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	---
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- mteb
	- transformers
	- transformers.js
	language:
	- de
	- en
	inference: false
	license: apache-2.0
	model-index:
	- name: jina-embeddings-v2-base-de
	results:
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (en)
	config: en
	split: test
	revision: e8379541af4e31359cca9fbcf4b00f2671dba205
	metrics:
	- type: accuracy
	value: 73.76119402985076
	- type: ap
	value: 35.99577188521176
	- type: f1
	value: 67.50397431543269
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (de)
	config: de
	split: test
	revision: e8379541af4e31359cca9fbcf4b00f2671dba205
	metrics:
	- type: accuracy
	value: 68.9186295503212
	- type: ap
	value: 79.73307115840507
	- type: f1
	value: 66.66245744831339
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_polarity
	name: MTEB AmazonPolarityClassification
	config: default
	split: test
	revision: e2d317d38cd51312af73b3d32a06d1a08b442046
	metrics:
	- type: accuracy
	value: 77.52215
	- type: ap
	value: 71.85051037177416
	- type: f1
	value: 77.4171096157774
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (en)
	config: en
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 38.498
	- type: f1
	value: 38.058193386555956
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (de)
	config: de
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 37.717999999999996
	- type: f1
	value: 37.22674371574757
	- task:
	type: Retrieval
	dataset:
	type: arguana
	name: MTEB ArguAna
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 25.319999999999997
	- type: map_at_10
	value: 40.351
	- type: map_at_100
	value: 41.435
	- type: map_at_1000
	value: 41.443000000000005
	- type: map_at_3
	value: 35.266
	- type: map_at_5
	value: 37.99
	- type: mrr_at_1
	value: 25.746999999999996
	- type: mrr_at_10
	value: 40.515
	- type: mrr_at_100
	value: 41.606
	- type: mrr_at_1000
	value: 41.614000000000004
	- type: mrr_at_3
	value: 35.42
	- type: mrr_at_5
	value: 38.112
	- type: ndcg_at_1
	value: 25.319999999999997
	- type: ndcg_at_10
	value: 49.332
	- type: ndcg_at_100
	value: 53.909
	- type: ndcg_at_1000
	value: 54.089
	- type: ndcg_at_3
	value: 38.705
	- type: ndcg_at_5
	value: 43.606
	- type: precision_at_1
	value: 25.319999999999997
	- type: precision_at_10
	value: 7.831
	- type: precision_at_100
	value: 0.9820000000000001
	- type: precision_at_1000
	value: 0.1
	- type: precision_at_3
	value: 16.24
	- type: precision_at_5
	value: 12.119
	- type: recall_at_1
	value: 25.319999999999997
	- type: recall_at_10
	value: 78.307
	- type: recall_at_100
	value: 98.222
	- type: recall_at_1000
	value: 99.57300000000001
	- type: recall_at_3
	value: 48.72
	- type: recall_at_5
	value: 60.597
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-p2p
	name: MTEB ArxivClusteringP2P
	config: default
	split: test
	revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
	metrics:
	- type: v_measure
	value: 41.43100588255654
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-s2s
	name: MTEB ArxivClusteringS2S
	config: default
	split: test
	revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
	metrics:
	- type: v_measure
	value: 32.08988904593667
	- task:
	type: Reranking
	dataset:
	type: mteb/askubuntudupquestions-reranking
	name: MTEB AskUbuntuDupQuestions
	config: default
	split: test
	revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
	metrics:
	- type: map
	value: 60.55514765595906
	- type: mrr
	value: 73.51393835465858
	- task:
	type: STS
	dataset:
	type: mteb/biosses-sts
	name: MTEB BIOSSES
	config: default
	split: test
	revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
	metrics:
	- type: cos_sim_pearson
	value: 79.6723823121172
	- type: cos_sim_spearman
	value: 76.90596922214986
	- type: euclidean_pearson
	value: 77.87910737957918
	- type: euclidean_spearman
	value: 76.66319260598262
	- type: manhattan_pearson
	value: 77.37039493457965
	- type: manhattan_spearman
	value: 76.09872191280964
	- task:
	type: BitextMining
	dataset:
	type: mteb/bucc-bitext-mining
	name: MTEB BUCC (de-en)
	config: de-en
	split: test
	revision: d51519689f32196a32af33b075a01d0e7c51e252
	metrics:
	- type: accuracy
	value: 98.97703549060543
	- type: f1
	value: 98.86569241475296
	- type: precision
	value: 98.81002087682673
	- type: recall
	value: 98.97703549060543
	- task:
	type: Classification
	dataset:
	type: mteb/banking77
	name: MTEB Banking77Classification
	config: default
	split: test
	revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
	metrics:
	- type: accuracy
	value: 83.93506493506493
	- type: f1
	value: 83.91014949949302
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-p2p
	name: MTEB BiorxivClusteringP2P
	config: default
	split: test
	revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
	metrics:
	- type: v_measure
	value: 34.970675877585144
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-s2s
	name: MTEB BiorxivClusteringS2S
	config: default
	split: test
	revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
	metrics:
	- type: v_measure
	value: 28.779230269190954
	- task:
	type: Clustering
	dataset:
	type: slvnwhrl/blurbs-clustering-p2p
	name: MTEB BlurbsClusteringP2P
	config: default
	split: test
	revision: a2dd5b02a77de3466a3eaa98ae586b5610314496
	metrics:
	- type: v_measure
	value: 35.490175601567216
	- task:
	type: Clustering
	dataset:
	type: slvnwhrl/blurbs-clustering-s2s
	name: MTEB BlurbsClusteringS2S
	config: default
	split: test
	revision: 9bfff9a7f8f6dc6ffc9da71c48dd48b68696471d
	metrics:
	- type: v_measure
	value: 16.16638280560168
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackAndroidRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 30.830999999999996
	- type: map_at_10
	value: 41.355
	- type: map_at_100
	value: 42.791000000000004
	- type: map_at_1000
	value: 42.918
	- type: map_at_3
	value: 38.237
	- type: map_at_5
	value: 40.066
	- type: mrr_at_1
	value: 38.484
	- type: mrr_at_10
	value: 47.593
	- type: mrr_at_100
	value: 48.388
	- type: mrr_at_1000
	value: 48.439
	- type: mrr_at_3
	value: 45.279
	- type: mrr_at_5
	value: 46.724
	- type: ndcg_at_1
	value: 38.484
	- type: ndcg_at_10
	value: 47.27
	- type: ndcg_at_100
	value: 52.568000000000005
	- type: ndcg_at_1000
	value: 54.729000000000006
	- type: ndcg_at_3
	value: 43.061
	- type: ndcg_at_5
	value: 45.083
	- type: precision_at_1
	value: 38.484
	- type: precision_at_10
	value: 8.927
	- type: precision_at_100
	value: 1.425
	- type: precision_at_1000
	value: 0.19
	- type: precision_at_3
	value: 20.791999999999998
	- type: precision_at_5
	value: 14.85
	- type: recall_at_1
	value: 30.830999999999996
	- type: recall_at_10
	value: 57.87799999999999
	- type: recall_at_100
	value: 80.124
	- type: recall_at_1000
	value: 94.208
	- type: recall_at_3
	value: 45.083
	- type: recall_at_5
	value: 51.154999999999994
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackEnglishRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 25.782
	- type: map_at_10
	value: 34.492
	- type: map_at_100
	value: 35.521
	- type: map_at_1000
	value: 35.638
	- type: map_at_3
	value: 31.735999999999997
	- type: map_at_5
	value: 33.339
	- type: mrr_at_1
	value: 32.357
	- type: mrr_at_10
	value: 39.965
	- type: mrr_at_100
	value: 40.644000000000005
	- type: mrr_at_1000
	value: 40.695
	- type: mrr_at_3
	value: 37.739
	- type: mrr_at_5
	value: 39.061
	- type: ndcg_at_1
	value: 32.357
	- type: ndcg_at_10
	value: 39.644
	- type: ndcg_at_100
	value: 43.851
	- type: ndcg_at_1000
	value: 46.211999999999996
	- type: ndcg_at_3
	value: 35.675000000000004
	- type: ndcg_at_5
	value: 37.564
	- type: precision_at_1
	value: 32.357
	- type: precision_at_10
	value: 7.344
	- type: precision_at_100
	value: 1.201
	- type: precision_at_1000
	value: 0.168
	- type: precision_at_3
	value: 17.155
	- type: precision_at_5
	value: 12.166
	- type: recall_at_1
	value: 25.782
	- type: recall_at_10
	value: 49.132999999999996
	- type: recall_at_100
	value: 67.24
	- type: recall_at_1000
	value: 83.045
	- type: recall_at_3
	value: 37.021
	- type: recall_at_5
	value: 42.548
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackGamingRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 35.778999999999996
	- type: map_at_10
	value: 47.038000000000004
	- type: map_at_100
	value: 48.064
	- type: map_at_1000
	value: 48.128
	- type: map_at_3
	value: 44.186
	- type: map_at_5
	value: 45.788000000000004
	- type: mrr_at_1
	value: 41.254000000000005
	- type: mrr_at_10
	value: 50.556999999999995
	- type: mrr_at_100
	value: 51.296
	- type: mrr_at_1000
	value: 51.331
	- type: mrr_at_3
	value: 48.318
	- type: mrr_at_5
	value: 49.619
	- type: ndcg_at_1
	value: 41.254000000000005
	- type: ndcg_at_10
	value: 52.454
	- type: ndcg_at_100
	value: 56.776
	- type: ndcg_at_1000
	value: 58.181000000000004
	- type: ndcg_at_3
	value: 47.713
	- type: ndcg_at_5
	value: 49.997
	- type: precision_at_1
	value: 41.254000000000005
	- type: precision_at_10
	value: 8.464
	- type: precision_at_100
	value: 1.157
	- type: precision_at_1000
	value: 0.133
	- type: precision_at_3
	value: 21.526
	- type: precision_at_5
	value: 14.696000000000002
	- type: recall_at_1
	value: 35.778999999999996
	- type: recall_at_10
	value: 64.85300000000001
	- type: recall_at_100
	value: 83.98400000000001
	- type: recall_at_1000
	value: 94.18299999999999
	- type: recall_at_3
	value: 51.929
	- type: recall_at_5
	value: 57.666
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackGisRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 21.719
	- type: map_at_10
	value: 29.326999999999998
	- type: map_at_100
	value: 30.314000000000004
	- type: map_at_1000
	value: 30.397000000000002
	- type: map_at_3
	value: 27.101
	- type: map_at_5
	value: 28.141
	- type: mrr_at_1
	value: 23.503
	- type: mrr_at_10
	value: 31.225
	- type: mrr_at_100
	value: 32.096000000000004
	- type: mrr_at_1000
	value: 32.159
	- type: mrr_at_3
	value: 29.076999999999998
	- type: mrr_at_5
	value: 30.083
	- type: ndcg_at_1
	value: 23.503
	- type: ndcg_at_10
	value: 33.842
	- type: ndcg_at_100
	value: 39.038000000000004
	- type: ndcg_at_1000
	value: 41.214
	- type: ndcg_at_3
	value: 29.347
	- type: ndcg_at_5
	value: 31.121
	- type: precision_at_1
	value: 23.503
	- type: precision_at_10
	value: 5.266
	- type: precision_at_100
	value: 0.831
	- type: precision_at_1000
	value: 0.106
	- type: precision_at_3
	value: 12.504999999999999
	- type: precision_at_5
	value: 8.565000000000001
	- type: recall_at_1
	value: 21.719
	- type: recall_at_10
	value: 46.024
	- type: recall_at_100
	value: 70.78999999999999
	- type: recall_at_1000
	value: 87.022
	- type: recall_at_3
	value: 33.64
	- type: recall_at_5
	value: 37.992
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackMathematicaRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 15.601
	- type: map_at_10
	value: 22.054000000000002
	- type: map_at_100
	value: 23.177
	- type: map_at_1000
	value: 23.308
	- type: map_at_3
	value: 19.772000000000002
	- type: map_at_5
	value: 21.055
	- type: mrr_at_1
	value: 19.403000000000002
	- type: mrr_at_10
	value: 26.409
	- type: mrr_at_100
	value: 27.356
	- type: mrr_at_1000
	value: 27.441
	- type: mrr_at_3
	value: 24.108999999999998
	- type: mrr_at_5
	value: 25.427
	- type: ndcg_at_1
	value: 19.403000000000002
	- type: ndcg_at_10
	value: 26.474999999999998
	- type: ndcg_at_100
	value: 32.086
	- type: ndcg_at_1000
	value: 35.231
	- type: ndcg_at_3
	value: 22.289
	- type: ndcg_at_5
	value: 24.271
	- type: precision_at_1
	value: 19.403000000000002
	- type: precision_at_10
	value: 4.813
	- type: precision_at_100
	value: 0.8869999999999999
	- type: precision_at_1000
	value: 0.13
	- type: precision_at_3
	value: 10.531
	- type: precision_at_5
	value: 7.710999999999999
	- type: recall_at_1
	value: 15.601
	- type: recall_at_10
	value: 35.916
	- type: recall_at_100
	value: 60.8
	- type: recall_at_1000
	value: 83.245
	- type: recall_at_3
	value: 24.321
	- type: recall_at_5
	value: 29.372999999999998
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackPhysicsRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 25.522
	- type: map_at_10
	value: 34.854
	- type: map_at_100
	value: 36.269
	- type: map_at_1000
	value: 36.387
	- type: map_at_3
	value: 32.187
	- type: map_at_5
	value: 33.692
	- type: mrr_at_1
	value: 31.375999999999998
	- type: mrr_at_10
	value: 40.471000000000004
	- type: mrr_at_100
	value: 41.481
	- type: mrr_at_1000
	value: 41.533
	- type: mrr_at_3
	value: 38.274
	- type: mrr_at_5
	value: 39.612
	- type: ndcg_at_1
	value: 31.375999999999998
	- type: ndcg_at_10
	value: 40.298
	- type: ndcg_at_100
	value: 46.255
	- type: ndcg_at_1000
	value: 48.522
	- type: ndcg_at_3
	value: 36.049
	- type: ndcg_at_5
	value: 38.095
	- type: precision_at_1
	value: 31.375999999999998
	- type: precision_at_10
	value: 7.305000000000001
	- type: precision_at_100
	value: 1.201
	- type: precision_at_1000
	value: 0.157
	- type: precision_at_3
	value: 17.132
	- type: precision_at_5
	value: 12.107999999999999
	- type: recall_at_1
	value: 25.522
	- type: recall_at_10
	value: 50.988
	- type: recall_at_100
	value: 76.005
	- type: recall_at_1000
	value: 91.11200000000001
	- type: recall_at_3
	value: 38.808
	- type: recall_at_5
	value: 44.279
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackProgrammersRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 24.615000000000002
	- type: map_at_10
	value: 32.843
	- type: map_at_100
	value: 34.172999999999995
	- type: map_at_1000
	value: 34.286
	- type: map_at_3
	value: 30.125
	- type: map_at_5
	value: 31.495
	- type: mrr_at_1
	value: 30.023
	- type: mrr_at_10
	value: 38.106
	- type: mrr_at_100
	value: 39.01
	- type: mrr_at_1000
	value: 39.071
	- type: mrr_at_3
	value: 35.674
	- type: mrr_at_5
	value: 36.924
	- type: ndcg_at_1
	value: 30.023
	- type: ndcg_at_10
	value: 38.091
	- type: ndcg_at_100
	value: 43.771
	- type: ndcg_at_1000
	value: 46.315
	- type: ndcg_at_3
	value: 33.507
	- type: ndcg_at_5
	value: 35.304
	- type: precision_at_1
	value: 30.023
	- type: precision_at_10
	value: 6.837999999999999
	- type: precision_at_100
	value: 1.124
	- type: precision_at_1000
	value: 0.152
	- type: precision_at_3
	value: 15.562999999999999
	- type: precision_at_5
	value: 10.936
	- type: recall_at_1
	value: 24.615000000000002
	- type: recall_at_10
	value: 48.691
	- type: recall_at_100
	value: 72.884
	- type: recall_at_1000
	value: 90.387
	- type: recall_at_3
	value: 35.659
	- type: recall_at_5
	value: 40.602
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 23.223666666666666
	- type: map_at_10
	value: 31.338166666666673
	- type: map_at_100
	value: 32.47358333333333
	- type: map_at_1000
	value: 32.5955
	- type: map_at_3
	value: 28.84133333333333
	- type: map_at_5
	value: 30.20808333333333
	- type: mrr_at_1
	value: 27.62483333333333
	- type: mrr_at_10
	value: 35.385916666666674
	- type: mrr_at_100
	value: 36.23325
	- type: mrr_at_1000
	value: 36.29966666666667
	- type: mrr_at_3
	value: 33.16583333333333
	- type: mrr_at_5
	value: 34.41983333333334
	- type: ndcg_at_1
	value: 27.62483333333333
	- type: ndcg_at_10
	value: 36.222
	- type: ndcg_at_100
	value: 41.29491666666666
	- type: ndcg_at_1000
	value: 43.85508333333333
	- type: ndcg_at_3
	value: 31.95116666666667
	- type: ndcg_at_5
	value: 33.88541666666667
	- type: precision_at_1
	value: 27.62483333333333
	- type: precision_at_10
	value: 6.339916666666667
	- type: precision_at_100
	value: 1.0483333333333333
	- type: precision_at_1000
	value: 0.14608333333333334
	- type: precision_at_3
	value: 14.726500000000003
	- type: precision_at_5
	value: 10.395
	- type: recall_at_1
	value: 23.223666666666666
	- type: recall_at_10
	value: 46.778999999999996
	- type: recall_at_100
	value: 69.27141666666667
	- type: recall_at_1000
	value: 87.27383333333334
	- type: recall_at_3
	value: 34.678749999999994
	- type: recall_at_5
	value: 39.79900000000001
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackStatsRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 21.677
	- type: map_at_10
	value: 27.828000000000003
	- type: map_at_100
	value: 28.538999999999998
	- type: map_at_1000
	value: 28.64
	- type: map_at_3
	value: 26.105
	- type: map_at_5
	value: 27.009
	- type: mrr_at_1
	value: 24.387
	- type: mrr_at_10
	value: 30.209999999999997
	- type: mrr_at_100
	value: 30.953000000000003
	- type: mrr_at_1000
	value: 31.029
	- type: mrr_at_3
	value: 28.707
	- type: mrr_at_5
	value: 29.610999999999997
	- type: ndcg_at_1
	value: 24.387
	- type: ndcg_at_10
	value: 31.378
	- type: ndcg_at_100
	value: 35.249
	- type: ndcg_at_1000
	value: 37.923
	- type: ndcg_at_3
	value: 28.213
	- type: ndcg_at_5
	value: 29.658
	- type: precision_at_1
	value: 24.387
	- type: precision_at_10
	value: 4.8309999999999995
	- type: precision_at_100
	value: 0.73
	- type: precision_at_1000
	value: 0.104
	- type: precision_at_3
	value: 12.168
	- type: precision_at_5
	value: 8.251999999999999
	- type: recall_at_1
	value: 21.677
	- type: recall_at_10
	value: 40.069
	- type: recall_at_100
	value: 58.077
	- type: recall_at_1000
	value: 77.97
	- type: recall_at_3
	value: 31.03
	- type: recall_at_5
	value: 34.838
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackTexRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 14.484
	- type: map_at_10
	value: 20.355
	- type: map_at_100
	value: 21.382
	- type: map_at_1000
	value: 21.511
	- type: map_at_3
	value: 18.448
	- type: map_at_5
	value: 19.451999999999998
	- type: mrr_at_1
	value: 17.584
	- type: mrr_at_10
	value: 23.825
	- type: mrr_at_100
	value: 24.704
	- type: mrr_at_1000
	value: 24.793000000000003
	- type: mrr_at_3
	value: 21.92
	- type: mrr_at_5
	value: 22.97
	- type: ndcg_at_1
	value: 17.584
	- type: ndcg_at_10
	value: 24.315
	- type: ndcg_at_100
	value: 29.354999999999997
	- type: ndcg_at_1000
	value: 32.641999999999996
	- type: ndcg_at_3
	value: 20.802
	- type: ndcg_at_5
	value: 22.335
	- type: precision_at_1
	value: 17.584
	- type: precision_at_10
	value: 4.443
	- type: precision_at_100
	value: 0.8160000000000001
	- type: precision_at_1000
	value: 0.128
	- type: precision_at_3
	value: 9.807
	- type: precision_at_5
	value: 7.0889999999999995
	- type: recall_at_1
	value: 14.484
	- type: recall_at_10
	value: 32.804
	- type: recall_at_100
	value: 55.679
	- type: recall_at_1000
	value: 79.63
	- type: recall_at_3
	value: 22.976
	- type: recall_at_5
	value: 26.939
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackUnixRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 22.983999999999998
	- type: map_at_10
	value: 30.812
	- type: map_at_100
	value: 31.938
	- type: map_at_1000
	value: 32.056000000000004
	- type: map_at_3
	value: 28.449999999999996
	- type: map_at_5
	value: 29.542
	- type: mrr_at_1
	value: 27.145999999999997
	- type: mrr_at_10
	value: 34.782999999999994
	- type: mrr_at_100
	value: 35.699
	- type: mrr_at_1000
	value: 35.768
	- type: mrr_at_3
	value: 32.572
	- type: mrr_at_5
	value: 33.607
	- type: ndcg_at_1
	value: 27.145999999999997
	- type: ndcg_at_10
	value: 35.722
	- type: ndcg_at_100
	value: 40.964
	- type: ndcg_at_1000
	value: 43.598
	- type: ndcg_at_3
	value: 31.379
	- type: ndcg_at_5
	value: 32.924
	- type: precision_at_1
	value: 27.145999999999997
	- type: precision_at_10
	value: 6.063000000000001
	- type: precision_at_100
	value: 0.9730000000000001
	- type: precision_at_1000
	value: 0.13
	- type: precision_at_3
	value: 14.366000000000001
	- type: precision_at_5
	value: 9.776
	- type: recall_at_1
	value: 22.983999999999998
	- type: recall_at_10
	value: 46.876
	- type: recall_at_100
	value: 69.646
	- type: recall_at_1000
	value: 88.305
	- type: recall_at_3
	value: 34.471000000000004
	- type: recall_at_5
	value: 38.76
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackWebmastersRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 23.017000000000003
	- type: map_at_10
	value: 31.049
	- type: map_at_100
	value: 32.582
	- type: map_at_1000
	value: 32.817
	- type: map_at_3
	value: 28.303
	- type: map_at_5
	value: 29.854000000000003
	- type: mrr_at_1
	value: 27.866000000000003
	- type: mrr_at_10
	value: 35.56
	- type: mrr_at_100
	value: 36.453
	- type: mrr_at_1000
	value: 36.519
	- type: mrr_at_3
	value: 32.938
	- type: mrr_at_5
	value: 34.391
	- type: ndcg_at_1
	value: 27.866000000000003
	- type: ndcg_at_10
	value: 36.506
	- type: ndcg_at_100
	value: 42.344
	- type: ndcg_at_1000
	value: 45.213
	- type: ndcg_at_3
	value: 31.805
	- type: ndcg_at_5
	value: 33.933
	- type: precision_at_1
	value: 27.866000000000003
	- type: precision_at_10
	value: 7.016
	- type: precision_at_100
	value: 1.468
	- type: precision_at_1000
	value: 0.23900000000000002
	- type: precision_at_3
	value: 14.822
	- type: precision_at_5
	value: 10.791
	- type: recall_at_1
	value: 23.017000000000003
	- type: recall_at_10
	value: 47.053
	- type: recall_at_100
	value: 73.177
	- type: recall_at_1000
	value: 91.47800000000001
	- type: recall_at_3
	value: 33.675
	- type: recall_at_5
	value: 39.36
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackWordpressRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 16.673
	- type: map_at_10
	value: 24.051000000000002
	- type: map_at_100
	value: 24.933
	- type: map_at_1000
	value: 25.06
	- type: map_at_3
	value: 21.446
	- type: map_at_5
	value: 23.064
	- type: mrr_at_1
	value: 18.115000000000002
	- type: mrr_at_10
	value: 25.927
	- type: mrr_at_100
	value: 26.718999999999998
	- type: mrr_at_1000
	value: 26.817999999999998
	- type: mrr_at_3
	value: 23.383000000000003
	- type: mrr_at_5
	value: 25.008999999999997
	- type: ndcg_at_1
	value: 18.115000000000002
	- type: ndcg_at_10
	value: 28.669
	- type: ndcg_at_100
	value: 33.282000000000004
	- type: ndcg_at_1000
	value: 36.481
	- type: ndcg_at_3
	value: 23.574
	- type: ndcg_at_5
	value: 26.340000000000003
	- type: precision_at_1
	value: 18.115000000000002
	- type: precision_at_10
	value: 4.769
	- type: precision_at_100
	value: 0.767
	- type: precision_at_1000
	value: 0.116
	- type: precision_at_3
	value: 10.351
	- type: precision_at_5
	value: 7.8
	- type: recall_at_1
	value: 16.673
	- type: recall_at_10
	value: 41.063
	- type: recall_at_100
	value: 62.851
	- type: recall_at_1000
	value: 86.701
	- type: recall_at_3
	value: 27.532
	- type: recall_at_5
	value: 34.076
	- task:
	type: Retrieval
	dataset:
	type: climate-fever
	name: MTEB ClimateFEVER
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 8.752
	- type: map_at_10
	value: 15.120000000000001
	- type: map_at_100
	value: 16.678
	- type: map_at_1000
	value: 16.854
	- type: map_at_3
	value: 12.603
	- type: map_at_5
	value: 13.918
	- type: mrr_at_1
	value: 19.283
	- type: mrr_at_10
	value: 29.145
	- type: mrr_at_100
	value: 30.281000000000002
	- type: mrr_at_1000
	value: 30.339
	- type: mrr_at_3
	value: 26.069
	- type: mrr_at_5
	value: 27.864
	- type: ndcg_at_1
	value: 19.283
	- type: ndcg_at_10
	value: 21.804000000000002
	- type: ndcg_at_100
	value: 28.576
	- type: ndcg_at_1000
	value: 32.063
	- type: ndcg_at_3
	value: 17.511
	- type: ndcg_at_5
	value: 19.112000000000002
	- type: precision_at_1
	value: 19.283
	- type: precision_at_10
	value: 6.873
	- type: precision_at_100
	value: 1.405
	- type: precision_at_1000
	value: 0.20500000000000002
	- type: precision_at_3
	value: 13.16
	- type: precision_at_5
	value: 10.189
	- type: recall_at_1
	value: 8.752
	- type: recall_at_10
	value: 27.004
	- type: recall_at_100
	value: 50.648
	- type: recall_at_1000
	value: 70.458
	- type: recall_at_3
	value: 16.461000000000002
	- type: recall_at_5
	value: 20.973
	- task:
	type: Retrieval
	dataset:
	type: dbpedia-entity
	name: MTEB DBPedia
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 6.81
	- type: map_at_10
	value: 14.056
	- type: map_at_100
	value: 18.961
	- type: map_at_1000
	value: 20.169
	- type: map_at_3
	value: 10.496
	- type: map_at_5
	value: 11.952
	- type: mrr_at_1
	value: 53.5
	- type: mrr_at_10
	value: 63.479
	- type: mrr_at_100
	value: 63.971999999999994
	- type: mrr_at_1000
	value: 63.993
	- type: mrr_at_3
	value: 61.541999999999994
	- type: mrr_at_5
	value: 62.778999999999996
	- type: ndcg_at_1
	value: 42.25
	- type: ndcg_at_10
	value: 31.471
	- type: ndcg_at_100
	value: 35.115
	- type: ndcg_at_1000
	value: 42.408
	- type: ndcg_at_3
	value: 35.458
	- type: ndcg_at_5
	value: 32.973
	- type: precision_at_1
	value: 53.5
	- type: precision_at_10
	value: 24.85
	- type: precision_at_100
	value: 7.79
	- type: precision_at_1000
	value: 1.599
	- type: precision_at_3
	value: 38.667
	- type: precision_at_5
	value: 31.55
	- type: recall_at_1
	value: 6.81
	- type: recall_at_10
	value: 19.344
	- type: recall_at_100
	value: 40.837
	- type: recall_at_1000
	value: 64.661
	- type: recall_at_3
	value: 11.942
	- type: recall_at_5
	value: 14.646
	- task:
	type: Classification
	dataset:
	type: mteb/emotion
	name: MTEB EmotionClassification
	config: default
	split: test
	revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
	metrics:
	- type: accuracy
	value: 44.64499999999999
	- type: f1
	value: 39.39106911352714
	- task:
	type: Retrieval
	dataset:
	type: fever
	name: MTEB FEVER
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 48.196
	- type: map_at_10
	value: 61.404
	- type: map_at_100
	value: 61.846000000000004
	- type: map_at_1000
	value: 61.866
	- type: map_at_3
	value: 58.975
	- type: map_at_5
	value: 60.525
	- type: mrr_at_1
	value: 52.025
	- type: mrr_at_10
	value: 65.43299999999999
	- type: mrr_at_100
	value: 65.80799999999999
	- type: mrr_at_1000
	value: 65.818
	- type: mrr_at_3
	value: 63.146
	- type: mrr_at_5
	value: 64.64
	- type: ndcg_at_1
	value: 52.025
	- type: ndcg_at_10
	value: 67.889
	- type: ndcg_at_100
	value: 69.864
	- type: ndcg_at_1000
	value: 70.337
	- type: ndcg_at_3
	value: 63.315
	- type: ndcg_at_5
	value: 65.91799999999999
	- type: precision_at_1
	value: 52.025
	- type: precision_at_10
	value: 9.182
	- type: precision_at_100
	value: 1.027
	- type: precision_at_1000
	value: 0.108
	- type: precision_at_3
	value: 25.968000000000004
	- type: precision_at_5
	value: 17.006
	- type: recall_at_1
	value: 48.196
	- type: recall_at_10
	value: 83.885
	- type: recall_at_100
	value: 92.671
	- type: recall_at_1000
	value: 96.018
	- type: recall_at_3
	value: 71.59
	- type: recall_at_5
	value: 77.946
	- task:
	type: Retrieval
	dataset:
	type: fiqa
	name: MTEB FiQA2018
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 15.193000000000001
	- type: map_at_10
	value: 25.168000000000003
	- type: map_at_100
	value: 27.017000000000003
	- type: map_at_1000
	value: 27.205000000000002
	- type: map_at_3
	value: 21.746
	- type: map_at_5
	value: 23.579
	- type: mrr_at_1
	value: 31.635999999999996
	- type: mrr_at_10
	value: 40.077
	- type: mrr_at_100
	value: 41.112
	- type: mrr_at_1000
	value: 41.160999999999994
	- type: mrr_at_3
	value: 37.937
	- type: mrr_at_5
	value: 39.18
	- type: ndcg_at_1
	value: 31.635999999999996
	- type: ndcg_at_10
	value: 32.298
	- type: ndcg_at_100
	value: 39.546
	- type: ndcg_at_1000
	value: 42.88
	- type: ndcg_at_3
	value: 29.221999999999998
	- type: ndcg_at_5
	value: 30.069000000000003
	- type: precision_at_1
	value: 31.635999999999996
	- type: precision_at_10
	value: 9.367
	- type: precision_at_100
	value: 1.645
	- type: precision_at_1000
	value: 0.22399999999999998
	- type: precision_at_3
	value: 20.01
	- type: precision_at_5
	value: 14.753
	- type: recall_at_1
	value: 15.193000000000001
	- type: recall_at_10
	value: 38.214999999999996
	- type: recall_at_100
	value: 65.95
	- type: recall_at_1000
	value: 85.85300000000001
	- type: recall_at_3
	value: 26.357000000000003
	- type: recall_at_5
	value: 31.319999999999997
	- task:
	type: Retrieval
	dataset:
	type: jinaai/ger_da_lir
	name: MTEB GerDaLIR
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 10.363
	- type: map_at_10
	value: 16.222
	- type: map_at_100
	value: 17.28
	- type: map_at_1000
	value: 17.380000000000003
	- type: map_at_3
	value: 14.054
	- type: map_at_5
	value: 15.203
	- type: mrr_at_1
	value: 11.644
	- type: mrr_at_10
	value: 17.625
	- type: mrr_at_100
	value: 18.608
	- type: mrr_at_1000
	value: 18.695999999999998
	- type: mrr_at_3
	value: 15.481
	- type: mrr_at_5
	value: 16.659
	- type: ndcg_at_1
	value: 11.628
	- type: ndcg_at_10
	value: 20.028000000000002
	- type: ndcg_at_100
	value: 25.505
	- type: ndcg_at_1000
	value: 28.288000000000004
	- type: ndcg_at_3
	value: 15.603
	- type: ndcg_at_5
	value: 17.642
	- type: precision_at_1
	value: 11.628
	- type: precision_at_10
	value: 3.5589999999999997
	- type: precision_at_100
	value: 0.664
	- type: precision_at_1000
	value: 0.092
	- type: precision_at_3
	value: 7.109999999999999
	- type: precision_at_5
	value: 5.401
	- type: recall_at_1
	value: 10.363
	- type: recall_at_10
	value: 30.586000000000002
	- type: recall_at_100
	value: 56.43
	- type: recall_at_1000
	value: 78.142
	- type: recall_at_3
	value: 18.651
	- type: recall_at_5
	value: 23.493
	- task:
	type: Retrieval
	dataset:
	type: deepset/germandpr
	name: MTEB GermanDPR
	config: default
	split: test
	revision: 5129d02422a66be600ac89cd3e8531b4f97d347d
	metrics:
	- type: map_at_1
	value: 60.78
	- type: map_at_10
	value: 73.91499999999999
	- type: map_at_100
	value: 74.089
	- type: map_at_1000
	value: 74.09400000000001
	- type: map_at_3
	value: 71.87
	- type: map_at_5
	value: 73.37700000000001
	- type: mrr_at_1
	value: 60.78
	- type: mrr_at_10
	value: 73.91499999999999
	- type: mrr_at_100
	value: 74.089
	- type: mrr_at_1000
	value: 74.09400000000001
	- type: mrr_at_3
	value: 71.87
	- type: mrr_at_5
	value: 73.37700000000001
	- type: ndcg_at_1
	value: 60.78
	- type: ndcg_at_10
	value: 79.35600000000001
	- type: ndcg_at_100
	value: 80.077
	- type: ndcg_at_1000
	value: 80.203
	- type: ndcg_at_3
	value: 75.393
	- type: ndcg_at_5
	value: 78.077
	- type: precision_at_1
	value: 60.78
	- type: precision_at_10
	value: 9.59
	- type: precision_at_100
	value: 0.9900000000000001
	- type: precision_at_1000
	value: 0.1
	- type: precision_at_3
	value: 28.52
	- type: precision_at_5
	value: 18.4
	- type: recall_at_1
	value: 60.78
	- type: recall_at_10
	value: 95.902
	- type: recall_at_100
	value: 99.024
	- type: recall_at_1000
	value: 100.0
	- type: recall_at_3
	value: 85.56099999999999
	- type: recall_at_5
	value: 92.0
	- task:
	type: STS
	dataset:
	type: jinaai/german-STSbenchmark
	name: MTEB GermanSTSBenchmark
	config: default
	split: test
	revision: 49d9b423b996fea62b483f9ee6dfb5ec233515ca
	metrics:
	- type: cos_sim_pearson
	value: 88.49524420894356
	- type: cos_sim_spearman
	value: 88.32407839427714
	- type: euclidean_pearson
	value: 87.25098779877104
	- type: euclidean_spearman
	value: 88.22738098593608
	- type: manhattan_pearson
	value: 87.23872691839607
	- type: manhattan_spearman
	value: 88.2002968380165
	- task:
	type: Retrieval
	dataset:
	type: hotpotqa
	name: MTEB HotpotQA
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 31.81
	- type: map_at_10
	value: 46.238
	- type: map_at_100
	value: 47.141
	- type: map_at_1000
	value: 47.213
	- type: map_at_3
	value: 43.248999999999995
	- type: map_at_5
	value: 45.078
	- type: mrr_at_1
	value: 63.619
	- type: mrr_at_10
	value: 71.279
	- type: mrr_at_100
	value: 71.648
	- type: mrr_at_1000
	value: 71.665
	- type: mrr_at_3
	value: 69.76599999999999
	- type: mrr_at_5
	value: 70.743
	- type: ndcg_at_1
	value: 63.619
	- type: ndcg_at_10
	value: 55.38999999999999
	- type: ndcg_at_100
	value: 58.80800000000001
	- type: ndcg_at_1000
	value: 60.331999999999994
	- type: ndcg_at_3
	value: 50.727
	- type: ndcg_at_5
	value: 53.284
	- type: precision_at_1
	value: 63.619
	- type: precision_at_10
	value: 11.668000000000001
	- type: precision_at_100
	value: 1.434
	- type: precision_at_1000
	value: 0.164
	- type: precision_at_3
	value: 32.001000000000005
	- type: precision_at_5
	value: 21.223
	- type: recall_at_1
	value: 31.81
	- type: recall_at_10
	value: 58.339
	- type: recall_at_100
	value: 71.708
	- type: recall_at_1000
	value: 81.85
	- type: recall_at_3
	value: 48.001
	- type: recall_at_5
	value: 53.059
	- task:
	type: Classification
	dataset:
	type: mteb/imdb
	name: MTEB ImdbClassification
	config: default
	split: test
	revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
	metrics:
	- type: accuracy
	value: 68.60640000000001
	- type: ap
	value: 62.84296904042086
	- type: f1
	value: 68.50643633327537
	- task:
	type: Reranking
	dataset:
	type: jinaai/miracl
	name: MTEB MIRACL
	config: default
	split: test
	revision: 8741c3b61cd36ed9ca1b3d4203543a41793239e2
	metrics:
	- type: map
	value: 64.29704335389768
	- type: mrr
	value: 72.11962197159565
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (en)
	config: en
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 89.3844049247606
	- type: f1
	value: 89.2124328528015
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (de)
	config: de
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 88.36855452240067
	- type: f1
	value: 87.35458822097442
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (en)
	config: en
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 66.48654810761514
	- type: f1
	value: 50.07229882504409
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (de)
	config: de
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 63.832065370526905
	- type: f1
	value: 46.283579383385806
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (de)
	config: de
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 63.89038332212509
	- type: f1
	value: 61.86279849685129
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (en)
	config: en
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 69.11230665770006
	- type: f1
	value: 67.44780095350535
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (de)
	config: de
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 71.25084061869536
	- type: f1
	value: 71.43965023016408
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (en)
	config: en
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 73.73907195696032
	- type: f1
	value: 73.69920814839061
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-p2p
	name: MTEB MedrxivClusteringP2P
	config: default
	split: test
	revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
	metrics:
	- type: v_measure
	value: 31.32577306498249
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-s2s
	name: MTEB MedrxivClusteringS2S
	config: default
	split: test
	revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
	metrics:
	- type: v_measure
	value: 28.759349326367783
	- task:
	type: Reranking
	dataset:
	type: mteb/mind_small
	name: MTEB MindSmallReranking
	config: default
	split: test
	revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
	metrics:
	- type: map
	value: 30.401342674703425
	- type: mrr
	value: 31.384379585660987
	- task:
	type: Retrieval
	dataset:
	type: nfcorpus
	name: MTEB NFCorpus
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 4.855
	- type: map_at_10
	value: 10.01
	- type: map_at_100
	value: 12.461
	- type: map_at_1000
	value: 13.776
	- type: map_at_3
	value: 7.252
	- type: map_at_5
	value: 8.679
	- type: mrr_at_1
	value: 41.176
	- type: mrr_at_10
	value: 49.323
	- type: mrr_at_100
	value: 49.954
	- type: mrr_at_1000
	value: 49.997
	- type: mrr_at_3
	value: 46.904
	- type: mrr_at_5
	value: 48.375
	- type: ndcg_at_1
	value: 39.318999999999996
	- type: ndcg_at_10
	value: 28.607
	- type: ndcg_at_100
	value: 26.554
	- type: ndcg_at_1000
	value: 35.731
	- type: ndcg_at_3
	value: 32.897999999999996
	- type: ndcg_at_5
	value: 31.53
	- type: precision_at_1
	value: 41.176
	- type: precision_at_10
	value: 20.867
	- type: precision_at_100
	value: 6.796
	- type: precision_at_1000
	value: 1.983
	- type: precision_at_3
	value: 30.547
	- type: precision_at_5
	value: 27.245
	- type: recall_at_1
	value: 4.855
	- type: recall_at_10
	value: 14.08
	- type: recall_at_100
	value: 28.188000000000002
	- type: recall_at_1000
	value: 60.07900000000001
	- type: recall_at_3
	value: 7.947
	- type: recall_at_5
	value: 10.786
	- task:
	type: Retrieval
	dataset:
	type: nq
	name: MTEB NQ
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 26.906999999999996
	- type: map_at_10
	value: 41.147
	- type: map_at_100
	value: 42.269
	- type: map_at_1000
	value: 42.308
	- type: map_at_3
	value: 36.638999999999996
	- type: map_at_5
	value: 39.285
	- type: mrr_at_1
	value: 30.359
	- type: mrr_at_10
	value: 43.607
	- type: mrr_at_100
	value: 44.454
	- type: mrr_at_1000
	value: 44.481
	- type: mrr_at_3
	value: 39.644
	- type: mrr_at_5
	value: 42.061
	- type: ndcg_at_1
	value: 30.330000000000002
	- type: ndcg_at_10
	value: 48.899
	- type: ndcg_at_100
	value: 53.612
	- type: ndcg_at_1000
	value: 54.51200000000001
	- type: ndcg_at_3
	value: 40.262
	- type: ndcg_at_5
	value: 44.787
	- type: precision_at_1
	value: 30.330000000000002
	- type: precision_at_10
	value: 8.323
	- type: precision_at_100
	value: 1.0959999999999999
	- type: precision_at_1000
	value: 0.11800000000000001
	- type: precision_at_3
	value: 18.395
	- type: precision_at_5
	value: 13.627
	- type: recall_at_1
	value: 26.906999999999996
	- type: recall_at_10
	value: 70.215
	- type: recall_at_100
	value: 90.61200000000001
	- type: recall_at_1000
	value: 97.294
	- type: recall_at_3
	value: 47.784
	- type: recall_at_5
	value: 58.251
	- task:
	type: PairClassification
	dataset:
	type: paws-x
	name: MTEB PawsX
	config: default
	split: test
	revision: 8a04d940a42cd40658986fdd8e3da561533a3646
	metrics:
	- type: cos_sim_accuracy
	value: 60.5
	- type: cos_sim_ap
	value: 57.606096528877494
	- type: cos_sim_f1
	value: 62.24240307369892
	- type: cos_sim_precision
	value: 45.27439024390244
	- type: cos_sim_recall
	value: 99.55307262569832
	- type: dot_accuracy
	value: 57.699999999999996
	- type: dot_ap
	value: 51.289351057160616
	- type: dot_f1
	value: 62.25953130465197
	- type: dot_precision
	value: 45.31568228105906
	- type: dot_recall
	value: 99.4413407821229
	- type: euclidean_accuracy
	value: 60.45
	- type: euclidean_ap
	value: 57.616461421424034
	- type: euclidean_f1
	value: 62.313697657913416
	- type: euclidean_precision
	value: 45.657826313052524
	- type: euclidean_recall
	value: 98.10055865921787
	- type: manhattan_accuracy
	value: 60.3
	- type: manhattan_ap
	value: 57.580565271667325
	- type: manhattan_f1
	value: 62.24240307369892
	- type: manhattan_precision
	value: 45.27439024390244
	- type: manhattan_recall
	value: 99.55307262569832
	- type: max_accuracy
	value: 60.5
	- type: max_ap
	value: 57.616461421424034
	- type: max_f1
	value: 62.313697657913416
	- task:
	type: Retrieval
	dataset:
	type: quora
	name: MTEB QuoraRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 70.21300000000001
	- type: map_at_10
	value: 84.136
	- type: map_at_100
	value: 84.796
	- type: map_at_1000
	value: 84.812
	- type: map_at_3
	value: 81.182
	- type: map_at_5
	value: 83.027
	- type: mrr_at_1
	value: 80.91000000000001
	- type: mrr_at_10
	value: 87.155
	- type: mrr_at_100
	value: 87.27000000000001
	- type: mrr_at_1000
	value: 87.271
	- type: mrr_at_3
	value: 86.158
	- type: mrr_at_5
	value: 86.828
	- type: ndcg_at_1
	value: 80.88
	- type: ndcg_at_10
	value: 87.926
	- type: ndcg_at_100
	value: 89.223
	- type: ndcg_at_1000
	value: 89.321
	- type: ndcg_at_3
	value: 85.036
	- type: ndcg_at_5
	value: 86.614
	- type: precision_at_1
	value: 80.88
	- type: precision_at_10
	value: 13.350000000000001
	- type: precision_at_100
	value: 1.5310000000000001
	- type: precision_at_1000
	value: 0.157
	- type: precision_at_3
	value: 37.173
	- type: precision_at_5
	value: 24.476
	- type: recall_at_1
	value: 70.21300000000001
	- type: recall_at_10
	value: 95.12
	- type: recall_at_100
	value: 99.535
	- type: recall_at_1000
	value: 99.977
	- type: recall_at_3
	value: 86.833
	- type: recall_at_5
	value: 91.26100000000001
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering
	name: MTEB RedditClustering
	config: default
	split: test
	revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
	metrics:
	- type: v_measure
	value: 47.754688783184875
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering-p2p
	name: MTEB RedditClusteringP2P
	config: default
	split: test
	revision: 282350215ef01743dc01b456c7f5241fa8937f16
	metrics:
	- type: v_measure
	value: 54.875736374329364
	- task:
	type: Retrieval
	dataset:
	type: scidocs
	name: MTEB SCIDOCS
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 3.773
	- type: map_at_10
	value: 9.447
	- type: map_at_100
	value: 11.1
	- type: map_at_1000
	value: 11.37
	- type: map_at_3
	value: 6.787
	- type: map_at_5
	value: 8.077
	- type: mrr_at_1
	value: 18.5
	- type: mrr_at_10
	value: 28.227000000000004
	- type: mrr_at_100
	value: 29.445
	- type: mrr_at_1000
	value: 29.515
	- type: mrr_at_3
	value: 25.2
	- type: mrr_at_5
	value: 27.055
	- type: ndcg_at_1
	value: 18.5
	- type: ndcg_at_10
	value: 16.29
	- type: ndcg_at_100
	value: 23.250999999999998
	- type: ndcg_at_1000
	value: 28.445999999999998
	- type: ndcg_at_3
	value: 15.376000000000001
	- type: ndcg_at_5
	value: 13.528
	- type: precision_at_1
	value: 18.5
	- type: precision_at_10
	value: 8.51
	- type: precision_at_100
	value: 1.855
	- type: precision_at_1000
	value: 0.311
	- type: precision_at_3
	value: 14.533
	- type: precision_at_5
	value: 12.0
	- type: recall_at_1
	value: 3.773
	- type: recall_at_10
	value: 17.282
	- type: recall_at_100
	value: 37.645
	- type: recall_at_1000
	value: 63.138000000000005
	- type: recall_at_3
	value: 8.853
	- type: recall_at_5
	value: 12.168
	- task:
	type: STS
	dataset:
	type: mteb/sickr-sts
	name: MTEB SICK-R
	config: default
	split: test
	revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
	metrics:
	- type: cos_sim_pearson
	value: 85.32789517976525
	- type: cos_sim_spearman
	value: 80.32750384145629
	- type: euclidean_pearson
	value: 81.5025131452508
	- type: euclidean_spearman
	value: 80.24797115147175
	- type: manhattan_pearson
	value: 81.51634463412002
	- type: manhattan_spearman
	value: 80.24614721495055
	- task:
	type: STS
	dataset:
	type: mteb/sts12-sts
	name: MTEB STS12
	config: default
	split: test
	revision: a0d554a64d88156834ff5ae9920b964011b16384
	metrics:
	- type: cos_sim_pearson
	value: 88.47050448992432
	- type: cos_sim_spearman
	value: 80.58919997743621
	- type: euclidean_pearson
	value: 85.83258918113664
	- type: euclidean_spearman
	value: 80.97441389240902
	- type: manhattan_pearson
	value: 85.7798262013878
	- type: manhattan_spearman
	value: 80.97208703064196
	- task:
	type: STS
	dataset:
	type: mteb/sts13-sts
	name: MTEB STS13
	config: default
	split: test
	revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
	metrics:
	- type: cos_sim_pearson
	value: 85.95341439711532
	- type: cos_sim_spearman
	value: 86.59127484634989
	- type: euclidean_pearson
	value: 85.57850603454227
	- type: euclidean_spearman
	value: 86.47130477363419
	- type: manhattan_pearson
	value: 85.59387925447652
	- type: manhattan_spearman
	value: 86.50665427391583
	- task:
	type: STS
	dataset:
	type: mteb/sts14-sts
	name: MTEB STS14
	config: default
	split: test
	revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
	metrics:
	- type: cos_sim_pearson
	value: 85.39810909161844
	- type: cos_sim_spearman
	value: 82.98595295546008
	- type: euclidean_pearson
	value: 84.04681129969951
	- type: euclidean_spearman
	value: 82.98197460689866
	- type: manhattan_pearson
	value: 83.9918798171185
	- type: manhattan_spearman
	value: 82.91148131768082
	- task:
	type: STS
	dataset:
	type: mteb/sts15-sts
	name: MTEB STS15
	config: default
	split: test
	revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
	metrics:
	- type: cos_sim_pearson
	value: 88.02072712147692
	- type: cos_sim_spearman
	value: 88.78821332623012
	- type: euclidean_pearson
	value: 88.12132045572747
	- type: euclidean_spearman
	value: 88.74273451067364
	- type: manhattan_pearson
	value: 88.05431550059166
	- type: manhattan_spearman
	value: 88.67610233020723
	- task:
	type: STS
	dataset:
	type: mteb/sts16-sts
	name: MTEB STS16
	config: default
	split: test
	revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
	metrics:
	- type: cos_sim_pearson
	value: 82.96134704624787
	- type: cos_sim_spearman
	value: 84.44062976314666
	- type: euclidean_pearson
	value: 84.03642536310323
	- type: euclidean_spearman
	value: 84.4535014579785
	- type: manhattan_pearson
	value: 83.92874228901483
	- type: manhattan_spearman
	value: 84.33634314951631
	- 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: 87.3154168064887
	- type: cos_sim_spearman
	value: 86.72393652571682
	- type: euclidean_pearson
	value: 86.04193246174164
	- type: euclidean_spearman
	value: 86.30482896608093
	- type: manhattan_pearson
	value: 85.95524084651859
	- type: manhattan_spearman
	value: 86.06031431994282
	- 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: 89.91079682750804
	- type: cos_sim_spearman
	value: 89.30961836617064
	- type: euclidean_pearson
	value: 88.86249564158628
	- type: euclidean_spearman
	value: 89.04772899592396
	- type: manhattan_pearson
	value: 88.85579791315043
	- type: manhattan_spearman
	value: 88.94190462541333
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (en)
	config: en
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 67.00558145551088
	- type: cos_sim_spearman
	value: 67.96601170393878
	- type: euclidean_pearson
	value: 67.87627043214336
	- type: euclidean_spearman
	value: 66.76402572303859
	- type: manhattan_pearson
	value: 67.88306560555452
	- type: manhattan_spearman
	value: 66.6273862035506
	- 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: 50.83759332748726
	- type: cos_sim_spearman
	value: 59.066344562858006
	- type: euclidean_pearson
	value: 50.08955848154131
	- type: euclidean_spearman
	value: 58.36517305855221
	- type: manhattan_pearson
	value: 50.05257267223111
	- type: manhattan_spearman
	value: 58.37570252804986
	- 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: 59.22749007956492
	- type: cos_sim_spearman
	value: 55.97282077657827
	- type: euclidean_pearson
	value: 62.10661533695752
	- type: euclidean_spearman
	value: 53.62780854854067
	- type: manhattan_pearson
	value: 62.37138085709719
	- type: manhattan_spearman
	value: 54.17556356828155
	- task:
	type: STS
	dataset:
	type: mteb/stsbenchmark-sts
	name: MTEB STSBenchmark
	config: default
	split: test
	revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
	metrics:
	- type: cos_sim_pearson
	value: 87.91145397065878
	- type: cos_sim_spearman
	value: 88.13960018389005
	- type: euclidean_pearson
	value: 87.67618876224006
	- type: euclidean_spearman
	value: 87.99119480810556
	- type: manhattan_pearson
	value: 87.67920297334753
	- type: manhattan_spearman
	value: 87.99113250064492
	- task:
	type: Reranking
	dataset:
	type: mteb/scidocs-reranking
	name: MTEB SciDocsRR
	config: default
	split: test
	revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
	metrics:
	- type: map
	value: 78.09133563707582
	- type: mrr
	value: 93.2415288052543
	- task:
	type: Retrieval
	dataset:
	type: scifact
	name: MTEB SciFact
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 47.760999999999996
	- type: map_at_10
	value: 56.424
	- type: map_at_100
	value: 57.24399999999999
	- type: map_at_1000
	value: 57.278
	- type: map_at_3
	value: 53.68000000000001
	- type: map_at_5
	value: 55.442
	- type: mrr_at_1
	value: 50.666999999999994
	- type: mrr_at_10
	value: 58.012
	- type: mrr_at_100
	value: 58.736
	- type: mrr_at_1000
	value: 58.769000000000005
	- type: mrr_at_3
	value: 56.056
	- type: mrr_at_5
	value: 57.321999999999996
	- type: ndcg_at_1
	value: 50.666999999999994
	- type: ndcg_at_10
	value: 60.67700000000001
	- type: ndcg_at_100
	value: 64.513
	- type: ndcg_at_1000
	value: 65.62400000000001
	- type: ndcg_at_3
	value: 56.186
	- type: ndcg_at_5
	value: 58.692
	- type: precision_at_1
	value: 50.666999999999994
	- type: precision_at_10
	value: 8.200000000000001
	- type: precision_at_100
	value: 1.023
	- type: precision_at_1000
	value: 0.11199999999999999
	- type: precision_at_3
	value: 21.889
	- type: precision_at_5
	value: 14.866999999999999
	- type: recall_at_1
	value: 47.760999999999996
	- type: recall_at_10
	value: 72.006
	- type: recall_at_100
	value: 89.767
	- type: recall_at_1000
	value: 98.833
	- type: recall_at_3
	value: 60.211000000000006
	- type: recall_at_5
	value: 66.3
	- task:
	type: PairClassification
	dataset:
	type: mteb/sprintduplicatequestions-pairclassification
	name: MTEB SprintDuplicateQuestions
	config: default
	split: test
	revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
	metrics:
	- type: cos_sim_accuracy
	value: 99.79009900990098
	- type: cos_sim_ap
	value: 94.86690691995835
	- type: cos_sim_f1
	value: 89.37875751503007
	- type: cos_sim_precision
	value: 89.5582329317269
	- type: cos_sim_recall
	value: 89.2
	- type: dot_accuracy
	value: 99.76336633663367
	- type: dot_ap
	value: 94.26453740761586
	- type: dot_f1
	value: 88.00783162016641
	- type: dot_precision
	value: 86.19367209971237
	- type: dot_recall
	value: 89.9
	- type: euclidean_accuracy
	value: 99.7940594059406
	- type: euclidean_ap
	value: 94.85459757524379
	- type: euclidean_f1
	value: 89.62779156327544
	- type: euclidean_precision
	value: 88.96551724137932
	- type: euclidean_recall
	value: 90.3
	- type: manhattan_accuracy
	value: 99.79009900990098
	- type: manhattan_ap
	value: 94.76971336654465
	- type: manhattan_f1
	value: 89.35323383084577
	- type: manhattan_precision
	value: 88.91089108910892
	- type: manhattan_recall
	value: 89.8
	- type: max_accuracy
	value: 99.7940594059406
	- type: max_ap
	value: 94.86690691995835
	- type: max_f1
	value: 89.62779156327544
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering
	name: MTEB StackExchangeClustering
	config: default
	split: test
	revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
	metrics:
	- type: v_measure
	value: 55.38197670064987
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering-p2p
	name: MTEB StackExchangeClusteringP2P
	config: default
	split: test
	revision: 815ca46b2622cec33ccafc3735d572c266efdb44
	metrics:
	- type: v_measure
	value: 33.08330158937971
	- task:
	type: Reranking
	dataset:
	type: mteb/stackoverflowdupquestions-reranking
	name: MTEB StackOverflowDupQuestions
	config: default
	split: test
	revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
	metrics:
	- type: map
	value: 49.50367079063226
	- type: mrr
	value: 50.30444943128768
	- task:
	type: Summarization
	dataset:
	type: mteb/summeval
	name: MTEB SummEval
	config: default
	split: test
	revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
	metrics:
	- type: cos_sim_pearson
	value: 30.37739520909561
	- type: cos_sim_spearman
	value: 31.548500943973913
	- type: dot_pearson
	value: 29.983610104303
	- type: dot_spearman
	value: 29.90185869098618
	- task:
	type: Retrieval
	dataset:
	type: trec-covid
	name: MTEB TRECCOVID
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 0.198
	- type: map_at_10
	value: 1.5810000000000002
	- type: map_at_100
	value: 9.064
	- type: map_at_1000
	value: 22.161
	- type: map_at_3
	value: 0.536
	- type: map_at_5
	value: 0.8370000000000001
	- type: mrr_at_1
	value: 80.0
	- type: mrr_at_10
	value: 86.75
	- type: mrr_at_100
	value: 86.799
	- type: mrr_at_1000
	value: 86.799
	- type: mrr_at_3
	value: 85.0
	- type: mrr_at_5
	value: 86.5
	- type: ndcg_at_1
	value: 73.0
	- type: ndcg_at_10
	value: 65.122
	- type: ndcg_at_100
	value: 51.853
	- type: ndcg_at_1000
	value: 47.275
	- type: ndcg_at_3
	value: 66.274
	- type: ndcg_at_5
	value: 64.826
	- type: precision_at_1
	value: 80.0
	- type: precision_at_10
	value: 70.19999999999999
	- type: precision_at_100
	value: 53.480000000000004
	- type: precision_at_1000
	value: 20.946
	- type: precision_at_3
	value: 71.333
	- type: precision_at_5
	value: 70.0
	- type: recall_at_1
	value: 0.198
	- type: recall_at_10
	value: 1.884
	- type: recall_at_100
	value: 12.57
	- type: recall_at_1000
	value: 44.208999999999996
	- type: recall_at_3
	value: 0.5890000000000001
	- type: recall_at_5
	value: 0.95
	- task:
	type: Clustering
	dataset:
	type: slvnwhrl/tenkgnad-clustering-p2p
	name: MTEB TenKGnadClusteringP2P
	config: default
	split: test
	revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558
	metrics:
	- type: v_measure
	value: 42.84199261133083
	- task:
	type: Clustering
	dataset:
	type: slvnwhrl/tenkgnad-clustering-s2s
	name: MTEB TenKGnadClusteringS2S
	config: default
	split: test
	revision: 6cddbe003f12b9b140aec477b583ac4191f01786
	metrics:
	- type: v_measure
	value: 23.689557114798838
	- task:
	type: Retrieval
	dataset:
	type: webis-touche2020
	name: MTEB Touche2020
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 1.941
	- type: map_at_10
	value: 8.222
	- type: map_at_100
	value: 14.277999999999999
	- type: map_at_1000
	value: 15.790000000000001
	- type: map_at_3
	value: 4.4670000000000005
	- type: map_at_5
	value: 5.762
	- type: mrr_at_1
	value: 24.490000000000002
	- type: mrr_at_10
	value: 38.784
	- type: mrr_at_100
	value: 39.724
	- type: mrr_at_1000
	value: 39.724
	- type: mrr_at_3
	value: 33.333
	- type: mrr_at_5
	value: 37.415
	- type: ndcg_at_1
	value: 22.448999999999998
	- type: ndcg_at_10
	value: 21.026
	- type: ndcg_at_100
	value: 33.721000000000004
	- type: ndcg_at_1000
	value: 45.045
	- type: ndcg_at_3
	value: 20.053
	- type: ndcg_at_5
	value: 20.09
	- type: precision_at_1
	value: 24.490000000000002
	- type: precision_at_10
	value: 19.796
	- type: precision_at_100
	value: 7.469
	- type: precision_at_1000
	value: 1.48
	- type: precision_at_3
	value: 21.769
	- type: precision_at_5
	value: 21.224
	- type: recall_at_1
	value: 1.941
	- type: recall_at_10
	value: 14.915999999999999
	- type: recall_at_100
	value: 46.155
	- type: recall_at_1000
	value: 80.664
	- type: recall_at_3
	value: 5.629
	- type: recall_at_5
	value: 8.437
	- task:
	type: Classification
	dataset:
	type: mteb/toxic_conversations_50k
	name: MTEB ToxicConversationsClassification
	config: default
	split: test
	revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
	metrics:
	- type: accuracy
	value: 69.64800000000001
	- type: ap
	value: 12.914826731261094
	- type: f1
	value: 53.05213503422915
	- task:
	type: Classification
	dataset:
	type: mteb/tweet_sentiment_extraction
	name: MTEB TweetSentimentExtractionClassification
	config: default
	split: test
	revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
	metrics:
	- type: accuracy
	value: 60.427277872099594
	- type: f1
	value: 60.78292007556828
	- task:
	type: Clustering
	dataset:
	type: mteb/twentynewsgroups-clustering
	name: MTEB TwentyNewsgroupsClustering
	config: default
	split: test
	revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
	metrics:
	- type: v_measure
	value: 40.48134168406559
	- task:
	type: PairClassification
	dataset:
	type: mteb/twittersemeval2015-pairclassification
	name: MTEB TwitterSemEval2015
	config: default
	split: test
	revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
	metrics:
	- type: cos_sim_accuracy
	value: 84.79465935506944
	- type: cos_sim_ap
	value: 70.24589055290592
	- type: cos_sim_f1
	value: 65.0994575045208
	- type: cos_sim_precision
	value: 63.76518218623482
	- type: cos_sim_recall
	value: 66.49076517150397
	- type: dot_accuracy
	value: 84.63968528342374
	- type: dot_ap
	value: 69.84683095084355
	- type: dot_f1
	value: 64.50606169727523
	- type: dot_precision
	value: 59.1719885487778
	- type: dot_recall
	value: 70.89709762532982
	- type: euclidean_accuracy
	value: 84.76485664898374
	- type: euclidean_ap
	value: 70.20556438685551
	- type: euclidean_f1
	value: 65.06796614516543
	- type: euclidean_precision
	value: 63.29840319361277
	- type: euclidean_recall
	value: 66.93931398416886
	- type: manhattan_accuracy
	value: 84.72313286046374
	- type: manhattan_ap
	value: 70.17151475534308
	- type: manhattan_f1
	value: 65.31379180759113
	- type: manhattan_precision
	value: 62.17505366086334
	- type: manhattan_recall
	value: 68.7862796833773
	- type: max_accuracy
	value: 84.79465935506944
	- type: max_ap
	value: 70.24589055290592
	- type: max_f1
	value: 65.31379180759113
	- task:
	type: PairClassification
	dataset:
	type: mteb/twitterurlcorpus-pairclassification
	name: MTEB TwitterURLCorpus
	config: default
	split: test
	revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
	metrics:
	- type: cos_sim_accuracy
	value: 88.95874568246207
	- type: cos_sim_ap
	value: 85.82517548264127
	- type: cos_sim_f1
	value: 78.22288041466125
	- type: cos_sim_precision
	value: 75.33875338753387
	- type: cos_sim_recall
	value: 81.33661841700031
	- type: dot_accuracy
	value: 88.836496293709
	- type: dot_ap
	value: 85.53430720252186
	- type: dot_f1
	value: 78.10616085869725
	- type: dot_precision
	value: 74.73269555430501
	- type: dot_recall
	value: 81.79858330766862
	- type: euclidean_accuracy
	value: 88.92769821865176
	- type: euclidean_ap
	value: 85.65904346964223
	- type: euclidean_f1
	value: 77.98774074208407
	- type: euclidean_precision
	value: 73.72282795035315
	- type: euclidean_recall
	value: 82.77640899291654
	- type: manhattan_accuracy
	value: 88.86366282454303
	- type: manhattan_ap
	value: 85.61599642231819
	- type: manhattan_f1
	value: 78.01480509061737
	- type: manhattan_precision
	value: 74.10460685833044
	- type: manhattan_recall
	value: 82.36064059131506
	- type: max_accuracy
	value: 88.95874568246207
	- type: max_ap
	value: 85.82517548264127
	- type: max_f1
	value: 78.22288041466125
	- task:
	type: Retrieval
	dataset:
	type: None
	name: MTEB WikiCLIR
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 3.9539999999999997
	- type: map_at_10
	value: 7.407
	- type: map_at_100
	value: 8.677999999999999
	- type: map_at_1000
	value: 9.077
	- type: map_at_3
	value: 5.987
	- type: map_at_5
	value: 6.6979999999999995
	- type: mrr_at_1
	value: 35.65
	- type: mrr_at_10
	value: 45.097
	- type: mrr_at_100
	value: 45.83
	- type: mrr_at_1000
	value: 45.871
	- type: mrr_at_3
	value: 42.63
	- type: mrr_at_5
	value: 44.104
	- type: ndcg_at_1
	value: 29.215000000000003
	- type: ndcg_at_10
	value: 22.694
	- type: ndcg_at_100
	value: 22.242
	- type: ndcg_at_1000
	value: 27.069
	- type: ndcg_at_3
	value: 27.641
	- type: ndcg_at_5
	value: 25.503999999999998
	- type: precision_at_1
	value: 35.65
	- type: precision_at_10
	value: 12.795000000000002
	- type: precision_at_100
	value: 3.354
	- type: precision_at_1000
	value: 0.743
	- type: precision_at_3
	value: 23.403
	- type: precision_at_5
	value: 18.474
	- type: recall_at_1
	value: 3.9539999999999997
	- type: recall_at_10
	value: 11.301
	- type: recall_at_100
	value: 22.919999999999998
	- type: recall_at_1000
	value: 40.146
	- type: recall_at_3
	value: 7.146
	- type: recall_at_5
	value: 8.844000000000001
	- task:
	type: Retrieval
	dataset:
	type: jinaai/xmarket_de
	name: MTEB XMarket
	config: default
	split: test
	revision: 2336818db4c06570fcdf263e1bcb9993b786f67a
	metrics:
	- type: map_at_1
	value: 4.872
	- type: map_at_10
	value: 10.658
	- type: map_at_100
	value: 13.422999999999998
	- type: map_at_1000
	value: 14.245
	- type: map_at_3
	value: 7.857
	- type: map_at_5
	value: 9.142999999999999
	- type: mrr_at_1
	value: 16.744999999999997
	- type: mrr_at_10
	value: 24.416
	- type: mrr_at_100
	value: 25.432
	- type: mrr_at_1000
	value: 25.502999999999997
	- type: mrr_at_3
	value: 22.096
	- type: mrr_at_5
	value: 23.421
	- type: ndcg_at_1
	value: 16.695999999999998
	- type: ndcg_at_10
	value: 18.66
	- type: ndcg_at_100
	value: 24.314
	- type: ndcg_at_1000
	value: 29.846
	- type: ndcg_at_3
	value: 17.041999999999998
	- type: ndcg_at_5
	value: 17.585
	- type: precision_at_1
	value: 16.695999999999998
	- type: precision_at_10
	value: 10.374
	- type: precision_at_100
	value: 3.988
	- type: precision_at_1000
	value: 1.1860000000000002
	- type: precision_at_3
	value: 14.21
	- type: precision_at_5
	value: 12.623000000000001
	- type: recall_at_1
	value: 4.872
	- type: recall_at_10
	value: 18.624
	- type: recall_at_100
	value: 40.988
	- type: recall_at_1000
	value: 65.33
	- type: recall_at_3
	value: 10.162
	- type: recall_at_5
	value: 13.517999999999999
	---
	<!-- TODO: add evaluation results here -->
	<br><br>

	<p align="center">
	<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Jina AI logo: Jina AI is your Portal to Multimodal AI" width="150px">
	</p>


	<p align="center">
	<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
	</p>

	## Quick Start

	The easiest way to starting using `jina-embeddings-v2-base-de` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).

	## Intended Usage & Model Info

	`jina-embeddings-v2-base-de` is a German/English bilingual text embedding model supporting 8192 sequence length.
	It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
	We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed German-English input without bias.
	Additionally, we provide the following embedding models:

	`jina-embeddings-v2-base-de` ist ein zweisprachiges Text Embedding Modell für Deutsch und Englisch,
	welches Texteingaben mit einer Länge von bis zu 8192 Token unterstützt.
	Es basiert auf der adaptierten Bert-Modell-Architektur JinaBERT,
	welche mithilfe einer symmetrische Variante von [ALiBi](https://arxiv.org/abs/2108.12409) längere Eingabetexte erlaubt.
	Wir haben, das Model für hohe Performance in einsprachigen und cross-lingual Anwendungen entwickelt und speziell darauf trainiert,
	gemischte deutsch-englische Eingaben ohne einen Bias zu kodieren.
	Des Weiteren stellen wir folgende Embedding-Modelle bereit:

	- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
	- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
	- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English Bilingual embeddings.
	- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English Bilingual embeddings (you are here).
	- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon).
	- [`jina-embeddings-v2-base-code`](https://huggingface.co/jinaai/jina-embeddings-v2-base-code): 161 million parameters code embeddings.

	## Data & Parameters

	The data and training details are described in this [technical report](https://arxiv.org/abs/2402.17016).

	## Usage

	<details><summary>Please apply mean pooling when integrating the model.</summary>
	<p>

	### Why mean pooling?

	`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
	It has been proved to be the most effective way to produce high-quality sentence embeddings.
	We offer an `encode` function to deal with this.

	However, if you would like to do it without using the default `encode` function:

	```python
	import torch
	import torch.nn.functional as F
	from transformers import AutoTokenizer, AutoModel

	def mean_pooling(model_output, attention_mask):
	token_embeddings = model_output[0]
	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)

	sentences = ['How is the weather today?', 'What is the current weather like today?']

	tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-base-de')
	model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True, torch_dtype=torch.bfloat16)

	encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

	with torch.no_grad():
	model_output = model(**encoded_input)

	embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
	embeddings = F.normalize(embeddings, p=2, dim=1)
	```

	</p>
	</details>

	You can use Jina Embedding models directly from transformers package.

	```python
	!pip install transformers
	import torch
	from transformers import AutoModel
	from numpy.linalg import norm

	cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
	model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True, torch_dtype=torch.bfloat16)
	embeddings = model.encode(['How is the weather today?', 'Wie ist das Wetter heute?'])
	print(cos_sim(embeddings[0], embeddings[1]))
	```

	If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function:

	```python
	embeddings = model.encode(
	['Very long ... document'],
	max_length=2048
	)
	```

	Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well):

	```python
	!pip install -U sentence-transformers
	from sentence_transformers import SentenceTransformer
	from sentence_transformers.util import cos_sim

	model = SentenceTransformer(
	"jinaai/jina-embeddings-v2-base-de", # switch to en/zh for English or Chinese
	trust_remote_code=True
	)

	# control your input sequence length up to 8192
	model.max_seq_length = 1024

	embeddings = model.encode([
	'How is the weather today?',
	'Wie ist das Wetter heute?'
	])
	print(cos_sim(embeddings[0], embeddings[1]))
	```

	## Alternatives to Using Transformers Package

	1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/).
	2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).

	## Benchmark Results

	We evaluated our Bilingual model on all German and English evaluation tasks availble on the [MTEB benchmark](https://huggingface.co/blog/mteb). In addition, we evaluated the models agains a couple of other German, English, and multilingual models on additional German evaluation tasks:

	<img src="de_evaluation_results.png" width="780px">

	## Use Jina Embeddings for RAG

	According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83),

	> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.

	<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px">

	## Contact

	Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.

	## Citation

	If you find Jina Embeddings useful in your research, please cite the following paper:

	```
	@article{mohr2024multi,
	title={Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings},
	author={Mohr, Isabelle and Krimmel, Markus and Sturua, Saba and Akram, Mohammad Kalim and Koukounas, Andreas and G{\"u}nther, Michael and Mastrapas, Georgios and Ravishankar, Vinit and Mart{\'\i}nez, Joan Fontanals and Wang, Feng and others},
	journal={arXiv preprint arXiv:2402.17016},
	year={2024}
	}
	```