---
tags:
- mteb
model-index:
- name: mmarco-sentence-flare-it
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 66.28358208955223
    - type: ap
      value: 28.583712225399804
    - type: f1
      value: 59.773975520814645
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (de)
      config: de
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 49.28265524625267
    - type: ap
      value: 70.12705711793366
    - type: f1
      value: 46.9152621753021
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en-ext)
      config: en-ext
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 61.13943028485757
    - type: ap
      value: 15.393299134540122
    - type: f1
      value: 50.441499676740754
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (ja)
      config: ja
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 44.85010706638115
    - type: ap
      value: 11.24959111812915
    - type: f1
      value: 38.4896899038441
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 53.786350000000006
    - type: ap
      value: 52.711619488611895
    - type: f1
      value: 52.08639681443221
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 22.954
    - type: f1
      value: 20.895324325359304
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (de)
      config: de
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 22.016
    - type: f1
      value: 20.141551433471214
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (es)
      config: es
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 23.842000000000002
    - type: f1
      value: 22.360764368564414
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (fr)
      config: fr
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 24.534000000000002
    - type: f1
      value: 23.348432665500937
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (ja)
      config: ja
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 20.183999999999997
    - type: f1
      value: 17.025753479408394
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (zh)
      config: zh
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 20.226000000000003
    - type: f1
      value: 17.949454130689396
  - task:
      type: Retrieval
    dataset:
      type: arguana
      name: MTEB ArguAna
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.64
    - type: map_at_10
      value: 1.109
    - type: map_at_100
      value: 1.214
    - type: map_at_1000
      value: 1.273
    - type: map_at_3
      value: 0.936
    - type: map_at_5
      value: 1.032
    - type: mrr_at_1
      value: 0.64
    - type: mrr_at_10
      value: 1.109
    - type: mrr_at_100
      value: 1.214
    - type: mrr_at_1000
      value: 1.273
    - type: mrr_at_3
      value: 0.936
    - type: mrr_at_5
      value: 1.032
    - type: ndcg_at_1
      value: 0.64
    - type: ndcg_at_10
      value: 1.401
    - type: ndcg_at_100
      value: 2.106
    - type: ndcg_at_1000
      value: 4.484
    - type: ndcg_at_3
      value: 1.042
    - type: ndcg_at_5
      value: 1.217
    - type: precision_at_1
      value: 0.64
    - type: precision_at_10
      value: 0.23500000000000001
    - type: precision_at_100
      value: 0.061
    - type: precision_at_1000
      value: 0.027
    - type: precision_at_3
      value: 0.44999999999999996
    - type: precision_at_5
      value: 0.356
    - type: recall_at_1
      value: 0.64
    - type: recall_at_10
      value: 2.347
    - type: recall_at_100
      value: 6.117
    - type: recall_at_1000
      value: 26.671
    - type: recall_at_3
      value: 1.351
    - type: recall_at_5
      value: 1.778
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-p2p
      name: MTEB ArxivClusteringP2P
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 10.337297492580117
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-s2s
      name: MTEB ArxivClusteringS2S
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 8.41067718068448
  - task:
      type: Reranking
    dataset:
      type: mteb/askubuntudupquestions-reranking
      name: MTEB AskUbuntuDupQuestions
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 39.45372039138711
    - type: mrr
      value: 49.48005979861936
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 2.1060056434908527
    - type: cos_sim_spearman
      value: -6.396531291473412
    - type: euclidean_pearson
      value: -1.0319749731423296
    - type: euclidean_spearman
      value: -5.283855335987313
    - type: manhattan_pearson
      value: -5.66609061890471
    - type: manhattan_spearman
      value: -7.173055009189482
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 17.435064935064933
    - type: f1
      value: 15.631665237965379
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-p2p
      name: MTEB BiorxivClusteringP2P
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 4.01285243931824
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-s2s
      name: MTEB BiorxivClusteringS2S
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 3.0046123718115685
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackAndroidRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.001
    - type: map_at_10
      value: 0.023
    - type: map_at_100
      value: 0.034999999999999996
    - type: map_at_1000
      value: 0.042
    - type: map_at_3
      value: 0.001
    - type: map_at_5
      value: 0.001
    - type: mrr_at_1
      value: 0.14300000000000002
    - type: mrr_at_10
      value: 0.173
    - type: mrr_at_100
      value: 0.203
    - type: mrr_at_1000
      value: 0.216
    - type: mrr_at_3
      value: 0.14300000000000002
    - type: mrr_at_5
      value: 0.14300000000000002
    - type: ndcg_at_1
      value: 0.14300000000000002
    - type: ndcg_at_10
      value: 0.11
    - type: ndcg_at_100
      value: 0.174
    - type: ndcg_at_1000
      value: 0.526
    - type: ndcg_at_3
      value: 0.067
    - type: ndcg_at_5
      value: 0.049
    - type: precision_at_1
      value: 0.14300000000000002
    - type: precision_at_10
      value: 0.056999999999999995
    - type: precision_at_100
      value: 0.02
    - type: precision_at_1000
      value: 0.01
    - type: precision_at_3
      value: 0.048
    - type: precision_at_5
      value: 0.029
    - type: recall_at_1
      value: 0.001
    - type: recall_at_10
      value: 0.216
    - type: recall_at_100
      value: 0.629
    - type: recall_at_1000
      value: 3.1940000000000004
    - type: recall_at_3
      value: 0.001
    - type: recall_at_5
      value: 0.001
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackEnglishRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.04
    - type: map_at_100
      value: 0.058
    - type: map_at_1000
      value: 0.065
    - type: map_at_3
      value: 0.033
    - type: map_at_5
      value: 0.04
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.066
    - type: mrr_at_100
      value: 0.099
    - type: mrr_at_1000
      value: 0.11
    - type: mrr_at_3
      value: 0.053
    - type: mrr_at_5
      value: 0.066
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.062
    - type: ndcg_at_100
      value: 0.182
    - type: ndcg_at_1000
      value: 0.494
    - type: ndcg_at_3
      value: 0.055
    - type: ndcg_at_5
      value: 0.066
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.019
    - type: precision_at_100
      value: 0.012
    - type: precision_at_1000
      value: 0.006
    - type: precision_at_3
      value: 0.042
    - type: precision_at_5
      value: 0.038
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.1
    - type: recall_at_100
      value: 0.626
    - type: recall_at_1000
      value: 3.012
    - type: recall_at_3
      value: 0.068
    - type: recall_at_5
      value: 0.1
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackGamingRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.01
    - type: map_at_100
      value: 0.019
    - type: map_at_1000
      value: 0.026
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.0
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.01
    - type: mrr_at_100
      value: 0.019
    - type: mrr_at_1000
      value: 0.027
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.0
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.022000000000000002
    - type: ndcg_at_100
      value: 0.09
    - type: ndcg_at_1000
      value: 0.35500000000000004
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.0
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.006
    - type: precision_at_100
      value: 0.004
    - type: precision_at_1000
      value: 0.003
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.0
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.063
    - type: recall_at_100
      value: 0.439
    - type: recall_at_1000
      value: 2.576
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.0
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackGisRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.22599999999999998
    - type: map_at_10
      value: 0.22599999999999998
    - type: map_at_100
      value: 0.23500000000000001
    - type: map_at_1000
      value: 0.241
    - type: map_at_3
      value: 0.22599999999999998
    - type: map_at_5
      value: 0.22599999999999998
    - type: mrr_at_1
      value: 0.22599999999999998
    - type: mrr_at_10
      value: 0.22599999999999998
    - type: mrr_at_100
      value: 0.23800000000000002
    - type: mrr_at_1000
      value: 0.244
    - type: mrr_at_3
      value: 0.22599999999999998
    - type: mrr_at_5
      value: 0.22599999999999998
    - type: ndcg_at_1
      value: 0.22599999999999998
    - type: ndcg_at_10
      value: 0.22599999999999998
    - type: ndcg_at_100
      value: 0.317
    - type: ndcg_at_1000
      value: 0.584
    - type: ndcg_at_3
      value: 0.22599999999999998
    - type: ndcg_at_5
      value: 0.22599999999999998
    - type: precision_at_1
      value: 0.22599999999999998
    - type: precision_at_10
      value: 0.023
    - type: precision_at_100
      value: 0.009000000000000001
    - type: precision_at_1000
      value: 0.004
    - type: precision_at_3
      value: 0.075
    - type: precision_at_5
      value: 0.045
    - type: recall_at_1
      value: 0.22599999999999998
    - type: recall_at_10
      value: 0.22599999999999998
    - type: recall_at_100
      value: 0.732
    - type: recall_at_1000
      value: 2.951
    - type: recall_at_3
      value: 0.22599999999999998
    - type: recall_at_5
      value: 0.22599999999999998
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackMathematicaRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.062
    - type: map_at_10
      value: 0.062
    - type: map_at_100
      value: 0.08800000000000001
    - type: map_at_1000
      value: 0.1
    - type: map_at_3
      value: 0.062
    - type: map_at_5
      value: 0.062
    - type: mrr_at_1
      value: 0.124
    - type: mrr_at_10
      value: 0.124
    - type: mrr_at_100
      value: 0.173
    - type: mrr_at_1000
      value: 0.191
    - type: mrr_at_3
      value: 0.124
    - type: mrr_at_5
      value: 0.124
    - type: ndcg_at_1
      value: 0.124
    - type: ndcg_at_10
      value: 0.076
    - type: ndcg_at_100
      value: 0.27
    - type: ndcg_at_1000
      value: 0.7849999999999999
    - type: ndcg_at_3
      value: 0.076
    - type: ndcg_at_5
      value: 0.076
    - type: precision_at_1
      value: 0.124
    - type: precision_at_10
      value: 0.012
    - type: precision_at_100
      value: 0.02
    - type: precision_at_1000
      value: 0.009000000000000001
    - type: precision_at_3
      value: 0.041
    - type: precision_at_5
      value: 0.025
    - type: recall_at_1
      value: 0.062
    - type: recall_at_10
      value: 0.062
    - type: recall_at_100
      value: 0.9119999999999999
    - type: recall_at_1000
      value: 4.809
    - type: recall_at_3
      value: 0.062
    - type: recall_at_5
      value: 0.062
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackPhysicsRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.043
    - type: map_at_100
      value: 0.061
    - type: map_at_1000
      value: 0.06999999999999999
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.043
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.043
    - type: mrr_at_100
      value: 0.06899999999999999
    - type: mrr_at_1000
      value: 0.079
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.043
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.079
    - type: ndcg_at_100
      value: 0.22599999999999998
    - type: ndcg_at_1000
      value: 0.5579999999999999
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.079
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.019
    - type: precision_at_100
      value: 0.013
    - type: precision_at_1000
      value: 0.005
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.038
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.192
    - type: recall_at_100
      value: 0.918
    - type: recall_at_1000
      value: 3.5909999999999997
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.192
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackProgrammersRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.04
    - type: map_at_100
      value: 0.044000000000000004
    - type: map_at_1000
      value: 0.052
    - type: map_at_3
      value: 0.038
    - type: map_at_5
      value: 0.038
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.054
    - type: mrr_at_100
      value: 0.07200000000000001
    - type: mrr_at_1000
      value: 0.084
    - type: mrr_at_3
      value: 0.038
    - type: mrr_at_5
      value: 0.038
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.067
    - type: ndcg_at_100
      value: 0.10300000000000001
    - type: ndcg_at_1000
      value: 0.488
    - type: ndcg_at_3
      value: 0.056999999999999995
    - type: ndcg_at_5
      value: 0.056999999999999995
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.023
    - type: precision_at_100
      value: 0.006999999999999999
    - type: precision_at_1000
      value: 0.006
    - type: precision_at_3
      value: 0.038
    - type: precision_at_5
      value: 0.023
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.128
    - type: recall_at_100
      value: 0.248
    - type: recall_at_1000
      value: 3.36
    - type: recall_at_3
      value: 0.11399999999999999
    - type: recall_at_5
      value: 0.11399999999999999
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackStatsRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.0
    - type: map_at_100
      value: 0.013999999999999999
    - type: map_at_1000
      value: 0.023
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.0
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.0
    - type: mrr_at_100
      value: 0.03
    - type: mrr_at_1000
      value: 0.045
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.0
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.0
    - type: ndcg_at_100
      value: 0.066
    - type: ndcg_at_1000
      value: 0.445
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.0
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.0
    - type: precision_at_100
      value: 0.005
    - type: precision_at_1000
      value: 0.005
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.0
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.0
    - type: recall_at_100
      value: 0.24
    - type: recall_at_1000
      value: 3.1559999999999997
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.0
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackTexRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.0
    - type: map_at_100
      value: 0.006999999999999999
    - type: map_at_1000
      value: 0.012
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.0
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.0
    - type: mrr_at_100
      value: 0.011000000000000001
    - type: mrr_at_1000
      value: 0.018000000000000002
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.0
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.0
    - type: ndcg_at_100
      value: 0.055
    - type: ndcg_at_1000
      value: 0.254
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.0
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.0
    - type: precision_at_100
      value: 0.004
    - type: precision_at_1000
      value: 0.003
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.0
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.0
    - type: recall_at_100
      value: 0.27599999999999997
    - type: recall_at_1000
      value: 1.828
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.0
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackUnixRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.023
    - type: map_at_100
      value: 0.031
    - type: map_at_1000
      value: 0.038
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.023
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.023
    - type: mrr_at_100
      value: 0.039
    - type: mrr_at_1000
      value: 0.048
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.023
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.04
    - type: ndcg_at_100
      value: 0.133
    - type: ndcg_at_1000
      value: 0.395
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.04
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.009000000000000001
    - type: precision_at_100
      value: 0.009000000000000001
    - type: precision_at_1000
      value: 0.004
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.019
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.093
    - type: recall_at_100
      value: 0.598
    - type: recall_at_1000
      value: 2.59
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.093
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackWebmastersRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.0
    - type: map_at_100
      value: 0.015
    - type: map_at_1000
      value: 0.03
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.0
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.0
    - type: mrr_at_100
      value: 0.062
    - type: mrr_at_1000
      value: 0.083
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.0
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.0
    - type: ndcg_at_100
      value: 0.17700000000000002
    - type: ndcg_at_1000
      value: 0.9299999999999999
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.0
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.0
    - type: precision_at_100
      value: 0.027999999999999997
    - type: precision_at_1000
      value: 0.023
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.0
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.0
    - type: recall_at_100
      value: 0.894
    - type: recall_at_1000
      value: 6.639
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.0
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackWordpressRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.0
    - type: map_at_100
      value: 0.0
    - type: map_at_1000
      value: 0.006999999999999999
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.0
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.0
    - type: mrr_at_100
      value: 0.0
    - type: mrr_at_1000
      value: 0.009000000000000001
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.0
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.0
    - type: ndcg_at_100
      value: 0.0
    - type: ndcg_at_1000
      value: 0.35200000000000004
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.0
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.0
    - type: precision_at_100
      value: 0.0
    - type: precision_at_1000
      value: 0.004
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.0
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.0
    - type: recall_at_100
      value: 0.0
    - type: recall_at_1000
      value: 2.864
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.0
  - task:
      type: Retrieval
    dataset:
      type: climate-fever
      name: MTEB ClimateFEVER
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.033
    - type: map_at_10
      value: 0.034
    - type: map_at_100
      value: 0.037
    - type: map_at_1000
      value: 0.039
    - type: map_at_3
      value: 0.033
    - type: map_at_5
      value: 0.033
    - type: mrr_at_1
      value: 0.065
    - type: mrr_at_10
      value: 0.07200000000000001
    - type: mrr_at_100
      value: 0.08
    - type: mrr_at_1000
      value: 0.086
    - type: mrr_at_3
      value: 0.065
    - type: mrr_at_5
      value: 0.065
    - type: ndcg_at_1
      value: 0.065
    - type: ndcg_at_10
      value: 0.047
    - type: ndcg_at_100
      value: 0.079
    - type: ndcg_at_1000
      value: 0.19
    - type: ndcg_at_3
      value: 0.04
    - type: ndcg_at_5
      value: 0.04
    - type: precision_at_1
      value: 0.065
    - type: precision_at_10
      value: 0.013
    - type: precision_at_100
      value: 0.005
    - type: precision_at_1000
      value: 0.002
    - type: precision_at_3
      value: 0.022000000000000002
    - type: precision_at_5
      value: 0.013
    - type: recall_at_1
      value: 0.033
    - type: recall_at_10
      value: 0.049
    - type: recall_at_100
      value: 0.186
    - type: recall_at_1000
      value: 0.9199999999999999
    - type: recall_at_3
      value: 0.033
    - type: recall_at_5
      value: 0.033
  - task:
      type: Retrieval
    dataset:
      type: dbpedia-entity
      name: MTEB DBPedia
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.0
    - type: map_at_100
      value: 0.008
    - type: map_at_1000
      value: 0.008
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.0
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.0
    - type: mrr_at_100
      value: 0.066
    - type: mrr_at_1000
      value: 0.077
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.0
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.0
    - type: ndcg_at_100
      value: 0.08
    - type: ndcg_at_1000
      value: 0.131
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.0
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.0
    - type: precision_at_100
      value: 0.018000000000000002
    - type: precision_at_1000
      value: 0.006999999999999999
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.0
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.0
    - type: recall_at_100
      value: 0.133
    - type: recall_at_1000
      value: 0.293
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.0
  - task:
      type: Classification
    dataset:
      type: mteb/emotion
      name: MTEB EmotionClassification
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 19.57
    - type: f1
      value: 16.51103261738041
  - task:
      type: Retrieval
    dataset:
      type: fever
      name: MTEB FEVER
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.068
    - type: map_at_10
      value: 0.104
    - type: map_at_100
      value: 0.11
    - type: map_at_1000
      value: 0.11299999999999999
    - type: map_at_3
      value: 0.095
    - type: map_at_5
      value: 0.098
    - type: mrr_at_1
      value: 0.075
    - type: mrr_at_10
      value: 0.11299999999999999
    - type: mrr_at_100
      value: 0.11900000000000001
    - type: mrr_at_1000
      value: 0.123
    - type: mrr_at_3
      value: 0.10300000000000001
    - type: mrr_at_5
      value: 0.106
    - type: ndcg_at_1
      value: 0.075
    - type: ndcg_at_10
      value: 0.128
    - type: ndcg_at_100
      value: 0.167
    - type: ndcg_at_1000
      value: 0.291
    - type: ndcg_at_3
      value: 0.105
    - type: ndcg_at_5
      value: 0.11100000000000002
    - type: precision_at_1
      value: 0.075
    - type: precision_at_10
      value: 0.021
    - type: precision_at_100
      value: 0.004
    - type: precision_at_1000
      value: 0.002
    - type: precision_at_3
      value: 0.045
    - type: precision_at_5
      value: 0.03
    - type: recall_at_1
      value: 0.068
    - type: recall_at_10
      value: 0.19499999999999998
    - type: recall_at_100
      value: 0.40299999999999997
    - type: recall_at_1000
      value: 1.448
    - type: recall_at_3
      value: 0.128
    - type: recall_at_5
      value: 0.14300000000000002
  - task:
      type: Retrieval
    dataset:
      type: fiqa
      name: MTEB FiQA2018
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.0
    - type: map_at_100
      value: 0.005
    - type: map_at_1000
      value: 0.01
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.0
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.0
    - type: mrr_at_100
      value: 0.011000000000000001
    - type: mrr_at_1000
      value: 0.026
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.0
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.0
    - type: ndcg_at_100
      value: 0.06999999999999999
    - type: ndcg_at_1000
      value: 0.38899999999999996
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.0
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.0
    - type: precision_at_100
      value: 0.008
    - type: precision_at_1000
      value: 0.006999999999999999
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.0
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.0
    - type: recall_at_100
      value: 0.383
    - type: recall_at_1000
      value: 2.435
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.0
  - task:
      type: Retrieval
    dataset:
      type: hotpotqa
      name: MTEB HotpotQA
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.013999999999999999
    - type: map_at_10
      value: 0.018000000000000002
    - type: map_at_100
      value: 0.019
    - type: map_at_1000
      value: 0.02
    - type: map_at_3
      value: 0.016
    - type: map_at_5
      value: 0.016
    - type: mrr_at_1
      value: 0.027
    - type: mrr_at_10
      value: 0.034999999999999996
    - type: mrr_at_100
      value: 0.038
    - type: mrr_at_1000
      value: 0.039
    - type: mrr_at_3
      value: 0.032
    - type: mrr_at_5
      value: 0.032
    - type: ndcg_at_1
      value: 0.027
    - type: ndcg_at_10
      value: 0.026
    - type: ndcg_at_100
      value: 0.038
    - type: ndcg_at_1000
      value: 0.064
    - type: ndcg_at_3
      value: 0.021
    - type: ndcg_at_5
      value: 0.021
    - type: precision_at_1
      value: 0.027
    - type: precision_at_10
      value: 0.006999999999999999
    - type: precision_at_100
      value: 0.002
    - type: precision_at_1000
      value: 0.001
    - type: precision_at_3
      value: 0.013999999999999999
    - type: precision_at_5
      value: 0.008
    - type: recall_at_1
      value: 0.013999999999999999
    - type: recall_at_10
      value: 0.034
    - type: recall_at_100
      value: 0.08800000000000001
    - type: recall_at_1000
      value: 0.27
    - type: recall_at_3
      value: 0.02
    - type: recall_at_5
      value: 0.02
  - task:
      type: Classification
    dataset:
      type: mteb/imdb
      name: MTEB ImdbClassification
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 55.4624
    - type: ap
      value: 53.20545827965495
    - type: f1
      value: 54.40019244805333
  - task:
      type: Retrieval
    dataset:
      type: msmarco
      name: MTEB MSMARCO
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 0.086
    - type: map_at_10
      value: 0.109
    - type: map_at_100
      value: 0.11499999999999999
    - type: map_at_1000
      value: 0.11800000000000001
    - type: map_at_3
      value: 0.091
    - type: map_at_5
      value: 0.101
    - type: mrr_at_1
      value: 0.086
    - type: mrr_at_10
      value: 0.109
    - type: mrr_at_100
      value: 0.11499999999999999
    - type: mrr_at_1000
      value: 0.11800000000000001
    - type: mrr_at_3
      value: 0.091
    - type: mrr_at_5
      value: 0.101
    - type: ndcg_at_1
      value: 0.086
    - type: ndcg_at_10
      value: 0.133
    - type: ndcg_at_100
      value: 0.168
    - type: ndcg_at_1000
      value: 0.259
    - type: ndcg_at_3
      value: 0.093
    - type: ndcg_at_5
      value: 0.11100000000000002
    - type: precision_at_1
      value: 0.086
    - type: precision_at_10
      value: 0.021
    - type: precision_at_100
      value: 0.004
    - type: precision_at_1000
      value: 0.001
    - type: precision_at_3
      value: 0.033
    - type: precision_at_5
      value: 0.029
    - type: recall_at_1
      value: 0.086
    - type: recall_at_10
      value: 0.215
    - type: recall_at_100
      value: 0.387
    - type: recall_at_1000
      value: 1.1560000000000001
    - type: recall_at_3
      value: 0.1
    - type: recall_at_5
      value: 0.14300000000000002
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 34.42544459644323
    - type: f1
      value: 33.610930846065315
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (de)
      config: de
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 27.511975204282894
    - type: f1
      value: 25.84277270994464
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (es)
      config: es
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 29.51967978652435
    - type: f1
      value: 27.67290779782277
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (fr)
      config: fr
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 26.003758221108676
    - type: f1
      value: 23.831315642315282
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (hi)
      config: hi
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 20.132664037289356
    - type: f1
      value: 16.737043939830457
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (th)
      config: th
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 14.701627486437612
    - type: f1
      value: 10.849797498613762
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 13.948928408572733
    - type: f1
      value: 8.562615846233708
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (de)
      config: de
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 11.814595660749507
    - type: f1
      value: 5.353787568647624
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (es)
      config: es
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 12.468312208138759
    - type: f1
      value: 7.566990405355253
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (fr)
      config: fr
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 9.320388349514563
    - type: f1
      value: 5.916218245687591
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (hi)
      config: hi
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 3.2377196127644314
    - type: f1
      value: 1.2053714075016808
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (th)
      config: th
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 4.4159132007233275
    - type: f1
      value: 1.1992998118559788
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (it)
      config: it
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 22.299932750504368
    - type: f1
      value: 20.147804322480262
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (it)
      config: it
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 27.40753194351042
    - type: f1
      value: 25.187141587127705
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-p2p
      name: MTEB MedrxivClusteringP2P
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 11.797082944399047
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-s2s
      name: MTEB MedrxivClusteringS2S
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 11.059126362649126
  - task:
      type: Reranking
    dataset:
      type: mteb/mind_small
      name: MTEB MindSmallReranking
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 24.452301182586798
    - type: mrr
      value: 24.374807287562085
  - task:
      type: Retrieval
    dataset:
      type: nfcorpus
      name: MTEB NFCorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.03
    - type: map_at_10
      value: 0.075
    - type: map_at_100
      value: 0.208
    - type: map_at_1000
      value: 0.529
    - type: map_at_3
      value: 0.051000000000000004
    - type: map_at_5
      value: 0.055999999999999994
    - type: mrr_at_1
      value: 1.238
    - type: mrr_at_10
      value: 2.939
    - type: mrr_at_100
      value: 3.927
    - type: mrr_at_1000
      value: 4.117
    - type: mrr_at_3
      value: 1.806
    - type: mrr_at_5
      value: 2.286
    - type: ndcg_at_1
      value: 1.084
    - type: ndcg_at_10
      value: 1.133
    - type: ndcg_at_100
      value: 2.1399999999999997
    - type: ndcg_at_1000
      value: 9.362
    - type: ndcg_at_3
      value: 0.9299999999999999
    - type: ndcg_at_5
      value: 0.958
    - type: precision_at_1
      value: 1.238
    - type: precision_at_10
      value: 1.269
    - type: precision_at_100
      value: 1.155
    - type: precision_at_1000
      value: 1.0250000000000001
    - type: precision_at_3
      value: 1.032
    - type: precision_at_5
      value: 1.053
    - type: recall_at_1
      value: 0.03
    - type: recall_at_10
      value: 0.22200000000000003
    - type: recall_at_100
      value: 3.779
    - type: recall_at_1000
      value: 29.471000000000004
    - type: recall_at_3
      value: 0.087
    - type: recall_at_5
      value: 0.11199999999999999
  - task:
      type: Retrieval
    dataset:
      type: nq
      name: MTEB NQ
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.012
    - type: map_at_100
      value: 0.025
    - type: map_at_1000
      value: 0.027
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.006999999999999999
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.012
    - type: mrr_at_100
      value: 0.026
    - type: mrr_at_1000
      value: 0.029
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.006999999999999999
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.023
    - type: ndcg_at_100
      value: 0.092
    - type: ndcg_at_1000
      value: 0.16999999999999998
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.012
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.006
    - type: precision_at_100
      value: 0.004
    - type: precision_at_1000
      value: 0.001
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.006
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.058
    - type: recall_at_100
      value: 0.377
    - type: recall_at_1000
      value: 1.009
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.029
  - task:
      type: Retrieval
    dataset:
      type: quora
      name: MTEB QuoraRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 8.943
    - type: map_at_10
      value: 10.557
    - type: map_at_100
      value: 10.777000000000001
    - type: map_at_1000
      value: 10.812
    - type: map_at_3
      value: 10.137
    - type: map_at_5
      value: 10.351
    - type: mrr_at_1
      value: 10.51
    - type: mrr_at_10
      value: 12.229
    - type: mrr_at_100
      value: 12.468
    - type: mrr_at_1000
      value: 12.504999999999999
    - type: mrr_at_3
      value: 11.777
    - type: mrr_at_5
      value: 12.014
    - type: ndcg_at_1
      value: 10.5
    - type: ndcg_at_10
      value: 11.715
    - type: ndcg_at_100
      value: 12.925
    - type: ndcg_at_1000
      value: 14.163
    - type: ndcg_at_3
      value: 10.968
    - type: ndcg_at_5
      value: 11.264000000000001
    - type: precision_at_1
      value: 10.5
    - type: precision_at_10
      value: 1.696
    - type: precision_at_100
      value: 0.248
    - type: precision_at_1000
      value: 0.039
    - type: precision_at_3
      value: 4.623
    - type: precision_at_5
      value: 3.012
    - type: recall_at_1
      value: 8.943
    - type: recall_at_10
      value: 13.746
    - type: recall_at_100
      value: 19.521
    - type: recall_at_1000
      value: 29.255
    - type: recall_at_3
      value: 11.448
    - type: recall_at_5
      value: 12.332
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering
      name: MTEB RedditClustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 4.845410629021448
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering-p2p
      name: MTEB RedditClusteringP2P
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 11.661900277329933
  - task:
      type: Retrieval
    dataset:
      type: scidocs
      name: MTEB SCIDOCS
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.02
    - type: map_at_10
      value: 0.036000000000000004
    - type: map_at_100
      value: 0.056999999999999995
    - type: map_at_1000
      value: 0.07200000000000001
    - type: map_at_3
      value: 0.03
    - type: map_at_5
      value: 0.03
    - type: mrr_at_1
      value: 0.1
    - type: mrr_at_10
      value: 0.181
    - type: mrr_at_100
      value: 0.27899999999999997
    - type: mrr_at_1000
      value: 0.335
    - type: mrr_at_3
      value: 0.15
    - type: mrr_at_5
      value: 0.15
    - type: ndcg_at_1
      value: 0.1
    - type: ndcg_at_10
      value: 0.079
    - type: ndcg_at_100
      value: 0.28200000000000003
    - type: ndcg_at_1000
      value: 1.228
    - type: ndcg_at_3
      value: 0.077
    - type: ndcg_at_5
      value: 0.055
    - type: precision_at_1
      value: 0.1
    - type: precision_at_10
      value: 0.04
    - type: precision_at_100
      value: 0.034
    - type: precision_at_1000
      value: 0.027999999999999997
    - type: precision_at_3
      value: 0.067
    - type: precision_at_5
      value: 0.04
    - type: recall_at_1
      value: 0.02
    - type: recall_at_10
      value: 0.08
    - type: recall_at_100
      value: 0.703
    - type: recall_at_1000
      value: 5.632000000000001
    - type: recall_at_3
      value: 0.04
    - type: recall_at_5
      value: 0.04
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 22.985682461739827
    - type: cos_sim_spearman
      value: 36.63211990852576
    - type: euclidean_pearson
      value: 30.883409587497358
    - type: euclidean_spearman
      value: 36.94600975857584
    - type: manhattan_pearson
      value: 36.736693988156894
    - type: manhattan_spearman
      value: 38.98446799028811
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 2.5604178523517063
    - type: cos_sim_spearman
      value: 13.628378324133767
    - type: euclidean_pearson
      value: 7.9904894312005865
    - type: euclidean_spearman
      value: 15.090689818973416
    - type: manhattan_pearson
      value: 14.011092205465575
    - type: manhattan_spearman
      value: 18.04386210573924
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 18.59414271348264
    - type: cos_sim_spearman
      value: 23.758346452530105
    - type: euclidean_pearson
      value: 22.985667268384162
    - type: euclidean_spearman
      value: 25.143580728437183
    - type: manhattan_pearson
      value: 28.109316236003
    - type: manhattan_spearman
      value: 29.403691387442727
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 8.292349216262673
    - type: cos_sim_spearman
      value: 15.648383623810028
    - type: euclidean_pearson
      value: 12.136605941196938
    - type: euclidean_spearman
      value: 16.37547051924145
    - type: manhattan_pearson
      value: 21.049918496319524
    - type: manhattan_spearman
      value: 22.168125518695295
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 21.0574858763695
    - type: cos_sim_spearman
      value: 28.24306393347735
    - type: euclidean_pearson
      value: 25.67620587891895
    - type: euclidean_spearman
      value: 28.802005577995292
    - type: manhattan_pearson
      value: 33.333168689238846
    - type: manhattan_spearman
      value: 33.7249701052437
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 23.345013082866608
    - type: cos_sim_spearman
      value: 32.08654087568418
    - type: euclidean_pearson
      value: 29.1302480053082
    - type: euclidean_spearman
      value: 32.723960824054224
    - type: manhattan_pearson
      value: 34.73363269084969
    - type: manhattan_spearman
      value: 35.946509333697016
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (ko-ko)
      config: ko-ko
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: -1.0510693376748088
    - type: cos_sim_spearman
      value: 3.7330446273344897
    - type: euclidean_pearson
      value: -0.2108306777168949
    - type: euclidean_spearman
      value: 3.627369552634812
    - type: manhattan_pearson
      value: 1.5031538964733262
    - type: manhattan_spearman
      value: 5.004910973166412
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (ar-ar)
      config: ar-ar
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 11.578393453214481
    - type: cos_sim_spearman
      value: 21.790827126422034
    - type: euclidean_pearson
      value: 19.06071141618503
    - type: euclidean_spearman
      value: 22.161779839314196
    - type: manhattan_pearson
      value: 17.725623325242474
    - type: manhattan_spearman
      value: 20.43157514666076
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (en-ar)
      config: en-ar
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 17.29239442081278
    - type: cos_sim_spearman
      value: 16.292056207420202
    - type: euclidean_pearson
      value: 16.503491974377727
    - type: euclidean_spearman
      value: 15.541440440884651
    - type: manhattan_pearson
      value: 21.158901085317268
    - type: manhattan_spearman
      value: 21.781541830999963
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (en-de)
      config: en-de
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 22.589778414759955
    - type: cos_sim_spearman
      value: 18.997838545450612
    - type: euclidean_pearson
      value: 21.90016323186628
    - type: euclidean_spearman
      value: 18.905160536986692
    - type: manhattan_pearson
      value: 16.913499882046576
    - type: manhattan_spearman
      value: 15.669617887287327
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (en-en)
      config: en-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 33.05366168385912
    - type: cos_sim_spearman
      value: 37.781952608504135
    - type: euclidean_pearson
      value: 37.085941074268966
    - type: euclidean_spearman
      value: 37.8364215997913
    - type: manhattan_pearson
      value: 42.316206028770736
    - type: manhattan_spearman
      value: 41.74208275697782
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (en-tr)
      config: en-tr
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 23.991765076225654
    - type: cos_sim_spearman
      value: 20.588105042260104
    - type: euclidean_pearson
      value: 19.712724760717997
    - type: euclidean_spearman
      value: 19.253030106327383
    - type: manhattan_pearson
      value: 16.84198288544301
    - type: manhattan_spearman
      value: 17.61549197324614
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (es-en)
      config: es-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 24.968980346008365
    - type: cos_sim_spearman
      value: 27.252856647926286
    - type: euclidean_pearson
      value: 24.58496162769602
    - type: euclidean_spearman
      value: 26.034323771297824
    - type: manhattan_pearson
      value: 22.40058722998031
    - type: manhattan_spearman
      value: 24.459230575688714
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (es-es)
      config: es-es
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 32.94464820482554
    - type: cos_sim_spearman
      value: 40.462017863354085
    - type: euclidean_pearson
      value: 38.88676423326504
    - type: euclidean_spearman
      value: 40.47190508444464
    - type: manhattan_pearson
      value: 39.692234741895874
    - type: manhattan_spearman
      value: 39.478725017400166
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (fr-en)
      config: fr-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 23.342163445031748
    - type: cos_sim_spearman
      value: 27.27097487628386
    - type: euclidean_pearson
      value: 24.76789948947651
    - type: euclidean_spearman
      value: 25.81926286149811
    - type: manhattan_pearson
      value: 25.475723026689685
    - type: manhattan_spearman
      value: 25.813420033921787
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (it-en)
      config: it-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 7.717130622537516
    - type: cos_sim_spearman
      value: 9.437435044381033
    - type: euclidean_pearson
      value: 7.9423592614250555
    - type: euclidean_spearman
      value: 8.010785684725303
    - type: manhattan_pearson
      value: 8.217253457576026
    - type: manhattan_spearman
      value: 8.247881477427692
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (nl-en)
      config: nl-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 16.16499446427259
    - type: cos_sim_spearman
      value: 17.21754435880688
    - type: euclidean_pearson
      value: 16.325415307049443
    - type: euclidean_spearman
      value: 16.293339731059863
    - type: manhattan_pearson
      value: 19.410680542804005
    - type: manhattan_spearman
      value: 18.95736158862253
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (it)
      config: it
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 30.67175493186678
    - type: cos_sim_spearman
      value: 37.92638638971281
    - type: euclidean_pearson
      value: 37.47072224334179
    - type: euclidean_spearman
      value: 39.23036609148336
    - type: manhattan_pearson
      value: 42.92657347688227
    - type: manhattan_spearman
      value: 43.93955531904934
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 15.51842992576514
    - type: cos_sim_spearman
      value: 22.738964472371965
    - type: euclidean_pearson
      value: 20.68981398028433
    - type: euclidean_spearman
      value: 23.35518754871016
    - type: manhattan_pearson
      value: 26.488467867333142
    - type: manhattan_spearman
      value: 27.318174781418055
  - task:
      type: Reranking
    dataset:
      type: mteb/scidocs-reranking
      name: MTEB SciDocsRR
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 30.728870040728083
    - type: mrr
      value: 45.1852849401869
  - task:
      type: Retrieval
    dataset:
      type: scifact
      name: MTEB SciFact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.104
    - type: map_at_100
      value: 0.265
    - type: map_at_1000
      value: 0.334
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.067
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.104
    - type: mrr_at_100
      value: 0.27
    - type: mrr_at_1000
      value: 0.345
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.067
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.22899999999999998
    - type: ndcg_at_100
      value: 1.044
    - type: ndcg_at_1000
      value: 3.911
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.129
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.067
    - type: precision_at_100
      value: 0.05
    - type: precision_at_1000
      value: 0.032
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.067
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.6669999999999999
    - type: recall_at_100
      value: 4.583
    - type: recall_at_1000
      value: 28.910999999999998
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.333
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.01089108910891
    - type: cos_sim_ap
      value: 2.649295518982714
    - type: cos_sim_f1
      value: 6.26322471434617
    - type: cos_sim_precision
      value: 3.972088030059045
    - type: cos_sim_recall
      value: 14.799999999999999
    - type: dot_accuracy
      value: 99.0089108910891
    - type: dot_ap
      value: 1.713700413108619
    - type: dot_f1
      value: 5.073705862187179
    - type: dot_precision
      value: 3.061646669424907
    - type: dot_recall
      value: 14.799999999999999
    - type: euclidean_accuracy
      value: 99.01089108910891
    - type: euclidean_ap
      value: 2.744099763470491
    - type: euclidean_f1
      value: 6.291706387035273
    - type: euclidean_precision
      value: 4.611085235211924
    - type: euclidean_recall
      value: 9.9
    - type: manhattan_accuracy
      value: 99.01089108910891
    - type: manhattan_ap
      value: 3.2781717730991327
    - type: manhattan_f1
      value: 7.68245838668374
    - type: manhattan_precision
      value: 10.676156583629894
    - type: manhattan_recall
      value: 6.0
    - type: max_accuracy
      value: 99.01089108910891
    - type: max_ap
      value: 3.2781717730991327
    - type: max_f1
      value: 7.68245838668374
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering
      name: MTEB StackExchangeClustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 8.602221384568187
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering-p2p
      name: MTEB StackExchangeClusteringP2P
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 25.619483506865205
  - task:
      type: Reranking
    dataset:
      type: mteb/stackoverflowdupquestions-reranking
      name: MTEB StackOverflowDupQuestions
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 20.02237719216371
    - type: mrr
      value: 18.132453739071387
  - task:
      type: Retrieval
    dataset:
      type: trec-covid
      name: MTEB TRECCOVID
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.004
    - type: map_at_10
      value: 0.009000000000000001
    - type: map_at_100
      value: 0.011000000000000001
    - type: map_at_1000
      value: 0.016
    - type: map_at_3
      value: 0.004
    - type: map_at_5
      value: 0.006999999999999999
    - type: mrr_at_1
      value: 2.0
    - type: mrr_at_10
      value: 4.233
    - type: mrr_at_100
      value: 4.936
    - type: mrr_at_1000
      value: 5.103
    - type: mrr_at_3
      value: 2.0
    - type: mrr_at_5
      value: 3.9
    - type: ndcg_at_1
      value: 1.0
    - type: ndcg_at_10
      value: 1.1039999999999999
    - type: ndcg_at_100
      value: 0.486
    - type: ndcg_at_1000
      value: 0.666
    - type: ndcg_at_3
      value: 0.469
    - type: ndcg_at_5
      value: 1.347
    - type: precision_at_1
      value: 2.0
    - type: precision_at_10
      value: 1.4000000000000001
    - type: precision_at_100
      value: 0.52
    - type: precision_at_1000
      value: 0.37
    - type: precision_at_3
      value: 0.6669999999999999
    - type: precision_at_5
      value: 2.0
    - type: recall_at_1
      value: 0.004
    - type: recall_at_10
      value: 0.024
    - type: recall_at_100
      value: 0.09
    - type: recall_at_1000
      value: 0.807
    - type: recall_at_3
      value: 0.004
    - type: recall_at_5
      value: 0.016
  - task:
      type: Retrieval
    dataset:
      type: webis-touche2020
      name: MTEB Touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.0
    - type: map_at_100
      value: 0.0
    - type: map_at_1000
      value: 0.0
    - type: map_at_3
      value: 0.0
    - type: map_at_5
      value: 0.0
    - type: mrr_at_1
      value: 0.0
    - type: mrr_at_10
      value: 0.0
    - type: mrr_at_100
      value: 0.0
    - type: mrr_at_1000
      value: 0.0
    - type: mrr_at_3
      value: 0.0
    - type: mrr_at_5
      value: 0.0
    - type: ndcg_at_1
      value: 0.0
    - type: ndcg_at_10
      value: 0.0
    - type: ndcg_at_100
      value: 0.0
    - type: ndcg_at_1000
      value: 0.0
    - type: ndcg_at_3
      value: 0.0
    - type: ndcg_at_5
      value: 0.0
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.0
    - type: precision_at_100
      value: 0.0
    - type: precision_at_1000
      value: 0.0
    - type: precision_at_3
      value: 0.0
    - type: precision_at_5
      value: 0.0
    - type: recall_at_1
      value: 0.0
    - type: recall_at_10
      value: 0.0
    - type: recall_at_100
      value: 0.0
    - type: recall_at_1000
      value: 0.0
    - type: recall_at_3
      value: 0.0
    - type: recall_at_5
      value: 0.0
  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 55.065799999999996
    - type: ap
      value: 8.3123142340845
    - type: f1
      value: 41.78797425886187
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 41.177136389360506
    - type: f1
      value: 40.882588170909244
  - task:
      type: Clustering
    dataset:
      type: mteb/twentynewsgroups-clustering
      name: MTEB TwentyNewsgroupsClustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 10.046104242285523
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 77.63604935328128
    - type: cos_sim_ap
      value: 32.380569186976906
    - type: cos_sim_f1
      value: 38.29160530191458
    - type: cos_sim_precision
      value: 27.110086708374492
    - type: cos_sim_recall
      value: 65.17150395778364
    - type: dot_accuracy
      value: 77.40954878702986
    - type: dot_ap
      value: 28.34039741384004
    - type: dot_f1
      value: 37.45059908412271
    - type: dot_precision
      value: 24.565879351493706
    - type: dot_recall
      value: 78.7598944591029
    - type: euclidean_accuracy
      value: 77.63604935328128
    - type: euclidean_ap
      value: 32.40705726976434
    - type: euclidean_f1
      value: 38.365584519430676
    - type: euclidean_precision
      value: 27.524093620927033
    - type: euclidean_recall
      value: 63.298153034300796
    - type: manhattan_accuracy
      value: 77.70757584788699
    - type: manhattan_ap
      value: 33.03410839977045
    - type: manhattan_f1
      value: 39.04353514063523
    - type: manhattan_precision
      value: 26.943524927274552
    - type: manhattan_recall
      value: 70.87071240105541
    - type: max_accuracy
      value: 77.70757584788699
    - type: max_ap
      value: 33.03410839977045
    - type: max_f1
      value: 39.04353514063523
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 75.80626382582373
    - type: cos_sim_ap
      value: 41.94768516713251
    - type: cos_sim_f1
      value: 44.69374385849984
    - type: cos_sim_precision
      value: 34.620263870094725
    - type: cos_sim_recall
      value: 63.035109331690784
    - type: dot_accuracy
      value: 74.79528078550084
    - type: dot_ap
      value: 33.69361208467778
    - type: dot_f1
      value: 44.620064092118845
    - type: dot_precision
      value: 34.467567340773684
    - type: dot_recall
      value: 63.250692947336006
    - type: euclidean_accuracy
      value: 75.98866767570924
    - type: euclidean_ap
      value: 42.65497342948604
    - type: euclidean_f1
      value: 44.794497753619176
    - type: euclidean_precision
      value: 35.006501950585175
    - type: euclidean_recall
      value: 62.180474283954425
    - type: manhattan_accuracy
      value: 76.37870143982613
    - type: manhattan_ap
      value: 46.65401496383161
    - type: manhattan_f1
      value: 48.14085011643678
    - type: manhattan_precision
      value: 36.0535091417839
    - type: manhattan_recall
      value: 72.42069602710194
    - type: max_accuracy
      value: 76.37870143982613
    - type: max_ap
      value: 46.65401496383161
    - type: max_f1
      value: 48.14085011643678
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
datasets:
- unicamp-dl/mmarco
language:
- it
library_name: sentence-transformers
---

# mmarco-sentence-flare-it

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer, util

query = "Quante persone vivono a Londra?"
docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"]

#Load the model
model = SentenceTransformer('nickprock/mmarco-sentence-flare-it')

#Encode query and documents
query_emb = model.encode(query)
doc_emb = model.encode(docs)

#Compute dot score between query and all document embeddings
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()

#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))

#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)

#Output passages & scores
for doc, score in doc_score_pairs:
    print(score, doc)

```



## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output.last_hidden_state
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


#Encode text
def encode(texts):
    # Tokenize sentences
    encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')

    # Compute token embeddings
    with torch.no_grad():
        model_output = model(**encoded_input, return_dict=True)

    # Perform pooling
    embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

    return embeddings


# Sentences we want sentence embeddings for
query = "Quante persone vivono a Londra?"
docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("nickprock/mmarco-sentence-flare-it")
model = AutoModel.from_pretrained("nickprock/mmarco-sentence-flare-it")

#Encode query and docs
query_emb = encode(query)
doc_emb = encode(docs)

#Compute dot score between query and all document embeddings
scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()

#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))

#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)

#Output passages & scores
print("Query:", query)
for doc, score in doc_score_pairs:
    print(score, doc)

```



## Evaluation Results

<!--- Describe how your model was evaluated -->

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})


## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 7500 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
  ```
  {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 10,
    "evaluation_steps": 500,
    "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": 1500,
    "warmup_steps": 7500,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

More information about the [base model here](https://huggingface.co/osiria/flare-it/)