sentence-transformers safetensors distilbert sentence-similarity feature-extraction mteb en base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: distilbert/distilbert-base-uncased
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- mteb
model-index:
- name: prdev/mini-gte
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 74.8955
    - type: f1
      value: 68.84209999999999
    - type: f1_weighted
      value: 77.1819
    - type: ap
      value: 37.731500000000004
    - type: ap_weighted
      value: 37.731500000000004
    - type: main_score
      value: 74.8955
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification (default)
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 92.9424
    - type: f1
      value: 92.9268
    - type: f1_weighted
      value: 92.9268
    - type: ap
      value: 89.2255
    - type: ap_weighted
      value: 89.2255
    - type: main_score
      value: 92.9424
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 53.09199999999999
    - type: f1
      value: 52.735299999999995
    - type: f1_weighted
      value: 52.735299999999995
    - type: main_score
      value: 53.09199999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna (default)
      type: mteb/arguana
      config: default
      split: test
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
    metrics:
    - type: ndcg_at_1
      value: 31.791999999999998
    - type: ndcg_at_3
      value: 47.205999999999996
    - type: ndcg_at_5
      value: 51.842999999999996
    - type: ndcg_at_10
      value: 56.614
    - type: ndcg_at_20
      value: 59.211999999999996
    - type: ndcg_at_100
      value: 60.148999999999994
    - type: ndcg_at_1000
      value: 60.231
    - type: map_at_1
      value: 31.791999999999998
    - type: map_at_3
      value: 43.35
    - type: map_at_5
      value: 45.928000000000004
    - type: map_at_10
      value: 47.929
    - type: map_at_20
      value: 48.674
    - type: map_at_100
      value: 48.825
    - type: map_at_1000
      value: 48.827999999999996
    - type: recall_at_1
      value: 31.791999999999998
    - type: recall_at_3
      value: 58.392999999999994
    - type: recall_at_5
      value: 69.63000000000001
    - type: recall_at_10
      value: 84.211
    - type: recall_at_20
      value: 94.23899999999999
    - type: recall_at_100
      value: 99.004
    - type: recall_at_1000
      value: 99.644
    - type: precision_at_1
      value: 31.791999999999998
    - type: precision_at_3
      value: 19.464000000000002
    - type: precision_at_5
      value: 13.926
    - type: precision_at_10
      value: 8.421
    - type: precision_at_20
      value: 4.712000000000001
    - type: precision_at_100
      value: 0.9900000000000001
    - type: precision_at_1000
      value: 0.1
    - type: mrr_at_1
      value: 32.4324
    - type: mrr_at_3
      value: 43.6463
    - type: mrr_at_5
      value: 46.1569
    - type: mrr_at_10
      value: 48.1582
    - type: mrr_at_20
      value: 48.9033
    - type: mrr_at_100
      value: 49.0537
    - type: mrr_at_1000
      value: 49.0569
    - type: nauc_ndcg_at_1_max
      value: -4.8705
    - type: nauc_ndcg_at_1_std
      value: -9.1757
    - type: nauc_ndcg_at_1_diff1
      value: 17.743000000000002
    - type: nauc_ndcg_at_3_max
      value: -3.916
    - type: nauc_ndcg_at_3_std
      value: -10.424
    - type: nauc_ndcg_at_3_diff1
      value: 12.3928
    - type: nauc_ndcg_at_5_max
      value: -2.5090000000000003
    - type: nauc_ndcg_at_5_std
      value: -10.1328
    - type: nauc_ndcg_at_5_diff1
      value: 13.3086
    - type: nauc_ndcg_at_10_max
      value: -1.4653
    - type: nauc_ndcg_at_10_std
      value: -9.3154
    - type: nauc_ndcg_at_10_diff1
      value: 13.7827
    - type: nauc_ndcg_at_20_max
      value: -2.4534000000000002
    - type: nauc_ndcg_at_20_std
      value: -9.0213
    - type: nauc_ndcg_at_20_diff1
      value: 13.764399999999998
    - type: nauc_ndcg_at_100_max
      value: -2.8207
    - type: nauc_ndcg_at_100_std
      value: -9.0492
    - type: nauc_ndcg_at_100_diff1
      value: 14.3422
    - type: nauc_ndcg_at_1000_max
      value: -3.0108
    - type: nauc_ndcg_at_1000_std
      value: -9.2507
    - type: nauc_ndcg_at_1000_diff1
      value: 14.2345
    - type: nauc_map_at_1_max
      value: -4.8705
    - type: nauc_map_at_1_std
      value: -9.1757
    - type: nauc_map_at_1_diff1
      value: 17.743000000000002
    - type: nauc_map_at_3_max
      value: -4.2874
    - type: nauc_map_at_3_std
      value: -10.1539
    - type: nauc_map_at_3_diff1
      value: 13.6101
    - type: nauc_map_at_5_max
      value: -3.5856
    - type: nauc_map_at_5_std
      value: -9.9657
    - type: nauc_map_at_5_diff1
      value: 14.1354
    - type: nauc_map_at_10_max
      value: -3.2553
    - type: nauc_map_at_10_std
      value: -9.6771
    - type: nauc_map_at_10_diff1
      value: 14.402899999999999
    - type: nauc_map_at_20_max
      value: -3.5541000000000005
    - type: nauc_map_at_20_std
      value: -9.6286
    - type: nauc_map_at_20_diff1
      value: 14.3927
    - type: nauc_map_at_100_max
      value: -3.5811999999999995
    - type: nauc_map_at_100_std
      value: -9.6278
    - type: nauc_map_at_100_diff1
      value: 14.4922
    - type: nauc_map_at_1000_max
      value: -3.5881000000000003
    - type: nauc_map_at_1000_std
      value: -9.6335
    - type: nauc_map_at_1000_diff1
      value: 14.488400000000002
    - type: nauc_recall_at_1_max
      value: -4.8705
    - type: nauc_recall_at_1_std
      value: -9.1757
    - type: nauc_recall_at_1_diff1
      value: 17.743000000000002
    - type: nauc_recall_at_3_max
      value: -2.7195
    - type: nauc_recall_at_3_std
      value: -11.2342
    - type: nauc_recall_at_3_diff1
      value: 8.7116
    - type: nauc_recall_at_5_max
      value: 1.7492
    - type: nauc_recall_at_5_std
      value: -10.6963
    - type: nauc_recall_at_5_diff1
      value: 10.569
    - type: nauc_recall_at_10_max
      value: 10.7433
    - type: nauc_recall_at_10_std
      value: -6.339599999999999
    - type: nauc_recall_at_10_diff1
      value: 10.6275
    - type: nauc_recall_at_20_max
      value: 14.802499999999998
    - type: nauc_recall_at_20_std
      value: 3.9196
    - type: nauc_recall_at_20_diff1
      value: 6.0286
    - type: nauc_recall_at_100_max
      value: 40.8859
    - type: nauc_recall_at_100_std
      value: 57.965500000000006
    - type: nauc_recall_at_100_diff1
      value: 30.7703
    - type: nauc_recall_at_1000_max
      value: 24.2175
    - type: nauc_recall_at_1000_std
      value: 70.9234
    - type: nauc_recall_at_1000_diff1
      value: 5.9272
    - type: nauc_precision_at_1_max
      value: -4.8705
    - type: nauc_precision_at_1_std
      value: -9.1757
    - type: nauc_precision_at_1_diff1
      value: 17.743000000000002
    - type: nauc_precision_at_3_max
      value: -2.7195
    - type: nauc_precision_at_3_std
      value: -11.2342
    - type: nauc_precision_at_3_diff1
      value: 8.7116
    - type: nauc_precision_at_5_max
      value: 1.7492
    - type: nauc_precision_at_5_std
      value: -10.6963
    - type: nauc_precision_at_5_diff1
      value: 10.569
    - type: nauc_precision_at_10_max
      value: 10.7433
    - type: nauc_precision_at_10_std
      value: -6.339599999999999
    - type: nauc_precision_at_10_diff1
      value: 10.6275
    - type: nauc_precision_at_20_max
      value: 14.802499999999998
    - type: nauc_precision_at_20_std
      value: 3.9196
    - type: nauc_precision_at_20_diff1
      value: 6.0286
    - type: nauc_precision_at_100_max
      value: 40.8859
    - type: nauc_precision_at_100_std
      value: 57.965500000000006
    - type: nauc_precision_at_100_diff1
      value: 30.7703
    - type: nauc_precision_at_1000_max
      value: 24.2175
    - type: nauc_precision_at_1000_std
      value: 70.9234
    - type: nauc_precision_at_1000_diff1
      value: 5.9272
    - type: nauc_mrr_at_1_max
      value: -5.1491
    - type: nauc_mrr_at_1_std
      value: -8.8127
    - type: nauc_mrr_at_1_diff1
      value: 15.857099999999999
    - type: nauc_mrr_at_3_max
      value: -5.083200000000001
    - type: nauc_mrr_at_3_std
      value: -9.8967
    - type: nauc_mrr_at_3_diff1
      value: 11.9042
    - type: nauc_mrr_at_5_max
      value: -4.530399999999999
    - type: nauc_mrr_at_5_std
      value: -9.900599999999999
    - type: nauc_mrr_at_5_diff1
      value: 12.2957
    - type: nauc_mrr_at_10_max
      value: -4.2387
    - type: nauc_mrr_at_10_std
      value: -9.6123
    - type: nauc_mrr_at_10_diff1
      value: 12.4769
    - type: nauc_mrr_at_20_max
      value: -4.5254
    - type: nauc_mrr_at_20_std
      value: -9.5502
    - type: nauc_mrr_at_20_diff1
      value: 12.4674
    - type: nauc_mrr_at_100_max
      value: -4.5576
    - type: nauc_mrr_at_100_std
      value: -9.549100000000001
    - type: nauc_mrr_at_100_diff1
      value: 12.556899999999999
    - type: nauc_mrr_at_1000_max
      value: -4.5645999999999995
    - type: nauc_mrr_at_1000_std
      value: -9.5548
    - type: nauc_mrr_at_1000_diff1
      value: 12.552900000000001
    - type: main_score
      value: 56.614
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P (default)
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 47.2524
    - type: v_measure_std
      value: 13.7772
    - type: main_score
      value: 47.2524
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S (default)
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 40.7262
    - type: v_measure_std
      value: 14.125499999999999
    - type: main_score
      value: 40.7262
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions (default)
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 61.57319999999999
    - type: mrr
      value: 74.6714
    - type: nAUC_map_max
      value: 21.8916
    - type: nAUC_map_std
      value: 17.9941
    - type: nAUC_map_diff1
      value: 1.5548
    - type: nAUC_mrr_max
      value: 34.139399999999995
    - type: nAUC_mrr_std
      value: 18.133499999999998
    - type: nAUC_mrr_diff1
      value: 13.3597
    - type: main_score
      value: 61.57319999999999
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES (default)
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: pearson
      value: 86.7849
    - type: spearman
      value: 84.7302
    - type: cosine_pearson
      value: 86.7849
    - type: cosine_spearman
      value: 84.7302
    - type: manhattan_pearson
      value: 84.48179999999999
    - type: manhattan_spearman
      value: 84.0507
    - type: euclidean_pearson
      value: 84.8613
    - type: euclidean_spearman
      value: 84.6266
    - type: main_score
      value: 84.7302
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification (default)
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 85.7175
    - type: f1
      value: 85.6781
    - type: f1_weighted
      value: 85.6781
    - type: main_score
      value: 85.7175
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P (default)
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 40.0588
    - type: v_measure_std
      value: 0.8872
    - type: main_score
      value: 40.0588
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S (default)
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 36.382799999999996
    - type: v_measure_std
      value: 1.167
    - type: main_score
      value: 36.382799999999996
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval (default)
      type: mteb/cqadupstack-android
      config: default
      split: test
      revision: f46a197baaae43b4f621051089b82a364682dfeb
    metrics:
    - type: ndcg_at_1
      value: 37.196
    - type: ndcg_at_3
      value: 42.778
    - type: ndcg_at_5
      value: 45.013999999999996
    - type: ndcg_at_10
      value: 47.973
    - type: ndcg_at_20
      value: 50.141000000000005
    - type: ndcg_at_100
      value: 53.31399999999999
    - type: ndcg_at_1000
      value: 55.52
    - type: map_at_1
      value: 30.598
    - type: map_at_3
      value: 38.173
    - type: map_at_5
      value: 40.093
    - type: map_at_10
      value: 41.686
    - type: map_at_20
      value: 42.522
    - type: map_at_100
      value: 43.191
    - type: map_at_1000
      value: 43.328
    - type: recall_at_1
      value: 30.598
    - type: recall_at_3
      value: 45.019999999999996
    - type: recall_at_5
      value: 51.357
    - type: recall_at_10
      value: 60.260000000000005
    - type: recall_at_20
      value: 67.93299999999999
    - type: recall_at_100
      value: 82.07
    - type: recall_at_1000
      value: 96.345
    - type: precision_at_1
      value: 37.196
    - type: precision_at_3
      value: 20.552999999999997
    - type: precision_at_5
      value: 14.707
    - type: precision_at_10
      value: 9.213000000000001
    - type: precision_at_20
      value: 5.522
    - type: precision_at_100
      value: 1.4949999999999999
    - type: precision_at_1000
      value: 0.198
    - type: mrr_at_1
      value: 37.196
    - type: mrr_at_3
      value: 44.4683
    - type: mrr_at_5
      value: 45.9776
    - type: mrr_at_10
      value: 47.1884
    - type: mrr_at_20
      value: 47.6763
    - type: mrr_at_100
      value: 47.957
    - type: mrr_at_1000
      value: 48.0103
    - type: nauc_ndcg_at_1_max
      value: 38.1056
    - type: nauc_ndcg_at_1_std
      value: -1.5731
    - type: nauc_ndcg_at_1_diff1
      value: 52.3965
    - type: nauc_ndcg_at_3_max
      value: 35.8655
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      value: 0.2057
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      value: 46.299600000000005
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      value: 1.542
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    - type: nauc_ndcg_at_10_max
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      value: 2.7934
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      value: 1.958
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      value: 48.1781
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      value: 48.166399999999996
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      value: -0.7946
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      value: 55.799699999999994
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      value: 0.7802
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      value: 42.728100000000005
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      value: 3.803
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      value: 43.0721
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  - task:
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  - task:
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  - task:
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  - task:
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    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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  - task:
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      type: mteb/msmarco
      config: default
      split: dev
      revision: c5a29a104738b98a9e76336939199e264163d4a0
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  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
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      value: 95.1756
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
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      value: 76.7601
    - type: f1
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    - type: f1_weighted
      value: 79.6763
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      value: 76.7601
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
    - type: accuracy
      value: 75.3262
    - type: f1
      value: 72.7127
    - type: f1_weighted
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  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
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      value: 80.2152
    - type: f1
      value: 80.06490000000001
    - type: f1_weighted
      value: 80.26830000000001
    - type: main_score
      value: 80.2152
  - 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: 1.6771999999999998
    - type: main_score
      value: 35.098800000000004
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S (default)
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
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      value: 32.9033
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      value: 1.4976
    - type: main_score
      value: 32.9033
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking (default)
      type: mteb/mind_small
      config: default
      split: test
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
    metrics:
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    - type: mrr
      value: 33.3476
    - type: nAUC_map_max
      value: -19.1719
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      value: 0.1456
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      value: 14.8056
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      value: -13.6261
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      value: 1.5634
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      value: 13.8537
    - type: main_score
      value: 32.1551
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus (default)
      type: mteb/nfcorpus
      config: default
      split: test
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
    metrics:
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      value: 48.607
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      value: 38.989000000000004
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      value: 10.333
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      value: 13.79
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    - type: recall_at_1
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      value: 13.376
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    - type: recall_at_1000
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    - type: nauc_recall_at_10_max
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    - type: nauc_precision_at_5_diff1
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    - type: nauc_precision_at_20_max
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    - type: nauc_precision_at_20_std
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    - type: nauc_precision_at_1000_diff1
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    - type: nauc_mrr_at_1_max
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    - type: nauc_mrr_at_3_max
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    - type: nauc_mrr_at_3_std
      value: -38.8782
    - type: nauc_mrr_at_3_diff1
      value: 76.4216
    - type: nauc_mrr_at_5_max
      value: 39.4401
    - type: nauc_mrr_at_5_std
      value: -39.0877
    - type: nauc_mrr_at_5_diff1
      value: 76.6241
    - type: nauc_mrr_at_10_max
      value: 39.4302
    - type: nauc_mrr_at_10_std
      value: -38.798500000000004
    - type: nauc_mrr_at_10_diff1
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    - type: nauc_mrr_at_20_max
      value: 39.4583
    - type: nauc_mrr_at_20_std
      value: -38.6556
    - type: nauc_mrr_at_20_diff1
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    - type: nauc_mrr_at_100_max
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    - type: nauc_mrr_at_100_std
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    - type: nauc_mrr_at_1000_max
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    - type: nauc_mrr_at_1000_std
      value: -38.6493
    - type: nauc_mrr_at_1000_diff1
      value: 76.7332
    - type: main_score
      value: 87.774
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering (default)
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 56.1172
    - type: v_measure_std
      value: 4.1773
    - type: main_score
      value: 56.1172
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P (default)
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
    metrics:
    - type: v_measure
      value: 65.1415
    - type: v_measure_std
      value: 12.804499999999999
    - type: main_score
      value: 65.1415
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS (default)
      type: mteb/scidocs
      config: default
      split: test
      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
    metrics:
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      value: 24.3
    - type: ndcg_at_3
      value: 20.275000000000002
    - type: ndcg_at_5
      value: 17.712
    - type: ndcg_at_10
      value: 21.317
    - type: ndcg_at_20
      value: 24.355
    - type: ndcg_at_100
      value: 30.115
    - type: ndcg_at_1000
      value: 35.803000000000004
    - type: map_at_1
      value: 4.928
    - type: map_at_3
      value: 9.15
    - type: map_at_5
      value: 10.914
    - type: map_at_10
      value: 12.808
    - type: map_at_20
      value: 13.968
    - type: map_at_100
      value: 15.113999999999999
    - type: map_at_1000
      value: 15.463
    - type: recall_at_1
      value: 4.928
    - type: recall_at_3
      value: 11.643
    - type: recall_at_5
      value: 15.873000000000001
    - type: recall_at_10
      value: 22.472
    - type: recall_at_20
      value: 29.612
    - type: recall_at_100
      value: 48.323
    - type: recall_at_1000
      value: 75.98
    - type: precision_at_1
      value: 24.3
    - type: precision_at_3
      value: 19.167
    - type: precision_at_5
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    - type: precision_at_10
      value: 11.08
    - type: precision_at_20
      value: 7.3
    - type: precision_at_100
      value: 2.382
    - type: precision_at_1000
      value: 0.374
    - type: mrr_at_1
      value: 24.3
    - type: mrr_at_3
      value: 32.2
    - type: mrr_at_5
      value: 34.25
    - type: mrr_at_10
      value: 35.6749
    - type: mrr_at_20
      value: 36.326
    - 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
      value: 8.174299999999999
    - type: nauc_ndcg_at_1_diff1
      value: 20.6462
    - type: nauc_ndcg_at_3_max
      value: 26.8438
    - type: nauc_ndcg_at_3_std
      value: 12.5593
    - type: nauc_ndcg_at_3_diff1
      value: 16.1086
    - type: nauc_ndcg_at_5_max
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    - type: nauc_ndcg_at_5_std
      value: 15.6557
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    - type: nauc_ndcg_at_10_max
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    - type: nauc_ndcg_at_10_std
      value: 18.8626
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    - type: nauc_ndcg_at_20_max
      value: 30.128
    - type: nauc_ndcg_at_20_std
      value: 22.264400000000002
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      value: 12.0184
    - type: nauc_ndcg_at_100_max
      value: 31.5267
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      value: 27.067000000000004
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    - type: nauc_ndcg_at_1000_max
      value: 31.4219
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      value: 26.9349
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    - type: nauc_map_at_1_max
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      value: 8.1475
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      value: 20.5305
    - type: nauc_map_at_3_max
      value: 27.1559
    - type: nauc_map_at_3_std
      value: 10.626199999999999
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      value: 17.2136
    - type: nauc_map_at_5_max
      value: 28.149800000000003
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      value: 13.549800000000001
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      value: 14.9097
    - type: nauc_map_at_10_max
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    - type: nauc_map_at_10_std
      value: 16.6128
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      value: 13.232199999999999
    - type: nauc_map_at_20_max
      value: 29.8518
    - type: nauc_map_at_20_std
      value: 18.9557
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      value: 13.1546
    - type: nauc_map_at_100_max
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    - type: nauc_map_at_100_std
      value: 20.7314
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      value: 13.3874
    - type: nauc_map_at_1000_max
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    - type: nauc_map_at_1000_std
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      value: 13.3881
    - type: nauc_recall_at_1_max
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    - type: nauc_recall_at_1_std
      value: 8.1475
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      value: 20.5305
    - type: nauc_recall_at_3_max
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      value: 13.8467
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    - type: nauc_recall_at_10_diff1
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    - type: nauc_recall_at_20_max
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    - type: nauc_recall_at_20_std
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    - type: nauc_recall_at_100_max
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    - type: nauc_recall_at_100_std
      value: 37.9775
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    - type: nauc_recall_at_1000_max
      value: 24.3488
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    - type: nauc_precision_at_1_max
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      value: 8.174299999999999
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      value: 20.6462
    - type: nauc_precision_at_3_max
      value: 27.107300000000002
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      value: 13.9846
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      value: 14.025199999999998
    - type: nauc_precision_at_5_max
      value: 27.940199999999997
    - type: nauc_precision_at_5_std
      value: 18.523500000000002
    - type: nauc_precision_at_5_diff1
      value: 10.6452
    - type: nauc_precision_at_10_max
      value: 28.9679
    - type: nauc_precision_at_10_std
      value: 23.2788
    - type: nauc_precision_at_10_diff1
      value: 7.0396
    - type: nauc_precision_at_20_max
      value: 28.799200000000003
    - type: nauc_precision_at_20_std
      value: 28.2269
    - type: nauc_precision_at_20_diff1
      value: 6.6255999999999995
    - type: nauc_precision_at_100_max
      value: 28.6629
    - type: nauc_precision_at_100_std
      value: 37.5551
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      value: 7.858999999999999
    - type: nauc_precision_at_1000_max
      value: 25.0545
    - type: nauc_precision_at_1000_std
      value: 37.301899999999996
    - type: nauc_precision_at_1000_diff1
      value: 7.5589
    - type: nauc_mrr_at_1_max
      value: 24.935499999999998
    - type: nauc_mrr_at_1_std
      value: 8.174299999999999
    - type: nauc_mrr_at_1_diff1
      value: 20.6462
    - type: nauc_mrr_at_3_max
      value: 26.037
    - type: nauc_mrr_at_3_std
      value: 13.3379
    - type: nauc_mrr_at_3_diff1
      value: 16.713
    - type: nauc_mrr_at_5_max
      value: 26.512200000000004
    - type: nauc_mrr_at_5_std
      value: 14.1804
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      value: 17.1186
    - type: nauc_mrr_at_10_max
      value: 26.7938
    - type: nauc_mrr_at_10_std
      value: 14.458699999999999
    - type: nauc_mrr_at_10_diff1
      value: 16.531299999999998
    - type: nauc_mrr_at_20_max
      value: 26.628
    - type: nauc_mrr_at_20_std
      value: 14.593200000000001
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      value: 16.4492
    - type: nauc_mrr_at_100_max
      value: 26.6627
    - type: nauc_mrr_at_100_std
      value: 14.5648
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      value: 16.614
    - type: nauc_mrr_at_1000_max
      value: 26.6506
    - type: nauc_mrr_at_1000_std
      value: 14.5124
    - type: nauc_mrr_at_1000_diff1
      value: 16.6315
    - type: main_score
      value: 21.317
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R (default)
      type: mteb/sickr-sts
      config: default
      split: test
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
    metrics:
    - type: pearson
      value: 85.2236
    - type: spearman
      value: 79.55109999999999
    - type: cosine_pearson
      value: 85.2236
    - type: cosine_spearman
      value: 79.55109999999999
    - type: manhattan_pearson
      value: 82.441
    - type: manhattan_spearman
      value: 79.65100000000001
    - type: euclidean_pearson
      value: 82.461
    - type: euclidean_spearman
      value: 79.58460000000001
    - type: main_score
      value: 79.55109999999999
  - task:
      type: STS
    dataset:
      name: MTEB STS12 (default)
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: pearson
      value: 82.6754
    - type: spearman
      value: 74.3345
    - type: cosine_pearson
      value: 82.6754
    - type: cosine_spearman
      value: 74.3336
    - type: manhattan_pearson
      value: 78.64330000000001
    - type: manhattan_spearman
      value: 74.23769999999999
    - type: euclidean_pearson
      value: 78.589
    - type: euclidean_spearman
      value: 74.1178
    - type: main_score
      value: 74.3336
  - task:
      type: STS
    dataset:
      name: MTEB STS13 (default)
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: pearson
      value: 82.4438
    - type: spearman
      value: 83.5213
    - type: cosine_pearson
      value: 82.4438
    - type: cosine_spearman
      value: 83.5213
    - type: manhattan_pearson
      value: 83.1769
    - type: manhattan_spearman
      value: 83.29039999999999
    - type: euclidean_pearson
      value: 83.0053
    - type: euclidean_spearman
      value: 83.1047
    - type: main_score
      value: 83.5213
  - task:
      type: STS
    dataset:
      name: MTEB STS14 (default)
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: pearson
      value: 82.8843
    - type: spearman
      value: 80.6162
    - type: cosine_pearson
      value: 82.8843
    - type: cosine_spearman
      value: 80.6161
    - type: manhattan_pearson
      value: 82.446
    - type: manhattan_spearman
      value: 80.5926
    - type: euclidean_pearson
      value: 82.33840000000001
    - type: euclidean_spearman
      value: 80.4619
    - type: main_score
      value: 80.6161
  - task:
      type: STS
    dataset:
      name: MTEB STS15 (default)
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: pearson
      value: 85.7467
    - type: spearman
      value: 86.85690000000001
    - type: cosine_pearson
      value: 85.7467
    - type: cosine_spearman
      value: 86.85690000000001
    - type: manhattan_pearson
      value: 86.469
    - type: manhattan_spearman
      value: 86.751
    - type: euclidean_pearson
      value: 86.4531
    - type: euclidean_spearman
      value: 86.7053
    - type: main_score
      value: 86.85690000000001
  - task:
      type: STS
    dataset:
      name: MTEB STS16 (default)
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: pearson
      value: 84.143
    - type: spearman
      value: 85.4366
    - type: cosine_pearson
      value: 84.143
    - type: cosine_spearman
      value: 85.4367
    - type: manhattan_pearson
      value: 84.6762
    - type: manhattan_spearman
      value: 85.1846
    - type: euclidean_pearson
      value: 84.6233
    - type: euclidean_spearman
      value: 85.1252
    - type: main_score
      value: 85.4367
  - 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: 89.10820000000001
    - type: spearman
      value: 89.7621
    - type: cosine_pearson
      value: 89.10820000000001
    - type: cosine_spearman
      value: 89.7621
    - type: manhattan_pearson
      value: 89.3624
    - type: manhattan_spearman
      value: 89.6515
    - type: euclidean_pearson
      value: 89.3729
    - type: euclidean_spearman
      value: 89.6836
    - type: main_score
      value: 89.7621
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: pearson
      value: 68.44019999999999
    - type: spearman
      value: 68.3308
    - type: cosine_pearson
      value: 68.4403
    - type: cosine_spearman
      value: 68.3308
    - type: manhattan_pearson
      value: 69.8199
    - type: manhattan_spearman
      value: 68.754
    - type: euclidean_pearson
      value: 69.5629
    - type: euclidean_spearman
      value: 68.55630000000001
    - type: main_score
      value: 68.3308
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark (default)
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: pearson
      value: 86.0748
    - type: spearman
      value: 86.2486
    - type: cosine_pearson
      value: 86.0748
    - type: cosine_spearman
      value: 86.2486
    - type: manhattan_pearson
      value: 86.2053
    - type: manhattan_spearman
      value: 86.0544
    - type: euclidean_pearson
      value: 86.1504
    - type: euclidean_spearman
      value: 85.985
    - type: main_score
      value: 86.2486
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR (default)
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 84.4152
    - type: mrr
      value: 95.4755
    - type: nAUC_map_max
      value: 55.565799999999996
    - type: nAUC_map_std
      value: 71.4307
    - type: nAUC_map_diff1
      value: 1.0619
    - type: nAUC_mrr_max
      value: 87.11959999999999
    - type: nAUC_mrr_std
      value: 82.7146
    - type: nAUC_mrr_diff1
      value: 44.2384
    - type: main_score
      value: 84.4152
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact (default)
      type: mteb/scifact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
    metrics:
    - type: ndcg_at_1
      value: 62.333000000000006
    - type: ndcg_at_3
      value: 67.297
    - type: ndcg_at_5
      value: 69.24900000000001
    - type: ndcg_at_10
      value: 72.63199999999999
    - type: ndcg_at_20
      value: 73.6
    - type: ndcg_at_100
      value: 74.888
    - type: ndcg_at_1000
      value: 75.452
    - type: map_at_1
      value: 59.733000000000004
    - type: map_at_3
      value: 65.194
    - type: map_at_5
      value: 66.616
    - type: map_at_10
      value: 68.158
    - type: map_at_20
      value: 68.464
    - type: map_at_100
      value: 68.685
    - type: map_at_1000
      value: 68.704
    - type: recall_at_1
      value: 59.733000000000004
    - type: recall_at_3
      value: 70.61699999999999
    - type: recall_at_5
      value: 75.5
    - type: recall_at_10
      value: 85.51100000000001
    - type: recall_at_20
      value: 89.133
    - type: recall_at_100
      value: 95.5
    - type: recall_at_1000
      value: 100
    - type: precision_at_1
      value: 62.333000000000006
    - type: precision_at_3
      value: 25.667
    - type: precision_at_5
      value: 16.8
    - type: precision_at_10
      value: 9.633
    - type: precision_at_20
      value: 5.050000000000001
    - type: precision_at_100
      value: 1.083
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: mrr_at_1
      value: 62.3333
    - type: mrr_at_3
      value: 67.11110000000001
    - type: mrr_at_5
      value: 68.0111
    - type: mrr_at_10
      value: 69.243
    - type: mrr_at_20
      value: 69.4702
    - type: mrr_at_100
      value: 69.661
    - type: mrr_at_1000
      value: 69.6798
    - type: nauc_ndcg_at_1_max
      value: 54.49850000000001
    - type: nauc_ndcg_at_1_std
      value: 7.1119
    - type: nauc_ndcg_at_1_diff1
      value: 66.1422
    - type: nauc_ndcg_at_3_max
      value: 56.4064
    - type: nauc_ndcg_at_3_std
      value: 5.8438
    - type: nauc_ndcg_at_3_diff1
      value: 63.8497
    - type: nauc_ndcg_at_5_max
      value: 57.1304
    - type: nauc_ndcg_at_5_std
      value: 7.220600000000001
    - type: nauc_ndcg_at_5_diff1
      value: 63.31250000000001
    - type: nauc_ndcg_at_10_max
      value: 55.722300000000004
    - type: nauc_ndcg_at_10_std
      value: 9.1649
    - type: nauc_ndcg_at_10_diff1
      value: 60.6626
    - type: nauc_ndcg_at_20_max
      value: 55.945299999999996
    - type: nauc_ndcg_at_20_std
      value: 9.843200000000001
    - type: nauc_ndcg_at_20_diff1
      value: 60.113099999999996
    - type: nauc_ndcg_at_100_max
      value: 56.1439
    - type: nauc_ndcg_at_100_std
      value: 10.7972
    - type: nauc_ndcg_at_100_diff1
      value: 60.854200000000006
    - type: nauc_ndcg_at_1000_max
      value: 56.2434
    - type: nauc_ndcg_at_1000_std
      value: 9.697899999999999
    - type: nauc_ndcg_at_1000_diff1
      value: 61.719
    - type: nauc_map_at_1_max
      value: 50.9749
    - type: nauc_map_at_1_std
      value: 3.6609999999999996
    - type: nauc_map_at_1_diff1
      value: 67.3719
    - type: nauc_map_at_3_max
      value: 54.31009999999999
    - type: nauc_map_at_3_std
      value: 4.3235
    - type: nauc_map_at_3_diff1
      value: 64.8984
    - type: nauc_map_at_5_max
      value: 55.328599999999994
    - type: nauc_map_at_5_std
      value: 5.959099999999999
    - type: nauc_map_at_5_diff1
      value: 64.0263
    - type: nauc_map_at_10_max
      value: 55.1577
    - type: nauc_map_at_10_std
      value: 7.147399999999999
    - type: nauc_map_at_10_diff1
      value: 62.943000000000005
    - type: nauc_map_at_20_max
      value: 55.367900000000006
    - type: nauc_map_at_20_std
      value: 7.5756000000000006
    - type: nauc_map_at_20_diff1
      value: 62.7916
    - type: nauc_map_at_100_max
      value: 55.3427
    - type: nauc_map_at_100_std
      value: 7.645499999999999
    - type: nauc_map_at_100_diff1
      value: 62.89940000000001
    - type: nauc_map_at_1000_max
      value: 55.35020000000001
    - type: nauc_map_at_1000_std
      value: 7.6221
    - type: nauc_map_at_1000_diff1
      value: 62.930299999999995
    - type: nauc_recall_at_1_max
      value: 50.9749
    - type: nauc_recall_at_1_std
      value: 3.6609999999999996
    - type: nauc_recall_at_1_diff1
      value: 67.3719
    - type: nauc_recall_at_3_max
      value: 56.184
    - type: nauc_recall_at_3_std
      value: 1.8581
    - type: nauc_recall_at_3_diff1
      value: 63.3395
    - type: nauc_recall_at_5_max
      value: 59.3021
    - type: nauc_recall_at_5_std
      value: 6.4342
    - type: nauc_recall_at_5_diff1
      value: 61.8267
    - type: nauc_recall_at_10_max
      value: 51.8488
    - type: nauc_recall_at_10_std
      value: 13.397
    - type: nauc_recall_at_10_diff1
      value: 47.7098
    - type: nauc_recall_at_20_max
      value: 51.607499999999995
    - type: nauc_recall_at_20_std
      value: 17.8583
    - type: nauc_recall_at_20_diff1
      value: 40.2701
    - type: nauc_recall_at_100_max
      value: 54.1844
    - type: nauc_recall_at_100_std
      value: 53.411500000000004
    - type: nauc_recall_at_100_diff1
      value: 31.0043
    - type: nauc_recall_at_1000_max
    - type: nauc_recall_at_1000_std
    - type: nauc_recall_at_1000_diff1
    - type: nauc_precision_at_1_max
      value: 54.49850000000001
    - type: nauc_precision_at_1_std
      value: 7.1119
    - type: nauc_precision_at_1_diff1
      value: 66.1422
    - type: nauc_precision_at_3_max
      value: 52.115
    - type: nauc_precision_at_3_std
      value: 16.1809
    - type: nauc_precision_at_3_diff1
      value: 41.6736
    - type: nauc_precision_at_5_max
      value: 46.3365
    - type: nauc_precision_at_5_std
      value: 22.7022
    - type: nauc_precision_at_5_diff1
      value: 25.564500000000002
    - type: nauc_precision_at_10_max
      value: 31.7504
    - type: nauc_precision_at_10_std
      value: 31.063499999999998
    - type: nauc_precision_at_10_diff1
      value: 0.61
    - type: nauc_precision_at_20_max
      value: 27.0162
    - type: nauc_precision_at_20_std
      value: 35.5844
    - type: nauc_precision_at_20_diff1
      value: -8.559899999999999
    - type: nauc_precision_at_100_max
      value: 17.9369
    - type: nauc_precision_at_100_std
      value: 45.360299999999995
    - type: nauc_precision_at_100_diff1
      value: -21.3734
    - type: nauc_precision_at_1000_max
      value: 9.6015
    - type: nauc_precision_at_1000_std
      value: 41.6207
    - type: nauc_precision_at_1000_diff1
      value: -31.4964
    - type: nauc_mrr_at_1_max
      value: 54.49850000000001
    - type: nauc_mrr_at_1_std
      value: 7.1119
    - type: nauc_mrr_at_1_diff1
      value: 66.1422
    - type: nauc_mrr_at_3_max
      value: 57.52589999999999
    - type: nauc_mrr_at_3_std
      value: 8.605400000000001
    - type: nauc_mrr_at_3_diff1
      value: 63.4207
    - type: nauc_mrr_at_5_max
      value: 57.809900000000006
    - type: nauc_mrr_at_5_std
      value: 9.2631
    - type: nauc_mrr_at_5_diff1
      value: 63.4016
    - type: nauc_mrr_at_10_max
      value: 57.02199999999999
    - type: nauc_mrr_at_10_std
      value: 9.542100000000001
    - type: nauc_mrr_at_10_diff1
      value: 62.527
    - type: nauc_mrr_at_20_max
      value: 56.942800000000005
    - type: nauc_mrr_at_20_std
      value: 9.3838
    - type: nauc_mrr_at_20_diff1
      value: 62.3991
    - type: nauc_mrr_at_100_max
      value: 56.9339
    - type: nauc_mrr_at_100_std
      value: 9.4351
    - type: nauc_mrr_at_100_diff1
      value: 62.5023
    - type: nauc_mrr_at_1000_max
      value: 56.943200000000004
    - type: nauc_mrr_at_1000_std
      value: 9.4141
    - type: nauc_mrr_at_1000_diff1
      value: 62.5335
    - type: main_score
      value: 72.63199999999999
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions (default)
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: similarity_accuracy
      value: 99.801
    - type: similarity_accuracy_threshold
      value: 79.4678
    - type: similarity_f1
      value: 89.64450000000001
    - type: similarity_f1_threshold
      value: 79.4678
    - type: similarity_precision
      value: 92.4548
    - type: similarity_recall
      value: 87
    - type: similarity_ap
      value: 95.45320000000001
    - type: cosine_accuracy
      value: 99.801
    - type: cosine_accuracy_threshold
      value: 79.4678
    - type: cosine_f1
      value: 89.64450000000001
    - type: cosine_f1_threshold
      value: 79.4678
    - type: cosine_precision
      value: 92.4548
    - type: cosine_recall
      value: 87
    - type: cosine_ap
      value: 95.45320000000001
    - type: manhattan_accuracy
      value: 99.798
    - type: manhattan_accuracy_threshold
      value: 23357.8888
    - type: manhattan_f1
      value: 89.4737
    - type: manhattan_f1_threshold
      value: 23387.7502
    - type: manhattan_precision
      value: 92.4307
    - type: manhattan_recall
      value: 86.7
    - type: manhattan_ap
      value: 95.4849
    - type: euclidean_accuracy
      value: 99.795
    - type: euclidean_accuracy_threshold
      value: 1058.165
    - type: euclidean_f1
      value: 89.35300000000001
    - type: euclidean_f1_threshold
      value: 1085.2129
    - type: euclidean_precision
      value: 91.0696
    - type: euclidean_recall
      value: 87.7
    - type: euclidean_ap
      value: 95.4203
    - type: dot_accuracy
      value: 99.79599999999999
    - type: dot_accuracy_threshold
      value: 22427.3651
    - type: dot_f1
      value: 89.6311
    - type: dot_f1_threshold
      value: 21924.3118
    - type: dot_precision
      value: 89.3638
    - type: dot_recall
      value: 89.9
    - type: dot_ap
      value: 95.2676
    - type: max_accuracy
      value: 99.801
    - type: max_f1
      value: 89.64450000000001
    - type: max_precision
      value: 92.4548
    - type: max_recall
      value: 89.9
    - type: max_ap
      value: 95.4849
    - type: main_score
      value: 95.4849
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering (default)
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 66.74849999999999
    - type: v_measure_std
      value: 5.3791
    - type: main_score
      value: 66.74849999999999
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P (default)
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 36.4297
    - type: v_measure_std
      value: 1.4817
    - type: main_score
      value: 36.4297
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions (default)
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 49.791000000000004
    - type: mrr
      value: 50.559200000000004
    - type: nAUC_map_max
      value: 12.2774
    - type: nAUC_map_std
      value: 8.179599999999999
    - type: nAUC_map_diff1
      value: 34.4755
    - type: nAUC_mrr_max
      value: 12.5622
    - type: nAUC_mrr_std
      value: 8.7019
    - type: nAUC_mrr_diff1
      value: 34.4394
    - type: main_score
      value: 49.791000000000004
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval (default)
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: pearson
      value: 29.8817
    - type: spearman
      value: 30.4779
    - type: cosine_spearman
      value: 30.4779
    - type: cosine_pearson
      value: 29.8817
    - type: dot_spearman
      value: 29.801499999999997
    - type: dot_pearson
      value: 30.2519
    - type: main_score
      value: 30.4779
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID (default)
      type: mteb/trec-covid
      config: default
      split: test
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
    metrics:
    - type: ndcg_at_1
      value: 83
    - type: ndcg_at_3
      value: 79.061
    - type: ndcg_at_5
      value: 74.477
    - type: ndcg_at_10
      value: 74.02499999999999
    - type: ndcg_at_20
      value: 71.031
    - type: ndcg_at_100
      value: 56.143
    - type: ndcg_at_1000
      value: 52.11
    - type: map_at_1
      value: 0.243
    - type: map_at_3
      value: 0.642
    - type: map_at_5
      value: 0.997
    - type: map_at_10
      value: 1.848
    - type: map_at_20
      value: 3.354
    - type: map_at_100
      value: 10.297
    - type: map_at_1000
      value: 25.901999999999997
    - type: recall_at_1
      value: 0.243
    - type: recall_at_3
      value: 0.668
    - type: recall_at_5
      value: 1.0630000000000002
    - type: recall_at_10
      value: 2.09
    - type: recall_at_20
      value: 3.92
    - type: recall_at_100
      value: 13.83
    - type: recall_at_1000
      value: 49.292
    - type: precision_at_1
      value: 88
    - type: precision_at_3
      value: 81.333
    - type: precision_at_5
      value: 76.4
    - type: precision_at_10
      value: 77.60000000000001
    - type: precision_at_20
      value: 74
    - type: precision_at_100
      value: 57.04
    - type: precision_at_1000
      value: 22.962
    - type: mrr_at_1
      value: 86
    - type: mrr_at_3
      value: 92
    - type: mrr_at_5
      value: 92.4
    - type: mrr_at_10
      value: 92.4
    - type: mrr_at_20
      value: 92.4
    - type: mrr_at_100
      value: 92.4
    - type: mrr_at_1000
      value: 92.4
    - type: nauc_ndcg_at_1_max
      value: 61.9438
    - type: nauc_ndcg_at_1_std
      value: 47.8895
    - type: nauc_ndcg_at_1_diff1
      value: -24.637500000000003
    - type: nauc_ndcg_at_3_max
      value: 61.8429
    - type: nauc_ndcg_at_3_std
      value: 54.91629999999999
    - type: nauc_ndcg_at_3_diff1
      value: -20.4604
    - type: nauc_ndcg_at_5_max
      value: 57.8672
    - type: nauc_ndcg_at_5_std
      value: 54.7293
    - type: nauc_ndcg_at_5_diff1
      value: -16.473599999999998
    - type: nauc_ndcg_at_10_max
      value: 53.545
    - type: nauc_ndcg_at_10_std
      value: 56.166799999999995
    - type: nauc_ndcg_at_10_diff1
      value: -10.5161
    - type: nauc_ndcg_at_20_max
      value: 57.28529999999999
    - type: nauc_ndcg_at_20_std
      value: 67.5691
    - type: nauc_ndcg_at_20_diff1
      value: -9.9578
    - type: nauc_ndcg_at_100_max
      value: 55.752100000000006
    - type: nauc_ndcg_at_100_std
      value: 79.8469
    - type: nauc_ndcg_at_100_diff1
      value: -6.660099999999999
    - type: nauc_ndcg_at_1000_max
      value: 59.807900000000004
    - type: nauc_ndcg_at_1000_std
      value: 72.4075
    - type: nauc_ndcg_at_1000_diff1
      value: -7.3533
    - type: nauc_map_at_1_max
      value: 6.4536999999999995
    - type: nauc_map_at_1_std
      value: -7.8119
    - type: nauc_map_at_1_diff1
      value: -14.0471
    - type: nauc_map_at_3_max
      value: 16.98
    - type: nauc_map_at_3_std
      value: -0.3721
    - type: nauc_map_at_3_diff1
      value: -15.7142
    - type: nauc_map_at_5_max
      value: 19.6888
    - type: nauc_map_at_5_std
      value: 1.4467
    - type: nauc_map_at_5_diff1
      value: -16.999200000000002
    - type: nauc_map_at_10_max
      value: 21.453400000000002
    - type: nauc_map_at_10_std
      value: 4.1453
    - type: nauc_map_at_10_diff1
      value: -13.9404
    - type: nauc_map_at_20_max
      value: 23.8514
    - type: nauc_map_at_20_std
      value: 11.505899999999999
    - type: nauc_map_at_20_diff1
      value: -10.5448
    - type: nauc_map_at_100_max
      value: 46.5883
    - type: nauc_map_at_100_std
      value: 57.91159999999999
    - type: nauc_map_at_100_diff1
      value: -8.8815
    - type: nauc_map_at_1000_max
      value: 63.9415
    - type: nauc_map_at_1000_std
      value: 79.9525
    - type: nauc_map_at_1000_diff1
      value: 2.9305000000000003
    - type: nauc_recall_at_1_max
      value: 6.4536999999999995
    - type: nauc_recall_at_1_std
      value: -7.8119
    - type: nauc_recall_at_1_diff1
      value: -14.0471
    - type: nauc_recall_at_3_max
      value: 13.3248
    - type: nauc_recall_at_3_std
      value: -3.4745999999999997
    - type: nauc_recall_at_3_diff1
      value: -16.9174
    - type: nauc_recall_at_5_max
      value: 14.6892
    - type: nauc_recall_at_5_std
      value: -2.0025999999999997
    - type: nauc_recall_at_5_diff1
      value: -17.622799999999998
    - type: nauc_recall_at_10_max
      value: 12.6493
    - type: nauc_recall_at_10_std
      value: -3.3624
    - type: nauc_recall_at_10_diff1
      value: -14.583599999999999
    - type: nauc_recall_at_20_max
      value: 12.4179
    - type: nauc_recall_at_20_std
      value: 2.6304000000000003
    - type: nauc_recall_at_20_diff1
      value: -12.0154
    - type: nauc_recall_at_100_max
      value: 33.3924
    - type: nauc_recall_at_100_std
      value: 41.6643
    - type: nauc_recall_at_100_diff1
      value: -13.6719
    - type: nauc_recall_at_1000_max
      value: 54.8435
    - type: nauc_recall_at_1000_std
      value: 59.816199999999995
    - type: nauc_recall_at_1000_diff1
      value: -2.3768000000000002
    - type: nauc_precision_at_1_max
      value: 83.2167
    - type: nauc_precision_at_1_std
      value: 71.8899
    - type: nauc_precision_at_1_diff1
      value: -18.970699999999997
    - type: nauc_precision_at_3_max
      value: 70.7754
    - type: nauc_precision_at_3_std
      value: 60.5541
    - type: nauc_precision_at_3_diff1
      value: -16.8234
    - type: nauc_precision_at_5_max
      value: 64.384
    - type: nauc_precision_at_5_std
      value: 54.879999999999995
    - type: nauc_precision_at_5_diff1
      value: -12.5072
    - type: nauc_precision_at_10_max
      value: 60.5951
    - type: nauc_precision_at_10_std
      value: 57.330000000000005
    - type: nauc_precision_at_10_diff1
      value: -4.029400000000001
    - type: nauc_precision_at_20_max
      value: 61.1634
    - type: nauc_precision_at_20_std
      value: 69.7819
    - type: nauc_precision_at_20_diff1
      value: -6.6238
    - type: nauc_precision_at_100_max
      value: 57.61619999999999
    - type: nauc_precision_at_100_std
      value: 82.3103
    - type: nauc_precision_at_100_diff1
      value: 0.8824000000000001
    - type: nauc_precision_at_1000_max
      value: 48.0414
    - type: nauc_precision_at_1000_std
      value: 54.315599999999996
    - type: nauc_precision_at_1000_diff1
      value: 8.9054
    - type: nauc_mrr_at_1_max
      value: 55.3901
    - type: nauc_mrr_at_1_std
      value: 45.5245
    - type: nauc_mrr_at_1_diff1
      value: -1.6835
    - type: nauc_mrr_at_3_max
      value: 61.1547
    - type: nauc_mrr_at_3_std
      value: 52.5639
    - type: nauc_mrr_at_3_diff1
      value: -23.9503
    - type: nauc_mrr_at_5_max
      value: 59.0374
    - type: nauc_mrr_at_5_std
      value: 49.9784
    - type: nauc_mrr_at_5_diff1
      value: -15.771799999999999
    - type: nauc_mrr_at_10_max
      value: 59.0374
    - type: nauc_mrr_at_10_std
      value: 49.9784
    - type: nauc_mrr_at_10_diff1
      value: -15.771799999999999
    - type: nauc_mrr_at_20_max
      value: 59.0374
    - type: nauc_mrr_at_20_std
      value: 49.9784
    - type: nauc_mrr_at_20_diff1
      value: -15.771799999999999
    - type: nauc_mrr_at_100_max
      value: 59.0374
    - type: nauc_mrr_at_100_std
      value: 49.9784
    - type: nauc_mrr_at_100_diff1
      value: -15.771799999999999
    - type: nauc_mrr_at_1000_max
      value: 59.0374
    - type: nauc_mrr_at_1000_std
      value: 49.9784
    - type: nauc_mrr_at_1000_diff1
      value: -15.771799999999999
    - type: main_score
      value: 74.02499999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020 (default)
      type: mteb/touche2020
      config: default
      split: test
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
    metrics:
    - type: ndcg_at_1
      value: 26.531
    - type: ndcg_at_3
      value: 25.533
    - type: ndcg_at_5
      value: 25.846999999999998
    - type: ndcg_at_10
      value: 23.86
    - type: ndcg_at_20
      value: 25.685999999999996
    - type: ndcg_at_100
      value: 35.339999999999996
    - type: ndcg_at_1000
      value: 46.949999999999996
    - type: map_at_1
      value: 2.253
    - type: map_at_3
      value: 4.737
    - type: map_at_5
      value: 6.550000000000001
    - type: map_at_10
      value: 9.114
    - type: map_at_20
      value: 11.928999999999998
    - type: map_at_100
      value: 15.082
    - type: map_at_1000
      value: 16.567
    - type: recall_at_1
      value: 2.253
    - type: recall_at_3
      value: 6.067
    - type: recall_at_5
      value: 9.985
    - type: recall_at_10
      value: 15.595
    - type: recall_at_20
      value: 24.709
    - type: recall_at_100
      value: 46.075
    - type: recall_at_1000
      value: 81.211
    - type: precision_at_1
      value: 30.612000000000002
    - type: precision_at_3
      value: 27.211000000000002
    - type: precision_at_5
      value: 27.346999999999998
    - type: precision_at_10
      value: 21.633
    - type: precision_at_20
      value: 17.347
    - type: precision_at_100
      value: 7.306
    - type: precision_at_1000
      value: 1.498
    - type: mrr_at_1
      value: 30.6122
    - type: mrr_at_3
      value: 41.8367
    - type: mrr_at_5
      value: 45.6122
    - type: mrr_at_10
      value: 46.827000000000005
    - type: mrr_at_20
      value: 47.652699999999996
    - type: mrr_at_100
      value: 47.9184
    - type: mrr_at_1000
      value: 47.9184
    - type: nauc_ndcg_at_1_max
      value: -39.9725
    - type: nauc_ndcg_at_1_std
      value: -16.2998
    - type: nauc_ndcg_at_1_diff1
      value: 10.729700000000001
    - type: nauc_ndcg_at_3_max
      value: -32.3198
    - type: nauc_ndcg_at_3_std
      value: -8.7066
    - type: nauc_ndcg_at_3_diff1
      value: 17.6297
    - type: nauc_ndcg_at_5_max
      value: -32.069900000000004
    - type: nauc_ndcg_at_5_std
      value: -0.3237
    - type: nauc_ndcg_at_5_diff1
      value: 6.7525
    - type: nauc_ndcg_at_10_max
      value: -32.9347
    - type: nauc_ndcg_at_10_std
      value: 0.506
    - type: nauc_ndcg_at_10_diff1
      value: 5.428999999999999
    - type: nauc_ndcg_at_20_max
      value: -30.7678
    - type: nauc_ndcg_at_20_std
      value: 0.2792
    - type: nauc_ndcg_at_20_diff1
      value: 8.7515
    - type: nauc_ndcg_at_100_max
      value: -30.291800000000002
    - type: nauc_ndcg_at_100_std
      value: 24.8031
    - type: nauc_ndcg_at_100_diff1
      value: 3.9330999999999996
    - type: nauc_ndcg_at_1000_max
      value: -27.448299999999996
    - type: nauc_ndcg_at_1000_std
      value: 35.2315
    - type: nauc_ndcg_at_1000_diff1
      value: 0.15059999999999998
    - type: nauc_map_at_1_max
      value: -41.288799999999995
    - type: nauc_map_at_1_std
      value: -24.5046
    - type: nauc_map_at_1_diff1
      value: 7.072100000000001
    - type: nauc_map_at_3_max
      value: -28.862199999999998
    - type: nauc_map_at_3_std
      value: -18.990299999999998
    - type: nauc_map_at_3_diff1
      value: 15.764700000000001
    - type: nauc_map_at_5_max
      value: -27.409699999999997
    - type: nauc_map_at_5_std
      value: -15.2501
    - type: nauc_map_at_5_diff1
      value: 8.8044
    - type: nauc_map_at_10_max
      value: -26.222099999999998
    - type: nauc_map_at_10_std
      value: -11.4798
    - type: nauc_map_at_10_diff1
      value: 7.2714
    - type: nauc_map_at_20_max
      value: -23.0414
    - type: nauc_map_at_20_std
      value: -7.985
    - type: nauc_map_at_20_diff1
      value: 7.704
    - type: nauc_map_at_100_max
      value: -21.3902
    - type: nauc_map_at_100_std
      value: 4.8129
    - type: nauc_map_at_100_diff1
      value: 6.401700000000001
    - type: nauc_map_at_1000_max
      value: -21.4197
    - type: nauc_map_at_1000_std
      value: 8.5824
    - type: nauc_map_at_1000_diff1
      value: 5.328
    - type: nauc_recall_at_1_max
      value: -41.288799999999995
    - type: nauc_recall_at_1_std
      value: -24.5046
    - type: nauc_recall_at_1_diff1
      value: 7.072100000000001
    - type: nauc_recall_at_3_max
      value: -24.351300000000002
    - type: nauc_recall_at_3_std
      value: -13.661000000000001
    - type: nauc_recall_at_3_diff1
      value: 14.1204
    - type: nauc_recall_at_5_max
      value: -22.767799999999998
    - type: nauc_recall_at_5_std
      value: -7.4171000000000005
    - type: nauc_recall_at_5_diff1
      value: 1.9924999999999997
    - type: nauc_recall_at_10_max
      value: -25.3874
    - type: nauc_recall_at_10_std
      value: -3.9967
    - type: nauc_recall_at_10_diff1
      value: 3.4776000000000002
    - type: nauc_recall_at_20_max
      value: -25.051099999999998
    - type: nauc_recall_at_20_std
      value: -2.0329
    - type: nauc_recall_at_20_diff1
      value: 2.2399
    - type: nauc_recall_at_100_max
      value: -20.6196
    - type: nauc_recall_at_100_std
      value: 39.644200000000005
    - type: nauc_recall_at_100_diff1
      value: -6.7455
    - type: nauc_recall_at_1000_max
      value: -6.2200999999999995
    - type: nauc_recall_at_1000_std
      value: 78.9064
    - type: nauc_recall_at_1000_diff1
      value: -23.044700000000002
    - type: nauc_precision_at_1_max
      value: -39.8407
    - type: nauc_precision_at_1_std
      value: -16.3352
    - type: nauc_precision_at_1_diff1
      value: 12.1075
    - type: nauc_precision_at_3_max
      value: -30.505900000000004
    - type: nauc_precision_at_3_std
      value: -6.6981
    - type: nauc_precision_at_3_diff1
      value: 22.1572
    - type: nauc_precision_at_5_max
      value: -26.9752
    - type: nauc_precision_at_5_std
      value: 9.2292
    - type: nauc_precision_at_5_diff1
      value: 6.7962
    - type: nauc_precision_at_10_max
      value: -29.9346
    - type: nauc_precision_at_10_std
      value: 13.3568
    - type: nauc_precision_at_10_diff1
      value: 6.8902
    - type: nauc_precision_at_20_max
      value: -22.7968
    - type: nauc_precision_at_20_std
      value: 21.0382
    - type: nauc_precision_at_20_diff1
      value: 9.033199999999999
    - type: nauc_precision_at_100_max
      value: -11.4519
    - type: nauc_precision_at_100_std
      value: 72.8881
    - type: nauc_precision_at_100_diff1
      value: -8.261000000000001
    - type: nauc_precision_at_1000_max
      value: 29.3926
    - type: nauc_precision_at_1000_std
      value: 44.936
    - type: nauc_precision_at_1000_diff1
      value: -15.2011
    - type: nauc_mrr_at_1_max
      value: -39.8407
    - type: nauc_mrr_at_1_std
      value: -16.3352
    - type: nauc_mrr_at_1_diff1
      value: 12.1075
    - type: nauc_mrr_at_3_max
      value: -37.689
    - type: nauc_mrr_at_3_std
      value: -8.757
    - type: nauc_mrr_at_3_diff1
      value: 6.916300000000001
    - type: nauc_mrr_at_5_max
      value: -36.2749
    - type: nauc_mrr_at_5_std
      value: -5.7966
    - type: nauc_mrr_at_5_diff1
      value: 4.8726
    - type: nauc_mrr_at_10_max
      value: -39.0726
    - type: nauc_mrr_at_10_std
      value: -6.830799999999999
    - type: nauc_mrr_at_10_diff1
      value: 5.1214
    - type: nauc_mrr_at_20_max
      value: -38.6519
    - type: nauc_mrr_at_20_std
      value: -8.6379
    - type: nauc_mrr_at_20_diff1
      value: 6.436699999999999
    - type: nauc_mrr_at_100_max
      value: -38.065599999999996
    - type: nauc_mrr_at_100_std
      value: -8.444
    - type: nauc_mrr_at_100_diff1
      value: 6.2007
    - type: nauc_mrr_at_1000_max
      value: -38.065599999999996
    - type: nauc_mrr_at_1000_std
      value: -8.444
    - type: nauc_mrr_at_1000_diff1
      value: 6.2007
    - type: main_score
      value: 23.86
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification (default)
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
    metrics:
    - type: accuracy
      value: 78.21289999999999
    - type: f1
      value: 60.9322
    - type: f1_weighted
      value: 82.69539999999999
    - type: ap
      value: 19.0474
    - type: ap_weighted
      value: 19.0474
    - type: main_score
      value: 78.21289999999999
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification (default)
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 61.3865
    - type: f1
      value: 61.8066
    - type: f1_weighted
      value: 60.887
    - type: main_score
      value: 61.3865
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering (default)
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 49.3006
    - type: v_measure_std
      value: 0.8814000000000001
    - type: main_score
      value: 49.3006
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015 (default)
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: similarity_accuracy
      value: 85.96289999999999
    - type: similarity_accuracy_threshold
      value: 81.7629
    - type: similarity_f1
      value: 67.1044
    - type: similarity_f1_threshold
      value: 77.98479999999999
    - type: similarity_precision
      value: 64.3497
    - type: similarity_recall
      value: 70.10549999999999
    - type: similarity_ap
      value: 73.406
    - type: cosine_accuracy
      value: 85.96289999999999
    - type: cosine_accuracy_threshold
      value: 81.7629
    - type: cosine_f1
      value: 67.1044
    - type: cosine_f1_threshold
      value: 77.98479999999999
    - type: cosine_precision
      value: 64.3497
    - type: cosine_recall
      value: 70.10549999999999
    - type: cosine_ap
      value: 73.406
    - type: manhattan_accuracy
      value: 85.8735
    - type: manhattan_accuracy_threshold
      value: 22606.6437
    - type: manhattan_f1
      value: 67.173
    - type: manhattan_f1_threshold
      value: 24147.3145
    - type: manhattan_precision
      value: 65.5857
    - type: manhattan_recall
      value: 68.8391
    - type: manhattan_ap
      value: 73.4081
    - type: euclidean_accuracy
      value: 85.94500000000001
    - type: euclidean_accuracy_threshold
      value: 1019.4165999999999
    - type: euclidean_f1
      value: 67.0857
    - type: euclidean_f1_threshold
      value: 1125.1016
    - type: euclidean_precision
      value: 64.07300000000001
    - type: euclidean_recall
      value: 70.3958
    - type: euclidean_ap
      value: 73.3824
    - type: dot_accuracy
      value: 85.3013
    - type: dot_accuracy_threshold
      value: 23726.3916
    - type: dot_f1
      value: 65.8888
    - type: dot_f1_threshold
      value: 22265.913399999998
    - type: dot_precision
      value: 62.2128
    - type: dot_recall
      value: 70.0264
    - type: dot_ap
      value: 71.5363
    - type: max_accuracy
      value: 85.96289999999999
    - type: max_f1
      value: 67.173
    - type: max_precision
      value: 65.5857
    - type: max_recall
      value: 70.3958
    - type: max_ap
      value: 73.4081
    - type: main_score
      value: 73.4081
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus (default)
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: similarity_accuracy
      value: 88.82100000000001
    - type: similarity_accuracy_threshold
      value: 75.9351
    - type: similarity_f1
      value: 77.7423
    - type: similarity_f1_threshold
      value: 72.788
    - type: similarity_precision
      value: 74.2279
    - type: similarity_recall
      value: 81.6061
    - type: similarity_ap
      value: 85.4324
    - type: cosine_accuracy
      value: 88.82100000000001
    - type: cosine_accuracy_threshold
      value: 75.9351
    - type: cosine_f1
      value: 77.7423
    - type: cosine_f1_threshold
      value: 72.788
    - type: cosine_precision
      value: 74.2279
    - type: cosine_recall
      value: 81.6061
    - type: cosine_ap
      value: 85.4324
    - type: manhattan_accuracy
      value: 88.786
    - type: manhattan_accuracy_threshold
      value: 25561.4807
    - type: manhattan_f1
      value: 77.5953
    - type: manhattan_f1_threshold
      value: 26504.0619
    - type: manhattan_precision
      value: 76.1432
    - type: manhattan_recall
      value: 79.1038
    - type: manhattan_ap
      value: 85.3477
    - type: euclidean_accuracy
      value: 88.76859999999999
    - type: euclidean_accuracy_threshold
      value: 1181.4543
    - type: euclidean_f1
      value: 77.645
    - type: euclidean_f1_threshold
      value: 1224.4793
    - type: euclidean_precision
      value: 75.5925
    - type: euclidean_recall
      value: 79.8121
    - type: euclidean_ap
      value: 85.3781
    - type: dot_accuracy
      value: 88.5707
    - type: dot_accuracy_threshold
      value: 21387.7899
    - type: dot_f1
      value: 77.4888
    - type: dot_f1_threshold
      value: 20875.6653
    - type: dot_precision
      value: 75.58009999999999
    - type: dot_recall
      value: 79.4965
    - type: dot_ap
      value: 84.88550000000001
    - type: max_accuracy
      value: 88.82100000000001
    - type: max_f1
      value: 77.7423
    - type: max_precision
      value: 76.1432
    - type: max_recall
      value: 81.6061
    - type: max_ap
      value: 85.4324
    - type: main_score
      value: 85.4324
---

# Mini-GTE

<p align="center">
  <img src="./qtack_logo.png" alt="QTACK Logo" style="width:33%;">
</p>

## Overview
This is the first model developed by QTACK and serves as a proof of concept for our distillation approach! Built upon a distillbert-based architecture, Mini-GTE is distilled from GTE and designed for efficiency without sacrificing accuracy at only 66M parameters. As a standalone sentence transformer, it ranks 2nd on the MTEB classic leaderboard in the <100M parameter category and 63rd overall which makes it a strong choice for real-time query encoding, semantic search, and similarity tasks.

## Model Details
- **Model Type:** Sentence Transformer
- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity

## Usage
- Optimized for quick inference
- Great at quickly generating high quality encodings
- Easy to plug and play since it is distilled from GTE
- **We want to see how you’re using our model so we’ll give you a free coffee/$10 gift card if you get on call with us and show us what you’ve built!**

## Getting Started
### Installation
Mini-GTE is built on the [Sentence Transformers](https://www.sbert.net/) framework. To install the required packages, run:

```bash
pip install -U sentence-transformers
```
### Quick Start
Here's a quick example to get you started:

```python
from sentence_transformers import SentenceTransformer

# Download directly from Hugging Face
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape) # Expected: [3, 768]

# Compute the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape) # Expected: [3, 3]
```

## Training Details
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.1.0a0+32f93b1
- Accelerate: 1.2.0
- Datasets: 2.21.0
- Tokenizers: 0.21.0

## Getting Help

For any questions, suggestions, or issues, please contact the QTACK team directly through our [contact page](https://www.qtack.com/contact).

