metadata
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
license: apache-2.0
model-index:
- name: gte-qwen2-7B-instruct
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 91.31343283582089
- type: ap
value: 67.64251402604096
- type: f1
value: 87.53372530755692
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.497825
- type: ap
value: 96.30329547047529
- type: f1
value: 97.49769793778039
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 62.564
- type: f1
value: 60.975777935041066
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 36.486000000000004
- type: map_at_10
value: 54.842
- type: map_at_100
value: 55.206999999999994
- type: map_at_1000
value: 55.206999999999994
- type: map_at_3
value: 49.893
- type: map_at_5
value: 53.105000000000004
- type: mrr_at_1
value: 37.34
- type: mrr_at_10
value: 55.143
- type: mrr_at_100
value: 55.509
- type: mrr_at_1000
value: 55.509
- type: mrr_at_3
value: 50.212999999999994
- type: mrr_at_5
value: 53.432
- type: ndcg_at_1
value: 36.486000000000004
- type: ndcg_at_10
value: 64.273
- type: ndcg_at_100
value: 65.66199999999999
- type: ndcg_at_1000
value: 65.66199999999999
- type: ndcg_at_3
value: 54.352999999999994
- type: ndcg_at_5
value: 60.131
- type: precision_at_1
value: 36.486000000000004
- type: precision_at_10
value: 9.395000000000001
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.428
- type: precision_at_5
value: 16.259
- type: recall_at_1
value: 36.486000000000004
- type: recall_at_10
value: 93.95400000000001
- type: recall_at_100
value: 99.644
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 67.283
- type: recall_at_5
value: 81.294
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 56.461169803700564
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 51.73600434466286
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 67.57827065898053
- type: mrr
value: 79.08136569493911
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 83.53324575999243
- type: cos_sim_spearman
value: 81.37173362822374
- type: euclidean_pearson
value: 82.19243335103444
- type: euclidean_spearman
value: 81.33679307304334
- type: manhattan_pearson
value: 82.38752665975699
- type: manhattan_spearman
value: 81.31510583189689
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.56818181818181
- type: f1
value: 87.25826722019875
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 50.09239610327673
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 46.64733054606282
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 33.997
- type: map_at_10
value: 48.176
- type: map_at_100
value: 49.82
- type: map_at_1000
value: 49.924
- type: map_at_3
value: 43.626
- type: map_at_5
value: 46.275
- type: mrr_at_1
value: 42.059999999999995
- type: mrr_at_10
value: 53.726
- type: mrr_at_100
value: 54.398
- type: mrr_at_1000
value: 54.416
- type: mrr_at_3
value: 50.714999999999996
- type: mrr_at_5
value: 52.639
- type: ndcg_at_1
value: 42.059999999999995
- type: ndcg_at_10
value: 55.574999999999996
- type: ndcg_at_100
value: 60.744
- type: ndcg_at_1000
value: 61.85699999999999
- type: ndcg_at_3
value: 49.363
- type: ndcg_at_5
value: 52.44
- type: precision_at_1
value: 42.059999999999995
- type: precision_at_10
value: 11.101999999999999
- type: precision_at_100
value: 1.73
- type: precision_at_1000
value: 0.218
- type: precision_at_3
value: 24.464
- type: precision_at_5
value: 18.026
- type: recall_at_1
value: 33.997
- type: recall_at_10
value: 70.35900000000001
- type: recall_at_100
value: 91.642
- type: recall_at_1000
value: 97.977
- type: recall_at_3
value: 52.76
- type: recall_at_5
value: 61.148
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 35.884
- type: map_at_10
value: 48.14
- type: map_at_100
value: 49.5
- type: map_at_1000
value: 49.63
- type: map_at_3
value: 44.646
- type: map_at_5
value: 46.617999999999995
- type: mrr_at_1
value: 44.458999999999996
- type: mrr_at_10
value: 53.751000000000005
- type: mrr_at_100
value: 54.37800000000001
- type: mrr_at_1000
value: 54.415
- type: mrr_at_3
value: 51.815
- type: mrr_at_5
value: 52.882
- type: ndcg_at_1
value: 44.458999999999996
- type: ndcg_at_10
value: 54.157
- type: ndcg_at_100
value: 58.362
- type: ndcg_at_1000
value: 60.178
- type: ndcg_at_3
value: 49.661
- type: ndcg_at_5
value: 51.74999999999999
- type: precision_at_1
value: 44.458999999999996
- type: precision_at_10
value: 10.248
- type: precision_at_100
value: 1.5890000000000002
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 23.928
- type: precision_at_5
value: 16.878999999999998
- type: recall_at_1
value: 35.884
- type: recall_at_10
value: 64.798
- type: recall_at_100
value: 82.345
- type: recall_at_1000
value: 93.267
- type: recall_at_3
value: 51.847
- type: recall_at_5
value: 57.601
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 39.383
- type: map_at_10
value: 53.714
- type: map_at_100
value: 54.838
- type: map_at_1000
value: 54.87800000000001
- type: map_at_3
value: 50.114999999999995
- type: map_at_5
value: 52.153000000000006
- type: mrr_at_1
value: 45.016
- type: mrr_at_10
value: 56.732000000000006
- type: mrr_at_100
value: 57.411
- type: mrr_at_1000
value: 57.431
- type: mrr_at_3
value: 54.044000000000004
- type: mrr_at_5
value: 55.639
- type: ndcg_at_1
value: 45.016
- type: ndcg_at_10
value: 60.228
- type: ndcg_at_100
value: 64.277
- type: ndcg_at_1000
value: 65.07
- type: ndcg_at_3
value: 54.124
- type: ndcg_at_5
value: 57.147000000000006
- type: precision_at_1
value: 45.016
- type: precision_at_10
value: 9.937
- type: precision_at_100
value: 1.288
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 24.471999999999998
- type: precision_at_5
value: 16.991
- type: recall_at_1
value: 39.383
- type: recall_at_10
value: 76.175
- type: recall_at_100
value: 93.02
- type: recall_at_1000
value: 98.60900000000001
- type: recall_at_3
value: 60.265
- type: recall_at_5
value: 67.46600000000001
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 27.426000000000002
- type: map_at_10
value: 37.397000000000006
- type: map_at_100
value: 38.61
- type: map_at_1000
value: 38.678000000000004
- type: map_at_3
value: 34.150999999999996
- type: map_at_5
value: 36.137
- type: mrr_at_1
value: 29.944
- type: mrr_at_10
value: 39.654
- type: mrr_at_100
value: 40.638000000000005
- type: mrr_at_1000
value: 40.691
- type: mrr_at_3
value: 36.817
- type: mrr_at_5
value: 38.524
- type: ndcg_at_1
value: 29.944
- type: ndcg_at_10
value: 43.094
- type: ndcg_at_100
value: 48.789
- type: ndcg_at_1000
value: 50.339999999999996
- type: ndcg_at_3
value: 36.984
- type: ndcg_at_5
value: 40.248
- type: precision_at_1
value: 29.944
- type: precision_at_10
value: 6.78
- type: precision_at_100
value: 1.024
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 15.895000000000001
- type: precision_at_5
value: 11.39
- type: recall_at_1
value: 27.426000000000002
- type: recall_at_10
value: 58.464000000000006
- type: recall_at_100
value: 84.193
- type: recall_at_1000
value: 95.52000000000001
- type: recall_at_3
value: 42.172
- type: recall_at_5
value: 50.101
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 19.721
- type: map_at_10
value: 31.604
- type: map_at_100
value: 32.972
- type: map_at_1000
value: 33.077
- type: map_at_3
value: 27.218999999999998
- type: map_at_5
value: 29.53
- type: mrr_at_1
value: 25
- type: mrr_at_10
value: 35.843
- type: mrr_at_100
value: 36.785000000000004
- type: mrr_at_1000
value: 36.842000000000006
- type: mrr_at_3
value: 32.193
- type: mrr_at_5
value: 34.264
- type: ndcg_at_1
value: 25
- type: ndcg_at_10
value: 38.606
- type: ndcg_at_100
value: 44.272
- type: ndcg_at_1000
value: 46.527
- type: ndcg_at_3
value: 30.985000000000003
- type: ndcg_at_5
value: 34.43
- type: precision_at_1
value: 25
- type: precision_at_10
value: 7.811
- type: precision_at_100
value: 1.203
- type: precision_at_1000
value: 0.15
- type: precision_at_3
value: 15.423
- type: precision_at_5
value: 11.791
- type: recall_at_1
value: 19.721
- type: recall_at_10
value: 55.625
- type: recall_at_100
value: 79.34400000000001
- type: recall_at_1000
value: 95.208
- type: recall_at_3
value: 35.19
- type: recall_at_5
value: 43.626
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 33.784
- type: map_at_10
value: 47.522
- type: map_at_100
value: 48.949999999999996
- type: map_at_1000
value: 49.038
- type: map_at_3
value: 43.284
- type: map_at_5
value: 45.629
- type: mrr_at_1
value: 41.482
- type: mrr_at_10
value: 52.830999999999996
- type: mrr_at_100
value: 53.559999999999995
- type: mrr_at_1000
value: 53.588
- type: mrr_at_3
value: 50.016000000000005
- type: mrr_at_5
value: 51.614000000000004
- type: ndcg_at_1
value: 41.482
- type: ndcg_at_10
value: 54.569
- type: ndcg_at_100
value: 59.675999999999995
- type: ndcg_at_1000
value: 60.989000000000004
- type: ndcg_at_3
value: 48.187000000000005
- type: ndcg_at_5
value: 51.183
- type: precision_at_1
value: 41.482
- type: precision_at_10
value: 10.221
- type: precision_at_100
value: 1.486
- type: precision_at_1000
value: 0.17500000000000002
- type: precision_at_3
value: 23.548
- type: precision_at_5
value: 16.805
- type: recall_at_1
value: 33.784
- type: recall_at_10
value: 69.798
- type: recall_at_100
value: 90.098
- type: recall_at_1000
value: 98.176
- type: recall_at_3
value: 52.127
- type: recall_at_5
value: 59.861
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 28.038999999999998
- type: map_at_10
value: 41.904
- type: map_at_100
value: 43.36
- type: map_at_1000
value: 43.453
- type: map_at_3
value: 37.785999999999994
- type: map_at_5
value: 40.105000000000004
- type: mrr_at_1
value: 35.046
- type: mrr_at_10
value: 46.926
- type: mrr_at_100
value: 47.815000000000005
- type: mrr_at_1000
value: 47.849000000000004
- type: mrr_at_3
value: 44.273
- type: mrr_at_5
value: 45.774
- type: ndcg_at_1
value: 35.046
- type: ndcg_at_10
value: 48.937000000000005
- type: ndcg_at_100
value: 54.544000000000004
- type: ndcg_at_1000
value: 56.069
- type: ndcg_at_3
value: 42.858000000000004
- type: ndcg_at_5
value: 45.644
- type: precision_at_1
value: 35.046
- type: precision_at_10
value: 9.452
- type: precision_at_100
value: 1.429
- type: precision_at_1000
value: 0.173
- type: precision_at_3
value: 21.346999999999998
- type: precision_at_5
value: 15.342
- type: recall_at_1
value: 28.038999999999998
- type: recall_at_10
value: 64.59700000000001
- type: recall_at_100
value: 87.735
- type: recall_at_1000
value: 97.41300000000001
- type: recall_at_3
value: 47.368
- type: recall_at_5
value: 54.93900000000001
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 28.17291666666667
- type: map_at_10
value: 40.025749999999995
- type: map_at_100
value: 41.39208333333333
- type: map_at_1000
value: 41.499249999999996
- type: map_at_3
value: 36.347
- type: map_at_5
value: 38.41391666666667
- type: mrr_at_1
value: 33.65925
- type: mrr_at_10
value: 44.085499999999996
- type: mrr_at_100
value: 44.94116666666667
- type: mrr_at_1000
value: 44.9855
- type: mrr_at_3
value: 41.2815
- type: mrr_at_5
value: 42.91491666666666
- type: ndcg_at_1
value: 33.65925
- type: ndcg_at_10
value: 46.430833333333325
- type: ndcg_at_100
value: 51.761
- type: ndcg_at_1000
value: 53.50899999999999
- type: ndcg_at_3
value: 40.45133333333333
- type: ndcg_at_5
value: 43.31483333333334
- type: precision_at_1
value: 33.65925
- type: precision_at_10
value: 8.4995
- type: precision_at_100
value: 1.3210000000000004
- type: precision_at_1000
value: 0.16591666666666666
- type: precision_at_3
value: 19.165083333333335
- type: precision_at_5
value: 13.81816666666667
- type: recall_at_1
value: 28.17291666666667
- type: recall_at_10
value: 61.12624999999999
- type: recall_at_100
value: 83.97266666666667
- type: recall_at_1000
value: 95.66550000000001
- type: recall_at_3
value: 44.661249999999995
- type: recall_at_5
value: 51.983333333333334
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 24.681
- type: map_at_10
value: 34.892
- type: map_at_100
value: 35.996
- type: map_at_1000
value: 36.083
- type: map_at_3
value: 31.491999999999997
- type: map_at_5
value: 33.632
- type: mrr_at_1
value: 28.528
- type: mrr_at_10
value: 37.694
- type: mrr_at_100
value: 38.613
- type: mrr_at_1000
value: 38.668
- type: mrr_at_3
value: 34.714
- type: mrr_at_5
value: 36.616
- type: ndcg_at_1
value: 28.528
- type: ndcg_at_10
value: 40.703
- type: ndcg_at_100
value: 45.993
- type: ndcg_at_1000
value: 47.847
- type: ndcg_at_3
value: 34.622
- type: ndcg_at_5
value: 38.035999999999994
- type: precision_at_1
value: 28.528
- type: precision_at_10
value: 6.902
- type: precision_at_100
value: 1.0370000000000001
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 15.798000000000002
- type: precision_at_5
value: 11.655999999999999
- type: recall_at_1
value: 24.681
- type: recall_at_10
value: 55.81
- type: recall_at_100
value: 79.785
- type: recall_at_1000
value: 92.959
- type: recall_at_3
value: 39.074
- type: recall_at_5
value: 47.568
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.627
- type: map_at_10
value: 27.872000000000003
- type: map_at_100
value: 29.237999999999996
- type: map_at_1000
value: 29.363
- type: map_at_3
value: 24.751
- type: map_at_5
value: 26.521
- type: mrr_at_1
value: 23.021
- type: mrr_at_10
value: 31.924000000000003
- type: mrr_at_100
value: 32.922000000000004
- type: mrr_at_1000
value: 32.988
- type: mrr_at_3
value: 29.192
- type: mrr_at_5
value: 30.798
- type: ndcg_at_1
value: 23.021
- type: ndcg_at_10
value: 33.535
- type: ndcg_at_100
value: 39.732
- type: ndcg_at_1000
value: 42.201
- type: ndcg_at_3
value: 28.153
- type: ndcg_at_5
value: 30.746000000000002
- type: precision_at_1
value: 23.021
- type: precision_at_10
value: 6.459
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 13.719000000000001
- type: precision_at_5
value: 10.193000000000001
- type: recall_at_1
value: 18.627
- type: recall_at_10
value: 46.463
- type: recall_at_100
value: 74.226
- type: recall_at_1000
value: 91.28500000000001
- type: recall_at_3
value: 31.357000000000003
- type: recall_at_5
value: 38.067
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 31.457
- type: map_at_10
value: 42.888
- type: map_at_100
value: 44.24
- type: map_at_1000
value: 44.327
- type: map_at_3
value: 39.588
- type: map_at_5
value: 41.423
- type: mrr_at_1
value: 37.126999999999995
- type: mrr_at_10
value: 47.083000000000006
- type: mrr_at_100
value: 47.997
- type: mrr_at_1000
value: 48.044
- type: mrr_at_3
value: 44.574000000000005
- type: mrr_at_5
value: 46.202
- type: ndcg_at_1
value: 37.126999999999995
- type: ndcg_at_10
value: 48.833
- type: ndcg_at_100
value: 54.327000000000005
- type: ndcg_at_1000
value: 56.011
- type: ndcg_at_3
value: 43.541999999999994
- type: ndcg_at_5
value: 46.127
- type: precision_at_1
value: 37.126999999999995
- type: precision_at_10
value: 8.376999999999999
- type: precision_at_100
value: 1.2309999999999999
- type: precision_at_1000
value: 0.146
- type: precision_at_3
value: 20.211000000000002
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 31.457
- type: recall_at_10
value: 62.369
- type: recall_at_100
value: 85.444
- type: recall_at_1000
value: 96.65599999999999
- type: recall_at_3
value: 47.961
- type: recall_at_5
value: 54.676
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 27.139999999999997
- type: map_at_10
value: 38.801
- type: map_at_100
value: 40.549
- type: map_at_1000
value: 40.802
- type: map_at_3
value: 35.05
- type: map_at_5
value: 36.884
- type: mrr_at_1
value: 33.004
- type: mrr_at_10
value: 43.864
- type: mrr_at_100
value: 44.667
- type: mrr_at_1000
value: 44.717
- type: mrr_at_3
value: 40.777
- type: mrr_at_5
value: 42.319
- type: ndcg_at_1
value: 33.004
- type: ndcg_at_10
value: 46.022
- type: ndcg_at_100
value: 51.542
- type: ndcg_at_1000
value: 53.742000000000004
- type: ndcg_at_3
value: 39.795
- type: ndcg_at_5
value: 42.272
- type: precision_at_1
value: 33.004
- type: precision_at_10
value: 9.012
- type: precision_at_100
value: 1.7770000000000001
- type: precision_at_1000
value: 0.26
- type: precision_at_3
value: 19.038
- type: precision_at_5
value: 13.675999999999998
- type: recall_at_1
value: 27.139999999999997
- type: recall_at_10
value: 60.961
- type: recall_at_100
value: 84.451
- type: recall_at_1000
value: 98.113
- type: recall_at_3
value: 43.001
- type: recall_at_5
value: 49.896
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 17.936
- type: map_at_10
value: 27.399
- type: map_at_100
value: 28.632
- type: map_at_1000
value: 28.738000000000003
- type: map_at_3
value: 24.456
- type: map_at_5
value: 26.06
- type: mrr_at_1
value: 19.224
- type: mrr_at_10
value: 28.998
- type: mrr_at_100
value: 30.11
- type: mrr_at_1000
value: 30.177
- type: mrr_at_3
value: 26.247999999999998
- type: mrr_at_5
value: 27.708
- type: ndcg_at_1
value: 19.224
- type: ndcg_at_10
value: 32.911
- type: ndcg_at_100
value: 38.873999999999995
- type: ndcg_at_1000
value: 41.277
- type: ndcg_at_3
value: 27.142
- type: ndcg_at_5
value: 29.755
- type: precision_at_1
value: 19.224
- type: precision_at_10
value: 5.6930000000000005
- type: precision_at_100
value: 0.9259999999999999
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 12.138
- type: precision_at_5
value: 8.909
- type: recall_at_1
value: 17.936
- type: recall_at_10
value: 48.096
- type: recall_at_100
value: 75.389
- type: recall_at_1000
value: 92.803
- type: recall_at_3
value: 32.812999999999995
- type: recall_at_5
value: 38.851
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 22.076999999999998
- type: map_at_10
value: 35.44
- type: map_at_100
value: 37.651
- type: map_at_1000
value: 37.824999999999996
- type: map_at_3
value: 30.764999999999997
- type: map_at_5
value: 33.26
- type: mrr_at_1
value: 50.163000000000004
- type: mrr_at_10
value: 61.207
- type: mrr_at_100
value: 61.675000000000004
- type: mrr_at_1000
value: 61.692
- type: mrr_at_3
value: 58.60999999999999
- type: mrr_at_5
value: 60.307
- type: ndcg_at_1
value: 50.163000000000004
- type: ndcg_at_10
value: 45.882
- type: ndcg_at_100
value: 53.239999999999995
- type: ndcg_at_1000
value: 55.852000000000004
- type: ndcg_at_3
value: 40.514
- type: ndcg_at_5
value: 42.038
- type: precision_at_1
value: 50.163000000000004
- type: precision_at_10
value: 13.466000000000001
- type: precision_at_100
value: 2.164
- type: precision_at_1000
value: 0.266
- type: precision_at_3
value: 29.707
- type: precision_at_5
value: 21.694
- type: recall_at_1
value: 22.076999999999998
- type: recall_at_10
value: 50.193
- type: recall_at_100
value: 74.993
- type: recall_at_1000
value: 89.131
- type: recall_at_3
value: 35.472
- type: recall_at_5
value: 41.814
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.953
- type: map_at_10
value: 24.515
- type: map_at_100
value: 36.173
- type: map_at_1000
value: 38.351
- type: map_at_3
value: 16.592000000000002
- type: map_at_5
value: 20.036
- type: mrr_at_1
value: 74.25
- type: mrr_at_10
value: 81.813
- type: mrr_at_100
value: 82.006
- type: mrr_at_1000
value: 82.011
- type: mrr_at_3
value: 80.875
- type: mrr_at_5
value: 81.362
- type: ndcg_at_1
value: 62.5
- type: ndcg_at_10
value: 52.42
- type: ndcg_at_100
value: 56.808
- type: ndcg_at_1000
value: 63.532999999999994
- type: ndcg_at_3
value: 56.654
- type: ndcg_at_5
value: 54.18300000000001
- type: precision_at_1
value: 74.25
- type: precision_at_10
value: 42.699999999999996
- type: precision_at_100
value: 13.675
- type: precision_at_1000
value: 2.664
- type: precision_at_3
value: 60.5
- type: precision_at_5
value: 52.800000000000004
- type: recall_at_1
value: 9.953
- type: recall_at_10
value: 30.253999999999998
- type: recall_at_100
value: 62.516000000000005
- type: recall_at_1000
value: 84.163
- type: recall_at_3
value: 18.13
- type: recall_at_5
value: 22.771
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 79.455
- type: f1
value: 74.16798697647569
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 87.531
- type: map_at_10
value: 93.16799999999999
- type: map_at_100
value: 93.341
- type: map_at_1000
value: 93.349
- type: map_at_3
value: 92.444
- type: map_at_5
value: 92.865
- type: mrr_at_1
value: 94.014
- type: mrr_at_10
value: 96.761
- type: mrr_at_100
value: 96.762
- type: mrr_at_1000
value: 96.762
- type: mrr_at_3
value: 96.672
- type: mrr_at_5
value: 96.736
- type: ndcg_at_1
value: 94.014
- type: ndcg_at_10
value: 95.112
- type: ndcg_at_100
value: 95.578
- type: ndcg_at_1000
value: 95.68900000000001
- type: ndcg_at_3
value: 94.392
- type: ndcg_at_5
value: 94.72500000000001
- type: precision_at_1
value: 94.014
- type: precision_at_10
value: 11.065
- type: precision_at_100
value: 1.157
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 35.259
- type: precision_at_5
value: 21.599
- type: recall_at_1
value: 87.531
- type: recall_at_10
value: 97.356
- type: recall_at_100
value: 98.965
- type: recall_at_1000
value: 99.607
- type: recall_at_3
value: 95.312
- type: recall_at_5
value: 96.295
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 32.055
- type: map_at_10
value: 53.114
- type: map_at_100
value: 55.235
- type: map_at_1000
value: 55.345
- type: map_at_3
value: 45.854
- type: map_at_5
value: 50.025
- type: mrr_at_1
value: 60.34
- type: mrr_at_10
value: 68.804
- type: mrr_at_100
value: 69.309
- type: mrr_at_1000
value: 69.32199999999999
- type: mrr_at_3
value: 66.40899999999999
- type: mrr_at_5
value: 67.976
- type: ndcg_at_1
value: 60.34
- type: ndcg_at_10
value: 62.031000000000006
- type: ndcg_at_100
value: 68.00500000000001
- type: ndcg_at_1000
value: 69.286
- type: ndcg_at_3
value: 56.355999999999995
- type: ndcg_at_5
value: 58.687
- type: precision_at_1
value: 60.34
- type: precision_at_10
value: 17.176
- type: precision_at_100
value: 2.36
- type: precision_at_1000
value: 0.259
- type: precision_at_3
value: 37.14
- type: precision_at_5
value: 27.809
- type: recall_at_1
value: 32.055
- type: recall_at_10
value: 70.91
- type: recall_at_100
value: 91.83
- type: recall_at_1000
value: 98.871
- type: recall_at_3
value: 51.202999999999996
- type: recall_at_5
value: 60.563
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 43.68
- type: map_at_10
value: 64.389
- type: map_at_100
value: 65.24
- type: map_at_1000
value: 65.303
- type: map_at_3
value: 61.309000000000005
- type: map_at_5
value: 63.275999999999996
- type: mrr_at_1
value: 87.36
- type: mrr_at_10
value: 91.12
- type: mrr_at_100
value: 91.227
- type: mrr_at_1000
value: 91.229
- type: mrr_at_3
value: 90.57600000000001
- type: mrr_at_5
value: 90.912
- type: ndcg_at_1
value: 87.36
- type: ndcg_at_10
value: 73.076
- type: ndcg_at_100
value: 75.895
- type: ndcg_at_1000
value: 77.049
- type: ndcg_at_3
value: 68.929
- type: ndcg_at_5
value: 71.28
- type: precision_at_1
value: 87.36
- type: precision_at_10
value: 14.741000000000001
- type: precision_at_100
value: 1.694
- type: precision_at_1000
value: 0.185
- type: precision_at_3
value: 43.043
- type: precision_at_5
value: 27.681
- type: recall_at_1
value: 43.68
- type: recall_at_10
value: 73.707
- type: recall_at_100
value: 84.7
- type: recall_at_1000
value: 92.309
- type: recall_at_3
value: 64.564
- type: recall_at_5
value: 69.203
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 96.75399999999999
- type: ap
value: 95.29389839242187
- type: f1
value: 96.75348377433475
- task:
type: Retrieval
dataset:
type: mteb/msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 25.176
- type: map_at_10
value: 38.598
- type: map_at_100
value: 39.707
- type: map_at_1000
value: 39.744
- type: map_at_3
value: 34.566
- type: map_at_5
value: 36.863
- type: mrr_at_1
value: 25.874000000000002
- type: mrr_at_10
value: 39.214
- type: mrr_at_100
value: 40.251
- type: mrr_at_1000
value: 40.281
- type: mrr_at_3
value: 35.291
- type: mrr_at_5
value: 37.545
- type: ndcg_at_1
value: 25.874000000000002
- type: ndcg_at_10
value: 45.98
- type: ndcg_at_100
value: 51.197
- type: ndcg_at_1000
value: 52.073
- type: ndcg_at_3
value: 37.785999999999994
- type: ndcg_at_5
value: 41.870000000000005
- type: precision_at_1
value: 25.874000000000002
- type: precision_at_10
value: 7.181
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 16.051000000000002
- type: precision_at_5
value: 11.713
- type: recall_at_1
value: 25.176
- type: recall_at_10
value: 68.67699999999999
- type: recall_at_100
value: 92.55
- type: recall_at_1000
value: 99.164
- type: recall_at_3
value: 46.372
- type: recall_at_5
value: 56.16
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 99.03784769721841
- type: f1
value: 98.97791641821495
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 91.88326493388054
- type: f1
value: 73.74809928034335
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 85.41358439811701
- type: f1
value: 83.503679460639
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 89.77135171486215
- type: f1
value: 88.89843747468366
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 46.22695362087359
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 44.132372165849425
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 33.35680810650402
- type: mrr
value: 34.72625715637218
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 7.165000000000001
- type: map_at_10
value: 15.424
- type: map_at_100
value: 20.28
- type: map_at_1000
value: 22.065
- type: map_at_3
value: 11.236
- type: map_at_5
value: 13.025999999999998
- type: mrr_at_1
value: 51.702999999999996
- type: mrr_at_10
value: 59.965
- type: mrr_at_100
value: 60.667
- type: mrr_at_1000
value: 60.702999999999996
- type: mrr_at_3
value: 58.772000000000006
- type: mrr_at_5
value: 59.267
- type: ndcg_at_1
value: 49.536
- type: ndcg_at_10
value: 40.6
- type: ndcg_at_100
value: 37.848
- type: ndcg_at_1000
value: 46.657
- type: ndcg_at_3
value: 46.117999999999995
- type: ndcg_at_5
value: 43.619
- type: precision_at_1
value: 51.393
- type: precision_at_10
value: 30.31
- type: precision_at_100
value: 9.972
- type: precision_at_1000
value: 2.329
- type: precision_at_3
value: 43.137
- type: precision_at_5
value: 37.585
- type: recall_at_1
value: 7.165000000000001
- type: recall_at_10
value: 19.689999999999998
- type: recall_at_100
value: 39.237
- type: recall_at_1000
value: 71.417
- type: recall_at_3
value: 12.247
- type: recall_at_5
value: 14.902999999999999
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 42.653999999999996
- type: map_at_10
value: 59.611999999999995
- type: map_at_100
value: 60.32300000000001
- type: map_at_1000
value: 60.336
- type: map_at_3
value: 55.584999999999994
- type: map_at_5
value: 58.19
- type: mrr_at_1
value: 47.683
- type: mrr_at_10
value: 62.06700000000001
- type: mrr_at_100
value: 62.537
- type: mrr_at_1000
value: 62.544999999999995
- type: mrr_at_3
value: 59.178
- type: mrr_at_5
value: 61.034
- type: ndcg_at_1
value: 47.654
- type: ndcg_at_10
value: 67.001
- type: ndcg_at_100
value: 69.73899999999999
- type: ndcg_at_1000
value: 69.986
- type: ndcg_at_3
value: 59.95700000000001
- type: ndcg_at_5
value: 64.025
- type: precision_at_1
value: 47.654
- type: precision_at_10
value: 10.367999999999999
- type: precision_at_100
value: 1.192
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 26.651000000000003
- type: precision_at_5
value: 18.459
- type: recall_at_1
value: 42.653999999999996
- type: recall_at_10
value: 86.619
- type: recall_at_100
value: 98.04899999999999
- type: recall_at_1000
value: 99.812
- type: recall_at_3
value: 68.987
- type: recall_at_5
value: 78.158
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 72.538
- type: map_at_10
value: 86.702
- type: map_at_100
value: 87.31
- type: map_at_1000
value: 87.323
- type: map_at_3
value: 83.87
- type: map_at_5
value: 85.682
- type: mrr_at_1
value: 83.31
- type: mrr_at_10
value: 89.225
- type: mrr_at_100
value: 89.30399999999999
- type: mrr_at_1000
value: 89.30399999999999
- type: mrr_at_3
value: 88.44300000000001
- type: mrr_at_5
value: 89.005
- type: ndcg_at_1
value: 83.32000000000001
- type: ndcg_at_10
value: 90.095
- type: ndcg_at_100
value: 91.12
- type: ndcg_at_1000
value: 91.179
- type: ndcg_at_3
value: 87.606
- type: ndcg_at_5
value: 89.031
- type: precision_at_1
value: 83.32000000000001
- type: precision_at_10
value: 13.641
- type: precision_at_100
value: 1.541
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.377
- type: precision_at_5
value: 25.162000000000003
- type: recall_at_1
value: 72.538
- type: recall_at_10
value: 96.47200000000001
- type: recall_at_100
value: 99.785
- type: recall_at_1000
value: 99.99900000000001
- type: recall_at_3
value: 89.278
- type: recall_at_5
value: 93.367
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 73.55219145406065
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 74.13437105242755
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.873
- type: map_at_10
value: 17.944
- type: map_at_100
value: 21.171
- type: map_at_1000
value: 21.528
- type: map_at_3
value: 12.415
- type: map_at_5
value: 15.187999999999999
- type: mrr_at_1
value: 33.800000000000004
- type: mrr_at_10
value: 46.455
- type: mrr_at_100
value: 47.378
- type: mrr_at_1000
value: 47.394999999999996
- type: mrr_at_3
value: 42.367
- type: mrr_at_5
value: 44.972
- type: ndcg_at_1
value: 33.800000000000004
- type: ndcg_at_10
value: 28.907
- type: ndcg_at_100
value: 39.695
- type: ndcg_at_1000
value: 44.582
- type: ndcg_at_3
value: 26.949
- type: ndcg_at_5
value: 23.988
- type: precision_at_1
value: 33.800000000000004
- type: precision_at_10
value: 15.079999999999998
- type: precision_at_100
value: 3.056
- type: precision_at_1000
value: 0.42100000000000004
- type: precision_at_3
value: 25.167
- type: precision_at_5
value: 21.26
- type: recall_at_1
value: 6.873
- type: recall_at_10
value: 30.568
- type: recall_at_100
value: 62.062
- type: recall_at_1000
value: 85.37700000000001
- type: recall_at_3
value: 15.312999999999999
- type: recall_at_5
value: 21.575
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.37009118256057
- type: cos_sim_spearman
value: 79.27986395671529
- type: euclidean_pearson
value: 79.18037715442115
- type: euclidean_spearman
value: 79.28004791561621
- type: manhattan_pearson
value: 79.34062972800541
- type: manhattan_spearman
value: 79.43106695543402
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.48474767383833
- type: cos_sim_spearman
value: 79.54505388752513
- type: euclidean_pearson
value: 83.43282704179565
- type: euclidean_spearman
value: 79.54579919925405
- type: manhattan_pearson
value: 83.77564492427952
- type: manhattan_spearman
value: 79.84558396989286
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.803698035802
- type: cos_sim_spearman
value: 88.83451367754881
- type: euclidean_pearson
value: 88.28939285711628
- type: euclidean_spearman
value: 88.83528996073112
- type: manhattan_pearson
value: 88.28017412671795
- type: manhattan_spearman
value: 88.9228828016344
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.27469288153428
- type: cos_sim_spearman
value: 83.87477064876288
- type: euclidean_pearson
value: 84.2601737035379
- type: euclidean_spearman
value: 83.87431082479074
- type: manhattan_pearson
value: 84.3621547772745
- type: manhattan_spearman
value: 84.12094375000423
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.12749863201587
- type: cos_sim_spearman
value: 88.54287568368565
- type: euclidean_pearson
value: 87.90429700607999
- type: euclidean_spearman
value: 88.5437689576261
- type: manhattan_pearson
value: 88.19276653356833
- type: manhattan_spearman
value: 88.99995393814679
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.68398747560902
- type: cos_sim_spearman
value: 86.48815303460574
- type: euclidean_pearson
value: 85.52356631237954
- type: euclidean_spearman
value: 86.486391949551
- type: manhattan_pearson
value: 85.67267981761788
- type: manhattan_spearman
value: 86.7073696332485
- 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: 88.9057107443124
- type: cos_sim_spearman
value: 88.7312168757697
- type: euclidean_pearson
value: 88.72810439714794
- type: euclidean_spearman
value: 88.71976185854771
- type: manhattan_pearson
value: 88.50433745949111
- type: manhattan_spearman
value: 88.51726175544195
- 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: 67.59391795109886
- type: cos_sim_spearman
value: 66.87613008631367
- type: euclidean_pearson
value: 69.23198488262217
- type: euclidean_spearman
value: 66.85427723013692
- type: manhattan_pearson
value: 69.50730124841084
- type: manhattan_spearman
value: 67.10404669820792
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.0820605344619
- type: cos_sim_spearman
value: 86.8518089863434
- type: euclidean_pearson
value: 86.31087134689284
- type: euclidean_spearman
value: 86.8518520517941
- type: manhattan_pearson
value: 86.47203796160612
- type: manhattan_spearman
value: 87.1080149734421
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 89.09255369305481
- type: mrr
value: 97.10323445617563
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 61.260999999999996
- type: map_at_10
value: 74.043
- type: map_at_100
value: 74.37700000000001
- type: map_at_1000
value: 74.384
- type: map_at_3
value: 71.222
- type: map_at_5
value: 72.875
- type: mrr_at_1
value: 64.333
- type: mrr_at_10
value: 74.984
- type: mrr_at_100
value: 75.247
- type: mrr_at_1000
value: 75.25500000000001
- type: mrr_at_3
value: 73.167
- type: mrr_at_5
value: 74.35000000000001
- type: ndcg_at_1
value: 64.333
- type: ndcg_at_10
value: 79.06
- type: ndcg_at_100
value: 80.416
- type: ndcg_at_1000
value: 80.55600000000001
- type: ndcg_at_3
value: 74.753
- type: ndcg_at_5
value: 76.97500000000001
- type: precision_at_1
value: 64.333
- type: precision_at_10
value: 10.567
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 29.889
- type: precision_at_5
value: 19.533
- type: recall_at_1
value: 61.260999999999996
- type: recall_at_10
value: 93.167
- type: recall_at_100
value: 99
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 81.667
- type: recall_at_5
value: 87.394
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.71980198019801
- type: cos_sim_ap
value: 92.81616007802704
- type: cos_sim_f1
value: 85.17548454688318
- type: cos_sim_precision
value: 89.43894389438944
- type: cos_sim_recall
value: 81.3
- type: dot_accuracy
value: 99.71980198019801
- type: dot_ap
value: 92.81398760591358
- type: dot_f1
value: 85.17548454688318
- type: dot_precision
value: 89.43894389438944
- type: dot_recall
value: 81.3
- type: euclidean_accuracy
value: 99.71980198019801
- type: euclidean_ap
value: 92.81560637245072
- type: euclidean_f1
value: 85.17548454688318
- type: euclidean_precision
value: 89.43894389438944
- type: euclidean_recall
value: 81.3
- type: manhattan_accuracy
value: 99.73069306930694
- type: manhattan_ap
value: 93.14005487480794
- type: manhattan_f1
value: 85.56263269639068
- type: manhattan_precision
value: 91.17647058823529
- type: manhattan_recall
value: 80.60000000000001
- type: max_accuracy
value: 99.73069306930694
- type: max_ap
value: 93.14005487480794
- type: max_f1
value: 85.56263269639068
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 79.86443362395185
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 49.40897096662564
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.66040806627947
- type: mrr
value: 56.58670475766064
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.51015090598575
- type: cos_sim_spearman
value: 31.35016454939226
- type: dot_pearson
value: 31.5150068731
- type: dot_spearman
value: 31.34790869023487
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.254
- type: map_at_10
value: 2.064
- type: map_at_100
value: 12.909
- type: map_at_1000
value: 31.761
- type: map_at_3
value: 0.738
- type: map_at_5
value: 1.155
- type: mrr_at_1
value: 96
- type: mrr_at_10
value: 98
- type: mrr_at_100
value: 98
- type: mrr_at_1000
value: 98
- type: mrr_at_3
value: 98
- type: mrr_at_5
value: 98
- type: ndcg_at_1
value: 93
- type: ndcg_at_10
value: 82.258
- type: ndcg_at_100
value: 64.34
- type: ndcg_at_1000
value: 57.912
- type: ndcg_at_3
value: 90.827
- type: ndcg_at_5
value: 86.79
- type: precision_at_1
value: 96
- type: precision_at_10
value: 84.8
- type: precision_at_100
value: 66
- type: precision_at_1000
value: 25.356
- type: precision_at_3
value: 94.667
- type: precision_at_5
value: 90.4
- type: recall_at_1
value: 0.254
- type: recall_at_10
value: 2.1950000000000003
- type: recall_at_100
value: 16.088
- type: recall_at_1000
value: 54.559000000000005
- type: recall_at_3
value: 0.75
- type: recall_at_5
value: 1.191
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.976
- type: map_at_10
value: 11.389000000000001
- type: map_at_100
value: 18.429000000000002
- type: map_at_1000
value: 20.113
- type: map_at_3
value: 6.483
- type: map_at_5
value: 8.770999999999999
- type: mrr_at_1
value: 40.816
- type: mrr_at_10
value: 58.118
- type: mrr_at_100
value: 58.489999999999995
- type: mrr_at_1000
value: 58.489999999999995
- type: mrr_at_3
value: 53.061
- type: mrr_at_5
value: 57.041
- type: ndcg_at_1
value: 40.816
- type: ndcg_at_10
value: 30.567
- type: ndcg_at_100
value: 42.44
- type: ndcg_at_1000
value: 53.480000000000004
- type: ndcg_at_3
value: 36.016
- type: ndcg_at_5
value: 34.257
- type: precision_at_1
value: 42.857
- type: precision_at_10
value: 25.714
- type: precision_at_100
value: 8.429
- type: precision_at_1000
value: 1.5939999999999999
- type: precision_at_3
value: 36.735
- type: precision_at_5
value: 33.878
- type: recall_at_1
value: 2.976
- type: recall_at_10
value: 17.854999999999997
- type: recall_at_100
value: 51.833
- type: recall_at_1000
value: 86.223
- type: recall_at_3
value: 7.887
- type: recall_at_5
value: 12.026
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 85.1174
- type: ap
value: 30.169441069345748
- type: f1
value: 69.79254701873245
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 72.58347481607245
- type: f1
value: 72.74877295564937
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 53.90586138221305
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.35769207844072
- type: cos_sim_ap
value: 77.9645072410354
- type: cos_sim_f1
value: 71.32352941176471
- type: cos_sim_precision
value: 66.5903890160183
- type: cos_sim_recall
value: 76.78100263852242
- type: dot_accuracy
value: 87.37557370209214
- type: dot_ap
value: 77.96250046429908
- type: dot_f1
value: 71.28932757557064
- type: dot_precision
value: 66.95249130938586
- type: dot_recall
value: 76.22691292875989
- type: euclidean_accuracy
value: 87.35173153722357
- type: euclidean_ap
value: 77.96520460741593
- type: euclidean_f1
value: 71.32470733210104
- type: euclidean_precision
value: 66.91329479768785
- type: euclidean_recall
value: 76.35883905013192
- type: manhattan_accuracy
value: 87.25636287774931
- type: manhattan_ap
value: 77.77752485611796
- type: manhattan_f1
value: 71.18148599269183
- type: manhattan_precision
value: 66.10859728506787
- type: manhattan_recall
value: 77.0976253298153
- type: max_accuracy
value: 87.37557370209214
- type: max_ap
value: 77.96520460741593
- type: max_f1
value: 71.32470733210104
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.38176737687739
- type: cos_sim_ap
value: 86.58811861657401
- type: cos_sim_f1
value: 79.09430644097604
- type: cos_sim_precision
value: 75.45085977911366
- type: cos_sim_recall
value: 83.10748383122882
- type: dot_accuracy
value: 89.38370784336554
- type: dot_ap
value: 86.58840606004333
- type: dot_f1
value: 79.10179860068133
- type: dot_precision
value: 75.44546153308643
- type: dot_recall
value: 83.13058207576223
- type: euclidean_accuracy
value: 89.38564830985369
- type: euclidean_ap
value: 86.58820721061164
- type: euclidean_f1
value: 79.09070942235888
- type: euclidean_precision
value: 75.38729937194697
- type: euclidean_recall
value: 83.17677856482906
- type: manhattan_accuracy
value: 89.40699344122326
- type: manhattan_ap
value: 86.60631843011362
- type: manhattan_f1
value: 79.14949970570925
- type: manhattan_precision
value: 75.78191039729502
- type: manhattan_recall
value: 82.83030489682784
- type: max_accuracy
value: 89.40699344122326
- type: max_ap
value: 86.60631843011362
- type: max_f1
value: 79.14949970570925
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_pearson
value: 65.58442135663871
- type: cos_sim_spearman
value: 72.2538631361313
- type: euclidean_pearson
value: 70.97255486607429
- type: euclidean_spearman
value: 72.25374250228647
- type: manhattan_pearson
value: 70.83250199989911
- type: manhattan_spearman
value: 72.14819496536272
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_pearson
value: 59.99478404929932
- type: cos_sim_spearman
value: 62.61836216999812
- type: euclidean_pearson
value: 66.86429811933593
- type: euclidean_spearman
value: 62.6183520374191
- type: manhattan_pearson
value: 66.8063778911633
- type: manhattan_spearman
value: 62.569607573241115
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 53.98400000000001
- type: f1
value: 51.21447361350723
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_pearson
value: 79.11941660686553
- type: cos_sim_spearman
value: 81.25029594540435
- type: euclidean_pearson
value: 82.06973504238826
- type: euclidean_spearman
value: 81.2501989488524
- type: manhattan_pearson
value: 82.10094630392753
- type: manhattan_spearman
value: 81.27987244392389
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 47.07270168705156
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 45.98511703185043
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 88.19895157194931
- type: mrr
value: 90.21424603174603
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 88.03317320980119
- type: mrr
value: 89.9461507936508
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: map_at_1
value: 29.037000000000003
- type: map_at_10
value: 42.001
- type: map_at_100
value: 43.773
- type: map_at_1000
value: 43.878
- type: map_at_3
value: 37.637
- type: map_at_5
value: 40.034
- type: mrr_at_1
value: 43.136
- type: mrr_at_10
value: 51.158
- type: mrr_at_100
value: 52.083
- type: mrr_at_1000
value: 52.12
- type: mrr_at_3
value: 48.733
- type: mrr_at_5
value: 50.025
- type: ndcg_at_1
value: 43.136
- type: ndcg_at_10
value: 48.685
- type: ndcg_at_100
value: 55.513
- type: ndcg_at_1000
value: 57.242000000000004
- type: ndcg_at_3
value: 43.329
- type: ndcg_at_5
value: 45.438
- type: precision_at_1
value: 43.136
- type: precision_at_10
value: 10.56
- type: precision_at_100
value: 1.6129999999999998
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 24.064
- type: precision_at_5
value: 17.269000000000002
- type: recall_at_1
value: 29.037000000000003
- type: recall_at_10
value: 59.245000000000005
- type: recall_at_100
value: 87.355
- type: recall_at_1000
value: 98.74000000000001
- type: recall_at_3
value: 42.99
- type: recall_at_5
value: 49.681999999999995
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_accuracy
value: 82.68190018039687
- type: cos_sim_ap
value: 90.18017125327886
- type: cos_sim_f1
value: 83.64080906868193
- type: cos_sim_precision
value: 79.7076890489303
- type: cos_sim_recall
value: 87.98223053542202
- type: dot_accuracy
value: 82.68190018039687
- type: dot_ap
value: 90.18782350103646
- type: dot_f1
value: 83.64242087729039
- type: dot_precision
value: 79.65313028764805
- type: dot_recall
value: 88.05237315875614
- type: euclidean_accuracy
value: 82.68190018039687
- type: euclidean_ap
value: 90.1801957900632
- type: euclidean_f1
value: 83.63636363636364
- type: euclidean_precision
value: 79.52772506852203
- type: euclidean_recall
value: 88.19265840542437
- type: manhattan_accuracy
value: 82.14070956103427
- type: manhattan_ap
value: 89.96178420101427
- type: manhattan_f1
value: 83.21087838578791
- type: manhattan_precision
value: 78.35605121850475
- type: manhattan_recall
value: 88.70703764320785
- type: max_accuracy
value: 82.68190018039687
- type: max_ap
value: 90.18782350103646
- type: max_f1
value: 83.64242087729039
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: map_at_1
value: 72.234
- type: map_at_10
value: 80.10000000000001
- type: map_at_100
value: 80.36
- type: map_at_1000
value: 80.363
- type: map_at_3
value: 78.315
- type: map_at_5
value: 79.607
- type: mrr_at_1
value: 72.392
- type: mrr_at_10
value: 80.117
- type: mrr_at_100
value: 80.36999999999999
- type: mrr_at_1000
value: 80.373
- type: mrr_at_3
value: 78.469
- type: mrr_at_5
value: 79.633
- type: ndcg_at_1
value: 72.392
- type: ndcg_at_10
value: 83.651
- type: ndcg_at_100
value: 84.749
- type: ndcg_at_1000
value: 84.83000000000001
- type: ndcg_at_3
value: 80.253
- type: ndcg_at_5
value: 82.485
- type: precision_at_1
value: 72.392
- type: precision_at_10
value: 9.557
- type: precision_at_100
value: 1.004
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 28.732000000000003
- type: precision_at_5
value: 18.377
- type: recall_at_1
value: 72.234
- type: recall_at_10
value: 94.573
- type: recall_at_100
value: 99.368
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 85.669
- type: recall_at_5
value: 91.01700000000001
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: map_at_1
value: 26.173999999999996
- type: map_at_10
value: 80.04
- type: map_at_100
value: 82.94500000000001
- type: map_at_1000
value: 82.98100000000001
- type: map_at_3
value: 55.562999999999995
- type: map_at_5
value: 69.89800000000001
- type: mrr_at_1
value: 89.5
- type: mrr_at_10
value: 92.996
- type: mrr_at_100
value: 93.06400000000001
- type: mrr_at_1000
value: 93.065
- type: mrr_at_3
value: 92.658
- type: mrr_at_5
value: 92.84599999999999
- type: ndcg_at_1
value: 89.5
- type: ndcg_at_10
value: 87.443
- type: ndcg_at_100
value: 90.253
- type: ndcg_at_1000
value: 90.549
- type: ndcg_at_3
value: 85.874
- type: ndcg_at_5
value: 84.842
- type: precision_at_1
value: 89.5
- type: precision_at_10
value: 41.805
- type: precision_at_100
value: 4.827
- type: precision_at_1000
value: 0.49
- type: precision_at_3
value: 76.85
- type: precision_at_5
value: 64.8
- type: recall_at_1
value: 26.173999999999996
- type: recall_at_10
value: 89.101
- type: recall_at_100
value: 98.08099999999999
- type: recall_at_1000
value: 99.529
- type: recall_at_3
value: 57.902
- type: recall_at_5
value: 74.602
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: map_at_1
value: 56.10000000000001
- type: map_at_10
value: 66.15299999999999
- type: map_at_100
value: 66.625
- type: map_at_1000
value: 66.636
- type: map_at_3
value: 63.632999999999996
- type: map_at_5
value: 65.293
- type: mrr_at_1
value: 56.10000000000001
- type: mrr_at_10
value: 66.15299999999999
- type: mrr_at_100
value: 66.625
- type: mrr_at_1000
value: 66.636
- type: mrr_at_3
value: 63.632999999999996
- type: mrr_at_5
value: 65.293
- type: ndcg_at_1
value: 56.10000000000001
- type: ndcg_at_10
value: 71.146
- type: ndcg_at_100
value: 73.27799999999999
- type: ndcg_at_1000
value: 73.529
- type: ndcg_at_3
value: 66.09
- type: ndcg_at_5
value: 69.08999999999999
- type: precision_at_1
value: 56.10000000000001
- type: precision_at_10
value: 8.68
- type: precision_at_100
value: 0.964
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 24.4
- type: precision_at_5
value: 16.1
- type: recall_at_1
value: 56.10000000000001
- type: recall_at_10
value: 86.8
- type: recall_at_100
value: 96.39999999999999
- type: recall_at_1000
value: 98.3
- type: recall_at_3
value: 73.2
- type: recall_at_5
value: 80.5
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 54.52096960369373
- type: f1
value: 40.930845295808695
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 86.51031894934334
- type: ap
value: 55.9516014323483
- type: f1
value: 81.54813679326381
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cos_sim_pearson
value: 69.67437838574276
- type: cos_sim_spearman
value: 73.81314174653045
- type: euclidean_pearson
value: 72.63430276680275
- type: euclidean_spearman
value: 73.81358736777001
- type: manhattan_pearson
value: 72.58743833842829
- type: manhattan_spearman
value: 73.7590419009179
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 31.648613483640254
- type: mrr
value: 30.37420634920635
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
- type: map_at_1
value: 73.28099999999999
- type: map_at_10
value: 81.977
- type: map_at_100
value: 82.222
- type: map_at_1000
value: 82.22699999999999
- type: map_at_3
value: 80.441
- type: map_at_5
value: 81.46600000000001
- type: mrr_at_1
value: 75.673
- type: mrr_at_10
value: 82.41000000000001
- type: mrr_at_100
value: 82.616
- type: mrr_at_1000
value: 82.621
- type: mrr_at_3
value: 81.094
- type: mrr_at_5
value: 81.962
- type: ndcg_at_1
value: 75.673
- type: ndcg_at_10
value: 85.15599999999999
- type: ndcg_at_100
value: 86.151
- type: ndcg_at_1000
value: 86.26899999999999
- type: ndcg_at_3
value: 82.304
- type: ndcg_at_5
value: 84.009
- type: precision_at_1
value: 75.673
- type: precision_at_10
value: 10.042
- type: precision_at_100
value: 1.052
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 30.673000000000002
- type: precision_at_5
value: 19.326999999999998
- type: recall_at_1
value: 73.28099999999999
- type: recall_at_10
value: 94.446
- type: recall_at_100
value: 98.737
- type: recall_at_1000
value: 99.649
- type: recall_at_3
value: 86.984
- type: recall_at_5
value: 91.024
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (zh-CN)
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 81.08607935440484
- type: f1
value: 78.24879986066307
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 86.05917955615332
- type: f1
value: 85.05279279434997
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: map_at_1
value: 56.2
- type: map_at_10
value: 62.57899999999999
- type: map_at_100
value: 63.154999999999994
- type: map_at_1000
value: 63.193
- type: map_at_3
value: 61.217
- type: map_at_5
value: 62.012
- type: mrr_at_1
value: 56.3
- type: mrr_at_10
value: 62.629000000000005
- type: mrr_at_100
value: 63.205999999999996
- type: mrr_at_1000
value: 63.244
- type: mrr_at_3
value: 61.267
- type: mrr_at_5
value: 62.062
- type: ndcg_at_1
value: 56.2
- type: ndcg_at_10
value: 65.592
- type: ndcg_at_100
value: 68.657
- type: ndcg_at_1000
value: 69.671
- type: ndcg_at_3
value: 62.808
- type: ndcg_at_5
value: 64.24499999999999
- type: precision_at_1
value: 56.2
- type: precision_at_10
value: 7.5
- type: precision_at_100
value: 0.899
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 22.467000000000002
- type: precision_at_5
value: 14.180000000000001
- type: recall_at_1
value: 56.2
- type: recall_at_10
value: 75
- type: recall_at_100
value: 89.9
- type: recall_at_1000
value: 97.89999999999999
- type: recall_at_3
value: 67.4
- type: recall_at_5
value: 70.89999999999999
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 76.87666666666667
- type: f1
value: 76.7317686219665
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 79.64266377910124
- type: cos_sim_ap
value: 84.78274442344829
- type: cos_sim_f1
value: 81.16947472745292
- type: cos_sim_precision
value: 76.47058823529412
- type: cos_sim_recall
value: 86.48363252375924
- type: dot_accuracy
value: 79.64266377910124
- type: dot_ap
value: 84.7851404063692
- type: dot_f1
value: 81.16947472745292
- type: dot_precision
value: 76.47058823529412
- type: dot_recall
value: 86.48363252375924
- type: euclidean_accuracy
value: 79.64266377910124
- type: euclidean_ap
value: 84.78068373762378
- type: euclidean_f1
value: 81.14794656110837
- type: euclidean_precision
value: 76.35009310986965
- type: euclidean_recall
value: 86.58922914466737
- type: manhattan_accuracy
value: 79.48023822414727
- type: manhattan_ap
value: 84.72928897427576
- type: manhattan_f1
value: 81.32084770823064
- type: manhattan_precision
value: 76.24768946395564
- type: manhattan_recall
value: 87.11721224920802
- type: max_accuracy
value: 79.64266377910124
- type: max_ap
value: 84.7851404063692
- type: max_f1
value: 81.32084770823064
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 94.3
- type: ap
value: 92.8664032274438
- type: f1
value: 94.29311102997727
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_pearson
value: 48.51392279882909
- type: cos_sim_spearman
value: 54.06338895994974
- type: euclidean_pearson
value: 52.58480559573412
- type: euclidean_spearman
value: 54.06417276612201
- type: manhattan_pearson
value: 52.69525121721343
- type: manhattan_spearman
value: 54.048147455389675
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_pearson
value: 29.728387290757325
- type: cos_sim_spearman
value: 31.366121633635284
- type: euclidean_pearson
value: 29.14588368552961
- type: euclidean_spearman
value: 31.36764411112844
- type: manhattan_pearson
value: 29.63517350523121
- type: manhattan_spearman
value: 31.94157020583762
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 63.64868296271406
- type: cos_sim_spearman
value: 66.12800618164744
- type: euclidean_pearson
value: 63.21405767340238
- type: euclidean_spearman
value: 66.12786567790748
- type: manhattan_pearson
value: 64.04300276525848
- type: manhattan_spearman
value: 66.5066857145652
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cos_sim_pearson
value: 81.2302623912794
- type: cos_sim_spearman
value: 81.16833673266562
- type: euclidean_pearson
value: 79.47647843876024
- type: euclidean_spearman
value: 81.16944349524972
- type: manhattan_pearson
value: 79.84947238492208
- type: manhattan_spearman
value: 81.64626599410026
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 67.80129586475687
- type: mrr
value: 77.77402311635554
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: map_at_1
value: 28.666999999999998
- type: map_at_10
value: 81.063
- type: map_at_100
value: 84.504
- type: map_at_1000
value: 84.552
- type: map_at_3
value: 56.897
- type: map_at_5
value: 70.073
- type: mrr_at_1
value: 92.087
- type: mrr_at_10
value: 94.132
- type: mrr_at_100
value: 94.19800000000001
- type: mrr_at_1000
value: 94.19999999999999
- type: mrr_at_3
value: 93.78999999999999
- type: mrr_at_5
value: 94.002
- type: ndcg_at_1
value: 92.087
- type: ndcg_at_10
value: 87.734
- type: ndcg_at_100
value: 90.736
- type: ndcg_at_1000
value: 91.184
- type: ndcg_at_3
value: 88.78
- type: ndcg_at_5
value: 87.676
- type: precision_at_1
value: 92.087
- type: precision_at_10
value: 43.46
- type: precision_at_100
value: 5.07
- type: precision_at_1000
value: 0.518
- type: precision_at_3
value: 77.49000000000001
- type: precision_at_5
value: 65.194
- type: recall_at_1
value: 28.666999999999998
- type: recall_at_10
value: 86.632
- type: recall_at_100
value: 96.646
- type: recall_at_1000
value: 98.917
- type: recall_at_3
value: 58.333999999999996
- type: recall_at_5
value: 72.974
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 52.971999999999994
- type: f1
value: 50.2898280984929
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 86.0797948663824
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 85.10759092255017
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: map_at_1
value: 65.60000000000001
- type: map_at_10
value: 74.773
- type: map_at_100
value: 75.128
- type: map_at_1000
value: 75.136
- type: map_at_3
value: 73.05
- type: map_at_5
value: 74.13499999999999
- type: mrr_at_1
value: 65.60000000000001
- type: mrr_at_10
value: 74.773
- type: mrr_at_100
value: 75.128
- type: mrr_at_1000
value: 75.136
- type: mrr_at_3
value: 73.05
- type: mrr_at_5
value: 74.13499999999999
- type: ndcg_at_1
value: 65.60000000000001
- type: ndcg_at_10
value: 78.84299999999999
- type: ndcg_at_100
value: 80.40899999999999
- type: ndcg_at_1000
value: 80.57
- type: ndcg_at_3
value: 75.40599999999999
- type: ndcg_at_5
value: 77.351
- type: precision_at_1
value: 65.60000000000001
- type: precision_at_10
value: 9.139999999999999
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 27.400000000000002
- type: precision_at_5
value: 17.380000000000003
- type: recall_at_1
value: 65.60000000000001
- type: recall_at_10
value: 91.4
- type: recall_at_100
value: 98.4
- type: recall_at_1000
value: 99.6
- type: recall_at_3
value: 82.19999999999999
- type: recall_at_5
value: 86.9
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 89.47
- type: ap
value: 75.59561751845389
- type: f1
value: 87.95207751382563
- dataset:
config: default
name: MTEB AlloProfClusteringP2P
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure
value: 76.05592323841036
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloProfClusteringS2S
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure
value: 64.51718058866508
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloprofReranking
revision: 666fdacebe0291776e86f29345663dfaf80a0db9
split: test
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
metrics:
- type: map
value: 73.08278490943373
- type: mrr
value: 74.66561454570449
task:
type: Reranking
- dataset:
config: default
name: MTEB AlloprofRetrieval
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: map_at_1
value: 38.912
- type: map_at_10
value: 52.437999999999995
- type: map_at_100
value: 53.38
- type: map_at_1000
value: 53.427
- type: map_at_3
value: 48.879
- type: map_at_5
value: 50.934000000000005
- type: mrr_at_1
value: 44.085
- type: mrr_at_10
value: 55.337
- type: mrr_at_100
value: 56.016999999999996
- type: mrr_at_1000
value: 56.043
- type: mrr_at_3
value: 52.55499999999999
- type: mrr_at_5
value: 54.20399999999999
- type: ndcg_at_1
value: 44.085
- type: ndcg_at_10
value: 58.876
- type: ndcg_at_100
value: 62.714000000000006
- type: ndcg_at_1000
value: 63.721000000000004
- type: ndcg_at_3
value: 52.444
- type: ndcg_at_5
value: 55.692
- type: precision_at_1
value: 44.085
- type: precision_at_10
value: 9.21
- type: precision_at_100
value: 1.164
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 23.043
- type: precision_at_5
value: 15.898000000000001
- type: recall_at_1
value: 38.912
- type: recall_at_10
value: 75.577
- type: recall_at_100
value: 92.038
- type: recall_at_1000
value: 99.325
- type: recall_at_3
value: 58.592
- type: recall_at_5
value: 66.235
task:
type: Retrieval
- dataset:
config: fr
name: MTEB AmazonReviewsClassification (fr)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 55.532000000000004
- type: f1
value: 52.5783943471605
task:
type: Classification
- dataset:
config: default
name: MTEB BSARDRetrieval
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
split: test
type: maastrichtlawtech/bsard
metrics:
- type: map_at_1
value: 8.108
- type: map_at_10
value: 14.710999999999999
- type: map_at_100
value: 15.891
- type: map_at_1000
value: 15.983
- type: map_at_3
value: 12.237
- type: map_at_5
value: 13.679
- type: mrr_at_1
value: 8.108
- type: mrr_at_10
value: 14.710999999999999
- type: mrr_at_100
value: 15.891
- type: mrr_at_1000
value: 15.983
- type: mrr_at_3
value: 12.237
- type: mrr_at_5
value: 13.679
- type: ndcg_at_1
value: 8.108
- type: ndcg_at_10
value: 18.796
- type: ndcg_at_100
value: 25.098
- type: ndcg_at_1000
value: 27.951999999999998
- type: ndcg_at_3
value: 13.712
- type: ndcg_at_5
value: 16.309
- type: precision_at_1
value: 8.108
- type: precision_at_10
value: 3.198
- type: precision_at_100
value: 0.626
- type: precision_at_1000
value: 0.086
- type: precision_at_3
value: 6.006
- type: precision_at_5
value: 4.865
- type: recall_at_1
value: 8.108
- type: recall_at_10
value: 31.982
- type: recall_at_100
value: 62.613
- type: recall_at_1000
value: 86.036
- type: recall_at_3
value: 18.018
- type: recall_at_5
value: 24.324
task:
type: Retrieval
- dataset:
config: default
name: MTEB HALClusteringS2S
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
split: test
type: lyon-nlp/clustering-hal-s2s
metrics:
- type: v_measure
value: 30.833269778867116
task:
type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringP2P
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure
value: 50.0281928004713
task:
type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringS2S
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure
value: 43.699961510636534
task:
type: Clustering
- dataset:
config: fr
name: MTEB MTOPDomainClassification (fr)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 96.68963357344191
- type: f1
value: 96.45175170820961
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPIntentClassification (fr)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 87.46946445349202
- type: f1
value: 65.79860440988624
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClassification (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: accuracy
value: 82.60663507109005
- type: f1
value: 77.20462646604777
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringP2P (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure
value: 60.19311264967803
task:
type: Clustering
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringS2S (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure
value: 63.6235764409785
task:
type: Clustering
- dataset:
config: fr
name: MTEB MassiveIntentClassification (fr)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 81.65097511768661
- type: f1
value: 78.77796091490924
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveScenarioClassification (fr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 86.64425016812373
- type: f1
value: 85.4912728670017
task:
type: Classification
- dataset:
config: fr
name: MTEB MintakaRetrieval (fr)
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
split: test
type: jinaai/mintakaqa
metrics:
- type: map_at_1
value: 35.913000000000004
- type: map_at_10
value: 48.147
- type: map_at_100
value: 48.91
- type: map_at_1000
value: 48.949
- type: map_at_3
value: 45.269999999999996
- type: map_at_5
value: 47.115
- type: mrr_at_1
value: 35.913000000000004
- type: mrr_at_10
value: 48.147
- type: mrr_at_100
value: 48.91
- type: mrr_at_1000
value: 48.949
- type: mrr_at_3
value: 45.269999999999996
- type: mrr_at_5
value: 47.115
- type: ndcg_at_1
value: 35.913000000000004
- type: ndcg_at_10
value: 54.03
- type: ndcg_at_100
value: 57.839
- type: ndcg_at_1000
value: 58.925000000000004
- type: ndcg_at_3
value: 48.217999999999996
- type: ndcg_at_5
value: 51.56699999999999
- type: precision_at_1
value: 35.913000000000004
- type: precision_at_10
value: 7.244000000000001
- type: precision_at_100
value: 0.9039999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 18.905
- type: precision_at_5
value: 12.981000000000002
- type: recall_at_1
value: 35.913000000000004
- type: recall_at_10
value: 72.441
- type: recall_at_100
value: 90.41799999999999
- type: recall_at_1000
value: 99.099
- type: recall_at_3
value: 56.716
- type: recall_at_5
value: 64.90599999999999
task:
type: Retrieval
- dataset:
config: fr
name: MTEB OpusparcusPC (fr)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test
type: GEM/opusparcus
metrics:
- type: cos_sim_accuracy
value: 99.90069513406156
- type: cos_sim_ap
value: 100
- type: cos_sim_f1
value: 99.95032290114257
- type: cos_sim_precision
value: 100
- type: cos_sim_recall
value: 99.90069513406156
- type: dot_accuracy
value: 99.90069513406156
- type: dot_ap
value: 100
- type: dot_f1
value: 99.95032290114257
- type: dot_precision
value: 100
- type: dot_recall
value: 99.90069513406156
- type: euclidean_accuracy
value: 99.90069513406156
- type: euclidean_ap
value: 100
- type: euclidean_f1
value: 99.95032290114257
- type: euclidean_precision
value: 100
- type: euclidean_recall
value: 99.90069513406156
- type: manhattan_accuracy
value: 99.90069513406156
- type: manhattan_ap
value: 100
- type: manhattan_f1
value: 99.95032290114257
- type: manhattan_precision
value: 100
- type: manhattan_recall
value: 99.90069513406156
- type: max_accuracy
value: 99.90069513406156
- type: max_ap
value: 100
- type: max_f1
value: 99.95032290114257
task:
type: PairClassification
- dataset:
config: fr
name: MTEB PawsX (fr)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: paws-x
metrics:
- type: cos_sim_accuracy
value: 75.25
- type: cos_sim_ap
value: 80.86376001270014
- type: cos_sim_f1
value: 73.65945437441204
- type: cos_sim_precision
value: 64.02289452166802
- type: cos_sim_recall
value: 86.71096345514951
- type: dot_accuracy
value: 75.25
- type: dot_ap
value: 80.93686107633002
- type: dot_f1
value: 73.65945437441204
- type: dot_precision
value: 64.02289452166802
- type: dot_recall
value: 86.71096345514951
- type: euclidean_accuracy
value: 75.25
- type: euclidean_ap
value: 80.86379136218862
- type: euclidean_f1
value: 73.65945437441204
- type: euclidean_precision
value: 64.02289452166802
- type: euclidean_recall
value: 86.71096345514951
- type: manhattan_accuracy
value: 75.3
- type: manhattan_ap
value: 80.87826606097734
- type: manhattan_f1
value: 73.68421052631581
- type: manhattan_precision
value: 64
- type: manhattan_recall
value: 86.82170542635659
- type: max_accuracy
value: 75.3
- type: max_ap
value: 80.93686107633002
- type: max_f1
value: 73.68421052631581
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICKFr
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
split: test
type: Lajavaness/SICK-fr
metrics:
- type: cos_sim_pearson
value: 81.42349425981143
- type: cos_sim_spearman
value: 78.90454327031226
- type: euclidean_pearson
value: 78.39086497435166
- type: euclidean_spearman
value: 78.9046133980509
- type: manhattan_pearson
value: 78.63743094286502
- type: manhattan_spearman
value: 79.12136348449269
task:
type: STS
- dataset:
config: fr
name: MTEB STS22 (fr)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 81.452697919749
- type: cos_sim_spearman
value: 82.58116836039301
- type: euclidean_pearson
value: 81.04038478932786
- type: euclidean_spearman
value: 82.58116836039301
- type: manhattan_pearson
value: 81.37075396187771
- type: manhattan_spearman
value: 82.73678231355368
task:
type: STS
- dataset:
config: fr
name: MTEB STSBenchmarkMultilingualSTS (fr)
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
split: test
type: stsb_multi_mt
metrics:
- type: cos_sim_pearson
value: 85.7419764013806
- type: cos_sim_spearman
value: 85.46085808849622
- type: euclidean_pearson
value: 83.70449639870063
- type: euclidean_spearman
value: 85.46159013076233
- type: manhattan_pearson
value: 83.95259510313929
- type: manhattan_spearman
value: 85.8029724659458
task:
type: STS
- dataset:
config: default
name: MTEB SummEvalFr
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
split: test
type: lyon-nlp/summarization-summeval-fr-p2p
metrics:
- type: cos_sim_pearson
value: 32.61063271753325
- type: cos_sim_spearman
value: 31.454589417353603
- type: dot_pearson
value: 32.6106288643431
- type: dot_spearman
value: 31.454589417353603
task:
type: Summarization
- dataset:
config: default
name: MTEB SyntecReranking
revision: b205c5084a0934ce8af14338bf03feb19499c84d
split: test
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
metrics:
- type: map
value: 84.31666666666666
- type: mrr
value: 84.31666666666666
task:
type: Reranking
- dataset:
config: default
name: MTEB SyntecRetrieval
revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
split: test
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
metrics:
- type: map_at_1
value: 63
- type: map_at_10
value: 73.471
- type: map_at_100
value: 73.87
- type: map_at_1000
value: 73.87
- type: map_at_3
value: 70.5
- type: map_at_5
value: 73.05
- type: mrr_at_1
value: 63
- type: mrr_at_10
value: 73.471
- type: mrr_at_100
value: 73.87
- type: mrr_at_1000
value: 73.87
- type: mrr_at_3
value: 70.5
- type: mrr_at_5
value: 73.05
- type: ndcg_at_1
value: 63
- type: ndcg_at_10
value: 78.255
- type: ndcg_at_100
value: 79.88
- type: ndcg_at_1000
value: 79.88
- type: ndcg_at_3
value: 72.702
- type: ndcg_at_5
value: 77.264
- type: precision_at_1
value: 63
- type: precision_at_10
value: 9.3
- type: precision_at_100
value: 1
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 26.333000000000002
- type: precision_at_5
value: 18
- type: recall_at_1
value: 63
- type: recall_at_10
value: 93
- type: recall_at_100
value: 100
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 79
- type: recall_at_5
value: 90
task:
type: Retrieval
- dataset:
config: fr
name: MTEB XPQARetrieval (fr)
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
split: test
type: jinaai/xpqa
metrics:
- type: map_at_1
value: 40.338
- type: map_at_10
value: 61.927
- type: map_at_100
value: 63.361999999999995
- type: map_at_1000
value: 63.405
- type: map_at_3
value: 55.479
- type: map_at_5
value: 59.732
- type: mrr_at_1
value: 63.551
- type: mrr_at_10
value: 71.006
- type: mrr_at_100
value: 71.501
- type: mrr_at_1000
value: 71.509
- type: mrr_at_3
value: 69.07
- type: mrr_at_5
value: 70.165
- type: ndcg_at_1
value: 63.551
- type: ndcg_at_10
value: 68.297
- type: ndcg_at_100
value: 73.13199999999999
- type: ndcg_at_1000
value: 73.751
- type: ndcg_at_3
value: 62.999
- type: ndcg_at_5
value: 64.89
- type: precision_at_1
value: 63.551
- type: precision_at_10
value: 15.661
- type: precision_at_100
value: 1.9789999999999999
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 38.273
- type: precision_at_5
value: 27.61
- type: recall_at_1
value: 40.338
- type: recall_at_10
value: 77.267
- type: recall_at_100
value: 95.892
- type: recall_at_1000
value: 99.75500000000001
- type: recall_at_3
value: 60.36
- type: recall_at_5
value: 68.825
task:
type: Retrieval
- dataset:
config: default
name: MTEB 8TagsClustering
revision: None
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 51.36126303874126
task:
type: Clustering
- dataset:
config: default
name: MTEB AllegroReviews
revision: None
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 67.13717693836979
- type: f1
value: 57.27609848003782
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna-PL
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
split: test
type: clarin-knext/arguana-pl
metrics:
- type: map_at_1
value: 35.276999999999994
- type: map_at_10
value: 51.086
- type: map_at_100
value: 51.788000000000004
- type: map_at_1000
value: 51.791
- type: map_at_3
value: 46.147
- type: map_at_5
value: 49.078
- type: mrr_at_1
value: 35.917
- type: mrr_at_10
value: 51.315999999999995
- type: mrr_at_100
value: 52.018
- type: mrr_at_1000
value: 52.022
- type: mrr_at_3
value: 46.349000000000004
- type: mrr_at_5
value: 49.297000000000004
- type: ndcg_at_1
value: 35.276999999999994
- type: ndcg_at_10
value: 59.870999999999995
- type: ndcg_at_100
value: 62.590999999999994
- type: ndcg_at_1000
value: 62.661
- type: ndcg_at_3
value: 49.745
- type: ndcg_at_5
value: 55.067
- type: precision_at_1
value: 35.276999999999994
- type: precision_at_10
value: 8.791
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.057
- type: precision_at_5
value: 14.637
- type: recall_at_1
value: 35.276999999999994
- type: recall_at_10
value: 87.909
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 60.171
- type: recall_at_5
value: 73.18599999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB CBD
revision: None
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 78.03000000000002
- type: ap
value: 29.12548553897622
- type: f1
value: 66.54857118886073
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: None
split: test
type: PL-MTEB/cdsce-pairclassification
metrics:
- type: cos_sim_accuracy
value: 89
- type: cos_sim_ap
value: 76.75437826834582
- type: cos_sim_f1
value: 66.4850136239782
- type: cos_sim_precision
value: 68.92655367231639
- type: cos_sim_recall
value: 64.21052631578948
- type: dot_accuracy
value: 89
- type: dot_ap
value: 76.75437826834582
- type: dot_f1
value: 66.4850136239782
- type: dot_precision
value: 68.92655367231639
- type: dot_recall
value: 64.21052631578948
- type: euclidean_accuracy
value: 89
- type: euclidean_ap
value: 76.75437826834582
- type: euclidean_f1
value: 66.4850136239782
- type: euclidean_precision
value: 68.92655367231639
- type: euclidean_recall
value: 64.21052631578948
- type: manhattan_accuracy
value: 89
- type: manhattan_ap
value: 76.66074220647083
- type: manhattan_f1
value: 66.47058823529412
- type: manhattan_precision
value: 75.33333333333333
- type: manhattan_recall
value: 59.473684210526315
- type: max_accuracy
value: 89
- type: max_ap
value: 76.75437826834582
- type: max_f1
value: 66.4850136239782
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: None
split: test
type: PL-MTEB/cdscr-sts
metrics:
- type: cos_sim_pearson
value: 93.12903172428328
- type: cos_sim_spearman
value: 92.66381487060741
- type: euclidean_pearson
value: 90.37278396708922
- type: euclidean_spearman
value: 92.66381487060741
- type: manhattan_pearson
value: 90.32503296540962
- type: manhattan_spearman
value: 92.6902938354313
task:
type: STS
- dataset:
config: default
name: MTEB DBPedia-PL
revision: 76afe41d9af165cc40999fcaa92312b8b012064a
split: test
type: clarin-knext/dbpedia-pl
metrics:
- type: map_at_1
value: 8.83
- type: map_at_10
value: 18.326
- type: map_at_100
value: 26.496
- type: map_at_1000
value: 28.455000000000002
- type: map_at_3
value: 12.933
- type: map_at_5
value: 15.168000000000001
- type: mrr_at_1
value: 66
- type: mrr_at_10
value: 72.76700000000001
- type: mrr_at_100
value: 73.203
- type: mrr_at_1000
value: 73.219
- type: mrr_at_3
value: 71.458
- type: mrr_at_5
value: 72.246
- type: ndcg_at_1
value: 55.375
- type: ndcg_at_10
value: 41.3
- type: ndcg_at_100
value: 45.891
- type: ndcg_at_1000
value: 52.905
- type: ndcg_at_3
value: 46.472
- type: ndcg_at_5
value: 43.734
- type: precision_at_1
value: 66
- type: precision_at_10
value: 33.074999999999996
- type: precision_at_100
value: 11.094999999999999
- type: precision_at_1000
value: 2.374
- type: precision_at_3
value: 48.583
- type: precision_at_5
value: 42
- type: recall_at_1
value: 8.83
- type: recall_at_10
value: 22.587
- type: recall_at_100
value: 50.61600000000001
- type: recall_at_1000
value: 73.559
- type: recall_at_3
value: 13.688
- type: recall_at_5
value: 16.855
task:
type: Retrieval
- dataset:
config: default
name: MTEB FiQA-PL
revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
split: test
type: clarin-knext/fiqa-pl
metrics:
- type: map_at_1
value: 20.587
- type: map_at_10
value: 33.095
- type: map_at_100
value: 35.24
- type: map_at_1000
value: 35.429
- type: map_at_3
value: 28.626
- type: map_at_5
value: 31.136999999999997
- type: mrr_at_1
value: 40.586
- type: mrr_at_10
value: 49.033
- type: mrr_at_100
value: 49.952999999999996
- type: mrr_at_1000
value: 49.992
- type: mrr_at_3
value: 46.553
- type: mrr_at_5
value: 48.035
- type: ndcg_at_1
value: 40.586
- type: ndcg_at_10
value: 41.046
- type: ndcg_at_100
value: 48.586
- type: ndcg_at_1000
value: 51.634
- type: ndcg_at_3
value: 36.773
- type: ndcg_at_5
value: 38.389
- type: precision_at_1
value: 40.586
- type: precision_at_10
value: 11.466
- type: precision_at_100
value: 1.909
- type: precision_at_1000
value: 0.245
- type: precision_at_3
value: 24.434
- type: precision_at_5
value: 18.426000000000002
- type: recall_at_1
value: 20.587
- type: recall_at_10
value: 47.986000000000004
- type: recall_at_100
value: 75.761
- type: recall_at_1000
value: 94.065
- type: recall_at_3
value: 33.339
- type: recall_at_5
value: 39.765
task:
type: Retrieval
- dataset:
config: default
name: MTEB HotpotQA-PL
revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907
split: test
type: clarin-knext/hotpotqa-pl
metrics:
- type: map_at_1
value: 40.878
- type: map_at_10
value: 58.775999999999996
- type: map_at_100
value: 59.632
- type: map_at_1000
value: 59.707
- type: map_at_3
value: 56.074
- type: map_at_5
value: 57.629
- type: mrr_at_1
value: 81.756
- type: mrr_at_10
value: 86.117
- type: mrr_at_100
value: 86.299
- type: mrr_at_1000
value: 86.30600000000001
- type: mrr_at_3
value: 85.345
- type: mrr_at_5
value: 85.832
- type: ndcg_at_1
value: 81.756
- type: ndcg_at_10
value: 67.608
- type: ndcg_at_100
value: 70.575
- type: ndcg_at_1000
value: 71.99600000000001
- type: ndcg_at_3
value: 63.723
- type: ndcg_at_5
value: 65.70700000000001
- type: precision_at_1
value: 81.756
- type: precision_at_10
value: 13.619
- type: precision_at_100
value: 1.5939999999999999
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 39.604
- type: precision_at_5
value: 25.332
- type: recall_at_1
value: 40.878
- type: recall_at_10
value: 68.096
- type: recall_at_100
value: 79.696
- type: recall_at_1000
value: 89.082
- type: recall_at_3
value: 59.406000000000006
- type: recall_at_5
value: 63.329
task:
type: Retrieval
- dataset:
config: default
name: MTEB MSMARCO-PL
revision: 8634c07806d5cce3a6138e260e59b81760a0a640
split: test
type: clarin-knext/msmarco-pl
metrics:
- type: map_at_1
value: 2.1839999999999997
- type: map_at_10
value: 11.346
- type: map_at_100
value: 30.325000000000003
- type: map_at_1000
value: 37.806
- type: map_at_3
value: 4.842
- type: map_at_5
value: 6.891
- type: mrr_at_1
value: 86.047
- type: mrr_at_10
value: 89.14699999999999
- type: mrr_at_100
value: 89.46600000000001
- type: mrr_at_1000
value: 89.46600000000001
- type: mrr_at_3
value: 89.14699999999999
- type: mrr_at_5
value: 89.14699999999999
- type: ndcg_at_1
value: 67.829
- type: ndcg_at_10
value: 62.222
- type: ndcg_at_100
value: 55.337
- type: ndcg_at_1000
value: 64.076
- type: ndcg_at_3
value: 68.12700000000001
- type: ndcg_at_5
value: 64.987
- type: precision_at_1
value: 86.047
- type: precision_at_10
value: 69.535
- type: precision_at_100
value: 32.93
- type: precision_at_1000
value: 6.6049999999999995
- type: precision_at_3
value: 79.845
- type: precision_at_5
value: 75.349
- type: recall_at_1
value: 2.1839999999999997
- type: recall_at_10
value: 12.866
- type: recall_at_100
value: 43.505
- type: recall_at_1000
value: 72.366
- type: recall_at_3
value: 4.947
- type: recall_at_5
value: 7.192
task:
type: Retrieval
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 80.75319435104238
- type: f1
value: 77.58961444860606
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 85.54472091459313
- type: f1
value: 84.29498563572106
task:
type: Classification
- dataset:
config: default
name: MTEB NFCorpus-PL
revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
split: test
type: clarin-knext/nfcorpus-pl
metrics:
- type: map_at_1
value: 4.367
- type: map_at_10
value: 10.38
- type: map_at_100
value: 13.516
- type: map_at_1000
value: 14.982000000000001
- type: map_at_3
value: 7.367
- type: map_at_5
value: 8.59
- type: mrr_at_1
value: 41.486000000000004
- type: mrr_at_10
value: 48.886
- type: mrr_at_100
value: 49.657000000000004
- type: mrr_at_1000
value: 49.713
- type: mrr_at_3
value: 46.904
- type: mrr_at_5
value: 48.065000000000005
- type: ndcg_at_1
value: 40.402
- type: ndcg_at_10
value: 30.885
- type: ndcg_at_100
value: 28.393
- type: ndcg_at_1000
value: 37.428
- type: ndcg_at_3
value: 35.394999999999996
- type: ndcg_at_5
value: 33.391999999999996
- type: precision_at_1
value: 41.486000000000004
- type: precision_at_10
value: 23.437
- type: precision_at_100
value: 7.638
- type: precision_at_1000
value: 2.0389999999999997
- type: precision_at_3
value: 32.817
- type: precision_at_5
value: 28.915999999999997
- type: recall_at_1
value: 4.367
- type: recall_at_10
value: 14.655000000000001
- type: recall_at_100
value: 29.665999999999997
- type: recall_at_1000
value: 62.073
- type: recall_at_3
value: 8.51
- type: recall_at_5
value: 10.689
task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ-PL
revision: f171245712cf85dd4700b06bef18001578d0ca8d
split: test
type: clarin-knext/nq-pl
metrics:
- type: map_at_1
value: 28.616000000000003
- type: map_at_10
value: 41.626000000000005
- type: map_at_100
value: 42.689
- type: map_at_1000
value: 42.733
- type: map_at_3
value: 37.729
- type: map_at_5
value: 39.879999999999995
- type: mrr_at_1
value: 32.068000000000005
- type: mrr_at_10
value: 44.029
- type: mrr_at_100
value: 44.87
- type: mrr_at_1000
value: 44.901
- type: mrr_at_3
value: 40.687
- type: mrr_at_5
value: 42.625
- type: ndcg_at_1
value: 32.068000000000005
- type: ndcg_at_10
value: 48.449999999999996
- type: ndcg_at_100
value: 53.13
- type: ndcg_at_1000
value: 54.186
- type: ndcg_at_3
value: 40.983999999999995
- type: ndcg_at_5
value: 44.628
- type: precision_at_1
value: 32.068000000000005
- type: precision_at_10
value: 7.9750000000000005
- type: precision_at_100
value: 1.061
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 18.404999999999998
- type: precision_at_5
value: 13.111
- type: recall_at_1
value: 28.616000000000003
- type: recall_at_10
value: 66.956
- type: recall_at_100
value: 87.657
- type: recall_at_1000
value: 95.548
- type: recall_at_3
value: 47.453
- type: recall_at_5
value: 55.87800000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB PAC
revision: None
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 69.04141326382856
- type: ap
value: 77.47589122111044
- type: f1
value: 66.6332277374775
task:
type: Classification
- dataset:
config: default
name: MTEB PPC
revision: None
split: test
type: PL-MTEB/ppc-pairclassification
metrics:
- type: cos_sim_accuracy
value: 86.4
- type: cos_sim_ap
value: 94.1044939667201
- type: cos_sim_f1
value: 88.78048780487805
- type: cos_sim_precision
value: 87.22044728434504
- type: cos_sim_recall
value: 90.39735099337747
- type: dot_accuracy
value: 86.4
- type: dot_ap
value: 94.1044939667201
- type: dot_f1
value: 88.78048780487805
- type: dot_precision
value: 87.22044728434504
- type: dot_recall
value: 90.39735099337747
- type: euclidean_accuracy
value: 86.4
- type: euclidean_ap
value: 94.1044939667201
- type: euclidean_f1
value: 88.78048780487805
- type: euclidean_precision
value: 87.22044728434504
- type: euclidean_recall
value: 90.39735099337747
- type: manhattan_accuracy
value: 86.4
- type: manhattan_ap
value: 94.11438365697387
- type: manhattan_f1
value: 88.77968877968877
- type: manhattan_precision
value: 87.84440842787681
- type: manhattan_recall
value: 89.73509933774835
- type: max_accuracy
value: 86.4
- type: max_ap
value: 94.11438365697387
- type: max_f1
value: 88.78048780487805
task:
type: PairClassification
- dataset:
config: default
name: MTEB PSC
revision: None
split: test
type: PL-MTEB/psc-pairclassification
metrics:
- type: cos_sim_accuracy
value: 97.86641929499072
- type: cos_sim_ap
value: 99.36904211868182
- type: cos_sim_f1
value: 96.56203288490283
- type: cos_sim_precision
value: 94.72140762463343
- type: cos_sim_recall
value: 98.47560975609755
- type: dot_accuracy
value: 97.86641929499072
- type: dot_ap
value: 99.36904211868183
- type: dot_f1
value: 96.56203288490283
- type: dot_precision
value: 94.72140762463343
- type: dot_recall
value: 98.47560975609755
- type: euclidean_accuracy
value: 97.86641929499072
- type: euclidean_ap
value: 99.36904211868183
- type: euclidean_f1
value: 96.56203288490283
- type: euclidean_precision
value: 94.72140762463343
- type: euclidean_recall
value: 98.47560975609755
- type: manhattan_accuracy
value: 98.14471243042672
- type: manhattan_ap
value: 99.43359540492416
- type: manhattan_f1
value: 96.98795180722892
- type: manhattan_precision
value: 95.83333333333334
- type: manhattan_recall
value: 98.17073170731707
- type: max_accuracy
value: 98.14471243042672
- type: max_ap
value: 99.43359540492416
- type: max_f1
value: 96.98795180722892
task:
type: PairClassification
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: None
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 89.39058171745152
- type: f1
value: 86.8552093529568
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: None
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 74.97975708502024
- type: f1
value: 58.73081628832407
task:
type: Classification
- dataset:
config: default
name: MTEB Quora-PL
revision: 0be27e93455051e531182b85e85e425aba12e9d4
split: test
type: clarin-knext/quora-pl
metrics:
- type: map_at_1
value: 64.917
- type: map_at_10
value: 78.74600000000001
- type: map_at_100
value: 79.501
- type: map_at_1000
value: 79.524
- type: map_at_3
value: 75.549
- type: map_at_5
value: 77.495
- type: mrr_at_1
value: 74.9
- type: mrr_at_10
value: 82.112
- type: mrr_at_100
value: 82.314
- type: mrr_at_1000
value: 82.317
- type: mrr_at_3
value: 80.745
- type: mrr_at_5
value: 81.607
- type: ndcg_at_1
value: 74.83999999999999
- type: ndcg_at_10
value: 83.214
- type: ndcg_at_100
value: 84.997
- type: ndcg_at_1000
value: 85.207
- type: ndcg_at_3
value: 79.547
- type: ndcg_at_5
value: 81.46600000000001
- type: precision_at_1
value: 74.83999999999999
- type: precision_at_10
value: 12.822
- type: precision_at_100
value: 1.506
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 34.903
- type: precision_at_5
value: 23.16
- type: recall_at_1
value: 64.917
- type: recall_at_10
value: 92.27199999999999
- type: recall_at_100
value: 98.715
- type: recall_at_1000
value: 99.854
- type: recall_at_3
value: 82.04599999999999
- type: recall_at_5
value: 87.2
task:
type: Retrieval
- dataset:
config: default
name: MTEB SCIDOCS-PL
revision: 45452b03f05560207ef19149545f168e596c9337
split: test
type: clarin-knext/scidocs-pl
metrics:
- type: map_at_1
value: 3.51
- type: map_at_10
value: 9.046999999999999
- type: map_at_100
value: 10.823
- type: map_at_1000
value: 11.144
- type: map_at_3
value: 6.257
- type: map_at_5
value: 7.648000000000001
- type: mrr_at_1
value: 17.299999999999997
- type: mrr_at_10
value: 27.419
- type: mrr_at_100
value: 28.618
- type: mrr_at_1000
value: 28.685
- type: mrr_at_3
value: 23.817
- type: mrr_at_5
value: 25.927
- type: ndcg_at_1
value: 17.299999999999997
- type: ndcg_at_10
value: 16.084
- type: ndcg_at_100
value: 23.729
- type: ndcg_at_1000
value: 29.476999999999997
- type: ndcg_at_3
value: 14.327000000000002
- type: ndcg_at_5
value: 13.017999999999999
- type: precision_at_1
value: 17.299999999999997
- type: precision_at_10
value: 8.63
- type: precision_at_100
value: 1.981
- type: precision_at_1000
value: 0.336
- type: precision_at_3
value: 13.4
- type: precision_at_5
value: 11.700000000000001
- type: recall_at_1
value: 3.51
- type: recall_at_10
value: 17.518
- type: recall_at_100
value: 40.275
- type: recall_at_1000
value: 68.203
- type: recall_at_3
value: 8.155
- type: recall_at_5
value: 11.875
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-E-PL
revision: None
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics:
- type: cos_sim_accuracy
value: 86.30248675091724
- type: cos_sim_ap
value: 83.6756734006714
- type: cos_sim_f1
value: 74.97367497367497
- type: cos_sim_precision
value: 73.91003460207612
- type: cos_sim_recall
value: 76.06837606837607
- type: dot_accuracy
value: 86.30248675091724
- type: dot_ap
value: 83.6756734006714
- type: dot_f1
value: 74.97367497367497
- type: dot_precision
value: 73.91003460207612
- type: dot_recall
value: 76.06837606837607
- type: euclidean_accuracy
value: 86.30248675091724
- type: euclidean_ap
value: 83.67566984333091
- type: euclidean_f1
value: 74.97367497367497
- type: euclidean_precision
value: 73.91003460207612
- type: euclidean_recall
value: 76.06837606837607
- type: manhattan_accuracy
value: 86.28210354667753
- type: manhattan_ap
value: 83.64216119130171
- type: manhattan_f1
value: 74.92152075340078
- type: manhattan_precision
value: 73.4107997265892
- type: manhattan_recall
value: 76.49572649572649
- type: max_accuracy
value: 86.30248675091724
- type: max_ap
value: 83.6756734006714
- type: max_f1
value: 74.97367497367497
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: None
split: test
type: PL-MTEB/sickr-pl-sts
metrics:
- type: cos_sim_pearson
value: 82.23295940859121
- type: cos_sim_spearman
value: 78.89329160768719
- type: euclidean_pearson
value: 79.56019107076818
- type: euclidean_spearman
value: 78.89330209904084
- type: manhattan_pearson
value: 79.76098513973719
- type: manhattan_spearman
value: 79.05490162570123
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 37.732606308062486
- type: cos_sim_spearman
value: 41.01645667030284
- type: euclidean_pearson
value: 26.61722556367085
- type: euclidean_spearman
value: 41.01645667030284
- type: manhattan_pearson
value: 26.60917378970807
- type: manhattan_spearman
value: 41.51335727617614
task:
type: STS
- dataset:
config: default
name: MTEB SciFact-PL
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
split: test
type: clarin-knext/scifact-pl
metrics:
- type: map_at_1
value: 54.31700000000001
- type: map_at_10
value: 65.564
- type: map_at_100
value: 66.062
- type: map_at_1000
value: 66.08699999999999
- type: map_at_3
value: 62.592999999999996
- type: map_at_5
value: 63.888
- type: mrr_at_1
value: 56.99999999999999
- type: mrr_at_10
value: 66.412
- type: mrr_at_100
value: 66.85900000000001
- type: mrr_at_1000
value: 66.88
- type: mrr_at_3
value: 64.22200000000001
- type: mrr_at_5
value: 65.206
- type: ndcg_at_1
value: 56.99999999999999
- type: ndcg_at_10
value: 70.577
- type: ndcg_at_100
value: 72.879
- type: ndcg_at_1000
value: 73.45
- type: ndcg_at_3
value: 65.5
- type: ndcg_at_5
value: 67.278
- type: precision_at_1
value: 56.99999999999999
- type: precision_at_10
value: 9.667
- type: precision_at_100
value: 1.083
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26
- type: precision_at_5
value: 16.933
- type: recall_at_1
value: 54.31700000000001
- type: recall_at_10
value: 85.056
- type: recall_at_100
value: 95.667
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 71
- type: recall_at_5
value: 75.672
task:
type: Retrieval
- dataset:
config: default
name: MTEB TRECCOVID-PL
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
split: test
type: clarin-knext/trec-covid-pl
metrics:
- type: map_at_1
value: 0.245
- type: map_at_10
value: 2.051
- type: map_at_100
value: 12.009
- type: map_at_1000
value: 27.448
- type: map_at_3
value: 0.721
- type: map_at_5
value: 1.13
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 93
- type: mrr_at_100
value: 93
- type: mrr_at_1000
value: 93
- type: mrr_at_3
value: 93
- type: mrr_at_5
value: 93
- type: ndcg_at_1
value: 85
- type: ndcg_at_10
value: 80.303
- type: ndcg_at_100
value: 61.23499999999999
- type: ndcg_at_1000
value: 52.978
- type: ndcg_at_3
value: 84.419
- type: ndcg_at_5
value: 82.976
- type: precision_at_1
value: 88
- type: precision_at_10
value: 83.39999999999999
- type: precision_at_100
value: 61.96
- type: precision_at_1000
value: 22.648
- type: precision_at_3
value: 89.333
- type: precision_at_5
value: 87.2
- type: recall_at_1
value: 0.245
- type: recall_at_10
value: 2.193
- type: recall_at_100
value: 14.938
- type: recall_at_1000
value: 48.563
- type: recall_at_3
value: 0.738
- type: recall_at_5
value: 1.173
task:
type: Retrieval
base_model: Qwen/Qwen2-7B
gte-Qwen2-7B-instruct
gte-Qwen2-7B-instruct is the latest model in the gte (General Text Embedding) model family that ranks No.1 in both English and Chinese evaluations on the Massive Text Embedding Benchmark MTEB benchmark (as of June 16, 2024).
Recently, the Qwen team released the Qwen2 series models, and we have trained the gte-Qwen2-7B-instruct model based on the Qwen2-7B LLM model. Compared to the gte-Qwen1.5-7B-instruct model, the gte-Qwen2-7B-instruct model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models.
The model incorporates several key advancements:
- Integration of bidirectional attention mechanisms, enriching its contextual understanding.
- Instruction tuning, applied solely on the query side for streamlined efficiency
- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.
Model Information
- Model Size: 7B
- Embedding Dimension: 3584
- Max Input Tokens: 32k
Requirements
transformers>=4.39.2
flash_attn>=2.5.6
Usage
Sentence Transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192
queries = [
"how much protein should a female eat",
"summit define",
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
]
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
Observe the config_sentence_transformers.json to see all pre-built prompt names. Otherwise, you can use model.encode(queries, prompt="Instruct: ...\nQuery: " to use a custom prompt of your choice.
Transformers
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)
max_length = 8192
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
Evaluation
MTEB & C-MTEB
You can use the scripts/eval_mteb.py to reproduce the following result of gte-Qwen2-7B-instruct on MTEB(English)/C-MTEB(Chinese):
| Model Name | MTEB(56) | C-MTEB(35) | MTEB-fr(26) | MTEB-pl(26) |
|---|---|---|---|---|
| bge-base-en-1.5 | 64.23 | - | - | - |
| bge-large-en-1.5 | 63.55 | - | - | - |
| gte-large-en-v1.5 | 65.39 | - | - | - |
| gte-base-en-v1.5 | 64.11 | - | - | - |
| mxbai-embed-large-v1 | 64.68 | - | - | - |
| acge_text_embedding | - | 69.07 | - | - |
| stella-mrl-large-zh-v3.5-1792d | - | 68.55 | - | - |
| gte-large-zh | - | 66.72 | - | - |
| multilingual-e5-base | 59.45 | 56.21 | - | - |
| multilingual-e5-large | 61.50 | 58.81 | - | - |
| e5-mistral-7b-instruct | 66.63 | 60.81 | - | - |
| gte-Qwen1.5-7B-instruct | 67.34 | 69.52 | - | - |
| NV-Embed-v1 | 69.32 | - | - | - |
| gte-Qwen2-7B-instruct | 70.24 | 72.05 | 68.25 | 67.86 |
| gte-Qwen2-1.5B-instruc(https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | 67.16 | 67.65 | 66.60 | 64.04 |
GTE Models
The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture).
| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) |
|---|---|---|---|---|
| GTE-large-zh | Chinese | 512 | 1024 | 1.25GB |
| GTE-base-zh | Chinese | 512 | 512 | 0.41GB |
| GTE-small-zh | Chinese | 512 | 512 | 0.12GB |
| GTE-large | English | 512 | 1024 | 1.25GB |
| GTE-base | English | 512 | 512 | 0.21GB |
| GTE-small | English | 512 | 384 | 0.10GB |
| GTE-large-en-v1.5 | English | 8192 | 1024 | 1.74GB |
| GTE-base-en-v1.5 | English | 8192 | 768 | 0.51GB |
| GTE-Qwen1.5-7B-instruct | Multilingual | 32000 | 4096 | 26.45GB |
| GTE-Qwen2-7B-instruct | Multilingual | 32000 | 3584 | 26.45GB |
| GTE-Qwen2-1.5B-instruct | Multilingual | 32000 | 1536 | 6.62GB |
Cloud API Services
In addition to the open-source GTE series models, GTE series models are also available as commercial API services on Alibaba Cloud.
- Embedding Models: Rhree versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service.
- ReRank Models: The gte-rerank model service is available.
Note that the models behind the commercial APIs are not entirely identical to the open-source models.
Citation
If you find our paper or models helpful, please consider cite:
@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}
}