intfloat_multilingual-e5-large-instruct
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tags:
- mteb
- sentence-transformers
- transformers model-index:
- name: multilingual-e5-large-instruct
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy value: 76.23880597014924
- type: ap value: 39.07351965022687
- type: f1 value: 70.04836733862683
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy value: 66.71306209850107
- type: ap value: 79.01499914759529
- type: f1 value: 64.81951817560703
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy value: 73.85307346326837
- type: ap value: 22.447519885878737
- type: f1 value: 61.0162730745633
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy value: 76.04925053533191
- type: ap value: 23.44983217128922
- type: f1 value: 62.5723230907759
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy value: 96.28742500000001
- type: ap value: 94.8449918887462
- type: f1 value: 96.28680923610432
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 56.716
- type: f1 value: 55.76510398266401
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 52.99999999999999
- type: f1 value: 52.00829994765178
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 48.806000000000004
- type: f1 value: 48.082345914983634
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 48.507999999999996
- type: f1 value: 47.68752844642045
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 47.709999999999994
- type: f1 value: 47.05870376637181
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 44.662000000000006
- type: f1 value: 43.42371965372771
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 31.721
- type: map_at_10 value: 49.221
- type: map_at_100 value: 49.884
- type: map_at_1000 value: 49.888
- type: map_at_3 value: 44.31
- type: map_at_5 value: 47.276
- type: mrr_at_1 value: 32.432
- type: mrr_at_10 value: 49.5
- type: mrr_at_100 value: 50.163000000000004
- type: mrr_at_1000 value: 50.166
- type: mrr_at_3 value: 44.618
- type: mrr_at_5 value: 47.541
- type: ndcg_at_1 value: 31.721
- type: ndcg_at_10 value: 58.384
- type: ndcg_at_100 value: 61.111000000000004
- type: ndcg_at_1000 value: 61.187999999999995
- type: ndcg_at_3 value: 48.386
- type: ndcg_at_5 value: 53.708999999999996
- type: precision_at_1 value: 31.721
- type: precision_at_10 value: 8.741
- type: precision_at_100 value: 0.991
- type: precision_at_1000 value: 0.1
- type: precision_at_3 value: 20.057
- type: precision_at_5 value: 14.609
- type: recall_at_1 value: 31.721
- type: recall_at_10 value: 87.411
- type: recall_at_100 value: 99.075
- type: recall_at_1000 value: 99.644
- type: recall_at_3 value: 60.171
- type: recall_at_5 value: 73.044
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure value: 46.40419580759799
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure value: 40.48593255007969
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map value: 63.889179122289995
- type: mrr value: 77.61146286769556
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson value: 88.15075203727929
- type: cos_sim_spearman value: 86.9622224570873
- type: euclidean_pearson value: 86.70473853624121
- type: euclidean_spearman value: 86.9622224570873
- type: manhattan_pearson value: 86.21089380980065
- type: manhattan_spearman value: 86.75318154937008
- 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: 99.65553235908142
- type: f1 value: 99.60681976339595
- type: precision value: 99.58246346555325
- type: recall value: 99.65553235908142
- 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: 99.26260180497468
- type: f1 value: 99.14520507740848
- type: precision value: 99.08650671362535
- type: recall value: 99.26260180497468
- 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: 98.07412538967787
- type: f1 value: 97.86629719431936
- type: precision value: 97.76238309664012
- type: recall value: 98.07412538967787
- 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: 99.42074776197998
- type: f1 value: 99.38564156573635
- type: precision value: 99.36808846761454
- type: recall value: 99.42074776197998
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy value: 85.73376623376623
- type: f1 value: 85.68480707214599
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure value: 40.935218072113855
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure value: 36.276389017675264
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 27.764166666666668
- type: map_at_10 value: 37.298166666666674
- type: map_at_100 value: 38.530166666666666
- type: map_at_1000 value: 38.64416666666667
- type: map_at_3 value: 34.484833333333334
- type: map_at_5 value: 36.0385
- type: mrr_at_1 value: 32.93558333333333
- type: mrr_at_10 value: 41.589749999999995
- type: mrr_at_100 value: 42.425333333333334
- type: mrr_at_1000 value: 42.476333333333336
- type: mrr_at_3 value: 39.26825
- type: mrr_at_5 value: 40.567083333333336
- type: ndcg_at_1 value: 32.93558333333333
- type: ndcg_at_10 value: 42.706583333333334
- type: ndcg_at_100 value: 47.82483333333333
- type: ndcg_at_1000 value: 49.95733333333334
- type: ndcg_at_3 value: 38.064750000000004
- type: ndcg_at_5 value: 40.18158333333333
- type: precision_at_1 value: 32.93558333333333
- type: precision_at_10 value: 7.459833333333334
- type: precision_at_100 value: 1.1830833333333335
- type: precision_at_1000 value: 0.15608333333333332
- type: precision_at_3 value: 17.5235
- type: precision_at_5 value: 12.349833333333333
- type: recall_at_1 value: 27.764166666666668
- type: recall_at_10 value: 54.31775
- type: recall_at_100 value: 76.74350000000001
- type: recall_at_1000 value: 91.45208333333332
- type: recall_at_3 value: 41.23425
- type: recall_at_5 value: 46.73983333333334
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 12.969
- type: map_at_10 value: 21.584999999999997
- type: map_at_100 value: 23.3
- type: map_at_1000 value: 23.5
- type: map_at_3 value: 18.218999999999998
- type: map_at_5 value: 19.983
- type: mrr_at_1 value: 29.316
- type: mrr_at_10 value: 40.033
- type: mrr_at_100 value: 40.96
- type: mrr_at_1000 value: 41.001
- type: mrr_at_3 value: 37.123
- type: mrr_at_5 value: 38.757999999999996
- type: ndcg_at_1 value: 29.316
- type: ndcg_at_10 value: 29.858
- type: ndcg_at_100 value: 36.756
- type: ndcg_at_1000 value: 40.245999999999995
- type: ndcg_at_3 value: 24.822
- type: ndcg_at_5 value: 26.565
- type: precision_at_1 value: 29.316
- type: precision_at_10 value: 9.186
- type: precision_at_100 value: 1.6549999999999998
- type: precision_at_1000 value: 0.22999999999999998
- type: precision_at_3 value: 18.436
- type: precision_at_5 value: 13.876
- type: recall_at_1 value: 12.969
- type: recall_at_10 value: 35.142
- type: recall_at_100 value: 59.143
- type: recall_at_1000 value: 78.594
- type: recall_at_3 value: 22.604
- type: recall_at_5 value: 27.883000000000003
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 8.527999999999999
- type: map_at_10 value: 17.974999999999998
- type: map_at_100 value: 25.665
- type: map_at_1000 value: 27.406000000000002
- type: map_at_3 value: 13.017999999999999
- type: map_at_5 value: 15.137
- type: mrr_at_1 value: 62.5
- type: mrr_at_10 value: 71.891
- type: mrr_at_100 value: 72.294
- type: mrr_at_1000 value: 72.296
- type: mrr_at_3 value: 69.958
- type: mrr_at_5 value: 71.121
- type: ndcg_at_1 value: 50.875
- type: ndcg_at_10 value: 38.36
- type: ndcg_at_100 value: 44.235
- type: ndcg_at_1000 value: 52.154
- type: ndcg_at_3 value: 43.008
- type: ndcg_at_5 value: 40.083999999999996
- type: precision_at_1 value: 62.5
- type: precision_at_10 value: 30.0
- type: precision_at_100 value: 10.038
- type: precision_at_1000 value: 2.0869999999999997
- type: precision_at_3 value: 46.833000000000006
- type: precision_at_5 value: 38.800000000000004
- type: recall_at_1 value: 8.527999999999999
- type: recall_at_10 value: 23.828
- type: recall_at_100 value: 52.322
- type: recall_at_1000 value: 77.143
- type: recall_at_3 value: 14.136000000000001
- type: recall_at_5 value: 17.761
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy value: 51.51
- type: f1 value: 47.632159862049896
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 60.734
- type: map_at_10 value: 72.442
- type: map_at_100 value: 72.735
- type: map_at_1000 value: 72.75
- type: map_at_3 value: 70.41199999999999
- type: map_at_5 value: 71.80499999999999
- type: mrr_at_1 value: 65.212
- type: mrr_at_10 value: 76.613
- type: mrr_at_100 value: 76.79899999999999
- type: mrr_at_1000 value: 76.801
- type: mrr_at_3 value: 74.8
- type: mrr_at_5 value: 76.12400000000001
- type: ndcg_at_1 value: 65.212
- type: ndcg_at_10 value: 77.988
- type: ndcg_at_100 value: 79.167
- type: ndcg_at_1000 value: 79.452
- type: ndcg_at_3 value: 74.362
- type: ndcg_at_5 value: 76.666
- type: precision_at_1 value: 65.212
- type: precision_at_10 value: 10.003
- type: precision_at_100 value: 1.077
- type: precision_at_1000 value: 0.11199999999999999
- type: precision_at_3 value: 29.518
- type: precision_at_5 value: 19.016
- type: recall_at_1 value: 60.734
- type: recall_at_10 value: 90.824
- type: recall_at_100 value: 95.71600000000001
- type: recall_at_1000 value: 97.577
- type: recall_at_3 value: 81.243
- type: recall_at_5 value: 86.90299999999999
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 23.845
- type: map_at_10 value: 39.281
- type: map_at_100 value: 41.422
- type: map_at_1000 value: 41.593
- type: map_at_3 value: 34.467
- type: map_at_5 value: 37.017
- type: mrr_at_1 value: 47.531
- type: mrr_at_10 value: 56.204
- type: mrr_at_100 value: 56.928999999999995
- type: mrr_at_1000 value: 56.962999999999994
- type: mrr_at_3 value: 54.115
- type: mrr_at_5 value: 55.373000000000005
- type: ndcg_at_1 value: 47.531
- type: ndcg_at_10 value: 47.711999999999996
- type: ndcg_at_100 value: 54.510999999999996
- type: ndcg_at_1000 value: 57.103
- type: ndcg_at_3 value: 44.145
- type: ndcg_at_5 value: 45.032
- type: precision_at_1 value: 47.531
- type: precision_at_10 value: 13.194
- type: precision_at_100 value: 2.045
- type: precision_at_1000 value: 0.249
- type: precision_at_3 value: 29.424
- type: precision_at_5 value: 21.451
- type: recall_at_1 value: 23.845
- type: recall_at_10 value: 54.967
- type: recall_at_100 value: 79.11399999999999
- type: recall_at_1000 value: 94.56700000000001
- type: recall_at_3 value: 40.256
- type: recall_at_5 value: 46.215
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 37.819
- type: map_at_10 value: 60.889
- type: map_at_100 value: 61.717999999999996
- type: map_at_1000 value: 61.778
- type: map_at_3 value: 57.254000000000005
- type: map_at_5 value: 59.541
- type: mrr_at_1 value: 75.638
- type: mrr_at_10 value: 82.173
- type: mrr_at_100 value: 82.362
- type: mrr_at_1000 value: 82.37
- type: mrr_at_3 value: 81.089
- type: mrr_at_5 value: 81.827
- type: ndcg_at_1 value: 75.638
- type: ndcg_at_10 value: 69.317
- type: ndcg_at_100 value: 72.221
- type: ndcg_at_1000 value: 73.382
- type: ndcg_at_3 value: 64.14
- type: ndcg_at_5 value: 67.07600000000001
- type: precision_at_1 value: 75.638
- type: precision_at_10 value: 14.704999999999998
- type: precision_at_100 value: 1.698
- type: precision_at_1000 value: 0.185
- type: precision_at_3 value: 41.394999999999996
- type: precision_at_5 value: 27.162999999999997
- type: recall_at_1 value: 37.819
- type: recall_at_10 value: 73.52499999999999
- type: recall_at_100 value: 84.875
- type: recall_at_1000 value: 92.559
- type: recall_at_3 value: 62.092999999999996
- type: recall_at_5 value: 67.907
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy value: 94.60079999999999
- type: ap value: 92.67396345347356
- type: f1 value: 94.5988098167121
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 21.285
- type: map_at_10 value: 33.436
- type: map_at_100 value: 34.63
- type: map_at_1000 value: 34.681
- type: map_at_3 value: 29.412
- type: map_at_5 value: 31.715
- type: mrr_at_1 value: 21.848
- type: mrr_at_10 value: 33.979
- type: mrr_at_100 value: 35.118
- type: mrr_at_1000 value: 35.162
- type: mrr_at_3 value: 30.036
- type: mrr_at_5 value: 32.298
- type: ndcg_at_1 value: 21.862000000000002
- type: ndcg_at_10 value: 40.43
- type: ndcg_at_100 value: 46.17
- type: ndcg_at_1000 value: 47.412
- type: ndcg_at_3 value: 32.221
- type: ndcg_at_5 value: 36.332
- type: precision_at_1 value: 21.862000000000002
- type: precision_at_10 value: 6.491
- type: precision_at_100 value: 0.935
- type: precision_at_1000 value: 0.104
- type: precision_at_3 value: 13.744
- type: precision_at_5 value: 10.331999999999999
- type: recall_at_1 value: 21.285
- type: recall_at_10 value: 62.083
- type: recall_at_100 value: 88.576
- type: recall_at_1000 value: 98.006
- type: recall_at_3 value: 39.729
- type: recall_at_5 value: 49.608000000000004
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy value: 93.92612859097127
- type: f1 value: 93.82370333372853
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy value: 92.67681036911807
- type: f1 value: 92.14191382411472
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy value: 92.26817878585723
- type: f1 value: 91.92824250337878
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy value: 89.96554963983714
- type: f1 value: 90.02859329630792
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: ko
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: lv
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: ml
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: my
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: nb
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: sl
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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name: MTEB MassiveScenarioClassification (tr)
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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name: MTEB MassiveScenarioClassification (vi)
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- type: f1 value: 75.17918654541515
- task:
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dataset:
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name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- type: f1 value: 78.90019070153316
- task:
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dataset:
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config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
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- task:
type: Clustering
dataset:
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name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
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dataset:
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name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
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- type: mrr value: 34.32436977159129
- task:
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dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
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- type: map_at_10 value: 13.297
- type: map_at_100 value: 16.907
- type: map_at_1000 value: 18.391
- type: map_at_3 value: 9.626999999999999
- type: map_at_5 value: 11.190999999999999
- type: mrr_at_1 value: 46.129999999999995
- type: mrr_at_10 value: 54.346000000000004
- type: mrr_at_100 value: 55.067
- type: mrr_at_1000 value: 55.1
- type: mrr_at_3 value: 51.961
- type: mrr_at_5 value: 53.246
- type: ndcg_at_1 value: 44.118
- type: ndcg_at_10 value: 35.534
- type: ndcg_at_100 value: 32.946999999999996
- type: ndcg_at_1000 value: 41.599000000000004
- type: ndcg_at_3 value: 40.25
- type: ndcg_at_5 value: 37.978
- type: precision_at_1 value: 46.129999999999995
- type: precision_at_10 value: 26.842
- type: precision_at_100 value: 8.427
- type: precision_at_1000 value: 2.128
- type: precision_at_3 value: 37.977
- type: precision_at_5 value: 32.879000000000005
- type: recall_at_1 value: 5.935
- type: recall_at_10 value: 17.211000000000002
- type: recall_at_100 value: 34.33
- type: recall_at_1000 value: 65.551
- type: recall_at_3 value: 10.483
- type: recall_at_5 value: 13.078999999999999
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
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- type: map_at_10 value: 50.202000000000005
- type: map_at_100 value: 51.154999999999994
- type: map_at_1000 value: 51.181
- type: map_at_3 value: 45.774
- type: map_at_5 value: 48.522
- type: mrr_at_1 value: 39.687
- type: mrr_at_10 value: 52.88
- type: mrr_at_100 value: 53.569
- type: mrr_at_1000 value: 53.58500000000001
- type: mrr_at_3 value: 49.228
- type: mrr_at_5 value: 51.525
- type: ndcg_at_1 value: 39.687
- type: ndcg_at_10 value: 57.754000000000005
- type: ndcg_at_100 value: 61.597
- type: ndcg_at_1000 value: 62.18900000000001
- type: ndcg_at_3 value: 49.55
- type: ndcg_at_5 value: 54.11899999999999
- type: precision_at_1 value: 39.687
- type: precision_at_10 value: 9.313
- type: precision_at_100 value: 1.146
- type: precision_at_1000 value: 0.12
- type: precision_at_3 value: 22.229
- type: precision_at_5 value: 15.939
- type: recall_at_1 value: 35.231
- type: recall_at_10 value: 78.083
- type: recall_at_100 value: 94.42099999999999
- type: recall_at_1000 value: 98.81
- type: recall_at_3 value: 57.047000000000004
- type: recall_at_5 value: 67.637
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
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- type: map_at_10 value: 85.462
- type: map_at_100 value: 86.083
- type: map_at_1000 value: 86.09700000000001
- type: map_at_3 value: 82.49499999999999
- type: map_at_5 value: 84.392
- type: mrr_at_1 value: 82.09
- type: mrr_at_10 value: 88.301
- type: mrr_at_100 value: 88.383
- type: mrr_at_1000 value: 88.384
- type: mrr_at_3 value: 87.37
- type: mrr_at_5 value: 88.035
- type: ndcg_at_1 value: 82.12
- type: ndcg_at_10 value: 89.149
- type: ndcg_at_100 value: 90.235
- type: ndcg_at_1000 value: 90.307
- type: ndcg_at_3 value: 86.37599999999999
- type: ndcg_at_5 value: 87.964
- type: precision_at_1 value: 82.12
- type: precision_at_10 value: 13.56
- type: precision_at_100 value: 1.539
- type: precision_at_1000 value: 0.157
- type: precision_at_3 value: 37.88
- type: precision_at_5 value: 24.92
- type: recall_at_1 value: 71.241
- type: recall_at_10 value: 96.128
- type: recall_at_100 value: 99.696
- type: recall_at_1000 value: 99.994
- type: recall_at_3 value: 88.181
- type: recall_at_5 value: 92.694
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
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- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure value: 64.27391998854624
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
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- type: map_at_10 value: 10.965
- type: map_at_100 value: 12.934999999999999
- type: map_at_1000 value: 13.256
- type: map_at_3 value: 7.907
- type: map_at_5 value: 9.435
- type: mrr_at_1 value: 20.9
- type: mrr_at_10 value: 31.849
- type: mrr_at_100 value: 32.964
- type: mrr_at_1000 value: 33.024
- type: mrr_at_3 value: 28.517
- type: mrr_at_5 value: 30.381999999999998
- type: ndcg_at_1 value: 20.9
- type: ndcg_at_10 value: 18.723
- type: ndcg_at_100 value: 26.384999999999998
- type: ndcg_at_1000 value: 32.114
- type: ndcg_at_3 value: 17.753
- type: ndcg_at_5 value: 15.558
- type: precision_at_1 value: 20.9
- type: precision_at_10 value: 9.8
- type: precision_at_100 value: 2.078
- type: precision_at_1000 value: 0.345
- type: precision_at_3 value: 16.900000000000002
- type: precision_at_5 value: 13.88
- type: recall_at_1 value: 4.243
- type: recall_at_10 value: 19.885
- type: recall_at_100 value: 42.17
- type: recall_at_1000 value: 70.12
- type: recall_at_3 value: 10.288
- type: recall_at_5 value: 14.072000000000001
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
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- type: cos_sim_spearman value: 81.73248048438833
- type: euclidean_pearson value: 83.02810070308149
- type: euclidean_spearman value: 81.73248295679514
- type: manhattan_pearson value: 82.95368060376002
- type: manhattan_spearman value: 81.60277910998718
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
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- type: cos_sim_spearman value: 82.5713913555672
- type: euclidean_pearson value: 85.8796774746988
- type: euclidean_spearman value: 82.57137506803424
- type: manhattan_pearson value: 85.79671002960058
- type: manhattan_spearman value: 82.49445981618027
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson value: 86.23682503505542
- type: cos_sim_spearman value: 87.15008956711806
- type: euclidean_pearson value: 86.79805401524959
- type: euclidean_spearman value: 87.15008956711806
- type: manhattan_pearson value: 86.65298502699244
- type: manhattan_spearman value: 86.97677821948562
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson value: 85.63370304677802
- type: cos_sim_spearman value: 84.97105553540318
- type: euclidean_pearson value: 85.28896108687721
- type: euclidean_spearman value: 84.97105553540318
- type: manhattan_pearson value: 85.09663190337331
- type: manhattan_spearman value: 84.79126831644619
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson value: 90.2614838800733
- type: cos_sim_spearman value: 91.0509162991835
- type: euclidean_pearson value: 90.33098317533373
- type: euclidean_spearman value: 91.05091625871644
- type: manhattan_pearson value: 90.26250435151107
- type: manhattan_spearman value: 90.97999594417519
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson value: 85.80480973335091
- type: cos_sim_spearman value: 87.313695492969
- type: euclidean_pearson value: 86.49267251576939
- type: euclidean_spearman value: 87.313695492969
- type: manhattan_pearson value: 86.44019901831935
- type: manhattan_spearman value: 87.24205395460392
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson value: 90.05662789380672
- type: cos_sim_spearman value: 90.02759424426651
- type: euclidean_pearson value: 90.4042483422981
- type: euclidean_spearman value: 90.02759424426651
- type: manhattan_pearson value: 90.51446975000226
- type: manhattan_spearman value: 90.08832889933616
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson value: 67.5975528273532
- type: cos_sim_spearman value: 67.62969861411354
- type: euclidean_pearson value: 69.224275734323
- type: euclidean_spearman value: 67.62969861411354
- type: manhattan_pearson value: 69.3761447059927
- type: manhattan_spearman value: 67.90921005611467
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson value: 87.11244327231684
- type: cos_sim_spearman value: 88.37902438979035
- type: euclidean_pearson value: 87.86054279847336
- type: euclidean_spearman value: 88.37902438979035
- type: manhattan_pearson value: 87.77257757320378
- type: manhattan_spearman value: 88.25208966098123
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map value: 85.87174608143563
- type: mrr value: 96.12836872640794
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 57.760999999999996
- type: map_at_10 value: 67.258
- type: map_at_100 value: 67.757
- type: map_at_1000 value: 67.78800000000001
- type: map_at_3 value: 64.602
- type: map_at_5 value: 65.64
- type: mrr_at_1 value: 60.667
- type: mrr_at_10 value: 68.441
- type: mrr_at_100 value: 68.825
- type: mrr_at_1000 value: 68.853
- type: mrr_at_3 value: 66.444
- type: mrr_at_5 value: 67.26100000000001
- type: ndcg_at_1 value: 60.667
- type: ndcg_at_10 value: 71.852
- type: ndcg_at_100 value: 73.9
- type: ndcg_at_1000 value: 74.628
- type: ndcg_at_3 value: 67.093
- type: ndcg_at_5 value: 68.58
- type: precision_at_1 value: 60.667
- type: precision_at_10 value: 9.6
- type: precision_at_100 value: 1.0670000000000002
- type: precision_at_1000 value: 0.11199999999999999
- type: precision_at_3 value: 26.111
- type: precision_at_5 value: 16.733
- type: recall_at_1 value: 57.760999999999996
- type: recall_at_10 value: 84.967
- type: recall_at_100 value: 93.833
- type: recall_at_1000 value: 99.333
- type: recall_at_3 value: 71.589
- type: recall_at_5 value: 75.483
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy value: 99.66633663366336
- type: cos_sim_ap value: 91.17685358899108
- type: cos_sim_f1 value: 82.16818642350559
- type: cos_sim_precision value: 83.26488706365504
- type: cos_sim_recall value: 81.10000000000001
- type: dot_accuracy value: 99.66633663366336
- type: dot_ap value: 91.17663411119032
- type: dot_f1 value: 82.16818642350559
- type: dot_precision value: 83.26488706365504
- type: dot_recall value: 81.10000000000001
- type: euclidean_accuracy value: 99.66633663366336
- type: euclidean_ap value: 91.17685189882275
- type: euclidean_f1 value: 82.16818642350559
- type: euclidean_precision value: 83.26488706365504
- type: euclidean_recall value: 81.10000000000001
- type: manhattan_accuracy value: 99.66633663366336
- type: manhattan_ap value: 91.2241619496737
- type: manhattan_f1 value: 82.20472440944883
- type: manhattan_precision value: 86.51933701657458
- type: manhattan_recall value: 78.3
- type: max_accuracy value: 99.66633663366336
- type: max_ap value: 91.2241619496737
- type: max_f1 value: 82.20472440944883
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure value: 66.85101268897951
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure value: 42.461184054706905
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map value: 51.44542568873886
- type: mrr value: 52.33656151854681
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson value: 30.75982974997539
- type: cos_sim_spearman value: 30.385405026539914
- type: dot_pearson value: 30.75982433546523
- type: dot_spearman value: 30.385405026539914
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 0.22799999999999998
- type: map_at_10 value: 2.064
- type: map_at_100 value: 13.056000000000001
- type: map_at_1000 value: 31.747999999999998
- type: map_at_3 value: 0.67
- type: map_at_5 value: 1.097
- type: mrr_at_1 value: 90.0
- type: mrr_at_10 value: 94.667
- type: mrr_at_100 value: 94.667
- type: mrr_at_1000 value: 94.667
- type: mrr_at_3 value: 94.667
- type: mrr_at_5 value: 94.667
- type: ndcg_at_1 value: 86.0
- type: ndcg_at_10 value: 82.0
- type: ndcg_at_100 value: 64.307
- type: ndcg_at_1000 value: 57.023999999999994
- type: ndcg_at_3 value: 85.816
- type: ndcg_at_5 value: 84.904
- type: precision_at_1 value: 90.0
- type: precision_at_10 value: 85.8
- type: precision_at_100 value: 66.46
- type: precision_at_1000 value: 25.202
- type: precision_at_3 value: 90.0
- type: precision_at_5 value: 89.2
- type: recall_at_1 value: 0.22799999999999998
- type: recall_at_10 value: 2.235
- type: recall_at_100 value: 16.185
- type: recall_at_1000 value: 53.620999999999995
- type: recall_at_3 value: 0.7040000000000001
- type: recall_at_5 value: 1.172
- 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: 97.39999999999999
- type: f1 value: 96.75
- type: precision value: 96.45
- type: recall value: 97.39999999999999
- 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: 85.54913294797689
- type: f1 value: 82.46628131021194
- type: precision value: 81.1175337186898
- type: recall value: 85.54913294797689
- 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: 81.21951219512195
- type: f1 value: 77.33333333333334
- type: precision value: 75.54878048780488
- type: recall value: 81.21951219512195
- 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: 98.6
- type: f1 value: 98.26666666666665
- type: precision value: 98.1
- type: recall value: 98.6
- 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: 99.5
- type: f1 value: 99.33333333333333
- type: precision value: 99.25
- type: recall value: 99.5
- 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: 97.8
- type: f1 value: 97.2
- type: precision value: 96.89999999999999
- type: recall value: 97.8
- 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: 97.8
- type: f1 value: 97.18333333333334
- type: precision value: 96.88333333333333
- type: recall value: 97.8
- 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: 77.61194029850746
- type: f1 value: 72.81094527363183
- type: precision value: 70.83333333333333
- type: recall value: 77.61194029850746
- 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: 93.7
- type: f1 value: 91.91666666666667
- type: precision value: 91.08333333333334
- type: recall value: 93.7
- 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: 85.27642276422765
- type: precision value: 84.01277584204414
- 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: 96.1
- type: f1 value: 95.0
- type: precision value: 94.46666666666668
- type: recall value: 96.1
- 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: 93.681652490887
- type: f1 value: 91.90765492102065
- type: precision value: 91.05913325232888
- type: recall value: 93.681652490887
- 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: 92.17391304347827
- type: f1 value: 89.97101449275361
- type: precision value: 88.96811594202899
- type: recall value: 92.17391304347827
- 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: 90.43478260869566
- type: f1 value: 87.72173913043478
- type: precision value: 86.42028985507245
- type: recall value: 90.43478260869566
- 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: 90.4
- type: f1 value: 88.03
- type: precision value: 86.95
- type: recall value: 90.4
- 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: 93.4
- type: f1 value: 91.45666666666666
- type: precision value: 90.525
- type: recall value: 93.4
- 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: 81.9059107358263
- type: f1 value: 78.32557872364869
- type: precision value: 76.78260286824823
- type: recall value: 81.9059107358263
- 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: 94.3
- type: f1 value: 92.58333333333333
- type: precision value: 91.73333333333332
- type: recall value: 94.3
- 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: 79.10000000000001
- type: f1 value: 74.50500000000001
- type: precision value: 72.58928571428571
- type: recall value: 79.10000000000001
- 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: 96.6
- type: f1 value: 95.55
- type: precision value: 95.05
- type: recall value: 96.6
- 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: 82.0952380952381
- type: f1 value: 77.98458049886621
- type: precision value: 76.1968253968254
- type: recall value: 82.0952380952381
- 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: 87.9
- type: f1 value: 84.99190476190476
- type: precision value: 83.65
- type: recall value: 87.9
- 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: 95.7
- type: f1 value: 94.56666666666666
- type: precision value: 94.01666666666667
- type: recall value: 95.7
- 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: 98.6
- type: f1 value: 98.2
- type: precision value: 98.0
- type: recall value: 98.6
- 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: 95.6
- type: f1 value: 94.38333333333334
- type: precision value: 93.78333333333335
- type: recall value: 95.6
- 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: 87.4
- type: f1 value: 84.10380952380952
- type: precision value: 82.67
- type: recall value: 87.4
- 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: 95.5
- type: f1 value: 94.33333333333334
- type: precision value: 93.78333333333333
- type: recall value: 95.5
- 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: 89.4
- type: f1 value: 86.82000000000001
- type: precision value: 85.64500000000001
- type: recall value: 89.4
- 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: 95.1
- type: f1 value: 93.56666666666668
- type: precision value: 92.81666666666666
- type: recall value: 95.1
- 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: 98.6
- type: precision value: 98.45
- 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: 95.01347708894879
- type: f1 value: 93.51752021563343
- type: precision value: 92.82794249775381
- type: recall value: 95.01347708894879
- task:
type: BitextMining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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- type: accuracy value: 94.10187667560321
- type: f1 value: 92.46648793565683
- type: precision value: 91.71134941912423
- type: recall value: 94.10187667560321
- 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: 97.0
- type: f1 value: 96.11666666666666
- type: precision value: 95.68333333333334
- type: recall value: 97.0
- 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: 72.72727272727273
- type: f1 value: 66.58949745906267
- type: precision value: 63.86693017127799
- type: recall value: 72.72727272727273
- 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: 90.14084507042254
- type: f1 value: 88.26291079812206
- type: precision value: 87.32394366197182
- type: recall value: 90.14084507042254
- 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: 64.67065868263472
- type: f1 value: 58.2876627696987
- type: precision value: 55.79255774165953
- type: recall value: 64.67065868263472
- 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: 95.6
- type: f1 value: 94.41666666666667
- type: precision value: 93.85
- type: recall value: 95.6
- 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: 55.172413793103445
- type: f1 value: 49.63992493549144
- type: precision value: 47.71405113769646
- type: recall value: 55.172413793103445
- 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: 77.46478873239437
- type: f1 value: 73.4417616811983
- type: precision value: 71.91607981220658
- type: recall value: 77.46478873239437
- 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: 84.61538461538461
- type: f1 value: 80.91452991452994
- type: precision value: 79.33760683760683
- type: recall value: 84.61538461538461
- 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: 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 (dsb-eng)
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy value: 75.5741127348643
- type: f1 value: 72.00417536534445
- type: precision value: 70.53467872883321
- type: recall value: 75.5741127348643
- 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: 62.2
- type: f1 value: 55.577460317460314
- type: precision value: 52.98583333333333
- type: recall value: 62.2
- 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: 92.18241042345277
- type: f1 value: 90.6468124709167
- type: precision value: 89.95656894679696
- type: recall value: 92.18241042345277
- 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: 96.1
- type: f1 value: 95.13333333333333
- type: precision value: 94.66666666666667
- type: recall value: 96.1
- 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: 96.8
- type: f1 value: 95.85000000000001
- type: precision value: 95.39999999999999
- type: recall value: 96.8
- 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: 92.1259842519685
- type: f1 value: 89.76377952755905
- type: precision value: 88.71391076115485
- type: recall value: 92.1259842519685
- 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: 94.1
- type: f1 value: 92.49
- type: precision value: 91.725
- type: recall value: 94.1
- 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: 77.5623268698061
- type: f1 value: 73.27364463791058
- type: precision value: 71.51947852086357
- type: recall value: 77.5623268698061
- 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: 97.39999999999999
- type: f1 value: 96.56666666666666
- type: precision value: 96.16666666666667
- type: recall value: 97.39999999999999
- 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: 66.34615384615384
- type: f1 value: 61.092032967032964
- type: precision value: 59.27197802197802
- type: recall value: 66.34615384615384
- 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: 94.89999999999999
- type: f1 value: 93.41190476190476
- type: precision value: 92.7
- type: recall value: 94.89999999999999
- 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: 93.10000000000001
- type: f1 value: 91.10000000000001
- type: precision value: 90.13333333333333
- type: recall value: 93.10000000000001
- 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: 93.7
- type: f1 value: 91.97333333333334
- type: precision value: 91.14166666666667
- type: recall value: 93.7
- 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: 92.21698113207547
- type: f1 value: 90.3796046720575
- type: precision value: 89.56367924528303
- type: recall value: 92.21698113207547
- 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: 97.6
- type: f1 value: 96.91666666666667
- type: precision value: 96.6
- type: recall value: 97.6
- 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: 97.44525547445255
- type: f1 value: 96.71532846715328
- type: precision value: 96.35036496350365
- type: recall value: 97.44525547445255
- 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: 94.1
- type: f1 value: 92.34000000000002
- type: precision value: 91.49166666666667
- type: recall value: 94.1
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 3.2910000000000004
- type: map_at_10 value: 10.373000000000001
- type: map_at_100 value: 15.612
- type: map_at_1000 value: 17.06
- type: map_at_3 value: 6.119
- type: map_at_5 value: 7.917000000000001
- type: mrr_at_1 value: 44.897999999999996
- type: mrr_at_10 value: 56.054
- type: mrr_at_100 value: 56.82000000000001
- type: mrr_at_1000 value: 56.82000000000001
- type: mrr_at_3 value: 52.381
- type: mrr_at_5 value: 53.81
- type: ndcg_at_1 value: 42.857
- type: ndcg_at_10 value: 27.249000000000002
- type: ndcg_at_100 value: 36.529
- type: ndcg_at_1000 value: 48.136
- type: ndcg_at_3 value: 33.938
- type: ndcg_at_5 value: 29.951
- type: precision_at_1 value: 44.897999999999996
- type: precision_at_10 value: 22.653000000000002
- type: precision_at_100 value: 7.000000000000001
- type: precision_at_1000 value: 1.48
- type: precision_at_3 value: 32.653
- type: precision_at_5 value: 27.755000000000003
- type: recall_at_1 value: 3.2910000000000004
- type: recall_at_10 value: 16.16
- type: recall_at_100 value: 43.908
- type: recall_at_1000 value: 79.823
- type: recall_at_3 value: 7.156
- type: recall_at_5 value: 10.204
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy value: 71.05879999999999
- type: ap value: 14.609748142799111
- type: f1 value: 54.878956295843096
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy value: 64.61799660441426
- type: f1 value: 64.8698191961434
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure value: 51.32860036611885
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy value: 88.34714192048638
- type: cos_sim_ap value: 80.26732975975634
- type: cos_sim_f1 value: 73.53415148134374
- type: cos_sim_precision value: 69.34767360299276
- type: cos_sim_recall value: 78.25857519788919
- type: dot_accuracy value: 88.34714192048638
- type: dot_ap value: 80.26733698491206
- type: dot_f1 value: 73.53415148134374
- type: dot_precision value: 69.34767360299276
- type: dot_recall value: 78.25857519788919
- type: euclidean_accuracy value: 88.34714192048638
- type: euclidean_ap value: 80.26734337771738
- type: euclidean_f1 value: 73.53415148134374
- type: euclidean_precision value: 69.34767360299276
- type: euclidean_recall value: 78.25857519788919
- type: manhattan_accuracy value: 88.30541813196639
- type: manhattan_ap value: 80.19415808104145
- type: manhattan_f1 value: 73.55143870713441
- type: manhattan_precision value: 73.25307511122743
- type: manhattan_recall value: 73.85224274406332
- type: max_accuracy value: 88.34714192048638
- type: max_ap value: 80.26734337771738
- type: max_f1 value: 73.55143870713441
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy value: 89.81061047075717
- type: cos_sim_ap value: 87.11747055081017
- type: cos_sim_f1 value: 80.04355498817256
- type: cos_sim_precision value: 78.1165262000733
- type: cos_sim_recall value: 82.06806282722513
- type: dot_accuracy value: 89.81061047075717
- type: dot_ap value: 87.11746902745236
- type: dot_f1 value: 80.04355498817256
- type: dot_precision value: 78.1165262000733
- type: dot_recall value: 82.06806282722513
- type: euclidean_accuracy value: 89.81061047075717
- type: euclidean_ap value: 87.11746919324248
- type: euclidean_f1 value: 80.04355498817256
- type: euclidean_precision value: 78.1165262000733
- type: euclidean_recall value: 82.06806282722513
- type: manhattan_accuracy value: 89.79508673885202
- type: manhattan_ap value: 87.11074390832218
- type: manhattan_f1 value: 80.13002540726349
- type: manhattan_precision value: 77.83826945412311
- type: manhattan_recall value: 82.56082537727133
- type: max_accuracy value: 89.81061047075717
- type: max_ap value: 87.11747055081017
- type: max_f1 value: 80.13002540726349
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh license: mit
Multilingual-E5-large-instruct
Multilingual E5 Text Embeddings: A Technical Report. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 24 layers and the embedding size is 1024.
Usage
Below are examples to encode queries and passages from the MS-MARCO passage ranking dataset.
Transformers
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, '南瓜的家常做法')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large-instruct')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-large-instruct')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# => [[91.92852783203125, 67.580322265625], [70.3814468383789, 92.1330795288086]]
Sentence Transformers
from sentence_transformers import SentenceTransformer
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, '南瓜的家常做法')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
input_texts = queries + documents
model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')
embeddings = model.encode(input_texts, convert_to_tensor=True, normalize_embeddings=True)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[91.92853546142578, 67.5802993774414], [70.38143157958984, 92.13307189941406]]
Infinity
Usage with Infinity:
docker run --gpus all -v $PWD/data:/app/.cache -e HF_TOKEN=$HF_TOKEN -p "7997":"7997" \
michaelf34/infinity:0.0.68 \
v2 --model-id intfloat/multilingual-e5-large-instruct --revision "main" --dtype float16 --batch-size 32 --engine torch --port 7997
Supported Languages
This model is initialized from xlm-roberta-large and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation.
Training Details
Initialization: xlm-roberta-large
First stage: contrastive pre-training with 1 billion weakly supervised text pairs.
Second stage: fine-tuning on datasets from the E5-mistral paper.
MTEB Benchmark Evaluation
Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark.
FAQ
1. Do I need to add instructions to the query?
Yes, this is how the model is trained, otherwise you will see a performance degradation. The task definition should be a one-sentence instruction that describes the task. This is a way to customize text embeddings for different scenarios through natural language instructions.
Please check out unilm/e5/utils.py for instructions we used for evaluation.
On the other hand, there is no need to add instructions to the document side.
2. Why are my reproduced results slightly different from reported in the model card?
Different versions of transformers and pytorch could cause negligible but non-zero performance differences.
3. Why does the cosine similarity scores distribute around 0.7 to 1.0?
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue.
Citation
If you find our paper or models helpful, please consider cite as follows:
@article{wang2024multilingual,
title={Multilingual E5 Text Embeddings: A Technical Report},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2402.05672},
year={2024}
}
Limitations
Long texts will be truncated to at most 512 tokens.
Magnet link
Opens the swarm directly in your torrent client — no file download needed. Copy-paste works too:
magnet:?xt=urn:btih:0e4c2fcb3049ee2f3d32826deec60d1c051f5584&dn=intfloat_multilingual-e5-large-instructOpen magnet in torrent client · infohash 0e4c2fcb3049ee2f3d32826deec60d1c051f5584
Files & hashes
| Path | Size | sha1 | sha256 |
|---|---|---|---|
| 1_Pooling/config.json | 271 B (271 B) | c5bf064bf9373d01df9cfe9437084f3bd5d33b14 | aa629215c1d83e73d9c51184e566f2c53456bc742f936984355a2990c8c8d046 |
| README.md | 136.9 KB (140,205 B) | f48430f7761354d4528fda264ffe828e6e6aa1c4 | 41676700d80691ac2f4397ee2b546ef58f9e3236af2f3d0770ab0fa61940ce51 |
| config.json | 690 B (690 B) | f92397ec9462da6e5e34fa22a43cf234093481e8 | d185df634c872f06fc6dcb3bad4375d3c447234baea8a6e548103f653b595a35 |
| config_sentence_transformers.json | 128 B (128 B) | 486b40b74e493da9295e67f851141d361e95b284 | 7e16d286cc304f7d34ffb4e21fa1a11f227cf2b6d49cb615aa44d87723d17e28 |
| model.safetensors | 1.04 GB (1,119,825,680 B) | e1fe7b266540704ea3452c428074bc66c7da0451 | dd6b6e4f52db0a7aff83a13d10e6c5342ef9f6ab799bad3221f4b35ef390fa85 |
| modules.json | 349 B (349 B) | 952a9b81c0bfd99800fabf352f69c7ccd46c5e43 | 84e40c8e006c9b1d6c122e02cba9b02458120b5fb0c87b746c41e0207cf642cf |
| onnx/config.json | 763 B (763 B) | d0f15baab5b1dab7e7297c901a941fe9fb93c8e7 | 3762389b621283a0466d96f699fc1a0f160de49c4b50adcc87b537931e458ba3 |
| onnx/model.onnx_data | 2.08 GB (2,235,367,440 B) | 40a38bb6017a9b593624f8d338561e6ec721d030 | 4c13ca44e40a7ee86b461398cdee003d656e39f3fa87e1b3152fbada3e61b506 |
| onnx/sentencepiece.bpe.model | 4.8 MB (5,069,051 B) | 7e88c49faff6c6c136fdf4a3402d0cb534c6ab10 | cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865 |
| onnx/special_tokens_map.json | 964 B (964 B) | b1879d702821e753ffe4245048eee415d54a9385 | 8c785abebea9ae3257b61681b4e6fd8365ceafde980c21970d001e834cf10835 |
| onnx/tokenizer.json | 16.3 MB (17,082,756 B) | bf09722a309d10b2344a54f2db9ac57b6543aebc | f59925fcb90c92b894cb93e51bb9b4a6105c5c249fe54ce1c704420ac39b81af |
| onnx/tokenizer_config.json | 1.2 KB (1,182 B) | bc9cb006ea6982868d4e32f58506b1d17873b21f | 49f06d15a18f81855c338f8ab6241ca2e502a65b62968be69b74f094489f8175 |
| sentence_xlm-roberta_config.json | 53 B (53 B) | f789d99277496b282d19020415c5ba9ca79ac875 | ec8e29d6dcb61b611b7d3fdd2982c4524e6ad985959fa7194eacfb655a8d0d51 |
| sentencepiece.bpe.model | 4.8 MB (5,069,051 B) | 7e88c49faff6c6c136fdf4a3402d0cb534c6ab10 | cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865 |
| special_tokens_map.json | 964 B (964 B) | b1879d702821e753ffe4245048eee415d54a9385 | 8c785abebea9ae3257b61681b4e6fd8365ceafde980c21970d001e834cf10835 |
| tokenizer.json | 16.3 MB (17,082,756 B) | bf09722a309d10b2344a54f2db9ac57b6543aebc | f59925fcb90c92b894cb93e51bb9b4a6105c5c249fe54ce1c704420ac39b81af |
| tokenizer_config.json | 1.2 KB (1,182 B) | bc9cb006ea6982868d4e32f58506b1d17873b21f | 49f06d15a18f81855c338f8ab6241ca2e502a65b62968be69b74f094489f8175 |
Cite this release
- Canonical URL
- https://aiseedbank.org/models/intfloat_multilingual-e5-large-instruct/
- Slug
- intfloat_multilingual-e5-large-instruct
- Infohash
- 0e4c2fcb3049ee2f3d32826deec60d1c051f5584
- License
- mit
- Signing key fingerprint
- 85a3b32c3712427b
Every file carries a locally computed sha256 — verify a download against the signed sums: intfloat_multilingual-e5-large-instruct.SHA256SUMS (+ minisign signature).
Provenance
| Upstream repository | intfloat/multilingual-e5-large-instruct |
|---|---|
| Revision (pinned) | 274baa43b0e13e37fafa6428dbc7938e62e5c439 |
| Fetched at | 2026-09-02T04:36:37Z |
| License at fetch | mit |
| Snapshot tool | huggingface · seedbank 0.1.0 |
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✓ verified · rehash-vs-hf-metadata at 2026-09-02T04:37:10Z
mit3.17 GB (3,399,643,485 bytes)sentence-transformersonnxsafetensorsxlm-robertafeature-extractionmtebtransformersmultilingualmodel-indextext-embeddings-inferenceendpoints_compatible93 languages (af, am, ar …)paper: 2402.05672paper: 2401.00368paper: 2104.08663paper: 2210.07316