---
tags:
- mteb
model-index:
- name: bge-m3
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 75.6268656716418
    - type: ap
      value: 39.50276109614102
    - type: f1
      value: 70.00224623431103
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 91.013675
    - type: ap
      value: 87.30227544778319
    - type: f1
      value: 91.00157923673694
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 46.986000000000004
    - type: f1
      value: 44.93316837240337
  - task:
      type: Retrieval
    dataset:
      type: arguana
      name: MTEB ArguAna
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 28.521
    - type: map_at_10
      value: 45.062999999999995
    - type: map_at_100
      value: 45.965
    - type: map_at_1000
      value: 45.972
    - type: map_at_3
      value: 40.078
    - type: map_at_5
      value: 43.158
    - type: mrr_at_1
      value: 29.232000000000003
    - type: mrr_at_10
      value: 45.305
    - type: mrr_at_100
      value: 46.213
    - type: mrr_at_1000
      value: 46.22
    - type: mrr_at_3
      value: 40.339000000000006
    - type: mrr_at_5
      value: 43.394
    - type: ndcg_at_1
      value: 28.521
    - type: ndcg_at_10
      value: 53.959999999999994
    - type: ndcg_at_100
      value: 57.691
    - type: ndcg_at_1000
      value: 57.858
    - type: ndcg_at_3
      value: 43.867
    - type: ndcg_at_5
      value: 49.38
    - type: precision_at_1
      value: 28.521
    - type: precision_at_10
      value: 8.222
    - type: precision_at_100
      value: 0.9820000000000001
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 18.279
    - type: precision_at_5
      value: 13.627
    - type: recall_at_1
      value: 28.521
    - type: recall_at_10
      value: 82.219
    - type: recall_at_100
      value: 98.222
    - type: recall_at_1000
      value: 99.502
    - type: recall_at_3
      value: 54.836
    - type: recall_at_5
      value: 68.137
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-p2p
      name: MTEB ArxivClusteringP2P
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 39.409674498704625
  - task:
      type: Reranking
    dataset:
      type: mteb/askubuntudupquestions-reranking
      name: MTEB AskUbuntuDupQuestions
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 61.52757354203137
    - type: mrr
      value: 74.28241656773513
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 84.39442490594014
    - type: cos_sim_spearman
      value: 83.37599616417513
    - type: euclidean_pearson
      value: 83.23317790460271
    - type: euclidean_spearman
      value: 83.37599616417513
    - type: manhattan_pearson
      value: 83.23182214744224
    - type: manhattan_spearman
      value: 83.5428674363298
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 81.93181818181819
    - type: f1
      value: 81.0852312152688
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackEnglishRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 28.784
    - type: map_at_10
      value: 38.879000000000005
    - type: map_at_100
      value: 40.161
    - type: map_at_1000
      value: 40.291
    - type: map_at_3
      value: 36.104
    - type: map_at_5
      value: 37.671
    - type: mrr_at_1
      value: 35.924
    - type: mrr_at_10
      value: 44.471
    - type: mrr_at_100
      value: 45.251000000000005
    - type: mrr_at_1000
      value: 45.296
    - type: mrr_at_3
      value: 42.367
    - type: mrr_at_5
      value: 43.635000000000005
    - type: ndcg_at_1
      value: 35.924
    - type: ndcg_at_10
      value: 44.369
    - type: ndcg_at_100
      value: 48.925999999999995
    - type: ndcg_at_1000
      value: 50.964
    - type: ndcg_at_3
      value: 40.416999999999994
    - type: ndcg_at_5
      value: 42.309999999999995
    - type: precision_at_1
      value: 35.924
    - type: precision_at_10
      value: 8.344
    - type: precision_at_100
      value: 1.367
    - type: precision_at_1000
      value: 0.181
    - type: precision_at_3
      value: 19.469
    - type: precision_at_5
      value: 13.771
    - type: recall_at_1
      value: 28.784
    - type: recall_at_10
      value: 53.92400000000001
    - type: recall_at_100
      value: 72.962
    - type: recall_at_1000
      value: 85.90100000000001
    - type: recall_at_3
      value: 42.574
    - type: recall_at_5
      value: 47.798
  - task:
      type: Classification
    dataset:
      type: mteb/emotion
      name: MTEB EmotionClassification
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 50.16499999999999
    - type: f1
      value: 43.57906972116264
  - task:
      type: Retrieval
    dataset:
      type: fiqa
      name: MTEB FiQA2018
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 20.737
    - type: map_at_10
      value: 33.566
    - type: map_at_100
      value: 35.367
    - type: map_at_1000
      value: 35.546
    - type: map_at_3
      value: 29.881999999999998
    - type: map_at_5
      value: 31.818
    - type: mrr_at_1
      value: 41.975
    - type: mrr_at_10
      value: 50.410999999999994
    - type: mrr_at_100
      value: 51.172
    - type: mrr_at_1000
      value: 51.214999999999996
    - type: mrr_at_3
      value: 48.611
    - type: mrr_at_5
      value: 49.522
    - type: ndcg_at_1
      value: 41.975
    - type: ndcg_at_10
      value: 41.299
    - type: ndcg_at_100
      value: 47.768
    - type: ndcg_at_1000
      value: 50.882000000000005
    - type: ndcg_at_3
      value: 38.769
    - type: ndcg_at_5
      value: 39.106
    - type: precision_at_1
      value: 41.975
    - type: precision_at_10
      value: 11.296000000000001
    - type: precision_at_100
      value: 1.7840000000000003
    - type: precision_at_1000
      value: 0.23500000000000001
    - type: precision_at_3
      value: 26.029000000000003
    - type: precision_at_5
      value: 18.457
    - type: recall_at_1
      value: 20.737
    - type: recall_at_10
      value: 47.284
    - type: recall_at_100
      value: 71.286
    - type: recall_at_1000
      value: 89.897
    - type: recall_at_3
      value: 35.411
    - type: recall_at_5
      value: 39.987
  - task:
      type: Classification
    dataset:
      type: mteb/imdb
      name: MTEB ImdbClassification
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 87.84
    - type: ap
      value: 82.68294664793142
    - type: f1
      value: 87.8226441992267
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 93.35841313269493
    - type: f1
      value: 93.060022693275
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 66.58002735978113
    - type: f1
      value: 46.995919480823055
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (en)
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 71.07935440484196
    - type: f1
      value: 69.13197875645403
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (en)
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 76.63752521856087
    - type: f1
      value: 75.61348469613843
  - task:
      type: Retrieval
    dataset:
      type: nfcorpus
      name: MTEB NFCorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 5.234
    - type: map_at_10
      value: 11.718
    - type: map_at_100
      value: 14.396
    - type: map_at_1000
      value: 15.661
    - type: map_at_3
      value: 8.951
    - type: map_at_5
      value: 10.233
    - type: mrr_at_1
      value: 43.034
    - type: mrr_at_10
      value: 52.161
    - type: mrr_at_100
      value: 52.729000000000006
    - type: mrr_at_1000
      value: 52.776
    - type: mrr_at_3
      value: 50.671
    - type: mrr_at_5
      value: 51.476
    - type: ndcg_at_1
      value: 41.331
    - type: ndcg_at_10
      value: 31.411
    - type: ndcg_at_100
      value: 28.459
    - type: ndcg_at_1000
      value: 37.114000000000004
    - type: ndcg_at_3
      value: 37.761
    - type: ndcg_at_5
      value: 35.118
    - type: precision_at_1
      value: 43.034
    - type: precision_at_10
      value: 22.878999999999998
    - type: precision_at_100
      value: 7.093000000000001
    - type: precision_at_1000
      value: 1.9560000000000002
    - type: precision_at_3
      value: 35.707
    - type: precision_at_5
      value: 30.279
    - type: recall_at_1
      value: 5.234
    - type: recall_at_10
      value: 14.745
    - type: recall_at_100
      value: 28.259
    - type: recall_at_1000
      value: 59.16400000000001
    - type: recall_at_3
      value: 10.08
    - type: recall_at_5
      value: 11.985
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 83.33269306539026
    - type: cos_sim_spearman
      value: 79.71441518631086
    - type: euclidean_pearson
      value: 80.98109404189279
    - type: euclidean_spearman
      value: 79.71444969096095
    - type: manhattan_pearson
      value: 80.97223989357175
    - type: manhattan_spearman
      value: 79.64929261210406
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 83.7127498314437
    - type: cos_sim_spearman
      value: 78.73426610516154
    - type: euclidean_pearson
      value: 79.72827173736742
    - type: euclidean_spearman
      value: 78.731973450314
    - type: manhattan_pearson
      value: 79.71391822179304
    - type: manhattan_spearman
      value: 78.69626503719782
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 78.33449726355023
    - type: cos_sim_spearman
      value: 79.59703323420547
    - type: euclidean_pearson
      value: 79.87238808505464
    - type: euclidean_spearman
      value: 79.59703323420547
    - type: manhattan_pearson
      value: 79.5006260085966
    - type: manhattan_spearman
      value: 79.21864659717262
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 79.00088445445654
    - type: cos_sim_spearman
      value: 78.99977508575147
    - type: euclidean_pearson
      value: 78.63222924140206
    - type: euclidean_spearman
      value: 78.99976994069327
    - type: manhattan_pearson
      value: 78.35504771673297
    - type: manhattan_spearman
      value: 78.76306077740067
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 87.13160613452308
    - type: cos_sim_spearman
      value: 87.81435104273643
    - type: euclidean_pearson
      value: 87.22395745487297
    - type: euclidean_spearman
      value: 87.81435041827874
    - type: manhattan_pearson
      value: 87.17630476262896
    - type: manhattan_spearman
      value: 87.76535338976686
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 83.76424652225954
    - type: cos_sim_spearman
      value: 85.39745570134193
    - type: euclidean_pearson
      value: 84.6971466556576
    - type: euclidean_spearman
      value: 85.39745570134193
    - type: manhattan_pearson
      value: 84.61210275324463
    - type: manhattan_spearman
      value: 85.30727114432379
  - 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: 86.87956530541486
    - type: cos_sim_spearman
      value: 87.13412608536781
    - type: euclidean_pearson
      value: 87.80084186244981
    - type: euclidean_spearman
      value: 87.13412608536781
    - type: manhattan_pearson
      value: 87.73101535306475
    - type: manhattan_spearman
      value: 87.05897655963285
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 83.70737517925419
    - type: cos_sim_spearman
      value: 84.84687698325351
    - type: euclidean_pearson
      value: 84.36525309890885
    - type: euclidean_spearman
      value: 84.84688249844098
    - type: manhattan_pearson
      value: 84.31171573973266
    - type: manhattan_spearman
      value: 84.79550448196474
  - task:
      type: Retrieval
    dataset:
      type: scifact
      name: MTEB SciFact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 48.178
    - type: map_at_10
      value: 59.24
    - type: map_at_100
      value: 59.902
    - type: map_at_1000
      value: 59.941
    - type: map_at_3
      value: 56.999
    - type: map_at_5
      value: 58.167
    - type: mrr_at_1
      value: 51.0
    - type: mrr_at_10
      value: 60.827
    - type: mrr_at_100
      value: 61.307
    - type: mrr_at_1000
      value: 61.341
    - type: mrr_at_3
      value: 59.0
    - type: mrr_at_5
      value: 60.033
    - type: ndcg_at_1
      value: 51.0
    - type: ndcg_at_10
      value: 64.366
    - type: ndcg_at_100
      value: 67.098
    - type: ndcg_at_1000
      value: 68.08
    - type: ndcg_at_3
      value: 60.409
    - type: ndcg_at_5
      value: 62.150000000000006
    - type: precision_at_1
      value: 51.0
    - type: precision_at_10
      value: 8.799999999999999
    - type: precision_at_100
      value: 1.027
    - type: precision_at_1000
      value: 0.11100000000000002
    - type: precision_at_3
      value: 24.444
    - type: precision_at_5
      value: 15.8
    - type: recall_at_1
      value: 48.178
    - type: recall_at_10
      value: 78.34400000000001
    - type: recall_at_100
      value: 90.36699999999999
    - type: recall_at_1000
      value: 98.0
    - type: recall_at_3
      value: 67.35
    - type: recall_at_5
      value: 71.989
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.87722772277228
    - type: cos_sim_ap
      value: 97.32479581402639
    - type: cos_sim_f1
      value: 93.74369323915236
    - type: cos_sim_precision
      value: 94.60285132382892
    - type: cos_sim_recall
      value: 92.9
    - type: dot_accuracy
      value: 99.87722772277228
    - type: dot_ap
      value: 97.32479581402637
    - type: dot_f1
      value: 93.74369323915236
    - type: dot_precision
      value: 94.60285132382892
    - type: dot_recall
      value: 92.9
    - type: euclidean_accuracy
      value: 99.87722772277228
    - type: euclidean_ap
      value: 97.32479581402639
    - type: euclidean_f1
      value: 93.74369323915236
    - type: euclidean_precision
      value: 94.60285132382892
    - type: euclidean_recall
      value: 92.9
    - type: manhattan_accuracy
      value: 99.87524752475248
    - type: manhattan_ap
      value: 97.29133330261223
    - type: manhattan_f1
      value: 93.59359359359361
    - type: manhattan_precision
      value: 93.687374749499
    - type: manhattan_recall
      value: 93.5
    - type: max_accuracy
      value: 99.87722772277228
    - type: max_ap
      value: 97.32479581402639
    - type: max_f1
      value: 93.74369323915236
  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 72.60060000000001
    - type: ap
      value: 15.719924742317021
    - type: f1
      value: 56.30561683159878
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 63.71250707413696
    - type: f1
      value: 63.54808116265952
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 85.110568039578
    - type: cos_sim_ap
      value: 70.28927714315245
    - type: cos_sim_f1
      value: 65.03893361488716
    - type: cos_sim_precision
      value: 65.06469500924214
    - type: cos_sim_recall
      value: 65.0131926121372
    - type: dot_accuracy
      value: 85.110568039578
    - type: dot_ap
      value: 70.28928082939848
    - type: dot_f1
      value: 65.03893361488716
    - type: dot_precision
      value: 65.06469500924214
    - type: dot_recall
      value: 65.0131926121372
    - type: euclidean_accuracy
      value: 85.110568039578
    - type: euclidean_ap
      value: 70.28928621260852
    - type: euclidean_f1
      value: 65.03893361488716
    - type: euclidean_precision
      value: 65.06469500924214
    - type: euclidean_recall
      value: 65.0131926121372
    - type: manhattan_accuracy
      value: 85.02115992132086
    - type: manhattan_ap
      value: 70.05813255171925
    - type: manhattan_f1
      value: 64.59658311510164
    - type: manhattan_precision
      value: 61.24379285883188
    - type: manhattan_recall
      value: 68.33773087071239
    - type: max_accuracy
      value: 85.110568039578
    - type: max_ap
      value: 70.28928621260852
    - type: max_f1
      value: 65.03893361488716
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.99949547871309
    - type: cos_sim_ap
      value: 85.82819569154559
    - type: cos_sim_f1
      value: 78.37315338318439
    - type: cos_sim_precision
      value: 74.46454564358494
    - type: cos_sim_recall
      value: 82.71481367416075
    - type: dot_accuracy
      value: 88.99949547871309
    - type: dot_ap
      value: 85.82820043407936
    - type: dot_f1
      value: 78.37315338318439
    - type: dot_precision
      value: 74.46454564358494
    - type: dot_recall
      value: 82.71481367416075
    - type: euclidean_accuracy
      value: 88.99949547871309
    - type: euclidean_ap
      value: 85.82819622588083
    - type: euclidean_f1
      value: 78.37315338318439
    - type: euclidean_precision
      value: 74.46454564358494
    - type: euclidean_recall
      value: 82.71481367416075
    - type: manhattan_accuracy
      value: 88.98009081383165
    - type: manhattan_ap
      value: 85.77393389750326
    - type: manhattan_f1
      value: 78.38852097130243
    - type: manhattan_precision
      value: 75.06341600901916
    - type: manhattan_recall
      value: 82.0218663381583
    - type: max_accuracy
      value: 88.99949547871309
    - type: max_ap
      value: 85.82820043407936
    - type: max_f1
      value: 78.38852097130243
---