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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
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	- type: recall_at_100
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	- 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
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	- 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
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	- type: mrr_at_10
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	- type: mrr_at_100
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	- type: mrr_at_1000
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	- type: mrr_at_3
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	- type: mrr_at_5
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	- type: ndcg_at_1
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	- type: ndcg_at_10
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	- type: ndcg_at_100
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	- type: ndcg_at_1000
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	- 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
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	config: en
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 93.92612859097127
	- type: f1
	value: 93.82370333372853
	- task:
	type: Classification
	dataset:
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	name: MTEB MTOPDomainClassification (de)
	config: de
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 92.67681036911807
	- type: f1
	value: 92.14191382411472
	- task:
	type: Classification
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	config: es
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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	- type: f1
	value: 91.92824250337878
	- task:
	type: Classification
	dataset:
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	name: MTEB MTOPDomainClassification (fr)
	config: fr
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 89.96554963983714
	- type: f1
	value: 90.02859329630792
	- task:
	type: Classification
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	- type: accuracy
	value: 90.02509860164935
	- type: f1
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	- task:
	type: Classification
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	- type: accuracy
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	- type: f1
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	- task:
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	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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	- type: accuracy
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	- type: f1
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	- task:
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	- task:
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	- task:
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	- type: f1
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
	type: Classification
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	- task:
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	metrics:
	- type: accuracy
	value: 78.69535978480162
	- type: f1
	value: 78.90019070153316
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (zh-TW)
	config: zh-TW
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 75.45729657027572
	- type: f1
	value: 76.19578371794672
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-p2p
	name: MTEB MedrxivClusteringP2P
	config: default
	split: test
	revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
	metrics:
	- type: v_measure
	value: 36.92715354123554
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-s2s
	name: MTEB MedrxivClusteringS2S
	config: default
	split: test
	revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
	metrics:
	- type: v_measure
	value: 35.53536244162518
	- task:
	type: Reranking
	dataset:
	type: mteb/mind_small
	name: MTEB MindSmallReranking
	config: default
	split: test
	revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
	metrics:
	- type: map
	value: 33.08507884504006
	- type: mrr
	value: 34.32436977159129
	- task:
	type: Retrieval
	dataset:
	type: nfcorpus
	name: MTEB NFCorpus
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 5.935
	- 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:
	- type: map_at_1
	value: 35.231
	- 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:
	- type: map_at_1
	value: 71.241
	- 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:
	- type: v_measure
	value: 56.59757799655151
	- 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:
	- type: map_at_1
	value: 4.243
	- 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:
	- type: cos_sim_pearson
	value: 85.84209174935282
	- 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:
	- type: cos_sim_pearson
	value: 88.52628804556943
	- 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
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	- type: recall_at_3
	value: 0.7040000000000001
	- type: recall_at_5
	value: 1.172
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	type: BitextMining
	dataset:
	type: mteb/tatoeba-bitext-mining
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	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
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	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
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	- 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
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	- type: precision
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	- 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
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	- 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
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	- 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
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	- 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
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	- 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
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	- 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
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	- type: precision
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	- 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
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	- 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
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	- type: precision
	value: 83.65
	- type: recall
	value: 87.9
	- task:
	type: BitextMining
	dataset:
	type: mteb/tatoeba-bitext-mining
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	config: pes-eng
	split: test
	revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
	metrics:
	- type: accuracy
	value: 95.7
	- type: f1
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	- 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
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	- 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
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	- type: precision
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	- 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
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	- 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
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	- type: precision
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	- 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
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	- type: recall
	value: 95.01347708894879
	- 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: 97.00854700854701
	- type: f1
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	- type: precision
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	- type: recall
	value: 97.00854700854701
	- 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: 96.5
	- type: f1
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	- type: precision
	value: 94.88333333333333
	- type: recall
	value: 96.5
	- 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: 96.5909090909091
	- type: f1
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	- type: precision
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	- type: recall
	value: 96.5909090909091
	- 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: 84.90566037735849
	- type: f1
	value: 81.85883997204752
	- type: precision
	value: 80.54507337526205
	- type: recall
	value: 84.90566037735849
	- 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: 97.5
	- type: f1
	value: 96.75
	- type: precision
	value: 96.38333333333333
	- type: recall
	value: 97.5
	- 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: 86.7704280155642
	- type: f1
	value: 82.99610894941635
	- type: precision
	value: 81.32295719844358
	- type: recall
	value: 86.7704280155642
	- 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: 67.52136752136752
	- type: f1
	value: 61.89662189662191
	- type: precision
	value: 59.68660968660969
	- type: recall
	value: 67.52136752136752
	- 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: 89.2
	- type: f1
	value: 86.32
	- type: precision
	value: 85.015
	- type: recall
	value: 89.2
	- 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: 96.0
	- type: f1
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	- type: precision
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	- type: recall
	value: 96.0
	- 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: 83.8785046728972
	- type: f1
	value: 80.54517133956385
	- type: precision
	value: 79.154984423676
	- type: recall
	value: 83.8785046728972
	- 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: 93.60000000000001
	- type: f1
	value: 92.01333333333334
	- type: precision
	value: 91.28333333333333
	- type: recall
	value: 93.60000000000001
	- 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: 97.1
	- type: f1
	value: 96.26666666666667
	- type: precision
	value: 95.85000000000001
	- type: recall
	value: 97.1
	- 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: 84.3
	- type: f1
	value: 80.67833333333333
	- type: precision
	value: 79.03928571428571
	- type: recall
	value: 84.3
	- 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: 97.3
	- type: f1
	value: 96.48333333333332
	- type: precision
	value: 96.08333333333331
	- type: recall
	value: 97.3
	- 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: 95.7
	- type: f1
	value: 94.66666666666667
	- type: precision
	value: 94.16666666666667
	- type: recall
	value: 95.7
	- 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: 97.2
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	metrics:
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	- task:
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	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
	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](https://arxiv.org/pdf/2402.05672).
	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

	```python
	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

	```python
	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]]
	```

	## Supported Languages

	This model is initialized from [xlm-roberta-large](https://huggingface.co/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](https://huggingface.co/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](https://arxiv.org/abs/2401.00368) paper.

	## MTEB Benchmark Evaluation

	Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
	on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).

	## 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](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) 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.