metadata
license: cc-by-nc-4.0
tags:
- mteb
model-index:
- name: text_sonar_basic_encoder_normalized
results:
- task:
type: Clustering
dataset:
type: PL-MTEB/8tags-clustering
name: MTEB 8TagsClustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 18.787544117314575
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_pearson
value: 17.97026675319667
- type: cos_sim_spearman
value: 17.63407829948615
- type: euclidean_pearson
value: 17.704571608660725
- type: euclidean_spearman
value: 17.634078298828143
- type: manhattan_pearson
value: 17.606959101509464
- type: manhattan_spearman
value: 17.549620164990085
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_pearson
value: 27.670887504789675
- type: cos_sim_spearman
value: 26.176629407301782
- type: euclidean_pearson
value: 28.878485717935586
- type: euclidean_spearman
value: 26.176635036613355
- type: manhattan_pearson
value: 28.782373978690103
- type: manhattan_spearman
value: 26.055266444113794
- task:
type: Classification
dataset:
type: PL-MTEB/allegro-reviews
name: MTEB AllegroReviews
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 29.62226640159046
- type: f1
value: 27.632722290701047
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 81.49253731343285
- type: ap
value: 46.61440947240349
- type: f1
value: 75.68925212232107
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.02355460385438
- type: ap
value: 83.13664983282676
- type: f1
value: 70.48997817871013
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 82.09145427286357
- type: ap
value: 31.45181004731995
- type: f1
value: 69.41750580313406
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.78800856531049
- type: ap
value: 19.65443896353892
- type: f1
value: 58.436688187826334
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 62.73074999999999
- type: ap
value: 58.2839375458089
- type: f1
value: 62.16204082406629
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 31.552000000000003
- type: f1
value: 31.125328770568277
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 34.611999999999995
- type: f1
value: 33.93738697105999
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 35.172
- type: f1
value: 34.14112656493798
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 34.910000000000004
- type: f1
value: 34.276631172288965
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 31.844
- type: f1
value: 31.478780923476368
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 31.912000000000003
- type: f1
value: 31.384992191831312
- task:
type: Classification
dataset:
type: DDSC/angry-tweets
name: MTEB AngryTweetsClassification
config: default
split: test
revision: 20b0e6081892e78179356fada741b7afa381443d
metrics:
- type: accuracy
value: 49.61795606494747
- type: f1
value: 48.63625944670304
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.677
- type: map_at_10
value: 14.732000000000001
- type: map_at_100
value: 15.501999999999999
- type: map_at_1000
value: 15.583
- type: map_at_3
value: 12.553
- type: map_at_5
value: 13.822999999999999
- type: mrr_at_1
value: 8.819
- type: mrr_at_10
value: 14.787
- type: mrr_at_100
value: 15.557000000000002
- type: mrr_at_1000
value: 15.638
- type: mrr_at_3
value: 12.648000000000001
- type: mrr_at_5
value: 13.879
- type: ndcg_at_1
value: 8.677
- type: ndcg_at_10
value: 18.295
- type: ndcg_at_100
value: 22.353
- type: ndcg_at_1000
value: 24.948999999999998
- type: ndcg_at_3
value: 13.789000000000001
- type: ndcg_at_5
value: 16.075
- type: precision_at_1
value: 8.677
- type: precision_at_10
value: 2.98
- type: precision_at_100
value: 0.49500000000000005
- type: precision_at_1000
value: 0.07100000000000001
- type: precision_at_3
value: 5.785
- type: precision_at_5
value: 4.58
- type: recall_at_1
value: 8.677
- type: recall_at_10
value: 29.801
- type: recall_at_100
value: 49.502
- type: recall_at_1000
value: 70.91
- type: recall_at_3
value: 17.354
- type: recall_at_5
value: 22.902
- task:
type: Retrieval
dataset:
type: arguana-pl
name: MTEB ArguAna-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.752000000000001
- type: map_at_10
value: 12.248000000000001
- type: map_at_100
value: 12.882
- type: map_at_1000
value: 12.963
- type: map_at_3
value: 10.574
- type: map_at_5
value: 11.566
- type: mrr_at_1
value: 7.824000000000001
- type: mrr_at_10
value: 12.293
- type: mrr_at_100
value: 12.928
- type: mrr_at_1000
value: 13.008000000000001
- type: mrr_at_3
value: 10.586
- type: mrr_at_5
value: 11.599
- type: ndcg_at_1
value: 7.752000000000001
- type: ndcg_at_10
value: 15.035000000000002
- type: ndcg_at_100
value: 18.497
- type: ndcg_at_1000
value: 20.896
- type: ndcg_at_3
value: 11.578
- type: ndcg_at_5
value: 13.38
- type: precision_at_1
value: 7.752000000000001
- type: precision_at_10
value: 2.404
- type: precision_at_100
value: 0.411
- type: precision_at_1000
value: 0.061
- type: precision_at_3
value: 4.836
- type: precision_at_5
value: 3.784
- type: recall_at_1
value: 7.752000000000001
- type: recall_at_10
value: 24.04
- type: recall_at_100
value: 41.11
- type: recall_at_1000
value: 60.597
- type: recall_at_3
value: 14.509
- type: recall_at_5
value: 18.919
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 26.81177290816682
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 24.346811178757022
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 50.88606427049027
- type: mrr
value: 65.13004001231148
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 77.15058512395619
- type: cos_sim_spearman
value: 79.10541692841936
- type: euclidean_pearson
value: 75.30525535929353
- type: euclidean_spearman
value: 79.10541692841936
- type: manhattan_pearson
value: 75.33508042552984
- type: manhattan_spearman
value: 78.84577245802708
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_pearson
value: 37.84739189558895
- type: cos_sim_spearman
value: 37.662710610486265
- type: euclidean_pearson
value: 37.5407537185213
- type: euclidean_spearman
value: 37.66272446700578
- type: manhattan_pearson
value: 37.863820146709706
- type: manhattan_spearman
value: 38.09120266204032
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (de-en)
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.97703549060543
- type: f1
value: 98.82393876130828
- type: precision
value: 98.74913013221992
- type: recall
value: 98.97703549060543
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (fr-en)
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.34910851860005
- type: f1
value: 98.09487123046446
- type: precision
value: 97.97032063981217
- type: recall
value: 98.34910851860005
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (ru-en)
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.60304814686526
- type: f1
value: 97.36520032328832
- type: precision
value: 97.24743101258517
- type: recall
value: 97.60304814686526
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.78883622959452
- type: f1
value: 98.71862383710724
- type: precision
value: 98.68351764086361
- type: recall
value: 98.78883622959452
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 73.49675324675324
- type: f1
value: 72.88538992490979
- task:
type: Clustering
dataset:
type: jinaai/big-patent-clustering
name: MTEB BigPatentClustering
config: default
split: test
revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
metrics:
- type: v_measure
value: 6.801245618724224
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 20.6156033971932
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 19.077587707743156
- task:
type: Clustering
dataset:
type: slvnwhrl/blurbs-clustering-p2p
name: MTEB BlurbsClusteringP2P
config: default
split: test
revision: a2dd5b02a77de3466a3eaa98ae586b5610314496
metrics:
- type: v_measure
value: 27.00349462858046
- task:
type: Clustering
dataset:
type: slvnwhrl/blurbs-clustering-s2s
name: MTEB BlurbsClusteringS2S
config: default
split: test
revision: 9bfff9a7f8f6dc6ffc9da71c48dd48b68696471d
metrics:
- type: v_measure
value: 14.845348131791589
- task:
type: BitextMining
dataset:
type: strombergnlp/bornholmsk_parallel
name: MTEB BornholmBitextMining
config: default
split: test
revision: 3bc5cfb4ec514264fe2db5615fac9016f7251552
metrics:
- type: accuracy
value: 54
- type: f1
value: 47.37026862026861
- type: precision
value: 45.0734126984127
- type: recall
value: 54
- task:
type: Classification
dataset:
type: PL-MTEB/cbd
name: MTEB CBD
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 63.83000000000001
- type: ap
value: 18.511972946438764
- type: f1
value: 53.16787370496645
- task:
type: PairClassification
dataset:
type: PL-MTEB/cdsce-pairclassification
name: MTEB CDSC-E
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 84.39999999999999
- type: cos_sim_ap
value: 59.968589741258036
- type: cos_sim_f1
value: 54.90909090909091
- type: cos_sim_precision
value: 41.94444444444444
- type: cos_sim_recall
value: 79.47368421052632
- type: dot_accuracy
value: 84.39999999999999
- type: dot_ap
value: 59.968589741258036
- type: dot_f1
value: 54.90909090909091
- type: dot_precision
value: 41.94444444444444
- type: dot_recall
value: 79.47368421052632
- type: euclidean_accuracy
value: 84.39999999999999
- type: euclidean_ap
value: 59.968589741258036
- type: euclidean_f1
value: 54.90909090909091
- type: euclidean_precision
value: 41.94444444444444
- type: euclidean_recall
value: 79.47368421052632
- type: manhattan_accuracy
value: 84.39999999999999
- type: manhattan_ap
value: 60.094893481041154
- type: manhattan_f1
value: 55.452865064695004
- type: manhattan_precision
value: 42.73504273504273
- type: manhattan_recall
value: 78.94736842105263
- type: max_accuracy
value: 84.39999999999999
- type: max_ap
value: 60.094893481041154
- type: max_f1
value: 55.452865064695004
- task:
type: STS
dataset:
type: PL-MTEB/cdscr-sts
name: MTEB CDSC-R
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 83.8427417206754
- type: cos_sim_spearman
value: 85.76946319798301
- type: euclidean_pearson
value: 79.43901249477852
- type: euclidean_spearman
value: 85.76946319798301
- type: manhattan_pearson
value: 79.81046681362531
- type: manhattan_spearman
value: 86.24115514951988
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 27.432031859995952
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 28.32367305628197
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 34.30720667137015
- type: mrr
value: 40.24416666666666
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 35.87700379259406
- type: mrr
value: 40.80206349206349
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.655000000000001
- type: map_at_10
value: 11.681999999999999
- type: map_at_100
value: 12.464
- type: map_at_1000
value: 12.603
- type: map_at_3
value: 10.514
- type: map_at_5
value: 11.083
- type: mrr_at_1
value: 10.157
- type: mrr_at_10
value: 14.773
- type: mrr_at_100
value: 15.581999999999999
- type: mrr_at_1000
value: 15.68
- type: mrr_at_3
value: 13.519
- type: mrr_at_5
value: 14.049
- type: ndcg_at_1
value: 10.157
- type: ndcg_at_10
value: 14.527999999999999
- type: ndcg_at_100
value: 18.695999999999998
- type: ndcg_at_1000
value: 22.709
- type: ndcg_at_3
value: 12.458
- type: ndcg_at_5
value: 13.152
- type: precision_at_1
value: 10.157
- type: precision_at_10
value: 2.976
- type: precision_at_100
value: 0.634
- type: precision_at_1000
value: 0.131
- type: precision_at_3
value: 6.152
- type: precision_at_5
value: 4.378
- type: recall_at_1
value: 7.655000000000001
- type: recall_at_10
value: 20.105
- type: recall_at_100
value: 39.181
- type: recall_at_1000
value: 68.06400000000001
- type: recall_at_3
value: 14.033000000000001
- type: recall_at_5
value: 16.209
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.2329999999999997
- type: map_at_10
value: 5.378
- type: map_at_100
value: 5.774
- type: map_at_1000
value: 5.863
- type: map_at_3
value: 4.598
- type: map_at_5
value: 4.9750000000000005
- type: mrr_at_1
value: 4.076
- type: mrr_at_10
value: 6.679
- type: mrr_at_100
value: 7.151000000000001
- type: mrr_at_1000
value: 7.24
- type: mrr_at_3
value: 5.722
- type: mrr_at_5
value: 6.2059999999999995
- type: ndcg_at_1
value: 4.076
- type: ndcg_at_10
value: 6.994
- type: ndcg_at_100
value: 9.366
- type: ndcg_at_1000
value: 12.181000000000001
- type: ndcg_at_3
value: 5.356000000000001
- type: ndcg_at_5
value: 6.008
- type: precision_at_1
value: 4.076
- type: precision_at_10
value: 1.459
- type: precision_at_100
value: 0.334
- type: precision_at_1000
value: 0.075
- type: precision_at_3
value: 2.718
- type: precision_at_5
value: 2.089
- type: recall_at_1
value: 3.2329999999999997
- type: recall_at_10
value: 10.749
- type: recall_at_100
value: 21.776
- type: recall_at_1000
value: 42.278999999999996
- type: recall_at_3
value: 6.146999999999999
- type: recall_at_5
value: 7.779999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.036
- type: map_at_10
value: 12.727
- type: map_at_100
value: 13.532
- type: map_at_1000
value: 13.653
- type: map_at_3
value: 11.15
- type: map_at_5
value: 11.965
- type: mrr_at_1
value: 9.404
- type: mrr_at_10
value: 14.493
- type: mrr_at_100
value: 15.274
- type: mrr_at_1000
value: 15.370000000000001
- type: mrr_at_3
value: 12.853
- type: mrr_at_5
value: 13.696
- type: ndcg_at_1
value: 9.404
- type: ndcg_at_10
value: 15.784
- type: ndcg_at_100
value: 20.104
- type: ndcg_at_1000
value: 23.357
- type: ndcg_at_3
value: 12.61
- type: ndcg_at_5
value: 13.988
- type: precision_at_1
value: 9.404
- type: precision_at_10
value: 2.947
- type: precision_at_100
value: 0.562
- type: precision_at_1000
value: 0.093
- type: precision_at_3
value: 6.04
- type: precision_at_5
value: 4.4639999999999995
- type: recall_at_1
value: 8.036
- type: recall_at_10
value: 23.429
- type: recall_at_100
value: 43.728
- type: recall_at_1000
value: 68.10000000000001
- type: recall_at_3
value: 14.99
- type: recall_at_5
value: 18.274
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.653
- type: map_at_10
value: 5.941
- type: map_at_100
value: 6.512
- type: map_at_1000
value: 6.6129999999999995
- type: map_at_3
value: 5.2540000000000004
- type: map_at_5
value: 5.645
- type: mrr_at_1
value: 3.955
- type: mrr_at_10
value: 6.4079999999999995
- type: mrr_at_100
value: 7.005999999999999
- type: mrr_at_1000
value: 7.105
- type: mrr_at_3
value: 5.593
- type: mrr_at_5
value: 6.051
- type: ndcg_at_1
value: 3.955
- type: ndcg_at_10
value: 7.342
- type: ndcg_at_100
value: 10.543
- type: ndcg_at_1000
value: 14.011000000000001
- type: ndcg_at_3
value: 5.853
- type: ndcg_at_5
value: 6.586
- type: precision_at_1
value: 3.955
- type: precision_at_10
value: 1.266
- type: precision_at_100
value: 0.315
- type: precision_at_1000
value: 0.066
- type: precision_at_3
value: 2.5989999999999998
- type: precision_at_5
value: 1.966
- type: recall_at_1
value: 3.653
- type: recall_at_10
value: 11.232000000000001
- type: recall_at_100
value: 26.625
- type: recall_at_1000
value: 54.476
- type: recall_at_3
value: 7.269
- type: recall_at_5
value: 8.982999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.257
- type: map_at_10
value: 3.881
- type: map_at_100
value: 4.279
- type: map_at_1000
value: 4.417
- type: map_at_3
value: 3.4070000000000005
- type: map_at_5
value: 3.744
- type: mrr_at_1
value: 2.9850000000000003
- type: mrr_at_10
value: 4.756
- type: mrr_at_100
value: 5.228
- type: mrr_at_1000
value: 5.354
- type: mrr_at_3
value: 4.125
- type: mrr_at_5
value: 4.567
- type: ndcg_at_1
value: 2.9850000000000003
- type: ndcg_at_10
value: 4.936999999999999
- type: ndcg_at_100
value: 7.664
- type: ndcg_at_1000
value: 12.045
- type: ndcg_at_3
value: 3.956
- type: ndcg_at_5
value: 4.584
- type: precision_at_1
value: 2.9850000000000003
- type: precision_at_10
value: 0.9329999999999999
- type: precision_at_100
value: 0.29
- type: precision_at_1000
value: 0.083
- type: precision_at_3
value: 1.949
- type: precision_at_5
value: 1.567
- type: recall_at_1
value: 2.257
- type: recall_at_10
value: 7.382
- type: recall_at_100
value: 20.689
- type: recall_at_1000
value: 53.586
- type: recall_at_3
value: 4.786
- type: recall_at_5
value: 6.2829999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.691
- type: map_at_10
value: 9.447
- type: map_at_100
value: 10.174
- type: map_at_1000
value: 10.308
- type: map_at_3
value: 8.187999999999999
- type: map_at_5
value: 8.852
- type: mrr_at_1
value: 8.566
- type: mrr_at_10
value: 12.036
- type: mrr_at_100
value: 12.817
- type: mrr_at_1000
value: 12.918
- type: mrr_at_3
value: 10.539
- type: mrr_at_5
value: 11.381
- type: ndcg_at_1
value: 8.566
- type: ndcg_at_10
value: 11.95
- type: ndcg_at_100
value: 15.831000000000001
- type: ndcg_at_1000
value: 19.561
- type: ndcg_at_3
value: 9.467
- type: ndcg_at_5
value: 10.544
- type: precision_at_1
value: 8.566
- type: precision_at_10
value: 2.387
- type: precision_at_100
value: 0.538
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 4.556
- type: precision_at_5
value: 3.5029999999999997
- type: recall_at_1
value: 6.691
- type: recall_at_10
value: 17.375
- type: recall_at_100
value: 34.503
- type: recall_at_1000
value: 61.492000000000004
- type: recall_at_3
value: 10.134
- type: recall_at_5
value: 13.056999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.68
- type: map_at_10
value: 6.776999999999999
- type: map_at_100
value: 7.207
- type: map_at_1000
value: 7.321999999999999
- type: map_at_3
value: 6.007
- type: map_at_5
value: 6.356000000000001
- type: mrr_at_1
value: 5.479
- type: mrr_at_10
value: 8.094999999999999
- type: mrr_at_100
value: 8.622
- type: mrr_at_1000
value: 8.729000000000001
- type: mrr_at_3
value: 7.249
- type: mrr_at_5
value: 7.6770000000000005
- type: ndcg_at_1
value: 5.479
- type: ndcg_at_10
value: 8.474
- type: ndcg_at_100
value: 11.134
- type: ndcg_at_1000
value: 14.759
- type: ndcg_at_3
value: 6.888
- type: ndcg_at_5
value: 7.504
- type: precision_at_1
value: 5.479
- type: precision_at_10
value: 1.575
- type: precision_at_100
value: 0.35000000000000003
- type: precision_at_1000
value: 0.08099999999999999
- type: precision_at_3
value: 3.272
- type: precision_at_5
value: 2.374
- type: recall_at_1
value: 4.68
- type: recall_at_10
value: 12.552
- type: recall_at_100
value: 24.91
- type: recall_at_1000
value: 52.019999999999996
- type: recall_at_3
value: 8.057
- type: recall_at_5
value: 9.629999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.741750000000001
- type: map_at_10
value: 7.103916666666667
- type: map_at_100
value: 7.656499999999998
- type: map_at_1000
value: 7.767583333333332
- type: map_at_3
value: 6.262416666666668
- type: map_at_5
value: 6.693916666666667
- type: mrr_at_1
value: 5.780583333333332
- type: mrr_at_10
value: 8.576333333333332
- type: mrr_at_100
value: 9.17975
- type: mrr_at_1000
value: 9.279083333333334
- type: mrr_at_3
value: 7.608833333333333
- type: mrr_at_5
value: 8.111333333333333
- type: ndcg_at_1
value: 5.780583333333332
- type: ndcg_at_10
value: 8.866166666666668
- type: ndcg_at_100
value: 12.037083333333333
- type: ndcg_at_1000
value: 15.4555
- type: ndcg_at_3
value: 7.179083333333335
- type: ndcg_at_5
value: 7.897166666666666
- type: precision_at_1
value: 5.780583333333332
- type: precision_at_10
value: 1.6935833333333334
- type: precision_at_100
value: 0.3921666666666667
- type: precision_at_1000
value: 0.08391666666666667
- type: precision_at_3
value: 3.425416666666666
- type: precision_at_5
value: 2.5570833333333334
- type: recall_at_1
value: 4.741750000000001
- type: recall_at_10
value: 12.889083333333334
- type: recall_at_100
value: 27.81866666666667
- type: recall_at_1000
value: 53.52316666666667
- type: recall_at_3
value: 8.179333333333332
- type: recall_at_5
value: 10.004083333333334
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.7130000000000005
- type: map_at_10
value: 5.734
- type: map_at_100
value: 6.297999999999999
- type: map_at_1000
value: 6.388000000000001
- type: map_at_3
value: 5.119
- type: map_at_5
value: 5.432
- type: mrr_at_1
value: 4.9079999999999995
- type: mrr_at_10
value: 7.2940000000000005
- type: mrr_at_100
value: 7.8549999999999995
- type: mrr_at_1000
value: 7.95
- type: mrr_at_3
value: 6.621
- type: mrr_at_5
value: 6.950000000000001
- type: ndcg_at_1
value: 4.9079999999999995
- type: ndcg_at_10
value: 7.167999999999999
- type: ndcg_at_100
value: 10.436
- type: ndcg_at_1000
value: 13.370999999999999
- type: ndcg_at_3
value: 5.959
- type: ndcg_at_5
value: 6.481000000000001
- type: precision_at_1
value: 4.9079999999999995
- type: precision_at_10
value: 1.3339999999999999
- type: precision_at_100
value: 0.33899999999999997
- type: precision_at_1000
value: 0.065
- type: precision_at_3
value: 2.965
- type: precision_at_5
value: 2.117
- type: recall_at_1
value: 3.7130000000000005
- type: recall_at_10
value: 10.156
- type: recall_at_100
value: 25.955000000000002
- type: recall_at_1000
value: 48.891
- type: recall_at_3
value: 6.795
- type: recall_at_5
value: 8.187999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.114
- type: map_at_10
value: 3.4290000000000003
- type: map_at_100
value: 3.789
- type: map_at_1000
value: 3.878
- type: map_at_3
value: 2.9139999999999997
- type: map_at_5
value: 3.148
- type: mrr_at_1
value: 2.65
- type: mrr_at_10
value: 4.252000000000001
- type: mrr_at_100
value: 4.689
- type: mrr_at_1000
value: 4.782
- type: mrr_at_3
value: 3.671
- type: mrr_at_5
value: 3.9370000000000003
- type: ndcg_at_1
value: 2.65
- type: ndcg_at_10
value: 4.47
- type: ndcg_at_100
value: 6.654
- type: ndcg_at_1000
value: 9.713
- type: ndcg_at_3
value: 3.424
- type: ndcg_at_5
value: 3.794
- type: precision_at_1
value: 2.65
- type: precision_at_10
value: 0.9119999999999999
- type: precision_at_100
value: 0.248
- type: precision_at_1000
value: 0.063
- type: precision_at_3
value: 1.7209999999999999
- type: precision_at_5
value: 1.287
- type: recall_at_1
value: 2.114
- type: recall_at_10
value: 6.927
- type: recall_at_100
value: 17.26
- type: recall_at_1000
value: 40.672999999999995
- type: recall_at_3
value: 3.8859999999999997
- type: recall_at_5
value: 4.861
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.055
- type: map_at_10
value: 7.704999999999999
- type: map_at_100
value: 8.169
- type: map_at_1000
value: 8.257
- type: map_at_3
value: 7.033
- type: map_at_5
value: 7.4079999999999995
- type: mrr_at_1
value: 6.81
- type: mrr_at_10
value: 8.955
- type: mrr_at_100
value: 9.497
- type: mrr_at_1000
value: 9.583
- type: mrr_at_3
value: 8.116
- type: mrr_at_5
value: 8.526
- type: ndcg_at_1
value: 6.81
- type: ndcg_at_10
value: 9.113
- type: ndcg_at_100
value: 11.884
- type: ndcg_at_1000
value: 14.762
- type: ndcg_at_3
value: 7.675999999999999
- type: ndcg_at_5
value: 8.325000000000001
- type: precision_at_1
value: 6.81
- type: precision_at_10
value: 1.558
- type: precision_at_100
value: 0.34299999999999997
- type: precision_at_1000
value: 0.068
- type: precision_at_3
value: 3.2960000000000003
- type: precision_at_5
value: 2.388
- type: recall_at_1
value: 6.055
- type: recall_at_10
value: 12.219
- type: recall_at_100
value: 25.304
- type: recall_at_1000
value: 47.204
- type: recall_at_3
value: 8.387
- type: recall_at_5
value: 9.991
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.043
- type: map_at_10
value: 7.394
- type: map_at_100
value: 8.096
- type: map_at_1000
value: 8.243
- type: map_at_3
value: 6.300999999999999
- type: map_at_5
value: 6.7780000000000005
- type: mrr_at_1
value: 6.126
- type: mrr_at_10
value: 9.308
- type: mrr_at_100
value: 10.091
- type: mrr_at_1000
value: 10.206
- type: mrr_at_3
value: 7.938000000000001
- type: mrr_at_5
value: 8.64
- type: ndcg_at_1
value: 6.126
- type: ndcg_at_10
value: 9.474
- type: ndcg_at_100
value: 13.238
- type: ndcg_at_1000
value: 17.366
- type: ndcg_at_3
value: 7.3260000000000005
- type: ndcg_at_5
value: 8.167
- type: precision_at_1
value: 6.126
- type: precision_at_10
value: 1.9959999999999998
- type: precision_at_100
value: 0.494
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 3.557
- type: precision_at_5
value: 2.9250000000000003
- type: recall_at_1
value: 5.043
- type: recall_at_10
value: 13.812
- type: recall_at_100
value: 31.375999999999998
- type: recall_at_1000
value: 61.309999999999995
- type: recall_at_3
value: 7.8020000000000005
- type: recall_at_5
value: 9.725999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.771
- type: map_at_10
value: 5.152
- type: map_at_100
value: 5.584
- type: map_at_1000
value: 5.666
- type: map_at_3
value: 4.664
- type: map_at_5
value: 4.941
- type: mrr_at_1
value: 4.251
- type: mrr_at_10
value: 5.867
- type: mrr_at_100
value: 6.345000000000001
- type: mrr_at_1000
value: 6.432
- type: mrr_at_3
value: 5.36
- type: mrr_at_5
value: 5.656
- type: ndcg_at_1
value: 4.251
- type: ndcg_at_10
value: 6.16
- type: ndcg_at_100
value: 8.895
- type: ndcg_at_1000
value: 11.631
- type: ndcg_at_3
value: 5.176
- type: ndcg_at_5
value: 5.633
- type: precision_at_1
value: 4.251
- type: precision_at_10
value: 0.98
- type: precision_at_100
value: 0.259
- type: precision_at_1000
value: 0.053
- type: precision_at_3
value: 2.2800000000000002
- type: precision_at_5
value: 1.627
- type: recall_at_1
value: 3.771
- type: recall_at_10
value: 8.731
- type: recall_at_100
value: 22.517
- type: recall_at_1000
value: 44.183
- type: recall_at_3
value: 5.866
- type: recall_at_5
value: 7.066999999999999
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.543
- type: map_at_10
value: 1.027
- type: map_at_100
value: 1.228
- type: map_at_1000
value: 1.266
- type: map_at_3
value: 0.756
- type: map_at_5
value: 0.877
- type: mrr_at_1
value: 1.3679999999999999
- type: mrr_at_10
value: 2.474
- type: mrr_at_100
value: 2.8369999999999997
- type: mrr_at_1000
value: 2.894
- type: mrr_at_3
value: 1.8780000000000001
- type: mrr_at_5
value: 2.1319999999999997
- type: ndcg_at_1
value: 1.3679999999999999
- type: ndcg_at_10
value: 1.791
- type: ndcg_at_100
value: 3.06
- type: ndcg_at_1000
value: 4.501
- type: ndcg_at_3
value: 1.16
- type: ndcg_at_5
value: 1.3419999999999999
- type: precision_at_1
value: 1.3679999999999999
- type: precision_at_10
value: 0.697
- type: precision_at_100
value: 0.193
- type: precision_at_1000
value: 0.045
- type: precision_at_3
value: 0.9339999999999999
- type: precision_at_5
value: 0.808
- type: recall_at_1
value: 0.543
- type: recall_at_10
value: 2.5149999999999997
- type: recall_at_100
value: 7.356999999999999
- type: recall_at_1000
value: 16.233
- type: recall_at_3
value: 1.018
- type: recall_at_5
value: 1.5150000000000001
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: map_at_1
value: 3.7289999999999996
- type: map_at_10
value: 5.524
- type: map_at_100
value: 5.984
- type: map_at_1000
value: 6.087
- type: map_at_3
value: 4.854
- type: map_at_5
value: 5.2299999999999995
- type: mrr_at_1
value: 6.177
- type: mrr_at_10
value: 8.541
- type: mrr_at_100
value: 9.073
- type: mrr_at_1000
value: 9.161
- type: mrr_at_3
value: 7.71
- type: mrr_at_5
value: 8.148
- type: ndcg_at_1
value: 6.177
- type: ndcg_at_10
value: 7.217999999999999
- type: ndcg_at_100
value: 9.927
- type: ndcg_at_1000
value: 13.062000000000001
- type: ndcg_at_3
value: 6.0569999999999995
- type: ndcg_at_5
value: 6.544999999999999
- type: precision_at_1
value: 6.177
- type: precision_at_10
value: 1.6729999999999998
- type: precision_at_100
value: 0.38999999999999996
- type: precision_at_1000
value: 0.082
- type: precision_at_3
value: 3.5090000000000003
- type: precision_at_5
value: 2.596
- type: recall_at_1
value: 3.7289999999999996
- type: recall_at_10
value: 9.501
- type: recall_at_100
value: 21.444
- type: recall_at_1000
value: 43.891999999999996
- type: recall_at_3
value: 6.053
- type: recall_at_5
value: 7.531000000000001
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_accuracy
value: 58.123872519543
- type: cos_sim_ap
value: 61.86046509726734
- type: cos_sim_f1
value: 68.18181818181817
- type: cos_sim_precision
value: 52.4198617221873
- type: cos_sim_recall
value: 97.49824643441664
- type: dot_accuracy
value: 58.123872519543
- type: dot_ap
value: 61.860555259802986
- type: dot_f1
value: 68.18181818181817
- type: dot_precision
value: 52.4198617221873
- type: dot_recall
value: 97.49824643441664
- type: euclidean_accuracy
value: 58.123872519543
- type: euclidean_ap
value: 61.87698627731538
- type: euclidean_f1
value: 68.18181818181817
- type: euclidean_precision
value: 52.4198617221873
- type: euclidean_recall
value: 97.49824643441664
- type: manhattan_accuracy
value: 58.123872519543
- type: manhattan_ap
value: 61.99468883207791
- type: manhattan_f1
value: 68.33675564681727
- type: manhattan_precision
value: 52.671562420866046
- type: manhattan_recall
value: 97.26443768996961
- type: max_accuracy
value: 58.123872519543
- type: max_ap
value: 61.99468883207791
- type: max_f1
value: 68.33675564681727
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: map_at_1
value: 6.428000000000001
- type: map_at_10
value: 8.883000000000001
- type: map_at_100
value: 9.549000000000001
- type: map_at_1000
value: 9.665
- type: map_at_3
value: 8.061
- type: map_at_5
value: 8.475000000000001
- type: mrr_at_1
value: 6.428000000000001
- type: mrr_at_10
value: 8.896999999999998
- type: mrr_at_100
value: 9.557
- type: mrr_at_1000
value: 9.674000000000001
- type: mrr_at_3
value: 8.061
- type: mrr_at_5
value: 8.488
- type: ndcg_at_1
value: 6.428000000000001
- type: ndcg_at_10
value: 10.382
- type: ndcg_at_100
value: 14.235999999999999
- type: ndcg_at_1000
value: 18.04
- type: ndcg_at_3
value: 8.613999999999999
- type: ndcg_at_5
value: 9.372
- type: precision_at_1
value: 6.428000000000001
- type: precision_at_10
value: 1.528
- type: precision_at_100
value: 0.349
- type: precision_at_1000
value: 0.067
- type: precision_at_3
value: 3.4070000000000005
- type: precision_at_5
value: 2.424
- type: recall_at_1
value: 6.428000000000001
- type: recall_at_10
value: 15.226999999999999
- type: recall_at_100
value: 34.694
- type: recall_at_1000
value: 66.07
- type: recall_at_3
value: 10.221
- type: recall_at_5
value: 12.065
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.541
- type: map_at_10
value: 1.1560000000000001
- type: map_at_100
value: 1.508
- type: map_at_1000
value: 1.598
- type: map_at_3
value: 0.918
- type: map_at_5
value: 0.992
- type: mrr_at_1
value: 9.5
- type: mrr_at_10
value: 13.446
- type: mrr_at_100
value: 13.935
- type: mrr_at_1000
value: 14.008999999999999
- type: mrr_at_3
value: 12.083
- type: mrr_at_5
value: 12.733
- type: ndcg_at_1
value: 5.75
- type: ndcg_at_10
value: 3.9210000000000003
- type: ndcg_at_100
value: 3.975
- type: ndcg_at_1000
value: 5.634
- type: ndcg_at_3
value: 4.87
- type: ndcg_at_5
value: 4.259
- type: precision_at_1
value: 9.5
- type: precision_at_10
value: 3.9
- type: precision_at_100
value: 1.015
- type: precision_at_1000
value: 0.297
- type: precision_at_3
value: 6.75
- type: precision_at_5
value: 5.25
- type: recall_at_1
value: 0.541
- type: recall_at_10
value: 2.228
- type: recall_at_100
value: 4.9430000000000005
- type: recall_at_1000
value: 11.661000000000001
- type: recall_at_3
value: 1.264
- type: recall_at_5
value: 1.4869999999999999
- task:
type: Classification
dataset:
type: DDSC/dkhate
name: MTEB DKHateClassification
config: default
split: test
revision: 59d12749a3c91a186063c7d729ec392fda94681c
metrics:
- type: accuracy
value: 69.96960486322187
- type: ap
value: 91.23131906690253
- type: f1
value: 57.11872970138122
- task:
type: Classification
dataset:
type: AI-Sweden/SuperLim
name: MTEB DalajClassification
config: default
split: test
revision: 7ebf0b4caa7b2ae39698a889de782c09e6f5ee56
metrics:
- type: accuracy
value: 49.75225225225225
- type: ap
value: 49.88223192425368
- type: f1
value: 49.55059044107012
- task:
type: Classification
dataset:
type: danish_political_comments
name: MTEB DanishPoliticalCommentsClassification
config: default
split: train
revision: edbb03726c04a0efab14fc8c3b8b79e4d420e5a1
metrics:
- type: accuracy
value: 37.58534554537886
- type: f1
value: 33.99440115952713
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: map_at_1
value: 0.608
- type: map_at_10
value: 0.882
- type: map_at_100
value: 0.962
- type: map_at_1000
value: 1.028
- type: map_at_3
value: 0.749
- type: map_at_5
value: 0.8240000000000001
- type: mrr_at_1
value: 2.0500000000000003
- type: mrr_at_10
value: 2.796
- type: mrr_at_100
value: 2.983
- type: mrr_at_1000
value: 3.09
- type: mrr_at_3
value: 2.483
- type: mrr_at_5
value: 2.661
- type: ndcg_at_1
value: 2.0500000000000003
- type: ndcg_at_10
value: 1.435
- type: ndcg_at_100
value: 1.991
- type: ndcg_at_1000
value: 4.961
- type: ndcg_at_3
value: 1.428
- type: ndcg_at_5
value: 1.369
- type: precision_at_1
value: 2.0500000000000003
- type: precision_at_10
value: 0.5349999999999999
- type: precision_at_100
value: 0.127
- type: precision_at_1000
value: 0.086
- type: precision_at_3
value: 1.05
- type: precision_at_5
value: 0.84
- type: recall_at_1
value: 0.608
- type: recall_at_10
value: 1.54
- type: recall_at_100
value: 3.5069999999999997
- type: recall_at_1000
value: 20.531
- type: recall_at_3
value: 0.901
- type: recall_at_5
value: 1.168
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: map_at_1
value: 3.1
- type: map_at_10
value: 4.016
- type: map_at_100
value: 4.455
- type: map_at_1000
value: 4.579
- type: map_at_3
value: 3.567
- type: map_at_5
value: 3.8019999999999996
- type: mrr_at_1
value: 3.1
- type: mrr_at_10
value: 4.016
- type: mrr_at_100
value: 4.455
- type: mrr_at_1000
value: 4.579
- type: mrr_at_3
value: 3.567
- type: mrr_at_5
value: 3.8019999999999996
- type: ndcg_at_1
value: 3.1
- type: ndcg_at_10
value: 4.684
- type: ndcg_at_100
value: 7.284
- type: ndcg_at_1000
value: 11.689
- type: ndcg_at_3
value: 3.7289999999999996
- type: ndcg_at_5
value: 4.146
- type: precision_at_1
value: 3.1
- type: precision_at_10
value: 0.69
- type: precision_at_100
value: 0.202
- type: precision_at_1000
value: 0.056999999999999995
- type: precision_at_3
value: 1.4000000000000001
- type: precision_at_5
value: 1.04
- type: recall_at_1
value: 3.1
- type: recall_at_10
value: 6.9
- type: recall_at_100
value: 20.200000000000003
- type: recall_at_1000
value: 57.3
- type: recall_at_3
value: 4.2
- type: recall_at_5
value: 5.2
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 38.285000000000004
- type: f1
value: 35.35979931355028
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.9249999999999999
- type: map_at_10
value: 1.311
- type: map_at_100
value: 1.363
- type: map_at_1000
value: 1.376
- type: map_at_3
value: 1.145
- type: map_at_5
value: 1.233
- type: mrr_at_1
value: 0.975
- type: mrr_at_10
value: 1.371
- type: mrr_at_100
value: 1.426
- type: mrr_at_1000
value: 1.439
- type: mrr_at_3
value: 1.195
- type: mrr_at_5
value: 1.286
- type: ndcg_at_1
value: 0.975
- type: ndcg_at_10
value: 1.5859999999999999
- type: ndcg_at_100
value: 1.8800000000000001
- type: ndcg_at_1000
value: 2.313
- type: ndcg_at_3
value: 1.229
- type: ndcg_at_5
value: 1.388
- type: precision_at_1
value: 0.975
- type: precision_at_10
value: 0.254
- type: precision_at_100
value: 0.041
- type: precision_at_1000
value: 0.008
- type: precision_at_3
value: 0.49
- type: precision_at_5
value: 0.375
- type: recall_at_1
value: 0.9249999999999999
- type: recall_at_10
value: 2.4250000000000003
- type: recall_at_100
value: 3.866
- type: recall_at_1000
value: 7.401000000000001
- type: recall_at_3
value: 1.4200000000000002
- type: recall_at_5
value: 1.81
- task:
type: Retrieval
dataset:
type: fiqa-pl
name: MTEB FiQA-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.959
- type: map_at_10
value: 1.952
- type: map_at_100
value: 2.281
- type: map_at_1000
value: 2.393
- type: map_at_3
value: 1.703
- type: map_at_5
value: 1.8319999999999999
- type: mrr_at_1
value: 2.469
- type: mrr_at_10
value: 4.547
- type: mrr_at_100
value: 5.021
- type: mrr_at_1000
value: 5.1339999999999995
- type: mrr_at_3
value: 3.884
- type: mrr_at_5
value: 4.223
- type: ndcg_at_1
value: 2.469
- type: ndcg_at_10
value: 3.098
- type: ndcg_at_100
value: 5.177
- type: ndcg_at_1000
value: 8.889
- type: ndcg_at_3
value: 2.7119999999999997
- type: ndcg_at_5
value: 2.8000000000000003
- type: precision_at_1
value: 2.469
- type: precision_at_10
value: 1.065
- type: precision_at_100
value: 0.321
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 2.109
- type: precision_at_5
value: 1.574
- type: recall_at_1
value: 0.959
- type: recall_at_10
value: 4.075
- type: recall_at_100
value: 12.487
- type: recall_at_1000
value: 36.854
- type: recall_at_3
value: 2.632
- type: recall_at_5
value: 3.231
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.032
- type: map_at_10
value: 1.8849999999999998
- type: map_at_100
value: 2.167
- type: map_at_1000
value: 2.266
- type: map_at_3
value: 1.609
- type: map_at_5
value: 1.7680000000000002
- type: mrr_at_1
value: 2.6229999999999998
- type: mrr_at_10
value: 4.479
- type: mrr_at_100
value: 4.92
- type: mrr_at_1000
value: 5.029999999999999
- type: mrr_at_3
value: 3.7289999999999996
- type: mrr_at_5
value: 4.138
- type: ndcg_at_1
value: 2.6229999999999998
- type: ndcg_at_10
value: 3.005
- type: ndcg_at_100
value: 5.01
- type: ndcg_at_1000
value: 8.312
- type: ndcg_at_3
value: 2.548
- type: ndcg_at_5
value: 2.735
- type: precision_at_1
value: 2.6229999999999998
- type: precision_at_10
value: 1.049
- type: precision_at_100
value: 0.31
- type: precision_at_1000
value: 0.089
- type: precision_at_3
value: 1.955
- type: precision_at_5
value: 1.574
- type: recall_at_1
value: 1.032
- type: recall_at_10
value: 3.888
- type: recall_at_100
value: 12.414
- type: recall_at_1000
value: 33.823
- type: recall_at_3
value: 2.37
- type: recall_at_5
value: 3.077
- task:
type: Retrieval
dataset:
type: jinaai/ger_da_lir
name: MTEB GerDaLIR
config: default
split: test
revision: 0bb47f1d73827e96964edb84dfe552f62f4fd5eb
metrics:
- type: map_at_1
value: 0.542
- type: map_at_10
value: 0.8130000000000001
- type: map_at_100
value: 0.898
- type: map_at_1000
value: 0.9209999999999999
- type: map_at_3
value: 0.709
- type: map_at_5
value: 0.764
- type: mrr_at_1
value: 0.594
- type: mrr_at_10
value: 0.8880000000000001
- type: mrr_at_100
value: 0.9820000000000001
- type: mrr_at_1000
value: 1.008
- type: mrr_at_3
value: 0.774
- type: mrr_at_5
value: 0.832
- type: ndcg_at_1
value: 0.594
- type: ndcg_at_10
value: 1.0030000000000001
- type: ndcg_at_100
value: 1.537
- type: ndcg_at_1000
value: 2.4330000000000003
- type: ndcg_at_3
value: 0.782
- type: ndcg_at_5
value: 0.882
- type: precision_at_1
value: 0.594
- type: precision_at_10
value: 0.16999999999999998
- type: precision_at_100
value: 0.048
- type: precision_at_1000
value: 0.013
- type: precision_at_3
value: 0.33899999999999997
- type: precision_at_5
value: 0.255
- type: recall_at_1
value: 0.542
- type: recall_at_10
value: 1.533
- type: recall_at_100
value: 4.204
- type: recall_at_1000
value: 11.574
- type: recall_at_3
value: 0.932
- type: recall_at_5
value: 1.172
- task:
type: Retrieval
dataset:
type: deepset/germandpr
name: MTEB GermanDPR
config: default
split: test
revision: 5129d02422a66be600ac89cd3e8531b4f97d347d
metrics:
- type: map_at_1
value: 25.561
- type: map_at_10
value: 38.873000000000005
- type: map_at_100
value: 40.004
- type: map_at_1000
value: 40.03
- type: map_at_3
value: 34.585
- type: map_at_5
value: 36.980000000000004
- type: mrr_at_1
value: 25.463
- type: mrr_at_10
value: 38.792
- type: mrr_at_100
value: 39.922000000000004
- type: mrr_at_1000
value: 39.949
- type: mrr_at_3
value: 34.504000000000005
- type: mrr_at_5
value: 36.899
- type: ndcg_at_1
value: 25.561
- type: ndcg_at_10
value: 46.477000000000004
- type: ndcg_at_100
value: 51.751999999999995
- type: ndcg_at_1000
value: 52.366
- type: ndcg_at_3
value: 37.645
- type: ndcg_at_5
value: 41.953
- type: precision_at_1
value: 25.561
- type: precision_at_10
value: 7.083
- type: precision_at_100
value: 0.9490000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 15.512
- type: precision_at_5
value: 11.395
- type: recall_at_1
value: 25.561
- type: recall_at_10
value: 70.829
- type: recall_at_100
value: 94.92699999999999
- type: recall_at_1000
value: 99.61
- type: recall_at_3
value: 46.537
- type: recall_at_5
value: 56.976000000000006
- task:
type: Retrieval
dataset:
type: mteb/germanquad-retrieval
name: MTEB GermanQuAD-Retrieval
config: default
split: test
revision: f5c87ae5a2e7a5106606314eef45255f03151bb3
metrics:
- type: map_at_1
value: 53.539
- type: map_at_10
value: 65.144
- type: map_at_100
value: 65.627
- type: map_at_1000
value: 65.63900000000001
- type: map_at_3
value: 62.598
- type: map_at_5
value: 64.302
- type: mrr_at_1
value: 53.539
- type: mrr_at_10
value: 65.144
- type: mrr_at_100
value: 65.627
- type: mrr_at_1000
value: 65.63900000000001
- type: mrr_at_3
value: 62.598
- type: mrr_at_5
value: 64.302
- type: ndcg_at_1
value: 53.539
- type: ndcg_at_10
value: 70.602
- type: ndcg_at_100
value: 72.886
- type: ndcg_at_1000
value: 73.14500000000001
- type: ndcg_at_3
value: 65.52900000000001
- type: ndcg_at_5
value: 68.596
- type: precision_at_1
value: 53.539
- type: precision_at_10
value: 8.757
- type: precision_at_100
value: 0.9809999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 24.667
- type: precision_at_5
value: 16.289
- type: recall_at_1
value: 53.539
- type: recall_at_10
value: 87.568
- type: recall_at_100
value: 98.09400000000001
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 74.002
- type: recall_at_5
value: 81.443
- task:
type: STS
dataset:
type: jinaai/german-STSbenchmark
name: MTEB GermanSTSBenchmark
config: default
split: test
revision: e36907544d44c3a247898ed81540310442329e20
metrics:
- type: cos_sim_pearson
value: 68.82052535790737
- type: cos_sim_spearman
value: 67.9356892072251
- type: euclidean_pearson
value: 67.2308663006278
- type: euclidean_spearman
value: 67.93572522920142
- type: manhattan_pearson
value: 67.23568952733595
- type: manhattan_spearman
value: 67.91660489262797
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.813
- type: map_at_10
value: 9.49
- type: map_at_100
value: 9.959
- type: map_at_1000
value: 10.024
- type: map_at_3
value: 8.618
- type: map_at_5
value: 9.084
- type: mrr_at_1
value: 13.626
- type: mrr_at_10
value: 17.818
- type: mrr_at_100
value: 18.412
- type: mrr_at_1000
value: 18.482000000000003
- type: mrr_at_3
value: 16.506999999999998
- type: mrr_at_5
value: 17.219
- type: ndcg_at_1
value: 13.626
- type: ndcg_at_10
value: 12.959999999999999
- type: ndcg_at_100
value: 15.562999999999999
- type: ndcg_at_1000
value: 17.571
- type: ndcg_at_3
value: 10.995000000000001
- type: ndcg_at_5
value: 11.908000000000001
- type: precision_at_1
value: 13.626
- type: precision_at_10
value: 2.995
- type: precision_at_100
value: 0.51
- type: precision_at_1000
value: 0.078
- type: precision_at_3
value: 7.000000000000001
- type: precision_at_5
value: 4.926
- type: recall_at_1
value: 6.813
- type: recall_at_10
value: 14.976
- type: recall_at_100
value: 25.517
- type: recall_at_1000
value: 39.095
- type: recall_at_3
value: 10.5
- type: recall_at_5
value: 12.316
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
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value: 38.01462100808003
- type: f1
value: 26.680357453754215
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type: Classification
dataset:
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config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
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value: 55.7508
- type: ap
value: 53.28158993124153
- type: f1
value: 55.34571379744637
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type: Classification
dataset:
type: C-MTEB/JDReview-classification
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config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
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value: 69.58724202626641
- type: ap
value: 30.04577466931377
- type: f1
value: 62.46921898313143
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type: STS
dataset:
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name: MTEB LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
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- type: cos_sim_spearman
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- type: euclidean_pearson
value: 50.055425341198934
- type: euclidean_spearman
value: 50.11024862622995
- type: manhattan_pearson
value: 50.029980024931064
- type: manhattan_spearman
value: 50.074388245963384
- task:
type: Classification
dataset:
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name: MTEB LccSentimentClassification
config: default
split: test
revision: de7ba3406ee55ea2cc52a0a41408fa6aede6d3c6
metrics:
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value: 54.266666666666666
- type: f1
value: 52.181931818742875
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dataset:
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config: default
split: test
revision: d28a029f35c4ff7f616df47b0edf54e6882395e6
metrics:
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value: 51.40745004398599
- type: mrr
value: 56.71940267335004
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dataset:
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split: dev
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
metrics:
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- type: mrr
value: 4.019047619047618
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config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
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- type: map_at_3
value: 7.757
- type: map_at_5
value: 8.373
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value: 6.046
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value: 9.251
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value: 10.044
- type: mrr_at_1000
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value: 8.028
- type: mrr_at_5
value: 8.66
- type: ndcg_at_1
value: 6.046
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- type: ndcg_at_1000
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value: 9.582
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value: 6.046
- type: precision_at_10
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- type: precision_at_100
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- type: precision_at_1000
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value: 5.826
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value: 17.291
- type: recall_at_100
value: 40.037
- type: recall_at_1000
value: 71.351
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- type: recall_at_5
value: 12.950000000000001
- task:
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dataset:
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config: default
split: dev
revision: None
metrics:
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value: 1.203
- type: map_at_10
value: 2.27
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value: 3.005
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value: 4.936
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value: 2.435
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value: 0.158
- type: precision_at_1000
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- type: precision_at_3
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- type: precision_at_5
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value: 5.332
- type: recall_at_100
value: 15.164
- type: recall_at_1000
value: 35.831
- type: recall_at_3
value: 2.622
- type: recall_at_5
value: 3.572
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type: Classification
dataset:
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config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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- type: f1
value: 89.30222882069823
- task:
type: Classification
dataset:
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name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 89.54353338968724
- type: f1
value: 88.23043644828002
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type: Classification
dataset:
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config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 89.68544977510335
- task:
type: Classification
dataset:
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config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 86.18540557469466
- type: f1
value: 85.7362674669331
- task:
type: Classification
dataset:
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config: hi
split: test
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metrics:
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dataset:
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config: th
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metrics:
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- type: f1
value: 85.57630467968119
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type: Classification
dataset:
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config: en
split: test
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metrics:
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value: 70.85043319653442
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value: 46.0794069318026
- task:
type: Classification
dataset:
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config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 73.43195266272188
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dataset:
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metrics:
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dataset:
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metrics:
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metrics:
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dataset:
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metrics:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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dataset:
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metrics:
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- task:
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dataset:
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split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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dataset:
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split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: map_at_1
value: 2.9000000000000004
- type: map_at_10
value: 3.5360000000000005
- type: map_at_100
value: 3.703
- type: map_at_1000
value: 3.734
- type: map_at_3
value: 3.167
- type: map_at_5
value: 3.322
- type: mrr_at_1
value: 2.9000000000000004
- type: mrr_at_10
value: 3.5360000000000005
- type: mrr_at_100
value: 3.703
- type: mrr_at_1000
value: 3.734
- type: mrr_at_3
value: 3.167
- type: mrr_at_5
value: 3.322
- type: ndcg_at_1
value: 2.9000000000000004
- type: ndcg_at_10
value: 4.079
- type: ndcg_at_100
value: 5.101
- type: ndcg_at_1000
value: 6.295000000000001
- type: ndcg_at_3
value: 3.276
- type: ndcg_at_5
value: 3.56
- type: precision_at_1
value: 2.9000000000000004
- type: precision_at_10
value: 0.59
- type: precision_at_100
value: 0.11199999999999999
- type: precision_at_1000
value: 0.022000000000000002
- type: precision_at_3
value: 1.2
- type: precision_at_5
value: 0.86
- type: recall_at_1
value: 2.9000000000000004
- type: recall_at_10
value: 5.8999999999999995
- type: recall_at_100
value: 11.200000000000001
- type: recall_at_1000
value: 21.5
- type: recall_at_3
value: 3.5999999999999996
- type: recall_at_5
value: 4.3
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 19.061819627060558
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 19.79520446745267
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 26.881162218991285
- type: mrr
value: 27.23201335662217
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 57.69
- type: f1
value: 57.370451927892695
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.443
- type: map_at_10
value: 1.189
- type: map_at_100
value: 2.221
- type: map_at_1000
value: 3.034
- type: map_at_3
value: 0.683
- type: map_at_5
value: 0.882
- type: mrr_at_1
value: 4.334
- type: mrr_at_10
value: 10.908
- type: mrr_at_100
value: 12.536
- type: mrr_at_1000
value: 12.642000000000001
- type: mrr_at_3
value: 7.481999999999999
- type: mrr_at_5
value: 9.324
- type: ndcg_at_1
value: 3.7150000000000003
- type: ndcg_at_10
value: 5.591
- type: ndcg_at_100
value: 9.522
- type: ndcg_at_1000
value: 19.705000000000002
- type: ndcg_at_3
value: 4.292
- type: ndcg_at_5
value: 5.038
- type: precision_at_1
value: 4.334
- type: precision_at_10
value: 5.077
- type: precision_at_100
value: 3.2910000000000004
- type: precision_at_1000
value: 1.568
- type: precision_at_3
value: 4.644
- type: precision_at_5
value: 5.139
- type: recall_at_1
value: 0.443
- type: recall_at_10
value: 3.3520000000000003
- type: recall_at_100
value: 15.515
- type: recall_at_1000
value: 50.505
- type: recall_at_3
value: 0.931
- type: recall_at_5
value: 1.698
- task:
type: Retrieval
dataset:
type: nfcorpus-pl
name: MTEB NFCorpus-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.307
- type: map_at_10
value: 0.835
- type: map_at_100
value: 1.503
- type: map_at_1000
value: 2.263
- type: map_at_3
value: 0.503
- type: map_at_5
value: 0.567
- type: mrr_at_1
value: 4.025
- type: mrr_at_10
value: 9.731
- type: mrr_at_100
value: 11.229
- type: mrr_at_1000
value: 11.34
- type: mrr_at_3
value: 6.811
- type: mrr_at_5
value: 8.126999999999999
- type: ndcg_at_1
value: 3.56
- type: ndcg_at_10
value: 4.596
- type: ndcg_at_100
value: 7.567
- type: ndcg_at_1000
value: 17.76
- type: ndcg_at_3
value: 3.52
- type: ndcg_at_5
value: 3.823
- type: precision_at_1
value: 4.025
- type: precision_at_10
value: 4.334
- type: precision_at_100
value: 2.842
- type: precision_at_1000
value: 1.506
- type: precision_at_3
value: 3.818
- type: precision_at_5
value: 4.149
- type: recall_at_1
value: 0.307
- type: recall_at_10
value: 2.543
- type: recall_at_100
value: 12.152000000000001
- type: recall_at_1000
value: 46.878
- type: recall_at_3
value: 0.755
- type: recall_at_5
value: 0.975
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.439
- type: map_at_10
value: 0.6839999999999999
- type: map_at_100
value: 0.769
- type: map_at_1000
value: 0.79
- type: map_at_3
value: 0.584
- type: map_at_5
value: 0.621
- type: mrr_at_1
value: 0.5499999999999999
- type: mrr_at_10
value: 0.819
- type: mrr_at_100
value: 0.9169999999999999
- type: mrr_at_1000
value: 0.9400000000000001
- type: mrr_at_3
value: 0.705
- type: mrr_at_5
value: 0.75
- type: ndcg_at_1
value: 0.5499999999999999
- type: ndcg_at_10
value: 0.886
- type: ndcg_at_100
value: 1.422
- type: ndcg_at_1000
value: 2.2079999999999997
- type: ndcg_at_3
value: 0.6629999999999999
- type: ndcg_at_5
value: 0.735
- type: precision_at_1
value: 0.5499999999999999
- type: precision_at_10
value: 0.16199999999999998
- type: precision_at_100
value: 0.048
- type: precision_at_1000
value: 0.012
- type: precision_at_3
value: 0.309
- type: precision_at_5
value: 0.22599999999999998
- type: recall_at_1
value: 0.439
- type: recall_at_10
value: 1.405
- type: recall_at_100
value: 4.051
- type: recall_at_1000
value: 10.487
- type: recall_at_3
value: 0.787
- type: recall_at_5
value: 0.9560000000000001
- task:
type: Retrieval
dataset:
type: narrativeqa
name: MTEB NarrativeQARetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.93
- type: map_at_10
value: 7.349
- type: map_at_100
value: 8.011
- type: map_at_1000
value: 8.351
- type: map_at_3
value: 6.787
- type: map_at_5
value: 7.02
- type: mrr_at_1
value: 5.93
- type: mrr_at_10
value: 7.349
- type: mrr_at_100
value: 8.011
- type: mrr_at_1000
value: 8.351
- type: mrr_at_3
value: 6.787
- type: mrr_at_5
value: 7.02
- type: ndcg_at_1
value: 5.93
- type: ndcg_at_10
value: 8.291
- type: ndcg_at_100
value: 12.833
- type: ndcg_at_1000
value: 21.253
- type: ndcg_at_3
value: 7.072000000000001
- type: ndcg_at_5
value: 7.495
- type: precision_at_1
value: 5.93
- type: precision_at_10
value: 1.1400000000000001
- type: precision_at_100
value: 0.359
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 2.633
- type: precision_at_5
value: 1.786
- type: recall_at_1
value: 5.93
- type: recall_at_10
value: 11.395
- type: recall_at_100
value: 35.929
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 7.9
- type: recall_at_5
value: 8.932
- task:
type: Classification
dataset:
type: ScandEval/norec-mini
name: MTEB NoRecClassification
config: default
split: test
revision: 07b99ab3363c2e7f8f87015b01c21f4d9b917ce3
metrics:
- type: accuracy
value: 48.251953125
- type: f1
value: 45.42526611578402
- task:
type: Classification
dataset:
type: strombergnlp/nordic_langid
name: MTEB NordicLangClassification
config: default
split: test
revision: e254179d18ab0165fdb6dbef91178266222bee2a
metrics:
- type: accuracy
value: 48.403333333333336
- type: f1
value: 47.9287124185198
- task:
type: BitextMining
dataset:
type: kardosdrur/norwegian-courts
name: MTEB NorwegianCourtsBitextMining
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 93.85964912280701
- type: f1
value: 92.98245614035088
- type: precision
value: 92.54385964912281
- type: recall
value: 93.85964912280701
- task:
type: Classification
dataset:
type: NbAiLab/norwegian_parliament
name: MTEB NorwegianParliament
config: default
split: test
revision: f7393532774c66312378d30b197610b43d751972
metrics:
- type: accuracy
value: 55.991666666666674
- type: ap
value: 53.417849849746226
- type: f1
value: 55.757916182475384
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 54.68327016783974
- type: cos_sim_ap
value: 55.175059616546406
- type: cos_sim_f1
value: 67.81733189500179
- type: cos_sim_precision
value: 51.41766630316249
- type: cos_sim_recall
value: 99.57761351636748
- type: dot_accuracy
value: 54.68327016783974
- type: dot_ap
value: 55.175059616546406
- type: dot_f1
value: 67.81733189500179
- type: dot_precision
value: 51.41766630316249
- type: dot_recall
value: 99.57761351636748
- type: euclidean_accuracy
value: 54.68327016783974
- type: euclidean_ap
value: 55.17510180566365
- type: euclidean_f1
value: 67.81733189500179
- type: euclidean_precision
value: 51.41766630316249
- type: euclidean_recall
value: 99.57761351636748
- type: manhattan_accuracy
value: 55.44125609095831
- type: manhattan_ap
value: 55.76283671826867
- type: manhattan_f1
value: 68.05905653583004
- type: manhattan_precision
value: 51.63934426229508
- type: manhattan_recall
value: 99.78880675818374
- type: max_accuracy
value: 55.44125609095831
- type: max_ap
value: 55.76283671826867
- type: max_f1
value: 68.05905653583004
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 75.64
- type: ap
value: 71.45085103287833
- type: f1
value: 75.52254495697326
- task:
type: Classification
dataset:
type: laugustyniak/abusive-clauses-pl
name: MTEB PAC
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 73.86620330147699
- type: ap
value: 80.58015815306322
- type: f1
value: 71.49082510883872
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_pearson
value: 29.52361689421863
- type: cos_sim_spearman
value: 32.750058577257875
- type: euclidean_pearson
value: 34.583472972871796
- type: euclidean_spearman
value: 32.75328764421994
- type: manhattan_pearson
value: 34.727366510326995
- type: manhattan_spearman
value: 32.787167142114214
- task:
type: PairClassification
dataset:
type: PL-MTEB/ppc-pairclassification
name: MTEB PPC
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 71.1
- type: cos_sim_ap
value: 85.36544548691205
- type: cos_sim_f1
value: 75.23393636930756
- type: cos_sim_precision
value: 60.36036036036037
- type: cos_sim_recall
value: 99.83443708609272
- type: dot_accuracy
value: 71.1
- type: dot_ap
value: 85.36544548691204
- type: dot_f1
value: 75.23393636930756
- type: dot_precision
value: 60.36036036036037
- type: dot_recall
value: 99.83443708609272
- type: euclidean_accuracy
value: 71.1
- type: euclidean_ap
value: 85.36544548691205
- type: euclidean_f1
value: 75.23393636930756
- type: euclidean_precision
value: 60.36036036036037
- type: euclidean_recall
value: 99.83443708609272
- type: manhattan_accuracy
value: 71.1
- type: manhattan_ap
value: 85.33853868545614
- type: manhattan_f1
value: 75.23393636930756
- type: manhattan_precision
value: 60.36036036036037
- type: manhattan_recall
value: 99.83443708609272
- type: max_accuracy
value: 71.1
- type: max_ap
value: 85.36544548691205
- type: max_f1
value: 75.23393636930756
- task:
type: PairClassification
dataset:
type: PL-MTEB/psc-pairclassification
name: MTEB PSC
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 90.81632653061224
- type: cos_sim_ap
value: 91.97693749083473
- type: cos_sim_f1
value: 85.55078683834049
- type: cos_sim_precision
value: 80.59299191374663
- type: cos_sim_recall
value: 91.15853658536585
- type: dot_accuracy
value: 90.81632653061224
- type: dot_ap
value: 91.97693749083473
- type: dot_f1
value: 85.55078683834049
- type: dot_precision
value: 80.59299191374663
- type: dot_recall
value: 91.15853658536585
- type: euclidean_accuracy
value: 90.81632653061224
- type: euclidean_ap
value: 91.97693749083473
- type: euclidean_f1
value: 85.55078683834049
- type: euclidean_precision
value: 80.59299191374663
- type: euclidean_recall
value: 91.15853658536585
- type: manhattan_accuracy
value: 90.9090909090909
- type: manhattan_ap
value: 92.043441286281
- type: manhattan_f1
value: 85.34482758620689
- type: manhattan_precision
value: 80.70652173913044
- type: manhattan_recall
value: 90.54878048780488
- type: max_accuracy
value: 90.9090909090909
- type: max_ap
value: 92.043441286281
- type: max_f1
value: 85.55078683834049
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (de)
config: de
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 70.35
- type: cos_sim_ap
value: 72.01641717127626
- type: cos_sim_f1
value: 64.49511400651467
- type: cos_sim_precision
value: 55.26315789473685
- type: cos_sim_recall
value: 77.43016759776536
- type: dot_accuracy
value: 70.35
- type: dot_ap
value: 72.06599137974572
- type: dot_f1
value: 64.49511400651467
- type: dot_precision
value: 55.26315789473685
- type: dot_recall
value: 77.43016759776536
- type: euclidean_accuracy
value: 70.35
- type: euclidean_ap
value: 71.92019289154159
- type: euclidean_f1
value: 64.49511400651467
- type: euclidean_precision
value: 55.26315789473685
- type: euclidean_recall
value: 77.43016759776536
- type: manhattan_accuracy
value: 70.35
- type: manhattan_ap
value: 71.92979188519502
- type: manhattan_f1
value: 64.60409019402202
- type: manhattan_precision
value: 60.86956521739131
- type: manhattan_recall
value: 68.8268156424581
- type: max_accuracy
value: 70.35
- type: max_ap
value: 72.06599137974572
- type: max_f1
value: 64.60409019402202
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (en)
config: en
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 71
- type: cos_sim_ap
value: 74.73017292645147
- type: cos_sim_f1
value: 66.73427991886409
- type: cos_sim_precision
value: 61.78403755868545
- type: cos_sim_recall
value: 72.54685777287762
- type: dot_accuracy
value: 71
- type: dot_ap
value: 74.73017292645147
- type: dot_f1
value: 66.73427991886409
- type: dot_precision
value: 61.78403755868545
- type: dot_recall
value: 72.54685777287762
- type: euclidean_accuracy
value: 71
- type: euclidean_ap
value: 74.73013082197343
- type: euclidean_f1
value: 66.73427991886409
- type: euclidean_precision
value: 61.78403755868545
- type: euclidean_recall
value: 72.54685777287762
- type: manhattan_accuracy
value: 70.95
- type: manhattan_ap
value: 74.71203917486744
- type: manhattan_f1
value: 66.86868686868686
- type: manhattan_precision
value: 61.696178937558244
- type: manhattan_recall
value: 72.98787210584344
- type: max_accuracy
value: 71
- type: max_ap
value: 74.73017292645147
- type: max_f1
value: 66.86868686868686
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (es)
config: es
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 67.7
- type: cos_sim_ap
value: 69.70320170421651
- type: cos_sim_f1
value: 62.55625562556255
- type: cos_sim_precision
value: 52.851711026615966
- type: cos_sim_recall
value: 76.62624035281146
- type: dot_accuracy
value: 67.7
- type: dot_ap
value: 69.70320170421651
- type: dot_f1
value: 62.55625562556255
- type: dot_precision
value: 52.851711026615966
- type: dot_recall
value: 76.62624035281146
- type: euclidean_accuracy
value: 67.7
- type: euclidean_ap
value: 69.70320170421651
- type: euclidean_f1
value: 62.55625562556255
- type: euclidean_precision
value: 52.851711026615966
- type: euclidean_recall
value: 76.62624035281146
- type: manhattan_accuracy
value: 67.75
- type: manhattan_ap
value: 69.67833816050764
- type: manhattan_f1
value: 62.734082397003746
- type: manhattan_precision
value: 54.515866558177386
- type: manhattan_recall
value: 73.8699007717751
- type: max_accuracy
value: 67.75
- type: max_ap
value: 69.70320170421651
- type: max_f1
value: 62.734082397003746
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (fr)
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 69
- type: cos_sim_ap
value: 71.36406639969131
- type: cos_sim_f1
value: 64.45993031358886
- type: cos_sim_precision
value: 53.12275664034458
- type: cos_sim_recall
value: 81.94905869324474
- type: dot_accuracy
value: 69
- type: dot_ap
value: 71.2599779415656
- type: dot_f1
value: 64.45993031358886
- type: dot_precision
value: 53.12275664034458
- type: dot_recall
value: 81.94905869324474
- type: euclidean_accuracy
value: 69
- type: euclidean_ap
value: 71.3126257271965
- type: euclidean_f1
value: 64.45993031358886
- type: euclidean_precision
value: 53.12275664034458
- type: euclidean_recall
value: 81.94905869324474
- type: manhattan_accuracy
value: 69
- type: manhattan_ap
value: 71.29361764028188
- type: manhattan_f1
value: 64.54789615040288
- type: manhattan_precision
value: 54.16979714500376
- type: manhattan_recall
value: 79.84496124031007
- type: max_accuracy
value: 69
- type: max_ap
value: 71.36406639969131
- type: max_f1
value: 64.54789615040288
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (ja)
config: ja
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 63.849999999999994
- type: cos_sim_ap
value: 60.914955950361026
- type: cos_sim_f1
value: 62.4556422995032
- type: cos_sim_precision
value: 45.47803617571059
- type: cos_sim_recall
value: 99.66024915062289
- type: dot_accuracy
value: 63.849999999999994
- type: dot_ap
value: 60.808056565465506
- type: dot_f1
value: 62.4556422995032
- type: dot_precision
value: 45.47803617571059
- type: dot_recall
value: 99.66024915062289
- type: euclidean_accuracy
value: 63.849999999999994
- type: euclidean_ap
value: 60.8231492677072
- type: euclidean_f1
value: 62.4556422995032
- type: euclidean_precision
value: 45.47803617571059
- type: euclidean_recall
value: 99.66024915062289
- type: manhattan_accuracy
value: 63.800000000000004
- type: manhattan_ap
value: 60.86392751846975
- type: manhattan_f1
value: 62.43348705214614
- type: manhattan_precision
value: 45.45454545454545
- type: manhattan_recall
value: 99.66024915062289
- type: max_accuracy
value: 63.849999999999994
- type: max_ap
value: 60.914955950361026
- type: max_f1
value: 62.4556422995032
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (ko)
config: ko
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 61.1
- type: cos_sim_ap
value: 58.40339411735916
- type: cos_sim_f1
value: 62.7906976744186
- type: cos_sim_precision
value: 46.55172413793103
- type: cos_sim_recall
value: 96.42857142857143
- type: dot_accuracy
value: 61.1
- type: dot_ap
value: 58.439189685586456
- type: dot_f1
value: 62.7906976744186
- type: dot_precision
value: 46.55172413793103
- type: dot_recall
value: 96.42857142857143
- type: euclidean_accuracy
value: 61.1
- type: euclidean_ap
value: 58.34968788203145
- type: euclidean_f1
value: 62.7906976744186
- type: euclidean_precision
value: 46.55172413793103
- type: euclidean_recall
value: 96.42857142857143
- type: manhattan_accuracy
value: 61.1
- type: manhattan_ap
value: 58.31504446861402
- type: manhattan_f1
value: 62.636562272396226
- type: manhattan_precision
value: 46.48648648648649
- type: manhattan_recall
value: 95.98214285714286
- type: max_accuracy
value: 61.1
- type: max_ap
value: 58.439189685586456
- type: max_f1
value: 62.7906976744186
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (zh)
config: zh
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 64.2
- type: cos_sim_ap
value: 63.73722153283802
- type: cos_sim_f1
value: 62.52707581227437
- type: cos_sim_precision
value: 46.16204690831556
- type: cos_sim_recall
value: 96.86800894854586
- type: dot_accuracy
value: 64.2
- type: dot_ap
value: 63.67335241021108
- type: dot_f1
value: 62.52707581227437
- type: dot_precision
value: 46.16204690831556
- type: dot_recall
value: 96.86800894854586
- type: euclidean_accuracy
value: 64.2
- type: euclidean_ap
value: 63.77399571117368
- type: euclidean_f1
value: 62.52707581227437
- type: euclidean_precision
value: 46.16204690831556
- type: euclidean_recall
value: 96.86800894854586
- type: manhattan_accuracy
value: 64.5
- type: manhattan_ap
value: 63.747406783360816
- type: manhattan_f1
value: 62.58601955813112
- type: manhattan_precision
value: 46.27745045527584
- type: manhattan_recall
value: 96.64429530201343
- type: max_accuracy
value: 64.5
- type: max_ap
value: 63.77399571117368
- type: max_f1
value: 62.58601955813112
- task:
type: Classification
dataset:
type: PL-MTEB/polemo2_in
name: MTEB PolEmo2.0-IN
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 52.797783933518005
- type: f1
value: 53.84971294048786
- task:
type: Classification
dataset:
type: PL-MTEB/polemo2_out
name: MTEB PolEmo2.0-OUT
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 38.40080971659919
- type: f1
value: 30.38990873840624
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_pearson
value: 23.34232568997104
- type: cos_sim_spearman
value: 24.47961936211083
- type: euclidean_pearson
value: 22.03140944610336
- type: euclidean_spearman
value: 24.47949166265398
- type: manhattan_pearson
value: 25.542406448726908
- type: manhattan_spearman
value: 28.655724283839533
- task:
type: Retrieval
dataset:
type: quora-pl
name: MTEB Quora-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 59.938
- type: map_at_10
value: 72.734
- type: map_at_100
value: 73.564
- type: map_at_1000
value: 73.602
- type: map_at_3
value: 69.707
- type: map_at_5
value: 71.515
- type: mrr_at_1
value: 69.28
- type: mrr_at_10
value: 76.97500000000001
- type: mrr_at_100
value: 77.27199999999999
- type: mrr_at_1000
value: 77.28
- type: mrr_at_3
value: 75.355
- type: mrr_at_5
value: 76.389
- type: ndcg_at_1
value: 69.33
- type: ndcg_at_10
value: 77.61099999999999
- type: ndcg_at_100
value: 80.02
- type: ndcg_at_1000
value: 80.487
- type: ndcg_at_3
value: 73.764
- type: ndcg_at_5
value: 75.723
- type: precision_at_1
value: 69.33
- type: precision_at_10
value: 11.917
- type: precision_at_100
value: 1.447
- type: precision_at_1000
value: 0.154
- type: precision_at_3
value: 32.29
- type: precision_at_5
value: 21.432000000000002
- type: recall_at_1
value: 59.938
- type: recall_at_10
value: 87.252
- type: recall_at_100
value: 96.612
- type: recall_at_1000
value: 99.388
- type: recall_at_3
value: 76.264
- type: recall_at_5
value: 81.71000000000001
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 61.458999999999996
- type: map_at_10
value: 73.90299999999999
- type: map_at_100
value: 74.733
- type: map_at_1000
value: 74.771
- type: map_at_3
value: 70.999
- type: map_at_5
value: 72.745
- type: mrr_at_1
value: 70.93
- type: mrr_at_10
value: 78.353
- type: mrr_at_100
value: 78.636
- type: mrr_at_1000
value: 78.644
- type: mrr_at_3
value: 76.908
- type: mrr_at_5
value: 77.807
- type: ndcg_at_1
value: 70.93
- type: ndcg_at_10
value: 78.625
- type: ndcg_at_100
value: 81.01
- type: ndcg_at_1000
value: 81.45700000000001
- type: ndcg_at_3
value: 75.045
- type: ndcg_at_5
value: 76.84299999999999
- type: precision_at_1
value: 70.93
- type: precision_at_10
value: 11.953
- type: precision_at_100
value: 1.4489999999999998
- type: precision_at_1000
value: 0.154
- type: precision_at_3
value: 32.65
- type: precision_at_5
value: 21.598
- type: recall_at_1
value: 61.458999999999996
- type: recall_at_10
value: 87.608
- type: recall_at_100
value: 96.818
- type: recall_at_1000
value: 99.445
- type: recall_at_3
value: 77.354
- type: recall_at_5
value: 82.334
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 28.519889100999958
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 38.62765374782771
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.52
- type: map_at_10
value: 0.893
- type: map_at_100
value: 1.113
- type: map_at_1000
value: 1.304
- type: map_at_3
value: 0.7779999999999999
- type: map_at_5
value: 0.8200000000000001
- type: mrr_at_1
value: 2.6
- type: mrr_at_10
value: 4.0680000000000005
- type: mrr_at_100
value: 4.6080000000000005
- type: mrr_at_1000
value: 4.797
- type: mrr_at_3
value: 3.5999999999999996
- type: mrr_at_5
value: 3.8150000000000004
- type: ndcg_at_1
value: 2.6
- type: ndcg_at_10
value: 1.79
- type: ndcg_at_100
value: 3.5549999999999997
- type: ndcg_at_1000
value: 9.942
- type: ndcg_at_3
value: 1.94
- type: ndcg_at_5
value: 1.543
- type: precision_at_1
value: 2.6
- type: precision_at_10
value: 0.8500000000000001
- type: precision_at_100
value: 0.361
- type: precision_at_1000
value: 0.197
- type: precision_at_3
value: 1.7670000000000001
- type: precision_at_5
value: 1.26
- type: recall_at_1
value: 0.52
- type: recall_at_10
value: 1.7149999999999999
- type: recall_at_100
value: 7.318
- type: recall_at_1000
value: 39.915
- type: recall_at_3
value: 1.0699999999999998
- type: recall_at_5
value: 1.27
- task:
type: Retrieval
dataset:
type: scidocs-pl
name: MTEB SCIDOCS-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.32
- type: map_at_10
value: 0.676
- type: map_at_100
value: 0.847
- type: map_at_1000
value: 1.032
- type: map_at_3
value: 0.5369999999999999
- type: map_at_5
value: 0.592
- type: mrr_at_1
value: 1.6
- type: mrr_at_10
value: 2.863
- type: mrr_at_100
value: 3.334
- type: mrr_at_1000
value: 3.5479999999999996
- type: mrr_at_3
value: 2.317
- type: mrr_at_5
value: 2.587
- type: ndcg_at_1
value: 1.6
- type: ndcg_at_10
value: 1.397
- type: ndcg_at_100
value: 2.819
- type: ndcg_at_1000
value: 9.349
- type: ndcg_at_3
value: 1.3
- type: ndcg_at_5
value: 1.1079999999999999
- type: precision_at_1
value: 1.6
- type: precision_at_10
value: 0.74
- type: precision_at_100
value: 0.295
- type: precision_at_1000
value: 0.194
- type: precision_at_3
value: 1.2
- type: precision_at_5
value: 0.96
- type: recall_at_1
value: 0.32
- type: recall_at_10
value: 1.505
- type: recall_at_100
value: 5.988
- type: recall_at_1000
value: 39.308
- type: recall_at_3
value: 0.72
- type: recall_at_5
value: 0.9650000000000001
- task:
type: PairClassification
dataset:
type: PL-MTEB/sicke-pl-pairclassification
name: MTEB SICK-E-PL
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 73.84834896045659
- type: cos_sim_ap
value: 55.484124732566606
- type: cos_sim_f1
value: 57.34228187919464
- type: cos_sim_precision
value: 46.01464885825076
- type: cos_sim_recall
value: 76.06837606837607
- type: dot_accuracy
value: 73.84834896045659
- type: dot_ap
value: 55.48400003295399
- type: dot_f1
value: 57.34228187919464
- type: dot_precision
value: 46.01464885825076
- type: dot_recall
value: 76.06837606837607
- type: euclidean_accuracy
value: 73.84834896045659
- type: euclidean_ap
value: 55.48407331902175
- type: euclidean_f1
value: 57.34228187919464
- type: euclidean_precision
value: 46.01464885825076
- type: euclidean_recall
value: 76.06837606837607
- type: manhattan_accuracy
value: 73.80758255197716
- type: manhattan_ap
value: 55.42477275597209
- type: manhattan_f1
value: 57.55860953920776
- type: manhattan_precision
value: 46.29388816644994
- type: manhattan_recall
value: 76.06837606837607
- type: max_accuracy
value: 73.84834896045659
- type: max_ap
value: 55.484124732566606
- type: max_f1
value: 57.55860953920776
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 67.03943120783973
- type: cos_sim_spearman
value: 62.93971145260584
- type: euclidean_pearson
value: 64.13947263916926
- type: euclidean_spearman
value: 62.93972324235839
- type: manhattan_pearson
value: 64.11295322654566
- type: manhattan_spearman
value: 62.92816122293202
- task:
type: STS
dataset:
type: PL-MTEB/sickr-pl-sts
name: MTEB SICK-R-PL
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 67.75034167381077
- type: cos_sim_spearman
value: 62.98158872758643
- type: euclidean_pearson
value: 64.25794794439082
- type: euclidean_spearman
value: 62.981566596223125
- type: manhattan_pearson
value: 64.25439446502435
- type: manhattan_spearman
value: 63.01301439900365
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 61.622204530882755
- type: cos_sim_spearman
value: 65.4632047656541
- type: euclidean_pearson
value: 59.21529585527598
- type: euclidean_spearman
value: 65.4638163967956
- type: manhattan_pearson
value: 59.39341472707122
- type: manhattan_spearman
value: 65.57635757250173
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 60.329743331971486
- type: cos_sim_spearman
value: 62.78607195958339
- type: euclidean_pearson
value: 62.07415212138581
- type: euclidean_spearman
value: 62.78618151904129
- type: manhattan_pearson
value: 62.41250554765521
- type: manhattan_spearman
value: 62.87580558029627
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 59.16277512775291
- type: cos_sim_spearman
value: 57.53693422381856
- type: euclidean_pearson
value: 57.85017283427473
- type: euclidean_spearman
value: 57.53697385589326
- type: manhattan_pearson
value: 58.049796184955596
- type: manhattan_spearman
value: 57.76174789162225
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 74.42588553600197
- type: cos_sim_spearman
value: 74.25087788257943
- type: euclidean_pearson
value: 73.35436018935222
- type: euclidean_spearman
value: 74.25087694991477
- type: manhattan_pearson
value: 73.33747415771185
- type: manhattan_spearman
value: 74.21504509447377
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 75.77242432372144
- type: cos_sim_spearman
value: 75.72930700521489
- type: euclidean_pearson
value: 75.6995220623788
- type: euclidean_spearman
value: 75.72930646047212
- type: manhattan_pearson
value: 75.65841087952896
- type: manhattan_spearman
value: 75.69567692328437
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 66.2495297342053
- type: cos_sim_spearman
value: 66.14124319602982
- type: euclidean_pearson
value: 66.49498096178358
- type: euclidean_spearman
value: 66.14121792287747
- type: manhattan_pearson
value: 66.51560623835172
- type: manhattan_spearman
value: 66.05794413582558
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.0045186560239
- type: cos_sim_spearman
value: 74.96504390762252
- type: euclidean_pearson
value: 74.20988464347049
- type: euclidean_spearman
value: 74.98114602301776
- type: manhattan_pearson
value: 74.37929169860529
- type: manhattan_spearman
value: 75.37049827509504
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 73.88478151514396
- type: cos_sim_spearman
value: 74.05322141272103
- type: euclidean_pearson
value: 73.52175483343693
- type: euclidean_spearman
value: 74.05322141272103
- type: manhattan_pearson
value: 73.35875118828287
- type: manhattan_spearman
value: 73.83972625384673
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.57014781622605
- type: cos_sim_spearman
value: 74.95329129562734
- type: euclidean_pearson
value: 75.5667786729257
- type: euclidean_spearman
value: 74.95329129562734
- type: manhattan_pearson
value: 75.39548673816147
- type: manhattan_spearman
value: 74.89428642503749
- 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: 80.04007129652777
- type: cos_sim_spearman
value: 79.94429611477106
- type: euclidean_pearson
value: 79.91583070858822
- type: euclidean_spearman
value: 79.94429611477106
- type: manhattan_pearson
value: 80.14382273152769
- type: manhattan_spearman
value: 80.23862855392836
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 77.28740870194635
- type: cos_sim_spearman
value: 77.18286391819586
- type: euclidean_pearson
value: 77.05644328687119
- type: euclidean_spearman
value: 77.18286391819586
- type: manhattan_pearson
value: 77.15625898067294
- type: manhattan_spearman
value: 77.03165154316278
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.99293002371301
- type: cos_sim_spearman
value: 72.24657859872468
- type: euclidean_pearson
value: 73.38839879755461
- type: euclidean_spearman
value: 72.24657859872468
- type: manhattan_pearson
value: 73.6627728800822
- type: manhattan_spearman
value: 72.70893449698669
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 81.37213723705916
- type: cos_sim_spearman
value: 80.64548512701263
- type: euclidean_pearson
value: 80.94992193351284
- type: euclidean_spearman
value: 80.64484963200427
- type: manhattan_pearson
value: 80.92246813841794
- type: manhattan_spearman
value: 80.68860823161657
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 77.54059604962391
- type: cos_sim_spearman
value: 77.19559169700682
- type: euclidean_pearson
value: 77.32739821317861
- type: euclidean_spearman
value: 77.19559169700682
- type: manhattan_pearson
value: 77.29224328831437
- type: manhattan_spearman
value: 77.24394878313191
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.06397062195414
- type: cos_sim_spearman
value: 78.66694637555244
- type: euclidean_pearson
value: 79.34923290885872
- type: euclidean_spearman
value: 78.66694637555244
- type: manhattan_pearson
value: 79.50802161625809
- type: manhattan_spearman
value: 78.79195213396169
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 78.66045829245238
- type: cos_sim_spearman
value: 78.14055373851183
- type: euclidean_pearson
value: 78.94489279300518
- type: euclidean_spearman
value: 78.14055373851183
- type: manhattan_pearson
value: 79.33473165536323
- type: manhattan_spearman
value: 78.5783429705299
- 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: 36.63454535818336
- type: cos_sim_spearman
value: 47.12016162570126
- type: euclidean_pearson
value: 39.07268779927362
- type: euclidean_spearman
value: 47.12016162570126
- type: manhattan_pearson
value: 41.723119770725944
- type: manhattan_spearman
value: 47.90334362422989
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 13.325547358617957
- type: cos_sim_spearman
value: 24.094051740693416
- type: euclidean_pearson
value: 10.39110006005262
- type: euclidean_spearman
value: 24.094051740693416
- type: manhattan_pearson
value: 12.4380555005162
- type: manhattan_spearman
value: 25.176800279885715
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 41.21281570342249
- type: cos_sim_spearman
value: 55.397885077207974
- type: euclidean_pearson
value: 43.96150945976646
- type: euclidean_spearman
value: 55.397885077207974
- type: manhattan_pearson
value: 49.58812224529121
- type: manhattan_spearman
value: 55.35874879475974
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 5.985012243744998
- type: cos_sim_spearman
value: 25.307464943919012
- type: euclidean_pearson
value: -4.080537702499046
- type: euclidean_spearman
value: 25.307464943919012
- type: manhattan_pearson
value: -2.5058642304196543
- type: manhattan_spearman
value: 26.751588484373233
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 34.44666578772084
- type: cos_sim_spearman
value: 46.45977141800899
- type: euclidean_pearson
value: 38.78305544036559
- type: euclidean_spearman
value: 46.45977141800899
- type: manhattan_pearson
value: 46.45101297876112
- type: manhattan_spearman
value: 50.642972694093814
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 28.095327083873055
- type: cos_sim_spearman
value: 40.24741745875892
- type: euclidean_pearson
value: 29.141496784653892
- type: euclidean_spearman
value: 40.24741745875892
- type: manhattan_pearson
value: 32.013290716034064
- type: manhattan_spearman
value: 40.85454084311211
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 27.46788309503312
- type: cos_sim_spearman
value: 43.57385391855994
- type: euclidean_pearson
value: 24.558349674326177
- type: euclidean_spearman
value: 43.57385391855994
- type: manhattan_pearson
value: 28.974505207055866
- type: manhattan_spearman
value: 44.111553205713
- 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: 34.87841073990563
- type: cos_sim_spearman
value: 52.8221686505807
- type: euclidean_pearson
value: 38.36114580544504
- type: euclidean_spearman
value: 52.8221686505807
- type: manhattan_pearson
value: 46.69329448756753
- type: manhattan_spearman
value: 53.9140781097337
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 49.999267528357
- type: cos_sim_spearman
value: 61.71837669697145
- type: euclidean_pearson
value: 53.578476744372274
- type: euclidean_spearman
value: 61.71837669697145
- type: manhattan_pearson
value: 56.410294227490795
- type: manhattan_spearman
value: 60.684457655864875
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 22.43564137760586
- type: cos_sim_spearman
value: 34.28346144104183
- type: euclidean_pearson
value: 27.41326011184764
- type: euclidean_spearman
value: 34.28346144104183
- type: manhattan_pearson
value: 35.62923154232163
- type: manhattan_spearman
value: 37.937151135297185
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 44.34071611983998
- type: cos_sim_spearman
value: 57.823185616169646
- type: euclidean_pearson
value: 49.29310650157244
- type: euclidean_spearman
value: 57.823185616169646
- type: manhattan_pearson
value: 55.93298736518848
- type: manhattan_spearman
value: 58.57556581684834
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 56.07027840344927
- type: cos_sim_spearman
value: 62.20158260763411
- type: euclidean_pearson
value: 55.887969718543616
- type: euclidean_spearman
value: 62.20158260763411
- type: manhattan_pearson
value: 56.081533365738444
- type: manhattan_spearman
value: 62.018651361750685
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 41.41816324477061
- type: cos_sim_spearman
value: 44.71684955996943
- type: euclidean_pearson
value: 42.74585025834968
- type: euclidean_spearman
value: 44.71684955996943
- type: manhattan_pearson
value: 47.992481632815256
- type: manhattan_spearman
value: 46.18587933349126
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 38.89140730917917
- type: cos_sim_spearman
value: 49.18633779347391
- type: euclidean_pearson
value: 43.27605428753535
- type: euclidean_spearman
value: 49.18633779347391
- type: manhattan_pearson
value: 48.22046568809415
- type: manhattan_spearman
value: 49.248416391249464
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 40.31620568726327
- type: cos_sim_spearman
value: 49.13034440774138
- type: euclidean_pearson
value: 43.95169508285692
- type: euclidean_spearman
value: 49.13034440774138
- type: manhattan_pearson
value: 48.84250981398146
- type: manhattan_spearman
value: 49.54216339903405
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 27.074582378144058
- type: cos_sim_spearman
value: 41.29498619968451
- type: euclidean_pearson
value: 28.993986097276505
- type: euclidean_spearman
value: 41.29498619968451
- type: manhattan_pearson
value: 32.079813951133254
- type: manhattan_spearman
value: 43.664111732941464
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 6.864334110072116
- type: cos_sim_spearman
value: 25.805458732687914
- type: euclidean_pearson
value: 11.435920047618103
- type: euclidean_spearman
value: 25.805458732687914
- type: manhattan_pearson
value: 15.036308569899552
- type: manhattan_spearman
value: 25.405135387192757
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 65.44029549925597
- type: cos_sim_spearman
value: 61.97797868009122
- type: euclidean_pearson
value: 65.92740669959876
- type: euclidean_spearman
value: 61.97797868009122
- type: manhattan_pearson
value: 70.29575044091207
- type: manhattan_spearman
value: 73.24670207647144
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cos_sim_pearson
value: 51.35413149349556
- type: cos_sim_spearman
value: 50.175051356729924
- type: euclidean_pearson
value: 53.12039152785364
- type: euclidean_spearman
value: 50.174289111089685
- type: manhattan_pearson
value: 53.0731746793555
- type: manhattan_spearman
value: 50.15176393928403
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 67.84222983023291
- type: cos_sim_spearman
value: 67.39086924655895
- type: euclidean_pearson
value: 67.3393327127967
- type: euclidean_spearman
value: 67.39088047106472
- type: manhattan_pearson
value: 67.40316731822271
- type: manhattan_spearman
value: 67.49067800994015
- task:
type: Classification
dataset:
type: ScandEval/scala-da
name: MTEB ScalaDaClassification
config: default
split: test
revision: 1de08520a7b361e92ffa2a2201ebd41942c54675
metrics:
- type: accuracy
value: 50.62988281250001
- type: ap
value: 50.32274824114816
- type: f1
value: 50.37741703766756
- task:
type: Classification
dataset:
type: ScandEval/scala-nb
name: MTEB ScalaNbClassification
config: default
split: test
revision: 237111a078ad5a834a55c57803d40bbe410ed03b
metrics:
- type: accuracy
value: 51.181640625
- type: ap
value: 50.60884394099696
- type: f1
value: 50.866988720930415
- task:
type: Classification
dataset:
type: ScandEval/scala-nn
name: MTEB ScalaNnClassification
config: default
split: test
revision: 9d9a2a4092ed3cacf0744592f6d2f32ab8ef4c0b
metrics:
- type: accuracy
value: 50.9375
- type: ap
value: 50.47969135089731
- type: f1
value: 50.62913552324756
- task:
type: Classification
dataset:
type: ScandEval/scala-sv
name: MTEB ScalaSvClassification
config: default
split: test
revision: 1b48e3dcb02872335ff985ff938a054a4ed99008
metrics:
- type: accuracy
value: 51.1474609375
- type: ap
value: 50.5894187272385
- type: f1
value: 50.901812392367916
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 68.36051662289248
- type: mrr
value: 89.39224265204656
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.721999999999998
- type: map_at_10
value: 31.335
- type: map_at_100
value: 32.461
- type: map_at_1000
value: 32.557
- type: map_at_3
value: 29.282000000000004
- type: map_at_5
value: 30.602
- type: mrr_at_1
value: 24.667
- type: mrr_at_10
value: 32.363
- type: mrr_at_100
value: 33.421
- type: mrr_at_1000
value: 33.499
- type: mrr_at_3
value: 30.444
- type: mrr_at_5
value: 31.628
- type: ndcg_at_1
value: 24.667
- type: ndcg_at_10
value: 35.29
- type: ndcg_at_100
value: 40.665
- type: ndcg_at_1000
value: 43.241
- type: ndcg_at_3
value: 31.238
- type: ndcg_at_5
value: 33.486
- type: precision_at_1
value: 24.667
- type: precision_at_10
value: 5.1
- type: precision_at_100
value: 0.7969999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.667
- type: precision_at_5
value: 8.933
- type: recall_at_1
value: 23.721999999999998
- type: recall_at_10
value: 46.417
- type: recall_at_100
value: 70.944
- type: recall_at_1000
value: 91.033
- type: recall_at_3
value: 35.693999999999996
- type: recall_at_5
value: 40.944
- task:
type: Retrieval
dataset:
type: scifact-pl
name: MTEB SciFact-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.706
- type: map_at_10
value: 28.333000000000002
- type: map_at_100
value: 29.364
- type: map_at_1000
value: 29.451
- type: map_at_3
value: 26.112999999999996
- type: map_at_5
value: 27.502
- type: mrr_at_1
value: 23
- type: mrr_at_10
value: 29.555999999999997
- type: mrr_at_100
value: 30.536
- type: mrr_at_1000
value: 30.606
- type: mrr_at_3
value: 27.333000000000002
- type: mrr_at_5
value: 28.717
- type: ndcg_at_1
value: 23
- type: ndcg_at_10
value: 32.238
- type: ndcg_at_100
value: 37.785999999999994
- type: ndcg_at_1000
value: 40.266999999999996
- type: ndcg_at_3
value: 27.961000000000002
- type: ndcg_at_5
value: 30.322
- type: precision_at_1
value: 23
- type: precision_at_10
value: 4.7669999999999995
- type: precision_at_100
value: 0.787
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_3
value: 11.444
- type: precision_at_5
value: 8.200000000000001
- type: recall_at_1
value: 21.706
- type: recall_at_10
value: 43.206
- type: recall_at_100
value: 69.678
- type: recall_at_1000
value: 89.333
- type: recall_at_3
value: 31.900000000000002
- type: recall_at_5
value: 37.594
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.5
- type: cos_sim_ap
value: 77.07584309978081
- type: cos_sim_f1
value: 71.8864950078823
- type: cos_sim_precision
value: 75.74750830564784
- type: cos_sim_recall
value: 68.4
- type: dot_accuracy
value: 99.5
- type: dot_ap
value: 77.07584309978081
- type: dot_f1
value: 71.8864950078823
- type: dot_precision
value: 75.74750830564784
- type: dot_recall
value: 68.4
- type: euclidean_accuracy
value: 99.5
- type: euclidean_ap
value: 77.07584309978081
- type: euclidean_f1
value: 71.8864950078823
- type: euclidean_precision
value: 75.74750830564784
- type: euclidean_recall
value: 68.4
- type: manhattan_accuracy
value: 99.50594059405941
- type: manhattan_ap
value: 77.41658577240027
- type: manhattan_f1
value: 71.91374663072777
- type: manhattan_precision
value: 78.01169590643275
- type: manhattan_recall
value: 66.7
- type: max_accuracy
value: 99.50594059405941
- type: max_ap
value: 77.41658577240027
- type: max_f1
value: 71.91374663072777
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 46.32521494308228
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 20.573273825125266
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 38.612724125942385
- type: mrr
value: 38.891130315762666
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.305330424238836
- type: cos_sim_spearman
value: 30.556621737388685
- type: dot_pearson
value: 29.30533056265583
- type: dot_spearman
value: 30.556621737388685
- task:
type: Classification
dataset:
type: ScandEval/swerec-mini
name: MTEB SweRecClassification
config: default
split: test
revision: 3c62f26bafdc4c4e1c16401ad4b32f0a94b46612
metrics:
- type: accuracy
value: 68.4716796875
- type: f1
value: 59.865730786092364
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 55.34794621490011
- type: mrr
value: 59.22764129348421
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: map_at_1
value: 0.586
- type: map_at_10
value: 0.819
- type: map_at_100
value: 0.8920000000000001
- type: map_at_1000
value: 0.928
- type: map_at_3
value: 0.729
- type: map_at_5
value: 0.771
- type: mrr_at_1
value: 1.9949999999999999
- type: mrr_at_10
value: 2.608
- type: mrr_at_100
value: 2.771
- type: mrr_at_1000
value: 2.8289999999999997
- type: mrr_at_3
value: 2.365
- type: mrr_at_5
value: 2.483
- type: ndcg_at_1
value: 1.9949999999999999
- type: ndcg_at_10
value: 1.314
- type: ndcg_at_100
value: 1.831
- type: ndcg_at_1000
value: 3.4139999999999997
- type: ndcg_at_3
value: 1.377
- type: ndcg_at_5
value: 1.2630000000000001
- type: precision_at_1
value: 1.9949999999999999
- type: precision_at_10
value: 0.488
- type: precision_at_100
value: 0.123
- type: precision_at_1000
value: 0.054
- type: precision_at_3
value: 1.027
- type: precision_at_5
value: 0.737
- type: recall_at_1
value: 0.586
- type: recall_at_10
value: 1.3390000000000002
- type: recall_at_100
value: 3.15
- type: recall_at_1000
value: 11.859
- type: recall_at_3
value: 0.8710000000000001
- type: recall_at_5
value: 1.0290000000000001
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 40.946
- type: f1
value: 39.56517169731474
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.08499999999999999
- type: map_at_10
value: 0.462
- type: map_at_100
value: 0.893
- type: map_at_1000
value: 1.129
- type: map_at_3
value: 0.232
- type: map_at_5
value: 0.3
- type: mrr_at_1
value: 38
- type: mrr_at_10
value: 50.629999999999995
- type: mrr_at_100
value: 51.315999999999995
- type: mrr_at_1000
value: 51.365
- type: mrr_at_3
value: 47
- type: mrr_at_5
value: 48.9
- type: ndcg_at_1
value: 31
- type: ndcg_at_10
value: 24.823
- type: ndcg_at_100
value: 10.583
- type: ndcg_at_1000
value: 6.497999999999999
- type: ndcg_at_3
value: 30.95
- type: ndcg_at_5
value: 27.899
- type: precision_at_1
value: 38
- type: precision_at_10
value: 25.6
- type: precision_at_100
value: 8.98
- type: precision_at_1000
value: 2.248
- type: precision_at_3
value: 34.666999999999994
- type: precision_at_5
value: 29.599999999999998
- type: recall_at_1
value: 0.08499999999999999
- type: recall_at_10
value: 0.641
- type: recall_at_100
value: 2.002
- type: recall_at_1000
value: 4.902
- type: recall_at_3
value: 0.28200000000000003
- type: recall_at_5
value: 0.379
- task:
type: Retrieval
dataset:
type: trec-covid-pl
name: MTEB TRECCOVID-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.124
- type: map_at_10
value: 0.45199999999999996
- type: map_at_100
value: 0.874
- type: map_at_1000
value: 1.1039999999999999
- type: map_at_3
value: 0.253
- type: map_at_5
value: 0.32299999999999995
- type: mrr_at_1
value: 36
- type: mrr_at_10
value: 47.56
- type: mrr_at_100
value: 48.532
- type: mrr_at_1000
value: 48.579
- type: mrr_at_3
value: 45
- type: mrr_at_5
value: 45.5
- type: ndcg_at_1
value: 34
- type: ndcg_at_10
value: 24.529
- type: ndcg_at_100
value: 10.427
- type: ndcg_at_1000
value: 6.457
- type: ndcg_at_3
value: 31.173000000000002
- type: ndcg_at_5
value: 27.738000000000003
- type: precision_at_1
value: 38
- type: precision_at_10
value: 25.4
- type: precision_at_100
value: 8.88
- type: precision_at_1000
value: 2.2159999999999997
- type: precision_at_3
value: 34.666999999999994
- type: precision_at_5
value: 29.2
- type: recall_at_1
value: 0.124
- type: recall_at_10
value: 0.618
- type: recall_at_100
value: 1.9349999999999998
- type: recall_at_1000
value: 4.808
- type: recall_at_3
value: 0.28300000000000003
- type: recall_at_5
value: 0.382
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (sqi-eng)
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.9
- type: f1
value: 98.55000000000001
- type: precision
value: 98.38333333333334
- type: recall
value: 98.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fry-eng)
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.3179190751445
- type: f1
value: 59.44582071749702
- type: precision
value: 57.49678869621066
- type: recall
value: 65.3179190751445
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kur-eng)
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 38.53658536585366
- type: f1
value: 34.217555952803785
- type: precision
value: 32.96511296649355
- type: recall
value: 38.53658536585366
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tur-eng)
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.7
- type: f1
value: 98.26666666666665
- type: precision
value: 98.05
- type: recall
value: 98.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (deu-eng)
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.3
- type: f1
value: 99.13333333333333
- type: precision
value: 99.05000000000001
- type: recall
value: 99.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nld-eng)
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.89999999999999
- type: f1
value: 97.2
- type: precision
value: 96.85000000000001
- type: recall
value: 97.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ron-eng)
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.6
- type: precision
value: 97.3
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ang-eng)
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 52.23880597014925
- type: f1
value: 46.340992406389105
- type: precision
value: 44.556384742951906
- type: recall
value: 52.23880597014925
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ido-eng)
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95
- type: f1
value: 93.67000000000002
- type: precision
value: 93.075
- type: recall
value: 95
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jav-eng)
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.29268292682927
- type: f1
value: 85.76422764227642
- type: precision
value: 84.84204413472706
- type: recall
value: 88.29268292682927
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (isl-eng)
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.2
- type: f1
value: 96.46666666666667
- type: precision
value: 96.1
- type: recall
value: 97.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slv-eng)
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.8408262454435
- type: f1
value: 95.9902794653706
- type: precision
value: 95.56500607533415
- type: recall
value: 96.8408262454435
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cym-eng)
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.3913043478261
- type: f1
value: 91.30434782608695
- type: precision
value: 90.28985507246377
- type: recall
value: 93.3913043478261
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kaz-eng)
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.6086956521739
- type: f1
value: 88.1159420289855
- type: precision
value: 86.9623188405797
- type: recall
value: 90.6086956521739
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (est-eng)
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.16666666666667
- type: precision
value: 96.86666666666667
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (heb-eng)
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94
- type: f1
value: 92.34
- type: precision
value: 91.54166666666667
- type: recall
value: 94
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gla-eng)
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.92159227985525
- type: f1
value: 80.8868975817106
- type: precision
value: 79.11540008041817
- type: recall
value: 84.92159227985525
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mar-eng)
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.35
- type: precision
value: 92.58333333333334
- type: recall
value: 94.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lat-eng)
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 43.3
- type: f1
value: 36.64473116255726
- type: precision
value: 34.64017752457381
- type: recall
value: 43.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bel-eng)
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.7
- type: f1
value: 95.68333333333332
- type: precision
value: 95.19999999999999
- type: recall
value: 96.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pms-eng)
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.47619047619048
- type: f1
value: 66.63032734461306
- type: precision
value: 65.46459191863879
- type: recall
value: 70.47619047619048
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gle-eng)
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.5
- type: f1
value: 91.63
- type: precision
value: 90.75
- type: recall
value: 93.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pes-eng)
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.5
- type: f1
value: 94.36666666666666
- type: precision
value: 93.83333333333333
- type: recall
value: 95.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nob-eng)
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.3
- type: f1
value: 99.06666666666666
- type: precision
value: 98.95
- type: recall
value: 99.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bul-eng)
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.8
- type: f1
value: 94.51666666666667
- type: precision
value: 93.88333333333334
- type: recall
value: 95.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cbk-eng)
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84
- type: f1
value: 80.46675324675326
- type: precision
value: 78.95999999999998
- type: recall
value: 84
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hun-eng)
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 96.93333333333332
- type: precision
value: 96.55
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uig-eng)
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.10000000000001
- type: f1
value: 90.07333333333334
- type: precision
value: 89.16166666666668
- type: recall
value: 92.10000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (rus-eng)
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.6
- type: f1
value: 94.35
- type: precision
value: 93.75
- type: recall
value: 95.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (spa-eng)
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.9
- type: f1
value: 98.53333333333335
- type: precision
value: 98.35000000000001
- type: recall
value: 98.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hye-eng)
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.22641509433963
- type: f1
value: 95.14824797843666
- type: precision
value: 94.60916442048517
- type: recall
value: 96.22641509433963
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tel-eng)
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.58974358974359
- type: f1
value: 91.59544159544159
- type: precision
value: 90.66951566951566
- type: recall
value: 93.58974358974359
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (afr-eng)
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.1
- type: f1
value: 97.46666666666668
- type: precision
value: 97.15
- type: recall
value: 98.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mon-eng)
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.4090909090909
- type: f1
value: 91.5909090909091
- type: precision
value: 90.71969696969697
- type: recall
value: 93.4090909090909
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arz-eng)
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.51781970649894
- type: f1
value: 86.76150544075072
- type: precision
value: 85.55206149545772
- type: recall
value: 89.51781970649894
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hrv-eng)
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.65
- type: precision
value: 97.38333333333333
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nov-eng)
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.87548638132296
- type: f1
value: 71.24698906800073
- type: precision
value: 69.66572338167668
- type: recall
value: 75.87548638132296
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gsw-eng)
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 61.53846153846154
- type: f1
value: 54.83234714003944
- type: precision
value: 52.06552706552707
- type: recall
value: 61.53846153846154
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nds-eng)
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.199999999999996
- type: f1
value: 54.183211233211225
- type: precision
value: 52.48751719986241
- type: recall
value: 59.199999999999996
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ukr-eng)
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.6
- type: f1
value: 94.3
- type: precision
value: 93.65
- type: recall
value: 95.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uzb-eng)
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.85046728971963
- type: f1
value: 85.25700934579439
- type: precision
value: 84.09267912772586
- type: recall
value: 87.85046728971963
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lit-eng)
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98
- type: f1
value: 97.43333333333332
- type: precision
value: 97.15
- type: recall
value: 98
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ina-eng)
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.8
- type: f1
value: 88.66055555555555
- type: precision
value: 87.81845238095238
- type: recall
value: 90.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lfn-eng)
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.6
- type: f1
value: 65.538895353013
- type: precision
value: 63.69531394330308
- type: recall
value: 70.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (zsm-eng)
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.89999999999999
- type: f1
value: 96.06666666666668
- type: precision
value: 95.68333333333334
- type: recall
value: 96.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ita-eng)
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.8
- type: f1
value: 95.95
- type: precision
value: 95.55
- type: recall
value: 96.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cmn-eng)
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 93.8
- type: precision
value: 93.13333333333334
- type: recall
value: 95.19999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lvs-eng)
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.5
- type: f1
value: 95.45
- type: precision
value: 94.93333333333334
- type: recall
value: 96.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (glg-eng)
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.89999999999999
- type: f1
value: 97.28333333333332
- type: precision
value: 96.98333333333333
- type: recall
value: 97.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ceb-eng)
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.16666666666666
- type: f1
value: 74.67336721249764
- type: precision
value: 73.26035353535354
- type: recall
value: 78.16666666666666
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bre-eng)
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 11.200000000000001
- type: f1
value: 8.48123815073815
- type: precision
value: 7.843657708032708
- type: recall
value: 11.200000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ben-eng)
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.3
- type: f1
value: 89.02333333333333
- type: precision
value: 87.97500000000001
- type: recall
value: 91.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swg-eng)
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.32142857142857
- type: f1
value: 67.69209956709956
- type: precision
value: 66.19047619047619
- type: recall
value: 72.32142857142857
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arq-eng)
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.69264544456641
- type: f1
value: 75.40693115885212
- type: precision
value: 73.67544822539335
- type: recall
value: 79.69264544456641
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kab-eng)
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.8
- type: f1
value: 83.65666666666667
- type: precision
value: 82.24833333333333
- type: recall
value: 86.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fra-eng)
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.36666666666666
- type: precision
value: 94.86666666666666
- type: recall
value: 96.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (por-eng)
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.3
- type: f1
value: 95.49
- type: precision
value: 95.10833333333333
- type: recall
value: 96.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tat-eng)
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.60000000000001
- type: f1
value: 87.04746031746032
- type: precision
value: 85.89583333333333
- type: recall
value: 89.60000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (oci-eng)
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.9
- type: f1
value: 84.57088023088022
- type: precision
value: 83.6475
- type: recall
value: 86.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pol-eng)
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.7
- type: precision
value: 97.46666666666668
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (war-eng)
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.39999999999999
- type: f1
value: 82.83333333333333
- type: precision
value: 81.80137426900586
- type: recall
value: 85.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (aze-eng)
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 89.11999999999999
- type: precision
value: 88.12777777777778
- type: recall
value: 91.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (vie-eng)
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.16666666666669
- type: precision
value: 96.85000000000001
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nno-eng)
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.89999999999999
- type: f1
value: 97.30666666666666
- type: precision
value: 97.02499999999999
- type: recall
value: 97.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cha-eng)
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 27.00729927007299
- type: f1
value: 25.114895917815623
- type: precision
value: 24.602283361407448
- type: recall
value: 27.00729927007299
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mhr-eng)
config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 14.099999999999998
- type: f1
value: 11.869284007509814
- type: precision
value: 11.199695454818405
- type: recall
value: 14.099999999999998
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dan-eng)
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 97.09
- type: precision
value: 96.80833333333332
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ell-eng)
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.5
- type: f1
value: 95.47333333333333
- type: precision
value: 94.975
- type: recall
value: 96.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (amh-eng)
config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.45238095238095
- type: f1
value: 91.66666666666666
- type: precision
value: 90.77380952380952
- type: recall
value: 93.45238095238095
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pam-eng)
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 11.899999999999999
- type: f1
value: 10.303261315113037
- type: precision
value: 9.902986584515606
- type: recall
value: 11.899999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hsb-eng)
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.57349896480332
- type: f1
value: 77.86519438693352
- type: precision
value: 76.35595081247254
- type: recall
value: 81.57349896480332
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (srp-eng)
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 94.86666666666667
- type: precision
value: 94.25
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (epo-eng)
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.8
- type: f1
value: 98.46666666666667
- type: precision
value: 98.3
- type: recall
value: 98.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kzj-eng)
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 10.7
- type: f1
value: 8.621683883854935
- type: precision
value: 8.188292731521031
- type: recall
value: 10.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (awa-eng)
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.47619047619048
- type: f1
value: 87.8581735724593
- type: precision
value: 86.72438672438673
- type: recall
value: 90.47619047619048
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fao-eng)
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.0381679389313
- type: f1
value: 93.60050890585242
- type: precision
value: 92.970737913486
- type: recall
value: 95.0381679389313
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mal-eng)
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2532751091703
- type: f1
value: 97.67103347889375
- type: precision
value: 97.37991266375546
- type: recall
value: 98.2532751091703
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ile-eng)
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.6
- type: f1
value: 80.99904761904763
- type: precision
value: 79.54634920634919
- type: recall
value: 84.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bos-eng)
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.89265536723164
- type: f1
value: 95.90395480225989
- type: precision
value: 95.4331450094162
- type: recall
value: 96.89265536723164
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cor-eng)
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 12.6
- type: f1
value: 9.981918087824628
- type: precision
value: 9.326319147606549
- type: recall
value: 12.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cat-eng)
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.65
- type: precision
value: 96.28333333333333
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (eus-eng)
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.5
- type: f1
value: 95.38333333333333
- type: precision
value: 94.83333333333333
- type: recall
value: 96.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yue-eng)
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.8
- type: f1
value: 88.43666666666665
- type: precision
value: 87.395
- type: recall
value: 90.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swe-eng)
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 97.03333333333333
- type: precision
value: 96.71666666666667
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dtp-eng)
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.4
- type: f1
value: 7.946889105220061
- type: precision
value: 7.665059865752875
- type: recall
value: 9.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kat-eng)
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.04021447721179
- type: f1
value: 93.68632707774799
- type: precision
value: 93.08534405719392
- type: recall
value: 95.04021447721179
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jpn-eng)
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.89999999999999
- type: f1
value: 94.66666666666667
- type: precision
value: 94.08333333333334
- type: recall
value: 95.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (csb-eng)
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.6086956521739
- type: f1
value: 77.98418972332016
- type: precision
value: 75.96837944664031
- type: recall
value: 82.6086956521739
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (xho-eng)
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.77464788732394
- type: f1
value: 94.8356807511737
- type: precision
value: 94.36619718309859
- type: recall
value: 95.77464788732394
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (orv-eng)
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 53.17365269461077
- type: f1
value: 47.07043056743655
- type: precision
value: 45.161363241830784
- type: recall
value: 53.17365269461077
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ind-eng)
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.5
- type: f1
value: 94.5
- type: precision
value: 94.03333333333333
- type: recall
value: 95.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tuk-eng)
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.59605911330048
- type: f1
value: 91.82266009852216
- type: precision
value: 91.09195402298852
- type: recall
value: 93.59605911330048
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (max-eng)
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.40845070422534
- type: f1
value: 72.73082942097027
- type: precision
value: 71.46686939820742
- type: recall
value: 76.40845070422534
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swh-eng)
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.58974358974359
- type: f1
value: 91.98290598290598
- type: precision
value: 91.3119658119658
- type: recall
value: 93.58974358974359
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hin-eng)
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.06666666666668
- type: precision
value: 96.7
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dsb-eng)
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.89352818371609
- type: f1
value: 64.47860652453555
- type: precision
value: 62.878651918592574
- type: recall
value: 68.89352818371609
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ber-eng)
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 33.800000000000004
- type: f1
value: 29.290774344112368
- type: precision
value: 28.066016735704647
- type: recall
value: 33.800000000000004
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tam-eng)
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.22801302931596
- type: f1
value: 88.07817589576547
- type: precision
value: 87.171552660152
- type: recall
value: 90.22801302931596
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slk-eng)
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.63333333333334
- type: precision
value: 97.36666666666667
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tgl-eng)
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 96.95
- type: precision
value: 96.58333333333331
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ast-eng)
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.91338582677166
- type: f1
value: 90.81364829396327
- type: precision
value: 89.89501312335958
- type: recall
value: 92.91338582677166
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mkd-eng)
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.89999999999999
- type: f1
value: 95.98333333333332
- type: precision
value: 95.56666666666668
- type: recall
value: 96.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (khm-eng)
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.51523545706371
- type: f1
value: 70.20346919931407
- type: precision
value: 68.6389565788895
- type: recall
value: 74.51523545706371
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ces-eng)
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.6
- type: f1
value: 96.88333333333333
- type: precision
value: 96.53333333333333
- type: recall
value: 97.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tzl-eng)
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.15384615384615
- type: f1
value: 39.47885447885448
- type: precision
value: 37.301528599605525
- type: recall
value: 46.15384615384615
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (urd-eng)
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.69999999999999
- type: f1
value: 93.16666666666667
- type: precision
value: 92.41666666666667
- type: recall
value: 94.69999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ara-eng)
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 93.83333333333333
- type: precision
value: 93.16666666666667
- type: recall
value: 95.19999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kor-eng)
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92
- type: f1
value: 89.98666666666666
- type: precision
value: 89.09166666666667
- type: recall
value: 92
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yid-eng)
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.51886792452831
- type: f1
value: 94.3003144654088
- type: precision
value: 93.75
- type: recall
value: 95.51886792452831
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fin-eng)
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.83333333333333
- type: precision
value: 97.65
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tha-eng)
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.8978102189781
- type: f1
value: 96.04622871046227
- type: precision
value: 95.62043795620438
- type: recall
value: 96.8978102189781
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (wuu-eng)
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.1
- type: f1
value: 81.78564213564214
- type: precision
value: 80.46416666666667
- type: recall
value: 85.1
- task:
type: Clustering
dataset:
type: slvnwhrl/tenkgnad-clustering-p2p
name: MTEB TenKGnadClusteringP2P
config: default
split: test
revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558
metrics:
- type: v_measure
value: 21.827519839402644
- task:
type: Clustering
dataset:
type: slvnwhrl/tenkgnad-clustering-s2s
name: MTEB TenKGnadClusteringS2S
config: default
split: test
revision: 6cddbe003f12b9b140aec477b583ac4191f01786
metrics:
- type: v_measure
value: 27.160188241713684
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 38.54459276932986
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 43.4460576234314
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.20500000000000002
- type: map_at_10
value: 0.391
- type: map_at_100
value: 0.612
- type: map_at_1000
value: 0.645
- type: map_at_3
value: 0.302
- type: map_at_5
value: 0.383
- type: mrr_at_1
value: 4.082
- type: mrr_at_10
value: 5.612
- type: mrr_at_100
value: 6.822
- type: mrr_at_1000
value: 6.929
- type: mrr_at_3
value: 4.082
- type: mrr_at_5
value: 5.408
- type: ndcg_at_1
value: 4.082
- type: ndcg_at_10
value: 1.6840000000000002
- type: ndcg_at_100
value: 2.876
- type: ndcg_at_1000
value: 4.114
- type: ndcg_at_3
value: 2.52
- type: ndcg_at_5
value: 2.3720000000000003
- type: precision_at_1
value: 4.082
- type: precision_at_10
value: 1.429
- type: precision_at_100
value: 0.755
- type: precision_at_1000
value: 0.18
- type: precision_at_3
value: 2.041
- type: precision_at_5
value: 2.4490000000000003
- type: recall_at_1
value: 0.20500000000000002
- type: recall_at_10
value: 0.761
- type: recall_at_100
value: 4.423
- type: recall_at_1000
value: 9.044
- type: recall_at_3
value: 0.302
- type: recall_at_5
value: 0.683
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 67.28359999999999
- type: ap
value: 12.424592214862038
- type: f1
value: 51.53630450055703
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.23372948500284
- type: f1
value: 56.440924587214234
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 24.410059815620116
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 80.3302139834297
- type: cos_sim_ap
value: 53.57723069745093
- type: cos_sim_f1
value: 51.58639580004565
- type: cos_sim_precision
value: 45.45454545454545
- type: cos_sim_recall
value: 59.63060686015831
- type: dot_accuracy
value: 80.3302139834297
- type: dot_ap
value: 53.57723006705641
- type: dot_f1
value: 51.58639580004565
- type: dot_precision
value: 45.45454545454545
- type: dot_recall
value: 59.63060686015831
- type: euclidean_accuracy
value: 80.3302139834297
- type: euclidean_ap
value: 53.57723050286929
- type: euclidean_f1
value: 51.58639580004565
- type: euclidean_precision
value: 45.45454545454545
- type: euclidean_recall
value: 59.63060686015831
- type: manhattan_accuracy
value: 80.31233235977827
- type: manhattan_ap
value: 53.44943961562638
- type: manhattan_f1
value: 51.24183006535947
- type: manhattan_precision
value: 43.63636363636363
- type: manhattan_recall
value: 62.05804749340369
- type: max_accuracy
value: 80.3302139834297
- type: max_ap
value: 53.57723069745093
- type: max_f1
value: 51.58639580004565
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.45876508712695
- type: cos_sim_ap
value: 83.5320716566614
- type: cos_sim_f1
value: 75.54560716284276
- type: cos_sim_precision
value: 73.27929362379678
- type: cos_sim_recall
value: 77.95657530027718
- type: dot_accuracy
value: 87.45876508712695
- type: dot_ap
value: 83.53209944887666
- type: dot_f1
value: 75.54560716284276
- type: dot_precision
value: 73.27929362379678
- type: dot_recall
value: 77.95657530027718
- type: euclidean_accuracy
value: 87.45876508712695
- type: euclidean_ap
value: 83.53205938307582
- type: euclidean_f1
value: 75.54560716284276
- type: euclidean_precision
value: 73.27929362379678
- type: euclidean_recall
value: 77.95657530027718
- type: manhattan_accuracy
value: 87.52280048123569
- type: manhattan_ap
value: 83.4884324728773
- type: manhattan_f1
value: 75.43366677906411
- type: manhattan_precision
value: 73.46566445303948
- type: manhattan_recall
value: 77.51000923929782
- type: max_accuracy
value: 87.52280048123569
- type: max_ap
value: 83.53209944887666
- type: max_f1
value: 75.54560716284276
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: map_at_1
value: 13.100000000000001
- type: map_at_10
value: 15.620000000000001
- type: map_at_100
value: 15.928
- type: map_at_1000
value: 15.976
- type: map_at_3
value: 14.817
- type: map_at_5
value: 15.322
- type: mrr_at_1
value: 13
- type: mrr_at_10
value: 15.57
- type: mrr_at_100
value: 15.878
- type: mrr_at_1000
value: 15.926000000000002
- type: mrr_at_3
value: 14.767
- type: mrr_at_5
value: 15.272
- type: ndcg_at_1
value: 13.100000000000001
- type: ndcg_at_10
value: 17.05
- type: ndcg_at_100
value: 18.801000000000002
- type: ndcg_at_1000
value: 20.436
- type: ndcg_at_3
value: 15.425
- type: ndcg_at_5
value: 16.333000000000002
- type: precision_at_1
value: 13.100000000000001
- type: precision_at_10
value: 2.16
- type: precision_at_100
value: 0.304
- type: precision_at_1000
value: 0.044000000000000004
- type: precision_at_3
value: 5.733
- type: precision_at_5
value: 3.88
- type: recall_at_1
value: 13.100000000000001
- type: recall_at_10
value: 21.6
- type: recall_at_100
value: 30.4
- type: recall_at_1000
value: 44.1
- type: recall_at_3
value: 17.2
- type: recall_at_5
value: 19.400000000000002
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 76.12
- type: ap
value: 54.1619589378045
- type: f1
value: 74.32372858884229
- task:
type: Clustering
dataset:
type: jinaai/cities_wiki_clustering
name: MTEB WikiCitiesClustering
config: default
split: test
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
metrics:
- type: v_measure
value: 50.71744674029636
- task:
type: Retrieval
dataset:
type: jinaai/xmarket_de
name: MTEB XMarketDE
config: default
split: test
revision: 2336818db4c06570fcdf263e1bcb9993b786f67a
metrics:
- type: map_at_1
value: 0.182
- type: map_at_10
value: 0.266
- type: map_at_100
value: 0.295
- type: map_at_1000
value: 0.313
- type: map_at_3
value: 0.232
- type: map_at_5
value: 0.23800000000000002
- type: mrr_at_1
value: 1.3379999999999999
- type: mrr_at_10
value: 1.918
- type: mrr_at_100
value: 2.051
- type: mrr_at_1000
value: 2.084
- type: mrr_at_3
value: 1.7049999999999998
- type: mrr_at_5
value: 1.791
- type: ndcg_at_1
value: 1.3379999999999999
- type: ndcg_at_10
value: 0.859
- type: ndcg_at_100
value: 0.8500000000000001
- type: ndcg_at_1000
value: 1.345
- type: ndcg_at_3
value: 1.032
- type: ndcg_at_5
value: 0.918
- type: precision_at_1
value: 1.3379999999999999
- type: precision_at_10
value: 0.528
- type: precision_at_100
value: 0.22699999999999998
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 0.8829999999999999
- type: precision_at_5
value: 0.6890000000000001
- type: recall_at_1
value: 0.182
- type: recall_at_10
value: 0.51
- type: recall_at_100
value: 1.2229999999999999
- type: recall_at_1000
value: 4.183
- type: recall_at_3
value: 0.292
- type: recall_at_5
value: 0.315
SONAR
We introduce SONAR, a new multilingual and multimodal fixed-size sentence embedding space, with a full suite of speech and text encoders and decoders. It substantially outperforms existing sentence embeddings such as LASER3 and LabSE on the xsim and xsim++ multilingual similarity search tasks.
Speech segments can be embedded in the same SONAR embedding space using language-specific speech encoders trained in a teacher-student setting on speech transcription data. We also provide a single text decoder, which allows us to perform text-to-text and speech-to-text machine translation, including for zero-shot language and modality combinations.
SONAR stands for Sentence-level multimOdal and laNguage-Agnostic Representations
The full list of supported languages (along with download links) can be found here below.
Installing
SONAR depends mainly on Fairseq2 and can be installed using (tested with python=3.8)
pip install --upgrade pip
pip config set global.extra-index-url https://test.pypi.org/simple/
pip install -e .
Usage
fairseq2 will automatically download models into your $TORCH_HOME/hub directory upon using the commands below.
Compute text sentence embeddings with SONAR:
from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline
t2vec_model = TextToEmbeddingModelPipeline(encoder="text_sonar_basic_encoder",
tokenizer="text_sonar_basic_encoder")
sentences = ['My name is SONAR.', 'I can embed the sentences into vectorial space.']
t2vec_model.predict(sentences, source_lang="eng_Latn").shape
# torch.Size([2, 1024])
Translate text with SONAR
from sonar.inference_pipelines.text import TextToTextModelPipeline
t2t_model = TextToTextModelPipeline(encoder="text_sonar_basic_encoder",
decoder="text_sonar_basic_decoder",
tokenizer="text_sonar_basic_encoder") # tokenizer is attached to both encoder and decoder cards
sentences = ['My name is SONAR.', 'I can embed the sentences into vectorial space.']
t2t_model.predict(sentences, source_lang="eng_Latn", target_lang="fra_Latn")
# ['Mon nom est SONAR.', "Je peux intégrer les phrases dans l'espace vectoriel."]
Compute speech sentence embeddings with SONAR
from sonar.inference_pipelines.speech import SpeechToEmbeddingModelPipeline
s2vec_model = SpeechToEmbeddingModelPipeline(encoder="sonar_speech_encoder_eng")
s2vec_model.predict(["./tests/integration_tests/data/audio_files/audio_1.wav",
"./tests/integration_tests/data/audio_files/audio_2.wav"]).shape
# torch.Size([2, 1024])
import torchaudio
inp, sr = torchaudio.load("./tests/integration_tests/data/audio_files/audio_1.wav")
assert sr == 16000, "Sample rate should be 16kHz"
s2vec_model.predict([inp]).shape
# torch.Size([1, 1024])
Speech-to-text translation with SONAR
from sonar.inference_pipelines.speech import SpeechToTextModelPipeline
s2t_model = SpeechToTextModelPipeline(encoder="sonar_speech_encoder_eng",
decoder="text_sonar_basic_decoder",
tokenizer="text_sonar_basic_decoder")
import torchaudio
inp, sr = torchaudio.load("./tests/integration_tests/data/audio_files/audio_1.wav")
assert sr == 16000, "Sample rate should be 16kHz"
# passing loaded audio files
s2t_model.predict([inp], target_lang="eng_Latn")
# ['Television reports show white smoke coming from the plant.']
# passing multiple wav files
s2t_model.predict(["./tests/integration_tests/data/audio_files/audio_1.wav",
"./tests/integration_tests/data/audio_files/audio_2.wav"], target_lang="eng_Latn")
# ['Television reports show white smoke coming from the plant.',
# 'These couples may choose to make an adoption plan for their baby.']
Predicting cross-lingual semantic similarity with BLASER 2 models
import torch
from sonar.models.blaser.loader import load_blaser_model
blaser_ref = load_blaser_model("blaser_st2st_ref_v2_0").eval()
blaser_qe = load_blaser_model("blaser_st2st_qe_v2_0").eval()
# BLASER-2 is supposed to work with SONAR speech and text embeddings,
# but we didn't include their extraction in this snippet, to keep it simple.
emb = torch.ones([1, 1024])
print(blaser_ref(src=emb, ref=emb, mt=emb).item()) # 5.2552
print(blaser_qe(src=emb, mt=emb).item()) # 4.9819
See more complete demo notebooks :
Model details
- Developed by: Paul-Ambroise Duquenne et al.
- License: CC-BY-NC 4.0 license
- Cite as:
@article{Duquenne:2023:sonar_arxiv,
author = {Paul-Ambroise Duquenne and Holger Schwenk and Benoit Sagot},
title = {{SONAR:} Sentence-Level Multimodal and Language-Agnostic Representations},
publisher = {arXiv},
year = {2023},
url = {https://arxiv.org/abs/unk},
}