sentence-transformers safetensors feature-extraction mteb transformers modernbert custom_code arxiv:2410.02525 base_model:answerdotai/ModernBERT-base base_model:finetune:answerdotai/ModernBERT-base model-index autotrain_compatible endpoints_compatible region:us
---
base_model: answerdotai/ModernBERT-base
tags:
- mteb
- transformers
- sentence-transformers
- modernbert
model-index:
- name: cde-small-v2
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 86.01490000000001
    - type: f1
      value: 80.938
    - type: f1_weighted
      value: 86.9232
    - type: ap
      value: 54.949099999999994
    - type: ap_weighted
      value: 54.949099999999994
    - type: main_score
      value: 86.01490000000001
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification (default)
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 96.0223
    - type: f1
      value: 96.0206
    - type: f1_weighted
      value: 96.0206
    - type: ap
      value: 93.8301
    - type: ap_weighted
      value: 93.8301
    - type: main_score
      value: 96.0223
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 55.096000000000004
    - type: f1
      value: 54.4353
    - type: f1_weighted
      value: 54.4353
    - type: main_score
      value: 55.096000000000004
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna (default)
      type: mteb/arguana
      config: default
      split: test
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
    metrics:
    - type: ndcg_at_1
      value: 54.125
    - type: ndcg_at_3
      value: 69.009
    - type: ndcg_at_5
      value: 72.722
    - type: ndcg_at_10
      value: 74.957
    - type: ndcg_at_20
      value: 75.801
    - type: ndcg_at_100
      value: 75.986
    - type: ndcg_at_1000
      value: 76.015
    - type: map_at_1
      value: 54.125
    - type: map_at_3
      value: 65.375
    - type: map_at_5
      value: 67.448
    - type: map_at_10
      value: 68.38499999999999
    - type: map_at_20
      value: 68.636
    - type: map_at_100
      value: 68.66600000000001
    - type: map_at_1000
      value: 68.66799999999999
    - type: recall_at_1
      value: 54.125
    - type: recall_at_3
      value: 79.51599999999999
    - type: recall_at_5
      value: 88.478
    - type: recall_at_10
      value: 95.306
    - type: recall_at_20
      value: 98.506
    - type: recall_at_100
      value: 99.431
    - type: recall_at_1000
      value: 99.644
    - type: precision_at_1
      value: 54.125
    - type: precision_at_3
      value: 26.505000000000003
    - type: precision_at_5
      value: 17.696
    - type: precision_at_10
      value: 9.531
    - type: precision_at_20
      value: 4.925
    - type: precision_at_100
      value: 0.9939999999999999
    - type: precision_at_1000
      value: 0.1
    - type: mrr_at_1
      value: 54.623
    - type: mrr_at_3
      value: 65.505
    - type: mrr_at_5
      value: 67.6174
    - type: mrr_at_10
      value: 68.5664
    - type: mrr_at_20
      value: 68.8173
    - type: mrr_at_100
      value: 68.8476
    - type: mrr_at_1000
      value: 68.8489
    - type: nauc_ndcg_at_1_max
      value: -14.4789
    - type: nauc_ndcg_at_1_std
      value: -25.5432
    - type: nauc_ndcg_at_1_diff1
      value: 23.7267
    - type: nauc_ndcg_at_3_max
      value: -8.1401
    - type: nauc_ndcg_at_3_std
      value: -22.9099
    - type: nauc_ndcg_at_3_diff1
      value: 21.069499999999998
    - type: nauc_ndcg_at_5_max
      value: -8.4301
    - type: nauc_ndcg_at_5_std
      value: -22.9185
    - type: nauc_ndcg_at_5_diff1
      value: 21.229100000000003
    - type: nauc_ndcg_at_10_max
      value: -8.6651
    - type: nauc_ndcg_at_10_std
      value: -23.5444
    - type: nauc_ndcg_at_10_diff1
      value: 21.9585
    - type: nauc_ndcg_at_20_max
      value: -9.285400000000001
    - type: nauc_ndcg_at_20_std
      value: -23.4297
    - type: nauc_ndcg_at_20_diff1
      value: 21.6731
    - type: nauc_ndcg_at_100_max
      value: -9.8693
    - type: nauc_ndcg_at_100_std
      value: -23.313
    - type: nauc_ndcg_at_100_diff1
      value: 21.5888
    - type: nauc_ndcg_at_1000_max
      value: -9.9675
    - type: nauc_ndcg_at_1000_std
      value: -23.3522
    - type: nauc_ndcg_at_1000_diff1
      value: 21.5714
    - type: nauc_map_at_1_max
      value: -14.4789
    - type: nauc_map_at_1_std
      value: -25.5432
    - type: nauc_map_at_1_diff1
      value: 23.7267
    - type: nauc_map_at_3_max
      value: -10.0484
    - type: nauc_map_at_3_std
      value: -23.3575
    - type: nauc_map_at_3_diff1
      value: 21.329
    - type: nauc_map_at_5_max
      value: -10.3514
    - type: nauc_map_at_5_std
      value: -23.3955
    - type: nauc_map_at_5_diff1
      value: 21.3531
    - type: nauc_map_at_10_max
      value: -10.484200000000001
    - type: nauc_map_at_10_std
      value: -23.6726
    - type: nauc_map_at_10_diff1
      value: 21.6458
    - type: nauc_map_at_20_max
      value: -10.638499999999999
    - type: nauc_map_at_20_std
      value: -23.6588
    - type: nauc_map_at_20_diff1
      value: 21.576600000000003
    - type: nauc_map_at_100_max
      value: -10.717400000000001
    - type: nauc_map_at_100_std
      value: -23.6559
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      value: 21.5688
    - type: nauc_map_at_1000_max
      value: -10.7203
    - type: nauc_map_at_1000_std
      value: -23.6557
    - type: nauc_map_at_1000_diff1
      value: 21.5682
    - type: nauc_recall_at_1_max
      value: -14.4789
    - type: nauc_recall_at_1_std
      value: -25.5432
    - type: nauc_recall_at_1_diff1
      value: 23.7267
    - type: nauc_recall_at_3_max
      value: -0.2134
    - type: nauc_recall_at_3_std
      value: -21.251800000000003
    - type: nauc_recall_at_3_diff1
      value: 20.3069
    - type: nauc_recall_at_5_max
      value: 4.109100000000001
    - type: nauc_recall_at_5_std
      value: -20.1382
    - type: nauc_recall_at_5_diff1
      value: 21.1976
    - type: nauc_recall_at_10_max
      value: 18.3416
    - type: nauc_recall_at_10_std
      value: -22.9791
    - type: nauc_recall_at_10_diff1
      value: 29.4668
    - type: nauc_recall_at_20_max
      value: 45.3219
    - type: nauc_recall_at_20_std
      value: -14.8366
    - type: nauc_recall_at_20_diff1
      value: 31.829800000000002
    - type: nauc_recall_at_100_max
      value: 38.8075
    - type: nauc_recall_at_100_std
      value: 25.4176
    - type: nauc_recall_at_100_diff1
      value: 32.2733
    - type: nauc_recall_at_1000_max
      value: 28.1372
    - type: nauc_recall_at_1000_std
      value: 35.442
    - type: nauc_recall_at_1000_diff1
      value: 31.8247
    - type: nauc_precision_at_1_max
      value: -14.4789
    - type: nauc_precision_at_1_std
      value: -25.5432
    - type: nauc_precision_at_1_diff1
      value: 23.7267
    - type: nauc_precision_at_3_max
      value: -0.2134
    - type: nauc_precision_at_3_std
      value: -21.251800000000003
    - type: nauc_precision_at_3_diff1
      value: 20.3069
    - type: nauc_precision_at_5_max
      value: 4.109100000000001
    - type: nauc_precision_at_5_std
      value: -20.1382
    - type: nauc_precision_at_5_diff1
      value: 21.1976
    - type: nauc_precision_at_10_max
      value: 18.3416
    - type: nauc_precision_at_10_std
      value: -22.9791
    - type: nauc_precision_at_10_diff1
      value: 29.4668
    - type: nauc_precision_at_20_max
      value: 45.3219
    - type: nauc_precision_at_20_std
      value: -14.8366
    - type: nauc_precision_at_20_diff1
      value: 31.829800000000002
    - type: nauc_precision_at_100_max
      value: 38.8075
    - type: nauc_precision_at_100_std
      value: 25.4176
    - type: nauc_precision_at_100_diff1
      value: 32.2733
    - type: nauc_precision_at_1000_max
      value: 28.1372
    - type: nauc_precision_at_1000_std
      value: 35.442
    - type: nauc_precision_at_1000_diff1
      value: 31.8247
    - type: nauc_mrr_at_1_max
      value: -14.066600000000001
    - type: nauc_mrr_at_1_std
      value: -25.0145
    - type: nauc_mrr_at_1_diff1
      value: 22.361900000000002
    - type: nauc_mrr_at_3_max
      value: -10.6465
    - type: nauc_mrr_at_3_std
      value: -23.4323
    - type: nauc_mrr_at_3_diff1
      value: 19.758899999999997
    - type: nauc_mrr_at_5_max
      value: -10.7144
    - type: nauc_mrr_at_5_std
      value: -23.2823
    - type: nauc_mrr_at_5_diff1
      value: 19.8552
    - type: nauc_mrr_at_10_max
      value: -10.7815
    - type: nauc_mrr_at_10_std
      value: -23.51
    - type: nauc_mrr_at_10_diff1
      value: 20.157
    - type: nauc_mrr_at_20_max
      value: -10.9391
    - type: nauc_mrr_at_20_std
      value: -23.4946
    - type: nauc_mrr_at_20_diff1
      value: 20.072400000000002
    - type: nauc_mrr_at_100_max
      value: -11.018500000000001
    - type: nauc_mrr_at_100_std
      value: -23.491400000000002
    - type: nauc_mrr_at_100_diff1
      value: 20.0627
    - type: nauc_mrr_at_1000_max
      value: -11.0214
    - type: nauc_mrr_at_1000_std
      value: -23.491300000000003
    - type: nauc_mrr_at_1000_diff1
      value: 20.061999999999998
    - type: main_score
      value: 74.957
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P (default)
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 50.5269
    - type: v_measure_std
      value: 14.0094
    - type: main_score
      value: 50.5269
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S (default)
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 41.620200000000004
    - type: v_measure_std
      value: 14.4842
    - type: main_score
      value: 41.620200000000004
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions (default)
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 61.790299999999995
    - type: mrr
      value: 75.8156
    - type: nAUC_map_max
      value: 26.151200000000003
    - type: nAUC_map_std
      value: 15.8953
    - type: nAUC_map_diff1
      value: 5.0684
    - type: nAUC_mrr_max
      value: 36.9643
    - type: nAUC_mrr_std
      value: 19.0749
    - type: nAUC_mrr_diff1
      value: 15.549399999999999
    - type: main_score
      value: 61.790299999999995
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES (default)
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: pearson
      value: 88.41590000000001
    - type: spearman
      value: 86.7116
    - type: cosine_pearson
      value: 88.41590000000001
    - type: cosine_spearman
      value: 86.7116
    - type: manhattan_pearson
      value: 86.2045
    - type: manhattan_spearman
      value: 85.7248
    - type: euclidean_pearson
      value: 86.2336
    - type: euclidean_spearman
      value: 85.861
    - type: main_score
      value: 86.7116
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification (default)
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 88.3052
    - type: f1
      value: 88.2617
    - type: f1_weighted
      value: 88.2617
    - type: main_score
      value: 88.3052
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P (default)
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 45.4377
    - type: v_measure_std
      value: 0.8543000000000001
    - type: main_score
      value: 45.4377
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S (default)
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 39.6472
    - type: v_measure_std
      value: 0.7081999999999999
    - type: main_score
      value: 39.6472
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval (default)
      type: mteb/cqadupstack-android
      config: default
      split: test
      revision: f46a197baaae43b4f621051089b82a364682dfeb
    metrics:
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      value: 39.342
    - type: ndcg_at_3
      value: 44.718999999999994
    - type: ndcg_at_5
      value: 47.449999999999996
    - type: ndcg_at_10
      value: 50.17
    - type: ndcg_at_20
      value: 52.366
    - type: ndcg_at_100
      value: 55.400000000000006
    - type: ndcg_at_1000
      value: 57.13399999999999
    - type: map_at_1
      value: 32.300000000000004
    - type: map_at_3
      value: 39.937
    - type: map_at_5
      value: 42.141
    - type: map_at_10
      value: 43.681
    - type: map_at_20
      value: 44.516
    - type: map_at_100
      value: 45.14
    - type: map_at_1000
      value: 45.25
    - type: recall_at_1
      value: 32.300000000000004
    - type: recall_at_3
      value: 47.12
    - type: recall_at_5
      value: 54.581
    - type: recall_at_10
      value: 62.873000000000005
    - type: recall_at_20
      value: 70.604
    - type: recall_at_100
      value: 84.465
    - type: recall_at_1000
      value: 95.299
    - type: precision_at_1
      value: 39.342
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      value: 21.459
    - type: precision_at_5
      value: 15.622
    - type: precision_at_10
      value: 9.514
    - type: precision_at_20
      value: 5.665
    - type: precision_at_100
      value: 1.5150000000000001
    - type: precision_at_1000
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    - type: mrr_at_1
      value: 39.3419
    - type: mrr_at_3
      value: 46.805
    - type: mrr_at_5
      value: 48.5861
    - type: mrr_at_10
      value: 49.6697
    - type: mrr_at_20
      value: 50.131
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      value: -1.8753
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  - task:
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  - task:
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  - task:
      type: Classification
    dataset:
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      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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    - type: f1_weighted
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    - type: main_score
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  - task:
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    dataset:
      name: MTEB MSMARCO (default)
      type: mteb/msmarco
      config: default
      split: dev
      revision: c5a29a104738b98a9e76336939199e264163d4a0
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    - type: ndcg_at_20
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    - type: map_at_20
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    - type: map_at_100
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  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 96.311
    - type: f1
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    - type: f1_weighted
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    - type: main_score
      value: 96.311
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 86.5048
    - type: f1
      value: 67.3883
    - type: f1_weighted
      value: 88.2687
    - type: main_score
      value: 86.5048
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
    - type: accuracy
      value: 75.7902
    - type: f1
      value: 73.2351
    - type: f1_weighted
      value: 75.5894
    - type: main_score
      value: 75.7902
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
    - type: accuracy
      value: 77.3571
    - type: f1
      value: 77.3086
    - type: f1_weighted
      value: 77.235
    - type: main_score
      value: 77.3571
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P (default)
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
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      value: 39.4623
    - type: v_measure_std
      value: 1.3405
    - type: main_score
      value: 39.4623
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S (default)
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 37.5047
    - type: v_measure_std
      value: 1.2052
    - type: main_score
      value: 37.5047
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking (default)
      type: mteb/mind_small
      config: default
      split: test
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
    metrics:
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      value: 28.9125
    - type: mrr
      value: 29.656900000000004
    - type: nAUC_map_max
      value: -21.7929
    - type: nAUC_map_std
      value: -4.2712
    - type: nAUC_map_diff1
      value: 11.698500000000001
    - type: nAUC_mrr_max
      value: -16.4251
    - type: nAUC_mrr_std
      value: -2.1364
    - type: nAUC_mrr_diff1
      value: 11.3017
    - type: main_score
      value: 28.9125
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus (default)
      type: mteb/nfcorpus
      config: default
      split: test
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
    metrics:
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      value: 44.737
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      value: 40.943000000000005
    - type: ndcg_at_5
      value: 38.914
    - type: ndcg_at_10
      value: 35.762
    - type: ndcg_at_20
      value: 33.274
    - type: ndcg_at_100
      value: 32.861000000000004
    - type: ndcg_at_1000
      value: 41.509
    - type: map_at_1
      value: 5.792
    - type: map_at_3
      value: 9.506
    - type: map_at_5
      value: 11.213
    - type: map_at_10
      value: 13.165
    - type: map_at_20
      value: 14.663
    - type: map_at_100
      value: 16.885
    - type: map_at_1000
      value: 18.368000000000002
    - type: recall_at_1
      value: 5.792
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      value: 10.517
    - type: recall_at_5
      value: 13.296
    - type: recall_at_10
      value: 17.37
    - type: recall_at_20
      value: 21.22
    - type: recall_at_100
      value: 33.953
    - type: recall_at_1000
      value: 65.462
    - type: precision_at_1
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    - type: precision_at_3
      value: 38.596000000000004
    - type: precision_at_5
      value: 34.303
    - type: precision_at_10
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    - type: precision_at_20
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    - type: precision_at_100
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    - type: precision_at_1000
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    - type: mrr_at_1
      value: 46.7492
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      value: 54.02479999999999
    - type: mrr_at_5
      value: 55.031
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    - type: nauc_mrr_at_3_max
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    - type: nauc_mrr_at_3_std
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      value: 35.8853
    - type: nauc_mrr_at_5_std
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    - type: nauc_mrr_at_10_max
      value: 35.8488
    - type: nauc_mrr_at_10_std
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    - type: nauc_mrr_at_20_max
      value: 35.7887
    - type: nauc_mrr_at_20_std
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    - type: nauc_mrr_at_100_max
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    - type: nauc_mrr_at_100_std
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      value: -53.744899999999994
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    - type: main_score
      value: 89.848
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering (default)
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
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      value: 58.794599999999996
    - type: v_measure_std
      value: 3.7606
    - type: main_score
      value: 58.794599999999996
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P (default)
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
    metrics:
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      value: 65.4871
    - type: v_measure_std
      value: 13.1853
    - type: main_score
      value: 65.4871
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS (default)
      type: mteb/scidocs
      config: default
      split: test
      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
    metrics:
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      value: 26.0
    - type: ndcg_at_3
      value: 21.369
    - type: ndcg_at_5
      value: 18.865000000000002
    - type: ndcg_at_10
      value: 22.847
    - type: ndcg_at_20
      value: 25.817
    - type: ndcg_at_100
      value: 31.824
    - type: ndcg_at_1000
      value: 37.997
    - type: map_at_1
      value: 5.268
    - type: map_at_3
      value: 9.604
    - type: map_at_5
      value: 11.797
    - type: map_at_10
      value: 13.891
    - type: map_at_20
      value: 15.062000000000001
    - type: map_at_100
      value: 16.323
    - type: map_at_1000
      value: 16.71
    - type: recall_at_1
      value: 5.268
    - type: recall_at_3
      value: 12.203
    - type: recall_at_5
      value: 16.963
    - type: recall_at_10
      value: 24.29
    - type: recall_at_20
      value: 31.267
    - type: recall_at_100
      value: 50.727
    - type: recall_at_1000
      value: 80.67800000000001
    - type: precision_at_1
      value: 26.0
    - type: precision_at_3
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    - type: precision_at_5
      value: 16.74
    - type: precision_at_10
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    - type: precision_at_20
      value: 7.7
    - type: precision_at_100
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    - type: precision_at_1000
      value: 0.398
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      value: 26.0
    - type: mrr_at_3
      value: 34.2833
    - type: mrr_at_5
      value: 35.9333
    - type: mrr_at_10
      value: 37.5791
    - type: mrr_at_20
      value: 38.1301
    - type: mrr_at_100
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    - type: mrr_at_1000
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    - type: nauc_ndcg_at_1_max
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    - type: nauc_ndcg_at_1_std
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    - type: nauc_ndcg_at_1_diff1
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    - type: nauc_ndcg_at_3_max
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    - type: nauc_ndcg_at_3_std
      value: 11.126
    - type: nauc_ndcg_at_3_diff1
      value: 17.1458
    - type: nauc_ndcg_at_5_max
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    - type: nauc_ndcg_at_5_std
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    - type: nauc_ndcg_at_5_diff1
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    - type: nauc_ndcg_at_10_max
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    - type: nauc_ndcg_at_20_max
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      value: 19.9009
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    - type: nauc_ndcg_at_100_max
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    - type: nauc_ndcg_at_1000_max
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    - type: nauc_map_at_1_max
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      value: 8.932
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    - type: nauc_map_at_3_max
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      value: 9.1566
    - type: nauc_map_at_3_diff1
      value: 17.2375
    - type: nauc_map_at_5_max
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    - type: nauc_map_at_5_std
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    - type: nauc_map_at_5_diff1
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    - type: nauc_map_at_10_max
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      value: 14.0863
    - type: nauc_map_at_100_max
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    - type: nauc_map_at_1000_max
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      value: 31.478
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    - type: nauc_recall_at_100_max
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    - type: nauc_recall_at_1000_max
      value: 30.4869
    - type: nauc_recall_at_1000_std
      value: 38.8306
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    - type: nauc_precision_at_1_max
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    - type: nauc_precision_at_1_std
      value: 8.761800000000001
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      value: 22.0695
    - type: nauc_precision_at_3_max
      value: 29.608600000000003
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      value: 12.3347
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      value: 14.810200000000002
    - type: nauc_precision_at_5_max
      value: 28.8061
    - type: nauc_precision_at_5_std
      value: 16.0502
    - type: nauc_precision_at_5_diff1
      value: 12.251900000000001
    - type: nauc_precision_at_10_max
      value: 31.3513
    - type: nauc_precision_at_10_std
      value: 21.226300000000002
    - type: nauc_precision_at_10_diff1
      value: 8.772499999999999
    - type: nauc_precision_at_20_max
      value: 31.692999999999998
    - type: nauc_precision_at_20_std
      value: 25.4628
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      value: 7.1315
    - type: nauc_precision_at_100_max
      value: 33.3115
    - type: nauc_precision_at_100_std
      value: 36.888799999999996
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      value: 7.820100000000001
    - type: nauc_precision_at_1000_max
      value: 29.1927
    - type: nauc_precision_at_1000_std
      value: 36.2523
    - type: nauc_precision_at_1000_diff1
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    - type: nauc_mrr_at_1_max
      value: 21.9327
    - type: nauc_mrr_at_1_std
      value: 8.761800000000001
    - type: nauc_mrr_at_1_diff1
      value: 22.0695
    - type: nauc_mrr_at_3_max
      value: 26.1187
    - type: nauc_mrr_at_3_std
      value: 12.5639
    - type: nauc_mrr_at_3_diff1
      value: 19.642599999999998
    - type: nauc_mrr_at_5_max
      value: 25.8562
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      value: 12.495000000000001
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      value: 19.3465
    - type: nauc_mrr_at_10_max
      value: 26.218200000000003
    - type: nauc_mrr_at_10_std
      value: 13.1243
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      value: 18.9542
    - type: nauc_mrr_at_20_max
      value: 26.422099999999997
    - type: nauc_mrr_at_20_std
      value: 13.4214
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      value: 19.0105
    - type: nauc_mrr_at_100_max
      value: 26.338
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      value: 13.4264
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      value: 18.9729
    - type: nauc_mrr_at_1000_max
      value: 26.3327
    - type: nauc_mrr_at_1000_std
      value: 13.3904
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      value: 19.004199999999997
    - type: main_score
      value: 22.847
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R (default)
      type: mteb/sickr-sts
      config: default
      split: test
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
    metrics:
    - type: pearson
      value: 81.13050000000001
    - type: spearman
      value: 79.01310000000001
    - type: cosine_pearson
      value: 81.13050000000001
    - type: cosine_spearman
      value: 79.01310000000001
    - type: manhattan_pearson
      value: 79.03999999999999
    - type: manhattan_spearman
      value: 79.1744
    - type: euclidean_pearson
      value: 79.0977
    - type: euclidean_spearman
      value: 79.2268
    - type: main_score
      value: 79.01310000000001
  - task:
      type: STS
    dataset:
      name: MTEB STS12 (default)
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: pearson
      value: 86.9675
    - type: spearman
      value: 80.3531
    - type: cosine_pearson
      value: 86.9675
    - type: cosine_spearman
      value: 80.3531
    - type: manhattan_pearson
      value: 82.2315
    - type: manhattan_spearman
      value: 79.7004
    - type: euclidean_pearson
      value: 82.3305
    - type: euclidean_spearman
      value: 79.8601
    - type: main_score
      value: 80.3531
  - task:
      type: STS
    dataset:
      name: MTEB STS13 (default)
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: pearson
      value: 85.6041
    - type: spearman
      value: 86.0453
    - type: cosine_pearson
      value: 85.6041
    - type: cosine_spearman
      value: 86.0453
    - type: manhattan_pearson
      value: 85.2548
    - type: manhattan_spearman
      value: 85.8908
    - type: euclidean_pearson
      value: 85.253
    - type: euclidean_spearman
      value: 85.9181
    - type: main_score
      value: 86.0453
  - task:
      type: STS
    dataset:
      name: MTEB STS14 (default)
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: pearson
      value: 82.8792
    - type: spearman
      value: 82.9681
    - type: cosine_pearson
      value: 82.8792
    - type: cosine_spearman
      value: 82.9681
    - type: manhattan_pearson
      value: 81.4789
    - type: manhattan_spearman
      value: 82.4797
    - type: euclidean_pearson
      value: 81.4674
    - type: euclidean_spearman
      value: 82.4547
    - type: main_score
      value: 82.9681
  - task:
      type: STS
    dataset:
      name: MTEB STS15 (default)
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: pearson
      value: 87.5356
    - type: spearman
      value: 88.06540000000001
    - type: cosine_pearson
      value: 87.5356
    - type: cosine_spearman
      value: 88.06540000000001
    - type: manhattan_pearson
      value: 87.10759999999999
    - type: manhattan_spearman
      value: 87.75309999999999
    - type: euclidean_pearson
      value: 87.1489
    - type: euclidean_spearman
      value: 87.7857
    - type: main_score
      value: 88.06540000000001
  - task:
      type: STS
    dataset:
      name: MTEB STS16 (default)
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: pearson
      value: 85.0208
    - type: spearman
      value: 86.0136
    - type: cosine_pearson
      value: 85.0208
    - type: cosine_spearman
      value: 86.0136
    - type: manhattan_pearson
      value: 85.22
    - type: manhattan_spearman
      value: 86.1101
    - type: euclidean_pearson
      value: 85.2043
    - type: euclidean_spearman
      value: 86.113
    - type: main_score
      value: 86.0136
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      config: en-en
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: pearson
      value: 89.4083
    - type: spearman
      value: 88.9498
    - type: cosine_pearson
      value: 89.4083
    - type: cosine_spearman
      value: 88.9498
    - type: manhattan_pearson
      value: 89.46539999999999
    - type: manhattan_spearman
      value: 88.8754
    - type: euclidean_pearson
      value: 89.4326
    - type: euclidean_spearman
      value: 88.8148
    - type: main_score
      value: 88.9498
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: pearson
      value: 66.60770000000001
    - type: spearman
      value: 67.1515
    - type: cosine_pearson
      value: 66.60770000000001
    - type: cosine_spearman
      value: 67.1515
    - type: manhattan_pearson
      value: 66.5604
    - type: manhattan_spearman
      value: 66.4621
    - type: euclidean_pearson
      value: 66.4628
    - type: euclidean_spearman
      value: 66.2979
    - type: main_score
      value: 67.1515
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark (default)
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: pearson
      value: 86.86399999999999
    - type: spearman
      value: 87.7139
    - type: cosine_pearson
      value: 86.86399999999999
    - type: cosine_spearman
      value: 87.7139
    - type: manhattan_pearson
      value: 86.6602
    - type: manhattan_spearman
      value: 87.2606
    - type: euclidean_pearson
      value: 86.5924
    - type: euclidean_spearman
      value: 87.241
    - type: main_score
      value: 87.7139
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR (default)
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 84.37360000000001
    - type: mrr
      value: 95.6275
    - type: nAUC_map_max
      value: 52.991699999999994
    - type: nAUC_map_std
      value: 66.8168
    - type: nAUC_map_diff1
      value: -3.2009999999999996
    - type: nAUC_mrr_max
      value: 85.7492
    - type: nAUC_mrr_std
      value: 77.3543
    - type: nAUC_mrr_diff1
      value: 38.014700000000005
    - type: main_score
      value: 84.37360000000001
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact (default)
      type: mteb/scifact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
    metrics:
    - type: ndcg_at_1
      value: 60.667
    - type: ndcg_at_3
      value: 68.209
    - type: ndcg_at_5
      value: 71.409
    - type: ndcg_at_10
      value: 73.476
    - type: ndcg_at_20
      value: 74.339
    - type: ndcg_at_100
      value: 75.57000000000001
    - type: ndcg_at_1000
      value: 75.955
    - type: map_at_1
      value: 58.178
    - type: map_at_3
      value: 65.71900000000001
    - type: map_at_5
      value: 67.73
    - type: map_at_10
      value: 68.821
    - type: map_at_20
      value: 69.07600000000001
    - type: map_at_100
      value: 69.245
    - type: map_at_1000
      value: 69.258
    - type: recall_at_1
      value: 58.178
    - type: recall_at_3
      value: 73.172
    - type: recall_at_5
      value: 81.0
    - type: recall_at_10
      value: 86.867
    - type: recall_at_20
      value: 90.267
    - type: recall_at_100
      value: 96.933
    - type: recall_at_1000
      value: 100.0
    - type: precision_at_1
      value: 60.667
    - type: precision_at_3
      value: 26.444000000000003
    - type: precision_at_5
      value: 18.0
    - type: precision_at_10
      value: 9.866999999999999
    - type: precision_at_20
      value: 5.133
    - type: precision_at_100
      value: 1.097
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: mrr_at_1
      value: 60.6667
    - type: mrr_at_3
      value: 67.1667
    - type: mrr_at_5
      value: 68.85
    - type: mrr_at_10
      value: 69.4799
    - type: mrr_at_20
      value: 69.6658
    - type: mrr_at_100
      value: 69.8134
    - type: mrr_at_1000
      value: 69.8257
    - type: nauc_ndcg_at_1_max
      value: 49.3608
    - type: nauc_ndcg_at_1_std
      value: 12.742400000000002
    - type: nauc_ndcg_at_1_diff1
      value: 74.5012
    - type: nauc_ndcg_at_3_max
      value: 49.524499999999996
    - type: nauc_ndcg_at_3_std
      value: 7.7241
    - type: nauc_ndcg_at_3_diff1
      value: 72.0127
    - type: nauc_ndcg_at_5_max
      value: 51.897099999999995
    - type: nauc_ndcg_at_5_std
      value: 12.8641
    - type: nauc_ndcg_at_5_diff1
      value: 69.7789
    - type: nauc_ndcg_at_10_max
      value: 55.1141
    - type: nauc_ndcg_at_10_std
      value: 17.136499999999998
    - type: nauc_ndcg_at_10_diff1
      value: 68.8711
    - type: nauc_ndcg_at_20_max
      value: 54.74719999999999
    - type: nauc_ndcg_at_20_std
      value: 17.0485
    - type: nauc_ndcg_at_20_diff1
      value: 69.4701
    - type: nauc_ndcg_at_100_max
      value: 53.7619
    - type: nauc_ndcg_at_100_std
      value: 15.335299999999998
    - type: nauc_ndcg_at_100_diff1
      value: 70.34479999999999
    - type: nauc_ndcg_at_1000_max
      value: 53.4516
    - type: nauc_ndcg_at_1000_std
      value: 14.7843
    - type: nauc_ndcg_at_1000_diff1
      value: 70.6041
    - type: nauc_map_at_1_max
      value: 44.9654
    - type: nauc_map_at_1_std
      value: 5.9821
    - type: nauc_map_at_1_diff1
      value: 76.2581
    - type: nauc_map_at_3_max
      value: 47.515299999999996
    - type: nauc_map_at_3_std
      value: 6.2703
    - type: nauc_map_at_3_diff1
      value: 73.5279
    - type: nauc_map_at_5_max
      value: 49.805899999999994
    - type: nauc_map_at_5_std
      value: 10.1001
    - type: nauc_map_at_5_diff1
      value: 72.1812
    - type: nauc_map_at_10_max
      value: 51.9276
    - type: nauc_map_at_10_std
      value: 12.698200000000002
    - type: nauc_map_at_10_diff1
      value: 71.6343
    - type: nauc_map_at_20_max
      value: 51.8856
    - type: nauc_map_at_20_std
      value: 12.814800000000002
    - type: nauc_map_at_20_diff1
      value: 71.78179999999999
    - type: nauc_map_at_100_max
      value: 51.7504
    - type: nauc_map_at_100_std
      value: 12.5353
    - type: nauc_map_at_100_diff1
      value: 71.8854
    - type: nauc_map_at_1000_max
      value: 51.739900000000006
    - type: nauc_map_at_1000_std
      value: 12.519
    - type: nauc_map_at_1000_diff1
      value: 71.8964
    - type: nauc_recall_at_1_max
      value: 44.9654
    - type: nauc_recall_at_1_std
      value: 5.9821
    - type: nauc_recall_at_1_diff1
      value: 76.2581
    - type: nauc_recall_at_3_max
      value: 47.9306
    - type: nauc_recall_at_3_std
      value: 3.5374000000000003
    - type: nauc_recall_at_3_diff1
      value: 68.4552
    - type: nauc_recall_at_5_max
      value: 54.374
    - type: nauc_recall_at_5_std
      value: 17.646700000000003
    - type: nauc_recall_at_5_diff1
      value: 60.5644
    - type: nauc_recall_at_10_max
      value: 69.6484
    - type: nauc_recall_at_10_std
      value: 38.3671
    - type: nauc_recall_at_10_diff1
      value: 54.39580000000001
    - type: nauc_recall_at_20_max
      value: 70.0061
    - type: nauc_recall_at_20_std
      value: 42.403999999999996
    - type: nauc_recall_at_20_diff1
      value: 55.3831
    - type: nauc_recall_at_100_max
      value: 69.02629999999999
    - type: nauc_recall_at_100_std
      value: 43.850699999999996
    - type: nauc_recall_at_100_diff1
      value: 57.837
    - 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: 49.3608
    - type: nauc_precision_at_1_std
      value: 12.742400000000002
    - type: nauc_precision_at_1_diff1
      value: 74.5012
    - type: nauc_precision_at_3_max
      value: 45.2627
    - type: nauc_precision_at_3_std
      value: 15.5113
    - type: nauc_precision_at_3_diff1
      value: 44.5108
    - type: nauc_precision_at_5_max
      value: 48.4003
    - type: nauc_precision_at_5_std
      value: 35.3791
    - type: nauc_precision_at_5_diff1
      value: 19.7518
    - type: nauc_precision_at_10_max
      value: 46.688
    - type: nauc_precision_at_10_std
      value: 47.9876
    - type: nauc_precision_at_10_diff1
      value: 0.1083
    - type: nauc_precision_at_20_max
      value: 41.281400000000005
    - type: nauc_precision_at_20_std
      value: 49.0662
    - type: nauc_precision_at_20_diff1
      value: -6.2035
    - type: nauc_precision_at_100_max
      value: 30.0167
    - type: nauc_precision_at_100_std
      value: 47.2561
    - type: nauc_precision_at_100_diff1
      value: -22.8584
    - type: nauc_precision_at_1000_max
      value: 23.724999999999998
    - type: nauc_precision_at_1000_std
      value: 45.342
    - type: nauc_precision_at_1000_diff1
      value: -33.29
    - type: nauc_mrr_at_1_max
      value: 49.3608
    - type: nauc_mrr_at_1_std
      value: 12.742400000000002
    - type: nauc_mrr_at_1_diff1
      value: 74.5012
    - type: nauc_mrr_at_3_max
      value: 51.1718
    - type: nauc_mrr_at_3_std
      value: 11.739700000000001
    - type: nauc_mrr_at_3_diff1
      value: 71.5992
    - type: nauc_mrr_at_5_max
      value: 52.2421
    - type: nauc_mrr_at_5_std
      value: 14.127
    - type: nauc_mrr_at_5_diff1
      value: 70.57
    - type: nauc_mrr_at_10_max
      value: 52.5587
    - type: nauc_mrr_at_10_std
      value: 14.5207
    - type: nauc_mrr_at_10_diff1
      value: 70.55709999999999
    - type: nauc_mrr_at_20_max
      value: 52.3699
    - type: nauc_mrr_at_20_std
      value: 14.310300000000002
    - type: nauc_mrr_at_20_diff1
      value: 70.6993
    - type: nauc_mrr_at_100_max
      value: 52.2734
    - type: nauc_mrr_at_100_std
      value: 14.0848
    - type: nauc_mrr_at_100_diff1
      value: 70.8146
    - type: nauc_mrr_at_1000_max
      value: 52.2622
    - type: nauc_mrr_at_1000_std
      value: 14.0715
    - type: nauc_mrr_at_1000_diff1
      value: 70.8239
    - type: main_score
      value: 73.476
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions (default)
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: similarity_accuracy
      value: 99.87819999999999
    - type: similarity_accuracy_threshold
      value: 74.8
    - type: similarity_f1
      value: 93.79729999999999
    - type: similarity_f1_threshold
      value: 74.6812
    - type: similarity_precision
      value: 94.6083
    - type: similarity_recall
      value: 93.0
    - type: similarity_ap
      value: 97.1971
    - type: cosine_accuracy
      value: 99.87819999999999
    - type: cosine_accuracy_threshold
      value: 74.8
    - type: cosine_f1
      value: 93.79729999999999
    - type: cosine_f1_threshold
      value: 74.6812
    - type: cosine_precision
      value: 94.6083
    - type: cosine_recall
      value: 93.0
    - type: cosine_ap
      value: 97.1971
    - type: manhattan_accuracy
      value: 99.8792
    - type: manhattan_accuracy_threshold
      value: 47567.8925
    - type: manhattan_f1
      value: 93.8508
    - type: manhattan_f1_threshold
      value: 47567.8925
    - type: manhattan_precision
      value: 94.6138
    - type: manhattan_recall
      value: 93.10000000000001
    - type: manhattan_ap
      value: 97.2177
    - type: euclidean_accuracy
      value: 99.8812
    - type: euclidean_accuracy_threshold
      value: 2164.0619
    - type: euclidean_f1
      value: 93.9759
    - type: euclidean_f1_threshold
      value: 2164.0619
    - type: euclidean_precision
      value: 94.35480000000001
    - type: euclidean_recall
      value: 93.60000000000001
    - type: euclidean_ap
      value: 97.2412
    - type: dot_accuracy
      value: 99.8446
    - type: dot_accuracy_threshold
      value: 68470.2454
    - type: dot_f1
      value: 91.9939
    - type: dot_f1_threshold
      value: 68470.2454
    - type: dot_precision
      value: 93.8606
    - type: dot_recall
      value: 90.2
    - type: dot_ap
      value: 96.36829999999999
    - type: max_accuracy
      value: 99.8812
    - type: max_f1
      value: 93.9759
    - type: max_precision
      value: 94.6138
    - type: max_recall
      value: 93.60000000000001
    - type: max_ap
      value: 97.2412
    - type: main_score
      value: 97.2412
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering (default)
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 70.04010000000001
    - type: v_measure_std
      value: 3.9558999999999997
    - type: main_score
      value: 70.04010000000001
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P (default)
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 42.4207
    - type: v_measure_std
      value: 1.3677
    - type: main_score
      value: 42.4207
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions (default)
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 52.7026
    - type: mrr
      value: 53.5668
    - type: nAUC_map_max
      value: 12.1758
    - type: nAUC_map_std
      value: 6.7148
    - type: nAUC_map_diff1
      value: 39.881499999999996
    - type: nAUC_mrr_max
      value: 13.0771
    - type: nAUC_mrr_std
      value: 7.7001
    - type: nAUC_mrr_diff1
      value: 39.6391
    - type: main_score
      value: 52.7026
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval (default)
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: pearson
      value: 31.346400000000003
    - type: spearman
      value: 31.5967
    - type: cosine_spearman
      value: 31.5967
    - type: cosine_pearson
      value: 31.346400000000003
    - type: dot_spearman
      value: 28.5388
    - type: dot_pearson
      value: 31.005300000000002
    - type: main_score
      value: 31.5967
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID (default)
      type: mteb/trec-covid
      config: default
      split: test
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
    metrics:
    - type: ndcg_at_1
      value: 87.0
    - type: ndcg_at_3
      value: 84.693
    - type: ndcg_at_5
      value: 82.211
    - type: ndcg_at_10
      value: 80.55
    - type: ndcg_at_20
      value: 77.766
    - type: ndcg_at_100
      value: 62.881
    - type: ndcg_at_1000
      value: 56.510000000000005
    - type: map_at_1
      value: 0.251
    - type: map_at_3
      value: 0.7000000000000001
    - type: map_at_5
      value: 1.124
    - type: map_at_10
      value: 2.114
    - type: map_at_20
      value: 3.837
    - type: map_at_100
      value: 12.903999999999998
    - type: map_at_1000
      value: 31.184
    - type: recall_at_1
      value: 0.251
    - type: recall_at_3
      value: 0.72
    - type: recall_at_5
      value: 1.179
    - type: recall_at_10
      value: 2.271
    - type: recall_at_20
      value: 4.242
    - type: recall_at_100
      value: 16.012999999999998
    - type: recall_at_1000
      value: 53.556000000000004
    - type: precision_at_1
      value: 92.0
    - type: precision_at_3
      value: 88.667
    - type: precision_at_5
      value: 86.8
    - type: precision_at_10
      value: 85.8
    - type: precision_at_20
      value: 82.39999999999999
    - type: precision_at_100
      value: 64.8
    - type: precision_at_1000
      value: 24.832
    - type: mrr_at_1
      value: 92.0
    - type: mrr_at_3
      value: 95.0
    - type: mrr_at_5
      value: 95.0
    - type: mrr_at_10
      value: 95.0
    - type: mrr_at_20
      value: 95.0
    - type: mrr_at_100
      value: 95.0
    - type: mrr_at_1000
      value: 95.0
    - type: nauc_ndcg_at_1_max
      value: 73.7596
    - type: nauc_ndcg_at_1_std
      value: 52.21130000000001
    - type: nauc_ndcg_at_1_diff1
      value: -8.4225
    - type: nauc_ndcg_at_3_max
      value: 68.513
    - type: nauc_ndcg_at_3_std
      value: 61.9698
    - type: nauc_ndcg_at_3_diff1
      value: -13.079099999999999
    - type: nauc_ndcg_at_5_max
      value: 60.7482
    - type: nauc_ndcg_at_5_std
      value: 66.56830000000001
    - type: nauc_ndcg_at_5_diff1
      value: -12.947500000000002
    - type: nauc_ndcg_at_10_max
      value: 57.4673
    - type: nauc_ndcg_at_10_std
      value: 65.25999999999999
    - type: nauc_ndcg_at_10_diff1
      value: -14.4235
    - type: nauc_ndcg_at_20_max
      value: 61.1214
    - type: nauc_ndcg_at_20_std
      value: 73.60640000000001
    - type: nauc_ndcg_at_20_diff1
      value: -18.1836
    - type: nauc_ndcg_at_100_max
      value: 55.3917
    - type: nauc_ndcg_at_100_std
      value: 80.9228
    - type: nauc_ndcg_at_100_diff1
      value: -13.6584
    - type: nauc_ndcg_at_1000_max
      value: 61.6035
    - type: nauc_ndcg_at_1000_std
      value: 77.73299999999999
    - type: nauc_ndcg_at_1000_diff1
      value: 9.456199999999999
    - type: nauc_map_at_1_max
      value: 3.0159
    - type: nauc_map_at_1_std
      value: -6.6826
    - type: nauc_map_at_1_diff1
      value: 19.3295
    - type: nauc_map_at_3_max
      value: 11.3326
    - type: nauc_map_at_3_std
      value: 0.2297
    - type: nauc_map_at_3_diff1
      value: 18.4889
    - type: nauc_map_at_5_max
      value: 12.8623
    - type: nauc_map_at_5_std
      value: 3.1086
    - type: nauc_map_at_5_diff1
      value: 15.2538
    - type: nauc_map_at_10_max
      value: 15.9145
    - type: nauc_map_at_10_std
      value: 5.8626
    - type: nauc_map_at_10_diff1
      value: 11.5455
    - type: nauc_map_at_20_max
      value: 24.6148
    - type: nauc_map_at_20_std
      value: 17.161199999999997
    - type: nauc_map_at_20_diff1
      value: 7.6256
    - type: nauc_map_at_100_max
      value: 42.070299999999996
    - type: nauc_map_at_100_std
      value: 48.926700000000004
    - type: nauc_map_at_100_diff1
      value: 0.16
    - type: nauc_map_at_1000_max
      value: 63.9887
    - type: nauc_map_at_1000_std
      value: 81.2657
    - type: nauc_map_at_1000_diff1
      value: 4.1088
    - type: nauc_recall_at_1_max
      value: 3.0159
    - type: nauc_recall_at_1_std
      value: -6.6826
    - type: nauc_recall_at_1_diff1
      value: 19.3295
    - type: nauc_recall_at_3_max
      value: 7.7778
    - type: nauc_recall_at_3_std
      value: -3.3724
    - type: nauc_recall_at_3_diff1
      value: 17.9181
    - type: nauc_recall_at_5_max
      value: 6.716900000000001
    - type: nauc_recall_at_5_std
      value: -2.6891000000000003
    - type: nauc_recall_at_5_diff1
      value: 16.3817
    - type: nauc_recall_at_10_max
      value: 7.7518
    - type: nauc_recall_at_10_std
      value: -1.9855
    - type: nauc_recall_at_10_diff1
      value: 13.4496
    - type: nauc_recall_at_20_max
      value: 14.4895
    - type: nauc_recall_at_20_std
      value: 7.2935
    - type: nauc_recall_at_20_diff1
      value: 11.2986
    - type: nauc_recall_at_100_max
      value: 29.8636
    - type: nauc_recall_at_100_std
      value: 33.5546
    - type: nauc_recall_at_100_diff1
      value: 7.0793
    - type: nauc_recall_at_1000_max
      value: 57.184000000000005
    - type: nauc_recall_at_1000_std
      value: 65.3208
    - type: nauc_recall_at_1000_diff1
      value: 15.7381
    - type: nauc_precision_at_1_max
      value: 93.4641
    - type: nauc_precision_at_1_std
      value: 80.6839
    - type: nauc_precision_at_1_diff1
      value: 21.592
    - type: nauc_precision_at_3_max
      value: 87.6596
    - type: nauc_precision_at_3_std
      value: 71.28370000000001
    - type: nauc_precision_at_3_diff1
      value: -0.5263
    - type: nauc_precision_at_5_max
      value: 69.3194
    - type: nauc_precision_at_5_std
      value: 67.4507
    - type: nauc_precision_at_5_diff1
      value: 5.8362
    - type: nauc_precision_at_10_max
      value: 62.393299999999996
    - type: nauc_precision_at_10_std
      value: 62.443599999999996
    - type: nauc_precision_at_10_diff1
      value: -5.3395
    - type: nauc_precision_at_20_max
      value: 63.4842
    - type: nauc_precision_at_20_std
      value: 68.95599999999999
    - type: nauc_precision_at_20_diff1
      value: -13.494100000000001
    - type: nauc_precision_at_100_max
      value: 59.24549999999999
    - type: nauc_precision_at_100_std
      value: 81.3779
    - type: nauc_precision_at_100_diff1
      value: -11.0792
    - type: nauc_precision_at_1000_max
      value: 44.8354
    - type: nauc_precision_at_1000_std
      value: 55.232099999999996
    - type: nauc_precision_at_1000_diff1
      value: -1.4931
    - type: nauc_mrr_at_1_max
      value: 93.4641
    - type: nauc_mrr_at_1_std
      value: 80.6839
    - type: nauc_mrr_at_1_diff1
      value: 21.592
    - type: nauc_mrr_at_3_max
      value: 93.8998
    - type: nauc_mrr_at_3_std
      value: 79.3962
    - type: nauc_mrr_at_3_diff1
      value: 19.3371
    - type: nauc_mrr_at_5_max
      value: 93.8998
    - type: nauc_mrr_at_5_std
      value: 79.3962
    - type: nauc_mrr_at_5_diff1
      value: 19.3371
    - type: nauc_mrr_at_10_max
      value: 93.8998
    - type: nauc_mrr_at_10_std
      value: 79.3962
    - type: nauc_mrr_at_10_diff1
      value: 19.3371
    - type: nauc_mrr_at_20_max
      value: 93.8998
    - type: nauc_mrr_at_20_std
      value: 79.3962
    - type: nauc_mrr_at_20_diff1
      value: 19.3371
    - type: nauc_mrr_at_100_max
      value: 93.8998
    - type: nauc_mrr_at_100_std
      value: 79.3962
    - type: nauc_mrr_at_100_diff1
      value: 19.3371
    - type: nauc_mrr_at_1000_max
      value: 93.8998
    - type: nauc_mrr_at_1000_std
      value: 79.3962
    - type: nauc_mrr_at_1000_diff1
      value: 19.3371
    - type: main_score
      value: 80.55
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020 (default)
      type: mteb/touche2020
      config: default
      split: test
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
    metrics:
    - type: ndcg_at_1
      value: 18.367
    - type: ndcg_at_3
      value: 23.105999999999998
    - type: ndcg_at_5
      value: 22.423000000000002
    - type: ndcg_at_10
      value: 21.83
    - type: ndcg_at_20
      value: 23.534
    - type: ndcg_at_100
      value: 33.332
    - type: ndcg_at_1000
      value: 44.842999999999996
    - type: map_at_1
      value: 1.52
    - type: map_at_3
      value: 3.811
    - type: map_at_5
      value: 5.4879999999999995
    - type: map_at_10
      value: 8.204
    - type: map_at_20
      value: 10.387
    - type: map_at_100
      value: 13.633000000000001
    - type: map_at_1000
      value: 15.156
    - type: recall_at_1
      value: 1.52
    - type: recall_at_3
      value: 5.466
    - type: recall_at_5
      value: 8.927
    - type: recall_at_10
      value: 15.237
    - type: recall_at_20
      value: 22.841
    - type: recall_at_100
      value: 44.586999999999996
    - type: recall_at_1000
      value: 79.199
    - type: precision_at_1
      value: 20.408
    - type: precision_at_3
      value: 25.169999999999998
    - type: precision_at_5
      value: 23.673
    - type: precision_at_10
      value: 20.408
    - type: precision_at_20
      value: 16.531000000000002
    - type: precision_at_100
      value: 7.204000000000001
    - type: precision_at_1000
      value: 1.473
    - type: mrr_at_1
      value: 20.4082
    - type: mrr_at_3
      value: 35.374100000000006
    - type: mrr_at_5
      value: 37.7211
    - type: mrr_at_10
      value: 39.7068
    - type: mrr_at_20
      value: 40.6272
    - type: mrr_at_100
      value: 40.7905
    - type: mrr_at_1000
      value: 40.805
    - type: nauc_ndcg_at_1_max
      value: -25.3799
    - type: nauc_ndcg_at_1_std
      value: -27.8526
    - type: nauc_ndcg_at_1_diff1
      value: 11.5616
    - type: nauc_ndcg_at_3_max
      value: -31.987900000000003
    - type: nauc_ndcg_at_3_std
      value: -18.1926
    - type: nauc_ndcg_at_3_diff1
      value: 15.4188
    - type: nauc_ndcg_at_5_max
      value: -29.2499
    - type: nauc_ndcg_at_5_std
      value: -18.8992
    - type: nauc_ndcg_at_5_diff1
      value: 9.677
    - type: nauc_ndcg_at_10_max
      value: -25.427899999999998
    - type: nauc_ndcg_at_10_std
      value: -19.0155
    - type: nauc_ndcg_at_10_diff1
      value: 1.5350000000000001
    - type: nauc_ndcg_at_20_max
      value: -25.007800000000003
    - type: nauc_ndcg_at_20_std
      value: -6.626899999999999
    - type: nauc_ndcg_at_20_diff1
      value: -2.0142
    - type: nauc_ndcg_at_100_max
      value: -24.7187
    - type: nauc_ndcg_at_100_std
      value: 18.587899999999998
    - type: nauc_ndcg_at_100_diff1
      value: -7.925599999999999
    - type: nauc_ndcg_at_1000_max
      value: -20.9609
    - type: nauc_ndcg_at_1000_std
      value: 27.360400000000002
    - type: nauc_ndcg_at_1000_diff1
      value: -5.3411
    - type: nauc_map_at_1_max
      value: -26.3166
    - type: nauc_map_at_1_std
      value: -27.701900000000002
    - type: nauc_map_at_1_diff1
      value: 14.4953
    - type: nauc_map_at_3_max
      value: -19.4984
    - type: nauc_map_at_3_std
      value: -26.0187
    - type: nauc_map_at_3_diff1
      value: 18.9316
    - type: nauc_map_at_5_max
      value: -17.6688
    - type: nauc_map_at_5_std
      value: -27.4662
    - type: nauc_map_at_5_diff1
      value: 16.3786
    - type: nauc_map_at_10_max
      value: -9.727
    - type: nauc_map_at_10_std
      value: -25.4592
    - type: nauc_map_at_10_diff1
      value: 8.434999999999999
    - type: nauc_map_at_20_max
      value: -14.2879
    - type: nauc_map_at_20_std
      value: -17.5881
    - type: nauc_map_at_20_diff1
      value: 2.4941
    - type: nauc_map_at_100_max
      value: -15.804499999999999
    - type: nauc_map_at_100_std
      value: -2.6222
    - type: nauc_map_at_100_diff1
      value: -4.3869
    - type: nauc_map_at_1000_max
      value: -15.4637
    - type: nauc_map_at_1000_std
      value: 1.8402000000000003
    - type: nauc_map_at_1000_diff1
      value: -5.3595
    - type: nauc_recall_at_1_max
      value: -26.3166
    - type: nauc_recall_at_1_std
      value: -27.701900000000002
    - type: nauc_recall_at_1_diff1
      value: 14.4953
    - type: nauc_recall_at_3_max
      value: -18.4525
    - type: nauc_recall_at_3_std
      value: -22.7019
    - type: nauc_recall_at_3_diff1
      value: 14.5105
    - type: nauc_recall_at_5_max
      value: -16.8608
    - type: nauc_recall_at_5_std
      value: -26.2799
    - type: nauc_recall_at_5_diff1
      value: 6.910299999999999
    - type: nauc_recall_at_10_max
      value: -11.498700000000001
    - type: nauc_recall_at_10_std
      value: -22.290499999999998
    - type: nauc_recall_at_10_diff1
      value: -1.6997000000000002
    - type: nauc_recall_at_20_max
      value: -16.319
    - type: nauc_recall_at_20_std
      value: -2.6968
    - type: nauc_recall_at_20_diff1
      value: -8.5511
    - type: nauc_recall_at_100_max
      value: -17.741
    - type: nauc_recall_at_100_std
      value: 36.1914
    - type: nauc_recall_at_100_diff1
      value: -20.1127
    - type: nauc_recall_at_1000_max
      value: 3.4278999999999997
    - type: nauc_recall_at_1000_std
      value: 65.7558
    - type: nauc_recall_at_1000_diff1
      value: -15.537899999999999
    - type: nauc_precision_at_1_max
      value: -27.3245
    - type: nauc_precision_at_1_std
      value: -28.615000000000002
    - type: nauc_precision_at_1_diff1
      value: 16.2275
    - type: nauc_precision_at_3_max
      value: -32.1286
    - type: nauc_precision_at_3_std
      value: -14.0653
    - type: nauc_precision_at_3_diff1
      value: 15.6075
    - type: nauc_precision_at_5_max
      value: -27.176299999999998
    - type: nauc_precision_at_5_std
      value: -15.5885
    - type: nauc_precision_at_5_diff1
      value: 7.3431999999999995
    - type: nauc_precision_at_10_max
      value: -26.9241
    - type: nauc_precision_at_10_std
      value: -11.737
    - type: nauc_precision_at_10_diff1
      value: -7.630000000000001
    - type: nauc_precision_at_20_max
      value: -26.901999999999997
    - type: nauc_precision_at_20_std
      value: 23.7519
    - type: nauc_precision_at_20_diff1
      value: -21.343799999999998
    - type: nauc_precision_at_100_max
      value: -16.9757
    - type: nauc_precision_at_100_std
      value: 70.6663
    - type: nauc_precision_at_100_diff1
      value: -32.3231
    - type: nauc_precision_at_1000_max
      value: 20.8431
    - type: nauc_precision_at_1000_std
      value: 37.8016
    - type: nauc_precision_at_1000_diff1
      value: -9.911200000000001
    - type: nauc_mrr_at_1_max
      value: -27.3245
    - type: nauc_mrr_at_1_std
      value: -28.615000000000002
    - type: nauc_mrr_at_1_diff1
      value: 16.2275
    - type: nauc_mrr_at_3_max
      value: -33.332499999999996
    - type: nauc_mrr_at_3_std
      value: -21.543499999999998
    - type: nauc_mrr_at_3_diff1
      value: 15.7577
    - type: nauc_mrr_at_5_max
      value: -34.56
    - type: nauc_mrr_at_5_std
      value: -21.0279
    - type: nauc_mrr_at_5_diff1
      value: 10.4699
    - type: nauc_mrr_at_10_max
      value: -35.4396
    - type: nauc_mrr_at_10_std
      value: -22.6385
    - type: nauc_mrr_at_10_diff1
      value: 8.4536
    - type: nauc_mrr_at_20_max
      value: -34.0343
    - type: nauc_mrr_at_20_std
      value: -21.4022
    - type: nauc_mrr_at_20_diff1
      value: 10.7134
    - type: nauc_mrr_at_100_max
      value: -34.190799999999996
    - type: nauc_mrr_at_100_std
      value: -21.5996
    - type: nauc_mrr_at_100_diff1
      value: 10.9828
    - type: nauc_mrr_at_1000_max
      value: -34.1503
    - type: nauc_mrr_at_1000_std
      value: -21.662300000000002
    - type: nauc_mrr_at_1000_diff1
      value: 10.96
    - type: main_score
      value: 21.83
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification (default)
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
    metrics:
    - type: accuracy
      value: 81.4014
    - type: f1
      value: 64.3103
    - type: f1_weighted
      value: 85.0047
    - type: ap
      value: 22.2804
    - type: ap_weighted
      value: 22.2804
    - type: main_score
      value: 81.4014
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification (default)
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 66.4403
    - type: f1
      value: 66.8774
    - type: f1_weighted
      value: 65.9999
    - type: main_score
      value: 66.4403
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering (default)
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 53.3153
    - type: v_measure_std
      value: 1.2923
    - type: main_score
      value: 53.3153
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015 (default)
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: similarity_accuracy
      value: 85.22380000000001
    - type: similarity_accuracy_threshold
      value: 74.7432
    - type: similarity_f1
      value: 66.2828
    - type: similarity_f1_threshold
      value: 69.9472
    - type: similarity_precision
      value: 60.765299999999996
    - type: similarity_recall
      value: 72.9024
    - type: similarity_ap
      value: 72.0492
    - type: cosine_accuracy
      value: 85.22380000000001
    - type: cosine_accuracy_threshold
      value: 74.7432
    - type: cosine_f1
      value: 66.2828
    - type: cosine_f1_threshold
      value: 69.9472
    - type: cosine_precision
      value: 60.765299999999996
    - type: cosine_recall
      value: 72.9024
    - type: cosine_ap
      value: 72.0492
    - type: manhattan_accuracy
      value: 85.10459999999999
    - type: manhattan_accuracy_threshold
      value: 48810.3699
    - type: manhattan_f1
      value: 65.7133
    - type: manhattan_f1_threshold
      value: 53724.462900000006
    - type: manhattan_precision
      value: 60.3399
    - type: manhattan_recall
      value: 72.1372
    - type: manhattan_ap
      value: 71.3681
    - type: euclidean_accuracy
      value: 85.1404
    - type: euclidean_accuracy_threshold
      value: 2203.8609
    - type: euclidean_f1
      value: 65.8107
    - type: euclidean_f1_threshold
      value: 2445.96
    - type: euclidean_precision
      value: 59.8875
    - type: euclidean_recall
      value: 73.0343
    - type: euclidean_ap
      value: 71.3938
    - type: dot_accuracy
      value: 84.8781
    - type: dot_accuracy_threshold
      value: 74077.38040000001
    - type: dot_f1
      value: 65.3706
    - type: dot_f1_threshold
      value: 69501.5808
    - type: dot_precision
      value: 60.58559999999999
    - type: dot_recall
      value: 70.97630000000001
    - type: dot_ap
      value: 71.0091
    - type: max_accuracy
      value: 85.22380000000001
    - type: max_f1
      value: 66.2828
    - type: max_precision
      value: 60.765299999999996
    - type: max_recall
      value: 73.0343
    - type: max_ap
      value: 72.0492
    - type: main_score
      value: 72.0492
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus (default)
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: similarity_accuracy
      value: 89.145
    - type: similarity_accuracy_threshold
      value: 65.00280000000001
    - type: similarity_f1
      value: 78.78150000000001
    - type: similarity_f1_threshold
      value: 61.2185
    - type: similarity_precision
      value: 75.0279
    - type: similarity_recall
      value: 82.9304
    - type: similarity_ap
      value: 86.39949999999999
    - type: cosine_accuracy
      value: 89.145
    - type: cosine_accuracy_threshold
      value: 65.00280000000001
    - type: cosine_f1
      value: 78.78150000000001
    - type: cosine_f1_threshold
      value: 61.2185
    - type: cosine_precision
      value: 75.0279
    - type: cosine_recall
      value: 82.9304
    - type: cosine_ap
      value: 86.39949999999999
    - type: manhattan_accuracy
      value: 89.05579999999999
    - type: manhattan_accuracy_threshold
      value: 55381.189
    - type: manhattan_f1
      value: 78.6152
    - type: manhattan_f1_threshold
      value: 58447.6685
    - type: manhattan_precision
      value: 74.77080000000001
    - type: manhattan_recall
      value: 82.8765
    - type: manhattan_ap
      value: 86.2899
    - type: euclidean_accuracy
      value: 89.1179
    - type: euclidean_accuracy_threshold
      value: 2552.2853999999998
    - type: euclidean_f1
      value: 78.6816
    - type: euclidean_f1_threshold
      value: 2660.0677
    - type: euclidean_precision
      value: 74.4317
    - type: euclidean_recall
      value: 83.4463
    - type: euclidean_ap
      value: 86.3158
    - type: dot_accuracy
      value: 88.81710000000001
    - type: dot_accuracy_threshold
      value: 58383.1421
    - type: dot_f1
      value: 78.2367
    - type: dot_f1_threshold
      value: 54826.550299999995
    - type: dot_precision
      value: 73.7657
    - type: dot_recall
      value: 83.2846
    - type: dot_ap
      value: 85.5699
    - type: max_accuracy
      value: 89.145
    - type: max_f1
      value: 78.78150000000001
    - type: max_precision
      value: 75.0279
    - type: max_recall
      value: 83.4463
    - type: max_ap
      value: 86.39949999999999
    - type: main_score
      value: 86.39949999999999
---

# cde-small-v2

> [!NOTE]
> **Note on parameter count:** Although HuggingFace reports the size of this model as 281M params, really it can be thought of as 140M. That's because our weights actually contain the weights of two models (dubbed "first stage" and "second stage"), and only the second-stage model is used to compute embeddings at search time.

<a href="https://github.com/jxmorris12/cde">Github</a>

Our new model that naturally integrates "context tokens" into the embedding process. As of January 13th, 2025, `cde-small-v2` is the best small model (under 400M params) on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for text embedding models, with an average score of 65.58.

👉  <b><a href="https://colab.research.google.com/drive/1r8xwbp7_ySL9lP-ve4XMJAHjidB9UkbL?usp=sharing">Try on Colab</a></b>
<br>
👉  <b><a href="https://arxiv.org/abs/2410.02525">Contextual Document Embeddings (ArXiv)</a></b>

![CDE Overview Figure](https://i.imgur.com/LyXJZjM.png)

<br>
<hr>

# How to use `cde-small-v2`

Our embedding model needs to be used in *two stages*. The first stage is to gather some dataset information by embedding a subset of the corpus using our "first-stage" model. The second stage is to actually embed queries and documents, conditioning on the corpus information from the first stage. Note that we can do the first stage part offline and only use the second-stage weights at inference time.

</details>

## With Transformers

<details>
<summary>Click to learn how to use cde-small-v2 with Transformers</summary>

### Loading the model

Our model can be loaded using `transformers` out-of-the-box with "trust remote code" enabled. We use the default BERT uncased tokenizer:
```python
import transformers

model = transformers.AutoModel.from_pretrained("jxm/cde-small-v2", trust_remote_code=True)
tokenizer = transformers.AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
```

#### Note on prefixes

*Nota bene*: Like all state-of-the-art embedding models, our model was trained with task-specific prefixes. To do retrieval, you can prepend the following strings to queries & documents:

```python
query_prefix = "search_query: "
document_prefix = "search_document: "
```

### First stage

```python
minicorpus_size = model.config.transductive_corpus_size
minicorpus_docs = [ ... ] # Put some strings here that are representative of your corpus, for example by calling random.sample(corpus, k=minicorpus_size)
assert len(minicorpus_docs) == minicorpus_size # You must use exactly this many documents in the minicorpus. You can oversample if your corpus is smaller.
minicorpus_docs = tokenizer(
    [document_prefix + doc for doc in minicorpus_docs],
    truncation=True,
    padding=True,
    max_length=512,
    return_tensors="pt"
).to(model.device)
import torch
from tqdm.autonotebook import tqdm

batch_size = 32

dataset_embeddings = []
for i in tqdm(range(0, len(minicorpus_docs["input_ids"]), batch_size)):
    minicorpus_docs_batch = {k: v[i:i+batch_size] for k,v in minicorpus_docs.items()}
    with torch.no_grad():
        dataset_embeddings.append(
            model.first_stage_model(**minicorpus_docs_batch)
        )

dataset_embeddings = torch.cat(dataset_embeddings)
```

### Running the second stage

Now that we have obtained "dataset embeddings" we can embed documents and queries like normal. Remember to use the document prefix for documents:
```python
docs = tokenizer(
    [document_prefix + doc for doc in docs],
    truncation=True,
    padding=True,
    max_length=512,
    return_tensors="pt"
).to(model.device)

with torch.no_grad():
  doc_embeddings = model.second_stage_model(
      input_ids=docs["input_ids"],
      attention_mask=docs["attention_mask"],
      dataset_embeddings=dataset_embeddings,
  )
doc_embeddings /= doc_embeddings.norm(p=2, dim=1, keepdim=True)
```

and the query prefix for queries:
```python
queries = queries.select(range(16))["text"]
queries = tokenizer(
    [query_prefix + query for query in queries],
    truncation=True,
    padding=True,
    max_length=512,
    return_tensors="pt"
).to(model.device)

with torch.no_grad():
  query_embeddings = model.second_stage_model(
      input_ids=queries["input_ids"],
      attention_mask=queries["attention_mask"],
      dataset_embeddings=dataset_embeddings,
  )
query_embeddings /= query_embeddings.norm(p=2, dim=1, keepdim=True)
```

these embeddings can be compared using dot product, since they're normalized.

</details>

### What if I don't know what my corpus will be ahead of time?

If you can't obtain corpus information ahead of time, you still have to pass *something* as the dataset embeddings; our model will work fine in this case, but not quite as well; without corpus information, our model performance drops from 65.0 to 63.8 on MTEB. We provide [some random strings](https://huggingface.co/jxm/cde-small-v2/resolve/main/random_strings.txt) that worked well for us that can be used as a substitute for corpus sampling.


## With Sentence Transformers

<details open="">
<summary>Click to learn how to use cde-small-v2 with Sentence Transformers</summary>

### Loading the model

Our model can be loaded using `sentence-transformers` out-of-the-box with "trust remote code" enabled:
```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("jxm/cde-small-v2", trust_remote_code=True)
```

#### Note on prefixes

*Nota bene*: Like all state-of-the-art embedding models, our model was trained with task-specific prefixes. To do retrieval, you can use `prompt_name="query"` and `prompt_name="document"` in the `encode` method of the model when embedding queries and documents, respectively.

### First stage

```python
minicorpus_size = model[0].config.transductive_corpus_size
minicorpus_docs = [ ... ] # Put some strings here that are representative of your corpus, for example by calling random.sample(corpus, k=minicorpus_size)
assert len(minicorpus_docs) == minicorpus_size # You must use exactly this many documents in the minicorpus. You can oversample if your corpus is smaller.

dataset_embeddings = model.encode(
    minicorpus_docs,
    prompt_name="document",
    convert_to_tensor=True
)
```

### Running the second stage

Now that we have obtained "dataset embeddings" we can embed documents and queries like normal. Remember to use the document prompt for documents:

```python
docs = [...]
queries = [...]

doc_embeddings = model.encode(
    docs,
    prompt_name="document",
    dataset_embeddings=dataset_embeddings,
    convert_to_tensor=True,
)
query_embeddings = model.encode(
    queries,
    prompt_name="query",
    dataset_embeddings=dataset_embeddings,
    convert_to_tensor=True,
) 
```

these embeddings can be compared using cosine similarity via `model.similarity`:
```python
similarities = model.similarity(query_embeddings, doc_embeddings)
topk_values, topk_indices = similarities.topk(5)
```

<details>
<summary>Click here for a full copy-paste ready example</summary>

```python
from sentence_transformers import SentenceTransformer
from datasets import load_dataset

# 1. Load the Sentence Transformer model
model = SentenceTransformer("jxm/cde-small-v2", trust_remote_code=True)
context_docs_size = model[0].config.transductive_corpus_size  # 512

# 2. Load the dataset: context dataset, docs, and queries
dataset = load_dataset("sentence-transformers/natural-questions", split="train")
dataset.shuffle(seed=42)
# 10 queries, 512 context docs, 500 docs
queries = dataset["query"][:10]
docs = dataset["answer"][:2000]
context_docs = dataset["answer"][-context_docs_size:] # Last 512 docs

# 3. First stage: embed the context docs
dataset_embeddings = model.encode(
    context_docs,
    prompt_name="document",
    convert_to_tensor=True,
)

# 4. Second stage: embed the docs and queries
doc_embeddings = model.encode(
    docs,
    prompt_name="document",
    dataset_embeddings=dataset_embeddings,
    convert_to_tensor=True,
)
query_embeddings = model.encode(
    queries,
    prompt_name="query",
    dataset_embeddings=dataset_embeddings,
    convert_to_tensor=True,
)

# 5. Compute the similarity between the queries and docs
similarities = model.similarity(query_embeddings, doc_embeddings)
topk_values, topk_indices = similarities.topk(5)
print(topk_values)
print(topk_indices)

"""
tensor([[0.5495, 0.5426, 0.5423, 0.5292, 0.5286],
        [0.6357, 0.6334, 0.6177, 0.5862, 0.5794],
        [0.7648, 0.5452, 0.5000, 0.4959, 0.4881],
        [0.6802, 0.5225, 0.5178, 0.5160, 0.5075],
        [0.6947, 0.5843, 0.5619, 0.5344, 0.5298],
        [0.7742, 0.7742, 0.7742, 0.7231, 0.6224],
        [0.8853, 0.6667, 0.5829, 0.5795, 0.5769],
        [0.6911, 0.6127, 0.6003, 0.5986, 0.5936],
        [0.6796, 0.6053, 0.6000, 0.5911, 0.5884],
        [0.7624, 0.5589, 0.5428, 0.5278, 0.5275]], device='cuda:0')
tensor([[   0,  296,  234, 1651, 1184],
        [1542,  466,  438, 1207, 1911],
        [   2, 1562,  632, 1852,  382],
        [   3,  694,  932, 1765,  662],
        [   4,   35,  747,   26,  432],
        [ 534,  175,    5, 1495,  575],
        [   6, 1802, 1875,  747,   21],
        [   7, 1913, 1936,  640,    6],
        [   8,  747,  167, 1318, 1743],
        [   9, 1583, 1145,  219,  357]], device='cuda:0')
"""
# As you can see, almost every query_i has document_i as the most similar document.

# 6. Print the top-k results
for query_idx, top_doc_idx in enumerate(topk_indices[:, 0]):
    print(f"Query {query_idx}: {queries[query_idx]}")
    print(f"Top Document: {docs[top_doc_idx]}")
    print()
"""
Query 0: when did richmond last play in a preliminary final
Top Document: Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next.

Query 1: who sang what in the world's come over you
Top Document: Life's What You Make It (Talk Talk song) "Life's What You Make It" is a song by the English band Talk Talk. It was released as a single in 1986, the first from the band's album The Colour of Spring. The single was a hit in the UK, peaking at No. 16, and charted in numerous other countries, often reaching the Top 20.

Query 2: who produces the most wool in the world
Top Document: Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.

Query 3: where does alaska the last frontier take place
Top Document: Alaska: The Last Frontier Alaska: The Last Frontier is an American reality cable television series on the Discovery Channel, currently in its 7th season of broadcast. The show documents the extended Kilcher family, descendants of Swiss immigrants and Alaskan pioneers, Yule and Ruth Kilcher, at their homestead 11 miles outside of Homer.[1] By living without plumbing or modern heating, the clan chooses to subsist by farming, hunting and preparing for the long winters.[2] The Kilcher family are relatives of the singer Jewel,[1][3] who has appeared on the show.[4]

Query 4: a day to remember all i want cameos
Top Document: All I Want (A Day to Remember song) The music video for the song, which was filmed in October 2010,[4] was released on January 6, 2011.[5] It features cameos of numerous popular bands and musicians. The cameos are: Tom Denney (A Day to Remember's former guitarist), Pete Wentz, Winston McCall of Parkway Drive, The Devil Wears Prada, Bring Me the Horizon, Sam Carter of Architects, Tim Lambesis of As I Lay Dying, Silverstein, Andrew WK, August Burns Red, Seventh Star, Matt Heafy of Trivium, Vic Fuentes of Pierce the Veil, Mike Herrera of MxPx, and Set Your Goals.[5] Rock Sound called the video "quite excellent".[5]

Query 5: what does the red stripes mean on the american flag
Top Document: Flag of the United States The flag of the United States of America, often referred to as the American flag, is the national flag of the United States. It consists of thirteen equal horizontal stripes of red (top and bottom) alternating with white, with a blue rectangle in the canton (referred to specifically as the "union") bearing fifty small, white, five-pointed stars arranged in nine offset horizontal rows, where rows of six stars (top and bottom) alternate with rows of five stars. The 50 stars on the flag represent the 50 states of the United States of America, and the 13 stripes represent the thirteen British colonies that declared independence from the Kingdom of Great Britain, and became the first states in the U.S.[1] Nicknames for the flag include The Stars and Stripes,[2] Old Glory,[3] and The Star-Spangled Banner.

Query 6: where did they film diary of a wimpy kid
Top Document: Diary of a Wimpy Kid (film) Filming of Diary of a Wimpy Kid was in Vancouver and wrapped up on October 16, 2009.

Query 7: where was beasts of the southern wild filmed
Top Document: Beasts of the Southern Wild The film's fictional setting, "Isle de Charles Doucet", known to its residents as the Bathtub, was inspired by several isolated and independent fishing communities threatened by erosion, hurricanes and rising sea levels in Louisiana's Terrebonne Parish, most notably the rapidly eroding Isle de Jean Charles. It was filmed in Terrebonne Parish town Montegut.[5]

Query 8: what part of the country are you likely to find the majority of the mollisols
Top Document: Mollisol Mollisols occur in savannahs and mountain valleys (such as Central Asia, or the North American Great Plains). These environments have historically been strongly influenced by fire and abundant pedoturbation from organisms such as ants and earthworms. It was estimated that in 2003, only 14 to 26 percent of grassland ecosystems still remained in a relatively natural state (that is, they were not used for agriculture due to the fertility of the A horizon). Globally, they represent ~7% of ice-free land area. As the world's most agriculturally productive soil order, the Mollisols represent one of the more economically important soil orders.

Query 9: when did fosters home for imaginary friends start
Top Document: Foster's Home for Imaginary Friends McCracken conceived the series after adopting two dogs from an animal shelter and applying the concept to imaginary friends. The show first premiered on Cartoon Network on August 13, 2004, as a 90-minute television film. On August 20, it began its normal run of twenty-to-thirty-minute episodes on Fridays, at 7 pm. The series finished its run on May 3, 2009, with a total of six seasons and seventy-nine episodes. McCracken left Cartoon Network shortly after the series ended. Reruns have aired on Boomerang from August 11, 2012 to November 3, 2013 and again from June 1, 2014 to April 3, 2017.
"""
```

</details>

### Colab demo

We've set up a short demo in a Colab notebook showing how you might use our model:
[Try our model in Colab:](https://colab.research.google.com/drive/1ddWeNj9nztHrwtoSEtaArfs7_NZhZA6k?usp=sharing)

### Training details

All non-mentioned other hyperparameters (learning, etc.) are either in the config or CDE paper. If not, please raise an issue here: https://github.com/jxmorris12/cde


#### Model details

cde-small-v2 includes a number of modeling changes from cde-small-v1:
- used the recently-released [ModernBERT](https://huggingface.co/blog/modernbert)
- added a residual connection between the model stages, which helps conditioning and gradient flow
- disabled pooling over instruction tokens
- disable position-embedding nullification over contextual tokens
- disable weight decay (not sure if this one helped or not)

#### Unsupervised training

Trained for six epochs on the nomic-unsupervised dataset with cluster size of 512 and batch size of 512, using GTR clusters and GTE-large filtering. (Probably would have performed better with GTE clustering too, but that's an expensive operation that we didn't rerun.)

#### Supervised training

Trained for four epochs on the BGE dataset with GTE clusters and GTE hard-negative filtering.

### Cite us

Used our model, method, or architecture? Want to cite us? Here's the ArXiv citation information:
```
@misc{morris2024contextualdocumentembeddings,
      title={Contextual Document Embeddings}, 
      author={John X. Morris and Alexander M. Rush},
      year={2024},
      eprint={2410.02525},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.02525}, 
}
```