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	---
	pipeline_tag: sentence-similarity
	license: apache-2.0
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- transformers
	- text2vec
	- mteb
	datasets:
	- shibing624/nli-zh-all
	language:
	- zh
	- en
	- de
	- fr
	- it
	- nl
	- pt
	- pl
	- ru
	metrics:
	- spearmanr
	model-index:
	- name: text2vec-base-multilingual
	results:
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (en)
	config: en
	split: test
	revision: e8379541af4e31359cca9fbcf4b00f2671dba205
	metrics:
	- type: accuracy
	value: 70.97014925373134
	- type: ap
	value: 33.95151328318672
	- type: f1
	value: 65.14740155705596
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (de)
	config: de
	split: test
	revision: e8379541af4e31359cca9fbcf4b00f2671dba205
	metrics:
	- type: accuracy
	value: 68.69379014989293
	- type: ap
	value: 79.68277579733802
	- type: f1
	value: 66.54960052336921
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (en-ext)
	config: en-ext
	split: test
	revision: e8379541af4e31359cca9fbcf4b00f2671dba205
	metrics:
	- type: accuracy
	value: 70.90704647676162
	- type: ap
	value: 20.747518928580437
	- type: f1
	value: 58.64365465884924
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (ja)
	config: ja
	split: test
	revision: e8379541af4e31359cca9fbcf4b00f2671dba205
	metrics:
	- type: accuracy
	value: 61.605995717344754
	- type: ap
	value: 14.135974879487028
	- type: f1
	value: 49.980224800472136
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_polarity
	name: MTEB AmazonPolarityClassification
	config: default
	split: test
	revision: e2d317d38cd51312af73b3d32a06d1a08b442046
	metrics:
	- type: accuracy
	value: 66.103375
	- type: ap
	value: 61.10087197664471
	- type: f1
	value: 65.75198509894145
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (en)
	config: en
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 33.134
	- type: f1
	value: 32.7905397597083
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (de)
	config: de
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 33.388
	- type: f1
	value: 33.190561196873084
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (es)
	config: es
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 34.824
	- type: f1
	value: 34.297290157740726
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (fr)
	config: fr
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 33.449999999999996
	- type: f1
	value: 33.08017234412433
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (ja)
	config: ja
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 30.046
	- type: f1
	value: 29.857141661482228
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (zh)
	config: zh
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 32.522
	- type: f1
	value: 31.854699911472174
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-p2p
	name: MTEB ArxivClusteringP2P
	config: default
	split: test
	revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
	metrics:
	- type: v_measure
	value: 32.31918856561886
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-s2s
	name: MTEB ArxivClusteringS2S
	config: default
	split: test
	revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
	metrics:
	- type: v_measure
	value: 25.503481615956137
	- task:
	type: Reranking
	dataset:
	type: mteb/askubuntudupquestions-reranking
	name: MTEB AskUbuntuDupQuestions
	config: default
	split: test
	revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
	metrics:
	- type: map
	value: 57.91471462820568
	- type: mrr
	value: 71.82990370663501
	- task:
	type: STS
	dataset:
	type: mteb/biosses-sts
	name: MTEB BIOSSES
	config: default
	split: test
	revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
	metrics:
	- type: cos_sim_pearson
	value: 68.83853315193127
	- type: cos_sim_spearman
	value: 66.16174850417771
	- type: euclidean_pearson
	value: 56.65313897263153
	- type: euclidean_spearman
	value: 52.69156205876939
	- type: manhattan_pearson
	value: 56.97282154658304
	- type: manhattan_spearman
	value: 53.167476517261015
	- task:
	type: Classification
	dataset:
	type: mteb/banking77
	name: MTEB Banking77Classification
	config: default
	split: test
	revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
	metrics:
	- type: accuracy
	value: 78.08441558441558
	- type: f1
	value: 77.99825264827898
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-p2p
	name: MTEB BiorxivClusteringP2P
	config: default
	split: test
	revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
	metrics:
	- type: v_measure
	value: 28.98583420521256
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-s2s
	name: MTEB BiorxivClusteringS2S
	config: default
	split: test
	revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
	metrics:
	- type: v_measure
	value: 23.195091778460892
	- task:
	type: Classification
	dataset:
	type: mteb/emotion
	name: MTEB EmotionClassification
	config: default
	split: test
	revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
	metrics:
	- type: accuracy
	value: 43.35
	- type: f1
	value: 38.80269436557695
	- task:
	type: Classification
	dataset:
	type: mteb/imdb
	name: MTEB ImdbClassification
	config: default
	split: test
	revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
	metrics:
	- type: accuracy
	value: 59.348
	- type: ap
	value: 55.75065220262251
	- type: f1
	value: 58.72117519082607
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (en)
	config: en
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 81.04879160966712
	- type: f1
	value: 80.86889779192701
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (de)
	config: de
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 78.59397013243168
	- type: f1
	value: 77.09902761555972
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (es)
	config: es
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 79.24282855236824
	- type: f1
	value: 78.75883867079015
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (fr)
	config: fr
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 76.16661446915127
	- type: f1
	value: 76.30204722831901
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (hi)
	config: hi
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 78.74506991753317
	- type: f1
	value: 77.50560442779701
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (th)
	config: th
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 77.67088607594937
	- type: f1
	value: 77.21442956887493
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (en)
	config: en
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 62.786137710898316
	- type: f1
	value: 46.23474201126368
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (de)
	config: de
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 55.285996055226825
	- type: f1
	value: 37.98039513682919
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (es)
	config: es
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 58.67911941294196
	- type: f1
	value: 40.541410807124954
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (fr)
	config: fr
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 53.257124960851854
	- type: f1
	value: 38.42982319259366
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (hi)
	config: hi
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 59.62352097525995
	- type: f1
	value: 41.28886486568534
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (th)
	config: th
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 58.799276672694404
	- type: f1
	value: 43.68379466247341
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (af)
	config: af
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 45.42030934767989
	- type: f1
	value: 44.12201543566376
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (am)
	config: am
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 37.67652992602556
	- type: f1
	value: 35.422091900843164
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (ar)
	config: ar
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 45.02353732347007
	- type: f1
	value: 41.852484084738194
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (az)
	config: az
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 48.70880968392737
	- type: f1
	value: 46.904360615435046
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (bn)
	config: bn
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 43.78950907868191
	- type: f1
	value: 41.58872353920405
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (cy)
	config: cy
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 28.759246805648957
	- type: f1
	value: 27.41182001374226
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (da)
	config: da
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 56.74176193678547
	- type: f1
	value: 53.82727354182497
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (de)
	config: de
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 51.55682582380632
	- type: f1
	value: 49.41963627941866
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (el)
	config: el
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 56.46940147948891
	- type: f1
	value: 55.28178711367465
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (en)
	config: en
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 63.83322125084063
	- type: f1
	value: 61.836172900845554
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (es)
	config: es
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 58.27505043712172
	- type: f1
	value: 57.642436374361154
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (fa)
	config: fa
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 59.05178211163417
	- type: f1
	value: 56.858998820504056
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (fi)
	config: fi
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 57.357094821788834
	- type: f1
	value: 54.79711189260453
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (fr)
	config: fr
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 58.79959650302623
	- type: f1
	value: 57.59158671719513
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (he)
	config: he
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 51.1768661735037
	- type: f1
	value: 48.886397276270515
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (hi)
	config: hi
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 57.06455951580362
	- type: f1
	value: 55.01530952684585
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (hu)
	config: hu
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 58.3591123066577
	- type: f1
	value: 55.9277783370191
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (hy)
	config: hy
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 52.108271687962336
	- type: f1
	value: 51.195023400664596
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (id)
	config: id
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 58.26832548755883
	- type: f1
	value: 56.60774065423401
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (is)
	config: is
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 35.806993947545394
	- type: f1
	value: 34.290418953173294
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (it)
	config: it
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 58.27841291190315
	- type: f1
	value: 56.9438998642419
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (ja)
	config: ja
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 60.78009414929389
	- type: f1
	value: 59.15780842483667
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (jv)
	config: jv
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 31.153328850033624
	- type: f1
	value: 30.11004596099605
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (ka)
	config: ka
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 44.50235373234701
	- type: f1
	value: 44.040585262624745
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (km)
	config: km
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 40.99193006052455
	- type: f1
	value: 39.505480119272484
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (kn)
	config: kn
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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	name: MTEB MassiveScenarioClassification (pl)
	config: pl
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 62.552118359112306
	- type: f1
	value: 61.354449605776765
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (pt)
	config: pt
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 62.40753194351043
	- type: f1
	value: 61.98779889528889
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (ro)
	config: ro
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 60.68258238063214
	- type: f1
	value: 60.59973978976571
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (ru)
	config: ru
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 62.31002017484868
	- type: f1
	value: 62.412312268503655
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (sl)
	config: sl
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 61.429051782111635
	- type: f1
	value: 61.60095590401424
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (sq)
	config: sq
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 62.229320780094156
	- type: f1
	value: 61.02251426747547
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (sv)
	config: sv
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 64.42501681237391
	- type: f1
	value: 63.461494430605235
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (sw)
	config: sw
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 38.51714862138534
	- type: f1
	value: 37.12466722986362
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (ta)
	config: ta
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 46.99731002017485
	- type: f1
	value: 45.859147049984834
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (te)
	config: te
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 51.01882985877605
	- type: f1
	value: 49.01040173136056
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (th)
	config: th
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 63.234700739744454
	- type: f1
	value: 62.732294595214746
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (tl)
	config: tl
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 38.72225958305312
	- type: f1
	value: 36.603231928120906
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (tr)
	config: tr
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 64.48554135843982
	- type: f1
	value: 63.97380562022752
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (ur)
	config: ur
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 56.7955615332885
	- type: f1
	value: 55.95308241204802
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (vi)
	config: vi
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 57.06455951580362
	- type: f1
	value: 56.95570494066693
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (zh-CN)
	config: zh-CN
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 65.8338937457969
	- type: f1
	value: 65.6778746906008
	- 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: 63.369199731002034
	- type: f1
	value: 63.527650116059945
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-p2p
	name: MTEB MedrxivClusteringP2P
	config: default
	split: test
	revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
	metrics:
	- type: v_measure
	value: 29.442504112215538
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-s2s
	name: MTEB MedrxivClusteringS2S
	config: default
	split: test
	revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
	metrics:
	- type: v_measure
	value: 26.16062814161053
	- task:
	type: Retrieval
	dataset:
	type: quora
	name: MTEB QuoraRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 65.319
	- type: map_at_10
	value: 78.72
	- type: map_at_100
	value: 79.44600000000001
	- type: map_at_1000
	value: 79.469
	- type: map_at_3
	value: 75.693
	- type: map_at_5
	value: 77.537
	- type: mrr_at_1
	value: 75.24
	- type: mrr_at_10
	value: 82.304
	- type: mrr_at_100
	value: 82.485
	- type: mrr_at_1000
	value: 82.489
	- type: mrr_at_3
	value: 81.002
	- type: mrr_at_5
	value: 81.817
	- type: ndcg_at_1
	value: 75.26
	- type: ndcg_at_10
	value: 83.07
	- type: ndcg_at_100
	value: 84.829
	- type: ndcg_at_1000
	value: 85.087
	- type: ndcg_at_3
	value: 79.67699999999999
	- type: ndcg_at_5
	value: 81.42
	- type: precision_at_1
	value: 75.26
	- type: precision_at_10
	value: 12.697
	- type: precision_at_100
	value: 1.4829999999999999
	- type: precision_at_1000
	value: 0.154
	- type: precision_at_3
	value: 34.849999999999994
	- type: precision_at_5
	value: 23.054
	- type: recall_at_1
	value: 65.319
	- type: recall_at_10
	value: 91.551
	- type: recall_at_100
	value: 98.053
	- type: recall_at_1000
	value: 99.516
	- type: recall_at_3
	value: 81.819
	- type: recall_at_5
	value: 86.66199999999999
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering
	name: MTEB RedditClustering
	config: default
	split: test
	revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
	metrics:
	- type: v_measure
	value: 31.249791587189996
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering-p2p
	name: MTEB RedditClusteringP2P
	config: default
	split: test
	revision: 282350215ef01743dc01b456c7f5241fa8937f16
	metrics:
	- type: v_measure
	value: 43.302922383029816
	- task:
	type: STS
	dataset:
	type: mteb/sickr-sts
	name: MTEB SICK-R
	config: default
	split: test
	revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
	metrics:
	- type: cos_sim_pearson
	value: 84.80670811345861
	- type: cos_sim_spearman
	value: 79.97373018384307
	- type: euclidean_pearson
	value: 83.40205934125837
	- type: euclidean_spearman
	value: 79.73331008251854
	- type: manhattan_pearson
	value: 83.3320983393412
	- type: manhattan_spearman
	value: 79.677919746045
	- task:
	type: STS
	dataset:
	type: mteb/sts12-sts
	name: MTEB STS12
	config: default
	split: test
	revision: a0d554a64d88156834ff5ae9920b964011b16384
	metrics:
	- type: cos_sim_pearson
	value: 86.3816087627948
	- type: cos_sim_spearman
	value: 80.91314664846955
	- type: euclidean_pearson
	value: 85.10603071031096
	- type: euclidean_spearman
	value: 79.42663939501841
	- type: manhattan_pearson
	value: 85.16096376014066
	- type: manhattan_spearman
	value: 79.51936545543191
	- task:
	type: STS
	dataset:
	type: mteb/sts13-sts
	name: MTEB STS13
	config: default
	split: test
	revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
	metrics:
	- type: cos_sim_pearson
	value: 80.44665329940209
	- type: cos_sim_spearman
	value: 82.86479010707745
	- type: euclidean_pearson
	value: 84.06719627734672
	- type: euclidean_spearman
	value: 84.9356099976297
	- type: manhattan_pearson
	value: 84.10370009572624
	- type: manhattan_spearman
	value: 84.96828040546536
	- task:
	type: STS
	dataset:
	type: mteb/sts14-sts
	name: MTEB STS14
	config: default
	split: test
	revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
	metrics:
	- type: cos_sim_pearson
	value: 86.05704260568437
	- type: cos_sim_spearman
	value: 87.36399473803172
	- type: euclidean_pearson
	value: 86.8895170159388
	- type: euclidean_spearman
	value: 87.16246440866921
	- type: manhattan_pearson
	value: 86.80814774538997
	- type: manhattan_spearman
	value: 87.09320142699522
	- task:
	type: STS
	dataset:
	type: mteb/sts15-sts
	name: MTEB STS15
	config: default
	split: test
	revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
	metrics:
	- type: cos_sim_pearson
	value: 85.97825118945852
	- type: cos_sim_spearman
	value: 88.31438033558268
	- type: euclidean_pearson
	value: 87.05174694758092
	- type: euclidean_spearman
	value: 87.80659468392355
	- type: manhattan_pearson
	value: 86.98831322198717
	- type: manhattan_spearman
	value: 87.72820615049285
	- task:
	type: STS
	dataset:
	type: mteb/sts16-sts
	name: MTEB STS16
	config: default
	split: test
	revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
	metrics:
	- type: cos_sim_pearson
	value: 78.68745420126719
	- type: cos_sim_spearman
	value: 81.6058424699445
	- type: euclidean_pearson
	value: 81.16540133861879
	- type: euclidean_spearman
	value: 81.86377535458067
	- type: manhattan_pearson
	value: 81.13813317937021
	- type: manhattan_spearman
	value: 81.87079962857256
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (ko-ko)
	config: ko-ko
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 68.06192660936868
	- type: cos_sim_spearman
	value: 68.2376353514075
	- type: euclidean_pearson
	value: 60.68326946956215
	- type: euclidean_spearman
	value: 59.19352349785952
	- type: manhattan_pearson
	value: 60.6592944683418
	- type: manhattan_spearman
	value: 59.167534419270865
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (ar-ar)
	config: ar-ar
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 76.78098264855684
	- type: cos_sim_spearman
	value: 78.02670452969812
	- type: euclidean_pearson
	value: 77.26694463661255
	- type: euclidean_spearman
	value: 77.47007626009587
	- type: manhattan_pearson
	value: 77.25070088632027
	- type: manhattan_spearman
	value: 77.36368265830724
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (en-ar)
	config: en-ar
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 78.45418506379532
	- type: cos_sim_spearman
	value: 78.60412019902428
	- type: euclidean_pearson
	value: 79.90303710850512
	- type: euclidean_spearman
	value: 78.67123625004957
	- type: manhattan_pearson
	value: 80.09189580897753
	- type: manhattan_spearman
	value: 79.02484481441483
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (en-de)
	config: en-de
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 82.35556731232779
	- type: cos_sim_spearman
	value: 81.48249735354844
	- type: euclidean_pearson
	value: 81.66748026636621
	- type: euclidean_spearman
	value: 80.35571574338547
	- type: manhattan_pearson
	value: 81.38214732806365
	- type: manhattan_spearman
	value: 79.9018202958774
	- 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: 86.4527703176897
	- type: cos_sim_spearman
	value: 85.81084095829584
	- type: euclidean_pearson
	value: 86.43489162324457
	- type: euclidean_spearman
	value: 85.27110976093296
	- type: manhattan_pearson
	value: 86.43674259444512
	- type: manhattan_spearman
	value: 85.05719308026032
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (en-tr)
	config: en-tr
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 76.00411240034492
	- type: cos_sim_spearman
	value: 76.33887356560854
	- type: euclidean_pearson
	value: 76.81730660019446
	- type: euclidean_spearman
	value: 75.04432185451306
	- type: manhattan_pearson
	value: 77.22298813168995
	- type: manhattan_spearman
	value: 75.56420330256725
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (es-en)
	config: es-en
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 79.1447136836213
	- type: cos_sim_spearman
	value: 81.80823850788917
	- type: euclidean_pearson
	value: 80.84505734814422
	- type: euclidean_spearman
	value: 81.714168092736
	- type: manhattan_pearson
	value: 80.84713816174187
	- type: manhattan_spearman
	value: 81.61267814749516
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (es-es)
	config: es-es
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 87.01257457052873
	- type: cos_sim_spearman
	value: 87.91146458004216
	- type: euclidean_pearson
	value: 88.36771859717994
	- type: euclidean_spearman
	value: 87.73182474597515
	- type: manhattan_pearson
	value: 88.26551451003671
	- type: manhattan_spearman
	value: 87.71675151388992
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (fr-en)
	config: fr-en
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 79.20121618382373
	- type: cos_sim_spearman
	value: 78.05794691968603
	- type: euclidean_pearson
	value: 79.93819925682054
	- type: euclidean_spearman
	value: 78.00586118701553
	- type: manhattan_pearson
	value: 80.05598625820885
	- type: manhattan_spearman
	value: 78.04802948866832
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (it-en)
	config: it-en
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 81.51743373871778
	- type: cos_sim_spearman
	value: 80.98266651818703
	- type: euclidean_pearson
	value: 81.11875722505269
	- type: euclidean_spearman
	value: 79.45188413284538
	- type: manhattan_pearson
	value: 80.7988457619225
	- type: manhattan_spearman
	value: 79.49643569311485
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (nl-en)
	config: nl-en
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 81.78679924046351
	- type: cos_sim_spearman
	value: 80.9986574147117
	- type: euclidean_pearson
	value: 82.09130079135713
	- type: euclidean_spearman
	value: 80.66215667390159
	- type: manhattan_pearson
	value: 82.0328610549654
	- type: manhattan_spearman
	value: 80.31047226932408
	- 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: 58.08082172994642
	- type: cos_sim_spearman
	value: 62.9940530222459
	- type: euclidean_pearson
	value: 58.47927303460365
	- type: euclidean_spearman
	value: 60.8440317609258
	- type: manhattan_pearson
	value: 58.32438211697841
	- type: manhattan_spearman
	value: 60.69642636776064
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (de)
	config: de
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 33.83985707464123
	- type: cos_sim_spearman
	value: 46.89093209603036
	- type: euclidean_pearson
	value: 34.63602187576556
	- type: euclidean_spearman
	value: 46.31087228200712
	- type: manhattan_pearson
	value: 34.66899391543166
	- type: manhattan_spearman
	value: 46.33049538425276
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (es)
	config: es
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 51.61315965767736
	- type: cos_sim_spearman
	value: 58.9434266730386
	- type: euclidean_pearson
	value: 50.35885602217862
	- type: euclidean_spearman
	value: 58.238679883286025
	- type: manhattan_pearson
	value: 53.01732044381151
	- type: manhattan_spearman
	value: 58.10482351761412
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (pl)
	config: pl
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 26.771738440430177
	- type: cos_sim_spearman
	value: 34.807259227816054
	- type: euclidean_pearson
	value: 17.82657835823811
	- type: euclidean_spearman
	value: 34.27912898498941
	- type: manhattan_pearson
	value: 19.121527758886312
	- type: manhattan_spearman
	value: 34.4940050226265
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (tr)
	config: tr
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 52.8354704676683
	- type: cos_sim_spearman
	value: 57.28629534815841
	- type: euclidean_pearson
	value: 54.10329332004385
	- type: euclidean_spearman
	value: 58.15030615859976
	- type: manhattan_pearson
	value: 55.42372087433115
	- type: manhattan_spearman
	value: 57.52270736584036
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (ar)
	config: ar
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 31.01976557986924
	- type: cos_sim_spearman
	value: 54.506959483927616
	- type: euclidean_pearson
	value: 36.917863022119086
	- type: euclidean_spearman
	value: 53.750194241538566
	- type: manhattan_pearson
	value: 37.200177833241085
	- type: manhattan_spearman
	value: 53.507659188082535
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (ru)
	config: ru
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 46.38635647225934
	- type: cos_sim_spearman
	value: 54.50892732637536
	- type: euclidean_pearson
	value: 40.8331015184763
	- type: euclidean_spearman
	value: 53.142903182230924
	- type: manhattan_pearson
	value: 43.07655692906317
	- type: manhattan_spearman
	value: 53.5833474125901
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (zh)
	config: zh
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 60.52525456662916
	- type: cos_sim_spearman
	value: 63.23975489531082
	- type: euclidean_pearson
	value: 58.989191722317514
	- type: euclidean_spearman
	value: 62.536326639863894
	- type: manhattan_pearson
	value: 61.32982866201855
	- type: manhattan_spearman
	value: 63.068262822520516
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (fr)
	config: fr
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 59.63798684577696
	- type: cos_sim_spearman
	value: 74.09937723367189
	- type: euclidean_pearson
	value: 63.77494904383906
	- type: euclidean_spearman
	value: 71.15932571292481
	- type: manhattan_pearson
	value: 63.69646122775205
	- type: manhattan_spearman
	value: 70.54960698541632
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (de-en)
	config: de-en
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 36.50262468726711
	- type: cos_sim_spearman
	value: 45.00322499674274
	- type: euclidean_pearson
	value: 32.58759216581778
	- type: euclidean_spearman
	value: 40.13720951315429
	- type: manhattan_pearson
	value: 34.88422299605277
	- type: manhattan_spearman
	value: 40.63516862200963
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (es-en)
	config: es-en
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 56.498552617040275
	- type: cos_sim_spearman
	value: 67.71358426124443
	- type: euclidean_pearson
	value: 57.16474781778287
	- type: euclidean_spearman
	value: 65.721515493531
	- type: manhattan_pearson
	value: 59.25227610738926
	- type: manhattan_spearman
	value: 65.89743680340739
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (it)
	config: it
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 55.97978814727984
	- type: cos_sim_spearman
	value: 65.85821395092104
	- type: euclidean_pearson
	value: 59.11117270978519
	- type: euclidean_spearman
	value: 64.50062069934965
	- type: manhattan_pearson
	value: 59.4436213778161
	- type: manhattan_spearman
	value: 64.4003273074382
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (pl-en)
	config: pl-en
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 58.00873192515712
	- type: cos_sim_spearman
	value: 60.167708809138745
	- type: euclidean_pearson
	value: 56.91950637760252
	- type: euclidean_spearman
	value: 58.50593399441014
	- type: manhattan_pearson
	value: 58.683747352584994
	- type: manhattan_spearman
	value: 59.38110066799761
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (zh-en)
	config: zh-en
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 54.26020658151187
	- type: cos_sim_spearman
	value: 61.29236187204147
	- type: euclidean_pearson
	value: 55.993896804147056
	- type: euclidean_spearman
	value: 58.654928232615354
	- type: manhattan_pearson
	value: 56.612492816099426
	- type: manhattan_spearman
	value: 58.65144067094258
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (es-it)
	config: es-it
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 49.13817835368122
	- type: cos_sim_spearman
	value: 50.78524216975442
	- type: euclidean_pearson
	value: 46.56046454501862
	- type: euclidean_spearman
	value: 50.3935060082369
	- type: manhattan_pearson
	value: 48.0232348418531
	- type: manhattan_spearman
	value: 50.79528358464199
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (de-fr)
	config: de-fr
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 44.274388638585286
	- type: cos_sim_spearman
	value: 49.43124017389838
	- type: euclidean_pearson
	value: 42.45909582681174
	- type: euclidean_spearman
	value: 49.661383797129055
	- type: manhattan_pearson
	value: 42.5771970142383
	- type: manhattan_spearman
	value: 50.14423414390715
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (de-pl)
	config: de-pl
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 26.119500839749776
	- type: cos_sim_spearman
	value: 39.324070169024424
	- type: euclidean_pearson
	value: 35.83247077201831
	- type: euclidean_spearman
	value: 42.61903924348457
	- type: manhattan_pearson
	value: 35.50415034487894
	- type: manhattan_spearman
	value: 41.87998075949351
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (fr-pl)
	config: fr-pl
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 72.62575835691209
	- type: cos_sim_spearman
	value: 73.24670207647144
	- type: euclidean_pearson
	value: 78.07793323914657
	- type: euclidean_spearman
	value: 73.24670207647144
	- type: manhattan_pearson
	value: 77.51429306378206
	- type: manhattan_spearman
	value: 73.24670207647144
	- task:
	type: STS
	dataset:
	type: mteb/stsbenchmark-sts
	name: MTEB STSBenchmark
	config: default
	split: test
	revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
	metrics:
	- type: cos_sim_pearson
	value: 84.09375596849891
	- type: cos_sim_spearman
	value: 86.44881302053585
	- type: euclidean_pearson
	value: 84.71259163967213
	- type: euclidean_spearman
	value: 85.63661992344069
	- type: manhattan_pearson
	value: 84.64466537502614
	- type: manhattan_spearman
	value: 85.53769949940238
	- task:
	type: Reranking
	dataset:
	type: mteb/scidocs-reranking
	name: MTEB SciDocsRR
	config: default
	split: test
	revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
	metrics:
	- type: map
	value: 70.2056154684549
	- type: mrr
	value: 89.52703161036494
	- task:
	type: PairClassification
	dataset:
	type: mteb/sprintduplicatequestions-pairclassification
	name: MTEB SprintDuplicateQuestions
	config: default
	split: test
	revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
	metrics:
	- type: cos_sim_accuracy
	value: 99.57623762376238
	- type: cos_sim_ap
	value: 83.53051588811371
	- type: cos_sim_f1
	value: 77.72704211060375
	- type: cos_sim_precision
	value: 78.88774459320288
	- type: cos_sim_recall
	value: 76.6
	- type: dot_accuracy
	value: 99.06435643564356
	- type: dot_ap
	value: 27.003124923857463
	- type: dot_f1
	value: 34.125269978401725
	- type: dot_precision
	value: 37.08920187793427
	- type: dot_recall
	value: 31.6
	- type: euclidean_accuracy
	value: 99.61485148514852
	- type: euclidean_ap
	value: 85.47332647001774
	- type: euclidean_f1
	value: 80.0808897876643
	- type: euclidean_precision
	value: 80.98159509202453
	- type: euclidean_recall
	value: 79.2
	- type: manhattan_accuracy
	value: 99.61683168316831
	- type: manhattan_ap
	value: 85.41969859598552
	- type: manhattan_f1
	value: 79.77755308392315
	- type: manhattan_precision
	value: 80.67484662576688
	- type: manhattan_recall
	value: 78.9
	- type: max_accuracy
	value: 99.61683168316831
	- type: max_ap
	value: 85.47332647001774
	- type: max_f1
	value: 80.0808897876643
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering
	name: MTEB StackExchangeClustering
	config: default
	split: test
	revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
	metrics:
	- type: v_measure
	value: 34.35688940053467
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering-p2p
	name: MTEB StackExchangeClusteringP2P
	config: default
	split: test
	revision: 815ca46b2622cec33ccafc3735d572c266efdb44
	metrics:
	- type: v_measure
	value: 30.64427069276576
	- task:
	type: Reranking
	dataset:
	type: mteb/stackoverflowdupquestions-reranking
	name: MTEB StackOverflowDupQuestions
	config: default
	split: test
	revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
	metrics:
	- type: map
	value: 44.89500754900078
	- type: mrr
	value: 45.33215558950853
	- task:
	type: Summarization
	dataset:
	type: mteb/summeval
	name: MTEB SummEval
	config: default
	split: test
	revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
	metrics:
	- type: cos_sim_pearson
	value: 30.653069624224084
	- type: cos_sim_spearman
	value: 30.10187112430319
	- type: dot_pearson
	value: 28.966278202103666
	- type: dot_spearman
	value: 28.342234095507767
	- task:
	type: Classification
	dataset:
	type: mteb/toxic_conversations_50k
	name: MTEB ToxicConversationsClassification
	config: default
	split: test
	revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
	metrics:
	- type: accuracy
	value: 65.96839999999999
	- type: ap
	value: 11.846327590186444
	- type: f1
	value: 50.518102944693574
	- task:
	type: Classification
	dataset:
	type: mteb/tweet_sentiment_extraction
	name: MTEB TweetSentimentExtractionClassification
	config: default
	split: test
	revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
	metrics:
	- type: accuracy
	value: 55.220713073005086
	- type: f1
	value: 55.47856175692088
	- task:
	type: Clustering
	dataset:
	type: mteb/twentynewsgroups-clustering
	name: MTEB TwentyNewsgroupsClustering
	config: default
	split: test
	revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
	metrics:
	- type: v_measure
	value: 31.581473892235877
	- task:
	type: PairClassification
	dataset:
	type: mteb/twittersemeval2015-pairclassification
	name: MTEB TwitterSemEval2015
	config: default
	split: test
	revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
	metrics:
	- type: cos_sim_accuracy
	value: 82.94093103653812
	- type: cos_sim_ap
	value: 62.48963249213361
	- type: cos_sim_f1
	value: 58.9541137429912
	- type: cos_sim_precision
	value: 52.05091937765205
	- type: cos_sim_recall
	value: 67.96833773087072
	- type: dot_accuracy
	value: 78.24998509864696
	- type: dot_ap
	value: 40.82371294480071
	- type: dot_f1
	value: 44.711163153786096
	- type: dot_precision
	value: 35.475379374419326
	- type: dot_recall
	value: 60.4485488126649
	- type: euclidean_accuracy
	value: 83.13166835548668
	- type: euclidean_ap
	value: 63.459878609769774
	- type: euclidean_f1
	value: 60.337199569532466
	- type: euclidean_precision
	value: 55.171659741963694
	- type: euclidean_recall
	value: 66.56992084432719
	- type: manhattan_accuracy
	value: 83.00649698992669
	- type: manhattan_ap
	value: 63.263161177904905
	- type: manhattan_f1
	value: 60.17122874713614
	- type: manhattan_precision
	value: 55.40750610703975
	- type: manhattan_recall
	value: 65.8311345646438
	- type: max_accuracy
	value: 83.13166835548668
	- type: max_ap
	value: 63.459878609769774
	- type: max_f1
	value: 60.337199569532466
	- task:
	type: PairClassification
	dataset:
	type: mteb/twitterurlcorpus-pairclassification
	name: MTEB TwitterURLCorpus
	config: default
	split: test
	revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
	metrics:
	- type: cos_sim_accuracy
	value: 87.80416812201653
	- type: cos_sim_ap
	value: 83.45540469219863
	- type: cos_sim_f1
	value: 75.58836427422892
	- type: cos_sim_precision
	value: 71.93934335002783
	- type: cos_sim_recall
	value: 79.62734832152756
	- type: dot_accuracy
	value: 83.04226336011176
	- type: dot_ap
	value: 70.63007268018524
	- type: dot_f1
	value: 65.35980325765405
	- type: dot_precision
	value: 60.84677151768532
	- type: dot_recall
	value: 70.59593470896212
	- type: euclidean_accuracy
	value: 87.60430007373773
	- type: euclidean_ap
	value: 83.10068502536592
	- type: euclidean_f1
	value: 75.02510506936439
	- type: euclidean_precision
	value: 72.56637168141593
	- type: euclidean_recall
	value: 77.65629812134279
	- type: manhattan_accuracy
	value: 87.60041914076145
	- type: manhattan_ap
	value: 83.05480769911229
	- type: manhattan_f1
	value: 74.98522895125554
	- type: manhattan_precision
	value: 72.04797047970479
	- type: manhattan_recall
	value: 78.17215891592238
	- type: max_accuracy
	value: 87.80416812201653
	- type: max_ap
	value: 83.45540469219863
	- type: max_f1
	value: 75.58836427422892
	---
	# shibing624/text2vec-base-multilingual
	This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-multilingual.

	It maps sentences to a 384 dimensional dense vector space and can be used for tasks
	like sentence embeddings, text matching or semantic search.



	- training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset
	- base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	- max_seq_length: 256
	- best epoch: 4
	- sentence embedding dim: 384

	## Evaluation
	For an automated evaluation of this model, see the Evaluation Benchmark: [text2vec](https://github.com/shibing624/text2vec)
	## Languages
	Available languages are: de, en, es, fr, it, nl, pl, pt, ru, zh

	### Release Models

	- 本项目release模型的中文匹配评测结果：

	\| Arch \| BaseModel \| Model \| ATEC \| BQ \| LCQMC \| PAWSX \| STS-B \| SOHU-dd \| SOHU-dc \| Avg \| QPS \|
	\|:-----------\|:-------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------\|:-----:\|:-----:\|:-----:\|:-----:\|:-----:\|:-------:\|:-------:\|:---------:\|:-----:\|
	\| Word2Vec \| word2vec \| [w2v-light-tencent-chinese](https://ai.tencent.com/ailab/nlp/en/download.html) \| 20.00 \| 31.49 \| 59.46 \| 2.57 \| 55.78 \| 55.04 \| 20.70 \| 35.03 \| 23769 \|
	\| SBERT \| xlm-roberta-base \| [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) \| 18.42 \| 38.52 \| 63.96 \| 10.14 \| 78.90 \| 63.01 \| 52.28 \| 46.46 \| 3138 \|
	\| Instructor \| hfl/chinese-roberta-wwm-ext \| [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base) \| 41.27 \| 63.81 \| 74.87 \| 12.20 \| 76.96 \| 75.83 \| 60.55 \| 57.93 \| 2980 \|
	\| CoSENT \| hfl/chinese-macbert-base \| [shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) \| 31.93 \| 42.67 \| 70.16 \| 17.21 \| 79.30 \| 70.27 \| 50.42 \| 51.61 \| 3008 \|
	\| CoSENT \| hfl/chinese-lert-large \| [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese) \| 32.61 \| 44.59 \| 69.30 \| 14.51 \| 79.44 \| 73.01 \| 59.04 \| 53.12 \| 2092 \|
	\| CoSENT \| nghuyong/ernie-3.0-base-zh \| [shibing624/text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence) \| 43.37 \| 61.43 \| 73.48 \| 38.90 \| 78.25 \| 70.60 \| 53.08 \| 59.87 \| 3089 \|
	\| CoSENT \| nghuyong/ernie-3.0-base-zh \| [shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase) \| 44.89 \| 63.58 \| 74.24 \| 40.90 \| 78.93 \| 76.70 \| 63.30 \| 63.08 \| 3066 \|
	\| CoSENT \| sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 \| [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual) \| 32.39 \| 50.33 \| 65.64 \| 32.56 \| 74.45 \| 68.88 \| 51.17 \| 53.67 \| 4004 \|


	说明：
	- 结果评测指标：spearman系数
	- `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行[examples/training_sup_text_matching_model.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model.py)代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用
	- `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)训练得到，并在中文各NLI测试集评估达到较好效果，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用
	- `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset)，数据集相对于[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用
	- `shibing624/text2vec-base-multilingual`模型，是用CoSENT方法训练，基于`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`用人工挑选后的多语言STS数据集[shibing624/nli-zh-all/text2vec-base-multilingual-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset)训练得到，并在中英文测试集评估相对于原模型效果有提升，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，多语言语义匹配任务推荐使用
	- `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况
	- QPS的GPU测试环境是Tesla V100，显存32GB

	模型训练实验报告：[实验报告](https://github.com/shibing624/text2vec/blob/master/docs/model_report.md)

	## Usage (text2vec)
	Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed:

	```
	pip install -U text2vec
	```

	Then you can use the model like this:

	```python
	from text2vec import SentenceModel
	sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card']

	model = SentenceModel('shibing624/text2vec-base-multilingual')
	embeddings = model.encode(sentences)
	print(embeddings)
	```

	## Usage (HuggingFace Transformers)
	Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this:

	First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

	Install transformers:
	```
	pip install transformers
	```

	Then load model and predict:
	```python
	from transformers import AutoTokenizer, AutoModel
	import torch

	# Mean Pooling - Take attention mask into account for correct averaging
	def mean_pooling(model_output, attention_mask):
	token_embeddings = model_output[0] # First element of model_output contains all token embeddings
	input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
	return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

	# Load model from HuggingFace Hub
	tokenizer = AutoTokenizer.from_pretrained('shibing624/text2vec-base-multilingual')
	model = AutoModel.from_pretrained('shibing624/text2vec-base-multilingual')
	sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card']
	# Tokenize sentences
	encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

	# Compute token embeddings
	with torch.no_grad():
	model_output = model(**encoded_input)
	# Perform pooling. In this case, mean pooling.
	sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
	print("Sentence embeddings:")
	print(sentence_embeddings)
	```

	## Usage (sentence-transformers)
	[sentence-transformers](https://github.com/UKPLab/sentence-transformers) is a popular library to compute dense vector representations for sentences.

	Install sentence-transformers:
	```
	pip install -U sentence-transformers
	```

	Then load model and predict:

	```python
	from sentence_transformers import SentenceTransformer

	m = SentenceTransformer("shibing624/text2vec-base-multilingual")
	sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card']

	sentence_embeddings = m.encode(sentences)
	print("Sentence embeddings:")
	print(sentence_embeddings)
	```


	## Full Model Architecture
	```
	CoSENT(
	(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_mean_tokens': True})
	)
	```


	## Intended uses

	Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
	the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.

	By default, input text longer than 256 word pieces is truncated.


	## Training procedure

	### Pre-training

	We use the pretrained [`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) model.
	Please refer to the model card for more detailed information about the pre-training procedure.

	### Fine-tuning

	We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each
	possible sentence pairs from the batch.
	We then apply the rank loss by comparing with true pairs and false pairs.


	## Citing & Authors
	This model was trained by [text2vec](https://github.com/shibing624/text2vec).

	If you find this model helpful, feel free to cite:
	```bibtex
	@software{text2vec,
	author = {Ming Xu},
	title = {text2vec: A Tool for Text to Vector},
	year = {2023},
	url = {https://github.com/shibing624/text2vec},
	}
	```