library_name: transformers tags:

• sentence-transformers
• gte
• mteb
• transformers.js
• sentence-similarity license: apache-2.0 language:
• en model-index:
• name: gte-base-en-v1.5 results:
  • task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics:
    • type: accuracy value: 74.7910447761194
    • type: ap value: 37.053785713650626
    • type: f1 value: 68.51101510998551
  • task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics:
    • type: accuracy value: 93.016875
    • type: ap value: 89.17750268426342
    • type: f1 value: 92.9970977240524
  • task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
    • type: accuracy value: 53.312000000000005
    • type: f1 value: 52.98175784163017
  • task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics:
    • type: map_at_1 value: 38.193
    • type: map_at_10 value: 54.848
    • type: map_at_100 value: 55.388000000000005
    • type: map_at_1000 value: 55.388999999999996
    • type: map_at_3 value: 50.427
    • type: map_at_5 value: 53.105000000000004
    • type: mrr_at_1 value: 39.047
    • type: mrr_at_10 value: 55.153
    • type: mrr_at_100 value: 55.686
    • type: mrr_at_1000 value: 55.688
    • type: mrr_at_3 value: 50.676
    • type: mrr_at_5 value: 53.417
    • type: ndcg_at_1 value: 38.193
    • type: ndcg_at_10 value: 63.486
    • type: ndcg_at_100 value: 65.58
    • type: ndcg_at_1000 value: 65.61
    • type: ndcg_at_3 value: 54.494
    • type: ndcg_at_5 value: 59.339
    • type: precision_at_1 value: 38.193
    • type: precision_at_10 value: 9.075
    • type: precision_at_100 value: 0.9939999999999999
    • type: precision_at_1000 value: 0.1
    • type: precision_at_3 value: 22.096
    • type: precision_at_5 value: 15.619
    • type: recall_at_1 value: 38.193
    • type: recall_at_10 value: 90.754
    • type: recall_at_100 value: 99.431
    • type: recall_at_1000 value: 99.644
    • type: recall_at_3 value: 66.28699999999999
    • type: recall_at_5 value: 78.094
  • task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics:
    • type: v_measure value: 47.508221208908964
  • task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics:
    • type: v_measure value: 42.04668382560096
  • task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics:
    • type: map value: 61.828759903716815
    • type: mrr value: 74.37343358395991
  • task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics:
    • type: cos_sim_pearson value: 85.03673698773017
    • type: cos_sim_spearman value: 83.6470866785058
    • type: euclidean_pearson value: 82.64048673096565
    • type: euclidean_spearman value: 83.63142367101115
    • type: manhattan_pearson value: 82.71493099760228
    • type: manhattan_spearman value: 83.60491704294326
  • task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics:
    • type: accuracy value: 86.73376623376623
    • type: f1 value: 86.70294049278262
  • task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics:
    • type: v_measure value: 40.31923804167062
  • task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics:
    • type: v_measure value: 37.552547125348454
  • task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics:
    • type: map_at_1 value: 30.567
    • type: map_at_10 value: 41.269
    • type: map_at_100 value: 42.689
    • type: map_at_1000 value: 42.84
    • type: map_at_3 value: 37.567
    • type: map_at_5 value: 39.706
    • type: mrr_at_1 value: 37.053000000000004
    • type: mrr_at_10 value: 46.900999999999996
    • type: mrr_at_100 value: 47.662
    • type: mrr_at_1000 value: 47.713
    • type: mrr_at_3 value: 43.801
    • type: mrr_at_5 value: 45.689
    • type: ndcg_at_1 value: 37.053000000000004
    • type: ndcg_at_10 value: 47.73
    • type: ndcg_at_100 value: 53.128
    • type: ndcg_at_1000 value: 55.300000000000004
    • type: ndcg_at_3 value: 42.046
    • type: ndcg_at_5 value: 44.782
    • type: precision_at_1 value: 37.053000000000004
    • type: precision_at_10 value: 9.142
    • type: precision_at_100 value: 1.485
    • type: precision_at_1000 value: 0.197
    • type: precision_at_3 value: 20.076
    • type: precision_at_5 value: 14.535
    • type: recall_at_1 value: 30.567
    • type: recall_at_10 value: 60.602999999999994
    • type: recall_at_100 value: 83.22800000000001
    • type: recall_at_1000 value: 96.696
    • type: recall_at_3 value: 44.336999999999996
    • type: recall_at_5 value: 51.949
  • task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics:
    • type: map_at_1 value: 28.538000000000004
    • type: map_at_10 value: 38.757999999999996
    • type: map_at_100 value: 40.129
    • type: map_at_1000 value: 40.262
    • type: map_at_3 value: 35.866
    • type: map_at_5 value: 37.417
    • type: mrr_at_1 value: 36.051
    • type: mrr_at_10 value: 44.868
    • type: mrr_at_100 value: 45.568999999999996
    • type: mrr_at_1000 value: 45.615
    • type: mrr_at_3 value: 42.558
    • type: mrr_at_5 value: 43.883
    • type: ndcg_at_1 value: 36.051
    • type: ndcg_at_10 value: 44.584
    • type: ndcg_at_100 value: 49.356
    • type: ndcg_at_1000 value: 51.39
    • type: ndcg_at_3 value: 40.389
    • type: ndcg_at_5 value: 42.14
    • type: precision_at_1 value: 36.051
    • type: precision_at_10 value: 8.446
    • type: precision_at_100 value: 1.411
    • type: precision_at_1000 value: 0.19
    • type: precision_at_3 value: 19.639
    • type: precision_at_5 value: 13.796
    • type: recall_at_1 value: 28.538000000000004
    • type: recall_at_10 value: 54.99000000000001
    • type: recall_at_100 value: 75.098
    • type: recall_at_1000 value: 87.848
    • type: recall_at_3 value: 42.236000000000004
    • type: recall_at_5 value: 47.377
  • task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics:
    • type: map_at_1 value: 37.188
    • type: map_at_10 value: 50.861000000000004
    • type: map_at_100 value: 51.917
    • type: map_at_1000 value: 51.964999999999996
    • type: map_at_3 value: 47.144000000000005
    • type: map_at_5 value: 49.417
    • type: mrr_at_1 value: 42.571
    • type: mrr_at_10 value: 54.086999999999996
    • type: mrr_at_100 value: 54.739000000000004
    • type: mrr_at_1000 value: 54.762
    • type: mrr_at_3 value: 51.285000000000004
    • type: mrr_at_5 value: 53.0
    • type: ndcg_at_1 value: 42.571
    • type: ndcg_at_10 value: 57.282
    • type: ndcg_at_100 value: 61.477000000000004
    • type: ndcg_at_1000 value: 62.426
    • type: ndcg_at_3 value: 51.0
    • type: ndcg_at_5 value: 54.346000000000004
    • type: precision_at_1 value: 42.571
    • type: precision_at_10 value: 9.467
    • type: precision_at_100 value: 1.2550000000000001
    • type: precision_at_1000 value: 0.13799999999999998
    • type: precision_at_3 value: 23.114
    • type: precision_at_5 value: 16.250999999999998
    • type: recall_at_1 value: 37.188
    • type: recall_at_10 value: 73.068
    • type: recall_at_100 value: 91.203
    • type: recall_at_1000 value: 97.916
    • type: recall_at_3 value: 56.552
    • type: recall_at_5 value: 64.567
  • task: type: Retrieval dataset: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics:
    • type: map_at_1 value: 25.041000000000004
    • type: map_at_10 value: 33.86
    • type: map_at_100 value: 34.988
    • type: map_at_1000 value: 35.064
    • type: map_at_3 value: 31.049
    • type: map_at_5 value: 32.845
    • type: mrr_at_1 value: 26.893
    • type: mrr_at_10 value: 35.594
    • type: mrr_at_100 value: 36.617
    • type: mrr_at_1000 value: 36.671
    • type: mrr_at_3 value: 33.051
    • type: mrr_at_5 value: 34.61
    • type: ndcg_at_1 value: 26.893
    • type: ndcg_at_10 value: 38.674
    • type: ndcg_at_100 value: 44.178
    • type: ndcg_at_1000 value: 46.089999999999996
    • type: ndcg_at_3 value: 33.485
    • type: ndcg_at_5 value: 36.402
    • type: precision_at_1 value: 26.893
    • type: precision_at_10 value: 5.989
    • type: precision_at_100 value: 0.918
    • type: precision_at_1000 value: 0.11100000000000002
    • type: precision_at_3 value: 14.2
    • type: precision_at_5 value: 10.26
    • type: recall_at_1 value: 25.041000000000004
    • type: recall_at_10 value: 51.666000000000004
    • type: recall_at_100 value: 76.896
    • type: recall_at_1000 value: 91.243
    • type: recall_at_3 value: 38.035999999999994
    • type: recall_at_5 value: 44.999
  • task: type: Retrieval dataset: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics:
    • type: map_at_1 value: 15.909999999999998
    • type: map_at_10 value: 23.901
    • type: map_at_100 value: 25.165
    • type: map_at_1000 value: 25.291000000000004
    • type: map_at_3 value: 21.356
    • type: map_at_5 value: 22.816
    • type: mrr_at_1 value: 20.025000000000002
    • type: mrr_at_10 value: 28.382
    • type: mrr_at_100 value: 29.465000000000003
    • type: mrr_at_1000 value: 29.535
    • type: mrr_at_3 value: 25.933
    • type: mrr_at_5 value: 27.332
    • type: ndcg_at_1 value: 20.025000000000002
    • type: ndcg_at_10 value: 29.099000000000004
    • type: ndcg_at_100 value: 35.127
    • type: ndcg_at_1000 value: 38.096000000000004
    • type: ndcg_at_3 value: 24.464
    • type: ndcg_at_5 value: 26.709
    • type: precision_at_1 value: 20.025000000000002
    • type: precision_at_10 value: 5.398
    • type: precision_at_100 value: 0.9690000000000001
    • type: precision_at_1000 value: 0.13699999999999998
    • type: precision_at_3 value: 11.774
    • type: precision_at_5 value: 8.632
    • type: recall_at_1 value: 15.909999999999998
    • type: recall_at_10 value: 40.672000000000004
    • type: recall_at_100 value: 66.855
    • type: recall_at_1000 value: 87.922
    • type: recall_at_3 value: 28.069
    • type: recall_at_5 value: 33.812
  • task: type: Retrieval dataset: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics:
    • type: map_at_1 value: 30.175
    • type: map_at_10 value: 41.36
    • type: map_at_100 value: 42.701
    • type: map_at_1000 value: 42.817
    • type: map_at_3 value: 37.931
    • type: map_at_5 value: 39.943
    • type: mrr_at_1 value: 35.611
    • type: mrr_at_10 value: 46.346
    • type: mrr_at_100 value: 47.160000000000004
    • type: mrr_at_1000 value: 47.203
    • type: mrr_at_3 value: 43.712
    • type: mrr_at_5 value: 45.367000000000004
    • type: ndcg_at_1 value: 35.611
    • type: ndcg_at_10 value: 47.532000000000004
    • type: ndcg_at_100 value: 53.003
    • type: ndcg_at_1000 value: 55.007
    • type: ndcg_at_3 value: 42.043
    • type: ndcg_at_5 value: 44.86
    • type: precision_at_1 value: 35.611
    • type: precision_at_10 value: 8.624
    • type: precision_at_100 value: 1.332
    • type: precision_at_1000 value: 0.169
    • type: precision_at_3 value: 20.083000000000002
    • type: precision_at_5 value: 14.437
    • type: recall_at_1 value: 30.175
    • type: recall_at_10 value: 60.5
    • type: recall_at_100 value: 83.399
    • type: recall_at_1000 value: 96.255
    • type: recall_at_3 value: 45.448
    • type: recall_at_5 value: 52.432
  • task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics:
    • type: map_at_1 value: 22.467000000000002
    • type: map_at_10 value: 33.812999999999995
    • type: map_at_100 value: 35.248000000000005
    • type: map_at_1000 value: 35.359
    • type: map_at_3 value: 30.316
    • type: map_at_5 value: 32.233000000000004
    • type: mrr_at_1 value: 28.310999999999996
    • type: mrr_at_10 value: 38.979
    • type: mrr_at_100 value: 39.937
    • type: mrr_at_1000 value: 39.989999999999995
    • type: mrr_at_3 value: 36.244
    • type: mrr_at_5 value: 37.871
    • type: ndcg_at_1 value: 28.310999999999996
    • type: ndcg_at_10 value: 40.282000000000004
    • type: ndcg_at_100 value: 46.22
    • type: ndcg_at_1000 value: 48.507
    • type: ndcg_at_3 value: 34.596
    • type: ndcg_at_5 value: 37.267
    • type: precision_at_1 value: 28.310999999999996
    • type: precision_at_10 value: 7.831
    • type: precision_at_100 value: 1.257
    • type: precision_at_1000 value: 0.164
    • type: precision_at_3 value: 17.275
    • type: precision_at_5 value: 12.556999999999999
    • type: recall_at_1 value: 22.467000000000002
    • type: recall_at_10 value: 54.14099999999999
    • type: recall_at_100 value: 79.593
    • type: recall_at_1000 value: 95.063
    • type: recall_at_3 value: 38.539
    • type: recall_at_5 value: 45.403
  • task: type: Retrieval dataset: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics:
    • type: map_at_1 value: 24.18591666666667
    • type: map_at_10 value: 33.84258333333333
    • type: map_at_100 value: 35.11391666666666
    • type: map_at_1000 value: 35.23258333333333
    • type: map_at_3 value: 30.764249999999997
    • type: map_at_5 value: 32.52333333333334
    • type: mrr_at_1 value: 28.54733333333333
    • type: mrr_at_10 value: 37.81725
    • type: mrr_at_100 value: 38.716499999999996
    • type: mrr_at_1000 value: 38.77458333333333
    • type: mrr_at_3 value: 35.157833333333336
    • type: mrr_at_5 value: 36.69816666666667
    • type: ndcg_at_1 value: 28.54733333333333
    • type: ndcg_at_10 value: 39.51508333333334
    • type: ndcg_at_100 value: 44.95316666666666
    • type: ndcg_at_1000 value: 47.257083333333334
    • type: ndcg_at_3 value: 34.205833333333324
    • type: ndcg_at_5 value: 36.78266666666667
    • type: precision_at_1 value: 28.54733333333333
    • type: precision_at_10 value: 7.082583333333334
    • type: precision_at_100 value: 1.1590833333333332
    • type: precision_at_1000 value: 0.15516666666666662
    • type: precision_at_3 value: 15.908750000000001
    • type: precision_at_5 value: 11.505416666666669
    • type: recall_at_1 value: 24.18591666666667
    • type: recall_at_10 value: 52.38758333333333
    • type: recall_at_100 value: 76.13666666666667
    • type: recall_at_1000 value: 91.99066666666667
    • type: recall_at_3 value: 37.78333333333334
    • type: recall_at_5 value: 44.30141666666666
  • task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics:
    • type: map_at_1 value: 21.975
    • type: map_at_10 value: 29.781000000000002
    • type: map_at_100 value: 30.847
    • type: map_at_1000 value: 30.94
    • type: map_at_3 value: 27.167
    • type: map_at_5 value: 28.633999999999997
    • type: mrr_at_1 value: 24.387
    • type: mrr_at_10 value: 32.476
    • type: mrr_at_100 value: 33.337
    • type: mrr_at_1000 value: 33.403
    • type: mrr_at_3 value: 29.881999999999998
    • type: mrr_at_5 value: 31.339
    • type: ndcg_at_1 value: 24.387
    • type: ndcg_at_10 value: 34.596
    • type: ndcg_at_100 value: 39.635
    • type: ndcg_at_1000 value: 42.079
    • type: ndcg_at_3 value: 29.516
    • type: ndcg_at_5 value: 31.959
    • type: precision_at_1 value: 24.387
    • type: precision_at_10 value: 5.6129999999999995
    • type: precision_at_100 value: 0.8909999999999999
    • type: precision_at_1000 value: 0.117
    • type: precision_at_3 value: 12.73
    • type: precision_at_5 value: 9.171999999999999
    • type: recall_at_1 value: 21.975
    • type: recall_at_10 value: 46.826
    • type: recall_at_100 value: 69.554
    • type: recall_at_1000 value: 87.749
    • type: recall_at_3 value: 33.016
    • type: recall_at_5 value: 38.97
  • task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics:
    • type: map_at_1 value: 15.614
    • type: map_at_10 value: 22.927
    • type: map_at_100 value: 24.185000000000002
    • type: map_at_1000 value: 24.319
    • type: map_at_3 value: 20.596
    • type: map_at_5 value: 21.854000000000003
    • type: mrr_at_1 value: 18.858
    • type: mrr_at_10 value: 26.535999999999998
    • type: mrr_at_100 value: 27.582
    • type: mrr_at_1000 value: 27.665
    • type: mrr_at_3 value: 24.295
    • type: mrr_at_5 value: 25.532
    • type: ndcg_at_1 value: 18.858
    • type: ndcg_at_10 value: 27.583000000000002
    • type: ndcg_at_100 value: 33.635
    • type: ndcg_at_1000 value: 36.647
    • type: ndcg_at_3 value: 23.348
    • type: ndcg_at_5 value: 25.257
    • type: precision_at_1 value: 18.858
    • type: precision_at_10 value: 5.158
    • type: precision_at_100 value: 0.964
    • type: precision_at_1000 value: 0.13999999999999999
    • type: precision_at_3 value: 11.092
    • type: precision_at_5 value: 8.1
    • type: recall_at_1 value: 15.614
    • type: recall_at_10 value: 37.916
    • type: recall_at_100 value: 65.205
    • type: recall_at_1000 value: 86.453
    • type: recall_at_3 value: 26.137
    • type: recall_at_5 value: 31.087999999999997
  • task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics:
    • type: map_at_1 value: 23.078000000000003
    • type: map_at_10 value: 31.941999999999997
    • type: map_at_100 value: 33.196999999999996
    • type: map_at_1000 value: 33.303
    • type: map_at_3 value: 28.927000000000003
    • type: map_at_5 value: 30.707
    • type: mrr_at_1 value: 26.866
    • type: mrr_at_10 value: 35.557
    • type: mrr_at_100 value: 36.569
    • type: mrr_at_1000 value: 36.632
    • type: mrr_at_3 value: 32.897999999999996
    • type: mrr_at_5 value: 34.437
    • type: ndcg_at_1 value: 26.866
    • type: ndcg_at_10 value: 37.372
    • type: ndcg_at_100 value: 43.248
    • type: ndcg_at_1000 value: 45.632
    • type: ndcg_at_3 value: 31.852999999999998
    • type: ndcg_at_5 value: 34.582
    • type: precision_at_1 value: 26.866
    • type: precision_at_10 value: 6.511
    • type: precision_at_100 value: 1.078
    • type: precision_at_1000 value: 0.13899999999999998
    • type: precision_at_3 value: 14.582999999999998
    • type: precision_at_5 value: 10.634
    • type: recall_at_1 value: 23.078000000000003
    • type: recall_at_10 value: 50.334
    • type: recall_at_100 value: 75.787
    • type: recall_at_1000 value: 92.485
    • type: recall_at_3 value: 35.386
    • type: recall_at_5 value: 42.225
  • task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics:
    • type: map_at_1 value: 22.203999999999997
    • type: map_at_10 value: 31.276
    • type: map_at_100 value: 32.844
    • type: map_at_1000 value: 33.062999999999995
    • type: map_at_3 value: 27.733999999999998
    • type: map_at_5 value: 29.64
    • type: mrr_at_1 value: 27.272999999999996
    • type: mrr_at_10 value: 36.083
    • type: mrr_at_100 value: 37.008
    • type: mrr_at_1000 value: 37.076
    • type: mrr_at_3 value: 33.004
    • type: mrr_at_5 value: 34.664
    • type: ndcg_at_1 value: 27.272999999999996
    • type: ndcg_at_10 value: 37.763000000000005
    • type: ndcg_at_100 value: 43.566
    • type: ndcg_at_1000 value: 46.356
    • type: ndcg_at_3 value: 31.673000000000002
    • type: ndcg_at_5 value: 34.501
    • type: precision_at_1 value: 27.272999999999996
    • type: precision_at_10 value: 7.470000000000001
    • type: precision_at_100 value: 1.502
    • type: precision_at_1000 value: 0.24
    • type: precision_at_3 value: 14.756
    • type: precision_at_5 value: 11.225
    • type: recall_at_1 value: 22.203999999999997
    • type: recall_at_10 value: 51.437999999999995
    • type: recall_at_100 value: 76.845
    • type: recall_at_1000 value: 94.38600000000001
    • type: recall_at_3 value: 34.258
    • type: recall_at_5 value: 41.512
  • task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics:
    • type: map_at_1 value: 17.474
    • type: map_at_10 value: 26.362999999999996
    • type: map_at_100 value: 27.456999999999997
    • type: map_at_1000 value: 27.567999999999998
    • type: map_at_3 value: 23.518
    • type: map_at_5 value: 25.068
    • type: mrr_at_1 value: 18.669
    • type: mrr_at_10 value: 27.998
    • type: mrr_at_100 value: 28.953
    • type: mrr_at_1000 value: 29.03
    • type: mrr_at_3 value: 25.230999999999998
    • type: mrr_at_5 value: 26.654
    • type: ndcg_at_1 value: 18.669
    • type: ndcg_at_10 value: 31.684
    • type: ndcg_at_100 value: 36.864999999999995
    • type: ndcg_at_1000 value: 39.555
    • type: ndcg_at_3 value: 26.057000000000002
    • type: ndcg_at_5 value: 28.587
    • type: precision_at_1 value: 18.669
    • type: precision_at_10 value: 5.3420000000000005
    • type: precision_at_100 value: 0.847
    • type: precision_at_1000 value: 0.12
    • type: precision_at_3 value: 11.583
    • type: precision_at_5 value: 8.466
    • type: recall_at_1 value: 17.474
    • type: recall_at_10 value: 46.497
    • type: recall_at_100 value: 69.977
    • type: recall_at_1000 value: 89.872
    • type: recall_at_3 value: 31.385999999999996
    • type: recall_at_5 value: 37.283
  • task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics:
    • type: map_at_1 value: 17.173
    • type: map_at_10 value: 30.407
    • type: map_at_100 value: 32.528
    • type: map_at_1000 value: 32.698
    • type: map_at_3 value: 25.523
    • type: map_at_5 value: 28.038
    • type: mrr_at_1 value: 38.958
    • type: mrr_at_10 value: 51.515
    • type: mrr_at_100 value: 52.214000000000006
    • type: mrr_at_1000 value: 52.237
    • type: mrr_at_3 value: 48.502
    • type: mrr_at_5 value: 50.251000000000005
    • type: ndcg_at_1 value: 38.958
    • type: ndcg_at_10 value: 40.355000000000004
    • type: ndcg_at_100 value: 47.68
    • type: ndcg_at_1000 value: 50.370000000000005
    • type: ndcg_at_3 value: 33.946
    • type: ndcg_at_5 value: 36.057
    • type: precision_at_1 value: 38.958
    • type: precision_at_10 value: 12.508
    • type: precision_at_100 value: 2.054
    • type: precision_at_1000 value: 0.256
    • type: precision_at_3 value: 25.581
    • type: precision_at_5 value: 19.256999999999998
    • type: recall_at_1 value: 17.173
    • type: recall_at_10 value: 46.967
    • type: recall_at_100 value: 71.47200000000001
    • type: recall_at_1000 value: 86.238
    • type: recall_at_3 value: 30.961
    • type: recall_at_5 value: 37.539
  • task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics:
    • type: map_at_1 value: 8.999
    • type: map_at_10 value: 18.989
    • type: map_at_100 value: 26.133
    • type: map_at_1000 value: 27.666
    • type: map_at_3 value: 13.918
    • type: map_at_5 value: 16.473
    • type: mrr_at_1 value: 66.25
    • type: mrr_at_10 value: 74.161
    • type: mrr_at_100 value: 74.516
    • type: mrr_at_1000 value: 74.524
    • type: mrr_at_3 value: 72.875
    • type: mrr_at_5 value: 73.613
    • type: ndcg_at_1 value: 54.37499999999999
    • type: ndcg_at_10 value: 39.902
    • type: ndcg_at_100 value: 44.212
    • type: ndcg_at_1000 value: 51.62
    • type: ndcg_at_3 value: 45.193
    • type: ndcg_at_5 value: 42.541000000000004
    • type: precision_at_1 value: 66.25
    • type: precision_at_10 value: 30.425
    • type: precision_at_100 value: 9.754999999999999
    • type: precision_at_1000 value: 2.043
    • type: precision_at_3 value: 48.25
    • type: precision_at_5 value: 40.65
    • type: recall_at_1 value: 8.999
    • type: recall_at_10 value: 24.133
    • type: recall_at_100 value: 49.138999999999996
    • type: recall_at_1000 value: 72.639
    • type: recall_at_3 value: 15.287999999999998
    • type: recall_at_5 value: 19.415
  • task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics:
    • type: accuracy value: 46.38999999999999
    • type: f1 value: 41.444205512055234
  • task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics:
    • type: map_at_1 value: 87.35000000000001
    • type: map_at_10 value: 92.837
    • type: map_at_100 value: 92.996
    • type: map_at_1000 value: 93.006
    • type: map_at_3 value: 92.187
    • type: map_at_5 value: 92.595
    • type: mrr_at_1 value: 93.864
    • type: mrr_at_10 value: 96.723
    • type: mrr_at_100 value: 96.72500000000001
    • type: mrr_at_1000 value: 96.72500000000001
    • type: mrr_at_3 value: 96.64
    • type: mrr_at_5 value: 96.71499999999999
    • type: ndcg_at_1 value: 93.864
    • type: ndcg_at_10 value: 94.813
    • type: ndcg_at_100 value: 95.243
    • type: ndcg_at_1000 value: 95.38600000000001
    • type: ndcg_at_3 value: 94.196
    • type: ndcg_at_5 value: 94.521
    • type: precision_at_1 value: 93.864
    • type: precision_at_10 value: 10.951
    • type: precision_at_100 value: 1.1400000000000001
    • type: precision_at_1000 value: 0.117
    • type: precision_at_3 value: 35.114000000000004
    • type: precision_at_5 value: 21.476
    • type: recall_at_1 value: 87.35000000000001
    • type: recall_at_10 value: 96.941
    • type: recall_at_100 value: 98.397
    • type: recall_at_1000 value: 99.21600000000001
    • type: recall_at_3 value: 95.149
    • type: recall_at_5 value: 96.131
  • task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics:
    • type: map_at_1 value: 24.476
    • type: map_at_10 value: 40.11
    • type: map_at_100 value: 42.229
    • type: map_at_1000 value: 42.378
    • type: map_at_3 value: 34.512
    • type: map_at_5 value: 38.037
    • type: mrr_at_1 value: 47.839999999999996
    • type: mrr_at_10 value: 57.053
    • type: mrr_at_100 value: 57.772
    • type: mrr_at_1000 value: 57.799
    • type: mrr_at_3 value: 54.552
    • type: mrr_at_5 value: 56.011
    • type: ndcg_at_1 value: 47.839999999999996
    • type: ndcg_at_10 value: 48.650999999999996
    • type: ndcg_at_100 value: 55.681000000000004
    • type: ndcg_at_1000 value: 57.979
    • type: ndcg_at_3 value: 43.923
    • type: ndcg_at_5 value: 46.037
    • type: precision_at_1 value: 47.839999999999996
    • type: precision_at_10 value: 13.395000000000001
    • type: precision_at_100 value: 2.0660000000000003
    • type: precision_at_1000 value: 0.248
    • type: precision_at_3 value: 29.064
    • type: precision_at_5 value: 22.006
    • type: recall_at_1 value: 24.476
    • type: recall_at_10 value: 56.216
    • type: recall_at_100 value: 81.798
    • type: recall_at_1000 value: 95.48299999999999
    • type: recall_at_3 value: 39.357
    • type: recall_at_5 value: 47.802
  • task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics:
    • type: map_at_1 value: 42.728
    • type: map_at_10 value: 57.737
    • type: map_at_100 value: 58.531
    • type: map_at_1000 value: 58.594
    • type: map_at_3 value: 54.869
    • type: map_at_5 value: 56.55
    • type: mrr_at_1 value: 85.456
    • type: mrr_at_10 value: 90.062
    • type: mrr_at_100 value: 90.159
    • type: mrr_at_1000 value: 90.16
    • type: mrr_at_3 value: 89.37899999999999
    • type: mrr_at_5 value: 89.81
    • type: ndcg_at_1 value: 85.456
    • type: ndcg_at_10 value: 67.755
    • type: ndcg_at_100 value: 70.341
    • type: ndcg_at_1000 value: 71.538
    • type: ndcg_at_3 value: 63.735
    • type: ndcg_at_5 value: 65.823
    • type: precision_at_1 value: 85.456
    • type: precision_at_10 value: 13.450000000000001
    • type: precision_at_100 value: 1.545
    • type: precision_at_1000 value: 0.16999999999999998
    • type: precision_at_3 value: 38.861000000000004
    • type: precision_at_5 value: 24.964
    • type: recall_at_1 value: 42.728
    • type: recall_at_10 value: 67.252
    • type: recall_at_100 value: 77.265
    • type: recall_at_1000 value: 85.246
    • type: recall_at_3 value: 58.292
    • type: recall_at_5 value: 62.41100000000001
  • task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics:
    • type: accuracy value: 87.4836
    • type: ap value: 82.29552224030336
    • type: f1 value: 87.42791432227448
  • task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics:
    • type: map_at_1 value: 23.015
    • type: map_at_10 value: 35.621
    • type: map_at_100 value: 36.809
    • type: map_at_1000 value: 36.853
    • type: map_at_3 value: 31.832
    • type: map_at_5 value: 34.006
    • type: mrr_at_1 value: 23.738999999999997
    • type: mrr_at_10 value: 36.309999999999995
    • type: mrr_at_100 value: 37.422
    • type: mrr_at_1000 value: 37.461
    • type: mrr_at_3 value: 32.592999999999996
    • type: mrr_at_5 value: 34.736
    • type: ndcg_at_1 value: 23.724999999999998
    • type: ndcg_at_10 value: 42.617
    • type: ndcg_at_100 value: 48.217999999999996
    • type: ndcg_at_1000 value: 49.309
    • type: ndcg_at_3 value: 34.905
    • type: ndcg_at_5 value: 38.769
    • type: precision_at_1 value: 23.724999999999998
    • type: precision_at_10 value: 6.689
    • type: precision_at_100 value: 0.9480000000000001
    • type: precision_at_1000 value: 0.104
    • type: precision_at_3 value: 14.89
    • type: precision_at_5 value: 10.897
    • type: recall_at_1 value: 23.015
    • type: recall_at_10 value: 64.041
    • type: recall_at_100 value: 89.724
    • type: recall_at_1000 value: 98.00999999999999
    • type: recall_at_3 value: 43.064
    • type: recall_at_5 value: 52.31099999999999
  • task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
    • type: accuracy value: 96.49794801641588
    • type: f1 value: 96.28931114498003
  • task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
    • type: accuracy value: 82.81121751025992
    • type: f1 value: 63.18740125901853
  • task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
    • type: accuracy value: 77.66644250168123
    • type: f1 value: 74.93211186867839
  • task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
    • type: accuracy value: 81.77202420981843
    • type: f1 value: 81.63681969283554
  • task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics:
    • type: v_measure value: 34.596687684870645
  • task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics:
    • type: v_measure value: 32.26965660101405
  • task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics:
    • type: map value: 31.33619694846802
    • type: mrr value: 32.53719657720334
  • task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics:
    • type: map_at_1 value: 6.0729999999999995
    • type: map_at_10 value: 13.245999999999999
    • type: map_at_100 value: 16.747999999999998
    • type: map_at_1000 value: 18.163
    • type: map_at_3 value: 10.064
    • type: map_at_5 value: 11.513
    • type: mrr_at_1 value: 49.536
    • type: mrr_at_10 value: 58.092
    • type: mrr_at_100 value: 58.752
    • type: mrr_at_1000 value: 58.78
    • type: mrr_at_3 value: 56.398
    • type: mrr_at_5 value: 57.389
    • type: ndcg_at_1 value: 47.059
    • type: ndcg_at_10 value: 35.881
    • type: ndcg_at_100 value: 32.751999999999995
    • type: ndcg_at_1000 value: 41.498000000000005
    • type: ndcg_at_3 value: 42.518
    • type: ndcg_at_5 value: 39.550999999999995
    • type: precision_at_1 value: 49.536
    • type: precision_at_10 value: 26.316
    • type: precision_at_100 value: 8.084
    • type: precision_at_1000 value: 2.081
    • type: precision_at_3 value: 39.938
    • type: precision_at_5 value: 34.056
    • type: recall_at_1 value: 6.0729999999999995
    • type: recall_at_10 value: 16.593
    • type: recall_at_100 value: 32.883
    • type: recall_at_1000 value: 64.654
    • type: recall_at_3 value: 11.174000000000001
    • type: recall_at_5 value: 13.528
  • task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics:
    • type: map_at_1 value: 30.043
    • type: map_at_10 value: 45.318999999999996
    • type: map_at_100 value: 46.381
    • type: map_at_1000 value: 46.412
    • type: map_at_3 value: 40.941
    • type: map_at_5 value: 43.662
    • type: mrr_at_1 value: 33.98
    • type: mrr_at_10 value: 47.870000000000005
    • type: mrr_at_100 value: 48.681999999999995
    • type: mrr_at_1000 value: 48.703
    • type: mrr_at_3 value: 44.341
    • type: mrr_at_5 value: 46.547
    • type: ndcg_at_1 value: 33.98
    • type: ndcg_at_10 value: 52.957
    • type: ndcg_at_100 value: 57.434
    • type: ndcg_at_1000 value: 58.103
    • type: ndcg_at_3 value: 44.896
    • type: ndcg_at_5 value: 49.353
    • type: precision_at_1 value: 33.98
    • type: precision_at_10 value: 8.786
    • type: precision_at_100 value: 1.1280000000000001
    • type: precision_at_1000 value: 0.11900000000000001
    • type: precision_at_3 value: 20.577
    • type: precision_at_5 value: 14.942
    • type: recall_at_1 value: 30.043
    • type: recall_at_10 value: 73.593
    • type: recall_at_100 value: 93.026
    • type: recall_at_1000 value: 97.943
    • type: recall_at_3 value: 52.955
    • type: recall_at_5 value: 63.132
  • task: type: Retrieval dataset: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 70.808
    • type: map_at_10 value: 84.675
    • type: map_at_100 value: 85.322
    • type: map_at_1000 value: 85.33800000000001
    • type: map_at_3 value: 81.68900000000001
    • type: map_at_5 value: 83.543
    • type: mrr_at_1 value: 81.5
    • type: mrr_at_10 value: 87.59700000000001
    • type: mrr_at_100 value: 87.705
    • type: mrr_at_1000 value: 87.70599999999999
    • type: mrr_at_3 value: 86.607
    • type: mrr_at_5 value: 87.289
    • type: ndcg_at_1 value: 81.51
    • type: ndcg_at_10 value: 88.41799999999999
    • type: ndcg_at_100 value: 89.644
    • type: ndcg_at_1000 value: 89.725
    • type: ndcg_at_3 value: 85.49900000000001
    • type: ndcg_at_5 value: 87.078
    • type: precision_at_1 value: 81.51
    • type: precision_at_10 value: 13.438
    • type: precision_at_100 value: 1.532
    • type: precision_at_1000 value: 0.157
    • type: precision_at_3 value: 37.363
    • type: precision_at_5 value: 24.57
    • type: recall_at_1 value: 70.808
    • type: recall_at_10 value: 95.575
    • type: recall_at_100 value: 99.667
    • type: recall_at_1000 value: 99.98899999999999
    • type: recall_at_3 value: 87.223
    • type: recall_at_5 value: 91.682
  • task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics:
    • type: v_measure value: 58.614831329137715
  • task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics:
    • type: v_measure value: 66.86580408560826
  • task: type: Retrieval dataset: type: mteb/scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics:
    • type: map_at_1 value: 5.093
    • type: map_at_10 value: 13.014000000000001
    • type: map_at_100 value: 15.412999999999998
    • type: map_at_1000 value: 15.756999999999998
    • type: map_at_3 value: 9.216000000000001
    • type: map_at_5 value: 11.036999999999999
    • type: mrr_at_1 value: 25.1
    • type: mrr_at_10 value: 37.133
    • type: mrr_at_100 value: 38.165
    • type: mrr_at_1000 value: 38.198
    • type: mrr_at_3 value: 33.217
    • type: mrr_at_5 value: 35.732
    • type: ndcg_at_1 value: 25.1
    • type: ndcg_at_10 value: 21.918000000000003
    • type: ndcg_at_100 value: 30.983
    • type: ndcg_at_1000 value: 36.629
    • type: ndcg_at_3 value: 20.544999999999998
    • type: ndcg_at_5 value: 18.192
    • type: precision_at_1 value: 25.1
    • type: precision_at_10 value: 11.44
    • type: precision_at_100 value: 2.459
    • type: precision_at_1000 value: 0.381
    • type: precision_at_3 value: 19.267
    • type: precision_at_5 value: 16.16
    • type: recall_at_1 value: 5.093
    • type: recall_at_10 value: 23.215
    • type: recall_at_100 value: 49.902
    • type: recall_at_1000 value: 77.403
    • type: recall_at_3 value: 11.733
    • type: recall_at_5 value: 16.372999999999998
  • task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics:
    • type: cos_sim_pearson value: 82.9365442977452
    • type: cos_sim_spearman value: 79.36960687383745
    • type: euclidean_pearson value: 79.6045204840714
    • type: euclidean_spearman value: 79.26382712751337
    • type: manhattan_pearson value: 79.4805084789529
    • type: manhattan_spearman value: 79.21847863209523
  • task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics:
    • type: cos_sim_pearson value: 83.27906192961453
    • type: cos_sim_spearman value: 74.38364712099211
    • type: euclidean_pearson value: 78.54358927241223
    • type: euclidean_spearman value: 74.22185560806376
    • type: manhattan_pearson value: 78.50904327377751
    • type: manhattan_spearman value: 74.2627500781748
  • task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics:
    • type: cos_sim_pearson value: 84.66863742649639
    • type: cos_sim_spearman value: 84.70630905216271
    • type: euclidean_pearson value: 84.64498334705334
    • type: euclidean_spearman value: 84.87204770690148
    • type: manhattan_pearson value: 84.65774227976077
    • type: manhattan_spearman value: 84.91251851797985
  • task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics:
    • type: cos_sim_pearson value: 83.1577763924467
    • type: cos_sim_spearman value: 80.10314039230198
    • type: euclidean_pearson value: 81.51346991046043
    • type: euclidean_spearman value: 80.08678485109435
    • type: manhattan_pearson value: 81.57058914661894
    • type: manhattan_spearman value: 80.1516230725106
  • task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics:
    • type: cos_sim_pearson value: 86.40310839662533
    • type: cos_sim_spearman value: 87.16293477217867
    • type: euclidean_pearson value: 86.50688711184775
    • type: euclidean_spearman value: 87.08651444923031
    • type: manhattan_pearson value: 86.54674677557857
    • type: manhattan_spearman value: 87.15079017870971
  • task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics:
    • type: cos_sim_pearson value: 84.32886275207817
    • type: cos_sim_spearman value: 85.0190460590732
    • type: euclidean_pearson value: 84.42553652784679
    • type: euclidean_spearman value: 85.20027364279328
    • type: manhattan_pearson value: 84.42926246281078
    • type: manhattan_spearman value: 85.20187419804306
  • 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: 90.76732216967812
    • type: cos_sim_spearman value: 90.63701653633909
    • type: euclidean_pearson value: 90.26678186114682
    • type: euclidean_spearman value: 90.67288073455427
    • type: manhattan_pearson value: 90.20772020584582
    • type: manhattan_spearman value: 90.60764863983702
  • task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics:
    • type: cos_sim_pearson value: 69.09280387698125
    • type: cos_sim_spearman value: 68.62743151172162
    • type: euclidean_pearson value: 69.89386398104689
    • type: euclidean_spearman value: 68.71191066733556
    • type: manhattan_pearson value: 69.92516500604872
    • type: manhattan_spearman value: 68.80452846992576
  • task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics:
    • type: cos_sim_pearson value: 86.13178592019887
    • type: cos_sim_spearman value: 86.03947178806887
    • type: euclidean_pearson value: 85.87029414285313
    • type: euclidean_spearman value: 86.04960843306998
    • type: manhattan_pearson value: 85.92946858580146
    • type: manhattan_spearman value: 86.12575341860442
  • task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics:
    • type: map value: 85.16657063002837
    • type: mrr value: 95.73671063867141
  • task: type: Retrieval dataset: type: mteb/scifact name: MTEB SciFact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics:
    • type: map_at_1 value: 63.510999999999996
    • type: map_at_10 value: 72.76899999999999
    • type: map_at_100 value: 73.303
    • type: map_at_1000 value: 73.32499999999999
    • type: map_at_3 value: 70.514
    • type: map_at_5 value: 71.929
    • type: mrr_at_1 value: 66.333
    • type: mrr_at_10 value: 73.75
    • type: mrr_at_100 value: 74.119
    • type: mrr_at_1000 value: 74.138
    • type: mrr_at_3 value: 72.222
    • type: mrr_at_5 value: 73.122
    • type: ndcg_at_1 value: 66.333
    • type: ndcg_at_10 value: 76.774
    • type: ndcg_at_100 value: 78.78500000000001
    • type: ndcg_at_1000 value: 79.254
    • type: ndcg_at_3 value: 73.088
    • type: ndcg_at_5 value: 75.002
    • type: precision_at_1 value: 66.333
    • type: precision_at_10 value: 9.833
    • type: precision_at_100 value: 1.093
    • type: precision_at_1000 value: 0.11299999999999999
    • type: precision_at_3 value: 28.222
    • type: precision_at_5 value: 18.333
    • type: recall_at_1 value: 63.510999999999996
    • type: recall_at_10 value: 87.98899999999999
    • type: recall_at_100 value: 96.5
    • type: recall_at_1000 value: 100.0
    • type: recall_at_3 value: 77.86699999999999
    • type: recall_at_5 value: 82.73899999999999
  • task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics:
    • type: cos_sim_accuracy value: 99.78514851485149
    • type: cos_sim_ap value: 94.94214383862038
    • type: cos_sim_f1 value: 89.02255639097744
    • type: cos_sim_precision value: 89.2462311557789
    • type: cos_sim_recall value: 88.8
    • type: dot_accuracy value: 99.78217821782178
    • type: dot_ap value: 94.69965247836805
    • type: dot_f1 value: 88.78695208970439
    • type: dot_precision value: 90.54054054054053
    • type: dot_recall value: 87.1
    • type: euclidean_accuracy value: 99.78118811881188
    • type: euclidean_ap value: 94.9865187695411
    • type: euclidean_f1 value: 88.99950223992036
    • type: euclidean_precision value: 88.60257680872151
    • type: euclidean_recall value: 89.4
    • type: manhattan_accuracy value: 99.78811881188119
    • type: manhattan_ap value: 95.0021236766459
    • type: manhattan_f1 value: 89.12071535022356
    • type: manhattan_precision value: 88.54886475814413
    • type: manhattan_recall value: 89.7
    • type: max_accuracy value: 99.78811881188119
    • type: max_ap value: 95.0021236766459
    • type: max_f1 value: 89.12071535022356
  • task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics:
    • type: v_measure value: 68.93190546593995
  • task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics:
    • type: v_measure value: 37.602808534760655
  • task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics:
    • type: map value: 52.29214480978073
    • type: mrr value: 53.123169722434426
  • task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics:
    • type: cos_sim_pearson value: 30.967800769650022
    • type: cos_sim_spearman value: 31.168490040206926
    • type: dot_pearson value: 30.888603021128553
    • type: dot_spearman value: 31.028241262520385
  • task: type: Retrieval dataset: type: mteb/trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics:
    • type: map_at_1 value: 0.22300000000000003
    • type: map_at_10 value: 1.781
    • type: map_at_100 value: 9.905999999999999
    • type: map_at_1000 value: 23.455000000000002
    • type: map_at_3 value: 0.569
    • type: map_at_5 value: 0.918
    • type: mrr_at_1 value: 84.0
    • type: mrr_at_10 value: 91.067
    • type: mrr_at_100 value: 91.067
    • type: mrr_at_1000 value: 91.067
    • type: mrr_at_3 value: 90.667
    • type: mrr_at_5 value: 91.067
    • type: ndcg_at_1 value: 78.0
    • type: ndcg_at_10 value: 73.13499999999999
    • type: ndcg_at_100 value: 55.32
    • type: ndcg_at_1000 value: 49.532
    • type: ndcg_at_3 value: 73.715
    • type: ndcg_at_5 value: 72.74199999999999
    • type: precision_at_1 value: 84.0
    • type: precision_at_10 value: 78.8
    • type: precision_at_100 value: 56.32
    • type: precision_at_1000 value: 21.504
    • type: precision_at_3 value: 77.333
    • type: precision_at_5 value: 78.0
    • type: recall_at_1 value: 0.22300000000000003
    • type: recall_at_10 value: 2.049
    • type: recall_at_100 value: 13.553
    • type: recall_at_1000 value: 46.367999999999995
    • type: recall_at_3 value: 0.604
    • type: recall_at_5 value: 1.015
  • task: type: Retrieval dataset: type: mteb/touche2020 name: MTEB Touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics:
    • type: map_at_1 value: 3.0380000000000003
    • type: map_at_10 value: 10.188
    • type: map_at_100 value: 16.395
    • type: map_at_1000 value: 18.024
    • type: map_at_3 value: 6.236
    • type: map_at_5 value: 7.276000000000001
    • type: mrr_at_1 value: 34.694
    • type: mrr_at_10 value: 46.292
    • type: mrr_at_100 value: 47.446
    • type: mrr_at_1000 value: 47.446
    • type: mrr_at_3 value: 41.156
    • type: mrr_at_5 value: 44.32
    • type: ndcg_at_1 value: 32.653
    • type: ndcg_at_10 value: 25.219
    • type: ndcg_at_100 value: 37.802
    • type: ndcg_at_1000 value: 49.274
    • type: ndcg_at_3 value: 28.605999999999998
    • type: ndcg_at_5 value: 26.21
    • type: precision_at_1 value: 34.694
    • type: precision_at_10 value: 21.837
    • type: precision_at_100 value: 7.776
    • type: precision_at_1000 value: 1.522
    • type: precision_at_3 value: 28.571
    • type: precision_at_5 value: 25.306
    • type: recall_at_1 value: 3.0380000000000003
    • type: recall_at_10 value: 16.298000000000002
    • type: recall_at_100 value: 48.712
    • type: recall_at_1000 value: 83.16799999999999
    • type: recall_at_3 value: 7.265000000000001
    • type: recall_at_5 value: 9.551
  • task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics:
    • type: accuracy value: 83.978
    • type: ap value: 24.751887949330015
    • type: f1 value: 66.8685134049279
  • task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics:
    • type: accuracy value: 61.573288058856825
    • type: f1 value: 61.973261751726604
  • task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics:
    • type: v_measure value: 48.75483298792469
  • task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics:
    • type: cos_sim_accuracy value: 86.36824223639506
    • type: cos_sim_ap value: 75.53126388573047
    • type: cos_sim_f1 value: 67.9912831688245
    • type: cos_sim_precision value: 66.11817501869858
    • type: cos_sim_recall value: 69.9736147757256
    • type: dot_accuracy value: 86.39804494248078
    • type: dot_ap value: 75.27598891718046
    • type: dot_f1 value: 67.91146284159763
    • type: dot_precision value: 63.90505003490807
    • type: dot_recall value: 72.45382585751979
    • type: euclidean_accuracy value: 86.36228169517793
    • type: euclidean_ap value: 75.51438087434647
    • type: euclidean_f1 value: 68.02370523061066
    • type: euclidean_precision value: 66.46525679758308
    • type: euclidean_recall value: 69.65699208443272
    • type: manhattan_accuracy value: 86.46361089586935
    • type: manhattan_ap value: 75.50800785730111
    • type: manhattan_f1 value: 67.9220437187253
    • type: manhattan_precision value: 67.79705573080967
    • type: manhattan_recall value: 68.04749340369392
    • type: max_accuracy value: 86.46361089586935
    • type: max_ap value: 75.53126388573047
    • type: max_f1 value: 68.02370523061066
  • task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics:
    • type: cos_sim_accuracy value: 88.80350836341057
    • type: cos_sim_ap value: 85.51101933260743
    • type: cos_sim_f1 value: 77.9152271629704
    • type: cos_sim_precision value: 75.27815662910056
    • type: cos_sim_recall value: 80.74376347397599
    • type: dot_accuracy value: 88.84425815966158
    • type: dot_ap value: 85.49726945962519
    • type: dot_f1 value: 77.94445269567801
    • type: dot_precision value: 75.27251864601261
    • type: dot_recall value: 80.81305820757623
    • type: euclidean_accuracy value: 88.80350836341057
    • type: euclidean_ap value: 85.4882880790211
    • type: euclidean_f1 value: 77.87063284615103
    • type: euclidean_precision value: 74.61022927689595
    • type: euclidean_recall value: 81.42901139513397
    • type: manhattan_accuracy value: 88.7161873714441
    • type: manhattan_ap value: 85.45753871906821
    • type: manhattan_f1 value: 77.8686401480111
    • type: manhattan_precision value: 74.95903683123174
    • type: manhattan_recall value: 81.01324299353249
    • type: max_accuracy value: 88.84425815966158
    • type: max_ap value: 85.51101933260743
    • type: max_f1 value: 77.94445269567801

gte-base-en-v1.5

We introduce gte-v1.5 series, upgraded gte embeddings that support the context length of up to 8192, while further enhancing model performance. The models are built upon the transformer++ encoder backbone (BERT + RoPE + GLU).

The gte-v1.5 series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to Evaluation).

We also present the gte-Qwen1.5-7B-instruct, a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.

• Developed by: Institute for Intelligent Computing, Alibaba Group
• Model type: Text Embeddings
• Paper: mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval

Model list

How to Get Started with the Model

Use the code below to get started with the model.

# Requires transformers>=4.36.0

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

input_texts = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "Beijing",
    "sorting algorithms"
]

model_path = 'Alibaba-NLP/gte-base-en-v1.5'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]
 
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())

It is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.

Use with sentence-transformers:

# Requires sentence_transformers>=2.7.0

from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

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

model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))

Use with transformers.js:

// npm i @xenova/transformers
import { pipeline, dot } from '@xenova/transformers';

// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-base-en-v1.5', {
    quantized: false, // Comment out this line to use the quantized version
});

// Generate sentence embeddings
const sentences = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "Beijing",
    "sorting algorithms"
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });

// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x));
console.log(similarities); // [34.504930869007296, 64.03973265120138, 19.520042686034362]

Use with infinity: Infinity is a MIT licensed server for OpenAI-compatible deployment.

docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.68 \
v2 --model-id Alibaba-NLP/gte-base-en-v1.5 --revision "4c742dc2b781e4ab062a4a77f4f7cbad4bdee970" --dtype bfloat16 --batch-size 32 --device cuda --engine torch --port 7997

Training Details

Training Data

• Masked language modeling (MLM): c4-en
• Weak-supervised contrastive pre-training (CPT): GTE pre-training data
• Supervised contrastive fine-tuning: GTE fine-tuning data

Training Procedure

To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy. The model first undergoes preliminary MLM pre-training on shorter lengths. And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.

The entire training process is as follows:

• MLM-2048: lr 5e-4, mlm_probability 0.3, batch_size 4096, num_steps 70000, rope_base 10000
• MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 20000, rope_base 500000
• CPT: max_len 512, lr 2e-4, batch_size 32768, num_steps 100000
• Fine-tuning: TODO

Evaluation

MTEB

The results of other models are retrieved from MTEB leaderboard.

The gte evaluation setting: mteb==1.2.0, fp16 auto mix precision, max_length=8192, and set ntk scaling factor to 2 (equivalent to rope_base * 2).

LoCo

Citation

If you find our paper or models helpful, please consider citing them as follows:

@misc{zhang2024mgte,
  title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, 
  author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang},
  year={2024},
  eprint={2407.19669},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2407.19669}, 
}
@misc{li2023gte,
  title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, 
  author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
  year={2023},
  eprint={2308.03281},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2308.03281}, 
}