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	---
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- mteb
	language:
	- es
	- en
	inference: false
	license: apache-2.0
	model-index:
	- name: jina-embeddings-v2-base-es
	results:
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (en)
	config: en
	split: test
	revision: e8379541af4e31359cca9fbcf4b00f2671dba205
	metrics:
	- type: accuracy
	value: 74.25373134328358
	- type: ap
	value: 37.05201236793268
	- type: f1
	value: 68.16770391201077
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_polarity
	name: MTEB AmazonPolarityClassification
	config: default
	split: test
	revision: e2d317d38cd51312af73b3d32a06d1a08b442046
	metrics:
	- type: accuracy
	value: 78.30885
	- type: ap
	value: 73.01622441156408
	- type: f1
	value: 78.20769284466313
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (en)
	config: en
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 38.324
	- type: f1
	value: 37.89543008761673
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (es)
	config: es
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 38.678000000000004
	- type: f1
	value: 38.122639506976
	- task:
	type: Retrieval
	dataset:
	type: arguana
	name: MTEB ArguAna
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 23.968999999999998
	- type: map_at_10
	value: 40.691
	- type: map_at_100
	value: 41.713
	- type: map_at_1000
	value: 41.719
	- type: map_at_3
	value: 35.42
	- type: map_at_5
	value: 38.442
	- type: mrr_at_1
	value: 24.395
	- type: mrr_at_10
	value: 40.853
	- type: mrr_at_100
	value: 41.869
	- type: mrr_at_1000
	value: 41.874
	- type: mrr_at_3
	value: 35.68
	- type: mrr_at_5
	value: 38.572
	- type: ndcg_at_1
	value: 23.968999999999998
	- type: ndcg_at_10
	value: 50.129999999999995
	- type: ndcg_at_100
	value: 54.364000000000004
	- type: ndcg_at_1000
	value: 54.494
	- type: ndcg_at_3
	value: 39.231
	- type: ndcg_at_5
	value: 44.694
	- type: precision_at_1
	value: 23.968999999999998
	- type: precision_at_10
	value: 8.036999999999999
	- type: precision_at_100
	value: 0.9860000000000001
	- type: precision_at_1000
	value: 0.1
	- type: precision_at_3
	value: 16.761
	- type: precision_at_5
	value: 12.717
	- type: recall_at_1
	value: 23.968999999999998
	- type: recall_at_10
	value: 80.36999999999999
	- type: recall_at_100
	value: 98.578
	- type: recall_at_1000
	value: 99.57300000000001
	- type: recall_at_3
	value: 50.28399999999999
	- type: recall_at_5
	value: 63.585
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-p2p
	name: MTEB ArxivClusteringP2P
	config: default
	split: test
	revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
	metrics:
	- type: v_measure
	value: 41.54886683150053
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-s2s
	name: MTEB ArxivClusteringS2S
	config: default
	split: test
	revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
	metrics:
	- type: v_measure
	value: 32.186028697637234
	- task:
	type: Reranking
	dataset:
	type: mteb/askubuntudupquestions-reranking
	name: MTEB AskUbuntuDupQuestions
	config: default
	split: test
	revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
	metrics:
	- type: map
	value: 61.19432643698725
	- type: mrr
	value: 75.28646176845622
	- task:
	type: STS
	dataset:
	type: mteb/biosses-sts
	name: MTEB BIOSSES
	config: default
	split: test
	revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
	metrics:
	- type: cos_sim_pearson
	value: 86.3828259381228
	- type: cos_sim_spearman
	value: 83.04647058342209
	- type: euclidean_pearson
	value: 84.02895346096244
	- type: euclidean_spearman
	value: 82.34524978635342
	- type: manhattan_pearson
	value: 84.35030723233426
	- type: manhattan_spearman
	value: 83.17177464337936
	- task:
	type: Classification
	dataset:
	type: mteb/banking77
	name: MTEB Banking77Classification
	config: default
	split: test
	revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
	metrics:
	- type: accuracy
	value: 85.25649350649351
	- type: f1
	value: 85.22320474023192
	- task:
	type: Clustering
	dataset:
	type: jinaai/big-patent-clustering
	name: MTEB BigPatentClustering
	config: default
	split: test
	revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
	metrics:
	- type: v_measure
	value: 20.42929408254094
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-p2p
	name: MTEB BiorxivClusteringP2P
	config: default
	split: test
	revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
	metrics:
	- type: v_measure
	value: 35.165318177498136
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-s2s
	name: MTEB BiorxivClusteringS2S
	config: default
	split: test
	revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
	metrics:
	- type: v_measure
	value: 28.89030154229562
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackAndroidRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 30.119
	- type: map_at_10
	value: 42.092
	- type: map_at_100
	value: 43.506
	- type: map_at_1000
	value: 43.631
	- type: map_at_3
	value: 38.373000000000005
	- type: map_at_5
	value: 40.501
	- type: mrr_at_1
	value: 38.196999999999996
	- type: mrr_at_10
	value: 48.237
	- type: mrr_at_100
	value: 48.914
	- type: mrr_at_1000
	value: 48.959
	- type: mrr_at_3
	value: 45.279
	- type: mrr_at_5
	value: 47.11
	- type: ndcg_at_1
	value: 38.196999999999996
	- type: ndcg_at_10
	value: 48.849
	- type: ndcg_at_100
	value: 53.713
	- type: ndcg_at_1000
	value: 55.678000000000004
	- type: ndcg_at_3
	value: 43.546
	- type: ndcg_at_5
	value: 46.009
	- type: precision_at_1
	value: 38.196999999999996
	- type: precision_at_10
	value: 9.642000000000001
	- type: precision_at_100
	value: 1.5190000000000001
	- type: precision_at_1000
	value: 0.199
	- type: precision_at_3
	value: 21.65
	- type: precision_at_5
	value: 15.708
	- type: recall_at_1
	value: 30.119
	- type: recall_at_10
	value: 61.788
	- type: recall_at_100
	value: 82.14399999999999
	- type: recall_at_1000
	value: 95.003
	- type: recall_at_3
	value: 45.772
	- type: recall_at_5
	value: 53.04600000000001
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackEnglishRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 28.979
	- type: map_at_10
	value: 37.785000000000004
	- type: map_at_100
	value: 38.945
	- type: map_at_1000
	value: 39.071
	- type: map_at_3
	value: 35.083999999999996
	- type: map_at_5
	value: 36.571999999999996
	- type: mrr_at_1
	value: 36.242000000000004
	- type: mrr_at_10
	value: 43.552
	- type: mrr_at_100
	value: 44.228
	- type: mrr_at_1000
	value: 44.275999999999996
	- type: mrr_at_3
	value: 41.359
	- type: mrr_at_5
	value: 42.598
	- type: ndcg_at_1
	value: 36.242000000000004
	- type: ndcg_at_10
	value: 42.94
	- type: ndcg_at_100
	value: 47.343
	- type: ndcg_at_1000
	value: 49.538
	- type: ndcg_at_3
	value: 39.086999999999996
	- type: ndcg_at_5
	value: 40.781
	- type: precision_at_1
	value: 36.242000000000004
	- type: precision_at_10
	value: 7.954999999999999
	- type: precision_at_100
	value: 1.303
	- type: precision_at_1000
	value: 0.178
	- type: precision_at_3
	value: 18.556
	- type: precision_at_5
	value: 13.145999999999999
	- type: recall_at_1
	value: 28.979
	- type: recall_at_10
	value: 51.835
	- type: recall_at_100
	value: 70.47
	- type: recall_at_1000
	value: 84.68299999999999
	- type: recall_at_3
	value: 40.410000000000004
	- type: recall_at_5
	value: 45.189
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackGamingRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 37.878
	- type: map_at_10
	value: 49.903
	- type: map_at_100
	value: 50.797000000000004
	- type: map_at_1000
	value: 50.858000000000004
	- type: map_at_3
	value: 46.526
	- type: map_at_5
	value: 48.615
	- type: mrr_at_1
	value: 43.135
	- type: mrr_at_10
	value: 53.067
	- type: mrr_at_100
	value: 53.668000000000006
	- type: mrr_at_1000
	value: 53.698
	- type: mrr_at_3
	value: 50.449
	- type: mrr_at_5
	value: 52.117000000000004
	- type: ndcg_at_1
	value: 43.135
	- type: ndcg_at_10
	value: 55.641
	- type: ndcg_at_100
	value: 59.427
	- type: ndcg_at_1000
	value: 60.655
	- type: ndcg_at_3
	value: 49.969
	- type: ndcg_at_5
	value: 53.075
	- type: precision_at_1
	value: 43.135
	- type: precision_at_10
	value: 8.997
	- type: precision_at_100
	value: 1.1809999999999998
	- type: precision_at_1000
	value: 0.133
	- type: precision_at_3
	value: 22.215
	- type: precision_at_5
	value: 15.586
	- type: recall_at_1
	value: 37.878
	- type: recall_at_10
	value: 69.405
	- type: recall_at_100
	value: 86.262
	- type: recall_at_1000
	value: 95.012
	- type: recall_at_3
	value: 54.458
	- type: recall_at_5
	value: 61.965
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackGisRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 24.853
	- type: map_at_10
	value: 32.402
	- type: map_at_100
	value: 33.417
	- type: map_at_1000
	value: 33.498
	- type: map_at_3
	value: 30.024
	- type: map_at_5
	value: 31.407
	- type: mrr_at_1
	value: 26.667
	- type: mrr_at_10
	value: 34.399
	- type: mrr_at_100
	value: 35.284
	- type: mrr_at_1000
	value: 35.345
	- type: mrr_at_3
	value: 32.109
	- type: mrr_at_5
	value: 33.375
	- type: ndcg_at_1
	value: 26.667
	- type: ndcg_at_10
	value: 36.854
	- type: ndcg_at_100
	value: 42.196
	- type: ndcg_at_1000
	value: 44.303
	- type: ndcg_at_3
	value: 32.186
	- type: ndcg_at_5
	value: 34.512
	- type: precision_at_1
	value: 26.667
	- type: precision_at_10
	value: 5.559
	- type: precision_at_100
	value: 0.88
	- type: precision_at_1000
	value: 0.109
	- type: precision_at_3
	value: 13.333
	- type: precision_at_5
	value: 9.379
	- type: recall_at_1
	value: 24.853
	- type: recall_at_10
	value: 48.636
	- type: recall_at_100
	value: 73.926
	- type: recall_at_1000
	value: 89.94
	- type: recall_at_3
	value: 36.266
	- type: recall_at_5
	value: 41.723
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackMathematicaRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 14.963999999999999
	- type: map_at_10
	value: 22.591
	- type: map_at_100
	value: 23.735999999999997
	- type: map_at_1000
	value: 23.868000000000002
	- type: map_at_3
	value: 20.093
	- type: map_at_5
	value: 21.499
	- type: mrr_at_1
	value: 18.407999999999998
	- type: mrr_at_10
	value: 26.863
	- type: mrr_at_100
	value: 27.87
	- type: mrr_at_1000
	value: 27.947
	- type: mrr_at_3
	value: 24.254
	- type: mrr_at_5
	value: 25.784000000000002
	- type: ndcg_at_1
	value: 18.407999999999998
	- type: ndcg_at_10
	value: 27.549
	- type: ndcg_at_100
	value: 33.188
	- type: ndcg_at_1000
	value: 36.312
	- type: ndcg_at_3
	value: 22.862
	- type: ndcg_at_5
	value: 25.130999999999997
	- type: precision_at_1
	value: 18.407999999999998
	- type: precision_at_10
	value: 5.087
	- type: precision_at_100
	value: 0.923
	- type: precision_at_1000
	value: 0.133
	- type: precision_at_3
	value: 10.987
	- type: precision_at_5
	value: 8.209
	- type: recall_at_1
	value: 14.963999999999999
	- type: recall_at_10
	value: 38.673
	- type: recall_at_100
	value: 63.224999999999994
	- type: recall_at_1000
	value: 85.443
	- type: recall_at_3
	value: 25.840000000000003
	- type: recall_at_5
	value: 31.503999999999998
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackPhysicsRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 27.861000000000004
	- type: map_at_10
	value: 37.562
	- type: map_at_100
	value: 38.906
	- type: map_at_1000
	value: 39.021
	- type: map_at_3
	value: 34.743
	- type: map_at_5
	value: 36.168
	- type: mrr_at_1
	value: 34.455999999999996
	- type: mrr_at_10
	value: 43.428
	- type: mrr_at_100
	value: 44.228
	- type: mrr_at_1000
	value: 44.278
	- type: mrr_at_3
	value: 41.001
	- type: mrr_at_5
	value: 42.315000000000005
	- type: ndcg_at_1
	value: 34.455999999999996
	- type: ndcg_at_10
	value: 43.477
	- type: ndcg_at_100
	value: 48.953
	- type: ndcg_at_1000
	value: 51.19200000000001
	- type: ndcg_at_3
	value: 38.799
	- type: ndcg_at_5
	value: 40.743
	- type: precision_at_1
	value: 34.455999999999996
	- type: precision_at_10
	value: 7.902000000000001
	- type: precision_at_100
	value: 1.244
	- type: precision_at_1000
	value: 0.161
	- type: precision_at_3
	value: 18.511
	- type: precision_at_5
	value: 12.859000000000002
	- type: recall_at_1
	value: 27.861000000000004
	- type: recall_at_10
	value: 55.36
	- type: recall_at_100
	value: 78.384
	- type: recall_at_1000
	value: 93.447
	- type: recall_at_3
	value: 41.926
	- type: recall_at_5
	value: 47.257
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackProgrammersRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 26.375
	- type: map_at_10
	value: 35.571000000000005
	- type: map_at_100
	value: 36.785000000000004
	- type: map_at_1000
	value: 36.905
	- type: map_at_3
	value: 32.49
	- type: map_at_5
	value: 34.123999999999995
	- type: mrr_at_1
	value: 32.647999999999996
	- type: mrr_at_10
	value: 40.598
	- type: mrr_at_100
	value: 41.484
	- type: mrr_at_1000
	value: 41.546
	- type: mrr_at_3
	value: 37.9
	- type: mrr_at_5
	value: 39.401
	- type: ndcg_at_1
	value: 32.647999999999996
	- type: ndcg_at_10
	value: 41.026
	- type: ndcg_at_100
	value: 46.365
	- type: ndcg_at_1000
	value: 48.876
	- type: ndcg_at_3
	value: 35.843
	- type: ndcg_at_5
	value: 38.118
	- type: precision_at_1
	value: 32.647999999999996
	- type: precision_at_10
	value: 7.443
	- type: precision_at_100
	value: 1.18
	- type: precision_at_1000
	value: 0.158
	- type: precision_at_3
	value: 16.819
	- type: precision_at_5
	value: 11.985999999999999
	- type: recall_at_1
	value: 26.375
	- type: recall_at_10
	value: 52.471000000000004
	- type: recall_at_100
	value: 75.354
	- type: recall_at_1000
	value: 92.35
	- type: recall_at_3
	value: 37.893
	- type: recall_at_5
	value: 43.935
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 25.012666666666668
	- type: map_at_10
	value: 33.685833333333335
	- type: map_at_100
	value: 34.849250000000005
	- type: map_at_1000
	value: 34.970083333333335
	- type: map_at_3
	value: 31.065083333333334
	- type: map_at_5
	value: 32.494416666666666
	- type: mrr_at_1
	value: 29.772666666666662
	- type: mrr_at_10
	value: 37.824666666666666
	- type: mrr_at_100
	value: 38.66741666666666
	- type: mrr_at_1000
	value: 38.72916666666666
	- type: mrr_at_3
	value: 35.54575
	- type: mrr_at_5
	value: 36.81524999999999
	- type: ndcg_at_1
	value: 29.772666666666662
	- type: ndcg_at_10
	value: 38.78241666666666
	- type: ndcg_at_100
	value: 43.84591666666667
	- type: ndcg_at_1000
	value: 46.275416666666665
	- type: ndcg_at_3
	value: 34.33416666666667
	- type: ndcg_at_5
	value: 36.345166666666664
	- type: precision_at_1
	value: 29.772666666666662
	- type: precision_at_10
	value: 6.794916666666667
	- type: precision_at_100
	value: 1.106416666666667
	- type: precision_at_1000
	value: 0.15033333333333335
	- type: precision_at_3
	value: 15.815083333333336
	- type: precision_at_5
	value: 11.184166666666664
	- type: recall_at_1
	value: 25.012666666666668
	- type: recall_at_10
	value: 49.748500000000014
	- type: recall_at_100
	value: 72.11341666666667
	- type: recall_at_1000
	value: 89.141
	- type: recall_at_3
	value: 37.242999999999995
	- type: recall_at_5
	value: 42.49033333333333
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackStatsRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 23.177
	- type: map_at_10
	value: 29.310000000000002
	- type: map_at_100
	value: 30.188
	- type: map_at_1000
	value: 30.29
	- type: map_at_3
	value: 27.356
	- type: map_at_5
	value: 28.410999999999998
	- type: mrr_at_1
	value: 26.074
	- type: mrr_at_10
	value: 32.002
	- type: mrr_at_100
	value: 32.838
	- type: mrr_at_1000
	value: 32.909
	- type: mrr_at_3
	value: 30.317
	- type: mrr_at_5
	value: 31.222
	- type: ndcg_at_1
	value: 26.074
	- type: ndcg_at_10
	value: 32.975
	- type: ndcg_at_100
	value: 37.621
	- type: ndcg_at_1000
	value: 40.253
	- type: ndcg_at_3
	value: 29.452
	- type: ndcg_at_5
	value: 31.020999999999997
	- type: precision_at_1
	value: 26.074
	- type: precision_at_10
	value: 5.077
	- type: precision_at_100
	value: 0.8049999999999999
	- type: precision_at_1000
	value: 0.11100000000000002
	- type: precision_at_3
	value: 12.526000000000002
	- type: precision_at_5
	value: 8.588999999999999
	- type: recall_at_1
	value: 23.177
	- type: recall_at_10
	value: 41.613
	- type: recall_at_100
	value: 63.287000000000006
	- type: recall_at_1000
	value: 83.013
	- type: recall_at_3
	value: 31.783
	- type: recall_at_5
	value: 35.769
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackTexRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 15.856
	- type: map_at_10
	value: 22.651
	- type: map_at_100
	value: 23.649
	- type: map_at_1000
	value: 23.783
	- type: map_at_3
	value: 20.591
	- type: map_at_5
	value: 21.684
	- type: mrr_at_1
	value: 19.408
	- type: mrr_at_10
	value: 26.51
	- type: mrr_at_100
	value: 27.356
	- type: mrr_at_1000
	value: 27.439999999999998
	- type: mrr_at_3
	value: 24.547
	- type: mrr_at_5
	value: 25.562
	- type: ndcg_at_1
	value: 19.408
	- type: ndcg_at_10
	value: 27.072000000000003
	- type: ndcg_at_100
	value: 31.980999999999998
	- type: ndcg_at_1000
	value: 35.167
	- type: ndcg_at_3
	value: 23.338
	- type: ndcg_at_5
	value: 24.94
	- type: precision_at_1
	value: 19.408
	- type: precision_at_10
	value: 4.9590000000000005
	- type: precision_at_100
	value: 0.8710000000000001
	- type: precision_at_1000
	value: 0.132
	- type: precision_at_3
	value: 11.138
	- type: precision_at_5
	value: 7.949000000000001
	- type: recall_at_1
	value: 15.856
	- type: recall_at_10
	value: 36.578
	- type: recall_at_100
	value: 58.89
	- type: recall_at_1000
	value: 81.743
	- type: recall_at_3
	value: 25.94
	- type: recall_at_5
	value: 30.153999999999996
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackUnixRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 25.892
	- type: map_at_10
	value: 33.899
	- type: map_at_100
	value: 34.955000000000005
	- type: map_at_1000
	value: 35.066
	- type: map_at_3
	value: 31.41
	- type: map_at_5
	value: 32.669
	- type: mrr_at_1
	value: 30.224
	- type: mrr_at_10
	value: 37.936
	- type: mrr_at_100
	value: 38.777
	- type: mrr_at_1000
	value: 38.85
	- type: mrr_at_3
	value: 35.821
	- type: mrr_at_5
	value: 36.894
	- type: ndcg_at_1
	value: 30.224
	- type: ndcg_at_10
	value: 38.766
	- type: ndcg_at_100
	value: 43.806
	- type: ndcg_at_1000
	value: 46.373999999999995
	- type: ndcg_at_3
	value: 34.325
	- type: ndcg_at_5
	value: 36.096000000000004
	- type: precision_at_1
	value: 30.224
	- type: precision_at_10
	value: 6.446000000000001
	- type: precision_at_100
	value: 1.0
	- type: precision_at_1000
	value: 0.133
	- type: precision_at_3
	value: 15.392
	- type: precision_at_5
	value: 10.671999999999999
	- type: recall_at_1
	value: 25.892
	- type: recall_at_10
	value: 49.573
	- type: recall_at_100
	value: 71.885
	- type: recall_at_1000
	value: 89.912
	- type: recall_at_3
	value: 37.226
	- type: recall_at_5
	value: 41.74
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackWebmastersRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 23.915
	- type: map_at_10
	value: 33.613
	- type: map_at_100
	value: 35.333999999999996
	- type: map_at_1000
	value: 35.563
	- type: map_at_3
	value: 31.203999999999997
	- type: map_at_5
	value: 32.479
	- type: mrr_at_1
	value: 29.447000000000003
	- type: mrr_at_10
	value: 38.440000000000005
	- type: mrr_at_100
	value: 39.459
	- type: mrr_at_1000
	value: 39.513999999999996
	- type: mrr_at_3
	value: 36.495
	- type: mrr_at_5
	value: 37.592
	- type: ndcg_at_1
	value: 29.447000000000003
	- type: ndcg_at_10
	value: 39.341
	- type: ndcg_at_100
	value: 45.382
	- type: ndcg_at_1000
	value: 47.921
	- type: ndcg_at_3
	value: 35.671
	- type: ndcg_at_5
	value: 37.299
	- type: precision_at_1
	value: 29.447000000000003
	- type: precision_at_10
	value: 7.648000000000001
	- type: precision_at_100
	value: 1.567
	- type: precision_at_1000
	value: 0.241
	- type: precision_at_3
	value: 17.194000000000003
	- type: precision_at_5
	value: 12.253
	- type: recall_at_1
	value: 23.915
	- type: recall_at_10
	value: 49.491
	- type: recall_at_100
	value: 76.483
	- type: recall_at_1000
	value: 92.674
	- type: recall_at_3
	value: 38.878
	- type: recall_at_5
	value: 43.492
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackWordpressRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 20.283
	- type: map_at_10
	value: 26.851000000000003
	- type: map_at_100
	value: 27.973
	- type: map_at_1000
	value: 28.087
	- type: map_at_3
	value: 24.887
	- type: map_at_5
	value: 25.804
	- type: mrr_at_1
	value: 22.366
	- type: mrr_at_10
	value: 28.864
	- type: mrr_at_100
	value: 29.903000000000002
	- type: mrr_at_1000
	value: 29.988
	- type: mrr_at_3
	value: 27.017999999999997
	- type: mrr_at_5
	value: 27.813
	- type: ndcg_at_1
	value: 22.366
	- type: ndcg_at_10
	value: 30.898999999999997
	- type: ndcg_at_100
	value: 36.176
	- type: ndcg_at_1000
	value: 39.036
	- type: ndcg_at_3
	value: 26.932000000000002
	- type: ndcg_at_5
	value: 28.416999999999998
	- type: precision_at_1
	value: 22.366
	- type: precision_at_10
	value: 4.824
	- type: precision_at_100
	value: 0.804
	- type: precision_at_1000
	value: 0.116
	- type: precision_at_3
	value: 11.459999999999999
	- type: precision_at_5
	value: 7.8740000000000006
	- type: recall_at_1
	value: 20.283
	- type: recall_at_10
	value: 41.559000000000005
	- type: recall_at_100
	value: 65.051
	- type: recall_at_1000
	value: 86.47200000000001
	- type: recall_at_3
	value: 30.524
	- type: recall_at_5
	value: 34.11
	- task:
	type: Retrieval
	dataset:
	type: climate-fever
	name: MTEB ClimateFEVER
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 11.326
	- type: map_at_10
	value: 19.357
	- type: map_at_100
	value: 21.014
	- type: map_at_1000
	value: 21.188000000000002
	- type: map_at_3
	value: 16.305
	- type: map_at_5
	value: 17.886
	- type: mrr_at_1
	value: 24.820999999999998
	- type: mrr_at_10
	value: 36.150999999999996
	- type: mrr_at_100
	value: 37.080999999999996
	- type: mrr_at_1000
	value: 37.123
	- type: mrr_at_3
	value: 32.952999999999996
	- type: mrr_at_5
	value: 34.917
	- type: ndcg_at_1
	value: 24.820999999999998
	- type: ndcg_at_10
	value: 27.131
	- type: ndcg_at_100
	value: 33.841
	- type: ndcg_at_1000
	value: 37.159
	- type: ndcg_at_3
	value: 22.311
	- type: ndcg_at_5
	value: 24.026
	- type: precision_at_1
	value: 24.820999999999998
	- type: precision_at_10
	value: 8.450000000000001
	- type: precision_at_100
	value: 1.557
	- type: precision_at_1000
	value: 0.218
	- type: precision_at_3
	value: 16.612
	- type: precision_at_5
	value: 12.808
	- type: recall_at_1
	value: 11.326
	- type: recall_at_10
	value: 32.548
	- type: recall_at_100
	value: 55.803000000000004
	- type: recall_at_1000
	value: 74.636
	- type: recall_at_3
	value: 20.549
	- type: recall_at_5
	value: 25.514
	- task:
	type: Retrieval
	dataset:
	type: dbpedia-entity
	name: MTEB DBPedia
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 7.481
	- type: map_at_10
	value: 15.043999999999999
	- type: map_at_100
	value: 20.194000000000003
	- type: map_at_1000
	value: 21.423000000000002
	- type: map_at_3
	value: 11.238
	- type: map_at_5
	value: 12.828999999999999
	- type: mrr_at_1
	value: 54.50000000000001
	- type: mrr_at_10
	value: 64.713
	- type: mrr_at_100
	value: 65.216
	- type: mrr_at_1000
	value: 65.23
	- type: mrr_at_3
	value: 62.74999999999999
	- type: mrr_at_5
	value: 63.87500000000001
	- type: ndcg_at_1
	value: 43.375
	- type: ndcg_at_10
	value: 32.631
	- type: ndcg_at_100
	value: 36.338
	- type: ndcg_at_1000
	value: 43.541000000000004
	- type: ndcg_at_3
	value: 36.746
	- type: ndcg_at_5
	value: 34.419
	- type: precision_at_1
	value: 54.50000000000001
	- type: precision_at_10
	value: 24.825
	- type: precision_at_100
	value: 7.698
	- type: precision_at_1000
	value: 1.657
	- type: precision_at_3
	value: 38.917
	- type: precision_at_5
	value: 32.35
	- type: recall_at_1
	value: 7.481
	- type: recall_at_10
	value: 20.341
	- type: recall_at_100
	value: 41.778
	- type: recall_at_1000
	value: 64.82
	- type: recall_at_3
	value: 12.748000000000001
	- type: recall_at_5
	value: 15.507000000000001
	- task:
	type: Classification
	dataset:
	type: mteb/emotion
	name: MTEB EmotionClassification
	config: default
	split: test
	revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
	metrics:
	- type: accuracy
	value: 46.580000000000005
	- type: f1
	value: 41.5149462395095
	- task:
	type: Retrieval
	dataset:
	type: fever
	name: MTEB FEVER
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 61.683
	- type: map_at_10
	value: 73.071
	- type: map_at_100
	value: 73.327
	- type: map_at_1000
	value: 73.341
	- type: map_at_3
	value: 71.446
	- type: map_at_5
	value: 72.557
	- type: mrr_at_1
	value: 66.44200000000001
	- type: mrr_at_10
	value: 77.725
	- type: mrr_at_100
	value: 77.89399999999999
	- type: mrr_at_1000
	value: 77.898
	- type: mrr_at_3
	value: 76.283
	- type: mrr_at_5
	value: 77.29700000000001
	- type: ndcg_at_1
	value: 66.44200000000001
	- type: ndcg_at_10
	value: 78.43
	- type: ndcg_at_100
	value: 79.462
	- type: ndcg_at_1000
	value: 79.754
	- type: ndcg_at_3
	value: 75.53800000000001
	- type: ndcg_at_5
	value: 77.332
	- type: precision_at_1
	value: 66.44200000000001
	- type: precision_at_10
	value: 9.878
	- type: precision_at_100
	value: 1.051
	- type: precision_at_1000
	value: 0.109
	- type: precision_at_3
	value: 29.878
	- type: precision_at_5
	value: 18.953
	- type: recall_at_1
	value: 61.683
	- type: recall_at_10
	value: 90.259
	- type: recall_at_100
	value: 94.633
	- type: recall_at_1000
	value: 96.60499999999999
	- type: recall_at_3
	value: 82.502
	- type: recall_at_5
	value: 86.978
	- task:
	type: Retrieval
	dataset:
	type: fiqa
	name: MTEB FiQA2018
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 17.724
	- type: map_at_10
	value: 29.487999999999996
	- type: map_at_100
	value: 31.243
	- type: map_at_1000
	value: 31.419999999999998
	- type: map_at_3
	value: 25.612000000000002
	- type: map_at_5
	value: 27.859
	- type: mrr_at_1
	value: 35.802
	- type: mrr_at_10
	value: 44.684000000000005
	- type: mrr_at_100
	value: 45.578
	- type: mrr_at_1000
	value: 45.621
	- type: mrr_at_3
	value: 42.361
	- type: mrr_at_5
	value: 43.85
	- type: ndcg_at_1
	value: 35.802
	- type: ndcg_at_10
	value: 37.009
	- type: ndcg_at_100
	value: 43.903
	- type: ndcg_at_1000
	value: 47.019
	- type: ndcg_at_3
	value: 33.634
	- type: ndcg_at_5
	value: 34.965
	- type: precision_at_1
	value: 35.802
	- type: precision_at_10
	value: 10.386
	- type: precision_at_100
	value: 1.7309999999999999
	- type: precision_at_1000
	value: 0.231
	- type: precision_at_3
	value: 22.84
	- type: precision_at_5
	value: 17.037
	- type: recall_at_1
	value: 17.724
	- type: recall_at_10
	value: 43.708000000000006
	- type: recall_at_100
	value: 69.902
	- type: recall_at_1000
	value: 88.51
	- type: recall_at_3
	value: 30.740000000000002
	- type: recall_at_5
	value: 36.742000000000004
	- task:
	type: Clustering
	dataset:
	type: jinaai/flores_clustering
	name: MTEB FloresClusteringS2S
	config: default
	split: test
	revision: 480b580487f53a46f881354a8348335d4edbb2de
	metrics:
	- type: v_measure
	value: 39.79120149869612
	- task:
	type: Retrieval
	dataset:
	type: hotpotqa
	name: MTEB HotpotQA
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 34.801
	- type: map_at_10
	value: 50.42100000000001
	- type: map_at_100
	value: 51.254
	- type: map_at_1000
	value: 51.327999999999996
	- type: map_at_3
	value: 47.56
	- type: map_at_5
	value: 49.379
	- type: mrr_at_1
	value: 69.602
	- type: mrr_at_10
	value: 76.385
	- type: mrr_at_100
	value: 76.668
	- type: mrr_at_1000
	value: 76.683
	- type: mrr_at_3
	value: 75.102
	- type: mrr_at_5
	value: 75.949
	- type: ndcg_at_1
	value: 69.602
	- type: ndcg_at_10
	value: 59.476
	- type: ndcg_at_100
	value: 62.527
	- type: ndcg_at_1000
	value: 64.043
	- type: ndcg_at_3
	value: 55.155
	- type: ndcg_at_5
	value: 57.623000000000005
	- type: precision_at_1
	value: 69.602
	- type: precision_at_10
	value: 12.292
	- type: precision_at_100
	value: 1.467
	- type: precision_at_1000
	value: 0.167
	- type: precision_at_3
	value: 34.634
	- type: precision_at_5
	value: 22.728
	- type: recall_at_1
	value: 34.801
	- type: recall_at_10
	value: 61.458
	- type: recall_at_100
	value: 73.363
	- type: recall_at_1000
	value: 83.43
	- type: recall_at_3
	value: 51.951
	- type: recall_at_5
	value: 56.82000000000001
	- task:
	type: Classification
	dataset:
	type: mteb/imdb
	name: MTEB ImdbClassification
	config: default
	split: test
	revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
	metrics:
	- type: accuracy
	value: 67.46079999999999
	- type: ap
	value: 61.81278199159353
	- type: f1
	value: 67.26505019954826
	- task:
	type: Reranking
	dataset:
	type: jinaai/miracl
	name: MTEB MIRACL
	config: default
	split: test
	revision: d28a029f35c4ff7f616df47b0edf54e6882395e6
	metrics:
	- type: map
	value: 73.90464144118539
	- type: mrr
	value: 82.44674693216022
	- task:
	type: Retrieval
	dataset:
	type: jinaai/miracl
	name: MTEB MIRACLRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 21.299
	- type: map_at_10
	value: 70.547
	- type: map_at_100
	value: 72.394
	- type: map_at_1000
	value: 72.39999999999999
	- type: map_at_3
	value: 41.317
	- type: map_at_5
	value: 53.756
	- type: mrr_at_1
	value: 72.84
	- type: mrr_at_10
	value: 82.466
	- type: mrr_at_100
	value: 82.52199999999999
	- type: mrr_at_1000
	value: 82.52199999999999
	- type: mrr_at_3
	value: 80.607
	- type: mrr_at_5
	value: 82.065
	- type: ndcg_at_1
	value: 72.994
	- type: ndcg_at_10
	value: 80.89
	- type: ndcg_at_100
	value: 83.30199999999999
	- type: ndcg_at_1000
	value: 83.337
	- type: ndcg_at_3
	value: 70.357
	- type: ndcg_at_5
	value: 72.529
	- type: precision_at_1
	value: 72.994
	- type: precision_at_10
	value: 43.056
	- type: precision_at_100
	value: 4.603
	- type: precision_at_1000
	value: 0.461
	- type: precision_at_3
	value: 61.626000000000005
	- type: precision_at_5
	value: 55.525000000000006
	- type: recall_at_1
	value: 21.299
	- type: recall_at_10
	value: 93.903
	- type: recall_at_100
	value: 99.86699999999999
	- type: recall_at_1000
	value: 100.0
	- type: recall_at_3
	value: 46.653
	- type: recall_at_5
	value: 65.72200000000001
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (en)
	config: en
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 90.37163702690378
	- type: f1
	value: 90.18615216514222
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (es)
	config: es
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 89.88992661774515
	- type: f1
	value: 89.3738963046966
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (en)
	config: en
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 71.97218422252622
	- type: f1
	value: 54.03096570916335
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (es)
	config: es
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 68.75917278185457
	- type: f1
	value: 49.144083814705844
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (en)
	config: en
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 70.75991930060525
	- type: f1
	value: 69.37993796176502
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (es)
	config: es
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 66.93006052454606
	- type: f1
	value: 66.04029135274683
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (en)
	config: en
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 73.81977135171486
	- type: f1
	value: 74.10477122507747
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (es)
	config: es
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 71.23402824478816
	- type: f1
	value: 71.75572665880296
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-p2p
	name: MTEB MedrxivClusteringP2P
	config: default
	split: test
	revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
	metrics:
	- type: v_measure
	value: 32.189750849969215
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-s2s
	name: MTEB MedrxivClusteringS2S
	config: default
	split: test
	revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
	metrics:
	- type: v_measure
	value: 28.78357393555938
	- task:
	type: Reranking
	dataset:
	type: mteb/mind_small
	name: MTEB MindSmallReranking
	config: default
	split: test
	revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
	metrics:
	- type: map
	value: 30.605612998328358
	- type: mrr
	value: 31.595529205695833
	- task:
	type: Retrieval
	dataset:
	type: jinaai/mintakaqa
	name: MTEB MintakaESRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 16.213
	- type: map_at_10
	value: 24.079
	- type: map_at_100
	value: 25.039
	- type: map_at_1000
	value: 25.142999999999997
	- type: map_at_3
	value: 21.823
	- type: map_at_5
	value: 23.069
	- type: mrr_at_1
	value: 16.213
	- type: mrr_at_10
	value: 24.079
	- type: mrr_at_100
	value: 25.039
	- type: mrr_at_1000
	value: 25.142999999999997
	- type: mrr_at_3
	value: 21.823
	- type: mrr_at_5
	value: 23.069
	- type: ndcg_at_1
	value: 16.213
	- type: ndcg_at_10
	value: 28.315
	- type: ndcg_at_100
	value: 33.475
	- type: ndcg_at_1000
	value: 36.838
	- type: ndcg_at_3
	value: 23.627000000000002
	- type: ndcg_at_5
	value: 25.879
	- type: precision_at_1
	value: 16.213
	- type: precision_at_10
	value: 4.183
	- type: precision_at_100
	value: 0.6709999999999999
	- type: precision_at_1000
	value: 0.095
	- type: precision_at_3
	value: 9.612
	- type: precision_at_5
	value: 6.865
	- type: recall_at_1
	value: 16.213
	- type: recall_at_10
	value: 41.832
	- type: recall_at_100
	value: 67.12
	- type: recall_at_1000
	value: 94.843
	- type: recall_at_3
	value: 28.837000000000003
	- type: recall_at_5
	value: 34.323
	- task:
	type: Retrieval
	dataset:
	type: nfcorpus
	name: MTEB NFCorpus
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 4.692
	- type: map_at_10
	value: 10.783
	- type: map_at_100
	value: 13.447999999999999
	- type: map_at_1000
	value: 14.756
	- type: map_at_3
	value: 7.646
	- type: map_at_5
	value: 9.311
	- type: mrr_at_1
	value: 42.415000000000006
	- type: mrr_at_10
	value: 50.471
	- type: mrr_at_100
	value: 51.251999999999995
	- type: mrr_at_1000
	value: 51.292
	- type: mrr_at_3
	value: 48.4
	- type: mrr_at_5
	value: 49.809
	- type: ndcg_at_1
	value: 40.867
	- type: ndcg_at_10
	value: 30.303
	- type: ndcg_at_100
	value: 27.915
	- type: ndcg_at_1000
	value: 36.734
	- type: ndcg_at_3
	value: 35.74
	- type: ndcg_at_5
	value: 33.938
	- type: precision_at_1
	value: 42.415000000000006
	- type: precision_at_10
	value: 22.105
	- type: precision_at_100
	value: 7.173
	- type: precision_at_1000
	value: 2.007
	- type: precision_at_3
	value: 33.437
	- type: precision_at_5
	value: 29.349999999999998
	- type: recall_at_1
	value: 4.692
	- type: recall_at_10
	value: 14.798
	- type: recall_at_100
	value: 28.948
	- type: recall_at_1000
	value: 59.939
	- type: recall_at_3
	value: 8.562
	- type: recall_at_5
	value: 11.818
	- task:
	type: Retrieval
	dataset:
	type: nq
	name: MTEB NQ
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 27.572999999999997
	- type: map_at_10
	value: 42.754
	- type: map_at_100
	value: 43.8
	- type: map_at_1000
	value: 43.838
	- type: map_at_3
	value: 38.157000000000004
	- type: map_at_5
	value: 40.9
	- type: mrr_at_1
	value: 31.373
	- type: mrr_at_10
	value: 45.321
	- type: mrr_at_100
	value: 46.109
	- type: mrr_at_1000
	value: 46.135
	- type: mrr_at_3
	value: 41.483
	- type: mrr_at_5
	value: 43.76
	- type: ndcg_at_1
	value: 31.373
	- type: ndcg_at_10
	value: 50.7
	- type: ndcg_at_100
	value: 55.103
	- type: ndcg_at_1000
	value: 55.955999999999996
	- type: ndcg_at_3
	value: 42.069
	- type: ndcg_at_5
	value: 46.595
	- type: precision_at_1
	value: 31.373
	- type: precision_at_10
	value: 8.601
	- type: precision_at_100
	value: 1.11
	- type: precision_at_1000
	value: 0.11900000000000001
	- type: precision_at_3
	value: 19.399
	- type: precision_at_5
	value: 14.224
	- type: recall_at_1
	value: 27.572999999999997
	- type: recall_at_10
	value: 72.465
	- type: recall_at_100
	value: 91.474
	- type: recall_at_1000
	value: 97.78099999999999
	- type: recall_at_3
	value: 50.087
	- type: recall_at_5
	value: 60.516000000000005
	- task:
	type: Retrieval
	dataset:
	type: quora
	name: MTEB QuoraRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 70.525
	- type: map_at_10
	value: 84.417
	- type: map_at_100
	value: 85.07000000000001
	- type: map_at_1000
	value: 85.085
	- type: map_at_3
	value: 81.45
	- type: map_at_5
	value: 83.317
	- type: mrr_at_1
	value: 81.17999999999999
	- type: mrr_at_10
	value: 87.34100000000001
	- type: mrr_at_100
	value: 87.461
	- type: mrr_at_1000
	value: 87.46199999999999
	- type: mrr_at_3
	value: 86.372
	- type: mrr_at_5
	value: 87.046
	- type: ndcg_at_1
	value: 81.17999999999999
	- type: ndcg_at_10
	value: 88.144
	- type: ndcg_at_100
	value: 89.424
	- type: ndcg_at_1000
	value: 89.517
	- type: ndcg_at_3
	value: 85.282
	- type: ndcg_at_5
	value: 86.874
	- type: precision_at_1
	value: 81.17999999999999
	- type: precision_at_10
	value: 13.385
	- type: precision_at_100
	value: 1.533
	- type: precision_at_1000
	value: 0.157
	- type: precision_at_3
	value: 37.29
	- type: precision_at_5
	value: 24.546
	- type: recall_at_1
	value: 70.525
	- type: recall_at_10
	value: 95.22500000000001
	- type: recall_at_100
	value: 99.572
	- type: recall_at_1000
	value: 99.98899999999999
	- type: recall_at_3
	value: 87.035
	- type: recall_at_5
	value: 91.526
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering
	name: MTEB RedditClustering
	config: default
	split: test
	revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
	metrics:
	- type: v_measure
	value: 48.284384328108736
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering-p2p
	name: MTEB RedditClusteringP2P
	config: default
	split: test
	revision: 282350215ef01743dc01b456c7f5241fa8937f16
	metrics:
	- type: v_measure
	value: 56.02508021518392
	- task:
	type: Retrieval
	dataset:
	type: scidocs
	name: MTEB SCIDOCS
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 4.023000000000001
	- type: map_at_10
	value: 10.046
	- type: map_at_100
	value: 11.802999999999999
	- type: map_at_1000
	value: 12.074
	- type: map_at_3
	value: 7.071
	- type: map_at_5
	value: 8.556
	- type: mrr_at_1
	value: 19.8
	- type: mrr_at_10
	value: 30.105999999999998
	- type: mrr_at_100
	value: 31.16
	- type: mrr_at_1000
	value: 31.224
	- type: mrr_at_3
	value: 26.633000000000003
	- type: mrr_at_5
	value: 28.768
	- type: ndcg_at_1
	value: 19.8
	- type: ndcg_at_10
	value: 17.358
	- type: ndcg_at_100
	value: 24.566
	- type: ndcg_at_1000
	value: 29.653000000000002
	- type: ndcg_at_3
	value: 16.052
	- type: ndcg_at_5
	value: 14.325
	- type: precision_at_1
	value: 19.8
	- type: precision_at_10
	value: 9.07
	- type: precision_at_100
	value: 1.955
	- type: precision_at_1000
	value: 0.318
	- type: precision_at_3
	value: 14.933
	- type: precision_at_5
	value: 12.68
	- type: recall_at_1
	value: 4.023000000000001
	- type: recall_at_10
	value: 18.398
	- type: recall_at_100
	value: 39.683
	- type: recall_at_1000
	value: 64.625
	- type: recall_at_3
	value: 9.113
	- type: recall_at_5
	value: 12.873000000000001
	- task:
	type: STS
	dataset:
	type: mteb/sickr-sts
	name: MTEB SICK-R
	config: default
	split: test
	revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
	metrics:
	- type: cos_sim_pearson
	value: 87.90508618312852
	- type: cos_sim_spearman
	value: 83.01323463129205
	- type: euclidean_pearson
	value: 84.35845059002891
	- type: euclidean_spearman
	value: 82.85508559018527
	- type: manhattan_pearson
	value: 84.3682368950498
	- type: manhattan_spearman
	value: 82.8619728517302
	- task:
	type: STS
	dataset:
	type: mteb/sts12-sts
	name: MTEB STS12
	config: default
	split: test
	revision: a0d554a64d88156834ff5ae9920b964011b16384
	metrics:
	- type: cos_sim_pearson
	value: 89.28294535873366
	- type: cos_sim_spearman
	value: 81.61879268131732
	- type: euclidean_pearson
	value: 85.99053604863724
	- type: euclidean_spearman
	value: 80.95176684739084
	- type: manhattan_pearson
	value: 85.98054086663903
	- type: manhattan_spearman
	value: 80.9911070430335
	- task:
	type: STS
	dataset:
	type: mteb/sts13-sts
	name: MTEB STS13
	config: default
	split: test
	revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
	metrics:
	- type: cos_sim_pearson
	value: 86.15898098455258
	- type: cos_sim_spearman
	value: 86.8247985072307
	- type: euclidean_pearson
	value: 86.25342429918649
	- type: euclidean_spearman
	value: 87.13468603023252
	- type: manhattan_pearson
	value: 86.2006134067688
	- type: manhattan_spearman
	value: 87.06135811996896
	- task:
	type: STS
	dataset:
	type: mteb/sts14-sts
	name: MTEB STS14
	config: default
	split: test
	revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
	metrics:
	- type: cos_sim_pearson
	value: 85.57403998481877
	- type: cos_sim_spearman
	value: 83.55947075172618
	- type: euclidean_pearson
	value: 84.97097562965358
	- type: euclidean_spearman
	value: 83.6287075601467
	- type: manhattan_pearson
	value: 84.87092197104133
	- type: manhattan_spearman
	value: 83.53783891641335
	- task:
	type: STS
	dataset:
	type: mteb/sts15-sts
	name: MTEB STS15
	config: default
	split: test
	revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
	metrics:
	- type: cos_sim_pearson
	value: 88.14632780204231
	- type: cos_sim_spearman
	value: 88.74903634923868
	- type: euclidean_pearson
	value: 88.03922995855112
	- type: euclidean_spearman
	value: 88.72852190525855
	- type: manhattan_pearson
	value: 87.9694791024271
	- type: manhattan_spearman
	value: 88.66461452107418
	- task:
	type: STS
	dataset:
	type: mteb/sts16-sts
	name: MTEB STS16
	config: default
	split: test
	revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
	metrics:
	- type: cos_sim_pearson
	value: 84.75989818558652
	- type: cos_sim_spearman
	value: 86.03107893122942
	- type: euclidean_pearson
	value: 85.21908960133018
	- type: euclidean_spearman
	value: 85.93012720153482
	- type: manhattan_pearson
	value: 85.1969170195502
	- type: manhattan_spearman
	value: 85.8975254197784
	- 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: 89.16803898789955
	- type: cos_sim_spearman
	value: 88.56139047950525
	- type: euclidean_pearson
	value: 88.09685325747859
	- type: euclidean_spearman
	value: 88.0457609458947
	- type: manhattan_pearson
	value: 88.07054413001431
	- type: manhattan_spearman
	value: 88.10784098889314
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (es-en)
	config: es-en
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 86.7160384474547
	- type: cos_sim_spearman
	value: 86.4899235500562
	- type: euclidean_pearson
	value: 85.90854477703468
	- type: euclidean_spearman
	value: 86.16085009124498
	- type: manhattan_pearson
	value: 85.9249735317884
	- type: manhattan_spearman
	value: 86.25038421339116
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (es-es)
	config: es-es
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 89.37914622360788
	- type: cos_sim_spearman
	value: 88.24619159322809
	- type: euclidean_pearson
	value: 89.00538382632769
	- type: euclidean_spearman
	value: 88.44675863524736
	- type: manhattan_pearson
	value: 88.97372120683606
	- type: manhattan_spearman
	value: 88.33509324222129
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (en)
	config: en
	split: test
	revision: eea2b4fe26a775864c896887d910b76a8098ad3f
	metrics:
	- type: cos_sim_pearson
	value: 66.22181360203069
	- type: cos_sim_spearman
	value: 65.6218291833768
	- type: euclidean_pearson
	value: 67.14543788822508
	- type: euclidean_spearman
	value: 65.21269939987857
	- type: manhattan_pearson
	value: 67.03304607195636
	- type: manhattan_spearman
	value: 65.18885316423805
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (es)
	config: es
	split: test
	revision: eea2b4fe26a775864c896887d910b76a8098ad3f
	metrics:
	- type: cos_sim_pearson
	value: 65.71694059677084
	- type: cos_sim_spearman
	value: 67.96591844540954
	- type: euclidean_pearson
	value: 65.6964079162296
	- type: euclidean_spearman
	value: 67.53027948900173
	- type: manhattan_pearson
	value: 65.93545097673741
	- type: manhattan_spearman
	value: 67.7261811805062
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (es-en)
	config: es-en
	split: test
	revision: eea2b4fe26a775864c896887d910b76a8098ad3f
	metrics:
	- type: cos_sim_pearson
	value: 75.43544796375058
	- type: cos_sim_spearman
	value: 78.80462701160789
	- type: euclidean_pearson
	value: 76.19135575163138
	- type: euclidean_spearman
	value: 78.4974732597096
	- type: manhattan_pearson
	value: 76.3254742699264
	- type: manhattan_spearman
	value: 78.51884307690416
	- task:
	type: STS
	dataset:
	type: mteb/stsbenchmark-sts
	name: MTEB STSBenchmark
	config: default
	split: test
	revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
	metrics:
	- type: cos_sim_pearson
	value: 87.46805293607684
	- type: cos_sim_spearman
	value: 87.83792784689113
	- type: euclidean_pearson
	value: 87.3872143683234
	- type: euclidean_spearman
	value: 87.61611384542778
	- type: manhattan_pearson
	value: 87.38542672601992
	- type: manhattan_spearman
	value: 87.61423971087297
	- task:
	type: STS
	dataset:
	type: PlanTL-GOB-ES/sts-es
	name: MTEB STSES
	config: default
	split: test
	revision: 0912bb6c9393c76d62a7c5ee81c4c817ff47c9f4
	metrics:
	- type: cos_sim_pearson
	value: 82.55286866116202
	- type: cos_sim_spearman
	value: 80.22150503320272
	- type: euclidean_pearson
	value: 83.27223445187087
	- type: euclidean_spearman
	value: 80.59078590992925
	- type: manhattan_pearson
	value: 83.23095887013197
	- type: manhattan_spearman
	value: 80.87994285189795
	- task:
	type: Reranking
	dataset:
	type: mteb/scidocs-reranking
	name: MTEB SciDocsRR
	config: default
	split: test
	revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
	metrics:
	- type: map
	value: 79.29717302265792
	- type: mrr
	value: 94.02156304117088
	- task:
	type: Retrieval
	dataset:
	type: scifact
	name: MTEB SciFact
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 49.9
	- type: map_at_10
	value: 58.626
	- type: map_at_100
	value: 59.519999999999996
	- type: map_at_1000
	value: 59.55200000000001
	- type: map_at_3
	value: 56.232000000000006
	- type: map_at_5
	value: 57.833
	- type: mrr_at_1
	value: 52.333
	- type: mrr_at_10
	value: 60.039
	- type: mrr_at_100
	value: 60.732
	- type: mrr_at_1000
	value: 60.75899999999999
	- type: mrr_at_3
	value: 58.278
	- type: mrr_at_5
	value: 59.428000000000004
	- type: ndcg_at_1
	value: 52.333
	- type: ndcg_at_10
	value: 62.67
	- type: ndcg_at_100
	value: 66.465
	- type: ndcg_at_1000
	value: 67.425
	- type: ndcg_at_3
	value: 58.711999999999996
	- type: ndcg_at_5
	value: 60.958999999999996
	- type: precision_at_1
	value: 52.333
	- type: precision_at_10
	value: 8.333
	- type: precision_at_100
	value: 1.027
	- type: precision_at_1000
	value: 0.11100000000000002
	- type: precision_at_3
	value: 22.778000000000002
	- type: precision_at_5
	value: 15.267
	- type: recall_at_1
	value: 49.9
	- type: recall_at_10
	value: 73.394
	- type: recall_at_100
	value: 90.43299999999999
	- type: recall_at_1000
	value: 98.167
	- type: recall_at_3
	value: 63.032999999999994
	- type: recall_at_5
	value: 68.444
	- task:
	type: Clustering
	dataset:
	type: jinaai/spanish_news_clustering
	name: MTEB SpanishNewsClusteringP2P
	config: default
	split: test
	revision: b5edc3d3d7c12c7b9f883e9da50f6732f3624142
	metrics:
	- type: v_measure
	value: 48.30543557796266
	- task:
	type: Retrieval
	dataset:
	type: jinaai/spanish_passage_retrieval
	name: MTEB SpanishPassageRetrievalS2P
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 14.443
	- type: map_at_10
	value: 28.736
	- type: map_at_100
	value: 34.514
	- type: map_at_1000
	value: 35.004000000000005
	- type: map_at_3
	value: 20.308
	- type: map_at_5
	value: 25.404
	- type: mrr_at_1
	value: 50.29900000000001
	- type: mrr_at_10
	value: 63.757
	- type: mrr_at_100
	value: 64.238
	- type: mrr_at_1000
	value: 64.24600000000001
	- type: mrr_at_3
	value: 59.480999999999995
	- type: mrr_at_5
	value: 62.924
	- type: ndcg_at_1
	value: 50.29900000000001
	- type: ndcg_at_10
	value: 42.126999999999995
	- type: ndcg_at_100
	value: 57.208000000000006
	- type: ndcg_at_1000
	value: 60.646
	- type: ndcg_at_3
	value: 38.722
	- type: ndcg_at_5
	value: 40.007999999999996
	- type: precision_at_1
	value: 50.29900000000001
	- type: precision_at_10
	value: 19.82
	- type: precision_at_100
	value: 4.82
	- type: precision_at_1000
	value: 0.5910000000000001
	- type: precision_at_3
	value: 31.537
	- type: precision_at_5
	value: 28.262999999999998
	- type: recall_at_1
	value: 14.443
	- type: recall_at_10
	value: 43.885999999999996
	- type: recall_at_100
	value: 85.231
	- type: recall_at_1000
	value: 99.07000000000001
	- type: recall_at_3
	value: 22.486
	- type: recall_at_5
	value: 33.035
	- task:
	type: Retrieval
	dataset:
	type: jinaai/spanish_passage_retrieval
	name: MTEB SpanishPassageRetrievalS2S
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 15.578
	- type: map_at_10
	value: 52.214000000000006
	- type: map_at_100
	value: 64.791
	- type: map_at_1000
	value: 64.791
	- type: map_at_3
	value: 33.396
	- type: map_at_5
	value: 41.728
	- type: mrr_at_1
	value: 73.653
	- type: mrr_at_10
	value: 85.116
	- type: mrr_at_100
	value: 85.205
	- type: mrr_at_1000
	value: 85.205
	- type: mrr_at_3
	value: 84.631
	- type: mrr_at_5
	value: 85.05
	- type: ndcg_at_1
	value: 76.64699999999999
	- type: ndcg_at_10
	value: 70.38600000000001
	- type: ndcg_at_100
	value: 82.27600000000001
	- type: ndcg_at_1000
	value: 82.27600000000001
	- type: ndcg_at_3
	value: 70.422
	- type: ndcg_at_5
	value: 69.545
	- type: precision_at_1
	value: 76.64699999999999
	- type: precision_at_10
	value: 43.653
	- type: precision_at_100
	value: 7.718999999999999
	- type: precision_at_1000
	value: 0.772
	- type: precision_at_3
	value: 64.671
	- type: precision_at_5
	value: 56.766000000000005
	- type: recall_at_1
	value: 15.578
	- type: recall_at_10
	value: 67.459
	- type: recall_at_100
	value: 100.0
	- type: recall_at_1000
	value: 100.0
	- type: recall_at_3
	value: 36.922
	- type: recall_at_5
	value: 49.424
	- task:
	type: PairClassification
	dataset:
	type: mteb/sprintduplicatequestions-pairclassification
	name: MTEB SprintDuplicateQuestions
	config: default
	split: test
	revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
	metrics:
	- type: cos_sim_accuracy
	value: 99.81683168316832
	- type: cos_sim_ap
	value: 95.61502659412484
	- type: cos_sim_f1
	value: 90.6813627254509
	- type: cos_sim_precision
	value: 90.86345381526104
	- type: cos_sim_recall
	value: 90.5
	- type: dot_accuracy
	value: 99.8039603960396
	- type: dot_ap
	value: 95.36783483182609
	- type: dot_f1
	value: 89.90825688073394
	- type: dot_precision
	value: 91.68399168399168
	- type: dot_recall
	value: 88.2
	- type: euclidean_accuracy
	value: 99.81188118811882
	- type: euclidean_ap
	value: 95.51583052324564
	- type: euclidean_f1
	value: 90.46214355948868
	- type: euclidean_precision
	value: 88.97485493230174
	- type: euclidean_recall
	value: 92.0
	- type: manhattan_accuracy
	value: 99.8079207920792
	- type: manhattan_ap
	value: 95.44030644653718
	- type: manhattan_f1
	value: 90.37698412698413
	- type: manhattan_precision
	value: 89.66535433070865
	- type: manhattan_recall
	value: 91.10000000000001
	- type: max_accuracy
	value: 99.81683168316832
	- type: max_ap
	value: 95.61502659412484
	- type: max_f1
	value: 90.6813627254509
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering
	name: MTEB StackExchangeClustering
	config: default
	split: test
	revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
	metrics:
	- type: v_measure
	value: 55.39046705023096
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering-p2p
	name: MTEB StackExchangeClusteringP2P
	config: default
	split: test
	revision: 815ca46b2622cec33ccafc3735d572c266efdb44
	metrics:
	- type: v_measure
	value: 33.57429225651293
	- task:
	type: Reranking
	dataset:
	type: mteb/stackoverflowdupquestions-reranking
	name: MTEB StackOverflowDupQuestions
	config: default
	split: test
	revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
	metrics:
	- type: map
	value: 50.17622570658746
	- type: mrr
	value: 50.99844293778118
	- task:
	type: Summarization
	dataset:
	type: mteb/summeval
	name: MTEB SummEval
	config: default
	split: test
	revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
	metrics:
	- type: cos_sim_pearson
	value: 29.97416289382191
	- type: cos_sim_spearman
	value: 29.871890597161432
	- type: dot_pearson
	value: 28.768845892613644
	- type: dot_spearman
	value: 28.872458999448686
	- task:
	type: Retrieval
	dataset:
	type: trec-covid
	name: MTEB TRECCOVID
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 0.22599999999999998
	- type: map_at_10
	value: 1.646
	- type: map_at_100
	value: 9.491
	- type: map_at_1000
	value: 23.75
	- type: map_at_3
	value: 0.588
	- type: map_at_5
	value: 0.9129999999999999
	- type: mrr_at_1
	value: 84.0
	- type: mrr_at_10
	value: 89.889
	- type: mrr_at_100
	value: 89.889
	- type: mrr_at_1000
	value: 89.889
	- type: mrr_at_3
	value: 89.667
	- type: mrr_at_5
	value: 89.667
	- type: ndcg_at_1
	value: 75.0
	- type: ndcg_at_10
	value: 67.368
	- type: ndcg_at_100
	value: 52.834
	- type: ndcg_at_1000
	value: 49.144
	- type: ndcg_at_3
	value: 72.866
	- type: ndcg_at_5
	value: 70.16
	- type: precision_at_1
	value: 84.0
	- type: precision_at_10
	value: 71.8
	- type: precision_at_100
	value: 54.04
	- type: precision_at_1000
	value: 21.709999999999997
	- type: precision_at_3
	value: 77.333
	- type: precision_at_5
	value: 74.0
	- type: recall_at_1
	value: 0.22599999999999998
	- type: recall_at_10
	value: 1.9029999999999998
	- type: recall_at_100
	value: 13.012
	- type: recall_at_1000
	value: 46.105000000000004
	- type: recall_at_3
	value: 0.63
	- type: recall_at_5
	value: 1.0030000000000001
	- task:
	type: Retrieval
	dataset:
	type: webis-touche2020
	name: MTEB Touche2020
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 1.5
	- type: map_at_10
	value: 8.193999999999999
	- type: map_at_100
	value: 14.01
	- type: map_at_1000
	value: 15.570999999999998
	- type: map_at_3
	value: 4.361000000000001
	- type: map_at_5
	value: 5.9270000000000005
	- type: mrr_at_1
	value: 16.326999999999998
	- type: mrr_at_10
	value: 33.326
	- type: mrr_at_100
	value: 34.592
	- type: mrr_at_1000
	value: 34.592
	- type: mrr_at_3
	value: 29.252
	- type: mrr_at_5
	value: 30.680000000000003
	- type: ndcg_at_1
	value: 15.306000000000001
	- type: ndcg_at_10
	value: 19.819
	- type: ndcg_at_100
	value: 33.428000000000004
	- type: ndcg_at_1000
	value: 45.024
	- type: ndcg_at_3
	value: 19.667
	- type: ndcg_at_5
	value: 19.625
	- type: precision_at_1
	value: 16.326999999999998
	- type: precision_at_10
	value: 18.367
	- type: precision_at_100
	value: 7.367
	- type: precision_at_1000
	value: 1.496
	- type: precision_at_3
	value: 23.128999999999998
	- type: precision_at_5
	value: 21.633
	- type: recall_at_1
	value: 1.5
	- type: recall_at_10
	value: 14.362
	- type: recall_at_100
	value: 45.842
	- type: recall_at_1000
	value: 80.42
	- type: recall_at_3
	value: 5.99
	- type: recall_at_5
	value: 8.701
	- task:
	type: Classification
	dataset:
	type: mteb/toxic_conversations_50k
	name: MTEB ToxicConversationsClassification
	config: default
	split: test
	revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
	metrics:
	- type: accuracy
	value: 70.04740000000001
	- type: ap
	value: 13.58661943759992
	- type: f1
	value: 53.727487131754195
	- task:
	type: Classification
	dataset:
	type: mteb/tweet_sentiment_extraction
	name: MTEB TweetSentimentExtractionClassification
	config: default
	split: test
	revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
	metrics:
	- type: accuracy
	value: 61.06395019807584
	- type: f1
	value: 61.36753664680866
	- task:
	type: Clustering
	dataset:
	type: mteb/twentynewsgroups-clustering
	name: MTEB TwentyNewsgroupsClustering
	config: default
	split: test
	revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
	metrics:
	- type: v_measure
	value: 40.19881263066229
	- task:
	type: PairClassification
	dataset:
	type: mteb/twittersemeval2015-pairclassification
	name: MTEB TwitterSemEval2015
	config: default
	split: test
	revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
	metrics:
	- type: cos_sim_accuracy
	value: 85.19401561661799
	- type: cos_sim_ap
	value: 71.62462506173092
	- type: cos_sim_f1
	value: 66.0641327225455
	- type: cos_sim_precision
	value: 62.234662934453
	- type: cos_sim_recall
	value: 70.3957783641161
	- type: dot_accuracy
	value: 84.69333015437802
	- type: dot_ap
	value: 69.83805526490895
	- type: dot_f1
	value: 64.85446235265817
	- type: dot_precision
	value: 59.59328028293546
	- type: dot_recall
	value: 71.13456464379946
	- type: euclidean_accuracy
	value: 85.38475293556655
	- type: euclidean_ap
	value: 72.05594596250286
	- type: euclidean_f1
	value: 66.53543307086615
	- type: euclidean_precision
	value: 62.332872291378514
	- type: euclidean_recall
	value: 71.34564643799473
	- type: manhattan_accuracy
	value: 85.3907134767837
	- type: manhattan_ap
	value: 72.04585410650152
	- type: manhattan_f1
	value: 66.57132642116554
	- type: manhattan_precision
	value: 60.704194740273856
	- type: manhattan_recall
	value: 73.6939313984169
	- type: max_accuracy
	value: 85.3907134767837
	- type: max_ap
	value: 72.05594596250286
	- type: max_f1
	value: 66.57132642116554
	- task:
	type: PairClassification
	dataset:
	type: mteb/twitterurlcorpus-pairclassification
	name: MTEB TwitterURLCorpus
	config: default
	split: test
	revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
	metrics:
	- type: cos_sim_accuracy
	value: 89.30414871735165
	- type: cos_sim_ap
	value: 86.4398673359918
	- type: cos_sim_f1
	value: 78.9243598692186
	- type: cos_sim_precision
	value: 75.47249350101876
	- type: cos_sim_recall
	value: 82.7071142593163
	- type: dot_accuracy
	value: 89.26145845461248
	- type: dot_ap
	value: 86.32172118414802
	- type: dot_f1
	value: 78.8277467755645
	- type: dot_precision
	value: 75.79418662497335
	- type: dot_recall
	value: 82.11425931629196
	- type: euclidean_accuracy
	value: 89.24205378973105
	- type: euclidean_ap
	value: 86.23988673522649
	- type: euclidean_f1
	value: 78.67984857951413
	- type: euclidean_precision
	value: 75.2689684269742
	- type: euclidean_recall
	value: 82.41453649522637
	- type: manhattan_accuracy
	value: 89.18189932859859
	- type: manhattan_ap
	value: 86.21003833972824
	- type: manhattan_f1
	value: 78.70972564850115
	- type: manhattan_precision
	value: 76.485544094145
	- type: manhattan_recall
	value: 81.0671388974438
	- type: max_accuracy
	value: 89.30414871735165
	- type: max_ap
	value: 86.4398673359918
	- type: max_f1
	value: 78.9243598692186
	- task:
	type: Clustering
	dataset:
	type: jinaai/cities_wiki_clustering
	name: MTEB WikiCitiesClustering
	config: default
	split: test
	revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
	metrics:
	- type: v_measure
	value: 73.254610626148
	- task:
	type: Retrieval
	dataset:
	type: jinaai/xmarket_ml
	name: MTEB XMarketES
	config: default
	split: test
	revision: 705db869e8107dfe6e34b832af90446e77d813e3
	metrics:
	- type: map_at_1
	value: 5.506
	- type: map_at_10
	value: 11.546
	- type: map_at_100
	value: 14.299999999999999
	- type: map_at_1000
	value: 15.146999999999998
	- type: map_at_3
	value: 8.748000000000001
	- type: map_at_5
	value: 10.036000000000001
	- type: mrr_at_1
	value: 17.902
	- type: mrr_at_10
	value: 25.698999999999998
	- type: mrr_at_100
	value: 26.634
	- type: mrr_at_1000
	value: 26.704
	- type: mrr_at_3
	value: 23.244999999999997
	- type: mrr_at_5
	value: 24.555
	- type: ndcg_at_1
	value: 17.902
	- type: ndcg_at_10
	value: 19.714000000000002
	- type: ndcg_at_100
	value: 25.363000000000003
	- type: ndcg_at_1000
	value: 30.903999999999996
	- type: ndcg_at_3
	value: 17.884
	- type: ndcg_at_5
	value: 18.462
	- type: precision_at_1
	value: 17.902
	- type: precision_at_10
	value: 10.467
	- type: precision_at_100
	value: 3.9699999999999998
	- type: precision_at_1000
	value: 1.1320000000000001
	- type: precision_at_3
	value: 14.387
	- type: precision_at_5
	value: 12.727
	- type: recall_at_1
	value: 5.506
	- type: recall_at_10
	value: 19.997999999999998
	- type: recall_at_100
	value: 42.947
	- type: recall_at_1000
	value: 67.333
	- type: recall_at_3
	value: 11.158
	- type: recall_at_5
	value: 14.577000000000002
	- task:
	type: Retrieval
	dataset:
	type: jinaai/xpqa
	name: MTEB XPQAESRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 32.53
	- type: map_at_10
	value: 58.68600000000001
	- type: map_at_100
	value: 60.45399999999999
	- type: map_at_1000
	value: 60.51499999999999
	- type: map_at_3
	value: 50.356
	- type: map_at_5
	value: 55.98
	- type: mrr_at_1
	value: 61.791
	- type: mrr_at_10
	value: 68.952
	- type: mrr_at_100
	value: 69.524
	- type: mrr_at_1000
	value: 69.538
	- type: mrr_at_3
	value: 67.087
	- type: mrr_at_5
	value: 68.052
	- type: ndcg_at_1
	value: 61.791
	- type: ndcg_at_10
	value: 65.359
	- type: ndcg_at_100
	value: 70.95700000000001
	- type: ndcg_at_1000
	value: 71.881
	- type: ndcg_at_3
	value: 59.999
	- type: ndcg_at_5
	value: 61.316
	- type: precision_at_1
	value: 61.791
	- type: precision_at_10
	value: 18.184
	- type: precision_at_100
	value: 2.317
	- type: precision_at_1000
	value: 0.245
	- type: precision_at_3
	value: 42.203
	- type: precision_at_5
	value: 31.374999999999996
	- type: recall_at_1
	value: 32.53
	- type: recall_at_10
	value: 73.098
	- type: recall_at_100
	value: 94.029
	- type: recall_at_1000
	value: 99.842
	- type: recall_at_3
	value: 54.525
	- type: recall_at_5
	value: 63.796
	---
	<!-- TODO: add evaluation results here -->
	<br><br>

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	<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." 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-es` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).


	## Intended Usage & Model Info

	`jina-embeddings-v2-base-es` is a Spanish/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 Spanish-English input without bias.
	Additionally, we provide the following embedding models:

	`jina-embeddings-v2-base-es` es un modelo (embedding) de texto bilingüe Inglés/Español que admite una longitud de secuencia de 8192.
	Se basa en la arquitectura BERT (JinaBERT) que incorpora la variante bi-direccional simétrica de [ALiBi](https://arxiv.org/abs/2108.12409) para permitir una mayor longitud de secuencia.
	Hemos diseñado este modelo para un alto rendimiento en aplicaciones monolingües y bilingües, y está entrenando específicamente para admitir entradas mixtas de español e inglés sin sesgo.
	Adicionalmente, proporcionamos los siguientes modelos (embeddings):

	- [`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): Chinese-English Bilingual embeddings.
	- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): German-English Bilingual embeddings.
	- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (you are here).

	## 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 pooling` 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-es')
	model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-es', trust_remote_code=True)

	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 the `transformers` package:
	```python
	!pip install transformers
	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-es', trust_remote_code=True) # trust_remote_code is needed to use the encode method
	embeddings = model.encode(['How is the weather today?', '¿Qué tiempo hace hoy?'])
	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
	)
	```

	Or you can use the model with the `sentence-transformers` package:
	```python
	from sentence_transformers import SentenceTransformer, util

	model = SentenceTransformer("jinaai/jina-embeddings-v2-base-es", trust_remote_code=True)
	embeddings = model.encode(['How is the weather today?', '¿Qué tiempo hace hoy?'])
	print(util.cos_sim(embeddings[0], embeddings[1]))
	```

	And if you only want to handle shorter sequence, such as 2k, then you can set the `model.max_seq_length`

	```python
	model.max_seq_length = 2048
	```

	## Alternatives to Transformers and Sentence Transformers

	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).

	## 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">


	## Plans

	1. Bilingual embedding models supporting more European & Asian languages, including French, Italian and Japanese.
	2. Multimodal embedding models enable Multimodal RAG applications.
	3. High-performt rerankers.

	## 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}
	}
	```