README.md

---
model-index:
- name: iamgroot42/rover_nexus
  results:
  - dataset:
      config: en
      name: MTEB AmazonCounterfactualClassification (en)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    metrics:
    - type: accuracy
      value: 95.7761
    - type: f1
      value: 93.8227
    - type: f1_weighted
      value: 95.9368
    - type: ap
      value: 82.63589999999999
    - type: ap_weighted
      value: 82.63589999999999
    - type: main_score
      value: 95.7761
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB AmazonPolarityClassification (default)
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
      split: test
      type: mteb/amazon_polarity
    metrics:
    - type: accuracy
      value: 97.7143
    - type: f1
      value: 97.7143
    - type: f1_weighted
      value: 97.7143
    - type: ap
      value: 96.5356
    - type: ap_weighted
      value: 96.5356
    - type: main_score
      value: 97.7143
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB AmazonReviewsClassification (en)
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
      split: test
      type: mteb/amazon_reviews_multi
    metrics:
    - type: accuracy
      value: 63.617999999999995
    - type: f1
      value: 61.487199999999994
    - type: f1_weighted
      value: 61.487199999999994
    - type: main_score
      value: 63.617999999999995
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB ArguAna (default)
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
      split: test
      type: mteb/arguana
    metrics:
    - type: ndcg_at_1
      value: 75.39099999999999
    - type: ndcg_at_3
      value: 88.795
    - type: ndcg_at_5
      value: 89.634
    - type: ndcg_at_10
      value: 89.786
    - type: ndcg_at_20
      value: 89.786
    - type: ndcg_at_100
      value: 89.786
    - type: ndcg_at_1000
      value: 89.786
    - type: map_at_1
      value: 75.39099999999999
    - type: map_at_3
      value: 85.835
    - type: map_at_5
      value: 86.311
    - type: map_at_10
      value: 86.382
    - type: map_at_20
      value: 86.382
    - type: map_at_100
      value: 86.382
    - type: map_at_1000
      value: 86.382
    - type: recall_at_1
      value: 75.39099999999999
    - type: recall_at_3
      value: 97.226
    - type: recall_at_5
      value: 99.21799999999999
    - type: recall_at_10
      value: 99.644
    - type: recall_at_20
      value: 99.644
    - type: recall_at_100
      value: 99.644
    - type: recall_at_1000
      value: 99.644
    - type: precision_at_1
      value: 75.39099999999999
    - type: precision_at_3
      value: 32.409
    - type: precision_at_5
      value: 19.844
    - type: precision_at_10
      value: 9.964
    - type: precision_at_20
      value: 4.982
    - type: precision_at_100
      value: 0.996
    - type: precision_at_1000
      value: 0.1
    - type: mrr_at_1
      value: 76.2447
    - type: mrr_at_3
      value: 86.1783
    - type: mrr_at_5
      value: 86.6015
    - type: mrr_at_10
      value: 86.6844
    - type: mrr_at_20
      value: 86.6844
    - type: mrr_at_100
      value: 86.6844
    - type: mrr_at_1000
      value: 86.6844
    - type: nauc_ndcg_at_1_max
      value: -0.7014
    - type: nauc_ndcg_at_1_std
      value: -34.0713
    - type: nauc_ndcg_at_1_diff1
      value: 72.04
    - type: nauc_ndcg_at_3_max
      value: -0.7801
    - type: nauc_ndcg_at_3_std
      value: -40.4138
    - type: nauc_ndcg_at_3_diff1
      value: 69.5318
    - type: nauc_ndcg_at_5_max
      value: 0.5993
    - type: nauc_ndcg_at_5_std
      value: -35.9135
    - type: nauc_ndcg_at_5_diff1
      value: 69.5877
    - type: nauc_ndcg_at_10_max
      value: 1.0135
    - type: nauc_ndcg_at_10_std
      value: -34.773399999999995
    - type: nauc_ndcg_at_10_diff1
      value: 69.7499
    - type: nauc_ndcg_at_20_max
      value: 1.0135
    - type: nauc_ndcg_at_20_std
      value: -34.773399999999995
    - type: nauc_ndcg_at_20_diff1
      value: 69.7499
    - type: nauc_ndcg_at_100_max
      value: 1.0135
    - type: nauc_ndcg_at_100_std
      value: -34.773399999999995
    - type: nauc_ndcg_at_100_diff1
      value: 69.7499
    - type: nauc_ndcg_at_1000_max
      value: 1.0135
    - type: nauc_ndcg_at_1000_std
      value: -34.773399999999995
    - type: nauc_ndcg_at_1000_diff1
      value: 69.7499
    - type: nauc_map_at_1_max
      value: -0.7014
    - type: nauc_map_at_1_std
      value: -34.0713
    - type: nauc_map_at_1_diff1
      value: 72.04
    - type: nauc_map_at_3_max
      value: -0.5740999999999999
    - type: nauc_map_at_3_std
      value: -37.9683
    - type: nauc_map_at_3_diff1
      value: 70.2016
    - type: nauc_map_at_5_max
      value: 0.0239
    - type: nauc_map_at_5_std
      value: -35.9525
    - type: nauc_map_at_5_diff1
      value: 70.233
    - type: nauc_map_at_10_max
      value: 0.1661
    - type: nauc_map_at_10_std
      value: -35.551899999999996
    - type: nauc_map_at_10_diff1
      value: 70.29379999999999
    - type: nauc_map_at_20_max
      value: 0.1661
    - type: nauc_map_at_20_std
      value: -35.551899999999996
    - type: nauc_map_at_20_diff1
      value: 70.29379999999999
    - type: nauc_map_at_100_max
      value: 0.1661
    - type: nauc_map_at_100_std
      value: -35.551899999999996
    - type: nauc_map_at_100_diff1
      value: 70.29379999999999
    - type: nauc_map_at_1000_max
      value: 0.1661
    - type: nauc_map_at_1000_std
      value: -35.551899999999996
    - type: nauc_map_at_1000_diff1
      value: 70.29379999999999
    - type: nauc_recall_at_1_max
      value: -0.7014
    - type: nauc_recall_at_1_std
      value: -34.0713
    - type: nauc_recall_at_1_diff1
      value: 72.04
    - type: nauc_recall_at_3_max
      value: -3.8722
    - type: nauc_recall_at_3_std
      value: -72.8357
    - type: nauc_recall_at_3_diff1
      value: 61.261500000000005
    - type: nauc_recall_at_5_max
      value: 27.9653
    - type: nauc_recall_at_5_std
      value: -25.6213
    - type: nauc_recall_at_5_diff1
      value: 43.013200000000005
    - type: nauc_recall_at_10_max
      value: 91.0821
    - type: nauc_recall_at_10_std
      value: 70.0735
    - type: nauc_recall_at_10_diff1
      value: 22.9874
    - type: nauc_recall_at_20_max
      value: 91.0821
    - type: nauc_recall_at_20_std
      value: 70.0735
    - type: nauc_recall_at_20_diff1
      value: 22.9874
    - type: nauc_recall_at_100_max
      value: 91.0821
    - type: nauc_recall_at_100_std
      value: 70.0735
    - type: nauc_recall_at_100_diff1
      value: 22.9874
    - type: nauc_recall_at_1000_max
      value: 91.0821
    - type: nauc_recall_at_1000_std
      value: 70.0735
    - type: nauc_recall_at_1000_diff1
      value: 22.9874
    - type: nauc_precision_at_1_max
      value: -0.7014
    - type: nauc_precision_at_1_std
      value: -34.0713
    - type: nauc_precision_at_1_diff1
      value: 72.04
    - type: nauc_precision_at_3_max
      value: -3.8722
    - type: nauc_precision_at_3_std
      value: -72.8357
    - type: nauc_precision_at_3_diff1
      value: 61.261500000000005
    - type: nauc_precision_at_5_max
      value: 27.9653
    - type: nauc_precision_at_5_std
      value: -25.6213
    - type: nauc_precision_at_5_diff1
      value: 43.013200000000005
    - type: nauc_precision_at_10_max
      value: 91.0821
    - type: nauc_precision_at_10_std
      value: 70.0735
    - type: nauc_precision_at_10_diff1
      value: 22.9874
    - type: nauc_precision_at_20_max
      value: 91.0821
    - type: nauc_precision_at_20_std
      value: 70.0735
    - type: nauc_precision_at_20_diff1
      value: 22.9874
    - type: nauc_precision_at_100_max
      value: 91.0821
    - type: nauc_precision_at_100_std
      value: 70.0735
    - type: nauc_precision_at_100_diff1
      value: 22.9874
    - type: nauc_precision_at_1000_max
      value: 91.0821
    - type: nauc_precision_at_1000_std
      value: 70.0735
    - type: nauc_precision_at_1000_diff1
      value: 22.9874
    - type: nauc_mrr_at_1_max
      value: -2.0376
    - type: nauc_mrr_at_1_std
      value: -34.260600000000004
    - type: nauc_mrr_at_1_diff1
      value: 69.606
    - type: nauc_mrr_at_3_max
      value: -2.3678999999999997
    - type: nauc_mrr_at_3_std
      value: -38.2381
    - type: nauc_mrr_at_3_diff1
      value: 67.3485
    - type: nauc_mrr_at_5_max
      value: -1.9768999999999999
    - type: nauc_mrr_at_5_std
      value: -36.711
    - type: nauc_mrr_at_5_diff1
      value: 67.2939
    - type: nauc_mrr_at_10_max
      value: -1.7911
    - type: nauc_mrr_at_10_std
      value: -36.1749
    - type: nauc_mrr_at_10_diff1
      value: 67.3921
    - type: nauc_mrr_at_20_max
      value: -1.7911
    - type: nauc_mrr_at_20_std
      value: -36.1749
    - type: nauc_mrr_at_20_diff1
      value: 67.3921
    - type: nauc_mrr_at_100_max
      value: -1.7911
    - type: nauc_mrr_at_100_std
      value: -36.1749
    - type: nauc_mrr_at_100_diff1
      value: 67.3921
    - type: nauc_mrr_at_1000_max
      value: -1.7911
    - type: nauc_mrr_at_1000_std
      value: -36.1749
    - type: nauc_mrr_at_1000_diff1
      value: 67.3921
    - type: main_score
      value: 89.786
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ArxivClusteringP2P (default)
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
      split: test
      type: mteb/arxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 57.1362
    - type: v_measure_std
      value: 14.7667
    - type: main_score
      value: 57.1362
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB ArxivClusteringS2S (default)
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
      split: test
      type: mteb/arxiv-clustering-s2s
    metrics:
    - type: v_measure
      value: 52.639199999999995
    - type: v_measure_std
      value: 15.049499999999998
    - type: main_score
      value: 52.639199999999995
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB AskUbuntuDupQuestions (default)
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
      split: test
      type: mteb/askubuntudupquestions-reranking
    metrics:
    - type: map
      value: 66.93430000000001
    - type: mrr
      value: 80.2153
    - type: nAUC_map_max
      value: 28.881600000000002
    - type: nAUC_map_std
      value: 27.9878
    - type: nAUC_map_diff1
      value: 15.101500000000001
    - type: nAUC_mrr_max
      value: 38.428200000000004
    - type: nAUC_mrr_std
      value: 32.2285
    - type: nAUC_mrr_diff1
      value: 18.8509
    - type: main_score
      value: 66.93430000000001
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB BIOSSES (default)
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
      split: test
      type: mteb/biosses-sts
    metrics:
    - type: pearson
      value: 89.4712
    - type: spearman
      value: 87.7044
    - type: cosine_pearson
      value: 89.4712
    - type: cosine_spearman
      value: 87.7044
    - type: manhattan_pearson
      value: 88.1497
    - type: manhattan_spearman
      value: 87.73570000000001
    - type: euclidean_pearson
      value: 87.9881
    - type: euclidean_spearman
      value: 87.7044
    - type: main_score
      value: 87.7044
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB Banking77Classification (default)
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
      split: test
      type: mteb/banking77
    metrics:
    - type: accuracy
      value: 93.8019
    - type: f1
      value: 93.7388
    - type: f1_weighted
      value: 93.7388
    - type: main_score
      value: 93.8019
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB BiorxivClusteringP2P (default)
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
      split: test
      type: mteb/biorxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 54.646300000000004
    - type: v_measure_std
      value: 1.2942
    - type: main_score
      value: 54.646300000000004
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB BiorxivClusteringS2S (default)
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
      split: test
      type: mteb/biorxiv-clustering-s2s
    metrics:
    - type: v_measure
      value: 49.9505
    - type: v_measure_std
      value: 1.1009
    - type: main_score
      value: 49.9505
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB CQADupstackAndroidRetrieval (default)
      revision: f46a197baaae43b4f621051089b82a364682dfeb
      split: test
      type: mteb/cqadupstack-android
    metrics:
    - type: ndcg_at_1
      value: 59.084
    - type: ndcg_at_3
      value: 68.133
    - type: ndcg_at_5
      value: 71.473
    - type: ndcg_at_10
      value: 74.25699999999999
    - type: ndcg_at_20
      value: 75.755
    - type: ndcg_at_100
      value: 76.778
    - type: ndcg_at_1000
      value: 77.05199999999999
    - type: map_at_1
      value: 47.410999999999994
    - type: map_at_3
      value: 60.514
    - type: map_at_5
      value: 64.048
    - type: map_at_10
      value: 66.34
    - type: map_at_20
      value: 67.31700000000001
    - type: map_at_100
      value: 67.778
    - type: map_at_1000
      value: 67.84700000000001
    - type: recall_at_1
      value: 47.410999999999994
    - type: recall_at_3
      value: 71.247
    - type: recall_at_5
      value: 80.908
    - type: recall_at_10
      value: 89.143
    - type: recall_at_20
      value: 94.536
    - type: recall_at_100
      value: 98.53
    - type: recall_at_1000
      value: 99.727
    - type: precision_at_1
      value: 59.084
    - type: precision_at_3
      value: 33.81
    - type: precision_at_5
      value: 24.549000000000003
    - type: precision_at_10
      value: 14.878
    - type: precision_at_20
      value: 8.469
    - type: precision_at_100
      value: 1.989
    - type: precision_at_1000
      value: 0.22899999999999998
    - type: mrr_at_1
      value: 59.0844
    - type: mrr_at_3
      value: 69.57560000000001
    - type: mrr_at_5
      value: 71.1636
    - type: mrr_at_10
      value: 71.8404
    - type: mrr_at_20
      value: 72.0815
    - type: mrr_at_100
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    - type: mrr_at_1000
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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:
      type: Classification
  - dataset:
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      revision: c5a29a104738b98a9e76336939199e264163d4a0
      split: dev
      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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    - type: f1_weighted
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      value: 99.95439999999999
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MTOPIntentClassification (en)
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
      split: test
      type: mteb/mtop_intent
    metrics:
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    - type: f1_weighted
      value: 92.0981
    - type: main_score
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    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveIntentClassification (en)
      revision: 4672e20407010da34463acc759c162ca9734bca6
      split: test
      type: mteb/amazon_massive_intent
    metrics:
    - type: accuracy
      value: 91.9435
    - type: f1
      value: 91.76389999999999
    - type: f1_weighted
      value: 92.1532
    - type: main_score
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    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: 99.2972
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      value: 99.2864
    - type: f1_weighted
      value: 99.2985
    - type: main_score
      value: 99.2972
    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: 1.4316
    - type: main_score
      value: 51.532199999999996
    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: 1.6781000000000001
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      value: 48.9936
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB MindSmallReranking (default)
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
      split: test
      type: mteb/mind_small
    metrics:
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      value: 30.547200000000004
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      value: -22.4106
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      value: -5.1465
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      value: 12.2824
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      value: -17.1894
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      value: -3.1673
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      value: 11.4619
    - type: main_score
      value: 29.6848
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB NFCorpus (default)
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
      split: test
      type: mteb/nfcorpus
    metrics:
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      value: 53.715
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      value: 50.865
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      value: 49.324
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      value: 46.989999999999995
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      value: 44.855000000000004
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      value: 45.639
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      value: 55.396
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      value: 10.83
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      value: 17.343
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      value: 19.912
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      value: 23.018
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      value: 25.335
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      value: 28.172000000000004
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      value: 30.006
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      value: 10.83
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      value: 19.541
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      value: 23.980999999999998
    - type: recall_at_10
      value: 29.866
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      value: 35.29
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      value: 50.1
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      value: 80.405
    - type: precision_at_1
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      value: 41.238
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      value: 32.291
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      value: 23.235
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      value: 74.8974
    - type: nauc_recall_at_1_max
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    - type: nauc_recall_at_1_std
      value: -43.6161
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    - type: nauc_recall_at_3_max
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    - type: nauc_recall_at_3_std
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      value: 71.25359999999999
    - type: nauc_recall_at_5_max
      value: 32.5847
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      value: -73.9396
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    - type: nauc_recall_at_10_max
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      value: 42.5506
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      value: 72.165
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      value: 16.6256
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      value: 61.7435
    - type: nauc_precision_at_1_max
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      value: 75.527
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    - type: nauc_precision_at_3_std
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    - type: nauc_precision_at_5_max
      value: 3.9085
    - type: nauc_precision_at_5_std
      value: 22.3557
    - type: nauc_precision_at_5_diff1
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      value: -0.7126
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      value: 34.4463
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    - type: nauc_precision_at_20_max
      value: -2.8043
    - type: nauc_precision_at_20_std
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      value: -4.9339
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      value: -5.6414
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      value: 47.656
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      value: -43.223099999999995
    - type: nauc_mrr_at_1_max
      value: 39.456599999999995
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      value: -38.451800000000006
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      value: 75.5085
    - type: nauc_mrr_at_3_max
      value: 40.3247
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      value: -41.2735
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      value: 74.36970000000001
    - type: nauc_mrr_at_5_max
      value: 40.1839
    - type: nauc_mrr_at_5_std
      value: -41.274
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      value: 74.5963
    - type: nauc_mrr_at_10_max
      value: 40.239000000000004
    - type: nauc_mrr_at_10_std
      value: -40.734500000000004
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      value: 74.66199999999999
    - type: nauc_mrr_at_20_max
      value: 40.18
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      value: -40.6285
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      value: 74.6781
    - type: nauc_mrr_at_100_max
      value: 40.1602
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      value: -40.6371
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      value: 74.6773
    - type: nauc_mrr_at_1000_max
      value: 40.1599
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      value: -40.6379
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      value: 74.6773
    - type: main_score
      value: 88.857
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB RedditClustering (default)
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
      split: test
      type: mteb/reddit-clustering
    metrics:
    - type: v_measure
      value: 77.1563
    - type: v_measure_std
      value: 3.3381000000000003
    - type: main_score
      value: 77.1563
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB RedditClusteringP2P (default)
      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
      split: test
      type: mteb/reddit-clustering-p2p
    metrics:
    - type: v_measure
      value: 70.2294
    - type: v_measure_std
      value: 11.2968
    - type: main_score
      value: 70.2294
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB SCIDOCS (default)
      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
      split: test
      type: mteb/scidocs
    metrics:
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      value: 38.7
    - type: ndcg_at_3
      value: 32.368
    - type: ndcg_at_5
      value: 28.589
    - type: ndcg_at_10
      value: 34.528999999999996
    - type: ndcg_at_20
      value: 39.028
    - type: ndcg_at_100
      value: 45.658
    - type: ndcg_at_1000
      value: 49.72
    - type: map_at_1
      value: 7.887
    - type: map_at_3
      value: 15.303
    - type: map_at_5
      value: 18.739
    - type: map_at_10
      value: 22.409000000000002
    - type: map_at_20
      value: 24.448
    - type: map_at_100
      value: 26.148
    - type: map_at_1000
      value: 26.456000000000003
    - type: recall_at_1
      value: 7.887
    - type: recall_at_3
      value: 18.678
    - type: recall_at_5
      value: 25.807999999999996
    - type: recall_at_10
      value: 36.685
    - type: recall_at_20
      value: 47.25
    - type: recall_at_100
      value: 68.477
    - type: recall_at_1000
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    - type: precision_at_1
      value: 38.7
    - type: precision_at_3
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    - type: precision_at_5
      value: 25.4
    - type: precision_at_10
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    - type: precision_at_20
      value: 11.635
    - type: precision_at_100
      value: 3.373
    - type: precision_at_1000
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    - type: mrr_at_1
      value: 38.6
    - type: mrr_at_3
      value: 48.8
    - type: mrr_at_5
      value: 51.23
    - type: mrr_at_10
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    - type: mrr_at_20
      value: 53.009499999999996
    - type: mrr_at_100
      value: 53.228500000000004
    - type: mrr_at_1000
      value: 53.2436
    - type: nauc_ndcg_at_1_max
      value: 6.0639
    - type: nauc_ndcg_at_1_std
      value: 14.1284
    - type: nauc_ndcg_at_1_diff1
      value: 21.0126
    - type: nauc_ndcg_at_3_max
      value: 6.8744
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      value: 17.058300000000003
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      value: 15.622900000000001
    - type: nauc_ndcg_at_5_max
      value: 5.8021
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      value: 21.4376
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      value: 14.2795
    - type: nauc_ndcg_at_10_max
      value: 7.1497
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      value: 26.2426
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    - type: nauc_ndcg_at_20_max
      value: 7.0079
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      value: 31.5785
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      value: 13.3766
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      value: 6.5833
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      value: 34.7066
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      value: 6.4925
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      value: 32.8567
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      value: 14.5588
    - type: nauc_map_at_1_max
      value: 6.0024
    - type: nauc_map_at_1_std
      value: 13.6135
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      value: 21.0303
    - type: nauc_map_at_3_max
      value: 6.812
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      value: 15.673
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      value: 14.632700000000002
    - type: nauc_map_at_5_max
      value: 5.3659
    - type: nauc_map_at_5_std
      value: 19.2072
    - type: nauc_map_at_5_diff1
      value: 13.4808
    - type: nauc_map_at_10_max
      value: 5.9001
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      value: 23.5749
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      value: 12.5175
    - type: nauc_map_at_20_max
      value: 6.0044
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      value: 27.192100000000003
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      value: 13.0855
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      value: 5.9662
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      value: 5.9823
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      value: 28.5657
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      value: 12.9164
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    - type: nauc_recall_at_1_std
      value: 13.6135
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      value: 21.0303
    - type: nauc_recall_at_3_max
      value: 6.7357
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      value: 4.5299
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      value: 30.9601
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      value: 5.6238
    - type: nauc_recall_at_20_std
      value: 40.4586
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      value: 7.7701
    - type: nauc_recall_at_100_max
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      value: 48.2792
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    - type: nauc_recall_at_1000_max
      value: -1.4181
    - type: nauc_recall_at_1000_std
      value: 47.2352
    - type: nauc_recall_at_1000_diff1
      value: 9.7315
    - type: nauc_precision_at_1_max
      value: 6.0639
    - type: nauc_precision_at_1_std
      value: 14.1284
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      value: 21.0126
    - type: nauc_precision_at_3_max
      value: 6.8919999999999995
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      value: 17.8512
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      value: 12.698
    - type: nauc_precision_at_5_max
      value: 4.696899999999999
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      value: 24.1872
    - type: nauc_precision_at_5_diff1
      value: 9.898800000000001
    - type: nauc_precision_at_10_max
      value: 6.840400000000001
    - type: nauc_precision_at_10_std
      value: 30.9012
    - type: nauc_precision_at_10_diff1
      value: 6.829300000000001
    - type: nauc_precision_at_20_max
      value: 5.930499999999999
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      value: 40.3715
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      value: 7.2597
    - type: nauc_precision_at_100_max
      value: 3.4431000000000003
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      value: 47.0829
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      value: 3.9010000000000002
    - type: nauc_precision_at_1000_max
      value: 0.6851999999999999
    - type: nauc_precision_at_1000_std
      value: 42.6842
    - type: nauc_precision_at_1000_diff1
      value: 7.481999999999999
    - type: nauc_mrr_at_1_max
      value: 6.1662
    - type: nauc_mrr_at_1_std
      value: 13.8154
    - type: nauc_mrr_at_1_diff1
      value: 21.3001
    - type: nauc_mrr_at_3_max
      value: 7.034
    - type: nauc_mrr_at_3_std
      value: 16.7161
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      value: 20.6858
    - type: nauc_mrr_at_5_max
      value: 7.059200000000001
    - type: nauc_mrr_at_5_std
      value: 18.2732
    - type: nauc_mrr_at_5_diff1
      value: 20.463700000000003
    - type: nauc_mrr_at_10_max
      value: 7.753400000000001
    - type: nauc_mrr_at_10_std
      value: 18.5582
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      value: 19.9613
    - type: nauc_mrr_at_20_max
      value: 7.744199999999999
    - type: nauc_mrr_at_20_std
      value: 18.5997
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      value: 19.950599999999998
    - type: nauc_mrr_at_100_max
      value: 7.6713000000000005
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      value: 18.3825
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      value: 20.0976
    - type: nauc_mrr_at_1000_max
      value: 7.6633000000000004
    - type: nauc_mrr_at_1000_std
      value: 18.3562
    - type: nauc_mrr_at_1000_diff1
      value: 20.0897
    - type: main_score
      value: 34.528999999999996
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SICK-R (default)
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
      split: test
      type: mteb/sickr-sts
    metrics:
    - type: pearson
      value: 86.1982
    - type: spearman
      value: 81.625
    - type: cosine_pearson
      value: 86.1982
    - type: cosine_spearman
      value: 81.625
    - type: manhattan_pearson
      value: 83.7364
    - type: manhattan_spearman
      value: 81.6094
    - type: euclidean_pearson
      value: 83.7609
    - type: euclidean_spearman
      value: 81.625
    - type: main_score
      value: 81.625
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS12 (default)
      revision: a0d554a64d88156834ff5ae9920b964011b16384
      split: test
      type: mteb/sts12-sts
    metrics:
    - type: pearson
      value: 87.0088
    - type: spearman
      value: 78.5656
    - type: cosine_pearson
      value: 87.0088
    - type: cosine_spearman
      value: 78.5685
    - type: manhattan_pearson
      value: 83.6701
    - type: manhattan_spearman
      value: 78.5915
    - type: euclidean_pearson
      value: 83.65559999999999
    - type: euclidean_spearman
      value: 78.5685
    - type: main_score
      value: 78.5685
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS13 (default)
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
      split: test
      type: mteb/sts13-sts
    metrics:
    - type: pearson
      value: 87.8312
    - type: spearman
      value: 88.1872
    - type: cosine_pearson
      value: 87.83109999999999
    - type: cosine_spearman
      value: 88.1872
    - type: manhattan_pearson
      value: 87.7746
    - type: manhattan_spearman
      value: 88.2053
    - type: euclidean_pearson
      value: 87.7815
    - type: euclidean_spearman
      value: 88.1872
    - type: main_score
      value: 88.1872
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS14 (default)
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
      split: test
      type: mteb/sts14-sts
    metrics:
    - type: pearson
      value: 85.604
    - type: spearman
      value: 84.0718
    - type: cosine_pearson
      value: 85.604
    - type: cosine_spearman
      value: 84.0718
    - type: manhattan_pearson
      value: 84.5478
    - type: manhattan_spearman
      value: 84.0521
    - type: euclidean_pearson
      value: 84.5694
    - type: euclidean_spearman
      value: 84.0718
    - type: main_score
      value: 84.0718
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS15 (default)
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
      split: test
      type: mteb/sts15-sts
    metrics:
    - type: pearson
      value: 89.01559999999999
    - type: spearman
      value: 89.4459
    - type: cosine_pearson
      value: 89.01559999999999
    - type: cosine_spearman
      value: 89.4459
    - type: manhattan_pearson
      value: 88.7875
    - type: manhattan_spearman
      value: 89.4203
    - type: euclidean_pearson
      value: 88.8119
    - type: euclidean_spearman
      value: 89.4459
    - type: main_score
      value: 89.4459
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS16 (default)
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
      split: test
      type: mteb/sts16-sts
    metrics:
    - type: pearson
      value: 85.7279
    - type: spearman
      value: 86.3643
    - type: cosine_pearson
      value: 85.7279
    - type: cosine_spearman
      value: 86.3643
    - type: manhattan_pearson
      value: 85.9517
    - type: manhattan_spearman
      value: 86.355
    - type: euclidean_pearson
      value: 85.9339
    - type: euclidean_spearman
      value: 86.3643
    - type: main_score
      value: 86.3643
    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: 89.9783
    - type: spearman
      value: 89.3624
    - type: cosine_pearson
      value: 89.9783
    - type: cosine_spearman
      value: 89.3624
    - type: manhattan_pearson
      value: 89.7846
    - type: manhattan_spearman
      value: 89.3142
    - type: euclidean_pearson
      value: 89.74170000000001
    - type: euclidean_spearman
      value: 89.3624
    - type: main_score
      value: 89.3624
    task:
      type: STS
  - dataset:
      config: en
      name: MTEB STS22 (en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 64.6745
    - type: spearman
      value: 65.4776
    - type: cosine_pearson
      value: 64.6745
    - type: cosine_spearman
      value: 65.4776
    - type: manhattan_pearson
      value: 65.6748
    - type: manhattan_spearman
      value: 65.3413
    - type: euclidean_pearson
      value: 65.7655
    - type: euclidean_spearman
      value: 65.4776
    - type: main_score
      value: 65.4776
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STSBenchmark (default)
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
      split: test
      type: mteb/stsbenchmark-sts
    metrics:
    - type: pearson
      value: 87.724
    - type: spearman
      value: 88.3237
    - type: cosine_pearson
      value: 87.724
    - type: cosine_spearman
      value: 88.3237
    - type: manhattan_pearson
      value: 87.9269
    - type: manhattan_spearman
      value: 88.301
    - type: euclidean_pearson
      value: 87.9367
    - type: euclidean_spearman
      value: 88.3237
    - type: main_score
      value: 88.3237
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SciDocsRR (default)
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
      split: test
      type: mteb/scidocs-reranking
    metrics:
    - type: map
      value: 86.7192
    - type: mrr
      value: 96.4221
    - type: nAUC_map_max
      value: 43.437799999999996
    - type: nAUC_map_std
      value: 67.55980000000001
    - type: nAUC_map_diff1
      value: 0.6785
    - type: nAUC_mrr_max
      value: 83.50840000000001
    - type: nAUC_mrr_std
      value: 84.7092
    - type: nAUC_mrr_diff1
      value: 45.8165
    - type: main_score
      value: 86.7192
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SciFact (default)
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
      split: test
      type: mteb/scifact
    metrics:
    - type: ndcg_at_1
      value: 72.333
    - type: ndcg_at_3
      value: 80.896
    - type: ndcg_at_5
      value: 83.758
    - type: ndcg_at_10
      value: 85.088
    - type: ndcg_at_20
      value: 85.464
    - type: ndcg_at_100
      value: 85.637
    - type: ndcg_at_1000
      value: 85.637
    - type: map_at_1
      value: 69.467
    - type: map_at_3
      value: 77.969
    - type: map_at_5
      value: 80.03
    - type: map_at_10
      value: 80.726
    - type: map_at_20
      value: 80.87299999999999
    - type: map_at_100
      value: 80.892
    - type: map_at_1000
      value: 80.892
    - type: recall_at_1
      value: 69.467
    - type: recall_at_3
      value: 86.861
    - type: recall_at_5
      value: 93.95
    - type: recall_at_10
      value: 97.667
    - type: recall_at_20
      value: 99.0
    - type: recall_at_100
      value: 100.0
    - type: recall_at_1000
      value: 100.0
    - type: precision_at_1
      value: 72.333
    - type: precision_at_3
      value: 31.667
    - type: precision_at_5
      value: 20.867
    - type: precision_at_10
      value: 11.0
    - type: precision_at_20
      value: 5.6000000000000005
    - type: precision_at_100
      value: 1.13
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: mrr_at_1
      value: 72.3333
    - type: mrr_at_3
      value: 79.2222
    - type: mrr_at_5
      value: 80.7889
    - type: mrr_at_10
      value: 81.2286
    - type: mrr_at_20
      value: 81.2656
    - type: mrr_at_100
      value: 81.2847
    - type: mrr_at_1000
      value: 81.2847
    - type: nauc_ndcg_at_1_max
      value: 38.9574
    - type: nauc_ndcg_at_1_std
      value: -0.1477
    - type: nauc_ndcg_at_1_diff1
      value: 76.0676
    - type: nauc_ndcg_at_3_max
      value: 43.899100000000004
    - type: nauc_ndcg_at_3_std
      value: 1.3967
    - type: nauc_ndcg_at_3_diff1
      value: 76.5965
    - type: nauc_ndcg_at_5_max
      value: 42.2858
    - type: nauc_ndcg_at_5_std
      value: -0.0241
    - type: nauc_ndcg_at_5_diff1
      value: 76.1049
    - type: nauc_ndcg_at_10_max
      value: 42.381
    - type: nauc_ndcg_at_10_std
      value: 2.6438
    - type: nauc_ndcg_at_10_diff1
      value: 75.9975
    - type: nauc_ndcg_at_20_max
      value: 42.9424
    - type: nauc_ndcg_at_20_std
      value: 2.8966
    - type: nauc_ndcg_at_20_diff1
      value: 76.175
    - type: nauc_ndcg_at_100_max
      value: 42.854
    - type: nauc_ndcg_at_100_std
      value: 2.8745
    - type: nauc_ndcg_at_100_diff1
      value: 75.9101
    - type: nauc_ndcg_at_1000_max
      value: 42.854
    - type: nauc_ndcg_at_1000_std
      value: 2.8745
    - type: nauc_ndcg_at_1000_diff1
      value: 75.9101
    - type: nauc_map_at_1_max
      value: 33.055299999999995
    - type: nauc_map_at_1_std
      value: -7.5394000000000005
    - type: nauc_map_at_1_diff1
      value: 77.3494
    - type: nauc_map_at_3_max
      value: 40.9863
    - type: nauc_map_at_3_std
      value: -1.8884
    - type: nauc_map_at_3_diff1
      value: 77.1306
    - type: nauc_map_at_5_max
      value: 41.2322
    - type: nauc_map_at_5_std
      value: -0.3544
    - type: nauc_map_at_5_diff1
      value: 76.129
    - type: nauc_map_at_10_max
      value: 41.6711
    - type: nauc_map_at_10_std
      value: 1.4658
    - type: nauc_map_at_10_diff1
      value: 75.9733
    - type: nauc_map_at_20_max
      value: 41.856500000000004
    - type: nauc_map_at_20_std
      value: 1.6687
    - type: nauc_map_at_20_diff1
      value: 75.9273
    - type: nauc_map_at_100_max
      value: 41.8402
    - type: nauc_map_at_100_std
      value: 1.6459000000000001
    - type: nauc_map_at_100_diff1
      value: 75.9041
    - type: nauc_map_at_1000_max
      value: 41.8402
    - type: nauc_map_at_1000_std
      value: 1.6459000000000001
    - type: nauc_map_at_1000_diff1
      value: 75.9041
    - type: nauc_recall_at_1_max
      value: 33.055299999999995
    - type: nauc_recall_at_1_std
      value: -7.5394000000000005
    - type: nauc_recall_at_1_diff1
      value: 77.3494
    - type: nauc_recall_at_3_max
      value: 46.97
    - type: nauc_recall_at_3_std
      value: -0.4234
    - type: nauc_recall_at_3_diff1
      value: 77.9507
    - type: nauc_recall_at_5_max
      value: 42.5995
    - type: nauc_recall_at_5_std
      value: -9.1448
    - type: nauc_recall_at_5_diff1
      value: 75.5352
    - type: nauc_recall_at_10_max
      value: 34.3737
    - type: nauc_recall_at_10_std
      value: 1.1004
    - type: nauc_recall_at_10_diff1
      value: 77.2476
    - type: nauc_recall_at_20_max
      value: 47.3389
    - type: nauc_recall_at_20_std
      value: -0.9648
    - type: nauc_recall_at_20_diff1
      value: 95.6427
    - type: nauc_recall_at_100_max
      value: .nan
    - type: nauc_recall_at_100_std
      value: .nan
    - type: nauc_recall_at_100_diff1
      value: .nan
    - 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: 38.9574
    - type: nauc_precision_at_1_std
      value: -0.1477
    - type: nauc_precision_at_1_diff1
      value: 76.0676
    - type: nauc_precision_at_3_max
      value: 35.383199999999995
    - type: nauc_precision_at_3_std
      value: 24.918000000000003
    - type: nauc_precision_at_3_diff1
      value: 19.4821
    - type: nauc_precision_at_5_max
      value: 25.366699999999998
    - type: nauc_precision_at_5_std
      value: 36.871900000000004
    - type: nauc_precision_at_5_diff1
      value: -13.1237
    - type: nauc_precision_at_10_max
      value: 18.4563
    - type: nauc_precision_at_10_std
      value: 48.8215
    - type: nauc_precision_at_10_diff1
      value: -32.047
    - type: nauc_precision_at_20_max
      value: 15.966
    - type: nauc_precision_at_20_std
      value: 47.1139
    - type: nauc_precision_at_20_diff1
      value: -36.0122
    - type: nauc_precision_at_100_max
      value: 14.579
    - type: nauc_precision_at_100_std
      value: 49.2394
    - type: nauc_precision_at_100_diff1
      value: -41.8326
    - type: nauc_precision_at_1000_max
      value: 14.579
    - type: nauc_precision_at_1000_std
      value: 49.2394
    - type: nauc_precision_at_1000_diff1
      value: -41.8326
    - type: nauc_mrr_at_1_max
      value: 38.9574
    - type: nauc_mrr_at_1_std
      value: -0.1477
    - type: nauc_mrr_at_1_diff1
      value: 76.0676
    - type: nauc_mrr_at_3_max
      value: 43.9004
    - type: nauc_mrr_at_3_std
      value: 3.3175999999999997
    - type: nauc_mrr_at_3_diff1
      value: 76.16969999999999
    - type: nauc_mrr_at_5_max
      value: 43.113600000000005
    - type: nauc_mrr_at_5_std
      value: 2.5391
    - type: nauc_mrr_at_5_diff1
      value: 76.0292
    - type: nauc_mrr_at_10_max
      value: 42.613099999999996
    - type: nauc_mrr_at_10_std
      value: 2.5191000000000003
    - type: nauc_mrr_at_10_diff1
      value: 75.9944
    - type: nauc_mrr_at_20_max
      value: 42.5833
    - type: nauc_mrr_at_20_std
      value: 2.3678999999999997
    - type: nauc_mrr_at_20_diff1
      value: 76.0872
    - type: nauc_mrr_at_100_max
      value: 42.5675
    - type: nauc_mrr_at_100_std
      value: 2.3453999999999997
    - type: nauc_mrr_at_100_diff1
      value: 76.0638
    - type: nauc_mrr_at_1000_max
      value: 42.5675
    - type: nauc_mrr_at_1000_std
      value: 2.3453999999999997
    - type: nauc_mrr_at_1000_diff1
      value: 76.0638
    - type: main_score
      value: 85.088
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SprintDuplicateQuestions (default)
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
      split: test
      type: mteb/sprintduplicatequestions-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 99.7129
    - type: similarity_accuracy_threshold
      value: 76.1587
    - type: similarity_f1
      value: 84.6398
    - type: similarity_f1_threshold
      value: 76.1587
    - type: similarity_precision
      value: 89.97749999999999
    - type: similarity_recall
      value: 79.9
    - type: similarity_ap
      value: 92.08539999999999
    - type: cosine_accuracy
      value: 99.7129
    - type: cosine_accuracy_threshold
      value: 76.1587
    - type: cosine_f1
      value: 84.6398
    - type: cosine_f1_threshold
      value: 76.1587
    - type: cosine_precision
      value: 89.97749999999999
    - type: cosine_recall
      value: 79.9
    - type: cosine_ap
      value: 92.08539999999999
    - type: manhattan_accuracy
      value: 99.7139
    - type: manhattan_accuracy_threshold
      value: 2490.9832
    - type: manhattan_f1
      value: 84.6522
    - type: manhattan_f1_threshold
      value: 2490.9832
    - type: manhattan_precision
      value: 90.2605
    - type: manhattan_recall
      value: 79.7
    - type: manhattan_ap
      value: 92.08
    - type: euclidean_accuracy
      value: 99.7129
    - type: euclidean_accuracy_threshold
      value: 69.0526
    - type: euclidean_f1
      value: 84.6398
    - type: euclidean_f1_threshold
      value: 69.0526
    - type: euclidean_precision
      value: 89.97749999999999
    - type: euclidean_recall
      value: 79.9
    - type: euclidean_ap
      value: 92.08539999999999
    - type: dot_accuracy
      value: 99.7129
    - type: dot_accuracy_threshold
      value: 76.1587
    - type: dot_f1
      value: 84.6398
    - type: dot_f1_threshold
      value: 76.1587
    - type: dot_precision
      value: 89.97749999999999
    - type: dot_recall
      value: 79.9
    - type: dot_ap
      value: 92.08539999999999
    - type: max_accuracy
      value: 99.7139
    - type: max_f1
      value: 84.6522
    - type: max_precision
      value: 90.2605
    - type: max_recall
      value: 79.9
    - type: max_ap
      value: 92.08539999999999
    - type: main_score
      value: 92.08539999999999
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB StackExchangeClustering (default)
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
      split: test
      type: mteb/stackexchange-clustering
    metrics:
    - type: v_measure
      value: 82.0328
    - type: v_measure_std
      value: 3.1285
    - type: main_score
      value: 82.0328
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB StackExchangeClusteringP2P (default)
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
      split: test
      type: mteb/stackexchange-clustering-p2p
    metrics:
    - type: v_measure
      value: 48.1665
    - type: v_measure_std
      value: 1.5214
    - type: main_score
      value: 48.1665
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB StackOverflowDupQuestions (default)
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
      split: test
      type: mteb/stackoverflowdupquestions-reranking
    metrics:
    - type: map
      value: 54.68769999999999
    - type: mrr
      value: 55.675
    - type: nAUC_map_max
      value: 10.8063
    - type: nAUC_map_std
      value: 8.713600000000001
    - type: nAUC_map_diff1
      value: 39.5509
    - type: nAUC_mrr_max
      value: 11.930399999999999
    - type: nAUC_mrr_std
      value: 9.6175
    - type: nAUC_mrr_diff1
      value: 40.0268
    - type: main_score
      value: 54.68769999999999
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SummEval (default)
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
      split: test
      type: mteb/summeval
    metrics:
    - type: pearson
      value: 30.859399999999997
    - type: spearman
      value: 30.4465
    - type: cosine_spearman
      value: 30.4465
    - type: cosine_pearson
      value: 30.859399999999997
    - type: dot_spearman
      value: 30.4465
    - type: dot_pearson
      value: 30.859399999999997
    - type: main_score
      value: 30.4465
    task:
      type: Summarization
  - dataset:
      config: default
      name: MTEB TRECCOVID (default)
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
      split: test
      type: mteb/trec-covid
    metrics:
    - type: ndcg_at_1
      value: 88.0
    - type: ndcg_at_3
      value: 84.358
    - type: ndcg_at_5
      value: 82.887
    - type: ndcg_at_10
      value: 82.747
    - type: ndcg_at_20
      value: 80.381
    - type: ndcg_at_100
      value: 67.338
    - type: ndcg_at_1000
      value: 53.469
    - type: map_at_1
      value: 0.242
    - type: map_at_3
      value: 0.674
    - type: map_at_5
      value: 1.082
    - type: map_at_10
      value: 2.109
    - type: map_at_20
      value: 3.955
    - type: map_at_100
      value: 14.123
    - type: map_at_1000
      value: 31.130000000000003
    - type: recall_at_1
      value: 0.242
    - type: recall_at_3
      value: 0.685
    - type: recall_at_5
      value: 1.125
    - type: recall_at_10
      value: 2.257
    - type: recall_at_20
      value: 4.352
    - type: recall_at_100
      value: 17.044
    - type: recall_at_1000
      value: 47.494
    - type: precision_at_1
      value: 92.0
    - type: precision_at_3
      value: 88.0
    - type: precision_at_5
      value: 86.4
    - type: precision_at_10
      value: 86.4
    - type: precision_at_20
      value: 84.5
    - type: precision_at_100
      value: 69.58
    - type: precision_at_1000
      value: 22.56
    - type: mrr_at_1
      value: 92.0
    - type: mrr_at_3
      value: 95.0
    - type: mrr_at_5
      value: 95.39999999999999
    - type: mrr_at_10
      value: 95.39999999999999
    - type: mrr_at_20
      value: 95.39999999999999
    - type: mrr_at_100
      value: 95.39999999999999
    - type: mrr_at_1000
      value: 95.39999999999999
    - type: nauc_ndcg_at_1_max
      value: -34.918
    - type: nauc_ndcg_at_1_std
      value: 64.9171
    - type: nauc_ndcg_at_1_diff1
      value: -25.7963
    - type: nauc_ndcg_at_3_max
      value: -16.3991
    - type: nauc_ndcg_at_3_std
      value: 54.9104
    - type: nauc_ndcg_at_3_diff1
      value: -26.3455
    - type: nauc_ndcg_at_5_max
      value: -0.9459000000000001
    - type: nauc_ndcg_at_5_std
      value: 53.632999999999996
    - type: nauc_ndcg_at_5_diff1
      value: -27.479
    - type: nauc_ndcg_at_10_max
      value: 10.2555
    - type: nauc_ndcg_at_10_std
      value: 50.5127
    - type: nauc_ndcg_at_10_diff1
      value: -29.7344
    - type: nauc_ndcg_at_20_max
      value: 16.7577
    - type: nauc_ndcg_at_20_std
      value: 49.2737
    - type: nauc_ndcg_at_20_diff1
      value: -30.2252
    - type: nauc_ndcg_at_100_max
      value: 17.642
    - type: nauc_ndcg_at_100_std
      value: 62.1844
    - type: nauc_ndcg_at_100_diff1
      value: -24.3677
    - type: nauc_ndcg_at_1000_max
      value: 3.9043
    - type: nauc_ndcg_at_1000_std
      value: 74.6529
    - type: nauc_ndcg_at_1000_diff1
      value: -19.9165
    - type: nauc_map_at_1_max
      value: 8.2594
    - type: nauc_map_at_1_std
      value: 15.198300000000001
    - type: nauc_map_at_1_diff1
      value: 9.5579
    - type: nauc_map_at_3_max
      value: 12.1452
    - type: nauc_map_at_3_std
      value: 26.5818
    - type: nauc_map_at_3_diff1
      value: -5.2755
    - type: nauc_map_at_5_max
      value: 14.930499999999999
    - type: nauc_map_at_5_std
      value: 29.0041
    - type: nauc_map_at_5_diff1
      value: -7.671500000000001
    - type: nauc_map_at_10_max
      value: 18.997
    - type: nauc_map_at_10_std
      value: 28.7638
    - type: nauc_map_at_10_diff1
      value: -9.5023
    - type: nauc_map_at_20_max
      value: 20.1326
    - type: nauc_map_at_20_std
      value: 25.7159
    - type: nauc_map_at_20_diff1
      value: -6.8202
    - type: nauc_map_at_100_max
      value: 19.9081
    - type: nauc_map_at_100_std
      value: 44.6124
    - type: nauc_map_at_100_diff1
      value: -4.2406
    - type: nauc_map_at_1000_max
      value: 9.8118
    - type: nauc_map_at_1000_std
      value: 76.6769
    - type: nauc_map_at_1000_diff1
      value: -18.8289
    - type: nauc_recall_at_1_max
      value: 8.2594
    - type: nauc_recall_at_1_std
      value: 15.198300000000001
    - type: nauc_recall_at_1_diff1
      value: 9.5579
    - type: nauc_recall_at_3_max
      value: 12.8061
    - type: nauc_recall_at_3_std
      value: 24.857000000000003
    - type: nauc_recall_at_3_diff1
      value: -2.7226
    - type: nauc_recall_at_5_max
      value: 15.4786
    - type: nauc_recall_at_5_std
      value: 24.0416
    - type: nauc_recall_at_5_diff1
      value: -1.3981999999999999
    - type: nauc_recall_at_10_max
      value: 19.867099999999997
    - type: nauc_recall_at_10_std
      value: 21.4911
    - type: nauc_recall_at_10_diff1
      value: -0.906
    - type: nauc_recall_at_20_max
      value: 18.826999999999998
    - type: nauc_recall_at_20_std
      value: 16.9991
    - type: nauc_recall_at_20_diff1
      value: 3.3348999999999998
    - type: nauc_recall_at_100_max
      value: 17.843
    - type: nauc_recall_at_100_std
      value: 34.0944
    - type: nauc_recall_at_100_diff1
      value: 2.2885
    - type: nauc_recall_at_1000_max
      value: -0.7106
    - type: nauc_recall_at_1000_std
      value: 69.6537
    - type: nauc_recall_at_1000_diff1
      value: -19.310299999999998
    - type: nauc_precision_at_1_max
      value: -7.0495
    - type: nauc_precision_at_1_std
      value: 75.3735
    - type: nauc_precision_at_1_diff1
      value: -52.91780000000001
    - type: nauc_precision_at_3_max
      value: 7.803
    - type: nauc_precision_at_3_std
      value: 72.3574
    - type: nauc_precision_at_3_diff1
      value: -56.178200000000004
    - type: nauc_precision_at_5_max
      value: 10.770100000000001
    - type: nauc_precision_at_5_std
      value: 69.3859
    - type: nauc_precision_at_5_diff1
      value: -51.3122
    - type: nauc_precision_at_10_max
      value: 22.3857
    - type: nauc_precision_at_10_std
      value: 60.5115
    - type: nauc_precision_at_10_diff1
      value: -45.9797
    - type: nauc_precision_at_20_max
      value: 22.3038
    - type: nauc_precision_at_20_std
      value: 49.4557
    - type: nauc_precision_at_20_diff1
      value: -36.5443
    - type: nauc_precision_at_100_max
      value: 16.0115
    - type: nauc_precision_at_100_std
      value: 61.7637
    - type: nauc_precision_at_100_diff1
      value: -26.223800000000004
    - type: nauc_precision_at_1000_max
      value: -3.9560999999999997
    - type: nauc_precision_at_1000_std
      value: 38.3159
    - type: nauc_precision_at_1000_diff1
      value: -24.9664
    - type: nauc_mrr_at_1_max
      value: -7.0495
    - type: nauc_mrr_at_1_std
      value: 75.3735
    - type: nauc_mrr_at_1_diff1
      value: -52.91780000000001
    - type: nauc_mrr_at_3_max
      value: -16.909399999999998
    - type: nauc_mrr_at_3_std
      value: 77.6844
    - type: nauc_mrr_at_3_diff1
      value: -35.1727
    - type: nauc_mrr_at_5_max
      value: -13.4799
    - type: nauc_mrr_at_5_std
      value: 76.8806
    - type: nauc_mrr_at_5_diff1
      value: -41.3449
    - type: nauc_mrr_at_10_max
      value: -13.4799
    - type: nauc_mrr_at_10_std
      value: 76.8806
    - type: nauc_mrr_at_10_diff1
      value: -41.3449
    - type: nauc_mrr_at_20_max
      value: -13.4799
    - type: nauc_mrr_at_20_std
      value: 76.8806
    - type: nauc_mrr_at_20_diff1
      value: -41.3449
    - type: nauc_mrr_at_100_max
      value: -13.4799
    - type: nauc_mrr_at_100_std
      value: 76.8806
    - type: nauc_mrr_at_100_diff1
      value: -41.3449
    - type: nauc_mrr_at_1000_max
      value: -13.4799
    - type: nauc_mrr_at_1000_std
      value: 76.8806
    - type: nauc_mrr_at_1000_diff1
      value: -41.3449
    - type: main_score
      value: 82.747
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB Touche2020 (default)
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
      split: test
      type: mteb/touche2020
    metrics:
    - type: ndcg_at_1
      value: 54.081999999999994
    - type: ndcg_at_3
      value: 47.305
    - type: ndcg_at_5
      value: 42.753
    - type: ndcg_at_10
      value: 39.385999999999996
    - type: ndcg_at_20
      value: 38.999
    - type: ndcg_at_100
      value: 47.315000000000005
    - type: ndcg_at_1000
      value: 56.769999999999996
    - type: map_at_1
      value: 4.228
    - type: map_at_3
      value: 9.459
    - type: map_at_5
      value: 13.135
    - type: map_at_10
      value: 18.221
    - type: map_at_20
      value: 21.956
    - type: map_at_100
      value: 25.232
    - type: map_at_1000
      value: 26.582
    - type: recall_at_1
      value: 4.228
    - type: recall_at_3
      value: 10.658
    - type: recall_at_5
      value: 15.754000000000001
    - type: recall_at_10
      value: 24.826
    - type: recall_at_20
      value: 34.111999999999995
    - type: recall_at_100
      value: 52.797000000000004
    - type: recall_at_1000
      value: 82.733
    - type: precision_at_1
      value: 59.184000000000005
    - type: precision_at_3
      value: 48.980000000000004
    - type: precision_at_5
      value: 42.041000000000004
    - type: precision_at_10
      value: 34.489999999999995
    - type: precision_at_20
      value: 25.306
    - type: precision_at_100
      value: 8.735
    - type: precision_at_1000
      value: 1.52
    - type: mrr_at_1
      value: 59.183699999999995
    - type: mrr_at_3
      value: 68.0272
    - type: mrr_at_5
      value: 68.9456
    - type: mrr_at_10
      value: 70.5491
    - type: mrr_at_20
      value: 70.8804
    - type: mrr_at_100
      value: 70.9484
    - type: mrr_at_1000
      value: 70.9484
    - type: nauc_ndcg_at_1_max
      value: 14.2895
    - type: nauc_ndcg_at_1_std
      value: 15.140500000000001
    - type: nauc_ndcg_at_1_diff1
      value: 14.7508
    - type: nauc_ndcg_at_3_max
      value: -3.0176000000000003
    - type: nauc_ndcg_at_3_std
      value: 13.8234
    - type: nauc_ndcg_at_3_diff1
      value: 18.348
    - type: nauc_ndcg_at_5_max
      value: -3.2785
    - type: nauc_ndcg_at_5_std
      value: 6.0827
    - type: nauc_ndcg_at_5_diff1
      value: 19.9722
    - type: nauc_ndcg_at_10_max
      value: 0.1953
    - type: nauc_ndcg_at_10_std
      value: 4.0055
    - type: nauc_ndcg_at_10_diff1
      value: 19.0452
    - type: nauc_ndcg_at_20_max
      value: -4.6253
    - type: nauc_ndcg_at_20_std
      value: 12.3256
    - type: nauc_ndcg_at_20_diff1
      value: 16.514899999999997
    - type: nauc_ndcg_at_100_max
      value: -1.4607
    - type: nauc_ndcg_at_100_std
      value: 27.7821
    - type: nauc_ndcg_at_100_diff1
      value: 14.4344
    - type: nauc_ndcg_at_1000_max
      value: 8.2149
    - type: nauc_ndcg_at_1000_std
      value: 33.6054
    - type: nauc_ndcg_at_1000_diff1
      value: 15.9317
    - type: nauc_map_at_1_max
      value: -3.3970000000000002
    - type: nauc_map_at_1_std
      value: 2.5947999999999998
    - type: nauc_map_at_1_diff1
      value: 32.8068
    - type: nauc_map_at_3_max
      value: -5.5302999999999995
    - type: nauc_map_at_3_std
      value: 2.9596999999999998
    - type: nauc_map_at_3_diff1
      value: 35.8593
    - type: nauc_map_at_5_max
      value: -9.0474
    - type: nauc_map_at_5_std
      value: -3.2526
    - type: nauc_map_at_5_diff1
      value: 39.263
    - type: nauc_map_at_10_max
      value: -4.7221
    - type: nauc_map_at_10_std
      value: -1.3847
    - type: nauc_map_at_10_diff1
      value: 32.1957
    - type: nauc_map_at_20_max
      value: -2.9691
    - type: nauc_map_at_20_std
      value: 4.922
    - type: nauc_map_at_20_diff1
      value: 24.6601
    - type: nauc_map_at_100_max
      value: -2.7695999999999996
    - type: nauc_map_at_100_std
      value: 14.2812
    - type: nauc_map_at_100_diff1
      value: 22.034599999999998
    - type: nauc_map_at_1000_max
      value: -1.4055
    - type: nauc_map_at_1000_std
      value: 15.9695
    - type: nauc_map_at_1000_diff1
      value: 22.348000000000003
    - type: nauc_recall_at_1_max
      value: -3.3970000000000002
    - type: nauc_recall_at_1_std
      value: 2.5947999999999998
    - type: nauc_recall_at_1_diff1
      value: 32.8068
    - type: nauc_recall_at_3_max
      value: -9.774
    - type: nauc_recall_at_3_std
      value: 4.5374
    - type: nauc_recall_at_3_diff1
      value: 36.1682
    - type: nauc_recall_at_5_max
      value: -12.770999999999999
    - type: nauc_recall_at_5_std
      value: -3.5658000000000003
    - type: nauc_recall_at_5_diff1
      value: 38.3296
    - type: nauc_recall_at_10_max
      value: -8.0558
    - type: nauc_recall_at_10_std
      value: 0.024800000000000003
    - type: nauc_recall_at_10_diff1
      value: 26.5627
    - type: nauc_recall_at_20_max
      value: -9.7074
    - type: nauc_recall_at_20_std
      value: 15.120700000000001
    - type: nauc_recall_at_20_diff1
      value: 14.4759
    - type: nauc_recall_at_100_max
      value: -5.7863999999999995
    - type: nauc_recall_at_100_std
      value: 40.9887
    - type: nauc_recall_at_100_diff1
      value: 6.2395
    - type: nauc_recall_at_1000_max
      value: 27.007599999999996
    - type: nauc_recall_at_1000_std
      value: 63.81250000000001
    - type: nauc_recall_at_1000_diff1
      value: 4.8708
    - type: nauc_precision_at_1_max
      value: 12.7638
    - type: nauc_precision_at_1_std
      value: 22.0685
    - type: nauc_precision_at_1_diff1
      value: 11.7399
    - type: nauc_precision_at_3_max
      value: -8.608699999999999
    - type: nauc_precision_at_3_std
      value: 15.267900000000001
    - type: nauc_precision_at_3_diff1
      value: 16.5462
    - type: nauc_precision_at_5_max
      value: -7.2258000000000004
    - type: nauc_precision_at_5_std
      value: 1.7056000000000002
    - type: nauc_precision_at_5_diff1
      value: 17.8119
    - type: nauc_precision_at_10_max
      value: -0.0044
    - type: nauc_precision_at_10_std
      value: 4.0112000000000005
    - type: nauc_precision_at_10_diff1
      value: 3.5520000000000005
    - type: nauc_precision_at_20_max
      value: -2.7077
    - type: nauc_precision_at_20_std
      value: 34.144000000000005
    - type: nauc_precision_at_20_diff1
      value: -14.833499999999999
    - type: nauc_precision_at_100_max
      value: 12.6555
    - type: nauc_precision_at_100_std
      value: 59.5965
    - type: nauc_precision_at_100_diff1
      value: -24.3212
    - type: nauc_precision_at_1000_max
      value: 38.3951
    - type: nauc_precision_at_1000_std
      value: 32.5441
    - type: nauc_precision_at_1000_diff1
      value: -31.4919
    - type: nauc_mrr_at_1_max
      value: 12.7638
    - type: nauc_mrr_at_1_std
      value: 22.0685
    - type: nauc_mrr_at_1_diff1
      value: 11.7399
    - type: nauc_mrr_at_3_max
      value: 2.6993
    - type: nauc_mrr_at_3_std
      value: 24.444499999999998
    - type: nauc_mrr_at_3_diff1
      value: 8.2343
    - type: nauc_mrr_at_5_max
      value: 5.6594999999999995
    - type: nauc_mrr_at_5_std
      value: 22.1027
    - type: nauc_mrr_at_5_diff1
      value: 5.3648
    - type: nauc_mrr_at_10_max
      value: 5.6309
    - type: nauc_mrr_at_10_std
      value: 23.0288
    - type: nauc_mrr_at_10_diff1
      value: 5.2199
    - type: nauc_mrr_at_20_max
      value: 4.8424000000000005
    - type: nauc_mrr_at_20_std
      value: 23.6116
    - type: nauc_mrr_at_20_diff1
      value: 6.7553
    - type: nauc_mrr_at_100_max
      value: 5.3414
    - type: nauc_mrr_at_100_std
      value: 23.394000000000002
    - type: nauc_mrr_at_100_diff1
      value: 6.7397
    - type: nauc_mrr_at_1000_max
      value: 5.3414
    - type: nauc_mrr_at_1000_std
      value: 23.394000000000002
    - type: nauc_mrr_at_1000_diff1
      value: 6.7397
    - type: main_score
      value: 39.385999999999996
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ToxicConversationsClassification (default)
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
      split: test
      type: mteb/toxic_conversations_50k
    metrics:
    - type: accuracy
      value: 97.5928
    - type: f1
      value: 92.9042
    - type: f1_weighted
      value: 97.7586
    - type: ap
      value: 77.5261
    - type: ap_weighted
      value: 77.5261
    - type: main_score
      value: 97.5928
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TweetSentimentExtractionClassification (default)
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
      split: test
      type: mteb/tweet_sentiment_extraction
    metrics:
    - type: accuracy
      value: 88.2343
    - type: f1
      value: 88.4321
    - type: f1_weighted
      value: 88.13069999999999
    - type: main_score
      value: 88.2343
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TwentyNewsgroupsClustering (default)
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
      split: test
      type: mteb/twentynewsgroups-clustering
    metrics:
    - type: v_measure
      value: 83.486
    - type: v_measure_std
      value: 2.0008000000000004
    - type: main_score
      value: 83.486
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB TwitterSemEval2015 (default)
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
      split: test
      type: mteb/twittersemeval2015-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 87.7988
    - type: similarity_accuracy_threshold
      value: 74.7442
    - type: similarity_f1
      value: 72.9982
    - type: similarity_f1_threshold
      value: 71.073
    - type: similarity_precision
      value: 67.4804
    - type: similarity_recall
      value: 79.4987
    - type: similarity_ap
      value: 79.1752
    - type: cosine_accuracy
      value: 87.7988
    - type: cosine_accuracy_threshold
      value: 74.7442
    - type: cosine_f1
      value: 72.9982
    - type: cosine_f1_threshold
      value: 71.073
    - type: cosine_precision
      value: 67.4804
    - type: cosine_recall
      value: 79.4987
    - type: cosine_ap
      value: 79.1752
    - type: manhattan_accuracy
      value: 87.8107
    - type: manhattan_accuracy_threshold
      value: 2587.3787
    - type: manhattan_f1
      value: 72.8159
    - type: manhattan_f1_threshold
      value: 2713.7127
    - type: manhattan_precision
      value: 69.176
    - type: manhattan_recall
      value: 76.8602
    - type: manhattan_ap
      value: 79.1243
    - type: euclidean_accuracy
      value: 87.7988
    - type: euclidean_accuracy_threshold
      value: 71.0715
    - type: euclidean_f1
      value: 72.9982
    - type: euclidean_f1_threshold
      value: 76.0618
    - type: euclidean_precision
      value: 67.4804
    - type: euclidean_recall
      value: 79.4987
    - type: euclidean_ap
      value: 79.1752
    - type: dot_accuracy
      value: 87.7988
    - type: dot_accuracy_threshold
      value: 74.7442
    - type: dot_f1
      value: 72.9982
    - type: dot_f1_threshold
      value: 71.073
    - type: dot_precision
      value: 67.4804
    - type: dot_recall
      value: 79.4987
    - type: dot_ap
      value: 79.1752
    - type: max_accuracy
      value: 87.8107
    - type: max_f1
      value: 72.9982
    - type: max_precision
      value: 69.176
    - type: max_recall
      value: 79.4987
    - type: max_ap
      value: 79.1752
    - type: main_score
      value: 79.1752
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB TwitterURLCorpus (default)
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
      split: test
      type: mteb/twitterurlcorpus-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 89.4749
    - type: similarity_accuracy_threshold
      value: 73.03659999999999
    - type: similarity_f1
      value: 79.4416
    - type: similarity_f1_threshold
      value: 70.7413
    - type: similarity_precision
      value: 75.9696
    - type: similarity_recall
      value: 83.2461
    - type: similarity_ap
      value: 87.1156
    - type: cosine_accuracy
      value: 89.4749
    - type: cosine_accuracy_threshold
      value: 73.03659999999999
    - type: cosine_f1
      value: 79.4416
    - type: cosine_f1_threshold
      value: 70.7413
    - type: cosine_precision
      value: 75.9696
    - type: cosine_recall
      value: 83.2461
    - type: cosine_ap
      value: 87.1156
    - type: manhattan_accuracy
      value: 89.471
    - type: manhattan_accuracy_threshold
      value: 2630.4434
    - type: manhattan_f1
      value: 79.4078
    - type: manhattan_f1_threshold
      value: 2764.4342
    - type: manhattan_precision
      value: 75.7675
    - type: manhattan_recall
      value: 83.4155
    - type: manhattan_ap
      value: 87.0938
    - type: euclidean_accuracy
      value: 89.4749
    - type: euclidean_accuracy_threshold
      value: 73.4349
    - type: euclidean_f1
      value: 79.4416
    - type: euclidean_f1_threshold
      value: 76.4966
    - type: euclidean_precision
      value: 75.9696
    - type: euclidean_recall
      value: 83.2461
    - type: euclidean_ap
      value: 87.1156
    - type: dot_accuracy
      value: 89.4749
    - type: dot_accuracy_threshold
      value: 73.03659999999999
    - type: dot_f1
      value: 79.4416
    - type: dot_f1_threshold
      value: 70.7413
    - type: dot_precision
      value: 75.9696
    - type: dot_recall
      value: 83.2461
    - type: dot_ap
      value: 87.1156
    - type: max_accuracy
      value: 89.4749
    - type: max_f1
      value: 79.4416
    - type: max_precision
      value: 75.9696
    - type: max_recall
      value: 83.4155
    - type: max_ap
      value: 87.1156
    - type: main_score
      value: 87.1156
    task:
      type: PairClassification
tags:
- mteb
- sentence-similarity
- sentence-transformers
- Sentence Transformers
language:
- en
---
license: mit
---

## Usage

Code example

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

def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]

input_texts = [
    "what is the capital of Japan?",
    "Kyoto",
    "Tokyo",
    "Beijing"
]

tokenizer = AutoTokenizer.from_pretrained("iamgroot42/rover_nexus")
model = AutoModel.from_pretrained("iamgroot42/rover_nexus")

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```

Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

sentences = ['That is a happy person', 'That is a sad person']

model = SentenceTransformer('iamgroot42/rover_nexus')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```

Model training details and data will be uploaded soon!