metadata
datasets:
- allenai/c4
library_name: transformers
tags:
- sentence-transformers
- gte
- mteb
- transformers.js
license: apache-2.0
language:
- en
model-index:
- name: gte-large-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: 73.01492537313432
- type: ap
value: 35.05341696659522
- type: f1
value: 66.71270310883853
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.97189999999999
- type: ap
value: 90.5952493948908
- type: f1
value: 93.95848137716877
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 54.196
- type: f1
value: 53.80122334012787
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 47.297
- type: map_at_10
value: 64.303
- type: map_at_100
value: 64.541
- type: map_at_1000
value: 64.541
- type: map_at_3
value: 60.728
- type: map_at_5
value: 63.114000000000004
- type: mrr_at_1
value: 48.435
- type: mrr_at_10
value: 64.657
- type: mrr_at_100
value: 64.901
- type: mrr_at_1000
value: 64.901
- type: mrr_at_3
value: 61.06
- type: mrr_at_5
value: 63.514
- type: ndcg_at_1
value: 47.297
- type: ndcg_at_10
value: 72.107
- type: ndcg_at_100
value: 72.963
- type: ndcg_at_1000
value: 72.963
- type: ndcg_at_3
value: 65.063
- type: ndcg_at_5
value: 69.352
- type: precision_at_1
value: 47.297
- type: precision_at_10
value: 9.623
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 25.865
- type: precision_at_5
value: 17.596
- type: recall_at_1
value: 47.297
- type: recall_at_10
value: 96.23
- type: recall_at_100
value: 99.644
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 77.596
- type: recall_at_5
value: 87.98
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.467787861077475
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 43.39198391914257
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.12794820591384
- type: mrr
value: 75.9331442641692
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.85062993863319
- type: cos_sim_spearman
value: 85.39049989733459
- type: euclidean_pearson
value: 86.00222680278333
- type: euclidean_spearman
value: 85.45556162077396
- type: manhattan_pearson
value: 85.88769871785621
- type: manhattan_spearman
value: 85.11760211290839
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.32792207792208
- type: f1
value: 87.29132945999555
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.5779328301945
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 37.94425623865118
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-android
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 32.978
- type: map_at_10
value: 44.45
- type: map_at_100
value: 46.19
- type: map_at_1000
value: 46.303
- type: map_at_3
value: 40.849000000000004
- type: map_at_5
value: 42.55
- type: mrr_at_1
value: 40.629
- type: mrr_at_10
value: 50.848000000000006
- type: mrr_at_100
value: 51.669
- type: mrr_at_1000
value: 51.705
- type: mrr_at_3
value: 47.997
- type: mrr_at_5
value: 49.506
- type: ndcg_at_1
value: 40.629
- type: ndcg_at_10
value: 51.102000000000004
- type: ndcg_at_100
value: 57.159000000000006
- type: ndcg_at_1000
value: 58.669000000000004
- type: ndcg_at_3
value: 45.738
- type: ndcg_at_5
value: 47.632999999999996
- type: precision_at_1
value: 40.629
- type: precision_at_10
value: 9.700000000000001
- type: precision_at_100
value: 1.5970000000000002
- type: precision_at_1000
value: 0.202
- type: precision_at_3
value: 21.698
- type: precision_at_5
value: 15.393
- type: recall_at_1
value: 32.978
- type: recall_at_10
value: 63.711
- type: recall_at_100
value: 88.39399999999999
- type: recall_at_1000
value: 97.513
- type: recall_at_3
value: 48.025
- type: recall_at_5
value: 53.52
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-english
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 30.767
- type: map_at_10
value: 42.195
- type: map_at_100
value: 43.541999999999994
- type: map_at_1000
value: 43.673
- type: map_at_3
value: 38.561
- type: map_at_5
value: 40.532000000000004
- type: mrr_at_1
value: 38.79
- type: mrr_at_10
value: 48.021
- type: mrr_at_100
value: 48.735
- type: mrr_at_1000
value: 48.776
- type: mrr_at_3
value: 45.594
- type: mrr_at_5
value: 46.986
- type: ndcg_at_1
value: 38.79
- type: ndcg_at_10
value: 48.468
- type: ndcg_at_100
value: 53.037
- type: ndcg_at_1000
value: 55.001999999999995
- type: ndcg_at_3
value: 43.409
- type: ndcg_at_5
value: 45.654
- type: precision_at_1
value: 38.79
- type: precision_at_10
value: 9.452
- type: precision_at_100
value: 1.518
- type: precision_at_1000
value: 0.201
- type: precision_at_3
value: 21.21
- type: precision_at_5
value: 15.171999999999999
- type: recall_at_1
value: 30.767
- type: recall_at_10
value: 60.118
- type: recall_at_100
value: 79.271
- type: recall_at_1000
value: 91.43299999999999
- type: recall_at_3
value: 45.36
- type: recall_at_5
value: 51.705
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gaming
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 40.007
- type: map_at_10
value: 53.529
- type: map_at_100
value: 54.602
- type: map_at_1000
value: 54.647
- type: map_at_3
value: 49.951
- type: map_at_5
value: 52.066
- type: mrr_at_1
value: 45.705
- type: mrr_at_10
value: 56.745000000000005
- type: mrr_at_100
value: 57.43899999999999
- type: mrr_at_1000
value: 57.462999999999994
- type: mrr_at_3
value: 54.25299999999999
- type: mrr_at_5
value: 55.842000000000006
- type: ndcg_at_1
value: 45.705
- type: ndcg_at_10
value: 59.809
- type: ndcg_at_100
value: 63.837999999999994
- type: ndcg_at_1000
value: 64.729
- type: ndcg_at_3
value: 53.994
- type: ndcg_at_5
value: 57.028
- type: precision_at_1
value: 45.705
- type: precision_at_10
value: 9.762
- type: precision_at_100
value: 1.275
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 24.368000000000002
- type: precision_at_5
value: 16.84
- type: recall_at_1
value: 40.007
- type: recall_at_10
value: 75.017
- type: recall_at_100
value: 91.99000000000001
- type: recall_at_1000
value: 98.265
- type: recall_at_3
value: 59.704
- type: recall_at_5
value: 67.109
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gis
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 26.639000000000003
- type: map_at_10
value: 35.926
- type: map_at_100
value: 37.126999999999995
- type: map_at_1000
value: 37.202
- type: map_at_3
value: 32.989000000000004
- type: map_at_5
value: 34.465
- type: mrr_at_1
value: 28.475
- type: mrr_at_10
value: 37.7
- type: mrr_at_100
value: 38.753
- type: mrr_at_1000
value: 38.807
- type: mrr_at_3
value: 35.066
- type: mrr_at_5
value: 36.512
- type: ndcg_at_1
value: 28.475
- type: ndcg_at_10
value: 41.245
- type: ndcg_at_100
value: 46.814
- type: ndcg_at_1000
value: 48.571
- type: ndcg_at_3
value: 35.528999999999996
- type: ndcg_at_5
value: 38.066
- type: precision_at_1
value: 28.475
- type: precision_at_10
value: 6.497
- type: precision_at_100
value: 0.9650000000000001
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 15.065999999999999
- type: precision_at_5
value: 10.599
- type: recall_at_1
value: 26.639000000000003
- type: recall_at_10
value: 55.759
- type: recall_at_100
value: 80.913
- type: recall_at_1000
value: 93.929
- type: recall_at_3
value: 40.454
- type: recall_at_5
value: 46.439
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-mathematica
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 15.767999999999999
- type: map_at_10
value: 24.811
- type: map_at_100
value: 26.064999999999998
- type: map_at_1000
value: 26.186999999999998
- type: map_at_3
value: 21.736
- type: map_at_5
value: 23.283
- type: mrr_at_1
value: 19.527
- type: mrr_at_10
value: 29.179
- type: mrr_at_100
value: 30.153999999999996
- type: mrr_at_1000
value: 30.215999999999998
- type: mrr_at_3
value: 26.223000000000003
- type: mrr_at_5
value: 27.733999999999998
- type: ndcg_at_1
value: 19.527
- type: ndcg_at_10
value: 30.786
- type: ndcg_at_100
value: 36.644
- type: ndcg_at_1000
value: 39.440999999999995
- type: ndcg_at_3
value: 24.958
- type: ndcg_at_5
value: 27.392
- type: precision_at_1
value: 19.527
- type: precision_at_10
value: 5.995
- type: precision_at_100
value: 1.03
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 12.520999999999999
- type: precision_at_5
value: 9.129
- type: recall_at_1
value: 15.767999999999999
- type: recall_at_10
value: 44.824000000000005
- type: recall_at_100
value: 70.186
- type: recall_at_1000
value: 89.934
- type: recall_at_3
value: 28.607
- type: recall_at_5
value: 34.836
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-physics
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 31.952
- type: map_at_10
value: 44.438
- type: map_at_100
value: 45.778
- type: map_at_1000
value: 45.883
- type: map_at_3
value: 41.044000000000004
- type: map_at_5
value: 42.986000000000004
- type: mrr_at_1
value: 39.172000000000004
- type: mrr_at_10
value: 49.76
- type: mrr_at_100
value: 50.583999999999996
- type: mrr_at_1000
value: 50.621
- type: mrr_at_3
value: 47.353
- type: mrr_at_5
value: 48.739
- type: ndcg_at_1
value: 39.172000000000004
- type: ndcg_at_10
value: 50.760000000000005
- type: ndcg_at_100
value: 56.084
- type: ndcg_at_1000
value: 57.865
- type: ndcg_at_3
value: 45.663
- type: ndcg_at_5
value: 48.178
- type: precision_at_1
value: 39.172000000000004
- type: precision_at_10
value: 9.22
- type: precision_at_100
value: 1.387
- type: precision_at_1000
value: 0.17099999999999999
- type: precision_at_3
value: 21.976000000000003
- type: precision_at_5
value: 15.457
- type: recall_at_1
value: 31.952
- type: recall_at_10
value: 63.900999999999996
- type: recall_at_100
value: 85.676
- type: recall_at_1000
value: 97.03699999999999
- type: recall_at_3
value: 49.781
- type: recall_at_5
value: 56.330000000000005
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-programmers
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 25.332
- type: map_at_10
value: 36.874
- type: map_at_100
value: 38.340999999999994
- type: map_at_1000
value: 38.452
- type: map_at_3
value: 33.068
- type: map_at_5
value: 35.324
- type: mrr_at_1
value: 30.822
- type: mrr_at_10
value: 41.641
- type: mrr_at_100
value: 42.519
- type: mrr_at_1000
value: 42.573
- type: mrr_at_3
value: 38.413000000000004
- type: mrr_at_5
value: 40.542
- type: ndcg_at_1
value: 30.822
- type: ndcg_at_10
value: 43.414
- type: ndcg_at_100
value: 49.196
- type: ndcg_at_1000
value: 51.237
- type: ndcg_at_3
value: 37.230000000000004
- type: ndcg_at_5
value: 40.405
- type: precision_at_1
value: 30.822
- type: precision_at_10
value: 8.379
- type: precision_at_100
value: 1.315
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 18.417
- type: precision_at_5
value: 13.744
- type: recall_at_1
value: 25.332
- type: recall_at_10
value: 57.774
- type: recall_at_100
value: 82.071
- type: recall_at_1000
value: 95.60600000000001
- type: recall_at_3
value: 40.722
- type: recall_at_5
value: 48.754999999999995
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 25.91033333333334
- type: map_at_10
value: 36.23225000000001
- type: map_at_100
value: 37.55766666666667
- type: map_at_1000
value: 37.672583333333336
- type: map_at_3
value: 32.95666666666667
- type: map_at_5
value: 34.73375
- type: mrr_at_1
value: 30.634
- type: mrr_at_10
value: 40.19449999999999
- type: mrr_at_100
value: 41.099250000000005
- type: mrr_at_1000
value: 41.15091666666667
- type: mrr_at_3
value: 37.4615
- type: mrr_at_5
value: 39.00216666666667
- type: ndcg_at_1
value: 30.634
- type: ndcg_at_10
value: 42.162166666666664
- type: ndcg_at_100
value: 47.60708333333333
- type: ndcg_at_1000
value: 49.68616666666666
- type: ndcg_at_3
value: 36.60316666666666
- type: ndcg_at_5
value: 39.15616666666668
- type: precision_at_1
value: 30.634
- type: precision_at_10
value: 7.6193333333333335
- type: precision_at_100
value: 1.2198333333333333
- type: precision_at_1000
value: 0.15975000000000003
- type: precision_at_3
value: 17.087
- type: precision_at_5
value: 12.298333333333334
- type: recall_at_1
value: 25.91033333333334
- type: recall_at_10
value: 55.67300000000001
- type: recall_at_100
value: 79.20608333333334
- type: recall_at_1000
value: 93.34866666666667
- type: recall_at_3
value: 40.34858333333333
- type: recall_at_5
value: 46.834083333333325
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-stats
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 25.006
- type: map_at_10
value: 32.177
- type: map_at_100
value: 33.324999999999996
- type: map_at_1000
value: 33.419
- type: map_at_3
value: 29.952
- type: map_at_5
value: 31.095
- type: mrr_at_1
value: 28.066999999999997
- type: mrr_at_10
value: 34.995
- type: mrr_at_100
value: 35.978
- type: mrr_at_1000
value: 36.042
- type: mrr_at_3
value: 33.103
- type: mrr_at_5
value: 34.001
- type: ndcg_at_1
value: 28.066999999999997
- type: ndcg_at_10
value: 36.481
- type: ndcg_at_100
value: 42.022999999999996
- type: ndcg_at_1000
value: 44.377
- type: ndcg_at_3
value: 32.394
- type: ndcg_at_5
value: 34.108
- type: precision_at_1
value: 28.066999999999997
- type: precision_at_10
value: 5.736
- type: precision_at_100
value: 0.9259999999999999
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 13.804
- type: precision_at_5
value: 9.508999999999999
- type: recall_at_1
value: 25.006
- type: recall_at_10
value: 46.972
- type: recall_at_100
value: 72.138
- type: recall_at_1000
value: 89.479
- type: recall_at_3
value: 35.793
- type: recall_at_5
value: 39.947
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-tex
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 16.07
- type: map_at_10
value: 24.447
- type: map_at_100
value: 25.685999999999996
- type: map_at_1000
value: 25.813999999999997
- type: map_at_3
value: 21.634
- type: map_at_5
value: 23.133
- type: mrr_at_1
value: 19.580000000000002
- type: mrr_at_10
value: 28.127999999999997
- type: mrr_at_100
value: 29.119
- type: mrr_at_1000
value: 29.192
- type: mrr_at_3
value: 25.509999999999998
- type: mrr_at_5
value: 26.878
- type: ndcg_at_1
value: 19.580000000000002
- type: ndcg_at_10
value: 29.804000000000002
- type: ndcg_at_100
value: 35.555
- type: ndcg_at_1000
value: 38.421
- type: ndcg_at_3
value: 24.654999999999998
- type: ndcg_at_5
value: 26.881
- type: precision_at_1
value: 19.580000000000002
- type: precision_at_10
value: 5.736
- type: precision_at_100
value: 1.005
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 12.033000000000001
- type: precision_at_5
value: 8.871
- type: recall_at_1
value: 16.07
- type: recall_at_10
value: 42.364000000000004
- type: recall_at_100
value: 68.01899999999999
- type: recall_at_1000
value: 88.122
- type: recall_at_3
value: 27.846
- type: recall_at_5
value: 33.638
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-unix
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 26.365
- type: map_at_10
value: 36.591
- type: map_at_100
value: 37.730000000000004
- type: map_at_1000
value: 37.84
- type: map_at_3
value: 33.403
- type: map_at_5
value: 35.272999999999996
- type: mrr_at_1
value: 30.503999999999998
- type: mrr_at_10
value: 39.940999999999995
- type: mrr_at_100
value: 40.818
- type: mrr_at_1000
value: 40.876000000000005
- type: mrr_at_3
value: 37.065
- type: mrr_at_5
value: 38.814
- type: ndcg_at_1
value: 30.503999999999998
- type: ndcg_at_10
value: 42.185
- type: ndcg_at_100
value: 47.416000000000004
- type: ndcg_at_1000
value: 49.705
- type: ndcg_at_3
value: 36.568
- type: ndcg_at_5
value: 39.416000000000004
- type: precision_at_1
value: 30.503999999999998
- type: precision_at_10
value: 7.276000000000001
- type: precision_at_100
value: 1.118
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 16.729
- type: precision_at_5
value: 12.107999999999999
- type: recall_at_1
value: 26.365
- type: recall_at_10
value: 55.616
- type: recall_at_100
value: 78.129
- type: recall_at_1000
value: 93.95599999999999
- type: recall_at_3
value: 40.686
- type: recall_at_5
value: 47.668
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-webmasters
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 22.750999999999998
- type: map_at_10
value: 33.446
- type: map_at_100
value: 35.235
- type: map_at_1000
value: 35.478
- type: map_at_3
value: 29.358
- type: map_at_5
value: 31.525
- type: mrr_at_1
value: 27.668
- type: mrr_at_10
value: 37.694
- type: mrr_at_100
value: 38.732
- type: mrr_at_1000
value: 38.779
- type: mrr_at_3
value: 34.223
- type: mrr_at_5
value: 36.08
- type: ndcg_at_1
value: 27.668
- type: ndcg_at_10
value: 40.557
- type: ndcg_at_100
value: 46.605999999999995
- type: ndcg_at_1000
value: 48.917
- type: ndcg_at_3
value: 33.677
- type: ndcg_at_5
value: 36.85
- type: precision_at_1
value: 27.668
- type: precision_at_10
value: 8.3
- type: precision_at_100
value: 1.6260000000000001
- type: precision_at_1000
value: 0.253
- type: precision_at_3
value: 16.008
- type: precision_at_5
value: 12.292
- type: recall_at_1
value: 22.750999999999998
- type: recall_at_10
value: 55.643
- type: recall_at_100
value: 82.151
- type: recall_at_1000
value: 95.963
- type: recall_at_3
value: 36.623
- type: recall_at_5
value: 44.708
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-wordpress
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 17.288999999999998
- type: map_at_10
value: 25.903
- type: map_at_100
value: 27.071
- type: map_at_1000
value: 27.173000000000002
- type: map_at_3
value: 22.935
- type: map_at_5
value: 24.573
- type: mrr_at_1
value: 18.669
- type: mrr_at_10
value: 27.682000000000002
- type: mrr_at_100
value: 28.691
- type: mrr_at_1000
value: 28.761
- type: mrr_at_3
value: 24.738
- type: mrr_at_5
value: 26.392
- type: ndcg_at_1
value: 18.669
- type: ndcg_at_10
value: 31.335
- type: ndcg_at_100
value: 36.913000000000004
- type: ndcg_at_1000
value: 39.300000000000004
- type: ndcg_at_3
value: 25.423000000000002
- type: ndcg_at_5
value: 28.262999999999998
- type: precision_at_1
value: 18.669
- type: precision_at_10
value: 5.379
- type: precision_at_100
value: 0.876
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 11.214
- type: precision_at_5
value: 8.466
- type: recall_at_1
value: 17.288999999999998
- type: recall_at_10
value: 46.377
- type: recall_at_100
value: 71.53500000000001
- type: recall_at_1000
value: 88.947
- type: recall_at_3
value: 30.581999999999997
- type: recall_at_5
value: 37.354
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 21.795
- type: map_at_10
value: 37.614999999999995
- type: map_at_100
value: 40.037
- type: map_at_1000
value: 40.184999999999995
- type: map_at_3
value: 32.221
- type: map_at_5
value: 35.154999999999994
- type: mrr_at_1
value: 50.358000000000004
- type: mrr_at_10
value: 62.129
- type: mrr_at_100
value: 62.613
- type: mrr_at_1000
value: 62.62
- type: mrr_at_3
value: 59.272999999999996
- type: mrr_at_5
value: 61.138999999999996
- type: ndcg_at_1
value: 50.358000000000004
- type: ndcg_at_10
value: 48.362
- type: ndcg_at_100
value: 55.932
- type: ndcg_at_1000
value: 58.062999999999995
- type: ndcg_at_3
value: 42.111
- type: ndcg_at_5
value: 44.063
- type: precision_at_1
value: 50.358000000000004
- type: precision_at_10
value: 14.677999999999999
- type: precision_at_100
value: 2.2950000000000004
- type: precision_at_1000
value: 0.271
- type: precision_at_3
value: 31.77
- type: precision_at_5
value: 23.375
- type: recall_at_1
value: 21.795
- type: recall_at_10
value: 53.846000000000004
- type: recall_at_100
value: 78.952
- type: recall_at_1000
value: 90.41900000000001
- type: recall_at_3
value: 37.257
- type: recall_at_5
value: 44.661
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.728
- type: map_at_10
value: 22.691
- type: map_at_100
value: 31.734
- type: map_at_1000
value: 33.464
- type: map_at_3
value: 16.273
- type: map_at_5
value: 19.016
- type: mrr_at_1
value: 73.25
- type: mrr_at_10
value: 80.782
- type: mrr_at_100
value: 81.01899999999999
- type: mrr_at_1000
value: 81.021
- type: mrr_at_3
value: 79.583
- type: mrr_at_5
value: 80.146
- type: ndcg_at_1
value: 59.62499999999999
- type: ndcg_at_10
value: 46.304
- type: ndcg_at_100
value: 51.23
- type: ndcg_at_1000
value: 58.048
- type: ndcg_at_3
value: 51.541000000000004
- type: ndcg_at_5
value: 48.635
- type: precision_at_1
value: 73.25
- type: precision_at_10
value: 36.375
- type: precision_at_100
value: 11.53
- type: precision_at_1000
value: 2.23
- type: precision_at_3
value: 55.583000000000006
- type: precision_at_5
value: 47.15
- type: recall_at_1
value: 9.728
- type: recall_at_10
value: 28.793999999999997
- type: recall_at_100
value: 57.885
- type: recall_at_1000
value: 78.759
- type: recall_at_3
value: 17.79
- type: recall_at_5
value: 21.733
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.775
- type: f1
value: 41.89794273264891
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 85.378
- type: map_at_10
value: 91.51
- type: map_at_100
value: 91.666
- type: map_at_1000
value: 91.676
- type: map_at_3
value: 90.757
- type: map_at_5
value: 91.277
- type: mrr_at_1
value: 91.839
- type: mrr_at_10
value: 95.49
- type: mrr_at_100
value: 95.493
- type: mrr_at_1000
value: 95.493
- type: mrr_at_3
value: 95.345
- type: mrr_at_5
value: 95.47200000000001
- type: ndcg_at_1
value: 91.839
- type: ndcg_at_10
value: 93.806
- type: ndcg_at_100
value: 94.255
- type: ndcg_at_1000
value: 94.399
- type: ndcg_at_3
value: 93.027
- type: ndcg_at_5
value: 93.51
- type: precision_at_1
value: 91.839
- type: precision_at_10
value: 10.93
- type: precision_at_100
value: 1.1400000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 34.873
- type: precision_at_5
value: 21.44
- type: recall_at_1
value: 85.378
- type: recall_at_10
value: 96.814
- type: recall_at_100
value: 98.386
- type: recall_at_1000
value: 99.21600000000001
- type: recall_at_3
value: 94.643
- type: recall_at_5
value: 95.976
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 32.190000000000005
- type: map_at_10
value: 53.605000000000004
- type: map_at_100
value: 55.550999999999995
- type: map_at_1000
value: 55.665
- type: map_at_3
value: 46.62
- type: map_at_5
value: 50.517999999999994
- type: mrr_at_1
value: 60.34
- type: mrr_at_10
value: 70.775
- type: mrr_at_100
value: 71.238
- type: mrr_at_1000
value: 71.244
- type: mrr_at_3
value: 68.72399999999999
- type: mrr_at_5
value: 69.959
- type: ndcg_at_1
value: 60.34
- type: ndcg_at_10
value: 63.226000000000006
- type: ndcg_at_100
value: 68.60300000000001
- type: ndcg_at_1000
value: 69.901
- type: ndcg_at_3
value: 58.048
- type: ndcg_at_5
value: 59.789
- type: precision_at_1
value: 60.34
- type: precision_at_10
value: 17.130000000000003
- type: precision_at_100
value: 2.29
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 38.323
- type: precision_at_5
value: 27.87
- type: recall_at_1
value: 32.190000000000005
- type: recall_at_10
value: 73.041
- type: recall_at_100
value: 91.31
- type: recall_at_1000
value: 98.104
- type: recall_at_3
value: 53.70399999999999
- type: recall_at_5
value: 62.358999999999995
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 43.511
- type: map_at_10
value: 58.15
- type: map_at_100
value: 58.95399999999999
- type: map_at_1000
value: 59.018
- type: map_at_3
value: 55.31700000000001
- type: map_at_5
value: 57.04900000000001
- type: mrr_at_1
value: 87.022
- type: mrr_at_10
value: 91.32000000000001
- type: mrr_at_100
value: 91.401
- type: mrr_at_1000
value: 91.403
- type: mrr_at_3
value: 90.77
- type: mrr_at_5
value: 91.156
- type: ndcg_at_1
value: 87.022
- type: ndcg_at_10
value: 68.183
- type: ndcg_at_100
value: 70.781
- type: ndcg_at_1000
value: 72.009
- type: ndcg_at_3
value: 64.334
- type: ndcg_at_5
value: 66.449
- type: precision_at_1
value: 87.022
- type: precision_at_10
value: 13.406
- type: precision_at_100
value: 1.542
- type: precision_at_1000
value: 0.17099999999999999
- type: precision_at_3
value: 39.023
- type: precision_at_5
value: 25.080000000000002
- type: recall_at_1
value: 43.511
- type: recall_at_10
value: 67.02900000000001
- type: recall_at_100
value: 77.11
- type: recall_at_1000
value: 85.294
- type: recall_at_3
value: 58.535000000000004
- type: recall_at_5
value: 62.70099999999999
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 92.0996
- type: ap
value: 87.86206089096373
- type: f1
value: 92.07554547510763
- task:
type: Retrieval
dataset:
type: mteb/msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 23.179
- type: map_at_10
value: 35.86
- type: map_at_100
value: 37.025999999999996
- type: map_at_1000
value: 37.068
- type: map_at_3
value: 31.921
- type: map_at_5
value: 34.172000000000004
- type: mrr_at_1
value: 23.926
- type: mrr_at_10
value: 36.525999999999996
- type: mrr_at_100
value: 37.627
- type: mrr_at_1000
value: 37.665
- type: mrr_at_3
value: 32.653
- type: mrr_at_5
value: 34.897
- type: ndcg_at_1
value: 23.910999999999998
- type: ndcg_at_10
value: 42.927
- type: ndcg_at_100
value: 48.464
- type: ndcg_at_1000
value: 49.533
- type: ndcg_at_3
value: 34.910000000000004
- type: ndcg_at_5
value: 38.937
- type: precision_at_1
value: 23.910999999999998
- type: precision_at_10
value: 6.758
- type: precision_at_100
value: 0.9520000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.838000000000001
- type: precision_at_5
value: 10.934000000000001
- type: recall_at_1
value: 23.179
- type: recall_at_10
value: 64.622
- type: recall_at_100
value: 90.135
- type: recall_at_1000
value: 98.301
- type: recall_at_3
value: 42.836999999999996
- type: recall_at_5
value: 52.512
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.59598723210215
- type: f1
value: 96.41913500001952
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 82.89557683538533
- type: f1
value: 63.379319722356264
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 78.93745796906524
- type: f1
value: 75.71616541785902
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 81.41223940820443
- type: f1
value: 81.2877893719078
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 35.03682528325662
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.942529406124
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.459949660460317
- type: mrr
value: 32.70509582031616
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.497
- type: map_at_10
value: 13.843
- type: map_at_100
value: 17.713
- type: map_at_1000
value: 19.241
- type: map_at_3
value: 10.096
- type: map_at_5
value: 11.85
- type: mrr_at_1
value: 48.916
- type: mrr_at_10
value: 57.764
- type: mrr_at_100
value: 58.251
- type: mrr_at_1000
value: 58.282999999999994
- type: mrr_at_3
value: 55.623999999999995
- type: mrr_at_5
value: 57.018
- type: ndcg_at_1
value: 46.594
- type: ndcg_at_10
value: 36.945
- type: ndcg_at_100
value: 34.06
- type: ndcg_at_1000
value: 43.05
- type: ndcg_at_3
value: 41.738
- type: ndcg_at_5
value: 39.330999999999996
- type: precision_at_1
value: 48.916
- type: precision_at_10
value: 27.43
- type: precision_at_100
value: 8.616
- type: precision_at_1000
value: 2.155
- type: precision_at_3
value: 39.112
- type: precision_at_5
value: 33.808
- type: recall_at_1
value: 6.497
- type: recall_at_10
value: 18.163
- type: recall_at_100
value: 34.566
- type: recall_at_1000
value: 67.15
- type: recall_at_3
value: 11.100999999999999
- type: recall_at_5
value: 14.205000000000002
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 31.916
- type: map_at_10
value: 48.123
- type: map_at_100
value: 49.103
- type: map_at_1000
value: 49.131
- type: map_at_3
value: 43.711
- type: map_at_5
value: 46.323
- type: mrr_at_1
value: 36.181999999999995
- type: mrr_at_10
value: 50.617999999999995
- type: mrr_at_100
value: 51.329
- type: mrr_at_1000
value: 51.348000000000006
- type: mrr_at_3
value: 47.010999999999996
- type: mrr_at_5
value: 49.175000000000004
- type: ndcg_at_1
value: 36.181999999999995
- type: ndcg_at_10
value: 56.077999999999996
- type: ndcg_at_100
value: 60.037
- type: ndcg_at_1000
value: 60.63499999999999
- type: ndcg_at_3
value: 47.859
- type: ndcg_at_5
value: 52.178999999999995
- type: precision_at_1
value: 36.181999999999995
- type: precision_at_10
value: 9.284
- type: precision_at_100
value: 1.149
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 22.006999999999998
- type: precision_at_5
value: 15.695
- type: recall_at_1
value: 31.916
- type: recall_at_10
value: 77.771
- type: recall_at_100
value: 94.602
- type: recall_at_1000
value: 98.967
- type: recall_at_3
value: 56.528
- type: recall_at_5
value: 66.527
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.486
- type: map_at_10
value: 85.978
- type: map_at_100
value: 86.587
- type: map_at_1000
value: 86.598
- type: map_at_3
value: 83.04899999999999
- type: map_at_5
value: 84.857
- type: mrr_at_1
value: 82.32000000000001
- type: mrr_at_10
value: 88.64
- type: mrr_at_100
value: 88.702
- type: mrr_at_1000
value: 88.702
- type: mrr_at_3
value: 87.735
- type: mrr_at_5
value: 88.36
- type: ndcg_at_1
value: 82.34
- type: ndcg_at_10
value: 89.67
- type: ndcg_at_100
value: 90.642
- type: ndcg_at_1000
value: 90.688
- type: ndcg_at_3
value: 86.932
- type: ndcg_at_5
value: 88.408
- type: precision_at_1
value: 82.34
- type: precision_at_10
value: 13.675999999999998
- type: precision_at_100
value: 1.544
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.24
- type: precision_at_5
value: 25.068
- type: recall_at_1
value: 71.486
- type: recall_at_10
value: 96.844
- type: recall_at_100
value: 99.843
- type: recall_at_1000
value: 99.996
- type: recall_at_3
value: 88.92099999999999
- type: recall_at_5
value: 93.215
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 59.75758437908334
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 68.03497914092789
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.808
- type: map_at_10
value: 16.059
- type: map_at_100
value: 19.048000000000002
- type: map_at_1000
value: 19.43
- type: map_at_3
value: 10.953
- type: map_at_5
value: 13.363
- type: mrr_at_1
value: 28.7
- type: mrr_at_10
value: 42.436
- type: mrr_at_100
value: 43.599
- type: mrr_at_1000
value: 43.62
- type: mrr_at_3
value: 38.45
- type: mrr_at_5
value: 40.89
- type: ndcg_at_1
value: 28.7
- type: ndcg_at_10
value: 26.346000000000004
- type: ndcg_at_100
value: 36.758
- type: ndcg_at_1000
value: 42.113
- type: ndcg_at_3
value: 24.254
- type: ndcg_at_5
value: 21.506
- type: precision_at_1
value: 28.7
- type: precision_at_10
value: 13.969999999999999
- type: precision_at_100
value: 2.881
- type: precision_at_1000
value: 0.414
- type: precision_at_3
value: 22.933
- type: precision_at_5
value: 19.220000000000002
- type: recall_at_1
value: 5.808
- type: recall_at_10
value: 28.310000000000002
- type: recall_at_100
value: 58.475
- type: recall_at_1000
value: 84.072
- type: recall_at_3
value: 13.957
- type: recall_at_5
value: 19.515
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.39274129958557
- type: cos_sim_spearman
value: 79.78021235170053
- type: euclidean_pearson
value: 79.35335401300166
- type: euclidean_spearman
value: 79.7271870968275
- type: manhattan_pearson
value: 79.35256263340601
- type: manhattan_spearman
value: 79.76036386976321
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.99130429246708
- type: cos_sim_spearman
value: 73.88322811171203
- type: euclidean_pearson
value: 80.7569419170376
- type: euclidean_spearman
value: 73.82542155409597
- type: manhattan_pearson
value: 80.79468183847625
- type: manhattan_spearman
value: 73.87027144047784
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.88548789489907
- type: cos_sim_spearman
value: 85.07535893847255
- type: euclidean_pearson
value: 84.6637222061494
- type: euclidean_spearman
value: 85.14200626702456
- type: manhattan_pearson
value: 84.75327892344734
- type: manhattan_spearman
value: 85.24406181838596
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.88140039325008
- type: cos_sim_spearman
value: 79.61211268112362
- type: euclidean_pearson
value: 81.29639728816458
- type: euclidean_spearman
value: 79.51284578041442
- type: manhattan_pearson
value: 81.3381797137111
- type: manhattan_spearman
value: 79.55683684039808
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.16716737270485
- type: cos_sim_spearman
value: 86.14823841857738
- type: euclidean_pearson
value: 85.36325733440725
- type: euclidean_spearman
value: 86.04919691402029
- type: manhattan_pearson
value: 85.3147511385052
- type: manhattan_spearman
value: 86.00676205857764
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 80.34266645861588
- type: cos_sim_spearman
value: 81.59914035005882
- type: euclidean_pearson
value: 81.15053076245988
- type: euclidean_spearman
value: 81.52776915798489
- type: manhattan_pearson
value: 81.1819647418673
- type: manhattan_spearman
value: 81.57479527353556
- 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: 89.38263326821439
- type: cos_sim_spearman
value: 89.10946308202642
- type: euclidean_pearson
value: 88.87831312540068
- type: euclidean_spearman
value: 89.03615865973664
- type: manhattan_pearson
value: 88.79835539970384
- type: manhattan_spearman
value: 88.9766156339753
- 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: 70.1574915581685
- type: cos_sim_spearman
value: 70.59144980004054
- type: euclidean_pearson
value: 71.43246306918755
- type: euclidean_spearman
value: 70.5544189562984
- type: manhattan_pearson
value: 71.4071414609503
- type: manhattan_spearman
value: 70.31799126163712
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.36215796635351
- type: cos_sim_spearman
value: 83.07276756467208
- type: euclidean_pearson
value: 83.06690453635584
- type: euclidean_spearman
value: 82.9635366303289
- type: manhattan_pearson
value: 83.04994049700815
- type: manhattan_spearman
value: 82.98120125356036
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.92530011616722
- type: mrr
value: 96.21826793395421
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 65.75
- type: map_at_10
value: 77.701
- type: map_at_100
value: 78.005
- type: map_at_1000
value: 78.006
- type: map_at_3
value: 75.48
- type: map_at_5
value: 76.927
- type: mrr_at_1
value: 68.333
- type: mrr_at_10
value: 78.511
- type: mrr_at_100
value: 78.704
- type: mrr_at_1000
value: 78.704
- type: mrr_at_3
value: 77
- type: mrr_at_5
value: 78.083
- type: ndcg_at_1
value: 68.333
- type: ndcg_at_10
value: 82.42699999999999
- type: ndcg_at_100
value: 83.486
- type: ndcg_at_1000
value: 83.511
- type: ndcg_at_3
value: 78.96300000000001
- type: ndcg_at_5
value: 81.028
- type: precision_at_1
value: 68.333
- type: precision_at_10
value: 10.667
- type: precision_at_100
value: 1.127
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 31.333
- type: precision_at_5
value: 20.133000000000003
- type: recall_at_1
value: 65.75
- type: recall_at_10
value: 95.578
- type: recall_at_100
value: 99.833
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 86.506
- type: recall_at_5
value: 91.75
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.75247524752476
- type: cos_sim_ap
value: 94.16065078045173
- type: cos_sim_f1
value: 87.22986247544205
- type: cos_sim_precision
value: 85.71428571428571
- type: cos_sim_recall
value: 88.8
- type: dot_accuracy
value: 99.74554455445545
- type: dot_ap
value: 93.90633887037264
- type: dot_f1
value: 86.9873417721519
- type: dot_precision
value: 88.1025641025641
- type: dot_recall
value: 85.9
- type: euclidean_accuracy
value: 99.75247524752476
- type: euclidean_ap
value: 94.17466319018055
- type: euclidean_f1
value: 87.3405299313052
- type: euclidean_precision
value: 85.74181117533719
- type: euclidean_recall
value: 89
- type: manhattan_accuracy
value: 99.75445544554455
- type: manhattan_ap
value: 94.27688371923577
- type: manhattan_f1
value: 87.74002954209749
- type: manhattan_precision
value: 86.42095053346266
- type: manhattan_recall
value: 89.1
- type: max_accuracy
value: 99.75445544554455
- type: max_ap
value: 94.27688371923577
- type: max_f1
value: 87.74002954209749
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 71.26500637517056
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 39.17507906280528
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.4848744828509
- type: mrr
value: 53.33678168236992
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.599864323827887
- type: cos_sim_spearman
value: 30.91116204665598
- type: dot_pearson
value: 30.82637894269936
- type: dot_spearman
value: 30.957573868416066
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.23600000000000002
- type: map_at_10
value: 1.892
- type: map_at_100
value: 11.586
- type: map_at_1000
value: 27.761999999999997
- type: map_at_3
value: 0.653
- type: map_at_5
value: 1.028
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 94
- type: mrr_at_100
value: 94
- type: mrr_at_1000
value: 94
- type: mrr_at_3
value: 94
- type: mrr_at_5
value: 94
- type: ndcg_at_1
value: 82
- type: ndcg_at_10
value: 77.48899999999999
- type: ndcg_at_100
value: 60.141
- type: ndcg_at_1000
value: 54.228
- type: ndcg_at_3
value: 82.358
- type: ndcg_at_5
value: 80.449
- type: precision_at_1
value: 88
- type: precision_at_10
value: 82.19999999999999
- type: precision_at_100
value: 61.760000000000005
- type: precision_at_1000
value: 23.684
- type: precision_at_3
value: 88
- type: precision_at_5
value: 85.6
- type: recall_at_1
value: 0.23600000000000002
- type: recall_at_10
value: 2.117
- type: recall_at_100
value: 14.985000000000001
- type: recall_at_1000
value: 51.107
- type: recall_at_3
value: 0.688
- type: recall_at_5
value: 1.1039999999999999
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.3040000000000003
- type: map_at_10
value: 9.025
- type: map_at_100
value: 15.312999999999999
- type: map_at_1000
value: 16.954
- type: map_at_3
value: 4.981
- type: map_at_5
value: 6.32
- type: mrr_at_1
value: 24.490000000000002
- type: mrr_at_10
value: 39.835
- type: mrr_at_100
value: 40.8
- type: mrr_at_1000
value: 40.8
- type: mrr_at_3
value: 35.034
- type: mrr_at_5
value: 37.687
- type: ndcg_at_1
value: 22.448999999999998
- type: ndcg_at_10
value: 22.545
- type: ndcg_at_100
value: 35.931999999999995
- type: ndcg_at_1000
value: 47.665
- type: ndcg_at_3
value: 23.311
- type: ndcg_at_5
value: 22.421
- type: precision_at_1
value: 24.490000000000002
- type: precision_at_10
value: 20.408
- type: precision_at_100
value: 7.815999999999999
- type: precision_at_1000
value: 1.553
- type: precision_at_3
value: 25.169999999999998
- type: precision_at_5
value: 23.265
- type: recall_at_1
value: 2.3040000000000003
- type: recall_at_10
value: 15.693999999999999
- type: recall_at_100
value: 48.917
- type: recall_at_1000
value: 84.964
- type: recall_at_3
value: 6.026
- type: recall_at_5
value: 9.066
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 82.6074
- type: ap
value: 23.187467098602013
- type: f1
value: 65.36829506379657
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.16355404640635
- type: f1
value: 63.534725639863346
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 50.91004094411276
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.55301901412649
- type: cos_sim_ap
value: 75.25312618556728
- type: cos_sim_f1
value: 68.76561719140429
- type: cos_sim_precision
value: 65.3061224489796
- type: cos_sim_recall
value: 72.61213720316623
- type: dot_accuracy
value: 86.29671574178936
- type: dot_ap
value: 75.11910195501207
- type: dot_f1
value: 68.44048376830045
- type: dot_precision
value: 66.12546125461255
- type: dot_recall
value: 70.92348284960423
- type: euclidean_accuracy
value: 86.5828217202122
- type: euclidean_ap
value: 75.22986344900924
- type: euclidean_f1
value: 68.81267797449549
- type: euclidean_precision
value: 64.8238861674831
- type: euclidean_recall
value: 73.3245382585752
- type: manhattan_accuracy
value: 86.61262442629791
- type: manhattan_ap
value: 75.24401608557328
- type: manhattan_f1
value: 68.80473982483257
- type: manhattan_precision
value: 67.21187720181177
- type: manhattan_recall
value: 70.47493403693932
- type: max_accuracy
value: 86.61262442629791
- type: max_ap
value: 75.25312618556728
- type: max_f1
value: 68.81267797449549
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.10688089416696
- type: cos_sim_ap
value: 84.17862178779863
- type: cos_sim_f1
value: 76.17305208781748
- type: cos_sim_precision
value: 71.31246641590543
- type: cos_sim_recall
value: 81.74468740375731
- type: dot_accuracy
value: 88.1844995536927
- type: dot_ap
value: 84.33816725235876
- type: dot_f1
value: 76.43554032918746
- type: dot_precision
value: 74.01557767200346
- type: dot_recall
value: 79.0190945488143
- type: euclidean_accuracy
value: 88.07001203089223
- type: euclidean_ap
value: 84.12267000814985
- type: euclidean_f1
value: 76.12232600180778
- type: euclidean_precision
value: 74.50604541433205
- type: euclidean_recall
value: 77.81028641823221
- type: manhattan_accuracy
value: 88.06419063142779
- type: manhattan_ap
value: 84.11648917164187
- type: manhattan_f1
value: 76.20579953925474
- type: manhattan_precision
value: 72.56772755762935
- type: manhattan_recall
value: 80.22790267939637
- type: max_accuracy
value: 88.1844995536927
- type: max_ap
value: 84.33816725235876
- type: max_f1
value: 76.43554032918746
gte-large-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-large-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-large-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-large-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); // [41.86354093370361, 77.07076371259589, 37.02981979677899]
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(https://arxiv.org/pdf/2308.03281.pdf) 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-512: lr 2e-4, mlm_probability 0.3, batch_size 4096, num_steps 300000, rope_base 10000
- MLM-2048: lr 5e-5, mlm_probability 0.3, batch_size 4096, num_steps 30000, rope_base 10000
- MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 30000, rope_base 160000
- WSC: max_len 512, lr 5e-5, batch_size 28672, 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 | 409 | 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}
}