README.md

---
model-index:
- name: jasper_en_vision_language_v1
  results:
  - dataset:
      config: en-ext
      name: MTEB AmazonCounterfactualClassification (en-ext)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    metrics:
    - type: accuracy
      value: 95.7271
    - type: f1
      value: 89.25450000000001
    - type: f1_weighted
      value: 95.8563
    - type: ap
      value: 67.1563
    - type: ap_weighted
      value: 67.1563
    - type: main_score
      value: 95.7271
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB AmazonCounterfactualClassification (en)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    metrics:
    - type: accuracy
      value: 93.7761
    - type: f1
      value: 90.7582
    - type: f1_weighted
      value: 93.974
    - type: ap
      value: 74.88759999999999
    - type: ap_weighted
      value: 74.88759999999999
    - type: main_score
      value: 93.7761
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB AmazonPolarityClassification (default)
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
      split: test
      type: mteb/amazon_polarity
    metrics:
    - type: accuracy
      value: 97.5809
    - type: f1
      value: 97.5808
    - type: f1_weighted
      value: 97.5808
    - type: ap
      value: 96.3911
    - type: ap_weighted
      value: 96.3911
    - type: main_score
      value: 97.5809
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB AmazonReviewsClassification (en)
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
      split: test
      type: mteb/amazon_reviews_multi
    metrics:
    - type: accuracy
      value: 62.918
    - type: f1
      value: 60.696099999999994
    - type: f1_weighted
      value: 60.696099999999994
    - type: main_score
      value: 62.918
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB ArguAna (default)
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
      split: test
      type: mteb/arguana
    metrics:
    - type: ndcg_at_1
      value: 41.323
    - type: ndcg_at_3
      value: 56.737
    - type: ndcg_at_5
      value: 61.254
    - type: ndcg_at_10
      value: 65.204
    - type: ndcg_at_20
      value: 66.75
    - type: ndcg_at_100
      value: 67.254
    - type: ndcg_at_1000
      value: 67.254
    - type: map_at_1
      value: 41.323
    - type: map_at_3
      value: 52.881
    - type: map_at_5
      value: 55.373000000000005
    - type: map_at_10
      value: 57.013999999999996
    - type: map_at_20
      value: 57.457
    - type: map_at_100
      value: 57.538
    - type: map_at_1000
      value: 57.538
    - type: recall_at_1
      value: 41.323
    - type: recall_at_3
      value: 67.923
    - type: recall_at_5
      value: 78.947
    - type: recall_at_10
      value: 91.11
    - type: recall_at_20
      value: 97.084
    - type: recall_at_100
      value: 99.644
    - type: recall_at_1000
      value: 99.644
    - type: precision_at_1
      value: 41.323
    - type: precision_at_3
      value: 22.641
    - type: precision_at_5
      value: 15.789
    - type: precision_at_10
      value: 9.110999999999999
    - type: precision_at_20
      value: 4.854
    - type: precision_at_100
      value: 0.996
    - type: precision_at_1000
      value: 0.1
    - type: mrr_at_1
      value: 41.6785
    - type: mrr_at_3
      value: 52.963499999999996
    - type: mrr_at_5
      value: 55.456399999999995
    - type: mrr_at_10
      value: 57.104299999999995
    - type: mrr_at_20
      value: 57.5402
    - type: mrr_at_100
      value: 57.6213
    - type: mrr_at_1000
      value: 57.6213
    - type: nauc_ndcg_at_1_max
      value: -12.633600000000001
    - type: nauc_ndcg_at_1_std
      value: -19.747700000000002
    - type: nauc_ndcg_at_1_diff1
      value: 25.820300000000003
    - type: nauc_ndcg_at_3_max
      value: -8.480799999999999
    - type: nauc_ndcg_at_3_std
      value: -18.928900000000002
    - type: nauc_ndcg_at_3_diff1
      value: 19.409499999999998
    - type: nauc_ndcg_at_5_max
      value: -7.9172
    - type: nauc_ndcg_at_5_std
      value: -19.2306
    - type: nauc_ndcg_at_5_diff1
      value: 18.5809
    - type: nauc_ndcg_at_10_max
      value: -9.7812
    - type: nauc_ndcg_at_10_std
      value: -19.9964
    - type: nauc_ndcg_at_10_diff1
      value: 18.0753
    - type: nauc_ndcg_at_20_max
      value: -8.6546
    - type: nauc_ndcg_at_20_std
      value: -19.3205
    - type: nauc_ndcg_at_20_diff1
      value: 20.1741
    - type: nauc_ndcg_at_100_max
      value: -9.376900000000001
    - type: nauc_ndcg_at_100_std
      value: -19.5093
    - type: nauc_ndcg_at_100_diff1
      value: 20.4997
    - type: nauc_ndcg_at_1000_max
      value: -9.376900000000001
    - type: nauc_ndcg_at_1000_std
      value: -19.5093
    - type: nauc_ndcg_at_1000_diff1
      value: 20.4997
    - type: nauc_map_at_1_max
      value: -12.633600000000001
    - type: nauc_map_at_1_std
      value: -19.747700000000002
    - type: nauc_map_at_1_diff1
      value: 25.820300000000003
    - type: nauc_map_at_3_max
      value: -9.5533
    - type: nauc_map_at_3_std
      value: -19.3235
    - type: nauc_map_at_3_diff1
      value: 20.9083
    - type: nauc_map_at_5_max
      value: -9.3126
    - type: nauc_map_at_5_std
      value: -19.4593
    - type: nauc_map_at_5_diff1
      value: 20.5718
    - type: nauc_map_at_10_max
      value: -10.0766
    - type: nauc_map_at_10_std
      value: -19.8322
    - type: nauc_map_at_10_diff1
      value: 20.484099999999998
    - type: nauc_map_at_20_max
      value: -9.7917
    - type: nauc_map_at_20_std
      value: -19.6759
    - type: nauc_map_at_20_diff1
      value: 21.0095
    - type: nauc_map_at_100_max
      value: -9.8837
    - type: nauc_map_at_100_std
      value: -19.741400000000002
    - type: nauc_map_at_100_diff1
      value: 21.067700000000002
    - type: nauc_map_at_1000_max
      value: -9.8837
    - type: nauc_map_at_1000_std
      value: -19.741400000000002
    - type: nauc_map_at_1000_diff1
      value: 21.067700000000002
    - type: nauc_recall_at_1_max
      value: -12.633600000000001
    - type: nauc_recall_at_1_std
      value: -19.747700000000002
    - type: nauc_recall_at_1_diff1
      value: 25.820300000000003
    - type: nauc_recall_at_3_max
      value: -4.7886999999999995
    - type: nauc_recall_at_3_std
      value: -17.4764
    - type: nauc_recall_at_3_diff1
      value: 14.3308
    - type: nauc_recall_at_5_max
      value: -1.4803
    - type: nauc_recall_at_5_std
      value: -18.2137
    - type: nauc_recall_at_5_diff1
      value: 9.4604
    - type: nauc_recall_at_10_max
      value: -8.8102
    - type: nauc_recall_at_10_std
      value: -21.8279
    - type: nauc_recall_at_10_diff1
      value: -5.5388
    - type: nauc_recall_at_20_max
      value: 25.202400000000004
    - type: nauc_recall_at_20_std
      value: -4.8613
    - type: nauc_recall_at_20_diff1
      value: 9.3917
    - type: nauc_recall_at_100_max
      value: 37.0551
    - type: nauc_recall_at_100_std
      value: 84.5472
    - type: nauc_recall_at_100_diff1
      value: 19.5116
    - type: nauc_recall_at_1000_max
      value: 37.0551
    - type: nauc_recall_at_1000_std
      value: 84.5472
    - type: nauc_recall_at_1000_diff1
      value: 19.5116
    - type: nauc_precision_at_1_max
      value: -12.633600000000001
    - type: nauc_precision_at_1_std
      value: -19.747700000000002
    - type: nauc_precision_at_1_diff1
      value: 25.820300000000003
    - type: nauc_precision_at_3_max
      value: -4.7886999999999995
    - type: nauc_precision_at_3_std
      value: -17.4764
    - type: nauc_precision_at_3_diff1
      value: 14.3308
    - type: nauc_precision_at_5_max
      value: -1.4803
    - type: nauc_precision_at_5_std
      value: -18.2137
    - type: nauc_precision_at_5_diff1
      value: 9.4604
    - type: nauc_precision_at_10_max
      value: -8.8102
    - type: nauc_precision_at_10_std
      value: -21.8279
    - type: nauc_precision_at_10_diff1
      value: -5.5388
    - type: nauc_precision_at_20_max
      value: 25.202400000000004
    - type: nauc_precision_at_20_std
      value: -4.8613
    - type: nauc_precision_at_20_diff1
      value: 9.3917
    - type: nauc_precision_at_100_max
      value: 37.0551
    - type: nauc_precision_at_100_std
      value: 84.5472
    - type: nauc_precision_at_100_diff1
      value: 19.5116
    - type: nauc_precision_at_1000_max
      value: 37.0551
    - type: nauc_precision_at_1000_std
      value: 84.5472
    - type: nauc_precision_at_1000_diff1
      value: 19.5116
    - type: nauc_mrr_at_1_max
      value: -11.9728
    - type: nauc_mrr_at_1_std
      value: -19.4014
    - type: nauc_mrr_at_1_diff1
      value: 24.8653
    - type: nauc_mrr_at_3_max
      value: -9.6607
    - type: nauc_mrr_at_3_std
      value: -19.1819
    - type: nauc_mrr_at_3_diff1
      value: 20.0205
    - type: nauc_mrr_at_5_max
      value: -9.4261
    - type: nauc_mrr_at_5_std
      value: -19.3098
    - type: nauc_mrr_at_5_diff1
      value: 19.6347
    - type: nauc_mrr_at_10_max
      value: -10.1698
    - type: nauc_mrr_at_10_std
      value: -19.683
    - type: nauc_mrr_at_10_diff1
      value: 19.4823
    - type: nauc_mrr_at_20_max
      value: -9.913
    - type: nauc_mrr_at_20_std
      value: -19.517300000000002
    - type: nauc_mrr_at_20_diff1
      value: 20.0066
    - type: nauc_mrr_at_100_max
      value: -10.0053
    - type: nauc_mrr_at_100_std
      value: -19.5824
    - type: nauc_mrr_at_100_diff1
      value: 20.061899999999998
    - type: nauc_mrr_at_1000_max
      value: -10.0053
    - type: nauc_mrr_at_1000_std
      value: -19.5824
    - type: nauc_mrr_at_1000_diff1
      value: 20.061899999999998
    - type: main_score
      value: 65.204
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ArxivClusteringP2P (default)
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
      split: test
      type: mteb/arxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 55.710300000000004
    - type: v_measure_std
      value: 14.551900000000002
    - type: main_score
      value: 55.710300000000004
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB ArxivClusteringS2S (default)
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
      split: test
      type: mteb/arxiv-clustering-s2s
    metrics:
    - type: v_measure
      value: 51.004599999999996
    - type: v_measure_std
      value: 14.868899999999998
    - type: main_score
      value: 51.004599999999996
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB AskUbuntuDupQuestions (default)
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
      split: test
      type: mteb/askubuntudupquestions-reranking
    metrics:
    - type: map
      value: 67.38119999999999
    - type: mrr
      value: 79.0692
    - type: nAUC_map_max
      value: 21.9757
    - type: nAUC_map_std
      value: 14.3545
    - type: nAUC_map_diff1
      value: 11.82
    - type: nAUC_mrr_max
      value: 40.3449
    - type: nAUC_mrr_std
      value: 18.2678
    - type: nAUC_mrr_diff1
      value: 21.1596
    - type: main_score
      value: 67.38119999999999
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB BIOSSES (default)
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
      split: test
      type: mteb/biosses-sts
    metrics:
    - type: pearson
      value: 87.4332
    - type: spearman
      value: 84.65979999999999
    - type: cosine_pearson
      value: 87.4332
    - type: cosine_spearman
      value: 84.65979999999999
    - type: manhattan_pearson
      value: 86.37270000000001
    - type: manhattan_spearman
      value: 85.20309999999999
    - type: euclidean_pearson
      value: 86.1672
    - type: euclidean_spearman
      value: 84.7453
    - type: main_score
      value: 84.65979999999999
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB Banking77Classification (default)
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
      split: test
      type: mteb/banking77
    metrics:
    - type: accuracy
      value: 87.224
    - type: f1
      value: 86.7235
    - type: f1_weighted
      value: 86.7235
    - type: main_score
      value: 87.224
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB BiorxivClusteringP2P (default)
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
      split: test
      type: mteb/biorxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 52.04990000000001
    - type: v_measure_std
      value: 0.7255
    - type: main_score
      value: 52.04990000000001
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB BiorxivClusteringS2S (default)
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
      split: test
      type: mteb/biorxiv-clustering-s2s
    metrics:
    - type: v_measure
      value: 47.5811
    - type: v_measure_std
      value: 0.8049000000000001
    - type: main_score
      value: 47.5811
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB CQADupstackAndroidRetrieval (default)
      revision: f46a197baaae43b4f621051089b82a364682dfeb
      split: test
      type: mteb/cqadupstack-android
    metrics:
    - type: ndcg_at_1
      value: 46.781
    - type: ndcg_at_3
      value: 52.111
    - type: ndcg_at_5
      value: 54.371
    - type: ndcg_at_10
      value: 57.247
    - type: ndcg_at_20
      value: 59.122
    - type: ndcg_at_100
      value: 62.071
    - type: ndcg_at_1000
      value: 63.32
    - type: map_at_1
      value: 37.2
    - type: map_at_3
      value: 46.005
    - type: map_at_5
      value: 48.32
    - type: map_at_10
      value: 50.181
    - type: map_at_20
      value: 51.062
    - type: map_at_100
      value: 51.735
    - type: map_at_1000
      value: 51.842
    - type: recall_at_1
      value: 37.2
    - type: recall_at_3
      value: 53.94
    - type: recall_at_5
      value: 60.88
    - type: recall_at_10
      value: 69.692
    - type: recall_at_20
      value: 76.711
    - type: recall_at_100
      value: 90.263
    - type: recall_at_1000
      value: 97.749
    - type: precision_at_1
      value: 46.781
    - type: precision_at_3
      value: 25.894000000000002
    - type: precision_at_5
      value: 18.541
    - type: precision_at_10
      value: 11.402
    - type: precision_at_20
      value: 6.567
    - type: precision_at_100
      value: 1.704
    - type: precision_at_1000
      value: 0.21
    - type: mrr_at_1
      value: 46.781099999999995
    - type: mrr_at_3
      value: 54.6257
    - type: mrr_at_5
      value: 56.0992
    - type: mrr_at_10
      value: 57.00769999999999
    - type: mrr_at_20
      value: 57.3576
    - type: mrr_at_100
      value: 57.6118
    - type: mrr_at_1000
      value: 57.6415
    - type: nauc_ndcg_at_1_max
      value: 41.3069
    - type: nauc_ndcg_at_1_std
      value: -3.9859999999999998
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      value: 56.355900000000005
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      value: 40.354299999999995
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      value: -1.4893
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      value: 51.473800000000004
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      value: 39.2091
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      value: -2.6139
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      value: 50.4382
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      value: 37.8136
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      value: -1.9053
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      value: 51.0321
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      value: 31.1237
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      value: -7.0686
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      value: 55.28189999999999
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      value: -4.502599999999999
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    - type: nauc_map_at_1000_diff1
      value: 51.807
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      value: -7.0686
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      value: 55.28189999999999
    - type: nauc_recall_at_3_max
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    - type: nauc_recall_at_3_std
      value: -2.1842
    - type: nauc_recall_at_3_diff1
      value: 46.806
    - type: nauc_recall_at_5_max
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    - type: nauc_recall_at_10_max
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    - type: nauc_recall_at_20_max
      value: 27.1235
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      value: 2.8238
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      value: 36.9813
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      type: Retrieval
  - dataset:
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      name: MTEB ImdbClassification (default)
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
      split: test
      type: mteb/imdb
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    task:
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  - dataset:
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      revision: c5a29a104738b98a9e76336939199e264163d4a0
      split: test
      type: mteb/msmarco
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    task:
      type: Retrieval
  - dataset:
      config: en
      name: MTEB MTOPDomainClassification (en)
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
      split: test
      type: mteb/mtop_domain
    metrics:
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      value: 99.1712
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      value: 99.1701
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MTOPIntentClassification (en)
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
      split: test
      type: mteb/mtop_intent
    metrics:
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      value: 72.55080000000001
    - type: f1_weighted
      value: 91.72449999999999
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      value: 91.48429999999999
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveIntentClassification (en)
      revision: 4672e20407010da34463acc759c162ca9734bca6
      split: test
      type: mteb/amazon_massive_intent
    metrics:
    - type: accuracy
      value: 85.306
    - type: f1
      value: 83.1076
    - type: f1_weighted
      value: 84.4685
    - type: main_score
      value: 85.306
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveScenarioClassification (en)
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
      split: test
      type: mteb/amazon_massive_scenario
    metrics:
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      value: 90.15950000000001
    - type: f1_weighted
      value: 90.802
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      value: 91.17349999999999
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB MedrxivClusteringP2P (default)
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
      split: test
      type: mteb/medrxiv-clustering-p2p
    metrics:
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      value: 1.2241
    - type: main_score
      value: 46.7494
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB MedrxivClusteringS2S (default)
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
      split: test
      type: mteb/medrxiv-clustering-s2s
    metrics:
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      value: 44.9016
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      value: 1.3978000000000002
    - type: main_score
      value: 44.9016
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB MindSmallReranking (default)
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
      split: test
      type: mteb/mind_small
    metrics:
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      value: -21.0204
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      value: -5.1638
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      value: 10.4531
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      value: -15.85
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      value: -2.7011
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      value: 9.8352
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      value: 32.628099999999996
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB NFCorpus (default)
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
      split: test
      type: mteb/nfcorpus
    metrics:
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      value: 54.954
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      value: 49.876
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      value: 47.038999999999994
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      value: 43.313
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      value: 41.086
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      value: 40.849999999999994
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      value: 49.852000000000004
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      value: 7.412000000000001
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      value: 12.438
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      value: 14.448
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      value: 19.273
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      value: 22.494
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      value: 24.490000000000002
    - type: recall_at_1
      value: 7.412000000000001
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      value: 13.682
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      value: 16.595
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      value: 21.726
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      value: 26.895999999999997
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      value: 42.345
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      value: 74.752
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      value: 46.646
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      value: 40.619
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      value: 24.195
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      value: 77.9596
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      value: 25.3507
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    - type: nauc_recall_at_3_max
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    - type: nauc_recall_at_3_std
      value: -62.044399999999996
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    - type: nauc_recall_at_5_max
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      value: -68.6455
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    - type: nauc_recall_at_100_max
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      value: -2.321
    - type: nauc_precision_at_10_std
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    - type: nauc_precision_at_20_max
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    - type: nauc_precision_at_20_std
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    - type: nauc_precision_at_100_max
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      value: 40.402100000000004
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      value: -46.054
    - type: nauc_mrr_at_1_max
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      value: -42.5286
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      value: 79.3747
    - type: nauc_mrr_at_3_max
      value: 36.3166
    - type: nauc_mrr_at_3_std
      value: -45.0789
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      value: 78.3276
    - type: nauc_mrr_at_5_max
      value: 36.365700000000004
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      value: -45.1793
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      value: 78.5424
    - type: nauc_mrr_at_10_max
      value: 36.3128
    - type: nauc_mrr_at_10_std
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    - type: nauc_mrr_at_10_diff1
      value: 78.5756
    - type: nauc_mrr_at_20_max
      value: 36.327
    - type: nauc_mrr_at_20_std
      value: -44.853500000000004
    - type: nauc_mrr_at_20_diff1
      value: 78.57220000000001
    - type: nauc_mrr_at_100_max
      value: 36.3322
    - type: nauc_mrr_at_100_std
      value: -44.8025
    - type: nauc_mrr_at_100_diff1
      value: 78.57730000000001
    - type: nauc_mrr_at_1000_max
      value: 36.3314
    - type: nauc_mrr_at_1000_std
      value: -44.804100000000005
    - type: nauc_mrr_at_1000_diff1
      value: 78.5778
    - type: main_score
      value: 90.038
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB RedditClustering (default)
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
      split: test
      type: mteb/reddit-clustering
    metrics:
    - type: v_measure
      value: 73.0223
    - type: v_measure_std
      value: 2.5644
    - type: main_score
      value: 73.0223
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB RedditClusteringP2P (default)
      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
      split: test
      type: mteb/reddit-clustering-p2p
    metrics:
    - type: v_measure
      value: 74.7154
    - type: v_measure_std
      value: 8.2851
    - type: main_score
      value: 74.7154
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB SCIDOCS (default)
      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
      split: test
      type: mteb/scidocs
    metrics:
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      value: 29.099999999999998
    - type: ndcg_at_3
      value: 22.891000000000002
    - type: ndcg_at_5
      value: 20.055
    - type: ndcg_at_10
      value: 24.637999999999998
    - type: ndcg_at_20
      value: 28.188999999999997
    - type: ndcg_at_100
      value: 34.589
    - type: ndcg_at_1000
      value: 39.93
    - type: map_at_1
      value: 5.903
    - type: map_at_3
      value: 10.375
    - type: map_at_5
      value: 12.503
    - type: map_at_10
      value: 14.997
    - type: map_at_20
      value: 16.383
    - type: map_at_100
      value: 17.769
    - type: map_at_1000
      value: 18.115000000000002
    - type: recall_at_1
      value: 5.903
    - type: recall_at_3
      value: 12.943
    - type: recall_at_5
      value: 17.818
    - type: recall_at_10
      value: 26.162999999999997
    - type: recall_at_20
      value: 34.46
    - type: recall_at_100
      value: 55.24
    - type: recall_at_1000
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    - type: precision_at_1
      value: 29.099999999999998
    - type: precision_at_3
      value: 21.3
    - type: precision_at_5
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    - type: precision_at_10
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    - type: precision_at_20
      value: 8.5
    - type: precision_at_100
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    - type: precision_at_1000
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    - type: mrr_at_1
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    - type: mrr_at_3
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    - type: mrr_at_5
      value: 38.9617
    - type: mrr_at_10
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    - type: mrr_at_20
      value: 41.3132
    - type: mrr_at_100
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    - type: mrr_at_1000
      value: 41.7584
    - type: nauc_ndcg_at_1_max
      value: 18.4908
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      value: 8.3766
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      value: 22.0483
    - type: nauc_ndcg_at_3_max
      value: 19.1712
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      value: 10.407399999999999
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    - type: nauc_ndcg_at_5_max
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      value: 11.8195
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      value: 16.4224
    - type: nauc_ndcg_at_10_max
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    - type: nauc_ndcg_at_20_max
      value: 21.5015
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      value: 17.292199999999998
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      value: 23.2308
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      value: 22.425800000000002
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      value: 22.764499999999998
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      value: 12.540000000000001
    - type: nauc_map_at_1_max
      value: 17.8236
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      value: 8.1625
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      value: 21.8362
    - type: nauc_map_at_3_max
      value: 19.1677
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      value: 16.2302
    - type: nauc_map_at_5_max
      value: 20.3156
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      value: 10.6218
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      value: 16.2404
    - type: nauc_map_at_10_max
      value: 20.2029
    - type: nauc_map_at_10_std
      value: 12.9342
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      value: 14.7992
    - type: nauc_map_at_20_max
      value: 20.7278
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      value: 16.746
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    - type: nauc_recall_at_1_max
      value: 17.8236
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      value: 21.8362
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      value: 18.5764
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    - type: nauc_recall_at_20_max
      value: 19.9903
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    - type: nauc_recall_at_1000_max
      value: 20.188
    - type: nauc_recall_at_1000_std
      value: 39.786
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      value: -4.0011
    - type: nauc_precision_at_1_max
      value: 18.4908
    - type: nauc_precision_at_1_std
      value: 8.3766
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      value: 22.0483
    - type: nauc_precision_at_3_max
      value: 19.178600000000003
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      value: 11.6744
    - type: nauc_precision_at_3_diff1
      value: 13.5931
    - type: nauc_precision_at_5_max
      value: 20.4581
    - type: nauc_precision_at_5_std
      value: 13.5076
    - type: nauc_precision_at_5_diff1
      value: 13.619600000000002
    - type: nauc_precision_at_10_max
      value: 19.437099999999997
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      value: 17.754900000000003
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      value: 10.6315
    - type: nauc_precision_at_20_max
      value: 20.9159
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      value: 21.9286
    - type: nauc_precision_at_20_diff1
      value: 8.251
    - type: nauc_precision_at_100_max
      value: 22.2412
    - type: nauc_precision_at_100_std
      value: 32.925900000000006
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      value: 1.5637999999999999
    - type: nauc_precision_at_1000_max
      value: 22.1
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      value: 37.227700000000006
    - type: nauc_precision_at_1000_diff1
      value: -2.3548
    - type: nauc_mrr_at_1_max
      value: 18.4908
    - type: nauc_mrr_at_1_std
      value: 8.3766
    - type: nauc_mrr_at_1_diff1
      value: 22.0483
    - type: nauc_mrr_at_3_max
      value: 18.3602
    - type: nauc_mrr_at_3_std
      value: 9.579600000000001
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      value: 18.7544
    - type: nauc_mrr_at_5_max
      value: 18.5224
    - type: nauc_mrr_at_5_std
      value: 9.747300000000001
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      value: 19.0625
    - type: nauc_mrr_at_10_max
      value: 19.067
    - type: nauc_mrr_at_10_std
      value: 10.7103
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      value: 19.444
    - type: nauc_mrr_at_20_max
      value: 19.118399999999998
    - type: nauc_mrr_at_20_std
      value: 10.6384
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      value: 19.5223
    - type: nauc_mrr_at_100_max
      value: 19.200300000000002
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      value: 10.6891
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      value: 19.4009
    - type: nauc_mrr_at_1000_max
      value: 19.1676
    - type: nauc_mrr_at_1000_std
      value: 10.649799999999999
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      value: 19.4127
    - type: main_score
      value: 24.637999999999998
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SICK-R (default)
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
      split: test
      type: mteb/sickr-sts
    metrics:
    - type: pearson
      value: 86.8981
    - type: spearman
      value: 82.234
    - type: cosine_pearson
      value: 86.8981
    - type: cosine_spearman
      value: 82.234
    - type: manhattan_pearson
      value: 84.1165
    - type: manhattan_spearman
      value: 82.063
    - type: euclidean_pearson
      value: 84.0688
    - type: euclidean_spearman
      value: 82.2329
    - type: main_score
      value: 82.234
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS12 (default)
      revision: a0d554a64d88156834ff5ae9920b964011b16384
      split: test
      type: mteb/sts12-sts
    metrics:
    - type: pearson
      value: 87.8269
    - type: spearman
      value: 80.2578
    - type: cosine_pearson
      value: 87.8269
    - type: cosine_spearman
      value: 80.2578
    - type: manhattan_pearson
      value: 85.09020000000001
    - type: manhattan_spearman
      value: 80.5777
    - type: euclidean_pearson
      value: 84.71990000000001
    - type: euclidean_spearman
      value: 80.2547
    - type: main_score
      value: 80.2578
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS13 (default)
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
      split: test
      type: mteb/sts13-sts
    metrics:
    - type: pearson
      value: 89.45769999999999
    - type: spearman
      value: 89.69200000000001
    - type: cosine_pearson
      value: 89.45769999999999
    - type: cosine_spearman
      value: 89.69200000000001
    - type: manhattan_pearson
      value: 89.1685
    - type: manhattan_spearman
      value: 89.8889
    - type: euclidean_pearson
      value: 89.07969999999999
    - type: euclidean_spearman
      value: 89.7003
    - type: main_score
      value: 89.69200000000001
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS14 (default)
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
      split: test
      type: mteb/sts14-sts
    metrics:
    - type: pearson
      value: 87.01989999999999
    - type: spearman
      value: 85.3155
    - type: cosine_pearson
      value: 87.01989999999999
    - type: cosine_spearman
      value: 85.3155
    - type: manhattan_pearson
      value: 86.24159999999999
    - type: manhattan_spearman
      value: 85.544
    - type: euclidean_pearson
      value: 86.1032
    - type: euclidean_spearman
      value: 85.31490000000001
    - type: main_score
      value: 85.3155
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS15 (default)
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
      split: test
      type: mteb/sts15-sts
    metrics:
    - type: pearson
      value: 89.593
    - type: spearman
      value: 90.1574
    - type: cosine_pearson
      value: 89.593
    - type: cosine_spearman
      value: 90.1574
    - type: manhattan_pearson
      value: 89.93910000000001
    - type: manhattan_spearman
      value: 90.4357
    - type: euclidean_pearson
      value: 89.76650000000001
    - type: euclidean_spearman
      value: 90.1554
    - type: main_score
      value: 90.1574
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS16 (default)
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
      split: test
      type: mteb/sts16-sts
    metrics:
    - type: pearson
      value: 85.03540000000001
    - type: spearman
      value: 86.4191
    - type: cosine_pearson
      value: 85.03540000000001
    - type: cosine_spearman
      value: 86.4191
    - type: manhattan_pearson
      value: 85.8992
    - type: manhattan_spearman
      value: 86.7274
    - type: euclidean_pearson
      value: 85.638
    - type: euclidean_spearman
      value: 86.42
    - type: main_score
      value: 86.4191
    task:
      type: STS
  - dataset:
      config: en-ar
      name: MTEB STS17 (en-ar)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 54.1098
    - type: spearman
      value: 52.721
    - type: cosine_pearson
      value: 54.1098
    - type: cosine_spearman
      value: 52.721
    - type: manhattan_pearson
      value: 54.890899999999995
    - type: manhattan_spearman
      value: 52.727900000000005
    - type: euclidean_pearson
      value: 54.65410000000001
    - type: euclidean_spearman
      value: 52.8582
    - type: main_score
      value: 52.721
    task:
      type: STS
  - dataset:
      config: fr-en
      name: MTEB STS17 (fr-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 84.3262
    - type: spearman
      value: 84.1421
    - type: cosine_pearson
      value: 84.3262
    - type: cosine_spearman
      value: 84.1421
    - type: manhattan_pearson
      value: 83.8999
    - type: manhattan_spearman
      value: 83.9987
    - type: euclidean_pearson
      value: 84.0174
    - type: euclidean_spearman
      value: 84.1711
    - type: main_score
      value: 84.1421
    task:
      type: STS
  - dataset:
      config: en-en
      name: MTEB STS17 (en-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 91.38969999999999
    - type: spearman
      value: 91.00789999999999
    - type: cosine_pearson
      value: 91.38969999999999
    - type: cosine_spearman
      value: 91.00789999999999
    - type: manhattan_pearson
      value: 90.8917
    - type: manhattan_spearman
      value: 90.7834
    - type: euclidean_pearson
      value: 91.0361
    - type: euclidean_spearman
      value: 91.011
    - type: main_score
      value: 91.00789999999999
    task:
      type: STS
  - dataset:
      config: nl-en
      name: MTEB STS17 (nl-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 84.636
    - type: spearman
      value: 84.1977
    - type: cosine_pearson
      value: 84.636
    - type: cosine_spearman
      value: 84.1977
    - type: manhattan_pearson
      value: 84.6895
    - type: manhattan_spearman
      value: 84.1849
    - type: euclidean_pearson
      value: 84.753
    - type: euclidean_spearman
      value: 84.2251
    - type: main_score
      value: 84.1977
    task:
      type: STS
  - dataset:
      config: es-en
      name: MTEB STS17 (es-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 85.8595
    - type: spearman
      value: 86.9965
    - type: cosine_pearson
      value: 85.8595
    - type: cosine_spearman
      value: 86.9965
    - type: manhattan_pearson
      value: 86.92450000000001
    - type: manhattan_spearman
      value: 87.39489999999999
    - type: euclidean_pearson
      value: 86.5575
    - type: euclidean_spearman
      value: 86.997
    - type: main_score
      value: 86.9965
    task:
      type: STS
  - dataset:
      config: it-en
      name: MTEB STS17 (it-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 85.959
    - type: spearman
      value: 86.3103
    - type: cosine_pearson
      value: 85.959
    - type: cosine_spearman
      value: 86.3103
    - type: manhattan_pearson
      value: 85.7486
    - type: manhattan_spearman
      value: 86.0977
    - type: euclidean_pearson
      value: 85.8361
    - type: euclidean_spearman
      value: 86.2741
    - type: main_score
      value: 86.3103
    task:
      type: STS
  - dataset:
      config: en-de
      name: MTEB STS17 (en-de)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 86.2629
    - type: spearman
      value: 85.8372
    - type: cosine_pearson
      value: 86.2629
    - type: cosine_spearman
      value: 85.8372
    - type: manhattan_pearson
      value: 86.2491
    - type: manhattan_spearman
      value: 85.4973
    - type: euclidean_pearson
      value: 86.5074
    - type: euclidean_spearman
      value: 85.83370000000001
    - type: main_score
      value: 85.8372
    task:
      type: STS
  - dataset:
      config: en-tr
      name: MTEB STS17 (en-tr)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 57.5675
    - type: spearman
      value: 55.564899999999994
    - type: cosine_pearson
      value: 57.5675
    - type: cosine_spearman
      value: 55.564899999999994
    - type: manhattan_pearson
      value: 57.4686
    - type: manhattan_spearman
      value: 55.3816
    - type: euclidean_pearson
      value: 57.635099999999994
    - type: euclidean_spearman
      value: 55.501900000000006
    - type: main_score
      value: 55.564899999999994
    task:
      type: STS
  - dataset:
      config: pl-en
      name: MTEB STS22 (pl-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 76.1237
    - type: spearman
      value: 76.4
    - type: cosine_pearson
      value: 76.1237
    - type: cosine_spearman
      value: 76.4
    - type: manhattan_pearson
      value: 78.6794
    - type: manhattan_spearman
      value: 77.2282
    - type: euclidean_pearson
      value: 77.1688
    - type: euclidean_spearman
      value: 76.4
    - type: main_score
      value: 76.4
    task:
      type: STS
  - dataset:
      config: es-en
      name: MTEB STS22 (es-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 73.89099999999999
    - type: spearman
      value: 77.8292
    - type: cosine_pearson
      value: 73.89099999999999
    - type: cosine_spearman
      value: 77.8292
    - type: manhattan_pearson
      value: 77.53229999999999
    - type: manhattan_spearman
      value: 78.3926
    - type: euclidean_pearson
      value: 76.6734
    - type: euclidean_spearman
      value: 77.8721
    - type: main_score
      value: 77.8292
    task:
      type: STS
  - dataset:
      config: en
      name: MTEB STS22 (en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 68.31099999999999
    - type: spearman
      value: 68.0764
    - type: cosine_pearson
      value: 68.31099999999999
    - type: cosine_spearman
      value: 68.0764
    - type: manhattan_pearson
      value: 69.7423
    - type: manhattan_spearman
      value: 68.3212
    - type: euclidean_pearson
      value: 69.5701
    - type: euclidean_spearman
      value: 68.2299
    - type: main_score
      value: 68.0764
    task:
      type: STS
  - dataset:
      config: de-en
      name: MTEB STS22 (de-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 65.0636
    - type: spearman
      value: 59.4669
    - type: cosine_pearson
      value: 65.0636
    - type: cosine_spearman
      value: 59.4669
    - type: manhattan_pearson
      value: 69.723
    - type: manhattan_spearman
      value: 60.3823
    - type: euclidean_pearson
      value: 68.6454
    - type: euclidean_spearman
      value: 59.384899999999995
    - type: main_score
      value: 59.4669
    task:
      type: STS
  - dataset:
      config: zh-en
      name: MTEB STS22 (zh-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 73.7603
    - type: spearman
      value: 73.0799
    - type: cosine_pearson
      value: 73.7603
    - type: cosine_spearman
      value: 73.0799
    - type: manhattan_pearson
      value: 76.2351
    - type: manhattan_spearman
      value: 73.60719999999999
    - type: euclidean_pearson
      value: 75.61529999999999
    - type: euclidean_spearman
      value: 72.9809
    - type: main_score
      value: 73.0799
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STSBenchmark (default)
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
      split: test
      type: mteb/stsbenchmark-sts
    metrics:
    - type: pearson
      value: 88.1242
    - type: spearman
      value: 88.8456
    - type: cosine_pearson
      value: 88.1242
    - type: cosine_spearman
      value: 88.8456
    - type: manhattan_pearson
      value: 88.6122
    - type: manhattan_spearman
      value: 89.0198
    - type: euclidean_pearson
      value: 88.5314
    - type: euclidean_spearman
      value: 88.8432
    - type: main_score
      value: 88.8456
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SciDocsRR (default)
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
      split: test
      type: mteb/scidocs-reranking
    metrics:
    - type: map
      value: 89.06920000000001
    - type: mrr
      value: 96.977
    - type: nAUC_map_max
      value: 49.1778
    - type: nAUC_map_std
      value: 62.091499999999996
    - type: nAUC_map_diff1
      value: -13.373899999999999
    - type: nAUC_mrr_max
      value: 86.7325
    - type: nAUC_mrr_std
      value: 74.958
    - type: nAUC_mrr_diff1
      value: 25.6955
    - type: main_score
      value: 89.06920000000001
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SciFact (default)
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
      split: test
      type: mteb/scifact
    metrics:
    - type: ndcg_at_1
      value: 67.333
    - type: ndcg_at_3
      value: 75.142
    - type: ndcg_at_5
      value: 78.873
    - type: ndcg_at_10
      value: 80.372
    - type: ndcg_at_20
      value: 80.914
    - type: ndcg_at_100
      value: 81.563
    - type: ndcg_at_1000
      value: 81.601
    - type: map_at_1
      value: 63.99399999999999
    - type: map_at_3
      value: 72.181
    - type: map_at_5
      value: 74.665
    - type: map_at_10
      value: 75.51299999999999
    - type: map_at_20
      value: 75.693
    - type: map_at_100
      value: 75.78
    - type: map_at_1000
      value: 75.78099999999999
    - type: recall_at_1
      value: 63.99399999999999
    - type: recall_at_3
      value: 80.63900000000001
    - type: recall_at_5
      value: 90.017
    - type: recall_at_10
      value: 94.167
    - type: recall_at_20
      value: 96.167
    - type: recall_at_100
      value: 99.667
    - type: recall_at_1000
      value: 100.0
    - type: precision_at_1
      value: 67.333
    - type: precision_at_3
      value: 29.444
    - type: precision_at_5
      value: 20.067
    - type: precision_at_10
      value: 10.667
    - type: precision_at_20
      value: 5.45
    - type: precision_at_100
      value: 1.127
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: mrr_at_1
      value: 67.3333
    - type: mrr_at_3
      value: 73.9444
    - type: mrr_at_5
      value: 76.0444
    - type: mrr_at_10
      value: 76.4517
    - type: mrr_at_20
      value: 76.5739
    - type: mrr_at_100
      value: 76.6556
    - type: mrr_at_1000
      value: 76.6563
    - type: nauc_ndcg_at_1_max
      value: 55.373099999999994
    - type: nauc_ndcg_at_1_std
      value: -11.362
    - type: nauc_ndcg_at_1_diff1
      value: 81.5232
    - type: nauc_ndcg_at_3_max
      value: 58.0376
    - type: nauc_ndcg_at_3_std
      value: -14.4854
    - type: nauc_ndcg_at_3_diff1
      value: 77.848
    - type: nauc_ndcg_at_5_max
      value: 55.789699999999996
    - type: nauc_ndcg_at_5_std
      value: -15.0412
    - type: nauc_ndcg_at_5_diff1
      value: 77.7873
    - type: nauc_ndcg_at_10_max
      value: 58.6243
    - type: nauc_ndcg_at_10_std
      value: -13.867799999999999
    - type: nauc_ndcg_at_10_diff1
      value: 78.071
    - type: nauc_ndcg_at_20_max
      value: 58.9032
    - type: nauc_ndcg_at_20_std
      value: -12.7998
    - type: nauc_ndcg_at_20_diff1
      value: 78.12010000000001
    - type: nauc_ndcg_at_100_max
      value: 58.0772
    - type: nauc_ndcg_at_100_std
      value: -12.4604
    - type: nauc_ndcg_at_100_diff1
      value: 78.7002
    - type: nauc_ndcg_at_1000_max
      value: 58.0835
    - type: nauc_ndcg_at_1000_std
      value: -12.662799999999999
    - type: nauc_ndcg_at_1000_diff1
      value: 78.7157
    - type: nauc_map_at_1_max
      value: 45.2711
    - type: nauc_map_at_1_std
      value: -21.685
    - type: nauc_map_at_1_diff1
      value: 80.8536
    - type: nauc_map_at_3_max
      value: 53.970600000000005
    - type: nauc_map_at_3_std
      value: -18.0178
    - type: nauc_map_at_3_diff1
      value: 78.95309999999999
    - type: nauc_map_at_5_max
      value: 54.7643
    - type: nauc_map_at_5_std
      value: -15.943999999999999
    - type: nauc_map_at_5_diff1
      value: 78.9665
    - type: nauc_map_at_10_max
      value: 56.737899999999996
    - type: nauc_map_at_10_std
      value: -14.355599999999999
    - type: nauc_map_at_10_diff1
      value: 79.095
    - type: nauc_map_at_20_max
      value: 56.804
    - type: nauc_map_at_20_std
      value: -14.033599999999998
    - type: nauc_map_at_20_diff1
      value: 79.0729
    - type: nauc_map_at_100_max
      value: 56.7168
    - type: nauc_map_at_100_std
      value: -14.000000000000002
    - type: nauc_map_at_100_diff1
      value: 79.1456
    - type: nauc_map_at_1000_max
      value: 56.7168
    - type: nauc_map_at_1000_std
      value: -14.003099999999998
    - type: nauc_map_at_1000_diff1
      value: 79.1458
    - type: nauc_recall_at_1_max
      value: 45.2711
    - type: nauc_recall_at_1_std
      value: -21.685
    - type: nauc_recall_at_1_diff1
      value: 80.8536
    - type: nauc_recall_at_3_max
      value: 55.911100000000005
    - type: nauc_recall_at_3_std
      value: -19.1603
    - type: nauc_recall_at_3_diff1
      value: 72.51689999999999
    - type: nauc_recall_at_5_max
      value: 50.6317
    - type: nauc_recall_at_5_std
      value: -21.4004
    - type: nauc_recall_at_5_diff1
      value: 68.3824
    - type: nauc_recall_at_10_max
      value: 67.3176
    - type: nauc_recall_at_10_std
      value: -23.6601
    - type: nauc_recall_at_10_diff1
      value: 67.20150000000001
    - type: nauc_recall_at_20_max
      value: 76.566
    - type: nauc_recall_at_20_std
      value: -12.4873
    - type: nauc_recall_at_20_diff1
      value: 63.6849
    - type: nauc_recall_at_100_max
      value: 55.415499999999994
    - type: nauc_recall_at_100_std
      value: 72.2222
    - type: nauc_recall_at_100_diff1
      value: 72.2222
    - type: nauc_recall_at_1000_max
      value: .nan
    - type: nauc_recall_at_1000_std
      value: .nan
    - type: nauc_recall_at_1000_diff1
      value: .nan
    - type: nauc_precision_at_1_max
      value: 55.373099999999994
    - type: nauc_precision_at_1_std
      value: -11.362
    - type: nauc_precision_at_1_diff1
      value: 81.5232
    - type: nauc_precision_at_3_max
      value: 59.23969999999999
    - type: nauc_precision_at_3_std
      value: 17.3868
    - type: nauc_precision_at_3_diff1
      value: 37.0438
    - type: nauc_precision_at_5_max
      value: 51.358599999999996
    - type: nauc_precision_at_5_std
      value: 39.9722
    - type: nauc_precision_at_5_diff1
      value: 9.2927
    - type: nauc_precision_at_10_max
      value: 44.6166
    - type: nauc_precision_at_10_std
      value: 46.4231
    - type: nauc_precision_at_10_diff1
      value: -7.435700000000001
    - type: nauc_precision_at_20_max
      value: 41.1852
    - type: nauc_precision_at_20_std
      value: 51.89359999999999
    - type: nauc_precision_at_20_diff1
      value: -14.6318
    - type: nauc_precision_at_100_max
      value: 33.6188
    - type: nauc_precision_at_100_std
      value: 60.621700000000004
    - type: nauc_precision_at_100_diff1
      value: -26.376500000000004
    - type: nauc_precision_at_1000_max
      value: 33.2976
    - type: nauc_precision_at_1000_std
      value: 60.4508
    - type: nauc_precision_at_1000_diff1
      value: -27.829500000000003
    - type: nauc_mrr_at_1_max
      value: 55.373099999999994
    - type: nauc_mrr_at_1_std
      value: -11.362
    - type: nauc_mrr_at_1_diff1
      value: 81.5232
    - type: nauc_mrr_at_3_max
      value: 58.7918
    - type: nauc_mrr_at_3_std
      value: -11.580300000000001
    - type: nauc_mrr_at_3_diff1
      value: 78.79310000000001
    - type: nauc_mrr_at_5_max
      value: 58.13270000000001
    - type: nauc_mrr_at_5_std
      value: -11.2346
    - type: nauc_mrr_at_5_diff1
      value: 78.8896
    - type: nauc_mrr_at_10_max
      value: 58.4249
    - type: nauc_mrr_at_10_std
      value: -11.842600000000001
    - type: nauc_mrr_at_10_diff1
      value: 79.0629
    - type: nauc_mrr_at_20_max
      value: 58.4409
    - type: nauc_mrr_at_20_std
      value: -11.7012
    - type: nauc_mrr_at_20_diff1
      value: 79.1097
    - type: nauc_mrr_at_100_max
      value: 58.3685
    - type: nauc_mrr_at_100_std
      value: -11.6333
    - type: nauc_mrr_at_100_diff1
      value: 79.1871
    - type: nauc_mrr_at_1000_max
      value: 58.3686
    - type: nauc_mrr_at_1000_std
      value: -11.6365
    - type: nauc_mrr_at_1000_diff1
      value: 79.1874
    - type: main_score
      value: 80.372
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SprintDuplicateQuestions (default)
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
      split: test
      type: mteb/sprintduplicatequestions-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 99.8317
    - type: similarity_accuracy_threshold
      value: 87.7015
    - type: similarity_f1
      value: 91.2821
    - type: similarity_f1_threshold
      value: 87.68599999999999
    - type: similarity_precision
      value: 93.68419999999999
    - type: similarity_recall
      value: 89.0
    - type: similarity_ap
      value: 96.249
    - type: cosine_accuracy
      value: 99.8317
    - type: cosine_accuracy_threshold
      value: 87.7015
    - type: cosine_f1
      value: 91.2821
    - type: cosine_f1_threshold
      value: 87.68599999999999
    - type: cosine_precision
      value: 93.68419999999999
    - type: cosine_recall
      value: 89.0
    - type: cosine_ap
      value: 96.249
    - type: manhattan_accuracy
      value: 99.83370000000001
    - type: manhattan_accuracy_threshold
      value: 4133.4675
    - type: manhattan_f1
      value: 91.52369999999999
    - type: manhattan_f1_threshold
      value: 4244.392400000001
    - type: manhattan_precision
      value: 92.3625
    - type: manhattan_recall
      value: 90.7
    - type: manhattan_ap
      value: 96.4021
    - type: euclidean_accuracy
      value: 99.83070000000001
    - type: euclidean_accuracy_threshold
      value: 49.684200000000004
    - type: euclidean_f1
      value: 91.24419999999999
    - type: euclidean_f1_threshold
      value: 49.7175
    - type: euclidean_precision
      value: 93.4942
    - type: euclidean_recall
      value: 89.1
    - type: euclidean_ap
      value: 96.24589999999999
    - type: dot_accuracy
      value: 99.8277
    - type: dot_accuracy_threshold
      value: 87.3743
    - type: dot_f1
      value: 91.1495
    - type: dot_f1_threshold
      value: 87.3743
    - type: dot_precision
      value: 92.7536
    - type: dot_recall
      value: 89.60000000000001
    - type: dot_ap
      value: 96.23509999999999
    - type: max_accuracy
      value: 99.83370000000001
    - type: max_f1
      value: 91.52369999999999
    - type: max_precision
      value: 93.68419999999999
    - type: max_recall
      value: 90.7
    - type: max_ap
      value: 96.4021
    - type: main_score
      value: 96.4021
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB StackExchangeClustering (default)
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
      split: test
      type: mteb/stackexchange-clustering
    metrics:
    - type: v_measure
      value: 80.19420000000001
    - type: v_measure_std
      value: 2.8956
    - type: main_score
      value: 80.19420000000001
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB StackExchangeClusteringP2P (default)
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
      split: test
      type: mteb/stackexchange-clustering-p2p
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      value: 49.416900000000005
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      value: 1.5938
    - type: main_score
      value: 49.416900000000005
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB StackOverflowDupQuestions (default)
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
      split: test
      type: mteb/stackoverflowdupquestions-reranking
    metrics:
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      value: 55.598800000000004
    - type: nAUC_map_max
      value: 9.1665
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      value: 7.8292
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      value: 38.831199999999995
    - type: nAUC_mrr_max
      value: 9.7435
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      value: 8.030800000000001
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      value: 39.2785
    - type: main_score
      value: 54.579
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SummEval (default)
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
      split: test
      type: mteb/summeval
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      value: 31.4212
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      value: 31.4212
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      value: 31.6006
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      value: 31.5459
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      value: 31.6412
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      value: 31.4212
    task:
      type: Summarization
  - dataset:
      config: default
      name: MTEB TRECCOVID (default)
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
      split: test
      type: mteb/trec-covid
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      value: 0.243
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      value: 0.703
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      value: 1.142
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      value: 0.243
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      value: 0.726
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      value: 97.0
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      value: 97.0
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      value: 53.2855
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      value: 26.559500000000003
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      value: 30.8674
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      value: 25.9597
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      value: 34.3742
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      value: 24.567
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      value: -1.028
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      value: 69.3433
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      value: 69.3433
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      value: 69.3433
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    - type: main_score
      value: 86.732
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB Touche2020 (default)
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
      split: test
      type: mteb/touche2020
    metrics:
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      value: 37.185
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      value: 32.564
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      value: 31.395
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      value: 30.494
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      value: 42.617
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      value: 53.324000000000005
    - type: map_at_1
      value: 2.7640000000000002
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      value: 6.970999999999999
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      value: 8.518
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      value: 11.919
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      value: 14.389
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      value: 18.799
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    - type: recall_at_1
      value: 2.7640000000000002
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      value: 8.323
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      value: 11.259
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      value: 19.088
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      value: 26.488
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      value: 53.437
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      value: 86.385
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      value: 31.429000000000002
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      value: 1.6119999999999999
    - type: mrr_at_1
      value: 38.7755
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      value: 52.7211
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      value: 55.9864
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      value: 57.2554
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      value: -20.4965
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    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ToxicConversationsClassification (default)
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
      split: test
      type: mteb/toxic_conversations_50k
    metrics:
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    - type: main_score
      value: 91.3135
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TweetSentimentExtractionClassification (default)
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
      split: test
      type: mteb/tweet_sentiment_extraction
    metrics:
    - type: accuracy
      value: 77.24109999999999
    - type: f1
      value: 77.4964
    - type: f1_weighted
      value: 77.0248
    - type: main_score
      value: 77.24109999999999
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TwentyNewsgroupsClustering (default)
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
      split: test
      type: mteb/twentynewsgroups-clustering
    metrics:
    - type: v_measure
      value: 63.132200000000005
    - type: v_measure_std
      value: 0.7163
    - type: main_score
      value: 63.132200000000005
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB TwitterSemEval2015 (default)
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
      split: test
      type: mteb/twittersemeval2015-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 88.061
    - type: similarity_accuracy_threshold
      value: 85.9546
    - type: similarity_f1
      value: 73.36240000000001
    - type: similarity_f1_threshold
      value: 84.0127
    - type: similarity_precision
      value: 71.47149999999999
    - type: similarity_recall
      value: 75.3562
    - type: similarity_ap
      value: 80.2787
    - type: cosine_accuracy
      value: 88.061
    - type: cosine_accuracy_threshold
      value: 85.9546
    - type: cosine_f1
      value: 73.36240000000001
    - type: cosine_f1_threshold
      value: 84.0127
    - type: cosine_precision
      value: 71.47149999999999
    - type: cosine_recall
      value: 75.3562
    - type: cosine_ap
      value: 80.2787
    - type: manhattan_accuracy
      value: 87.8941
    - type: manhattan_accuracy_threshold
      value: 4374.3263
    - type: manhattan_f1
      value: 72.929
    - type: manhattan_f1_threshold
      value: 4637.7289
    - type: manhattan_precision
      value: 71.3636
    - type: manhattan_recall
      value: 74.5646
    - type: manhattan_ap
      value: 79.98140000000001
    - type: euclidean_accuracy
      value: 88.0789
    - type: euclidean_accuracy_threshold
      value: 52.9742
    - type: euclidean_f1
      value: 73.3591
    - type: euclidean_f1_threshold
      value: 56.4815
    - type: euclidean_precision
      value: 71.608
    - type: euclidean_recall
      value: 75.19789999999999
    - type: euclidean_ap
      value: 80.2656
    - type: dot_accuracy
      value: 88.0789
    - type: dot_accuracy_threshold
      value: 85.8034
    - type: dot_f1
      value: 73.40379999999999
    - type: dot_f1_threshold
      value: 84.3213
    - type: dot_precision
      value: 72.6544
    - type: dot_recall
      value: 74.16890000000001
    - type: dot_ap
      value: 80.32010000000001
    - type: max_accuracy
      value: 88.0789
    - type: max_f1
      value: 73.40379999999999
    - type: max_precision
      value: 72.6544
    - type: max_recall
      value: 75.3562
    - type: max_ap
      value: 80.32010000000001
    - type: main_score
      value: 80.32010000000001
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB TwitterURLCorpus (default)
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
      split: test
      type: mteb/twitterurlcorpus-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 89.93090000000001
    - type: similarity_accuracy_threshold
      value: 82.1349
    - type: similarity_f1
      value: 80.1377
    - type: similarity_f1_threshold
      value: 80.5047
    - type: similarity_precision
      value: 77.1539
    - type: similarity_recall
      value: 83.3616
    - type: similarity_ap
      value: 87.6917
    - type: cosine_accuracy
      value: 89.93090000000001
    - type: cosine_accuracy_threshold
      value: 82.1349
    - type: cosine_f1
      value: 80.1377
    - type: cosine_f1_threshold
      value: 80.5047
    - type: cosine_precision
      value: 77.1539
    - type: cosine_recall
      value: 83.3616
    - type: cosine_ap
      value: 87.6917
    - type: manhattan_accuracy
      value: 89.86880000000001
    - type: manhattan_accuracy_threshold
      value: 4882.7347
    - type: manhattan_f1
      value: 80.2323
    - type: manhattan_f1_threshold
      value: 5185.1944
    - type: manhattan_precision
      value: 76.74889999999999
    - type: manhattan_recall
      value: 84.0468
    - type: manhattan_ap
      value: 87.70750000000001
    - type: euclidean_accuracy
      value: 89.94640000000001
    - type: euclidean_accuracy_threshold
      value: 59.9149
    - type: euclidean_f1
      value: 80.1527
    - type: euclidean_f1_threshold
      value: 62.3611
    - type: euclidean_precision
      value: 77.2744
    - type: euclidean_recall
      value: 83.2538
    - type: euclidean_ap
      value: 87.6922
    - type: dot_accuracy
      value: 89.9038
    - type: dot_accuracy_threshold
      value: 82.53049999999999
    - type: dot_f1
      value: 80.0969
    - type: dot_f1_threshold
      value: 80.7285
    - type: dot_precision
      value: 77.5853
    - type: dot_recall
      value: 82.77640000000001
    - type: dot_ap
      value: 87.668
    - type: max_accuracy
      value: 89.94640000000001
    - type: max_f1
      value: 80.2323
    - type: max_precision
      value: 77.5853
    - type: max_recall
      value: 84.0468
    - type: max_ap
      value: 87.70750000000001
    - type: main_score
      value: 87.70750000000001
    task:
      type: PairClassification
datasets:
- BAAI/Infinity-MM
- HuggingFaceFW/fineweb-edu
language:
- en
base_model:
- dunzhang/stella_en_1.5B_v5
- google/siglip-so400m-patch14-384
tags:
- mteb
---


## Introduction 

Based on dunzhang/stella_en_1.5B_v5 and google/siglip-so400m-patch14-384.

It can encode both text and image.

The training code, data and report will be released soon.

The core training code will be integrated into the rag-retrieval library(https://github.com/NLPJCL/RAG-Retrieval) in the near future. (Welcome to star)

This work was accomplished during my free time; please grant time a little time.


Here's a short introduction to the training method:

The core idea of jasper and stella is distillation: **Let student model learn teacher model's vectors.**
The training process of jasper have 4 stage:

Stage1&2: Distill from teacher vectors. In jasper model the teacher model is nvidia/NV-Embed-v2 and dunzhang/stella_en_1.5B_v5 (Stage1 and Stage2 will freeze different parameters.)

Stage3: MRL training, I made some modifications to MRL to enable training on unsupervised text

Stage4: Alignment between *jasper token embeddings from image's detailed caption* and *vision embeddings from google/siglip-so400m-patch14-384*.

I use a AdaptiveAvgPool2d to do an adjustment on vision tokens' number and dimensions, this method does not need additional parameters.

**The meaning of distillation is to achieve better results with smaller models or as a way of pre-training, not to hit the top of the leaderboards.**
Actually, I've got first place on MTEB (Chinese and English), I will not release the two models, as I said before, it's meaningless. 



## Usage
```python
import torch
from sentence_transformers import SentenceTransformer


DOC1 = """
Blue light is scattered in all directions by the tiny molecules of air in Earth's atmosphere. 
Blue is scattered more than other colors because it travels as shorter, smaller waves. This is why we see a blue sky most of the time. 
Closer to the horizon, the sky fades to a lighter blue or white.
"""
DOC2 = """
When choosing colors, you can consider the following factors:
Color theory: Understand how colors work together and how they can evoke different reactions. 
Color psychology: Consider how colors affect emotions, behaviors, and responses. 
Brand identity: Colors can convey meaning and information about a brand. 
Mood: Consider the mood you want to create. For example, brighter colors can feel cheerful, while cooler colors can be calming.
Space: Consider the size of the space and the amount of natural light it receives. Dark colors can make a room feel smaller, while light colors can make it feel larger.
Color wheel: Use the color wheel to identify primary, secondary, and tertiary colors. 
Color combinations: Decide how to best complement your preferred color with others. 
Color palette: Limit your color palette to a main color and one or two additional colors. 
60-30-10 rule: Use a primary color 60% of the time, a secondary color 30% of the time, and an accent color 10% of the time
"""
if __name__ == "__main__":
    # load model
    use_gpu = False
    model_name = "infgrad/jasper_en_vision_language_v1"
    model = SentenceTransformer(
        model_name,
        trust_remote_code=True,
        device="cpu" if not use_gpu else "cuda",
        model_kwargs={
            "torch_dtype": torch.bfloat16 if use_gpu else torch.float32,
            "attn_implementation": "sdpa"
        },
        # vector_dim must be 12288, 1024, 512, 256
        ## 1024 is recommended
        # set is_text_encoder 'True', if you do not encode image
        config_kwargs={"is_text_encoder": False, "vector_dim": 1024},
    )
    # We can reduce the max_seq_length from the default of 2048 for faster encoding
    model.max_seq_length = 1024

    # data
    q_list = [
        "Why the sky is blue?",
        "how to choose suitable color",
    ]
    doc_list = [
        DOC1,
        [{"type": "image_path", "content": "./assets/img1.png"}, {"type": "text", "content": "Hope this image helps!"}],
        DOC2,
        [{"type": "image_path", "content": "./assets/img2.png"}],
    ]
    q_vecs = model.encode(q_list, prompt_name="s2p_query")
    doc_vecs = model.encode(doc_list)

    # calculate similarity
    similarities = model.similarity(q_vecs, doc_vecs)
    print(similarities)
    # the output is:
    # tensor([[0.7775, 0.7594, 0.2429, 0.2187],
    #         [0.3226, 0.3054, 0.7421, 0.5484]])
```

## Evaluation on MTEB

script: ./scripts/evaluate_en_mteb/run_evaluate_mteb.py

## License
**This model should not be used for any commercial purpose!**