sentence-t5-base / README.md

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	---
	pipeline_tag: sentence-similarity
	language: en
	license: apache-2.0
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- transformers
	- mteb
	model-index:
	- name: sentence-t5-base
	results:
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (en)
	config: en
	split: test
	metrics:
	- type: accuracy
	value: 75.82089552238807
	- type: ap
	value: 40.58809426967639
	- type: f1
	value: 70.5050115572668
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (de)
	config: de
	split: test
	metrics:
	- type: accuracy
	value: 69.97858672376874
	- type: ap
	value: 80.89622545806847
	- type: f1
	value: 68.09770164363411
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (en-ext)
	config: en-ext
	split: test
	metrics:
	- type: accuracy
	value: 76.80659670164917
	- type: ap
	value: 26.663544686227127
	- type: f1
	value: 64.52406535274052
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (ja)
	config: ja
	split: test
	metrics:
	- type: accuracy
	value: 46.04925053533191
	- type: ap
	value: 10.574096802771448
	- type: f1
	value: 36.74441737116304
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_polarity
	name: MTEB AmazonPolarityClassification
	config: default
	split: test
	metrics:
	- type: accuracy
	value: 85.11737500000001
	- type: ap
	value: 81.28435308927632
	- type: f1
	value: 85.01612484917347
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (en)
	config: en
	split: test
	metrics:
	- type: accuracy
	value: 44.943999999999996
	- type: f1
	value: 42.681783855948844
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (de)
	config: de
	split: test
	metrics:
	- type: accuracy
	value: 37.895999999999994
	- type: f1
	value: 35.428429230946115
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (es)
	config: es
	split: test
	metrics:
	- type: accuracy
	value: 37.328
	- type: f1
	value: 34.26335456752553
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (fr)
	config: fr
	split: test
	metrics:
	- type: accuracy
	value: 37.35
	- type: f1
	value: 34.644931974230495
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (ja)
	config: ja
	split: test
	metrics:
	- type: accuracy
	value: 22.290000000000003
	- type: f1
	value: 20.438677904046305
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (zh)
	config: zh
	split: test
	metrics:
	- type: accuracy
	value: 21.529999999999998
	- type: f1
	value: 18.273004097867844
	- task:
	type: Retrieval
	dataset:
	type: arguana
	name: MTEB ArguAna
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 21.906
	- type: map_at_10
	value: 35.993
	- type: map_at_100
	value: 37.14
	- type: map_at_1000
	value: 37.153999999999996
	- type: map_at_3
	value: 30.642000000000003
	- type: map_at_5
	value: 33.534000000000006
	- type: ndcg_at_1
	value: 21.906
	- type: ndcg_at_10
	value: 44.846000000000004
	- type: ndcg_at_100
	value: 49.95
	- type: ndcg_at_1000
	value: 50.29
	- type: ndcg_at_3
	value: 33.579
	- type: ndcg_at_5
	value: 38.807
	- type: precision_at_1
	value: 21.906
	- type: precision_at_10
	value: 7.367999999999999
	- type: precision_at_100
	value: 0.966
	- type: precision_at_1000
	value: 0.099
	- type: precision_at_3
	value: 14.035
	- type: precision_at_5
	value: 10.967
	- type: recall_at_1
	value: 21.906
	- type: recall_at_10
	value: 73.68400000000001
	- type: recall_at_100
	value: 96.586
	- type: recall_at_1000
	value: 99.14699999999999
	- type: recall_at_3
	value: 42.105
	- type: recall_at_5
	value: 54.836
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-p2p
	name: MTEB ArxivClusteringP2P
	config: default
	split: test
	metrics:
	- type: v_measure
	value: 39.27529166223639
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-s2s
	name: MTEB ArxivClusteringS2S
	config: default
	split: test
	metrics:
	- type: v_measure
	value: 27.261128959373327
	- task:
	type: Reranking
	dataset:
	type: mteb/askubuntudupquestions-reranking
	name: MTEB AskUbuntuDupQuestions
	config: default
	split: test
	metrics:
	- type: map
	value: 59.72875661091822
	- type: mrr
	value: 72.76997317856043
	- task:
	type: STS
	dataset:
	type: mteb/biosses-sts
	name: MTEB BIOSSES
	config: default
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 75.50587493517146
	- type: cos_sim_spearman
	value: 75.89088585182279
	- type: euclidean_pearson
	value: 75.74627833999679
	- type: euclidean_spearman
	value: 75.89088585182279
	- type: manhattan_pearson
	value: 76.10746255262428
	- type: manhattan_spearman
	value: 75.93968214440233
	- task:
	type: Classification
	dataset:
	type: mteb/banking77
	name: MTEB Banking77Classification
	config: default
	split: test
	metrics:
	- type: accuracy
	value: 76.47727272727273
	- type: f1
	value: 75.41900393828456
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-p2p
	name: MTEB BiorxivClusteringP2P
	config: default
	split: test
	metrics:
	- type: v_measure
	value: 33.98533095653499
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-s2s
	name: MTEB BiorxivClusteringS2S
	config: default
	split: test
	metrics:
	- type: v_measure
	value: 22.921149832439514
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackAndroidRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 27.97
	- type: map_at_10
	value: 39.523
	- type: map_at_100
	value: 41.101
	- type: map_at_1000
	value: 41.221000000000004
	- type: map_at_3
	value: 36.193999999999996
	- type: map_at_5
	value: 37.952000000000005
	- type: ndcg_at_1
	value: 34.621
	- type: ndcg_at_10
	value: 46.18
	- type: ndcg_at_100
	value: 51.93600000000001
	- type: ndcg_at_1000
	value: 53.833
	- type: ndcg_at_3
	value: 41.091
	- type: ndcg_at_5
	value: 43.230000000000004
	- type: precision_at_1
	value: 34.621
	- type: precision_at_10
	value: 9.041
	- type: precision_at_100
	value: 1.525
	- type: precision_at_1000
	value: 0.19499999999999998
	- type: precision_at_3
	value: 20.029
	- type: precision_at_5
	value: 14.335
	- type: recall_at_1
	value: 27.97
	- type: recall_at_10
	value: 59.325
	- type: recall_at_100
	value: 82.917
	- type: recall_at_1000
	value: 95.175
	- type: recall_at_3
	value: 44.251000000000005
	- type: recall_at_5
	value: 50.383
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackEnglishRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 23.701
	- type: map_at_10
	value: 32.094
	- type: map_at_100
	value: 33.293
	- type: map_at_1000
	value: 33.434999999999995
	- type: map_at_3
	value: 29.609999999999996
	- type: map_at_5
	value: 31.16
	- type: ndcg_at_1
	value: 30.573
	- type: ndcg_at_10
	value: 37.031
	- type: ndcg_at_100
	value: 42.001
	- type: ndcg_at_1000
	value: 44.714
	- type: ndcg_at_3
	value: 33.434999999999995
	- type: ndcg_at_5
	value: 35.356
	- type: precision_at_1
	value: 30.573
	- type: precision_at_10
	value: 6.854
	- type: precision_at_100
	value: 1.192
	- type: precision_at_1000
	value: 0.174
	- type: precision_at_3
	value: 16.178
	- type: precision_at_5
	value: 11.567
	- type: recall_at_1
	value: 23.701
	- type: recall_at_10
	value: 45.755
	- type: recall_at_100
	value: 67.035
	- type: recall_at_1000
	value: 84.893
	- type: recall_at_3
	value: 34.977999999999994
	- type: recall_at_5
	value: 40.357
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackGamingRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 35.617
	- type: map_at_10
	value: 47.774
	- type: map_at_100
	value: 48.943999999999996
	- type: map_at_1000
	value: 49.007
	- type: map_at_3
	value: 44.214999999999996
	- type: map_at_5
	value: 46.291
	- type: ndcg_at_1
	value: 40.627
	- type: ndcg_at_10
	value: 53.952
	- type: ndcg_at_100
	value: 58.55200000000001
	- type: ndcg_at_1000
	value: 59.824
	- type: ndcg_at_3
	value: 47.911
	- type: ndcg_at_5
	value: 50.966
	- type: precision_at_1
	value: 40.627
	- type: precision_at_10
	value: 8.884
	- type: precision_at_100
	value: 1.213
	- type: precision_at_1000
	value: 0.13699999999999998
	- type: precision_at_3
	value: 21.337999999999997
	- type: precision_at_5
	value: 15.034
	- type: recall_at_1
	value: 35.617
	- type: recall_at_10
	value: 68.73599999999999
	- type: recall_at_100
	value: 88.42999999999999
	- type: recall_at_1000
	value: 97.455
	- type: recall_at_3
	value: 52.915
	- type: recall_at_5
	value: 60.182
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackGisRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 21.029999999999998
	- type: map_at_10
	value: 27.915
	- type: map_at_100
	value: 28.924
	- type: map_at_1000
	value: 29.023
	- type: map_at_3
	value: 25.634
	- type: map_at_5
	value: 26.934
	- type: ndcg_at_1
	value: 22.599
	- type: ndcg_at_10
	value: 32.340999999999994
	- type: ndcg_at_100
	value: 37.422
	- type: ndcg_at_1000
	value: 40.014
	- type: ndcg_at_3
	value: 27.604
	- type: ndcg_at_5
	value: 29.872
	- type: precision_at_1
	value: 22.599
	- type: precision_at_10
	value: 5.051
	- type: precision_at_100
	value: 0.799
	- type: precision_at_1000
	value: 0.106
	- type: precision_at_3
	value: 11.562999999999999
	- type: precision_at_5
	value: 8.225999999999999
	- type: recall_at_1
	value: 21.029999999999998
	- type: recall_at_10
	value: 44.226
	- type: recall_at_100
	value: 67.902
	- type: recall_at_1000
	value: 87.497
	- type: recall_at_3
	value: 31.389
	- type: recall_at_5
	value: 36.888
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackMathematicaRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 12.592
	- type: map_at_10
	value: 20.054
	- type: map_at_100
	value: 21.384
	- type: map_at_1000
	value: 21.52
	- type: map_at_3
	value: 17.718999999999998
	- type: map_at_5
	value: 19.189999999999998
	- type: ndcg_at_1
	value: 15.299
	- type: ndcg_at_10
	value: 24.698
	- type: ndcg_at_100
	value: 31.080000000000002
	- type: ndcg_at_1000
	value: 34.266000000000005
	- type: ndcg_at_3
	value: 20.331
	- type: ndcg_at_5
	value: 22.735
	- type: precision_at_1
	value: 15.299
	- type: precision_at_10
	value: 4.776
	- type: precision_at_100
	value: 0.928
	- type: precision_at_1000
	value: 0.133
	- type: precision_at_3
	value: 10.033
	- type: precision_at_5
	value: 7.761
	- type: recall_at_1
	value: 12.592
	- type: recall_at_10
	value: 35.386
	- type: recall_at_100
	value: 63.412
	- type: recall_at_1000
	value: 86.20400000000001
	- type: recall_at_3
	value: 23.768
	- type: recall_at_5
	value: 29.557
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackPhysicsRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 23.549
	- type: map_at_10
	value: 32.875
	- type: map_at_100
	value: 34.247
	- type: map_at_1000
	value: 34.374
	- type: map_at_3
	value: 29.774
	- type: map_at_5
	value: 31.535000000000004
	- type: ndcg_at_1
	value: 28.874
	- type: ndcg_at_10
	value: 38.801
	- type: ndcg_at_100
	value: 44.727
	- type: ndcg_at_1000
	value: 47.154
	- type: ndcg_at_3
	value: 33.643
	- type: ndcg_at_5
	value: 36.046
	- type: precision_at_1
	value: 28.874
	- type: precision_at_10
	value: 7.305000000000001
	- type: precision_at_100
	value: 1.21
	- type: precision_at_1000
	value: 0.16199999999999998
	- type: precision_at_3
	value: 16.009
	- type: precision_at_5
	value: 11.741999999999999
	- type: recall_at_1
	value: 23.549
	- type: recall_at_10
	value: 51.15
	- type: recall_at_100
	value: 76.32900000000001
	- type: recall_at_1000
	value: 92.167
	- type: recall_at_3
	value: 36.544
	- type: recall_at_5
	value: 42.75
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackProgrammersRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 24.524
	- type: map_at_10
	value: 34.288999999999994
	- type: map_at_100
	value: 35.67
	- type: map_at_1000
	value: 35.788
	- type: map_at_3
	value: 31.029
	- type: map_at_5
	value: 32.767
	- type: ndcg_at_1
	value: 29.794999999999998
	- type: ndcg_at_10
	value: 40.164
	- type: ndcg_at_100
	value: 46.278999999999996
	- type: ndcg_at_1000
	value: 48.698
	- type: ndcg_at_3
	value: 34.648
	- type: ndcg_at_5
	value: 36.982
	- type: precision_at_1
	value: 29.794999999999998
	- type: precision_at_10
	value: 7.580000000000001
	- type: precision_at_100
	value: 1.248
	- type: precision_at_1000
	value: 0.165
	- type: precision_at_3
	value: 16.628999999999998
	- type: precision_at_5
	value: 12.055
	- type: recall_at_1
	value: 24.524
	- type: recall_at_10
	value: 52.782
	- type: recall_at_100
	value: 79.108
	- type: recall_at_1000
	value: 95.62899999999999
	- type: recall_at_3
	value: 37.330999999999996
	- type: recall_at_5
	value: 43.502
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 21.669083333333333
	- type: map_at_10
	value: 30.095166666666668
	- type: map_at_100
	value: 31.35275
	- type: map_at_1000
	value: 31.476166666666668
	- type: map_at_3
	value: 27.41675
	- type: map_at_5
	value: 28.91216666666667
	- type: ndcg_at_1
	value: 25.666833333333333
	- type: ndcg_at_10
	value: 35.23175
	- type: ndcg_at_100
	value: 40.822833333333335
	- type: ndcg_at_1000
	value: 43.33783333333334
	- type: ndcg_at_3
	value: 30.516333333333336
	- type: ndcg_at_5
	value: 32.723
	- type: precision_at_1
	value: 25.666833333333333
	- type: precision_at_10
	value: 6.345583333333332
	- type: precision_at_100
	value: 1.0886666666666667
	- type: precision_at_1000
	value: 0.14974999999999997
	- type: precision_at_3
	value: 14.185583333333335
	- type: precision_at_5
	value: 10.265333333333334
	- type: recall_at_1
	value: 21.669083333333333
	- type: recall_at_10
	value: 46.69591666666667
	- type: recall_at_100
	value: 71.36999999999999
	- type: recall_at_1000
	value: 88.98216666666666
	- type: recall_at_3
	value: 33.59675
	- type: recall_at_5
	value: 39.2065
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackStatsRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 18.587999999999997
	- type: map_at_10
	value: 25.452
	- type: map_at_100
	value: 26.296999999999997
	- type: map_at_1000
	value: 26.394000000000002
	- type: map_at_3
	value: 23.474
	- type: map_at_5
	value: 24.629
	- type: ndcg_at_1
	value: 21.012
	- type: ndcg_at_10
	value: 29.369
	- type: ndcg_at_100
	value: 33.782000000000004
	- type: ndcg_at_1000
	value: 36.406
	- type: ndcg_at_3
	value: 25.45
	- type: ndcg_at_5
	value: 27.384999999999998
	- type: precision_at_1
	value: 21.012
	- type: precision_at_10
	value: 4.723999999999999
	- type: precision_at_100
	value: 0.753
	- type: precision_at_1000
	value: 0.105
	- type: precision_at_3
	value: 11.094
	- type: precision_at_5
	value: 7.914000000000001
	- type: recall_at_1
	value: 18.587999999999997
	- type: recall_at_10
	value: 39.413
	- type: recall_at_100
	value: 59.78
	- type: recall_at_1000
	value: 79.49199999999999
	- type: recall_at_3
	value: 28.485
	- type: recall_at_5
	value: 33.367999999999995
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackTexRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 12.76
	- type: map_at_10
	value: 18.859
	- type: map_at_100
	value: 19.865
	- type: map_at_1000
	value: 19.994
	- type: map_at_3
	value: 16.817
	- type: map_at_5
	value: 17.837
	- type: ndcg_at_1
	value: 15.415999999999999
	- type: ndcg_at_10
	value: 23.037
	- type: ndcg_at_100
	value: 28.164
	- type: ndcg_at_1000
	value: 31.404
	- type: ndcg_at_3
	value: 19.134999999999998
	- type: ndcg_at_5
	value: 20.711
	- type: precision_at_1
	value: 15.415999999999999
	- type: precision_at_10
	value: 4.387
	- type: precision_at_100
	value: 0.826
	- type: precision_at_1000
	value: 0.127
	- type: precision_at_3
	value: 9.257
	- type: precision_at_5
	value: 6.696000000000001
	- type: recall_at_1
	value: 12.76
	- type: recall_at_10
	value: 32.657000000000004
	- type: recall_at_100
	value: 56.023
	- type: recall_at_1000
	value: 79.572
	- type: recall_at_3
	value: 21.608
	- type: recall_at_5
	value: 25.726
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackUnixRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 22.415
	- type: map_at_10
	value: 29.957
	- type: map_at_100
	value: 31.234
	- type: map_at_1000
	value: 31.351000000000003
	- type: map_at_3
	value: 27.261999999999997
	- type: map_at_5
	value: 28.708
	- type: ndcg_at_1
	value: 26.118999999999996
	- type: ndcg_at_10
	value: 34.961999999999996
	- type: ndcg_at_100
	value: 40.876000000000005
	- type: ndcg_at_1000
	value: 43.586000000000006
	- type: ndcg_at_3
	value: 29.958000000000002
	- type: ndcg_at_5
	value: 32.228
	- type: precision_at_1
	value: 26.118999999999996
	- type: precision_at_10
	value: 6.053999999999999
	- type: precision_at_100
	value: 1.012
	- type: precision_at_1000
	value: 0.13699999999999998
	- type: precision_at_3
	value: 13.65
	- type: precision_at_5
	value: 9.795
	- type: recall_at_1
	value: 22.415
	- type: recall_at_10
	value: 46.339000000000006
	- type: recall_at_100
	value: 72.30799999999999
	- type: recall_at_1000
	value: 91.448
	- type: recall_at_3
	value: 32.673
	- type: recall_at_5
	value: 38.467
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackWebmastersRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 21.624
	- type: map_at_10
	value: 30.0
	- type: map_at_100
	value: 31.776
	- type: map_at_1000
	value: 32.005
	- type: map_at_3
	value: 27.314
	- type: map_at_5
	value: 28.741
	- type: ndcg_at_1
	value: 25.691999999999997
	- type: ndcg_at_10
	value: 35.64
	- type: ndcg_at_100
	value: 42.488
	- type: ndcg_at_1000
	value: 44.978
	- type: ndcg_at_3
	value: 31.147000000000002
	- type: ndcg_at_5
	value: 33.241
	- type: precision_at_1
	value: 25.691999999999997
	- type: precision_at_10
	value: 7.0360000000000005
	- type: precision_at_100
	value: 1.547
	- type: precision_at_1000
	value: 0.244
	- type: precision_at_3
	value: 15.02
	- type: precision_at_5
	value: 11.146
	- type: recall_at_1
	value: 21.624
	- type: recall_at_10
	value: 46.415
	- type: recall_at_100
	value: 77.086
	- type: recall_at_1000
	value: 92.72500000000001
	- type: recall_at_3
	value: 33.911
	- type: recall_at_5
	value: 39.116
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackWordpressRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 15.659
	- type: map_at_10
	value: 22.35
	- type: map_at_100
	value: 23.498
	- type: map_at_1000
	value: 23.602
	- type: map_at_3
	value: 19.959
	- type: map_at_5
	value: 21.201999999999998
	- type: ndcg_at_1
	value: 17.375
	- type: ndcg_at_10
	value: 26.606
	- type: ndcg_at_100
	value: 32.567
	- type: ndcg_at_1000
	value: 35.177
	- type: ndcg_at_3
	value: 21.843
	- type: ndcg_at_5
	value: 23.924
	- type: precision_at_1
	value: 17.375
	- type: precision_at_10
	value: 4.455
	- type: precision_at_100
	value: 0.8109999999999999
	- type: precision_at_1000
	value: 0.11199999999999999
	- type: precision_at_3
	value: 9.427000000000001
	- type: precision_at_5
	value: 6.912999999999999
	- type: recall_at_1
	value: 15.659
	- type: recall_at_10
	value: 38.167
	- type: recall_at_100
	value: 66.11
	- type: recall_at_1000
	value: 85.529
	- type: recall_at_3
	value: 25.308000000000003
	- type: recall_at_5
	value: 30.182
	- task:
	type: Retrieval
	dataset:
	type: climate-fever
	name: MTEB ClimateFEVER
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 3.9469999999999996
	- type: map_at_10
	value: 6.816999999999999
	- type: map_at_100
	value: 7.7170000000000005
	- type: map_at_1000
	value: 7.887
	- type: map_at_3
	value: 5.6739999999999995
	- type: map_at_5
	value: 6.243
	- type: ndcg_at_1
	value: 8.73
	- type: ndcg_at_10
	value: 10.366999999999999
	- type: ndcg_at_100
	value: 15.343000000000002
	- type: ndcg_at_1000
	value: 19.535
	- type: ndcg_at_3
	value: 7.976
	- type: ndcg_at_5
	value: 8.786
	- type: precision_at_1
	value: 8.73
	- type: precision_at_10
	value: 3.3160000000000003
	- type: precision_at_100
	value: 0.857
	- type: precision_at_1000
	value: 0.16199999999999998
	- type: precision_at_3
	value: 5.776
	- type: precision_at_5
	value: 4.534
	- type: recall_at_1
	value: 3.9469999999999996
	- type: recall_at_10
	value: 13.385
	- type: recall_at_100
	value: 31.612000000000002
	- type: recall_at_1000
	value: 56.252
	- type: recall_at_3
	value: 7.686
	- type: recall_at_5
	value: 9.879
	- task:
	type: Retrieval
	dataset:
	type: dbpedia-entity
	name: MTEB DBPedia
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 5.75
	- type: map_at_10
	value: 11.632000000000001
	- type: map_at_100
	value: 16.400000000000002
	- type: map_at_1000
	value: 17.580000000000002
	- type: map_at_3
	value: 8.49
	- type: map_at_5
	value: 9.626999999999999
	- type: ndcg_at_1
	value: 35.75
	- type: ndcg_at_10
	value: 27.766000000000002
	- type: ndcg_at_100
	value: 31.424000000000003
	- type: ndcg_at_1000
	value: 38.998
	- type: ndcg_at_3
	value: 30.807000000000002
	- type: ndcg_at_5
	value: 28.62
	- type: precision_at_1
	value: 44.25
	- type: precision_at_10
	value: 22.625
	- type: precision_at_100
	value: 7.163
	- type: precision_at_1000
	value: 1.619
	- type: precision_at_3
	value: 33.75
	- type: precision_at_5
	value: 28.199999999999996
	- type: recall_at_1
	value: 5.75
	- type: recall_at_10
	value: 16.918
	- type: recall_at_100
	value: 37.645
	- type: recall_at_1000
	value: 62.197
	- type: recall_at_3
	value: 9.721
	- type: recall_at_5
	value: 11.974
	- task:
	type: Classification
	dataset:
	type: mteb/emotion
	name: MTEB EmotionClassification
	config: default
	split: test
	metrics:
	- type: accuracy
	value: 51.355000000000004
	- type: f1
	value: 44.27505726378252
	- task:
	type: Retrieval
	dataset:
	type: fever
	name: MTEB FEVER
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 13.81
	- type: map_at_10
	value: 21.567
	- type: map_at_100
	value: 22.461000000000002
	- type: map_at_1000
	value: 22.545
	- type: map_at_3
	value: 19.282
	- type: map_at_5
	value: 20.535999999999998
	- type: ndcg_at_1
	value: 15.032
	- type: ndcg_at_10
	value: 26.165
	- type: ndcg_at_100
	value: 30.819999999999997
	- type: ndcg_at_1000
	value: 33.209
	- type: ndcg_at_3
	value: 21.488
	- type: ndcg_at_5
	value: 23.721999999999998
	- type: precision_at_1
	value: 15.032
	- type: precision_at_10
	value: 4.292
	- type: precision_at_100
	value: 0.6779999999999999
	- type: precision_at_1000
	value: 0.09
	- type: precision_at_3
	value: 9.551
	- type: precision_at_5
	value: 6.927999999999999
	- type: recall_at_1
	value: 13.81
	- type: recall_at_10
	value: 39.009
	- type: recall_at_100
	value: 60.99400000000001
	- type: recall_at_1000
	value: 79.703
	- type: recall_at_3
	value: 26.221
	- type: recall_at_5
	value: 31.604
	- task:
	type: Retrieval
	dataset:
	type: fiqa
	name: MTEB FiQA2018
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 16.55
	- type: map_at_10
	value: 27.101
	- type: map_at_100
	value: 28.941
	- type: map_at_1000
	value: 29.137
	- type: map_at_3
	value: 22.926
	- type: map_at_5
	value: 25.217
	- type: ndcg_at_1
	value: 33.951
	- type: ndcg_at_10
	value: 34.832
	- type: ndcg_at_100
	value: 41.989
	- type: ndcg_at_1000
	value: 45.262
	- type: ndcg_at_3
	value: 30.427
	- type: ndcg_at_5
	value: 31.985999999999997
	- type: precision_at_1
	value: 33.951
	- type: precision_at_10
	value: 10.139
	- type: precision_at_100
	value: 1.735
	- type: precision_at_1000
	value: 0.233
	- type: precision_at_3
	value: 20.576
	- type: precision_at_5
	value: 15.556000000000001
	- type: recall_at_1
	value: 16.55
	- type: recall_at_10
	value: 42.153
	- type: recall_at_100
	value: 69.19999999999999
	- type: recall_at_1000
	value: 88.631
	- type: recall_at_3
	value: 27.071
	- type: recall_at_5
	value: 33.432
	- task:
	type: Retrieval
	dataset:
	type: hotpotqa
	name: MTEB HotpotQA
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 18.102999999999998
	- type: map_at_10
	value: 26.006
	- type: map_at_100
	value: 27.060000000000002
	- type: map_at_1000
	value: 27.173000000000002
	- type: map_at_3
	value: 23.815
	- type: map_at_5
	value: 24.978
	- type: ndcg_at_1
	value: 36.205
	- type: ndcg_at_10
	value: 33.198
	- type: ndcg_at_100
	value: 37.836999999999996
	- type: ndcg_at_1000
	value: 40.499
	- type: ndcg_at_3
	value: 29.108
	- type: ndcg_at_5
	value: 30.993
	- type: precision_at_1
	value: 36.205
	- type: precision_at_10
	value: 7.404
	- type: precision_at_100
	value: 1.109
	- type: precision_at_1000
	value: 0.146
	- type: precision_at_3
	value: 18.479
	- type: precision_at_5
	value: 12.581000000000001
	- type: recall_at_1
	value: 18.102999999999998
	- type: recall_at_10
	value: 37.022
	- type: recall_at_100
	value: 55.449000000000005
	- type: recall_at_1000
	value: 73.214
	- type: recall_at_3
	value: 27.717999999999996
	- type: recall_at_5
	value: 31.452
	- task:
	type: Classification
	dataset:
	type: mteb/imdb
	name: MTEB ImdbClassification
	config: default
	split: test
	metrics:
	- type: accuracy
	value: 77.3372
	- type: ap
	value: 71.64946791935137
	- type: f1
	value: 77.13428403424751
	- task:
	type: Retrieval
	dataset:
	type: msmarco
	name: MTEB MSMARCO
	config: default
	split: dev
	metrics:
	- type: map_at_1
	value: 9.093
	- type: map_at_10
	value: 16.227
	- type: map_at_100
	value: 17.477999999999998
	- type: map_at_1000
	value: 17.579
	- type: map_at_3
	value: 13.541
	- type: map_at_5
	value: 14.921000000000001
	- type: ndcg_at_1
	value: 9.370000000000001
	- type: ndcg_at_10
	value: 20.705000000000002
	- type: ndcg_at_100
	value: 27.331
	- type: ndcg_at_1000
	value: 30.104
	- type: ndcg_at_3
	value: 15.081
	- type: ndcg_at_5
	value: 17.551
	- type: precision_at_1
	value: 9.370000000000001
	- type: precision_at_10
	value: 3.633
	- type: precision_at_100
	value: 0.7040000000000001
	- type: precision_at_1000
	value: 0.094
	- type: precision_at_3
	value: 6.648
	- type: precision_at_5
	value: 5.241
	- type: recall_at_1
	value: 9.093
	- type: recall_at_10
	value: 34.777
	- type: recall_at_100
	value: 66.673
	- type: recall_at_1000
	value: 88.44999999999999
	- type: recall_at_3
	value: 19.194
	- type: recall_at_5
	value: 25.124999999999996
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (en)
	config: en
	split: test
	metrics:
	- type: accuracy
	value: 90.3374373005016
	- type: f1
	value: 90.25497662319412
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (de)
	config: de
	split: test
	metrics:
	- type: accuracy
	value: 76.98224852071004
	- type: f1
	value: 75.10443724253962
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (es)
	config: es
	split: test
	metrics:
	- type: accuracy
	value: 73.60907271514343
	- type: f1
	value: 73.15530983235772
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (fr)
	config: fr
	split: test
	metrics:
	- type: accuracy
	value: 75.02975258377701
	- type: f1
	value: 75.53083321964739
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (hi)
	config: hi
	split: test
	metrics:
	- type: accuracy
	value: 21.40193617784152
	- type: f1
	value: 15.465217146460256
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (th)
	config: th
	split: test
	metrics:
	- type: accuracy
	value: 16.206148282097647
	- type: f1
	value: 11.580229602870345
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (en)
	config: en
	split: test
	metrics:
	- type: accuracy
	value: 63.32421340629275
	- type: f1
	value: 45.42341063027956
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (de)
	config: de
	split: test
	metrics:
	- type: accuracy
	value: 44.426599041983664
	- type: f1
	value: 27.205947872504428
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (es)
	config: es
	split: test
	metrics:
	- type: accuracy
	value: 42.02801867911942
	- type: f1
	value: 26.314909946795733
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (fr)
	config: fr
	split: test
	metrics:
	- type: accuracy
	value: 43.845912934544316
	- type: f1
	value: 29.519701972859792
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (hi)
	config: hi
	split: test
	metrics:
	- type: accuracy
	value: 3.80064539261384
	- type: f1
	value: 1.2078686392462628
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (th)
	config: th
	split: test
	metrics:
	- type: accuracy
	value: 5.207956600361665
	- type: f1
	value: 1.5365513001536746
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (af)
	config: af
	split: test
	metrics:
	- type: accuracy
	value: 34.32078009414929
	- type: f1
	value: 31.969428974847435
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (am)
	config: am
	split: test
	metrics:
	- type: accuracy
	value: 2.3772696704774714
	- type: f1
	value: 1.027013290806954
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (ar)
	config: ar
	split: test
	metrics:
	- type: accuracy
	value: 4.53261600537996
	- type: f1
	value: 2.793131265571347
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (az)
	config: az
	split: test
	metrics:
	- type: accuracy
	value: 31.758574310692673
	- type: f1
	value: 30.162299253522708
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (bn)
	config: bn
	split: test
	metrics:
	- type: accuracy
	value: 2.5823806321452594
	- type: f1
	value: 1.0918434877949255
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (cy)
	config: cy
	split: test
	metrics:
	- type: accuracy
	value: 28.94418291862811
	- type: f1
	value: 27.498874158049468
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (da)
	config: da
	split: test
	metrics:
	- type: accuracy
	value: 38.81977135171486
	- type: f1
	value: 36.44688565156101
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (de)
	config: de
	split: test
	metrics:
	- type: accuracy
	value: 45.22864828513786
	- type: f1
	value: 41.61460113481098
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (el)
	config: el
	split: test
	metrics:
	- type: accuracy
	value: 10.053799596503026
	- type: f1
	value: 5.1615743271775285
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (en)
	config: en
	split: test
	metrics:
	- type: accuracy
	value: 69.74445191661063
	- type: f1
	value: 67.00099408297854
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (es)
	config: es
	split: test
	metrics:
	- type: accuracy
	value: 45.31943510423672
	- type: f1
	value: 43.92469151179908
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (fa)
	config: fa
	split: test
	metrics:
	- type: accuracy
	value: 3.5810356422326834
	- type: f1
	value: 0.8057464198110936
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (fi)
	config: fi
	split: test
	metrics:
	- type: accuracy
	value: 33.52387357094821
	- type: f1
	value: 30.686159550520415
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (fr)
	config: fr
	split: test
	metrics:
	- type: accuracy
	value: 51.13315400134499
	- type: f1
	value: 48.84533274433444
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (he)
	config: he
	split: test
	metrics:
	- type: accuracy
	value: 2.632817753866846
	- type: f1
	value: 0.7565304035292157
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (hi)
	config: hi
	split: test
	metrics:
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	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (sv)
	config: sv
	split: test
	metrics:
	- type: accuracy
	value: 46.68796234028245
	- type: f1
	value: 42.65714705554807
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (sw)
	config: sw
	split: test
	metrics:
	- type: accuracy
	value: 40.480833893745796
	- type: f1
	value: 37.28728896543833
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (ta)
	config: ta
	split: test
	metrics:
	- type: accuracy
	value: 7.474781439139207
	- type: f1
	value: 2.114620869965788
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (te)
	config: te
	split: test
	metrics:
	- type: accuracy
	value: 6.866173503698722
	- type: f1
	value: 3.0078405064872755
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (th)
	config: th
	split: test
	metrics:
	- type: accuracy
	value: 8.258238063214526
	- type: f1
	value: 4.082391072869187
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (tl)
	config: tl
	split: test
	metrics:
	- type: accuracy
	value: 48.93745796906523
	- type: f1
	value: 46.427786382184266
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (tr)
	config: tr
	split: test
	metrics:
	- type: accuracy
	value: 41.82918628110289
	- type: f1
	value: 40.642360044818325
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (ur)
	config: ur
	split: test
	metrics:
	- type: accuracy
	value: 9.767989240080698
	- type: f1
	value: 4.6812848634278925
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (vi)
	config: vi
	split: test
	metrics:
	- type: accuracy
	value: 30.013449899125753
	- type: f1
	value: 28.091947569862718
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (zh-CN)
	config: zh-CN
	split: test
	metrics:
	- type: accuracy
	value: 4.169468728984533
	- type: f1
	value: 0.8211582847545612
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (zh-TW)
	config: zh-TW
	split: test
	metrics:
	- type: accuracy
	value: 7.908540685944855
	- type: f1
	value: 3.5358754800289534
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-p2p
	name: MTEB MedrxivClusteringP2P
	config: default
	split: test
	metrics:
	- type: v_measure
	value: 33.20164128507875
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-s2s
	name: MTEB MedrxivClusteringS2S
	config: default
	split: test
	metrics:
	- type: v_measure
	value: 26.13067187536383
	- task:
	type: Reranking
	dataset:
	type: mteb/mind_small
	name: MTEB MindSmallReranking
	config: default
	split: test
	metrics:
	- type: map
	value: 30.196798710053223
	- type: mrr
	value: 31.098179519159814
	- task:
	type: Retrieval
	dataset:
	type: nfcorpus
	name: MTEB NFCorpus
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 4.7010000000000005
	- type: map_at_10
	value: 9.756
	- type: map_at_100
	value: 12.631999999999998
	- type: map_at_1000
	value: 14.046
	- type: map_at_3
	value: 7.053
	- type: map_at_5
	value: 8.244
	- type: ndcg_at_1
	value: 35.604
	- type: ndcg_at_10
	value: 28.645
	- type: ndcg_at_100
	value: 27.431
	- type: ndcg_at_1000
	value: 36.378
	- type: ndcg_at_3
	value: 32.533
	- type: ndcg_at_5
	value: 30.737
	- type: precision_at_1
	value: 37.771
	- type: precision_at_10
	value: 21.517
	- type: precision_at_100
	value: 7.567
	- type: precision_at_1000
	value: 2.026
	- type: precision_at_3
	value: 30.753000000000004
	- type: precision_at_5
	value: 26.811
	- type: recall_at_1
	value: 4.7010000000000005
	- type: recall_at_10
	value: 14.302999999999999
	- type: recall_at_100
	value: 29.304000000000002
	- type: recall_at_1000
	value: 62.202999999999996
	- type: recall_at_3
	value: 8.419
	- type: recall_at_5
	value: 10.656
	- task:
	type: Retrieval
	dataset:
	type: nq
	name: MTEB NQ
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 17.444000000000003
	- type: map_at_10
	value: 29.198
	- type: map_at_100
	value: 30.553
	- type: map_at_1000
	value: 30.614
	- type: map_at_3
	value: 24.992
	- type: map_at_5
	value: 27.322999999999997
	- type: ndcg_at_1
	value: 19.641000000000002
	- type: ndcg_at_10
	value: 36.324
	- type: ndcg_at_100
	value: 42.641
	- type: ndcg_at_1000
	value: 44.089
	- type: ndcg_at_3
	value: 28.000000000000004
	- type: ndcg_at_5
	value: 32.025
	- type: precision_at_1
	value: 19.641000000000002
	- type: precision_at_10
	value: 6.6339999999999995
	- type: precision_at_100
	value: 1.024
	- type: precision_at_1000
	value: 0.116
	- type: precision_at_3
	value: 13.209999999999999
	- type: precision_at_5
	value: 10.162
	- type: recall_at_1
	value: 17.444000000000003
	- type: recall_at_10
	value: 56.230999999999995
	- type: recall_at_100
	value: 84.61800000000001
	- type: recall_at_1000
	value: 95.416
	- type: recall_at_3
	value: 34.245999999999995
	- type: recall_at_5
	value: 43.617
	- task:
	type: Retrieval
	dataset:
	type: quora
	name: MTEB QuoraRetrieval
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 67.604
	- type: map_at_10
	value: 81.364
	- type: map_at_100
	value: 82.092
	- type: map_at_1000
	value: 82.112
	- type: map_at_3
	value: 78.321
	- type: map_at_5
	value: 80.203
	- type: ndcg_at_1
	value: 77.92
	- type: ndcg_at_10
	value: 85.491
	- type: ndcg_at_100
	value: 87.102
	- type: ndcg_at_1000
	value: 87.246
	- type: ndcg_at_3
	value: 82.219
	- type: ndcg_at_5
	value: 83.991
	- type: precision_at_1
	value: 77.92
	- type: precision_at_10
	value: 13.065
	- type: precision_at_100
	value: 1.525
	- type: precision_at_1000
	value: 0.157
	- type: precision_at_3
	value: 35.96
	- type: precision_at_5
	value: 23.785999999999998
	- type: recall_at_1
	value: 67.604
	- type: recall_at_10
	value: 93.57
	- type: recall_at_100
	value: 99.20400000000001
	- type: recall_at_1000
	value: 99.958
	- type: recall_at_3
	value: 84.38900000000001
	- type: recall_at_5
	value: 89.223
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering
	name: MTEB RedditClustering
	config: default
	split: test
	metrics:
	- type: v_measure
	value: 52.930534839708464
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering-p2p
	name: MTEB RedditClusteringP2P
	config: default
	split: test
	metrics:
	- type: v_measure
	value: 59.6686566444821
	- task:
	type: Retrieval
	dataset:
	type: scidocs
	name: MTEB SCIDOCS
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 3.267
	- type: map_at_10
	value: 8.061
	- type: map_at_100
	value: 9.66
	- type: map_at_1000
	value: 9.926
	- type: map_at_3
	value: 5.733
	- type: map_at_5
	value: 6.894
	- type: ndcg_at_1
	value: 16.0
	- type: ndcg_at_10
	value: 14.155000000000001
	- type: ndcg_at_100
	value: 20.973
	- type: ndcg_at_1000
	value: 26.163999999999998
	- type: ndcg_at_3
	value: 12.994
	- type: ndcg_at_5
	value: 11.58
	- type: precision_at_1
	value: 16.0
	- type: precision_at_10
	value: 7.470000000000001
	- type: precision_at_100
	value: 1.7389999999999999
	- type: precision_at_1000
	value: 0.299
	- type: precision_at_3
	value: 12.167
	- type: precision_at_5
	value: 10.280000000000001
	- type: recall_at_1
	value: 3.267
	- type: recall_at_10
	value: 15.152
	- type: recall_at_100
	value: 35.248000000000005
	- type: recall_at_1000
	value: 60.742
	- type: recall_at_3
	value: 7.4319999999999995
	- type: recall_at_5
	value: 10.452
	- task:
	type: STS
	dataset:
	type: mteb/sickr-sts
	name: MTEB SICK-R
	config: default
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 84.12684378047692
	- type: cos_sim_spearman
	value: 80.18231249099851
	- type: euclidean_pearson
	value: 81.10311004134292
	- type: euclidean_spearman
	value: 80.18231162371262
	- type: manhattan_pearson
	value: 81.06660654194627
	- type: manhattan_spearman
	value: 80.15421301055235
	- task:
	type: STS
	dataset:
	type: mteb/sts12-sts
	name: MTEB STS12
	config: default
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 86.39022108792102
	- type: cos_sim_spearman
	value: 78.0511871449349
	- type: euclidean_pearson
	value: 83.55414895785707
	- type: euclidean_spearman
	value: 78.04999900363751
	- type: manhattan_pearson
	value: 83.58122709700247
	- type: manhattan_spearman
	value: 78.09617051485085
	- task:
	type: STS
	dataset:
	type: mteb/sts13-sts
	name: MTEB STS13
	config: default
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 85.20665643089602
	- type: cos_sim_spearman
	value: 85.84897342040492
	- type: euclidean_pearson
	value: 85.07344348481206
	- type: euclidean_spearman
	value: 85.84897334409469
	- type: manhattan_pearson
	value: 85.05095172720918
	- type: manhattan_spearman
	value: 85.82539599484174
	- task:
	type: STS
	dataset:
	type: mteb/sts14-sts
	name: MTEB STS14
	config: default
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 83.89550144541676
	- type: cos_sim_spearman
	value: 82.18926664662587
	- type: euclidean_pearson
	value: 83.2979886572065
	- type: euclidean_spearman
	value: 82.18927470901535
	- type: manhattan_pearson
	value: 83.26470031355984
	- type: manhattan_spearman
	value: 82.18712042624048
	- task:
	type: STS
	dataset:
	type: mteb/sts15-sts
	name: MTEB STS15
	config: default
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 86.24309164032012
	- type: cos_sim_spearman
	value: 87.45860981918769
	- type: euclidean_pearson
	value: 87.04473506428359
	- type: euclidean_spearman
	value: 87.45861561864089
	- type: manhattan_pearson
	value: 87.02002615328881
	- type: manhattan_spearman
	value: 87.43661746711435
	- task:
	type: STS
	dataset:
	type: mteb/sts16-sts
	name: MTEB STS16
	config: default
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 82.24202172291855
	- type: cos_sim_spearman
	value: 84.03233567112525
	- type: euclidean_pearson
	value: 83.5361433714169
	- type: euclidean_spearman
	value: 84.03233506665642
	- type: manhattan_pearson
	value: 83.51738829906122
	- type: manhattan_spearman
	value: 84.02036537979589
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (ko-ko)
	config: ko-ko
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 9.160685666083912
	- type: cos_sim_spearman
	value: 10.0553422118037
	- type: euclidean_pearson
	value: 9.589155152132493
	- type: euclidean_spearman
	value: 10.215143153291868
	- type: manhattan_pearson
	value: 9.570908402796292
	- type: manhattan_spearman
	value: 10.214075999964175
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (ar-ar)
	config: ar-ar
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 10.60353259635145
	- type: cos_sim_spearman
	value: 13.355557088500165
	- type: euclidean_pearson
	value: 14.636463268109537
	- type: euclidean_spearman
	value: 14.35296057730866
	- type: manhattan_pearson
	value: 14.553161459629774
	- type: manhattan_spearman
	value: 14.267005982719008
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (en-ar)
	config: en-ar
	split: test
	metrics:
	- type: cos_sim_pearson
	value: -4.869359628676264
	- type: cos_sim_spearman
	value: -5.6460908267056835
	- type: euclidean_pearson
	value: -4.9763689688023245
	- type: euclidean_spearman
	value: -5.642707032163295
	- type: manhattan_pearson
	value: -2.1980242988428276
	- type: manhattan_spearman
	value: -1.854801657544592
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (en-de)
	config: en-de
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 66.79239834758799
	- type: cos_sim_spearman
	value: 67.11298548130333
	- type: euclidean_pearson
	value: 66.77948456698994
	- type: euclidean_spearman
	value: 67.11298548130333
	- type: manhattan_pearson
	value: 66.5459479074496
	- type: manhattan_spearman
	value: 66.85517071449804
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (en-en)
	config: en-en
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 89.5692743691406
	- type: cos_sim_spearman
	value: 89.56885540021487
	- type: euclidean_pearson
	value: 89.78111903652413
	- type: euclidean_spearman
	value: 89.56885540021487
	- type: manhattan_pearson
	value: 89.68974590722112
	- type: manhattan_spearman
	value: 89.40694757290255
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (en-tr)
	config: en-tr
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 2.5434531383947427
	- type: cos_sim_spearman
	value: -0.015686409614414636
	- type: euclidean_pearson
	value: 3.3562612023763454
	- type: euclidean_spearman
	value: -0.015686409614414636
	- type: manhattan_pearson
	value: 3.06029066490911
	- type: manhattan_spearman
	value: 0.9087736864115655
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (es-en)
	config: es-en
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 48.19554059380143
	- type: cos_sim_spearman
	value: 47.72387836409936
	- type: euclidean_pearson
	value: 48.566966490440386
	- type: euclidean_spearman
	value: 47.72387836409936
	- type: manhattan_pearson
	value: 48.47970171544757
	- type: manhattan_spearman
	value: 48.06448477123342
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (es-es)
	config: es-es
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 80.72325991736295
	- type: cos_sim_spearman
	value: 79.94411571627043
	- type: euclidean_pearson
	value: 81.66909260117279
	- type: euclidean_spearman
	value: 79.94284742229813
	- type: manhattan_pearson
	value: 81.78261278000369
	- type: manhattan_spearman
	value: 80.18524960358721
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (fr-en)
	config: fr-en
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 57.385906804319916
	- type: cos_sim_spearman
	value: 56.60927284389835
	- type: euclidean_pearson
	value: 58.220472472555414
	- type: euclidean_spearman
	value: 56.60927284389835
	- type: manhattan_pearson
	value: 57.974972842168704
	- type: manhattan_spearman
	value: 56.38609220634484
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (it-en)
	config: it-en
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 33.56148532200449
	- type: cos_sim_spearman
	value: 30.46169688812801
	- type: euclidean_pearson
	value: 34.03749511332228
	- type: euclidean_spearman
	value: 30.46169688812801
	- type: manhattan_pearson
	value: 33.51842606041771
	- type: manhattan_spearman
	value: 30.87826743052681
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (nl-en)
	config: nl-en
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 37.01008110523631
	- type: cos_sim_spearman
	value: 36.46124293832437
	- type: euclidean_pearson
	value: 37.860431566730725
	- type: euclidean_spearman
	value: 36.46124293832437
	- type: manhattan_pearson
	value: 37.84974555851177
	- type: manhattan_spearman
	value: 37.026498066678556
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (en)
	config: en
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 56.563590445291226
	- type: cos_sim_spearman
	value: 62.65994888539158
	- type: euclidean_pearson
	value: 61.43083003163841
	- type: euclidean_spearman
	value: 62.65994888539158
	- type: manhattan_pearson
	value: 61.530512036243564
	- type: manhattan_spearman
	value: 62.65300646176863
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (de)
	config: de
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 28.29024604941182
	- type: cos_sim_spearman
	value: 42.084625834786046
	- type: euclidean_pearson
	value: 30.271611311423545
	- type: euclidean_spearman
	value: 42.084625834786046
	- type: manhattan_pearson
	value: 30.19034939394144
	- type: manhattan_spearman
	value: 42.02260224541176
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (es)
	config: es
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 42.79846662914459
	- type: cos_sim_spearman
	value: 53.8129210907069
	- type: euclidean_pearson
	value: 48.21779716691527
	- type: euclidean_spearman
	value: 53.8129210907069
	- type: manhattan_pearson
	value: 48.35900342355713
	- type: manhattan_spearman
	value: 53.94896150957018
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (pl)
	config: pl
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 9.818882867657061
	- type: cos_sim_spearman
	value: 24.41994279795319
	- type: euclidean_pearson
	value: 4.813919367736767
	- type: euclidean_spearman
	value: 24.41994279795319
	- type: manhattan_pearson
	value: 4.602063702670144
	- type: manhattan_spearman
	value: 24.218951967147824
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (tr)
	config: tr
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 27.410972788257048
	- type: cos_sim_spearman
	value: 40.44872572093327
	- type: euclidean_pearson
	value: 33.742359285090565
	- type: euclidean_spearman
	value: 40.44872572093327
	- type: manhattan_pearson
	value: 33.90231904900396
	- type: manhattan_spearman
	value: 40.19149257794821
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (ar)
	config: ar
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 31.3322380268429
	- type: cos_sim_spearman
	value: 31.200490449337714
	- type: euclidean_pearson
	value: 32.130848968646525
	- type: euclidean_spearman
	value: 31.200490449337714
	- type: manhattan_pearson
	value: 32.14834980954443
	- type: manhattan_spearman
	value: 31.427049121627025
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (ru)
	config: ru
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 3.4365537430337683
	- type: cos_sim_spearman
	value: 12.125486695771288
	- type: euclidean_pearson
	value: 8.134889656987513
	- type: euclidean_spearman
	value: 12.125486695771288
	- type: manhattan_pearson
	value: 8.163310600014055
	- type: manhattan_spearman
	value: 12.129258700591722
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (zh)
	config: zh
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 27.320332340418773
	- type: cos_sim_spearman
	value: 32.900042162025
	- type: euclidean_pearson
	value: 30.195166197236723
	- type: euclidean_spearman
	value: 32.900041812396196
	- type: manhattan_pearson
	value: 30.146557575933087
	- type: manhattan_spearman
	value: 32.96907086076731
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (fr)
	config: fr
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 69.2830779511937
	- type: cos_sim_spearman
	value: 77.68846630995027
	- type: euclidean_pearson
	value: 73.034747757096
	- type: euclidean_spearman
	value: 77.68846630995027
	- type: manhattan_pearson
	value: 73.03548141166142
	- type: manhattan_spearman
	value: 77.65745427658017
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (de-en)
	config: de-en
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 39.71606949409573
	- type: cos_sim_spearman
	value: 46.8990508231622
	- type: euclidean_pearson
	value: 46.606091669710025
	- type: euclidean_spearman
	value: 46.8990508231622
	- type: manhattan_pearson
	value: 46.39554347396642
	- type: manhattan_spearman
	value: 46.59771734872816
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (es-en)
	config: es-en
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 54.53158773665186
	- type: cos_sim_spearman
	value: 65.18822674266846
	- type: euclidean_pearson
	value: 58.19324925326185
	- type: euclidean_spearman
	value: 65.18822674266846
	- type: manhattan_pearson
	value: 57.83750769005698
	- type: manhattan_spearman
	value: 65.02074812497972
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (it)
	config: it
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 56.77648080772914
	- type: cos_sim_spearman
	value: 60.64694762935356
	- type: euclidean_pearson
	value: 58.1456140359783
	- type: euclidean_spearman
	value: 60.64694762935356
	- type: manhattan_pearson
	value: 58.03342495626636
	- type: manhattan_spearman
	value: 60.384166246014914
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (pl-en)
	config: pl-en
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 47.2314368564716
	- type: cos_sim_spearman
	value: 42.96651621279448
	- type: euclidean_pearson
	value: 47.136522518411184
	- type: euclidean_spearman
	value: 42.96651621279448
	- type: manhattan_pearson
	value: 48.71469489220069
	- type: manhattan_spearman
	value: 44.518895193324646
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (zh-en)
	config: zh-en
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 25.589949160802995
	- type: cos_sim_spearman
	value: 20.153084379882284
	- type: euclidean_pearson
	value: 26.82363451623337
	- type: euclidean_spearman
	value: 20.153084379882284
	- type: manhattan_pearson
	value: 25.843715884495634
	- type: manhattan_spearman
	value: 18.901328744286676
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (es-it)
	config: es-it
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 48.45790617159233
	- type: cos_sim_spearman
	value: 55.28609467652911
	- type: euclidean_pearson
	value: 51.88464425822175
	- type: euclidean_spearman
	value: 55.28609467652911
	- type: manhattan_pearson
	value: 51.815736921803136
	- type: manhattan_spearman
	value: 55.33932627352348
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (de-fr)
	config: de-fr
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 44.7093430670243
	- type: cos_sim_spearman
	value: 55.04493953270152
	- type: euclidean_pearson
	value: 47.90591946944973
	- type: euclidean_spearman
	value: 55.04493953270152
	- type: manhattan_pearson
	value: 47.964230618301606
	- type: manhattan_spearman
	value: 56.09186738739794
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (de-pl)
	config: de-pl
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 25.093485946833393
	- type: cos_sim_spearman
	value: 33.93510205658959
	- type: euclidean_pearson
	value: 27.454896639869027
	- type: euclidean_spearman
	value: 33.93510205658959
	- type: manhattan_pearson
	value: 24.299109196300538
	- type: manhattan_spearman
	value: 32.51857329560673
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (fr-pl)
	config: fr-pl
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 40.9753045484768
	- type: cos_sim_spearman
	value: 28.17180849095055
	- type: euclidean_pearson
	value: 40.382800203298906
	- type: euclidean_spearman
	value: 28.17180849095055
	- type: manhattan_pearson
	value: 34.084425723423486
	- type: manhattan_spearman
	value: 28.17180849095055
	- task:
	type: STS
	dataset:
	type: mteb/stsbenchmark-sts
	name: MTEB STSBenchmark
	config: default
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 84.76003618726351
	- type: cos_sim_spearman
	value: 85.52030817522575
	- type: euclidean_pearson
	value: 85.5039926987335
	- type: euclidean_spearman
	value: 85.52030817522575
	- type: manhattan_pearson
	value: 85.51493965359182
	- type: manhattan_spearman
	value: 85.52189380846832
	- task:
	type: Reranking
	dataset:
	type: mteb/scidocs-reranking
	name: MTEB SciDocsRR
	config: default
	split: test
	metrics:
	- type: map
	value: 73.96228332723271
	- type: mrr
	value: 91.34847813769382
	- task:
	type: Retrieval
	dataset:
	type: scifact
	name: MTEB SciFact
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 32.372
	- type: map_at_10
	value: 41.02
	- type: map_at_100
	value: 41.907
	- type: map_at_1000
	value: 41.967
	- type: map_at_3
	value: 38.244
	- type: map_at_5
	value: 39.786
	- type: ndcg_at_1
	value: 34.666999999999994
	- type: ndcg_at_10
	value: 45.76
	- type: ndcg_at_100
	value: 50.163999999999994
	- type: ndcg_at_1000
	value: 51.956
	- type: ndcg_at_3
	value: 40.687
	- type: ndcg_at_5
	value: 43.143
	- type: precision_at_1
	value: 34.666999999999994
	- type: precision_at_10
	value: 6.7
	- type: precision_at_100
	value: 0.907
	- type: precision_at_1000
	value: 0.107
	- type: precision_at_3
	value: 16.667
	- type: precision_at_5
	value: 11.466999999999999
	- type: recall_at_1
	value: 32.372
	- type: recall_at_10
	value: 59.061
	- type: recall_at_100
	value: 79.733
	- type: recall_at_1000
	value: 94.167
	- type: recall_at_3
	value: 45.161
	- type: recall_at_5
	value: 51.439
	- task:
	type: PairClassification
	dataset:
	type: mteb/sprintduplicatequestions-pairclassification
	name: MTEB SprintDuplicateQuestions
	config: default
	split: test
	metrics:
	- type: cos_sim_accuracy
	value: 99.6960396039604
	- type: cos_sim_ap
	value: 91.22814257221482
	- type: cos_sim_f1
	value: 84.43775100401606
	- type: cos_sim_precision
	value: 84.77822580645162
	- type: cos_sim_recall
	value: 84.1
	- type: dot_accuracy
	value: 99.6960396039604
	- type: dot_ap
	value: 91.22814257221482
	- type: dot_f1
	value: 84.43775100401606
	- type: dot_precision
	value: 84.77822580645162
	- type: dot_recall
	value: 84.1
	- type: euclidean_accuracy
	value: 99.6960396039604
	- type: euclidean_ap
	value: 91.22814257221482
	- type: euclidean_f1
	value: 84.43775100401606
	- type: euclidean_precision
	value: 84.77822580645162
	- type: euclidean_recall
	value: 84.1
	- type: manhattan_accuracy
	value: 99.6960396039604
	- type: manhattan_ap
	value: 91.18887077921163
	- type: manhattan_f1
	value: 84.27991886409735
	- type: manhattan_precision
	value: 85.49382716049382
	- type: manhattan_recall
	value: 83.1
	- type: max_accuracy
	value: 99.6960396039604
	- type: max_ap
	value: 91.22814257221482
	- type: max_f1
	value: 84.43775100401606
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering
	name: MTEB StackExchangeClustering
	config: default
	split: test
	metrics:
	- type: v_measure
	value: 63.13072579524015
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering-p2p
	name: MTEB StackExchangeClusteringP2P
	config: default
	split: test
	metrics:
	- type: v_measure
	value: 35.681141375580225
	- task:
	type: Reranking
	dataset:
	type: mteb/stackoverflowdupquestions-reranking
	name: MTEB StackOverflowDupQuestions
	config: default
	split: test
	metrics:
	- type: map
	value: 48.46269194141537
	- type: mrr
	value: 49.11958343943638
	- task:
	type: Summarization
	dataset:
	type: mteb/summeval
	name: MTEB SummEval
	config: default
	split: test
	metrics:
	- type: cos_sim_pearson
	value: 30.709572612837498
	- type: cos_sim_spearman
	value: 31.3940211538976
	- type: dot_pearson
	value: 30.709578240668765
	- type: dot_spearman
	value: 31.3940211538976
	- task:
	type: Retrieval
	dataset:
	type: trec-covid
	name: MTEB TRECCOVID
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 0.151
	- type: map_at_10
	value: 0.822
	- type: map_at_100
	value: 4.846
	- type: map_at_1000
	value: 13.117
	- type: map_at_3
	value: 0.349
	- type: map_at_5
	value: 0.49500000000000005
	- type: ndcg_at_1
	value: 48.0
	- type: ndcg_at_10
	value: 40.699000000000005
	- type: ndcg_at_100
	value: 35.455
	- type: ndcg_at_1000
	value: 35.067
	- type: ndcg_at_3
	value: 44.519999999999996
	- type: ndcg_at_5
	value: 42.697
	- type: precision_at_1
	value: 54.0
	- type: precision_at_10
	value: 44.0
	- type: precision_at_100
	value: 37.72
	- type: precision_at_1000
	value: 16.302
	- type: precision_at_3
	value: 50.0
	- type: precision_at_5
	value: 47.199999999999996
	- type: recall_at_1
	value: 0.151
	- type: recall_at_10
	value: 1.109
	- type: recall_at_100
	value: 8.644
	- type: recall_at_1000
	value: 34.566
	- type: recall_at_3
	value: 0.394
	- type: recall_at_5
	value: 0.601
	- task:
	type: Retrieval
	dataset:
	type: webis-touche2020
	name: MTEB Touche2020
	config: default
	split: test
	metrics:
	- type: map_at_1
	value: 1.786
	- type: map_at_10
	value: 8.379
	- type: map_at_100
	value: 13.618
	- type: map_at_1000
	value: 15.15
	- type: map_at_3
	value: 3.7900000000000005
	- type: map_at_5
	value: 6.1530000000000005
	- type: ndcg_at_1
	value: 19.387999999999998
	- type: ndcg_at_10
	value: 20.296
	- type: ndcg_at_100
	value: 31.828
	- type: ndcg_at_1000
	value: 43.968
	- type: ndcg_at_3
	value: 19.583000000000002
	- type: ndcg_at_5
	value: 21.066
	- type: precision_at_1
	value: 22.448999999999998
	- type: precision_at_10
	value: 19.592000000000002
	- type: precision_at_100
	value: 7.041
	- type: precision_at_1000
	value: 1.49
	- type: precision_at_3
	value: 22.448999999999998
	- type: precision_at_5
	value: 24.490000000000002
	- type: recall_at_1
	value: 1.786
	- type: recall_at_10
	value: 14.571000000000002
	- type: recall_at_100
	value: 44.247
	- type: recall_at_1000
	value: 80.36
	- type: recall_at_3
	value: 5.117
	- type: recall_at_5
	value: 9.449
	- task:
	type: Classification
	dataset:
	type: mteb/toxic_conversations_50k
	name: MTEB ToxicConversationsClassification
	config: default
	split: test
	metrics:
	- type: accuracy
	value: 68.19919999999999
	- type: ap
	value: 14.328836562980976
	- type: f1
	value: 53.33893474325896
	- task:
	type: Classification
	dataset:
	type: mteb/tweet_sentiment_extraction
	name: MTEB TweetSentimentExtractionClassification
	config: default
	split: test
	metrics:
	- type: accuracy
	value: 62.71080928126768
	- type: f1
	value: 62.35221892617029
	- task:
	type: Clustering
	dataset:
	type: mteb/twentynewsgroups-clustering
	name: MTEB TwentyNewsgroupsClustering
	config: default
	split: test
	metrics:
	- type: v_measure
	value: 48.099101871064484
	- task:
	type: PairClassification
	dataset:
	type: mteb/twittersemeval2015-pairclassification
	name: MTEB TwitterSemEval2015
	config: default
	split: test
	metrics:
	- type: cos_sim_accuracy
	value: 87.60207426834357
	- type: cos_sim_ap
	value: 78.25096573546108
	- type: cos_sim_f1
	value: 71.740233384069
	- type: cos_sim_precision
	value: 69.07669760625306
	- type: cos_sim_recall
	value: 74.6174142480211
	- type: dot_accuracy
	value: 87.60207426834357
	- type: dot_ap
	value: 78.25097910093768
	- type: dot_f1
	value: 71.740233384069
	- type: dot_precision
	value: 69.07669760625306
	- type: dot_recall
	value: 74.6174142480211
	- type: euclidean_accuracy
	value: 87.60207426834357
	- type: euclidean_ap
	value: 78.25097099603116
	- type: euclidean_f1
	value: 71.740233384069
	- type: euclidean_precision
	value: 69.07669760625306
	- type: euclidean_recall
	value: 74.6174142480211
	- type: manhattan_accuracy
	value: 87.61399535077786
	- type: manhattan_ap
	value: 78.238484943708
	- type: manhattan_f1
	value: 71.77797490812317
	- type: manhattan_precision
	value: 69.05632772494513
	- type: manhattan_recall
	value: 74.72295514511873
	- type: max_accuracy
	value: 87.61399535077786
	- type: max_ap
	value: 78.25097910093768
	- type: max_f1
	value: 71.77797490812317
	- task:
	type: PairClassification
	dataset:
	type: mteb/twitterurlcorpus-pairclassification
	name: MTEB TwitterURLCorpus
	config: default
	split: test
	metrics:
	- type: cos_sim_accuracy
	value: 89.17413746264602
	- type: cos_sim_ap
	value: 86.04575990028458
	- type: cos_sim_f1
	value: 78.52034894604814
	- type: cos_sim_precision
	value: 76.42300123897675
	- type: cos_sim_recall
	value: 80.73606405913151
	- type: dot_accuracy
	value: 89.17413746264602
	- type: dot_ap
	value: 86.04575880500646
	- type: dot_f1
	value: 78.52034894604814
	- type: dot_precision
	value: 76.42300123897675
	- type: dot_recall
	value: 80.73606405913151
	- type: euclidean_accuracy
	value: 89.17413746264602
	- type: euclidean_ap
	value: 86.04575106124874
	- type: euclidean_f1
	value: 78.52034894604814
	- type: euclidean_precision
	value: 76.42300123897675
	- type: euclidean_recall
	value: 80.73606405913151
	- type: manhattan_accuracy
	value: 89.14891139830016
	- type: manhattan_ap
	value: 86.01748033351211
	- type: manhattan_f1
	value: 78.48817724818471
	- type: manhattan_precision
	value: 76.00057690920892
	- type: manhattan_recall
	value: 81.14413304588851
	- type: max_accuracy
	value: 89.17413746264602
	- type: max_ap
	value: 86.04575990028458
	- type: max_f1
	value: 78.52034894604814
	---

	# sentence-transformers/sentence-t5-base

	This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.

	This model was converted from the Tensorflow model [st5-base-1](https://tfhub.dev/google/sentence-t5/st5-base/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.

	The model uses only the encoder from a T5-base model. The weights are stored in FP16.


	## Usage (Sentence-Transformers)

	Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

	```
	pip install -U sentence-transformers
	```

	Then you can use the model like this:

	```python
	from sentence_transformers import SentenceTransformer
	sentences = ["This is an example sentence", "Each sentence is converted"]

	model = SentenceTransformer('sentence-transformers/sentence-t5-base')
	embeddings = model.encode(sentences)
	print(embeddings)
	```

	The model requires sentence-transformers version 2.2.0 or newer.

	## Evaluation Results

	For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/sentence-t5-base)



	## Citing & Authors

	If you find this model helpful, please cite the respective publication:
	[Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)