---
model-index:
- name: ai-sage/Giga-Embeddings-instruct
  results:
  - dataset:
      config: en-ext
      name: MTEB AmazonCounterfactualClassification (en-ext)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    metrics:
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      value: 94.5352323838081
    - type: ap
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    - type: ap_weighted
      value: 62.422648408367344
    - type: f1
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    - type: f1_weighted
      value: 94.85637995412655
    - type: main_score
      value: 94.5352323838081
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB AmazonCounterfactualClassification (en)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    metrics:
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    - type: ap
      value: 63.42364739316405
    - type: ap_weighted
      value: 63.42364739316405
    - type: f1
      value: 85.54214552412623
    - type: f1_weighted
      value: 90.59539168268289
    - type: main_score
      value: 90.31343283582089
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB AmazonPolarityClassification (default)
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
      split: test
      type: mteb/amazon_polarity
    metrics:
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    - type: ap_weighted
      value: 91.30887530384256
    - type: f1
      value: 94.29070662237378
    - type: f1_weighted
      value: 94.29070662237378
    - type: main_score
      value: 94.29605000000001
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB ArguAna (default)
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
      split: test
      type: mteb/arguana
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      value: 43.756
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    - type: map_at_5
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      value: 53.227999999999994
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      value: 56.931
    - type: ndcg_at_1000
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      value: 55.921
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    - type: precision_at_1
      value: 27.595999999999997
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    - type: recall_at_1
      value: 27.595999999999997
    - type: recall_at_10
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    - type: recall_at_100
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    - type: recall_at_1000
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    - type: recall_at_20
      value: 94.23899999999999
    - type: recall_at_3
      value: 52.347
    - type: recall_at_5
      value: 65.505
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB AskUbuntuDupQuestions (default)
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
      split: test
      type: mteb/askubuntudupquestions-reranking
    metrics:
    - type: main_score
      value: 58.18725222465893
    - type: map
      value: 58.18725222465893
    - type: mrr
      value: 69.84335839598998
    - type: nAUC_map_diff1
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      value: 24.2962940173052
    - type: nAUC_mrr_std
      value: 20.846599304343176
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB BIOSSES (default)
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
      split: test
      type: mteb/biosses-sts
    metrics:
    - type: cosine_pearson
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    - type: cosine_spearman
      value: 84.21758160533057
    - type: euclidean_pearson
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    - type: euclidean_spearman
      value: 84.21758160533057
    - type: main_score
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    - type: manhattan_spearman
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    - type: pearson
      value: 87.40814966131607
    - type: spearman
      value: 84.21758160533057
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB Banking77Classification (default)
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
      split: test
      type: mteb/banking77
    metrics:
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      value: 87.55183393575304
    - type: f1_weighted
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    - type: main_score
      value: 87.78571428571429
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB CEDRClassification (default)
      revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
      split: test
      type: ai-forever/cedr-classification
    metrics:
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      value: 84.46838469713137
    - type: main_score
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    task:
      type: MultilabelClassification
  - dataset:
      config: default
      name: MTEB CQADupstackAndroidRetrieval (default)
      revision: f46a197baaae43b4f621051089b82a364682dfeb
      split: test
      type: mteb/cqadupstack-android
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  - dataset:
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      value: 42.2828223866538
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      value: 39.17361365092243
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      value: 0.28376558066016455
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      value: 25.928388012449936
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      value: 3.649374343629417
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      value: 11.075555507772332
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      value: 18.3299768701232
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      value: 15.415187438641182
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      value: 16.52239778879397
    - type: nauc_precision_at_10_std
      value: 6.837276762823966
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      value: 69.54414023975188
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      value: 40.65799250364921
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      value: -5.03532335561708
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      value: 11.421479372333689
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      value: 14.65873574938081
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      value: 9.377346877954563
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      value: 29.286389164119818
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      value: 24.94345281552621
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      value: 1.2358684556900705
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      value: 22.154044068448584
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      value: 21.268338920126155
    - type: nauc_precision_at_5_std
      value: 3.352963627463402
    - type: nauc_recall_at_1000_diff1
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      value: 12.287873903193287
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      value: 25.92838801245011
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      value: 3.649374343629381
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      value: 11.075555507772298
    - type: nauc_recall_at_100_std
      value: 18.329976870123144
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      value: 15.415187438641109
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      value: 16.522397788793903
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      value: 6.837276762824004
    - type: nauc_recall_at_1_diff1
      value: 69.54414023975188
    - type: nauc_recall_at_1_max
      value: 40.65799250364921
    - type: nauc_recall_at_1_std
      value: -5.03532335561708
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      value: 11.42147937233372
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      value: 14.65873574938076
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      value: 9.377346877954627
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      value: 29.286389164119786
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      value: 24.943452815526214
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      value: 1.2358684556899975
    - type: nauc_recall_at_5_diff1
      value: 22.1540440684486
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      value: 21.268338920126187
    - type: nauc_recall_at_5_std
      value: 3.3529636274634282
    - type: ndcg_at_1
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    - type: ndcg_at_10
      value: 56.936
    - type: ndcg_at_100
      value: 60.723000000000006
    - type: ndcg_at_1000
      value: 62.260000000000005
    - type: ndcg_at_20
      value: 58.472
    - type: ndcg_at_3
      value: 51.526
    - type: ndcg_at_5
      value: 54.37800000000001
    - type: precision_at_1
      value: 65.672
    - type: precision_at_10
      value: 12.105
    - type: precision_at_100
      value: 1.5070000000000001
    - type: precision_at_1000
      value: 0.17099999999999999
    - type: precision_at_20
      value: 6.547
    - type: precision_at_3
      value: 32.312000000000005
    - type: precision_at_5
      value: 21.648
    - type: recall_at_1
      value: 32.836
    - type: recall_at_10
      value: 60.527
    - type: recall_at_100
      value: 75.375
    - type: recall_at_1000
      value: 85.544
    - type: recall_at_20
      value: 65.469
    - type: recall_at_3
      value: 48.467
    - type: recall_at_5
      value: 54.11899999999999
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ImdbClassification (default)
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
      split: test
      type: mteb/imdb
    metrics:
    - type: accuracy
      value: 94.90639999999999
    - type: ap
      value: 92.50413995343256
    - type: ap_weighted
      value: 92.50413995343256
    - type: f1
      value: 94.90353518558736
    - type: f1_weighted
      value: 94.90353518558736
    - type: main_score
      value: 94.90639999999999
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB InappropriatenessClassification (default)
      revision: 601651fdc45ef243751676e62dd7a19f491c0285
      split: test
      type: ai-forever/inappropriateness-classification
    metrics:
    - type: accuracy
      value: 83.29023437500001
    - type: ap
      value: 77.50711273019213
    - type: ap_weighted
      value: 77.50711273019213
    - type: f1
      value: 82.42886423677278
    - type: f1_weighted
      value: 82.42886423677278
    - type: main_score
      value: 83.29023437500001
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB KinopoiskClassification (default)
      revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
      split: test
      type: ai-forever/kinopoisk-sentiment-classification
    metrics:
    - type: accuracy
      value: 74.88666666666667
    - type: f1
      value: 72.6314130184491
    - type: f1_weighted
      value: 72.63141301844908
    - type: main_score
      value: 74.88666666666667
    task:
      type: Classification
  - dataset:
      config: ru
      name: MTEB MIRACLReranking (ru)
      revision: 6d1962c527217f8927fca80f890f14f36b2802af
      split: dev
      type: miracl/mmteb-miracl-reranking
    metrics:
    - type: MAP@1(MIRACL)
      value: 34.256
    - type: MAP@10(MIRACL)
      value: 54.418
    - type: MAP@100(MIRACL)
      value: 56.224
    - type: MAP@1000(MIRACL)
      value: 56.224
    - type: MAP@20(MIRACL)
      value: 55.726
    - type: MAP@3(MIRACL)
      value: 47.811
    - type: MAP@5(MIRACL)
      value: 51.604000000000006
    - type: NDCG@1(MIRACL)
      value: 55.493
    - type: NDCG@10(MIRACL)
      value: 61.209
    - type: NDCG@100(MIRACL)
      value: 64.768
    - type: NDCG@1000(MIRACL)
      value: 64.768
    - type: NDCG@20(MIRACL)
      value: 63.497
    - type: NDCG@3(MIRACL)
      value: 56.259
    - type: NDCG@5(MIRACL)
      value: 58.164
    - type: P@1(MIRACL)
      value: 55.493
    - type: P@10(MIRACL)
      value: 15.533
    - type: P@100(MIRACL)
      value: 1.9529999999999998
    - type: P@1000(MIRACL)
      value: 0.19499999999999998
    - type: P@20(MIRACL)
      value: 8.896999999999998
    - type: P@3(MIRACL)
      value: 34.509
    - type: P@5(MIRACL)
      value: 25.436999999999998
    - type: Recall@1(MIRACL)
      value: 34.256
    - type: Recall@10(MIRACL)
      value: 69.61500000000001
    - type: Recall@100(MIRACL)
      value: 79.952
    - type: Recall@1000(MIRACL)
      value: 79.952
    - type: Recall@20(MIRACL)
      value: 75.753
    - type: Recall@3(MIRACL)
      value: 54.217999999999996
    - type: Recall@5(MIRACL)
      value: 61.617999999999995
    - type: main_score
      value: 61.209
    - type: nAUC_MAP@1000_diff1(MIRACL)
      value: 25.375679947366475
    - type: nAUC_MAP@1000_max(MIRACL)
      value: 39.17949421634556
    - type: nAUC_MAP@1000_std(MIRACL)
      value: 16.668975000160252
    - type: nAUC_MAP@100_diff1(MIRACL)
      value: 25.375679947366475
    - type: nAUC_MAP@100_max(MIRACL)
      value: 39.17949421634556
    - type: nAUC_MAP@100_std(MIRACL)
      value: 16.668975000160252
    - type: nAUC_MAP@10_diff1(MIRACL)
      value: 26.475686007625093
    - type: nAUC_MAP@10_max(MIRACL)
      value: 36.450804891327074
    - type: nAUC_MAP@10_std(MIRACL)
      value: 15.488075690428888
    - type: nAUC_MAP@1_diff1(MIRACL)
      value: 40.99538964417129
    - type: nAUC_MAP@1_max(MIRACL)
      value: 19.924627548929934
    - type: nAUC_MAP@1_std(MIRACL)
      value: 2.3190113335598794
    - type: nAUC_MAP@20_diff1(MIRACL)
      value: 25.752468618876932
    - type: nAUC_MAP@20_max(MIRACL)
      value: 38.34566370834793
    - type: nAUC_MAP@20_std(MIRACL)
      value: 16.461203040560456
    - type: nAUC_MAP@3_diff1(MIRACL)
      value: 30.535358312013727
    - type: nAUC_MAP@3_max(MIRACL)
      value: 29.944731853212186
    - type: nAUC_MAP@3_std(MIRACL)
      value: 9.103557696794429
    - type: nAUC_MAP@5_diff1(MIRACL)
      value: 28.014426658247842
    - type: nAUC_MAP@5_max(MIRACL)
      value: 32.85404483202943
    - type: nAUC_MAP@5_std(MIRACL)
      value: 12.450517715414893
    - type: nAUC_NDCG@1000_diff1(MIRACL)
      value: 18.961103796856186
    - type: nAUC_NDCG@1000_max(MIRACL)
      value: 49.36987471280251
    - type: nAUC_NDCG@1000_std(MIRACL)
      value: 21.734657550451335
    - type: nAUC_NDCG@100_diff1(MIRACL)
      value: 18.961103796856186
    - type: nAUC_NDCG@100_max(MIRACL)
      value: 49.36987471280251
    - type: nAUC_NDCG@100_std(MIRACL)
      value: 21.734657550451335
    - type: nAUC_NDCG@10_diff1(MIRACL)
      value: 21.983623979331
    - type: nAUC_NDCG@10_max(MIRACL)
      value: 43.683160399908964
    - type: nAUC_NDCG@10_std(MIRACL)
      value: 20.01372618506444
    - type: nAUC_NDCG@1_diff1(MIRACL)
      value: 30.203859075266475
    - type: nAUC_NDCG@1_max(MIRACL)
      value: 41.79737245030743
    - type: nAUC_NDCG@1_std(MIRACL)
      value: 16.05372763120693
    - type: nAUC_NDCG@20_diff1(MIRACL)
      value: 20.149932418933712
    - type: nAUC_NDCG@20_max(MIRACL)
      value: 47.065303606034306
    - type: nAUC_NDCG@20_std(MIRACL)
      value: 21.44815256127275
    - type: nAUC_NDCG@3_diff1(MIRACL)
      value: 25.42407181388333
    - type: nAUC_NDCG@3_max(MIRACL)
      value: 40.24154284953538
    - type: nAUC_NDCG@3_std(MIRACL)
      value: 14.972072673678152
    - type: nAUC_NDCG@5_diff1(MIRACL)
      value: 24.153034992206194
    - type: nAUC_NDCG@5_max(MIRACL)
      value: 39.82178245941784
    - type: nAUC_NDCG@5_std(MIRACL)
      value: 16.58411685965955
    - type: nAUC_P@1000_diff1(MIRACL)
      value: -19.79420161864485
    - type: nAUC_P@1000_max(MIRACL)
      value: 34.3616294815911
    - type: nAUC_P@1000_std(MIRACL)
      value: 20.166807624722257
    - type: nAUC_P@100_diff1(MIRACL)
      value: -19.794201618644824
    - type: nAUC_P@100_max(MIRACL)
      value: 34.361629481591166
    - type: nAUC_P@100_std(MIRACL)
      value: 20.166807624722274
    - type: nAUC_P@10_diff1(MIRACL)
      value: -16.354560145786806
    - type: nAUC_P@10_max(MIRACL)
      value: 36.05760962789994
    - type: nAUC_P@10_std(MIRACL)
      value: 25.417471718298163
    - type: nAUC_P@1_diff1(MIRACL)
      value: 30.203859075266475
    - type: nAUC_P@1_max(MIRACL)
      value: 41.79737245030743
    - type: nAUC_P@1_std(MIRACL)
      value: 16.05372763120693
    - type: nAUC_P@20_diff1(MIRACL)
      value: -18.621011544449317
    - type: nAUC_P@20_max(MIRACL)
      value: 35.77327581203754
    - type: nAUC_P@20_std(MIRACL)
      value: 23.905223904634525
    - type: nAUC_P@3_diff1(MIRACL)
      value: -6.046172734824364
    - type: nAUC_P@3_max(MIRACL)
      value: 41.50549514127416
    - type: nAUC_P@3_std(MIRACL)
      value: 22.16102424923467
    - type: nAUC_P@5_diff1(MIRACL)
      value: -12.42960360332892
    - type: nAUC_P@5_max(MIRACL)
      value: 37.02162714427216
    - type: nAUC_P@5_std(MIRACL)
      value: 23.478907037340967
    - type: nAUC_Recall@1000_diff1(MIRACL)
      value: -2.1015821397499126
    - type: nAUC_Recall@1000_max(MIRACL)
      value: 72.4788381130068
    - type: nAUC_Recall@1000_std(MIRACL)
      value: 33.13079374757504
    - type: nAUC_Recall@100_diff1(MIRACL)
      value: -2.1015821397499126
    - type: nAUC_Recall@100_max(MIRACL)
      value: 72.4788381130068
    - type: nAUC_Recall@100_std(MIRACL)
      value: 33.13079374757504
    - type: nAUC_Recall@10_diff1(MIRACL)
      value: 12.578351372233119
    - type: nAUC_Recall@10_max(MIRACL)
      value: 46.853083044539666
    - type: nAUC_Recall@10_std(MIRACL)
      value: 25.220743565118504
    - type: nAUC_Recall@1_diff1(MIRACL)
      value: 40.99538964417129
    - type: nAUC_Recall@1_max(MIRACL)
      value: 19.924627548929934
    - type: nAUC_Recall@1_std(MIRACL)
      value: 2.3190113335598794
    - type: nAUC_Recall@20_diff1(MIRACL)
      value: 4.959175120863266
    - type: nAUC_Recall@20_max(MIRACL)
      value: 58.70067350548401
    - type: nAUC_Recall@20_std(MIRACL)
      value: 30.461409638792137
    - type: nAUC_Recall@3_diff1(MIRACL)
      value: 23.891732917363356
    - type: nAUC_Recall@3_max(MIRACL)
      value: 31.214738377868407
    - type: nAUC_Recall@3_std(MIRACL)
      value: 10.22032515759191
    - type: nAUC_Recall@5_diff1(MIRACL)
      value: 18.887826092373857
    - type: nAUC_Recall@5_max(MIRACL)
      value: 35.333905802137075
    - type: nAUC_Recall@5_std(MIRACL)
      value: 15.818853782953793
    task:
      type: Reranking
  - dataset:
      config: ru
      name: MTEB MIRACLRetrieval (ru)
      revision: main
      split: dev
      type: miracl/mmteb-miracl
    metrics:
    - type: main_score
      value: 58.919
    - type: map_at_1
      value: 28.105000000000004
    - type: map_at_10
      value: 48.485
    - type: map_at_100
      value: 51.092000000000006
    - type: map_at_1000
      value: 51.197
    - type: map_at_20
      value: 49.964
    - type: map_at_3
      value: 40.702
    - type: map_at_5
      value: 44.908
    - type: mrr_at_1
      value: 53.674121405750796
    - type: mrr_at_10
      value: 65.94696105279179
    - type: mrr_at_100
      value: 66.32118832568914
    - type: mrr_at_1000
      value: 66.32790976216316
    - type: mrr_at_20
      value: 66.1976638975423
    - type: mrr_at_3
      value: 63.205537806176835
    - type: mrr_at_5
      value: 65.0745473908414
    - type: nauc_map_at_1000_diff1
      value: 28.692875448815812
    - type: nauc_map_at_1000_max
      value: 12.491779369284659
    - type: nauc_map_at_1000_std
      value: -2.62205081625825
    - type: nauc_map_at_100_diff1
      value: 28.645110165399174
    - type: nauc_map_at_100_max
      value: 12.456098789482798
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      value: -2.6613261048769608
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      value: 28.472937541575334
    - type: nauc_map_at_10_max
      value: 10.810116535339406
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      value: -5.286588474512575
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      value: 32.84937124647936
    - type: nauc_map_at_1_max
      value: 6.422329814718059
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      value: -9.338607410867883
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      value: 28.599327426899933
    - type: nauc_map_at_20_max
      value: 11.82906430468277
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      value: -4.021436740087691
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      value: 27.482658420810434
    - type: nauc_map_at_3_max
      value: 8.017407826476449
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      value: -7.867867276064061
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      value: 27.800427415149926
    - type: nauc_map_at_5_max
      value: 9.039207813157995
    - type: nauc_map_at_5_std
      value: -7.591204490113271
    - type: nauc_mrr_at_1000_diff1
      value: 35.78578282699573
    - type: nauc_mrr_at_1000_max
      value: 20.786252125904436
    - type: nauc_mrr_at_1000_std
      value: 8.583860329566223
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      value: 35.775930857323694
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      value: 20.791530168252752
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      value: 8.59657930717071
    - type: nauc_mrr_at_10_diff1
      value: 35.69769923988554
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      value: 20.901225274243266
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      value: 8.708683664351218
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      value: 37.68512205937764
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      value: 17.880526496865198
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      value: 4.0110867274399515
    - type: nauc_mrr_at_20_diff1
      value: 35.761942530285836
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      value: 20.834018834025102
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      value: 8.68188170294387
    - type: nauc_mrr_at_3_diff1
      value: 35.65799054328497
    - type: nauc_mrr_at_3_max
      value: 20.406872858177756
    - type: nauc_mrr_at_3_std
      value: 7.9695230492304105
    - type: nauc_mrr_at_5_diff1
      value: 36.00029293304689
    - type: nauc_mrr_at_5_max
      value: 20.893681203274397
    - type: nauc_mrr_at_5_std
      value: 8.608032961961529
    - type: nauc_ndcg_at_1000_diff1
      value: 30.283370916340395
    - type: nauc_ndcg_at_1000_max
      value: 17.45515207920199
    - type: nauc_ndcg_at_1000_std
      value: 5.500817122521028
    - type: nauc_ndcg_at_100_diff1
      value: 29.37087750574036
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      value: 17.1659856137934
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      value: 5.740672030272012
    - type: nauc_ndcg_at_10_diff1
      value: 29.190898853994153
    - type: nauc_ndcg_at_10_max
      value: 13.581733885756453
    - type: nauc_ndcg_at_10_std
      value: -1.1925279328006109
    - type: nauc_ndcg_at_1_diff1
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  - dataset:
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  - dataset:
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  - dataset:
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  - dataset:
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  - dataset:
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  - dataset:
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    task:
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  - dataset:
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  - dataset:
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    task:
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  - dataset:
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      type: ai-forever/rubq-reranking
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  - dataset:
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    task:
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      type: ai-forever/ru-stsbenchmark-sts
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  - dataset:
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      type: mteb/scidocs
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      value: 20.94698008166532
    - type: nauc_recall_at_5_std
      value: 19.962609439107787
    - type: ndcg_at_1
      value: 22.2
    - type: ndcg_at_10
      value: 19.162000000000003
    - type: ndcg_at_100
      value: 28.321
    - type: ndcg_at_1000
      value: 34.266999999999996
    - type: ndcg_at_20
      value: 22.253999999999998
    - type: ndcg_at_3
      value: 17.798
    - type: ndcg_at_5
      value: 15.975
    - type: precision_at_1
      value: 22.2
    - type: precision_at_10
      value: 9.98
    - type: precision_at_100
      value: 2.329
    - type: precision_at_1000
      value: 0.376
    - type: precision_at_20
      value: 6.795
    - type: precision_at_3
      value: 16.567
    - type: precision_at_5
      value: 14.24
    - type: recall_at_1
      value: 4.475
    - type: recall_at_10
      value: 20.307
    - type: recall_at_100
      value: 47.242
    - type: recall_at_1000
      value: 76.242
    - type: recall_at_20
      value: 27.596999999999998
    - type: recall_at_3
      value: 10.05
    - type: recall_at_5
      value: 14.432
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SICK-R (default)
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
      split: test
      type: mteb/sickr-sts
    metrics:
    - type: cosine_pearson
      value: 85.571141897898
    - type: cosine_spearman
      value: 81.06253068558962
    - type: euclidean_pearson
      value: 83.27075641353736
    - type: euclidean_spearman
      value: 81.06253622684207
    - type: main_score
      value: 81.06253068558962
    - type: manhattan_pearson
      value: 83.35659901147551
    - type: manhattan_spearman
      value: 81.13591158643307
    - type: pearson
      value: 85.571141897898
    - type: spearman
      value: 81.06253068558962
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS12 (default)
      revision: a0d554a64d88156834ff5ae9920b964011b16384
      split: test
      type: mteb/sts12-sts
    metrics:
    - type: cosine_pearson
      value: 81.78267795485768
    - type: cosine_spearman
      value: 70.09877188862463
    - type: euclidean_pearson
      value: 78.49440835422978
    - type: euclidean_spearman
      value: 70.09877188862463
    - type: main_score
      value: 70.09877188862463
    - type: manhattan_pearson
      value: 78.7282306108082
    - type: manhattan_spearman
      value: 70.29557277377175
    - type: pearson
      value: 81.78267795485768
    - type: spearman
      value: 70.09877188862463
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS13 (default)
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
      split: test
      type: mteb/sts13-sts
    metrics:
    - type: cosine_pearson
      value: 80.25745663712165
    - type: cosine_spearman
      value: 81.01483036919429
    - type: euclidean_pearson
      value: 81.14541657219165
    - type: euclidean_spearman
      value: 81.01478607738545
    - type: main_score
      value: 81.01483036919429
    - type: manhattan_pearson
      value: 81.40111412290987
    - type: manhattan_spearman
      value: 81.28649812590226
    - type: pearson
      value: 80.25745663712165
    - type: spearman
      value: 81.01483036919429
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS14 (default)
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
      split: test
      type: mteb/sts14-sts
    metrics:
    - type: cosine_pearson
      value: 81.7451052491344
    - type: cosine_spearman
      value: 77.81666029040504
    - type: euclidean_pearson
      value: 81.31747977745773
    - type: euclidean_spearman
      value: 77.81666029040504
    - type: main_score
      value: 77.81666029040504
    - type: manhattan_pearson
      value: 81.46161935201552
    - type: manhattan_spearman
      value: 77.98131880904403
    - type: pearson
      value: 81.7451052491344
    - type: spearman
      value: 77.81666029040504
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS15 (default)
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
      split: test
      type: mteb/sts15-sts
    metrics:
    - type: cosine_pearson
      value: 83.69084648758516
    - type: cosine_spearman
      value: 84.52134445704115
    - type: euclidean_pearson
      value: 84.13552443709717
    - type: euclidean_spearman
      value: 84.52134445704115
    - type: main_score
      value: 84.52134445704115
    - type: manhattan_pearson
      value: 84.21966806051941
    - type: manhattan_spearman
      value: 84.61306373914836
    - type: pearson
      value: 83.69084648758516
    - type: spearman
      value: 84.52134445704115
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS16 (default)
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
      split: test
      type: mteb/sts16-sts
    metrics:
    - type: cosine_pearson
      value: 79.22411263296355
    - type: cosine_spearman
      value: 81.0920748151385
    - type: euclidean_pearson
      value: 80.69903573722831
    - type: euclidean_spearman
      value: 81.0920748151385
    - type: main_score
      value: 81.0920748151385
    - type: manhattan_pearson
      value: 80.82176565456413
    - type: manhattan_spearman
      value: 81.22812310589154
    - type: pearson
      value: 79.22411263296355
    - type: spearman
      value: 81.0920748151385
    task:
      type: STS
  - dataset:
      config: es-en
      name: MTEB STS22 (es-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 55.65638854673621
    - type: cosine_spearman
      value: 67.8978765123834
    - type: euclidean_pearson
      value: 60.91272349917401
    - type: euclidean_spearman
      value: 67.8978765123834
    - type: main_score
      value: 67.8978765123834
    - type: manhattan_pearson
      value: 62.43178407548512
    - type: manhattan_spearman
      value: 68.28262888978558
    - type: pearson
      value: 55.65638854673621
    - type: spearman
      value: 67.8978765123834
    task:
      type: STS
  - dataset:
      config: pl-en
      name: MTEB STS22 (pl-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 63.86172304063621
    - type: cosine_spearman
      value: 64.15805209466257
    - type: euclidean_pearson
      value: 64.74688851141593
    - type: euclidean_spearman
      value: 64.15805209466257
    - type: main_score
      value: 64.15805209466257
    - type: manhattan_pearson
      value: 66.12890961014374
    - type: manhattan_spearman
      value: 64.79879912353911
    - type: pearson
      value: 63.86172304063621
    - type: spearman
      value: 64.15805209466257
    task:
      type: STS
  - dataset:
      config: en
      name: MTEB STS22 (en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 63.234279359667276
    - type: cosine_spearman
      value: 63.624116896576034
    - type: euclidean_pearson
      value: 65.91000068718458
    - type: euclidean_spearman
      value: 63.624116896576034
    - type: main_score
      value: 63.624116896576034
    - type: manhattan_pearson
      value: 66.1029310140917
    - type: manhattan_spearman
      value: 63.787459541390234
    - type: pearson
      value: 63.234279359667276
    - type: spearman
      value: 63.624116896576034
    task:
      type: STS
  - dataset:
      config: zh-en
      name: MTEB STS22 (zh-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 60.926022204317896
    - type: cosine_spearman
      value: 64.75327857364532
    - type: euclidean_pearson
      value: 62.69516497034605
    - type: euclidean_spearman
      value: 64.75327857364532
    - type: main_score
      value: 64.75327857364532
    - type: manhattan_pearson
      value: 63.90894015427736
    - type: manhattan_spearman
      value: 65.48329498299246
    - type: pearson
      value: 60.926022204317896
    - type: spearman
      value: 64.75327857364532
    task:
      type: STS
  - dataset:
      config: de-en
      name: MTEB STS22 (de-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 52.69990619148788
    - type: cosine_spearman
      value: 54.334501201655925
    - type: euclidean_pearson
      value: 58.32806987651424
    - type: euclidean_spearman
      value: 54.334501201655925
    - type: main_score
      value: 54.334501201655925
    - type: manhattan_pearson
      value: 60.004209516880834
    - type: manhattan_spearman
      value: 54.965988386618704
    - type: pearson
      value: 52.69990619148788
    - type: spearman
      value: 54.334501201655925
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STSBenchmark (default)
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
      split: test
      type: mteb/stsbenchmark-sts
    metrics:
    - type: cosine_pearson
      value: 82.25427804989813
    - type: cosine_spearman
      value: 82.20135109121625
    - type: euclidean_pearson
      value: 83.21943775385887
    - type: euclidean_spearman
      value: 82.2013271737365
    - type: main_score
      value: 82.20135109121625
    - type: manhattan_pearson
      value: 83.30139032750424
    - type: manhattan_spearman
      value: 82.313788704099
    - type: pearson
      value: 82.25427804989813
    - type: spearman
      value: 82.20135109121625
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SciDocsRR (default)
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
      split: test
      type: mteb/scidocs-reranking
    metrics:
    - type: main_score
      value: 88.00790793717358
    - type: map
      value: 88.00790793717358
    - type: mrr
      value: 96.2317149081855
    - type: nAUC_map_diff1
      value: -4.285706894005613
    - type: nAUC_map_max
      value: 47.69800035323553
    - type: nAUC_map_std
      value: 66.05509605566249
    - type: nAUC_mrr_diff1
      value: 37.59327273978339
    - type: nAUC_mrr_max
      value: 74.83336690604901
    - type: nAUC_mrr_std
      value: 79.76400854713901
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SciFact (default)
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
      split: test
      type: mteb/scifact
    metrics:
    - type: main_score
      value: 72.902
    - type: map_at_1
      value: 58.160999999999994
    - type: map_at_10
      value: 68.15
    - type: map_at_100
      value: 68.76299999999999
    - type: map_at_1000
      value: 68.777
    - type: map_at_20
      value: 68.636
    - type: map_at_3
      value: 65.496
    - type: map_at_5
      value: 67.166
    - type: mrr_at_1
      value: 61.66666666666667
    - type: mrr_at_10
      value: 69.90119047619046
    - type: mrr_at_100
      value: 70.35924850016258
    - type: mrr_at_1000
      value: 70.36956277343268
    - type: mrr_at_20
      value: 70.2427525490219
    - type: mrr_at_3
      value: 68.0
    - type: mrr_at_5
      value: 69.23333333333332
    - type: nauc_map_at_1000_diff1
      value: 67.60995698057089
    - type: nauc_map_at_1000_max
      value: 46.068809020612704
    - type: nauc_map_at_1000_std
      value: 1.1948672845462098
    - type: nauc_map_at_100_diff1
      value: 67.60952762159904
    - type: nauc_map_at_100_max
      value: 46.07379744229154
    - type: nauc_map_at_100_std
      value: 1.182970323131957
    - type: nauc_map_at_10_diff1
      value: 67.57824321545665
    - type: nauc_map_at_10_max
      value: 46.32612750524493
    - type: nauc_map_at_10_std
      value: 1.2781320788709092
    - type: nauc_map_at_1_diff1
      value: 70.5130136513353
    - type: nauc_map_at_1_max
      value: 40.673408888331544
    - type: nauc_map_at_1_std
      value: -1.3408323300249747
    - type: nauc_map_at_20_diff1
      value: 67.52032735442157
    - type: nauc_map_at_20_max
      value: 46.12651713618736
    - type: nauc_map_at_20_std
      value: 1.1355480686211972
    - type: nauc_map_at_3_diff1
      value: 68.16380401477547
    - type: nauc_map_at_3_max
      value: 43.88680563184851
    - type: nauc_map_at_3_std
      value: -3.720201195392404
    - type: nauc_map_at_5_diff1
      value: 67.99170514346827
    - type: nauc_map_at_5_max
      value: 45.1182341963952
    - type: nauc_map_at_5_std
      value: -0.8736684979875234
    - type: nauc_mrr_at_1000_diff1
      value: 65.5906860945105
    - type: nauc_mrr_at_1000_max
      value: 47.56601805618958
    - type: nauc_mrr_at_1000_std
      value: 5.9136892538197126
    - type: nauc_mrr_at_100_diff1
      value: 65.59130506472648
    - type: nauc_mrr_at_100_max
      value: 47.560766539048224
    - type: nauc_mrr_at_100_std
      value: 5.886452280327445
    - type: nauc_mrr_at_10_diff1
      value: 65.38789898290564
    - type: nauc_mrr_at_10_max
      value: 47.82853216971601
    - type: nauc_mrr_at_10_std
      value: 6.362470864688538
    - type: nauc_mrr_at_1_diff1
      value: 67.64455543946382
    - type: nauc_mrr_at_1_max
      value: 44.87347417242169
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      value: 7.4488417726357365
    - type: nauc_mrr_at_20_diff1
      value: 65.47605317912657
    - type: nauc_mrr_at_20_max
      value: 47.58709668066147
    - type: nauc_mrr_at_20_std
      value: 5.809216165052655
    - type: nauc_mrr_at_3_diff1
      value: 65.77227321919354
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      value: 47.54231580750389
    - type: nauc_mrr_at_3_std
      value: 4.222335116280824
    - type: nauc_mrr_at_5_diff1
      value: 65.54309281879533
    - type: nauc_mrr_at_5_max
      value: 47.89198835887271
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      value: 5.990782526521852
    - type: nauc_ndcg_at_1000_diff1
      value: 66.50501237344044
    - type: nauc_ndcg_at_1000_max
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      value: 3.858567312387061
    - type: nauc_ndcg_at_100_diff1
      value: 66.52978786688968
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      value: 3.2827316431460147
    - type: nauc_ndcg_at_10_diff1
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    - type: ndcg_at_10
      value: 72.902
    - type: ndcg_at_100
      value: 75.368
    - type: ndcg_at_1000
      value: 75.657
    - type: ndcg_at_20
      value: 74.453
    - type: ndcg_at_3
      value: 68.57
    - type: ndcg_at_5
      value: 70.926
    - type: precision_at_1
      value: 61.667
    - type: precision_at_10
      value: 9.667
    - type: precision_at_100
      value: 1.093
    - type: precision_at_1000
      value: 0.11199999999999999
    - type: precision_at_20
      value: 5.2
    - type: precision_at_3
      value: 27.0
    - type: precision_at_5
      value: 17.8
    - type: recall_at_1
      value: 58.160999999999994
    - type: recall_at_10
      value: 85.68900000000001
    - type: recall_at_100
      value: 96.5
    - type: recall_at_1000
      value: 98.667
    - type: recall_at_20
      value: 91.60000000000001
    - type: recall_at_3
      value: 74.083
    - type: recall_at_5
      value: 80.022
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SensitiveTopicsClassification (default)
      revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
      split: test
      type: ai-forever/sensitive-topics-classification
    metrics:
    - type: accuracy
      value: 30.009765625
    - type: f1
      value: 31.97592344768223
    - type: lrap
      value: 46.34419759114506
    - type: main_score
      value: 30.009765625
    task:
      type: MultilabelClassification
  - dataset:
      config: default
      name: MTEB SprintDuplicateQuestions (default)
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
      split: test
      type: mteb/sprintduplicatequestions-pairclassification
    metrics:
    - type: cosine_accuracy
      value: 99.6059405940594
    - type: cosine_accuracy_threshold
      value: 80.66068887710571
    - type: cosine_ap
      value: 86.29950883344696
    - type: cosine_f1
      value: 79.280205655527
    - type: cosine_f1_threshold
      value: 80.31163215637207
    - type: cosine_precision
      value: 81.58730158730158
    - type: cosine_recall
      value: 77.10000000000001
    - type: dot_accuracy
      value: 99.6059405940594
    - type: dot_accuracy_threshold
      value: 80.66068291664124
    - type: dot_ap
      value: 86.29952127770177
    - type: dot_f1
      value: 79.280205655527
    - type: dot_f1_threshold
      value: 80.31163215637207
    - type: dot_precision
      value: 81.58730158730158
    - type: dot_recall
      value: 77.10000000000001
    - type: euclidean_accuracy
      value: 99.6059405940594
    - type: euclidean_accuracy_threshold
      value: 62.19213604927063
    - type: euclidean_ap
      value: 86.29950883344696
    - type: euclidean_f1
      value: 79.280205655527
    - type: euclidean_f1_threshold
      value: 62.75087594985962
    - type: euclidean_precision
      value: 81.58730158730158
    - type: euclidean_recall
      value: 77.10000000000001
    - type: main_score
      value: 86.97106165735022
    - type: manhattan_accuracy
      value: 99.61683168316831
    - type: manhattan_accuracy_threshold
      value: 2190.611457824707
    - type: manhattan_ap
      value: 86.97106165735022
    - type: manhattan_f1
      value: 79.76999477260847
    - type: manhattan_f1_threshold
      value: 2214.816665649414
    - type: manhattan_precision
      value: 83.57064622124864
    - type: manhattan_recall
      value: 76.3
    - type: max_accuracy
      value: 99.61683168316831
    - type: max_ap
      value: 86.97106165735022
    - type: max_f1
      value: 79.76999477260847
    - type: max_precision
      value: 83.57064622124864
    - type: max_recall
      value: 77.10000000000001
    - type: similarity_accuracy
      value: 99.6059405940594
    - type: similarity_accuracy_threshold
      value: 80.66068887710571
    - type: similarity_ap
      value: 86.29950883344696
    - type: similarity_f1
      value: 79.280205655527
    - type: similarity_f1_threshold
      value: 80.31163215637207
    - type: similarity_precision
      value: 81.58730158730158
    - type: similarity_recall
      value: 77.10000000000001
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB StackOverflowDupQuestions (default)
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
      split: test
      type: mteb/stackoverflowdupquestions-reranking
    metrics:
    - type: main_score
      value: 47.7154636461295
    - type: map
      value: 47.7154636461295
    - type: mrr
      value: 48.28928730583142
    - type: nAUC_map_diff1
      value: 35.220975539918406
    - type: nAUC_map_max
      value: 11.301549750395234
    - type: nAUC_map_std
      value: 10.780322232649885
    - type: nAUC_mrr_diff1
      value: 36.03068695413537
    - type: nAUC_mrr_max
      value: 11.937927361734655
    - type: nAUC_mrr_std
      value: 10.594734349234532
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SummEval (default)
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
      split: test
      type: mteb/summeval
    metrics:
    - type: cosine_pearson
      value: 29.3884630972395
    - type: cosine_spearman
      value: 27.85662716499775
    - type: dot_pearson
      value: 29.388462913794346
    - type: dot_spearman
      value: 27.888760562108097
    - type: main_score
      value: 27.85662716499775
    - type: pearson
      value: 29.3884630972395
    - type: spearman
      value: 27.85662716499775
    task:
      type: Summarization
  - dataset:
      config: default
      name: MTEB TERRa (default)
      revision: 7b58f24536063837d644aab9a023c62199b2a612
      split: dev
      type: ai-forever/terra-pairclassification
    metrics:
    - type: cosine_accuracy
      value: 60.91205211726385
    - type: cosine_accuracy_threshold
      value: 71.89492583274841
    - type: cosine_ap
      value: 59.975232226567556
    - type: cosine_f1
      value: 67.8733031674208
    - type: cosine_f1_threshold
      value: 47.70580530166626
    - type: cosine_precision
      value: 51.903114186851205
    - type: cosine_recall
      value: 98.0392156862745
    - type: dot_accuracy
      value: 60.91205211726385
    - type: dot_accuracy_threshold
      value: 71.89492583274841
    - type: dot_ap
      value: 59.975232226567556
    - type: dot_f1
      value: 67.8733031674208
    - type: dot_f1_threshold
      value: 47.7058082818985
    - type: dot_precision
      value: 51.903114186851205
    - type: dot_recall
      value: 98.0392156862745
    - type: euclidean_accuracy
      value: 60.91205211726385
    - type: euclidean_accuracy_threshold
      value: 74.97339844703674
    - type: euclidean_ap
      value: 59.975232226567556
    - type: euclidean_f1
      value: 67.8733031674208
    - type: euclidean_f1_threshold
      value: 102.26843357086182
    - type: euclidean_precision
      value: 51.903114186851205
    - type: euclidean_recall
      value: 98.0392156862745
    - type: main_score
      value: 60.456656396523144
    - type: manhattan_accuracy
      value: 61.563517915309454
    - type: manhattan_accuracy_threshold
      value: 2685.3965759277344
    - type: manhattan_ap
      value: 60.456656396523144
    - type: manhattan_f1
      value: 67.72009029345372
    - type: manhattan_f1_threshold
      value: 3692.0753479003906
    - type: manhattan_precision
      value: 51.724137931034484
    - type: manhattan_recall
      value: 98.0392156862745
    - type: max_accuracy
      value: 61.563517915309454
    - type: max_ap
      value: 60.456656396523144
    - type: max_f1
      value: 67.8733031674208
    - type: max_precision
      value: 51.903114186851205
    - type: max_recall
      value: 98.0392156862745
    - type: similarity_accuracy
      value: 60.91205211726385
    - type: similarity_accuracy_threshold
      value: 71.89492583274841
    - type: similarity_ap
      value: 59.975232226567556
    - type: similarity_f1
      value: 67.8733031674208
    - type: similarity_f1_threshold
      value: 47.70580530166626
    - type: similarity_precision
      value: 51.903114186851205
    - type: similarity_recall
      value: 98.0392156862745
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB TRECCOVID (default)
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
      split: test
      type: mteb/trec-covid
    metrics:
    - type: main_score
      value: 68.527
    - type: map_at_1
      value: 0.22799999999999998
    - type: map_at_10
      value: 1.6310000000000002
    - type: map_at_100
      value: 7.9990000000000006
    - type: map_at_1000
      value: 19.209
    - type: map_at_20
      value: 2.6550000000000002
    - type: map_at_3
      value: 0.562
    - type: map_at_5
      value: 0.914
    - type: mrr_at_1
      value: 84.0
    - type: mrr_at_10
      value: 91.66666666666666
    - type: mrr_at_100
      value: 91.66666666666666
    - type: mrr_at_1000
      value: 91.66666666666666
    - type: mrr_at_20
      value: 91.66666666666666
    - type: mrr_at_3
      value: 91.66666666666666
    - type: mrr_at_5
      value: 91.66666666666666
    - type: nauc_map_at_1000_diff1
      value: -24.9289165556578
    - type: nauc_map_at_1000_max
      value: 36.82343869931691
    - type: nauc_map_at_1000_std
      value: 69.76693302223156
    - type: nauc_map_at_100_diff1
      value: -14.93215489004332
    - type: nauc_map_at_100_max
      value: 26.903137343383193
    - type: nauc_map_at_100_std
      value: 51.93554226264329
    - type: nauc_map_at_10_diff1
      value: -9.589639096178356
    - type: nauc_map_at_10_max
      value: 0.9848575880881552
    - type: nauc_map_at_10_std
      value: 11.949732717032333
    - type: nauc_map_at_1_diff1
      value: -0.02833948708031013
    - type: nauc_map_at_1_max
      value: -9.58520783682369
    - type: nauc_map_at_1_std
      value: -5.49929878351851
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      value: -10.488150815671766
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      value: 7.636306025857953
    - type: nauc_map_at_20_std
      value: 21.697516125766192
    - type: nauc_map_at_3_diff1
      value: -12.564747985952957
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      value: -5.974361276237696
    - type: nauc_map_at_3_std
      value: 2.104636022300902
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      value: -11.586078080097174
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      value: -3.6533992703743476
    - type: nauc_map_at_5_std
      value: 6.216862673600229
    - type: nauc_mrr_at_1000_diff1
      value: -29.749167156574195
    - type: nauc_mrr_at_1000_max
      value: 25.24789339604172
    - type: nauc_mrr_at_1000_std
      value: 47.55242014501294
    - type: nauc_mrr_at_100_diff1
      value: -29.749167156574195
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      value: 25.24789339604172
    - type: nauc_mrr_at_100_std
      value: 47.55242014501294
    - type: nauc_mrr_at_10_diff1
      value: -29.749167156574195
    - type: nauc_mrr_at_10_max
      value: 25.24789339604172
    - type: nauc_mrr_at_10_std
      value: 47.55242014501294
    - type: nauc_mrr_at_1_diff1
      value: -23.429062346588083
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      value: 23.92611683848804
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      value: 45.82719685812465
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      value: -29.749167156574195
    - type: nauc_mrr_at_20_max
      value: 25.24789339604172
    - type: nauc_mrr_at_20_std
      value: 47.55242014501294
    - type: nauc_mrr_at_3_diff1
      value: -29.749167156574195
    - type: nauc_mrr_at_3_max
      value: 25.24789339604172
    - type: nauc_mrr_at_3_std
      value: 47.55242014501294
    - type: nauc_mrr_at_5_diff1
      value: -29.749167156574195
    - type: nauc_mrr_at_5_max
      value: 25.24789339604172
    - type: nauc_mrr_at_5_std
      value: 47.55242014501294
    - type: nauc_ndcg_at_1000_diff1
      value: -22.98398058353528
    - type: nauc_ndcg_at_1000_max
      value: 29.7425796111613
    - type: nauc_ndcg_at_1000_std
      value: 66.79973979577099
    - type: nauc_ndcg_at_100_diff1
      value: -24.302637816697096
    - type: nauc_ndcg_at_100_max
      value: 35.02366417892295
    - type: nauc_ndcg_at_100_std
      value: 73.11481424467178
    - type: nauc_ndcg_at_10_diff1
      value: -36.28539128782605
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      value: 22.467365655768294
    - type: nauc_ndcg_at_10_std
      value: 57.019631548174964
    - type: nauc_ndcg_at_1_diff1
      value: -31.390887834555393
    - type: nauc_ndcg_at_1_max
      value: 34.12862364077993
    - type: nauc_ndcg_at_1_std
      value: 42.54840979773444
    - type: nauc_ndcg_at_20_diff1
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      value: 26.976879472234245
    - type: nauc_ndcg_at_20_std
      value: 62.61724664065122
    - type: nauc_ndcg_at_3_diff1
      value: -42.816038008840906
    - type: nauc_ndcg_at_3_max
      value: 22.661518863218703
    - type: nauc_ndcg_at_3_std
      value: 43.96298336797927
    - type: nauc_ndcg_at_5_diff1
      value: -41.33190790544439
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      value: 21.470060099934962
    - type: nauc_ndcg_at_5_std
      value: 52.68059068309917
    - type: nauc_precision_at_1000_diff1
      value: -25.175496297929094
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      value: 31.092099302925973
    - type: nauc_precision_at_1000_std
      value: 55.28335657441642
    - type: nauc_precision_at_100_diff1
      value: -23.82235063790865
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      value: 38.82935361601115
    - type: nauc_precision_at_100_std
      value: 74.16286052994596
    - type: nauc_precision_at_10_diff1
      value: -31.256678895241897
    - type: nauc_precision_at_10_max
      value: 18.62764539335249
    - type: nauc_precision_at_10_std
      value: 55.088078791684566
    - type: nauc_precision_at_1_diff1
      value: -23.429062346588083
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      value: 23.92611683848804
    - type: nauc_precision_at_1_std
      value: 45.82719685812465
    - type: nauc_precision_at_20_diff1
      value: -25.457473488205647
    - type: nauc_precision_at_20_max
      value: 27.56443770951519
    - type: nauc_precision_at_20_std
      value: 62.132518431743186
    - type: nauc_precision_at_3_diff1
      value: -43.08727076791597
    - type: nauc_precision_at_3_max
      value: 10.02582635030932
    - type: nauc_precision_at_3_std
      value: 36.253506995410675
    - type: nauc_precision_at_5_diff1
      value: -40.1801527075686
    - type: nauc_precision_at_5_max
      value: 14.947044956076013
    - type: nauc_precision_at_5_std
      value: 50.97875933897098
    - type: nauc_recall_at_1000_diff1
      value: -21.578407961752355
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      value: 26.522130981068624
    - type: nauc_recall_at_1000_std
      value: 55.695556653573455
    - type: nauc_recall_at_100_diff1
      value: -10.077178296188565
    - type: nauc_recall_at_100_max
      value: 17.0587187509344
    - type: nauc_recall_at_100_std
      value: 36.697069293841245
    - type: nauc_recall_at_10_diff1
      value: -6.87042245147553
    - type: nauc_recall_at_10_max
      value: -4.148521545209006
    - type: nauc_recall_at_10_std
      value: 6.698236085537414
    - type: nauc_recall_at_1_diff1
      value: -0.02833948708031013
    - type: nauc_recall_at_1_max
      value: -9.58520783682369
    - type: nauc_recall_at_1_std
      value: -5.49929878351851
    - type: nauc_recall_at_20_diff1
      value: -5.820248045074897
    - type: nauc_recall_at_20_max
      value: 2.8626171367487934
    - type: nauc_recall_at_20_std
      value: 13.449577793915923
    - type: nauc_recall_at_3_diff1
      value: -9.773766102342227
    - type: nauc_recall_at_3_max
      value: -5.58368366828728
    - type: nauc_recall_at_3_std
      value: 0.10625733818166493
    - type: nauc_recall_at_5_diff1
      value: -7.531560179183814
    - type: nauc_recall_at_5_max
      value: -5.10431775402095
    - type: nauc_recall_at_5_std
      value: 2.9170078559993304
    - type: ndcg_at_1
      value: 77.0
    - type: ndcg_at_10
      value: 68.527
    - type: ndcg_at_100
      value: 48.405
    - type: ndcg_at_1000
      value: 44.595
    - type: ndcg_at_20
      value: 62.306
    - type: ndcg_at_3
      value: 72.939
    - type: ndcg_at_5
      value: 72.416
    - type: precision_at_1
      value: 84.0
    - type: precision_at_10
      value: 73.0
    - type: precision_at_100
      value: 49.08
    - type: precision_at_1000
      value: 19.836000000000002
    - type: precision_at_20
      value: 64.7
    - type: precision_at_3
      value: 78.0
    - type: precision_at_5
      value: 78.0
    - type: recall_at_1
      value: 0.22799999999999998
    - type: recall_at_10
      value: 1.881
    - type: recall_at_100
      value: 11.863
    - type: recall_at_1000
      value: 42.081
    - type: recall_at_20
      value: 3.2520000000000002
    - type: recall_at_3
      value: 0.5950000000000001
    - type: recall_at_5
      value: 1.016
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB Touche2020 (default)
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
      split: test
      type: mteb/touche2020
    metrics:
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      value: 13.937
    - type: map_at_1
      value: 1.2930000000000001
    - type: map_at_10
      value: 5.743
    - type: map_at_100
      value: 9.267
    - type: map_at_1000
      value: 10.742
    - type: map_at_20
      value: 7.303
    - type: map_at_3
      value: 2.896
    - type: map_at_5
      value: 3.939
    - type: mrr_at_1
      value: 14.285714285714285
    - type: mrr_at_10
      value: 26.15889212827988
    - type: mrr_at_100
      value: 27.768355317823477
    - type: mrr_at_1000
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    - type: mrr_at_20
      value: 27.13341930178665
    - type: mrr_at_3
      value: 22.448979591836736
    - type: mrr_at_5
      value: 25.102040816326532
    - type: nauc_map_at_1000_diff1
      value: -6.418619908635835
    - type: nauc_map_at_1000_max
      value: -29.16827935908967
    - type: nauc_map_at_1000_std
      value: 8.072487536940496
    - type: nauc_map_at_100_diff1
      value: -4.085439678921278
    - type: nauc_map_at_100_max
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    - type: nauc_map_at_100_std
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    - type: nauc_map_at_10_diff1
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    - type: nauc_map_at_10_max
      value: -35.69459573312886
    - type: nauc_map_at_10_std
      value: -12.779839188453062
    - type: nauc_map_at_1_diff1
      value: -2.3057823615493915
    - type: nauc_map_at_1_max
      value: -33.66603052318725
    - type: nauc_map_at_1_std
      value: -13.676412668949379
    - type: nauc_map_at_20_diff1
      value: -6.3310356612575545
    - type: nauc_map_at_20_max
      value: -32.01339434864334
    - type: nauc_map_at_20_std
      value: -6.815509360579602
    - type: nauc_map_at_3_diff1
      value: -1.133178599145889
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      value: 16.54
    - type: recall_at_3
      value: 3.9669999999999996
    - type: recall_at_5
      value: 6.354
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ToxicConversationsClassification (default)
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
      split: test
      type: mteb/toxic_conversations_50k
    metrics:
    - type: accuracy
      value: 75.3662109375
    - type: ap
      value: 17.520455604109124
    - type: ap_weighted
      value: 17.520455604109124
    - type: f1
      value: 58.701834404711065
    - type: f1_weighted
      value: 80.58084750047006
    - type: main_score
      value: 75.3662109375
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TweetSentimentExtractionClassification (default)
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
      split: test
      type: mteb/tweet_sentiment_extraction
    metrics:
    - type: accuracy
      value: 67.64855687606112
    - type: f1
      value: 67.95235466328752
    - type: f1_weighted
      value: 67.05421511780088
    - type: main_score
      value: 67.64855687606112
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TwitterSemEval2015 (default)
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
      split: test
      type: mteb/twittersemeval2015-pairclassification
    metrics:
    - type: cosine_accuracy
      value: 83.14358943792097
    - type: cosine_accuracy_threshold
      value: 82.20133781433105
    - type: cosine_ap
      value: 63.42168849627343
    - type: cosine_f1
      value: 60.19512195121952
    - type: cosine_f1_threshold
      value: 78.19918394088745
    - type: cosine_precision
      value: 55.96371882086167
    - type: cosine_recall
      value: 65.11873350923483
    - type: dot_accuracy
      value: 83.14358943792097
    - type: dot_accuracy_threshold
      value: 82.20133781433105
    - type: dot_ap
      value: 63.42166690978694
    - type: dot_f1
      value: 60.19512195121952
    - type: dot_f1_threshold
      value: 78.19918394088745
    - type: dot_precision
      value: 55.96371882086167
    - type: dot_recall
      value: 65.11873350923483
    - type: euclidean_accuracy
      value: 83.14358943792097
    - type: euclidean_accuracy_threshold
      value: 59.66349244117737
    - type: euclidean_ap
      value: 63.42169082838296
    - type: euclidean_f1
      value: 60.19512195121952
    - type: euclidean_f1_threshold
      value: 66.03152751922607
    - type: euclidean_precision
      value: 55.96371882086167
    - type: euclidean_recall
      value: 65.11873350923483
    - type: main_score
      value: 63.562161347378336
    - type: manhattan_accuracy
      value: 83.17339214400667
    - type: manhattan_accuracy_threshold
      value: 2131.719970703125
    - type: manhattan_ap
      value: 63.562161347378336
    - type: manhattan_f1
      value: 60.42120551924474
    - type: manhattan_f1_threshold
      value: 2370.509910583496
    - type: manhattan_precision
      value: 55.81395348837209
    - type: manhattan_recall
      value: 65.85751978891821
    - type: max_accuracy
      value: 83.17339214400667
    - type: max_ap
      value: 63.562161347378336
    - type: max_f1
      value: 60.42120551924474
    - type: max_precision
      value: 55.96371882086167
    - type: max_recall
      value: 65.85751978891821
    - type: similarity_accuracy
      value: 83.14358943792097
    - type: similarity_accuracy_threshold
      value: 82.20133781433105
    - type: similarity_ap
      value: 63.42168849627343
    - type: similarity_f1
      value: 60.19512195121952
    - type: similarity_f1_threshold
      value: 78.19918394088745
    - type: similarity_precision
      value: 55.96371882086167
    - type: similarity_recall
      value: 65.11873350923483
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB TwitterURLCorpus (default)
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
      split: test
      type: mteb/twitterurlcorpus-pairclassification
    metrics:
    - type: cosine_accuracy
      value: 88.78798463150541
    - type: cosine_accuracy_threshold
      value: 79.96333837509155
    - type: cosine_ap
      value: 86.19745333095996
    - type: cosine_f1
      value: 77.74792925901053
    - type: cosine_f1_threshold
      value: 77.78993844985962
    - type: cosine_precision
      value: 75.42348342261474
    - type: cosine_recall
      value: 80.2202032645519
    - type: dot_accuracy
      value: 88.78798463150541
    - type: dot_accuracy_threshold
      value: 79.96333241462708
    - type: dot_ap
      value: 86.19745428025062
    - type: dot_f1
      value: 77.74792925901053
    - type: dot_f1_threshold
      value: 77.78993844985962
    - type: dot_precision
      value: 75.42348342261474
    - type: dot_recall
      value: 80.2202032645519
    - type: euclidean_accuracy
      value: 88.78798463150541
    - type: euclidean_accuracy_threshold
      value: 63.303494453430176
    - type: euclidean_ap
      value: 86.19745308448735
    - type: euclidean_f1
      value: 77.74792925901053
    - type: euclidean_f1_threshold
      value: 66.64841771125793
    - type: euclidean_precision
      value: 75.42348342261474
    - type: euclidean_recall
      value: 80.2202032645519
    - type: main_score
      value: 86.25813794250404
    - type: manhattan_accuracy
      value: 88.79962743043428
    - type: manhattan_accuracy_threshold
      value: 2239.702606201172
    - type: manhattan_ap
      value: 86.25813794250404
    - type: manhattan_f1
      value: 77.84831272970264
    - type: manhattan_f1_threshold
      value: 2393.1716918945312
    - type: manhattan_precision
      value: 75.16667861495448
    - type: manhattan_recall
      value: 80.72836464428703
    - type: max_accuracy
      value: 88.79962743043428
    - type: max_ap
      value: 86.25813794250404
    - type: max_f1
      value: 77.84831272970264
    - type: max_precision
      value: 75.42348342261474
    - type: max_recall
      value: 80.72836464428703
    - type: similarity_accuracy
      value: 88.78798463150541
    - type: similarity_accuracy_threshold
      value: 79.96333837509155
    - type: similarity_ap
      value: 86.19745333095996
    - type: similarity_f1
      value: 77.74792925901053
    - type: similarity_f1_threshold
      value: 77.78993844985962
    - type: similarity_precision
      value: 75.42348342261474
    - type: similarity_recall
      value: 80.2202032645519
    task:
      type: PairClassification
tags:
- mteb
license: mit
language:
- ru
- en
pipeline_tag: feature-extraction
---
## Giga-Embeddings-instruct
- Base Decoder-only LLM: GigaChat-3b
- Pooling Type: Latent-Attention
- Embedding Dimension: 2048

## Использование

Ниже приведен пример кодирования запросов и текстов.


### Transformers

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

# Each query needs to be accompanied by an corresponding instruction describing the task.
task_name_to_instruct = {"example": "Given a question, retrieve passages that answer the question",}

query_prefix = task_name_to_instruct["example"] + "\nquestion: "
queries = [
    'are judo throws allowed in wrestling?', 
    'how to become a radiology technician in michigan?'
]

# No instruction needed for retrieval passages
passage_prefix = ""
passages = [
    "Since you're reading this, you are probably someone from a judo background or someone who is just wondering how judo techniques can be applied under wrestling rules. So without further ado, let's get to the question. Are Judo throws allowed in wrestling? Yes, judo throws are allowed in freestyle and folkstyle wrestling. You only need to be careful to follow the slam rules when executing judo throws. In wrestling, a slam is lifting and returning an opponent to the mat with unnecessary force.",
    "Below are the basic steps to becoming a radiologic technologist in Michigan:Earn a high school diploma. As with most careers in health care, a high school education is the first step to finding entry-level employment. Taking classes in math and science, such as anatomy, biology, chemistry, physiology, and physics, can help prepare students for their college studies and future careers.Earn an associate degree. Entry-level radiologic positions typically require at least an Associate of Applied Science. Before enrolling in one of these degree programs, students should make sure it has been properly accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT).Get licensed or certified in the state of Michigan."
]

# load model with tokenizer
model = AutoModel.from_pretrained('ai-sage/Giga-Embeddings-instruct', trust_remote_code=True)

# get the embeddings
query_embeddings = model.encode(queries, instruction=query_prefix)
passage_embeddings = model.encode(passages, instruction=passage_prefix)

# normalize embeddings
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
passage_embeddings = F.normalize(passage_embeddings, p=2, dim=1)

scores = (query_embeddings @ passage_embeddings.T) * 100
print(scores.tolist())
```

## Поддерживаемые языки

Эта модель инициализирована pretrain моделью GigaChat и дополнительно обучена на смеси английских и русских данных. Однако, поскольку pretrain GigaChat'a делался в основном на русскоязычных данных, мы рекомендуем использовать эту модель только для русского языка.

## FAQ

1. Нужно ли добавлять инструкции к запросу?

Да, именно так модель обучалась, иначе вы увидите снижение производительности. Определение задачи должно быть инструкцией в одном предложении, которая описывает задачу. Это способ настройки текстовых эмбеддингов для разных сценариев с помощью инструкций на естественном языке.

С другой стороны, добавлять инструкции на сторону документа не требуется.

2. Почему мои воспроизведённые результаты немного отличаются от указанных в карточке модели?

Разные версии библиотек transformers и pytorch могут вызывать незначительные, но ненулевые различия в производительности.


## Ограничения

Использование этой модели для входных данных, содержащих более 4096 токенов, невозможно.