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metadata
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - mteb
  - transformers
  - transformers.js
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



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.

The text embedding set trained by Jina AI.

Quick Start

The easiest way to starting using jina-embeddings-v2-base-zh is to use Jina AI's Embedding API.

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 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:

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.

Usage

Please apply mean pooling when integrating the model.

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:

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)

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:

huggingface-cli login

Alternatively, you can provide the access token as an environment variable in the shell:

export HF_TOKEN="<your token here>"

or in Python:

import os

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

Then, you can use load and use the model via the AutoModel class:

!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:

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

If you want to use the model together with the sentence-transformers package, make sure that you have installed the latest release and set trust_remote_code=True as well:

!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):

!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.
  2. Private and high-performance deployment: Get started by picking from our suite of models and deploy them on AWS Sagemaker.

Use Jina Embeddings for RAG

According to the latest blog post from LLamaIndex,

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.

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:

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 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}
}