metadata
library_name: transformers
tags:
- sentence-transformers
- gte
- mteb
- transformers.js
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
- 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
- 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
- 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
- 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
- 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
- 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
- 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: Coming soon.
Model list
| Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo |
|---|---|---|---|---|---|---|
gte-Qwen1.5-7B-instruct |
Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 |
gte-large-en-v1.5 |
English | 434 | 8192 | 1024 | 65.39 | 86.71 |
gte-base-en-v1.5 |
English | 137 | 8192 | 768 | 64.11 | 87.44 |
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:
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]
Training Details
Training Data
- Masked language modeling (MLM):
c4-en - Weak-supervised contrastive (WSC) pre-training: 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
- WSC: 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).
| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| gte-large-en-v1.5 | 434 | 1024 | 8192 | 65.39 | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 |
| mxbai-embed-large-v1 | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 |
| multilingual-e5-large-instruct | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 |
| bge-large-en-v1.5 | 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
| gte-base-en-v1.5 | 137 | 768 | 8192 | 64.11 | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 |
| bge-base-en-v1.5 | 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 |
LoCo
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
|---|---|---|---|---|---|---|---|---|
| gte-qwen1.5-7b | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 |
| gte-large-v1.5 | 1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 |
| gte-base-v1.5 | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 |
Citation
If you find our paper or models helpful, please consider citing them as follows:
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}