935003587
    - type: manhattan_pearson
      value: 73.89870445158144
    - type: manhattan_spearman
      value: 75.31714936339398
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/Mmarco-reranking
      name: MTEB MMarcoReranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 31.5372713650176
    - type: mrr
      value: 30.163095238095238
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/MMarcoRetrieval
      name: MTEB MMarcoRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 65.054
    - type: map_at_10
      value: 74.156
    - type: map_at_100
      value: 74.523
    - type: map_at_1000
      value: 74.535
    - type: map_at_3
      value: 72.269
    - type: map_at_5
      value: 73.41
    - type: mrr_at_1
      value: 67.24900000000001
    - type: mrr_at_10
      value: 74.78399999999999
    - type: mrr_at_100
      value: 75.107
    - type: mrr_at_1000
      value: 75.117
    - type: mrr_at_3
      value: 73.13499999999999
    - type: mrr_at_5
      value: 74.13499999999999
    - type: ndcg_at_1
      value: 67.24900000000001
    - type: ndcg_at_10
      value: 77.96300000000001
    - type: ndcg_at_100
      value: 79.584
    - type: ndcg_at_1000
      value: 79.884
    - type: ndcg_at_3
      value: 74.342
    - type: ndcg_at_5
      value: 76.278
    - type: precision_at_1
      value: 67.24900000000001
    - type: precision_at_10
      value: 9.466
    - type: precision_at_100
      value: 1.027
    - type: precision_at_1000
      value: 0.105
    - type: precision_at_3
      value: 27.955999999999996
    - type: precision_at_5
      value: 17.817
    - type: recall_at_1
      value: 65.054
    - type: recall_at_10
      value: 89.113
    - type: recall_at_100
      value: 96.369
    - type: recall_at_1000
      value: 98.714
    - type: recall_at_3
      value: 79.45400000000001
    - type: recall_at_5
      value: 84.06
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (zh-CN)
      config: zh-CN
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 68.1977135171486
    - type: f1
      value: 67.23114308718404
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (zh-CN)
      config: zh-CN
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 71.92669804976462
    - type: f1
      value: 72.90628475628779
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/MedicalRetrieval
      name: MTEB MedicalRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 49.2
    - type: map_at_10
      value: 54.539
    - type: map_at_100
      value: 55.135
    - type: map_at_1000
      value: 55.19199999999999
    - type: map_at_3
      value: 53.383
    - type: map_at_5
      value: 54.142999999999994
    - type: mrr_at_1
      value: 49.2
    - type: mrr_at_10
      value: 54.539
    - type: mrr_at_100
      value: 55.135999999999996
    - type: mrr_at_1000
      value: 55.19199999999999
    - type: mrr_at_3
      value: 53.383
    - type: mrr_at_5
      value: 54.142999999999994
    - type: ndcg_at_1
      value: 49.2
    - type: ndcg_at_10
      value: 57.123000000000005
    - type: ndcg_at_100
      value: 60.21300000000001
    - type: ndcg_at_1000
      value: 61.915
    - type: ndcg_at_3
      value: 54.772
    - type: ndcg_at_5
      value: 56.157999999999994
    - type: precision_at_1
      value: 49.2
    - type: precision_at_10
      value: 6.52
    - type: precision_at_100
      value: 0.8009999999999999
    - type: precision_at_1000
      value: 0.094
    - type: precision_at_3
      value: 19.6
    - type: precision_at_5
      value: 12.44
    - type: recall_at_1
      value: 49.2
    - type: recall_at_10
      value: 65.2
    - type: recall_at_100
      value: 80.10000000000001
    - type: recall_at_1000
      value: 93.89999999999999
    - type: recall_at_3
      value: 58.8
    - type: recall_at_5
      value: 62.2
  - task:
      type: Classification
    dataset:
      type: C-MTEB/MultilingualSentiment-classification
      name: MTEB MultilingualSentiment
      config: default
      split: validation
      revision: None
    metrics:
    - type: accuracy
      value: 63.29333333333334
    - type: f1
      value: 63.03293854259612
  - task:
      type: PairClassification
    dataset:
      type: C-MTEB/OCNLI
      name: MTEB Ocnli
      config: default
      split: validation
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 75.69030860855442
    - type: cos_sim_ap
      value: 80.6157833772759
    - type: cos_sim_f1
      value: 77.87524366471735
    - type: cos_sim_precision
      value: 72.3076923076923
    - type: cos_sim_recall
      value: 84.37170010559663
    - type: dot_accuracy
      value: 67.78559826746074
    - type: dot_ap
      value: 72.00871467527499
    - type: dot_f1
      value: 72.58722247394654
    - type: dot_precision
      value: 63.57142857142857
    - type: dot_recall
      value: 84.58289334741288
    - type: euclidean_accuracy
      value: 75.20303194369248
    - type: euclidean_ap
      value: 80.98587256415605
    - type: euclidean_f1
      value: 77.26396917148362
    - type: euclidean_precision
      value: 71.03631532329496
    - type: euclidean_recall
      value: 84.68848996832101
    - type: manhattan_accuracy
      value: 75.20303194369248
    - type: manhattan_ap
      value: 80.93460699513219
    - type: manhattan_f1
      value: 77.124773960217
    - type: manhattan_precision
      value: 67.43083003952569
    - type: manhattan_recall
      value: 90.07391763463569
    - type: max_accuracy
      value: 75.69030860855442
    - type: max_ap
      value: 80.98587256415605
    - type: max_f1
      value: 77.87524366471735
  - task:
      type: Classification
    dataset:
      type: C-MTEB/OnlineShopping-classification
      name: MTEB OnlineShopping
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 87.00000000000001
    - type: ap
      value: 83.24372135949511
    - type: f1
      value: 86.95554191530607
  - task:
      type: STS
    dataset:
      type: C-MTEB/PAWSX
      name: MTEB PAWSX
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 37.57616811591219
    - type: cos_sim_spearman
      value: 41.490259084930045
    - type: euclidean_pearson
      value: 38.9155043692188
    - type: euclidean_spearman
      value: 39.16056534305623
    - type: manhattan_pearson
      value: 38.76569892264335
    - type: manhattan_spearman
      value: 38.99891685590743
  - task:
      type: STS
    dataset:
      type: C-MTEB/QBQTC
      name: MTEB QBQTC
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 35.44858610359665
    - type: cos_sim_spearman
      value: 38.11128146262466
    - type: euclidean_pearson
      value: 31.928644189822457
    - type: euclidean_spearman
      value: 34.384936631696554
    - type: manhattan_pearson
      value: 31.90586687414376
    - type: manhattan_spearman
      value: 34.35770153777186
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (zh)
      config: zh
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 66.54931957553592
    - type: cos_sim_spearman
      value: 69.25068863016632
    - type: euclidean_pearson
      value: 50.26525596106869
    - type: euclidean_spearman
      value: 63.83352741910006
    - type: manhattan_pearson
      value: 49.98798282198196
    - type: manhattan_spearman
      value: 63.87649521907841
  - task:
      type: STS
    dataset:
      type: C-MTEB/STSB
      name: MTEB STSB
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 82.52782476625825
    - type: cos_sim_spearman
      value: 82.55618986168398
    - type: euclidean_pearson
      value: 78.48190631687673
    - type: euclidean_spearman
      value: 78.39479731354655
    - type: manhattan_pearson
      value: 78.51176592165885
    - type: manhattan_spearman
      value: 78.42363787303265
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/T2Reranking
      name: MTEB T2Reranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 67.36693873615643
    - type: mrr
      value: 77.83847701797939
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/T2Retrieval
      name: MTEB T2Retrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 25.795
    - type: map_at_10
      value: 72.258
    - type: map_at_100
      value: 76.049
    - type: map_at_1000
      value: 76.134
    - type: map_at_3
      value: 50.697
    - type: map_at_5
      value: 62.324999999999996
    - type: mrr_at_1
      value: 86.634
    - type: mrr_at_10
      value: 89.792
    - type: mrr_at_100
      value: 89.91900000000001
    - type: mrr_at_1000
      value: 89.923
    - type: mrr_at_3
      value: 89.224
    - type: mrr_at_5
      value: 89.608
    - type: ndcg_at_1
      value: 86.634
    - type: ndcg_at_10
      value: 80.589
    - type: ndcg_at_100
      value: 84.812
    - type: ndcg_at_1000
      value: 85.662
    - type: ndcg_at_3
      value: 82.169
    - type: ndcg_at_5
      value: 80.619
    - type: precision_at_1
      value: 86.634
    - type: precision_at_10
      value: 40.389
    - type: precision_at_100
      value: 4.93
    - type: precision_at_1000
      value: 0.513
    - type: precision_at_3
      value: 72.104
    - type: precision_at_5
      value: 60.425
    - type: recall_at_1
      value: 25.795
    - type: recall_at_10
      value: 79.565
    - type: recall_at_100
      value: 93.24799999999999
    - type: recall_at_1000
      value: 97.595
    - type: recall_at_3
      value: 52.583999999999996
    - type: recall_at_5
      value: 66.175
  - task:
      type: Classification
    dataset:
      type: C-MTEB/TNews-classification
      name: MTEB TNews
      config: default
      split: validation
      revision: None
    metrics:
    - type: accuracy
      value: 47.648999999999994
    - type: f1
      value: 46.28925837008413
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/ThuNewsClusteringP2P
      name: MTEB ThuNewsClusteringP2P
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 54.07641891287953
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/ThuNewsClusteringS2S
      name: MTEB ThuNewsClusteringS2S
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 53.423702062353954
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/VideoRetrieval
      name: MTEB VideoRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 55.7
    - type: map_at_10
      value: 65.923
    - type: map_at_100
      value: 66.42
    - type: map_at_1000
      value: 66.431
    - type: map_at_3
      value: 63.9
    - type: map_at_5
      value: 65.225
    - type: mrr_at_1
      value: 55.60000000000001
    - type: mrr_at_10
      value: 65.873
    - type: mrr_at_100
      value: 66.36999999999999
    - type: mrr_at_1000
      value: 66.381
    - type: mrr_at_3
      value: 63.849999999999994
    - type: mrr_at_5
      value: 65.17500000000001
    - type: ndcg_at_1
      value: 55.7
    - type: ndcg_at_10
      value: 70.621
    - type: ndcg_at_100
      value: 72.944
    - type: ndcg_at_1000
      value: 73.25399999999999
    - type: ndcg_at_3
      value: 66.547
    - type: ndcg_at_5
      value: 68.93599999999999
    - type: precision_at_1
      value: 55.7
    - type: precision_at_10
      value: 8.52
    - type: precision_at_100
      value: 0.958
    - type: precision_at_1000
      value: 0.098
    - type: precision_at_3
      value: 24.733
    - type: precision_at_5
      value: 16
    - type: recall_at_1
      value: 55.7
    - type: recall_at_10
      value: 85.2
    - type: recall_at_100
      value: 95.8
    - type: recall_at_1000
      value: 98.3
    - type: recall_at_3
      value: 74.2
    - type: recall_at_5
      value: 80
  - task:
      type: Classification
    dataset:
      type: C-MTEB/waimai-classification
      name: MTEB Waimai
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 84.54
    - type: ap
      value: 66.13603199670062
    - type: f1
      value: 82.61420654584116
---
<!-- TODO: add evaluation results here -->
<br><br>

<p align="center">
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>


<p align="center">
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>

## Quick Start

The easiest way to starting using `jina-embeddings-v2-base-zh` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).

## Intended Usage & Model Info

`jina-embeddings-v2-base-zh` is a Chinese/English bilingual text **embedding model** supporting **8192 sequence length**.
It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed Chinese-English input without bias. 
Additionally, we provide the following embedding models:

`jina-embeddings-v2-base-zh` 是支持中英双语的**文本向量**模型，它支持长达**8192字符**的文本编码。
该模型的研发基于BERT架构(JinaBERT)，JinaBERT是在BERT架构基础上的改进，首次将[ALiBi](https://arxiv.org/abs/2108.12409)应用到编码器架构中以支持更长的序列。
不同于以往的单语言/多语言向量模型，我们设计双语模型来更好的支持单语言（中搜中）以及跨语言（中搜英）文档检索。
除此之外，我们也提供其它向量模型:

- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English Bilingual embeddings **(you are here)**.
- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English Bilingual embeddings.
- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon).

## Data & Parameters

We will publish a report with technical details about the training of the bilingual models soon.
The training of the English model is described in this [technical report](https://arxiv.org/abs/2310.19923).

## Usage

**<details><summary>Please apply mean pooling when integrating the model.</summary>**
<p>

### Why mean pooling?

`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
It has been proved to be the most effective way to produce high-quality sentence embeddings.
We offer an `encode` function to deal with this.

However, if you would like to do it without using the default `encode` function:

```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

sentences = ['How is the weather today?', '今天天气怎么样?']

tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-base-zh')
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-zh', trust_remote_code=True)

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

with torch.no_grad():
    model_output = model(**encoded_input)

embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
```

</p>
</details>

You can use Jina Embedding models directly from transformers package.

First, you need to make sure that you are logged into huggingface. You can either use the huggingface-cli tool (after installing the `transformers` package) and pass your [hugginface access token](https://huggingface.co/docs/hub/security-tokens):
```bash
huggingface-cli login
```
Alternatively, you can provide the access token as an environment variable in the shell:
```bash
export HF_TOKEN="<your token here>"
```
or in Python:
```python
import os

os.environ['HF_TOKEN'] = "<your token here>"
```

Then, you can use load and use the model via the `AutoModel` class:
```python
!pip install transformers
from transformers import AutoModel
from numpy.linalg import norm

cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-zh', trust_remote_code=True) # trust_remote_code is needed to use the encode method
embeddings = model.encode(['How is the weather today?', '今天天气怎么样?'])
print(cos_sim(embeddings[0], embeddings[1]))
```

If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function:

```python
embeddings = model.encode(
    ['Very long ... document'],
    max_length=2048
)
```

If you want to use the model together with the [sentence-transformers package](https://github.com/UKPLab/sentence-transformers/), make sure that you have installed the latest release and set `trust_remote_code=True` as well:

```python
!pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
from numpy.linalg import norm

cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model = SentenceTransformer('jinaai/jina-embeddings-v2-base-zh', trust_remote_code=True)
embeddings = model.encode(['How is the weather today?', '今天天气怎么样?'])
print(cos_sim(embeddings[0], embeddings[1]))
```

Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well):

```python
!pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

model = SentenceTransformer(
    "jinaai/jina-embeddings-v2-base-de", # switch to en/zh for English or Chinese
    trust_remote_code=True
)

# control your input sequence length up to 8192
model.max_seq_length = 1024

embeddings = model.encode([
    'How is the weather today?',
    'Wie ist das Wetter heute?'
])
print(cos_sim(embeddings[0], embeddings[1]))
```

## Alternatives to Using Transformers Package

1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/). 
2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).

## Use Jina Embeddings for RAG

According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83),

> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.

<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px">

## Trouble Shooting

**Loading of Model Code failed**

If you forgot to pass the `trust_remote_code=True` flag when calling `AutoModel.from_pretrained` or initializing the model via the `SentenceTransformer` class, you will receive an error that the model weights could not be initialized.
This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model:

```bash
Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-en were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ...
```

## Contact

Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.

## Citation

If you find Jina Embeddings useful in your research, please cite the following paper:

```
@misc{günther2023jina,
      title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents}, 
      author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao},
      year={2023},
      eprint={2310.19923},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```