snowflake-arctic-embed-m-long / README.md

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	---
	license: apache-2.0
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- mteb
	- arctic
	- snowflake-arctic-embed
	- transformers.js
	model-index:
	- name: snowflake-arctic-m-long
	results:
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (en)
	config: en
	split: test
	revision: e8379541af4e31359cca9fbcf4b00f2671dba205
	metrics:
	- type: accuracy
	value: 78.4776119402985
	- type: ap
	value: 42.34374238166049
	- type: f1
	value: 72.51164234732224
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_polarity
	name: MTEB AmazonPolarityClassification
	config: default
	split: test
	revision: e2d317d38cd51312af73b3d32a06d1a08b442046
	metrics:
	- type: accuracy
	value: 78.7416
	- type: ap
	value: 73.12074819362377
	- type: f1
	value: 78.64057339708795
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (en)
	config: en
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 39.926
	- type: f1
	value: 39.35531993117573
	- task:
	type: Retrieval
	dataset:
	type: mteb/arguana
	name: MTEB ArguAna
	config: default
	split: test
	revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
	metrics:
	- type: map_at_1
	value: 34.851
	- type: map_at_10
	value: 51.473
	- type: map_at_100
	value: 52.103
	- type: map_at_1000
	value: 52.105000000000004
	- type: map_at_3
	value: 46.776
	- type: map_at_5
	value: 49.617
	- type: mrr_at_1
	value: 35.491
	- type: mrr_at_10
	value: 51.73799999999999
	- type: mrr_at_100
	value: 52.37500000000001
	- type: mrr_at_1000
	value: 52.378
	- type: mrr_at_3
	value: 46.965
	- type: mrr_at_5
	value: 49.878
	- type: ndcg_at_1
	value: 34.851
	- type: ndcg_at_10
	value: 60.364
	- type: ndcg_at_100
	value: 62.888999999999996
	- type: ndcg_at_1000
	value: 62.946000000000005
	- type: ndcg_at_3
	value: 50.807
	- type: ndcg_at_5
	value: 55.901
	- type: precision_at_1
	value: 34.851
	- type: precision_at_10
	value: 8.855
	- type: precision_at_100
	value: 0.992
	- type: precision_at_1000
	value: 0.1
	- type: precision_at_3
	value: 20.839
	- type: precision_at_5
	value: 14.963999999999999
	- type: recall_at_1
	value: 34.851
	- type: recall_at_10
	value: 88.549
	- type: recall_at_100
	value: 99.21799999999999
	- type: recall_at_1000
	value: 99.644
	- type: recall_at_3
	value: 62.517999999999994
	- type: recall_at_5
	value: 74.822
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-p2p
	name: MTEB ArxivClusteringP2P
	config: default
	split: test
	revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
	metrics:
	- type: v_measure
	value: 45.5554998405317
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-s2s
	name: MTEB ArxivClusteringS2S
	config: default
	split: test
	revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
	metrics:
	- type: v_measure
	value: 35.614248811397005
	- task:
	type: Reranking
	dataset:
	type: mteb/askubuntudupquestions-reranking
	name: MTEB AskUbuntuDupQuestions
	config: default
	split: test
	revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
	metrics:
	- type: map
	value: 61.355489424753884
	- type: mrr
	value: 75.49443784900849
	- task:
	type: STS
	dataset:
	type: mteb/biosses-sts
	name: MTEB BIOSSES
	config: default
	split: test
	revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
	metrics:
	- type: cos_sim_pearson
	value: 89.17311056578292
	- type: cos_sim_spearman
	value: 88.24237210809322
	- type: euclidean_pearson
	value: 87.3188065853646
	- type: euclidean_spearman
	value: 88.24237210809322
	- type: manhattan_pearson
	value: 86.89499710049658
	- type: manhattan_spearman
	value: 87.85441146091777
	- task:
	type: Classification
	dataset:
	type: mteb/banking77
	name: MTEB Banking77Classification
	config: default
	split: test
	revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
	metrics:
	- type: accuracy
	value: 80.26298701298703
	- type: f1
	value: 79.68356764080303
	- task:
	type: Clustering
	dataset:
	type: jinaai/big-patent-clustering
	name: MTEB BigPatentClustering
	config: default
	split: test
	revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
	metrics:
	- type: v_measure
	value: 20.923883720813706
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-p2p
	name: MTEB BiorxivClusteringP2P
	config: default
	split: test
	revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
	metrics:
	- type: v_measure
	value: 36.16058801465044
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-s2s
	name: MTEB BiorxivClusteringS2S
	config: default
	split: test
	revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
	metrics:
	- type: v_measure
	value: 30.1402356118627
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-android
	name: MTEB CQADupstackAndroidRetrieval
	config: default
	split: test
	revision: f46a197baaae43b4f621051089b82a364682dfeb
	metrics:
	- type: map_at_1
	value: 35.612
	- type: map_at_10
	value: 47.117
	- type: map_at_100
	value: 48.711
	- type: map_at_1000
	value: 48.826
	- type: map_at_3
	value: 43.858999999999995
	- type: map_at_5
	value: 45.612
	- type: mrr_at_1
	value: 42.918
	- type: mrr_at_10
	value: 52.806
	- type: mrr_at_100
	value: 53.564
	- type: mrr_at_1000
	value: 53.596999999999994
	- type: mrr_at_3
	value: 50.453
	- type: mrr_at_5
	value: 51.841
	- type: ndcg_at_1
	value: 42.918
	- type: ndcg_at_10
	value: 53.291999999999994
	- type: ndcg_at_100
	value: 58.711999999999996
	- type: ndcg_at_1000
	value: 60.317
	- type: ndcg_at_3
	value: 48.855
	- type: ndcg_at_5
	value: 50.778
	- type: precision_at_1
	value: 42.918
	- type: precision_at_10
	value: 9.927999999999999
	- type: precision_at_100
	value: 1.592
	- type: precision_at_1000
	value: 0.201
	- type: precision_at_3
	value: 23.366999999999997
	- type: precision_at_5
	value: 16.366
	- type: recall_at_1
	value: 35.612
	- type: recall_at_10
	value: 64.671
	- type: recall_at_100
	value: 86.97
	- type: recall_at_1000
	value: 96.99600000000001
	- type: recall_at_3
	value: 51.37199999999999
	- type: recall_at_5
	value: 57.094
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-english
	name: MTEB CQADupstackEnglishRetrieval
	config: default
	split: test
	revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
	metrics:
	- type: map_at_1
	value: 33.742
	- type: map_at_10
	value: 44.49
	- type: map_at_100
	value: 45.781
	- type: map_at_1000
	value: 45.902
	- type: map_at_3
	value: 41.453
	- type: map_at_5
	value: 43.251
	- type: mrr_at_1
	value: 42.357
	- type: mrr_at_10
	value: 50.463
	- type: mrr_at_100
	value: 51.17
	- type: mrr_at_1000
	value: 51.205999999999996
	- type: mrr_at_3
	value: 48.397
	- type: mrr_at_5
	value: 49.649
	- type: ndcg_at_1
	value: 42.357
	- type: ndcg_at_10
	value: 50.175000000000004
	- type: ndcg_at_100
	value: 54.491
	- type: ndcg_at_1000
	value: 56.282
	- type: ndcg_at_3
	value: 46.159
	- type: ndcg_at_5
	value: 48.226
	- type: precision_at_1
	value: 42.357
	- type: precision_at_10
	value: 9.382
	- type: precision_at_100
	value: 1.473
	- type: precision_at_1000
	value: 0.191
	- type: precision_at_3
	value: 22.187
	- type: precision_at_5
	value: 15.758
	- type: recall_at_1
	value: 33.742
	- type: recall_at_10
	value: 59.760999999999996
	- type: recall_at_100
	value: 77.89500000000001
	- type: recall_at_1000
	value: 89.005
	- type: recall_at_3
	value: 47.872
	- type: recall_at_5
	value: 53.559
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-gaming
	name: MTEB CQADupstackGamingRetrieval
	config: default
	split: test
	revision: 4885aa143210c98657558c04aaf3dc47cfb54340
	metrics:
	- type: map_at_1
	value: 43.883
	- type: map_at_10
	value: 56.464999999999996
	- type: map_at_100
	value: 57.394
	- type: map_at_1000
	value: 57.443999999999996
	- type: map_at_3
	value: 53.169
	- type: map_at_5
	value: 54.984
	- type: mrr_at_1
	value: 50.470000000000006
	- type: mrr_at_10
	value: 59.997
	- type: mrr_at_100
	value: 60.586
	- type: mrr_at_1000
	value: 60.61
	- type: mrr_at_3
	value: 57.837
	- type: mrr_at_5
	value: 59.019
	- type: ndcg_at_1
	value: 50.470000000000006
	- type: ndcg_at_10
	value: 62.134
	- type: ndcg_at_100
	value: 65.69500000000001
	- type: ndcg_at_1000
	value: 66.674
	- type: ndcg_at_3
	value: 56.916999999999994
	- type: ndcg_at_5
	value: 59.312
	- type: precision_at_1
	value: 50.470000000000006
	- type: precision_at_10
	value: 9.812
	- type: precision_at_100
	value: 1.25
	- type: precision_at_1000
	value: 0.13699999999999998
	- type: precision_at_3
	value: 25.119999999999997
	- type: precision_at_5
	value: 17.016000000000002
	- type: recall_at_1
	value: 43.883
	- type: recall_at_10
	value: 75.417
	- type: recall_at_100
	value: 90.545
	- type: recall_at_1000
	value: 97.44500000000001
	- type: recall_at_3
	value: 61.306000000000004
	- type: recall_at_5
	value: 67.244
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-gis
	name: MTEB CQADupstackGisRetrieval
	config: default
	split: test
	revision: 5003b3064772da1887988e05400cf3806fe491f2
	metrics:
	- type: map_at_1
	value: 29.813000000000002
	- type: map_at_10
	value: 38.627
	- type: map_at_100
	value: 39.735
	- type: map_at_1000
	value: 39.806000000000004
	- type: map_at_3
	value: 36.283
	- type: map_at_5
	value: 37.491
	- type: mrr_at_1
	value: 32.316
	- type: mrr_at_10
	value: 40.752
	- type: mrr_at_100
	value: 41.699000000000005
	- type: mrr_at_1000
	value: 41.749
	- type: mrr_at_3
	value: 38.531
	- type: mrr_at_5
	value: 39.706
	- type: ndcg_at_1
	value: 32.316
	- type: ndcg_at_10
	value: 43.524
	- type: ndcg_at_100
	value: 48.648
	- type: ndcg_at_1000
	value: 50.405
	- type: ndcg_at_3
	value: 38.928000000000004
	- type: ndcg_at_5
	value: 40.967
	- type: precision_at_1
	value: 32.316
	- type: precision_at_10
	value: 6.451999999999999
	- type: precision_at_100
	value: 0.9490000000000001
	- type: precision_at_1000
	value: 0.11299999999999999
	- type: precision_at_3
	value: 16.384
	- type: precision_at_5
	value: 11.006
	- type: recall_at_1
	value: 29.813000000000002
	- type: recall_at_10
	value: 56.562999999999995
	- type: recall_at_100
	value: 79.452
	- type: recall_at_1000
	value: 92.715
	- type: recall_at_3
	value: 43.985
	- type: recall_at_5
	value: 49.001
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-mathematica
	name: MTEB CQADupstackMathematicaRetrieval
	config: default
	split: test
	revision: 90fceea13679c63fe563ded68f3b6f06e50061de
	metrics:
	- type: map_at_1
	value: 19.961000000000002
	- type: map_at_10
	value: 28.026
	- type: map_at_100
	value: 29.212
	- type: map_at_1000
	value: 29.332
	- type: map_at_3
	value: 25.296999999999997
	- type: map_at_5
	value: 26.832
	- type: mrr_at_1
	value: 24.627
	- type: mrr_at_10
	value: 33.045
	- type: mrr_at_100
	value: 33.944
	- type: mrr_at_1000
	value: 34.013
	- type: mrr_at_3
	value: 30.307000000000002
	- type: mrr_at_5
	value: 31.874000000000002
	- type: ndcg_at_1
	value: 24.627
	- type: ndcg_at_10
	value: 33.414
	- type: ndcg_at_100
	value: 39.061
	- type: ndcg_at_1000
	value: 41.795
	- type: ndcg_at_3
	value: 28.377000000000002
	- type: ndcg_at_5
	value: 30.781999999999996
	- type: precision_at_1
	value: 24.627
	- type: precision_at_10
	value: 6.02
	- type: precision_at_100
	value: 1.035
	- type: precision_at_1000
	value: 0.13899999999999998
	- type: precision_at_3
	value: 13.516
	- type: precision_at_5
	value: 9.851
	- type: recall_at_1
	value: 19.961000000000002
	- type: recall_at_10
	value: 45.174
	- type: recall_at_100
	value: 69.69
	- type: recall_at_1000
	value: 89.24600000000001
	- type: recall_at_3
	value: 31.062
	- type: recall_at_5
	value: 37.193
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-physics
	name: MTEB CQADupstackPhysicsRetrieval
	config: default
	split: test
	revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
	metrics:
	- type: map_at_1
	value: 32.080999999999996
	- type: map_at_10
	value: 42.177
	- type: map_at_100
	value: 43.431999999999995
	- type: map_at_1000
	value: 43.533
	- type: map_at_3
	value: 38.721
	- type: map_at_5
	value: 40.669
	- type: mrr_at_1
	value: 38.787
	- type: mrr_at_10
	value: 47.762
	- type: mrr_at_100
	value: 48.541000000000004
	- type: mrr_at_1000
	value: 48.581
	- type: mrr_at_3
	value: 45.123999999999995
	- type: mrr_at_5
	value: 46.639
	- type: ndcg_at_1
	value: 38.787
	- type: ndcg_at_10
	value: 48.094
	- type: ndcg_at_100
	value: 53.291
	- type: ndcg_at_1000
	value: 55.21
	- type: ndcg_at_3
	value: 42.721
	- type: ndcg_at_5
	value: 45.301
	- type: precision_at_1
	value: 38.787
	- type: precision_at_10
	value: 8.576
	- type: precision_at_100
	value: 1.306
	- type: precision_at_1000
	value: 0.164
	- type: precision_at_3
	value: 19.698
	- type: precision_at_5
	value: 14.013
	- type: recall_at_1
	value: 32.080999999999996
	- type: recall_at_10
	value: 59.948
	- type: recall_at_100
	value: 81.811
	- type: recall_at_1000
	value: 94.544
	- type: recall_at_3
	value: 44.903999999999996
	- type: recall_at_5
	value: 51.763999999999996
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-programmers
	name: MTEB CQADupstackProgrammersRetrieval
	config: default
	split: test
	revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
	metrics:
	- type: map_at_1
	value: 28.869
	- type: map_at_10
	value: 38.954
	- type: map_at_100
	value: 40.233000000000004
	- type: map_at_1000
	value: 40.332
	- type: map_at_3
	value: 35.585
	- type: map_at_5
	value: 37.476
	- type: mrr_at_1
	value: 35.959
	- type: mrr_at_10
	value: 44.800000000000004
	- type: mrr_at_100
	value: 45.609
	- type: mrr_at_1000
	value: 45.655
	- type: mrr_at_3
	value: 42.333
	- type: mrr_at_5
	value: 43.68
	- type: ndcg_at_1
	value: 35.959
	- type: ndcg_at_10
	value: 44.957
	- type: ndcg_at_100
	value: 50.275000000000006
	- type: ndcg_at_1000
	value: 52.29899999999999
	- type: ndcg_at_3
	value: 39.797
	- type: ndcg_at_5
	value: 42.128
	- type: precision_at_1
	value: 35.959
	- type: precision_at_10
	value: 8.185
	- type: precision_at_100
	value: 1.261
	- type: precision_at_1000
	value: 0.159
	- type: precision_at_3
	value: 18.988
	- type: precision_at_5
	value: 13.516
	- type: recall_at_1
	value: 28.869
	- type: recall_at_10
	value: 57.154
	- type: recall_at_100
	value: 79.764
	- type: recall_at_1000
	value: 93.515
	- type: recall_at_3
	value: 42.364000000000004
	- type: recall_at_5
	value: 48.756
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack
	name: MTEB CQADupstackRetrieval
	config: default
	split: test
	revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
	metrics:
	- type: map_at_1
	value: 29.31008333333333
	- type: map_at_10
	value: 38.81849999999999
	- type: map_at_100
	value: 40.05058333333334
	- type: map_at_1000
	value: 40.16116666666667
	- type: map_at_3
	value: 35.91441666666667
	- type: map_at_5
	value: 37.526583333333335
	- type: mrr_at_1
	value: 34.60066666666667
	- type: mrr_at_10
	value: 43.08858333333333
	- type: mrr_at_100
	value: 43.927749999999996
	- type: mrr_at_1000
	value: 43.97866666666667
	- type: mrr_at_3
	value: 40.72775
	- type: mrr_at_5
	value: 42.067249999999994
	- type: ndcg_at_1
	value: 34.60066666666667
	- type: ndcg_at_10
	value: 44.20841666666667
	- type: ndcg_at_100
	value: 49.32866666666667
	- type: ndcg_at_1000
	value: 51.373999999999995
	- type: ndcg_at_3
	value: 39.452083333333334
	- type: ndcg_at_5
	value: 41.67
	- type: precision_at_1
	value: 34.60066666666667
	- type: precision_at_10
	value: 7.616583333333334
	- type: precision_at_100
	value: 1.20175
	- type: precision_at_1000
	value: 0.156
	- type: precision_at_3
	value: 17.992
	- type: precision_at_5
	value: 12.658416666666666
	- type: recall_at_1
	value: 29.31008333333333
	- type: recall_at_10
	value: 55.81900000000001
	- type: recall_at_100
	value: 78.06308333333334
	- type: recall_at_1000
	value: 92.10641666666668
	- type: recall_at_3
	value: 42.50166666666667
	- type: recall_at_5
	value: 48.26108333333333
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-stats
	name: MTEB CQADupstackStatsRetrieval
	config: default
	split: test
	revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
	metrics:
	- type: map_at_1
	value: 26.773000000000003
	- type: map_at_10
	value: 34.13
	- type: map_at_100
	value: 35.113
	- type: map_at_1000
	value: 35.211
	- type: map_at_3
	value: 31.958
	- type: map_at_5
	value: 33.080999999999996
	- type: mrr_at_1
	value: 30.061
	- type: mrr_at_10
	value: 37.061
	- type: mrr_at_100
	value: 37.865
	- type: mrr_at_1000
	value: 37.939
	- type: mrr_at_3
	value: 34.995
	- type: mrr_at_5
	value: 36.092
	- type: ndcg_at_1
	value: 30.061
	- type: ndcg_at_10
	value: 38.391999999999996
	- type: ndcg_at_100
	value: 43.13
	- type: ndcg_at_1000
	value: 45.449
	- type: ndcg_at_3
	value: 34.411
	- type: ndcg_at_5
	value: 36.163000000000004
	- type: precision_at_1
	value: 30.061
	- type: precision_at_10
	value: 5.982
	- type: precision_at_100
	value: 0.911
	- type: precision_at_1000
	value: 0.11800000000000001
	- type: precision_at_3
	value: 14.673
	- type: precision_at_5
	value: 10.030999999999999
	- type: recall_at_1
	value: 26.773000000000003
	- type: recall_at_10
	value: 48.445
	- type: recall_at_100
	value: 69.741
	- type: recall_at_1000
	value: 86.59
	- type: recall_at_3
	value: 37.576
	- type: recall_at_5
	value: 41.948
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-tex
	name: MTEB CQADupstackTexRetrieval
	config: default
	split: test
	revision: 46989137a86843e03a6195de44b09deda022eec7
	metrics:
	- type: map_at_1
	value: 18.556
	- type: map_at_10
	value: 26.340999999999998
	- type: map_at_100
	value: 27.560000000000002
	- type: map_at_1000
	value: 27.685
	- type: map_at_3
	value: 24.136
	- type: map_at_5
	value: 25.34
	- type: mrr_at_1
	value: 22.368
	- type: mrr_at_10
	value: 30.192999999999998
	- type: mrr_at_100
	value: 31.183
	- type: mrr_at_1000
	value: 31.258000000000003
	- type: mrr_at_3
	value: 28.223
	- type: mrr_at_5
	value: 29.294999999999998
	- type: ndcg_at_1
	value: 22.368
	- type: ndcg_at_10
	value: 31.029
	- type: ndcg_at_100
	value: 36.768
	- type: ndcg_at_1000
	value: 39.572
	- type: ndcg_at_3
	value: 27.197
	- type: ndcg_at_5
	value: 28.912
	- type: precision_at_1
	value: 22.368
	- type: precision_at_10
	value: 5.606
	- type: precision_at_100
	value: 0.9979999999999999
	- type: precision_at_1000
	value: 0.14100000000000001
	- type: precision_at_3
	value: 12.892999999999999
	- type: precision_at_5
	value: 9.16
	- type: recall_at_1
	value: 18.556
	- type: recall_at_10
	value: 41.087
	- type: recall_at_100
	value: 66.92
	- type: recall_at_1000
	value: 86.691
	- type: recall_at_3
	value: 30.415
	- type: recall_at_5
	value: 34.813
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-unix
	name: MTEB CQADupstackUnixRetrieval
	config: default
	split: test
	revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
	metrics:
	- type: map_at_1
	value: 29.953999999999997
	- type: map_at_10
	value: 39.633
	- type: map_at_100
	value: 40.923
	- type: map_at_1000
	value: 41.016000000000005
	- type: map_at_3
	value: 36.609
	- type: map_at_5
	value: 38.443
	- type: mrr_at_1
	value: 35.354
	- type: mrr_at_10
	value: 43.718
	- type: mrr_at_100
	value: 44.651999999999994
	- type: mrr_at_1000
	value: 44.696000000000005
	- type: mrr_at_3
	value: 41.154
	- type: mrr_at_5
	value: 42.730000000000004
	- type: ndcg_at_1
	value: 35.354
	- type: ndcg_at_10
	value: 44.933
	- type: ndcg_at_100
	value: 50.577000000000005
	- type: ndcg_at_1000
	value: 52.428
	- type: ndcg_at_3
	value: 39.833
	- type: ndcg_at_5
	value: 42.465
	- type: precision_at_1
	value: 35.354
	- type: precision_at_10
	value: 7.416
	- type: precision_at_100
	value: 1.157
	- type: precision_at_1000
	value: 0.14100000000000001
	- type: precision_at_3
	value: 17.817
	- type: precision_at_5
	value: 12.687000000000001
	- type: recall_at_1
	value: 29.953999999999997
	- type: recall_at_10
	value: 56.932
	- type: recall_at_100
	value: 80.93900000000001
	- type: recall_at_1000
	value: 93.582
	- type: recall_at_3
	value: 43.192
	- type: recall_at_5
	value: 49.757
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-webmasters
	name: MTEB CQADupstackWebmastersRetrieval
	config: default
	split: test
	revision: 160c094312a0e1facb97e55eeddb698c0abe3571
	metrics:
	- type: map_at_1
	value: 27.85
	- type: map_at_10
	value: 37.68
	- type: map_at_100
	value: 39.295
	- type: map_at_1000
	value: 39.527
	- type: map_at_3
	value: 35.036
	- type: map_at_5
	value: 36.269
	- type: mrr_at_1
	value: 33.004
	- type: mrr_at_10
	value: 42.096000000000004
	- type: mrr_at_100
	value: 43.019
	- type: mrr_at_1000
	value: 43.071
	- type: mrr_at_3
	value: 39.987
	- type: mrr_at_5
	value: 40.995
	- type: ndcg_at_1
	value: 33.004
	- type: ndcg_at_10
	value: 43.461
	- type: ndcg_at_100
	value: 49.138
	- type: ndcg_at_1000
	value: 51.50900000000001
	- type: ndcg_at_3
	value: 39.317
	- type: ndcg_at_5
	value: 40.760999999999996
	- type: precision_at_1
	value: 33.004
	- type: precision_at_10
	value: 8.161999999999999
	- type: precision_at_100
	value: 1.583
	- type: precision_at_1000
	value: 0.245
	- type: precision_at_3
	value: 18.445
	- type: precision_at_5
	value: 12.885
	- type: recall_at_1
	value: 27.85
	- type: recall_at_10
	value: 54.419
	- type: recall_at_100
	value: 79.742
	- type: recall_at_1000
	value: 93.97
	- type: recall_at_3
	value: 42.149
	- type: recall_at_5
	value: 46.165
	- task:
	type: Retrieval
	dataset:
	type: mteb/cqadupstack-wordpress
	name: MTEB CQADupstackWordpressRetrieval
	config: default
	split: test
	revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
	metrics:
	- type: map_at_1
	value: 24.627
	- type: map_at_10
	value: 32.182
	- type: map_at_100
	value: 33.217999999999996
	- type: map_at_1000
	value: 33.32
	- type: map_at_3
	value: 28.866999999999997
	- type: map_at_5
	value: 30.871
	- type: mrr_at_1
	value: 26.987
	- type: mrr_at_10
	value: 34.37
	- type: mrr_at_100
	value: 35.301
	- type: mrr_at_1000
	value: 35.369
	- type: mrr_at_3
	value: 31.391999999999996
	- type: mrr_at_5
	value: 33.287
	- type: ndcg_at_1
	value: 26.987
	- type: ndcg_at_10
	value: 37.096000000000004
	- type: ndcg_at_100
	value: 42.158
	- type: ndcg_at_1000
	value: 44.548
	- type: ndcg_at_3
	value: 30.913
	- type: ndcg_at_5
	value: 34.245
	- type: precision_at_1
	value: 26.987
	- type: precision_at_10
	value: 5.878
	- type: precision_at_100
	value: 0.906
	- type: precision_at_1000
	value: 0.123
	- type: precision_at_3
	value: 12.815999999999999
	- type: precision_at_5
	value: 9.612
	- type: recall_at_1
	value: 24.627
	- type: recall_at_10
	value: 50.257
	- type: recall_at_100
	value: 73.288
	- type: recall_at_1000
	value: 90.97800000000001
	- type: recall_at_3
	value: 33.823
	- type: recall_at_5
	value: 41.839
	- task:
	type: Retrieval
	dataset:
	type: mteb/climate-fever
	name: MTEB ClimateFEVER
	config: default
	split: test
	revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
	metrics:
	- type: map_at_1
	value: 17.343
	- type: map_at_10
	value: 28.59
	- type: map_at_100
	value: 30.591
	- type: map_at_1000
	value: 30.759999999999998
	- type: map_at_3
	value: 24.197
	- type: map_at_5
	value: 26.433
	- type: mrr_at_1
	value: 39.609
	- type: mrr_at_10
	value: 51.107
	- type: mrr_at_100
	value: 51.87199999999999
	- type: mrr_at_1000
	value: 51.894
	- type: mrr_at_3
	value: 48.154
	- type: mrr_at_5
	value: 49.939
	- type: ndcg_at_1
	value: 39.609
	- type: ndcg_at_10
	value: 38.329
	- type: ndcg_at_100
	value: 45.573
	- type: ndcg_at_1000
	value: 48.405
	- type: ndcg_at_3
	value: 32.506
	- type: ndcg_at_5
	value: 34.331
	- type: precision_at_1
	value: 39.609
	- type: precision_at_10
	value: 11.668000000000001
	- type: precision_at_100
	value: 1.9539999999999997
	- type: precision_at_1000
	value: 0.249
	- type: precision_at_3
	value: 23.952
	- type: precision_at_5
	value: 17.902
	- type: recall_at_1
	value: 17.343
	- type: recall_at_10
	value: 43.704
	- type: recall_at_100
	value: 68.363
	- type: recall_at_1000
	value: 84.04599999999999
	- type: recall_at_3
	value: 29.028
	- type: recall_at_5
	value: 35.022
	- task:
	type: Retrieval
	dataset:
	type: mteb/dbpedia
	name: MTEB DBPedia
	config: default
	split: test
	revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
	metrics:
	- type: map_at_1
	value: 9.934999999999999
	- type: map_at_10
	value: 22.081
	- type: map_at_100
	value: 32.036
	- type: map_at_1000
	value: 33.803
	- type: map_at_3
	value: 15.687999999999999
	- type: map_at_5
	value: 18.357
	- type: mrr_at_1
	value: 70.75
	- type: mrr_at_10
	value: 78.506
	- type: mrr_at_100
	value: 78.874
	- type: mrr_at_1000
	value: 78.88300000000001
	- type: mrr_at_3
	value: 77.667
	- type: mrr_at_5
	value: 78.342
	- type: ndcg_at_1
	value: 57.25
	- type: ndcg_at_10
	value: 45.286
	- type: ndcg_at_100
	value: 50.791
	- type: ndcg_at_1000
	value: 58.021
	- type: ndcg_at_3
	value: 49.504
	- type: ndcg_at_5
	value: 47.03
	- type: precision_at_1
	value: 70.75
	- type: precision_at_10
	value: 36.425000000000004
	- type: precision_at_100
	value: 11.953
	- type: precision_at_1000
	value: 2.248
	- type: precision_at_3
	value: 53.25
	- type: precision_at_5
	value: 46.150000000000006
	- type: recall_at_1
	value: 9.934999999999999
	- type: recall_at_10
	value: 27.592
	- type: recall_at_100
	value: 58.089
	- type: recall_at_1000
	value: 81.025
	- type: recall_at_3
	value: 17.048
	- type: recall_at_5
	value: 20.834
	- task:
	type: Classification
	dataset:
	type: mteb/emotion
	name: MTEB EmotionClassification
	config: default
	split: test
	revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
	metrics:
	- type: accuracy
	value: 47.25999999999999
	- type: f1
	value: 43.83371155132253
	- task:
	type: Retrieval
	dataset:
	type: mteb/fever
	name: MTEB FEVER
	config: default
	split: test
	revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
	metrics:
	- type: map_at_1
	value: 73.68900000000001
	- type: map_at_10
	value: 82.878
	- type: map_at_100
	value: 83.084
	- type: map_at_1000
	value: 83.097
	- type: map_at_3
	value: 81.528
	- type: map_at_5
	value: 82.432
	- type: mrr_at_1
	value: 79.49300000000001
	- type: mrr_at_10
	value: 87.24300000000001
	- type: mrr_at_100
	value: 87.3
	- type: mrr_at_1000
	value: 87.301
	- type: mrr_at_3
	value: 86.359
	- type: mrr_at_5
	value: 87.01
	- type: ndcg_at_1
	value: 79.49300000000001
	- type: ndcg_at_10
	value: 86.894
	- type: ndcg_at_100
	value: 87.6
	- type: ndcg_at_1000
	value: 87.79299999999999
	- type: ndcg_at_3
	value: 84.777
	- type: ndcg_at_5
	value: 86.08
	- type: precision_at_1
	value: 79.49300000000001
	- type: precision_at_10
	value: 10.578
	- type: precision_at_100
	value: 1.117
	- type: precision_at_1000
	value: 0.11499999999999999
	- type: precision_at_3
	value: 32.592999999999996
	- type: precision_at_5
	value: 20.423
	- type: recall_at_1
	value: 73.68900000000001
	- type: recall_at_10
	value: 94.833
	- type: recall_at_100
	value: 97.554
	- type: recall_at_1000
	value: 98.672
	- type: recall_at_3
	value: 89.236
	- type: recall_at_5
	value: 92.461
	- task:
	type: Retrieval
	dataset:
	type: mteb/fiqa
	name: MTEB FiQA2018
	config: default
	split: test
	revision: 27a168819829fe9bcd655c2df245fb19452e8e06
	metrics:
	- type: map_at_1
	value: 20.59
	- type: map_at_10
	value: 34.089000000000006
	- type: map_at_100
	value: 35.796
	- type: map_at_1000
	value: 35.988
	- type: map_at_3
	value: 29.877
	- type: map_at_5
	value: 32.202999999999996
	- type: mrr_at_1
	value: 41.049
	- type: mrr_at_10
	value: 50.370000000000005
	- type: mrr_at_100
	value: 51.209
	- type: mrr_at_1000
	value: 51.247
	- type: mrr_at_3
	value: 48.122
	- type: mrr_at_5
	value: 49.326
	- type: ndcg_at_1
	value: 41.049
	- type: ndcg_at_10
	value: 42.163000000000004
	- type: ndcg_at_100
	value: 48.638999999999996
	- type: ndcg_at_1000
	value: 51.775000000000006
	- type: ndcg_at_3
	value: 38.435
	- type: ndcg_at_5
	value: 39.561
	- type: precision_at_1
	value: 41.049
	- type: precision_at_10
	value: 11.481
	- type: precision_at_100
	value: 1.8239999999999998
	- type: precision_at_1000
	value: 0.24
	- type: precision_at_3
	value: 25.257
	- type: precision_at_5
	value: 18.519
	- type: recall_at_1
	value: 20.59
	- type: recall_at_10
	value: 49.547999999999995
	- type: recall_at_100
	value: 73.676
	- type: recall_at_1000
	value: 92.269
	- type: recall_at_3
	value: 35.656
	- type: recall_at_5
	value: 41.455
	- task:
	type: Retrieval
	dataset:
	type: mteb/hotpotqa
	name: MTEB HotpotQA
	config: default
	split: test
	revision: ab518f4d6fcca38d87c25209f94beba119d02014
	metrics:
	- type: map_at_1
	value: 39.932
	- type: map_at_10
	value: 64.184
	- type: map_at_100
	value: 65.06
	- type: map_at_1000
	value: 65.109
	- type: map_at_3
	value: 60.27
	- type: map_at_5
	value: 62.732
	- type: mrr_at_1
	value: 79.865
	- type: mrr_at_10
	value: 85.99799999999999
	- type: mrr_at_100
	value: 86.13
	- type: mrr_at_1000
	value: 86.13300000000001
	- type: mrr_at_3
	value: 85.136
	- type: mrr_at_5
	value: 85.69200000000001
	- type: ndcg_at_1
	value: 79.865
	- type: ndcg_at_10
	value: 72.756
	- type: ndcg_at_100
	value: 75.638
	- type: ndcg_at_1000
	value: 76.589
	- type: ndcg_at_3
	value: 67.38199999999999
	- type: ndcg_at_5
	value: 70.402
	- type: precision_at_1
	value: 79.865
	- type: precision_at_10
	value: 15.387999999999998
	- type: precision_at_100
	value: 1.7610000000000001
	- type: precision_at_1000
	value: 0.189
	- type: precision_at_3
	value: 43.394
	- type: precision_at_5
	value: 28.424
	- type: recall_at_1
	value: 39.932
	- type: recall_at_10
	value: 76.941
	- type: recall_at_100
	value: 88.062
	- type: recall_at_1000
	value: 94.396
	- type: recall_at_3
	value: 65.091
	- type: recall_at_5
	value: 71.06
	- task:
	type: Classification
	dataset:
	type: mteb/imdb
	name: MTEB ImdbClassification
	config: default
	split: test
	revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
	metrics:
	- type: accuracy
	value: 71.7904
	- type: ap
	value: 65.82899456730257
	- type: f1
	value: 71.56611877410202
	- task:
	type: Retrieval
	dataset:
	type: mteb/msmarco
	name: MTEB MSMARCO
	config: default
	split: dev
	revision: c5a29a104738b98a9e76336939199e264163d4a0
	metrics:
	- type: map_at_1
	value: 21.931
	- type: map_at_10
	value: 34.849999999999994
	- type: map_at_100
	value: 36.033
	- type: map_at_1000
	value: 36.08
	- type: map_at_3
	value: 30.842000000000002
	- type: map_at_5
	value: 33.229
	- type: mrr_at_1
	value: 22.55
	- type: mrr_at_10
	value: 35.436
	- type: mrr_at_100
	value: 36.563
	- type: mrr_at_1000
	value: 36.604
	- type: mrr_at_3
	value: 31.507
	- type: mrr_at_5
	value: 33.851
	- type: ndcg_at_1
	value: 22.55
	- type: ndcg_at_10
	value: 41.969
	- type: ndcg_at_100
	value: 47.576
	- type: ndcg_at_1000
	value: 48.731
	- type: ndcg_at_3
	value: 33.894000000000005
	- type: ndcg_at_5
	value: 38.133
	- type: precision_at_1
	value: 22.55
	- type: precision_at_10
	value: 6.660000000000001
	- type: precision_at_100
	value: 0.946
	- type: precision_at_1000
	value: 0.104
	- type: precision_at_3
	value: 14.532
	- type: precision_at_5
	value: 10.865
	- type: recall_at_1
	value: 21.931
	- type: recall_at_10
	value: 63.841
	- type: recall_at_100
	value: 89.47699999999999
	- type: recall_at_1000
	value: 98.259
	- type: recall_at_3
	value: 42.063
	- type: recall_at_5
	value: 52.21
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (en)
	config: en
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 93.03921568627452
	- type: f1
	value: 92.56400672314416
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (en)
	config: en
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 63.515731874145
	- type: f1
	value: 44.922310875523216
	- task:
	type: Classification
	dataset:
	type: masakhane/masakhanews
	name: MTEB MasakhaNEWSClassification (eng)
	config: eng
	split: test
	revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
	metrics:
	- type: accuracy
	value: 77.57383966244727
	- type: f1
	value: 76.55222378218293
	- task:
	type: Clustering
	dataset:
	type: masakhane/masakhanews
	name: MTEB MasakhaNEWSClusteringP2P (eng)
	config: eng
	split: test
	revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
	metrics:
	- type: v_measure
	value: 62.74836240280833
	- task:
	type: Clustering
	dataset:
	type: masakhane/masakhanews
	name: MTEB MasakhaNEWSClusteringS2S (eng)
	config: eng
	split: test
	revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
	metrics:
	- type: v_measure
	value: 24.414348715238184
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (en)
	config: en
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 66.54673839946201
	- type: f1
	value: 64.61004101532164
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (en)
	config: en
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 73.11365164761264
	- type: f1
	value: 72.01684013680978
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-p2p
	name: MTEB MedrxivClusteringP2P
	config: default
	split: test
	revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
	metrics:
	- type: v_measure
	value: 31.123671999617297
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-s2s
	name: MTEB MedrxivClusteringS2S
	config: default
	split: test
	revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
	metrics:
	- type: v_measure
	value: 26.72684341430875
	- task:
	type: Reranking
	dataset:
	type: mteb/mind_small
	name: MTEB MindSmallReranking
	config: default
	split: test
	revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
	metrics:
	- type: map
	value: 29.910228061734816
	- type: mrr
	value: 30.835255982532477
	- task:
	type: Retrieval
	dataset:
	type: mteb/nfcorpus
	name: MTEB NFCorpus
	config: default
	split: test
	revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
	metrics:
	- type: map_at_1
	value: 5.6770000000000005
	- type: map_at_10
	value: 13.15
	- type: map_at_100
	value: 16.205
	- type: map_at_1000
	value: 17.580000000000002
	- type: map_at_3
	value: 9.651
	- type: map_at_5
	value: 11.142000000000001
	- type: mrr_at_1
	value: 47.678
	- type: mrr_at_10
	value: 56.257000000000005
	- type: mrr_at_100
	value: 56.708000000000006
	- type: mrr_at_1000
	value: 56.751
	- type: mrr_at_3
	value: 54.128
	- type: mrr_at_5
	value: 55.181000000000004
	- type: ndcg_at_1
	value: 45.511
	- type: ndcg_at_10
	value: 35.867
	- type: ndcg_at_100
	value: 31.566
	- type: ndcg_at_1000
	value: 40.077
	- type: ndcg_at_3
	value: 41.9
	- type: ndcg_at_5
	value: 39.367999999999995
	- type: precision_at_1
	value: 47.678
	- type: precision_at_10
	value: 26.842
	- type: precision_at_100
	value: 7.991
	- type: precision_at_1000
	value: 2.0469999999999997
	- type: precision_at_3
	value: 39.938
	- type: precision_at_5
	value: 34.613
	- type: recall_at_1
	value: 5.6770000000000005
	- type: recall_at_10
	value: 17.119999999999997
	- type: recall_at_100
	value: 30.828
	- type: recall_at_1000
	value: 62.082
	- type: recall_at_3
	value: 10.456
	- type: recall_at_5
	value: 12.903999999999998
	- task:
	type: Retrieval
	dataset:
	type: mteb/nq
	name: MTEB NQ
	config: default
	split: test
	revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
	metrics:
	- type: map_at_1
	value: 39.021
	- type: map_at_10
	value: 54.976
	- type: map_at_100
	value: 55.793000000000006
	- type: map_at_1000
	value: 55.811
	- type: map_at_3
	value: 50.759
	- type: map_at_5
	value: 53.429
	- type: mrr_at_1
	value: 43.308
	- type: mrr_at_10
	value: 57.118
	- type: mrr_at_100
	value: 57.69499999999999
	- type: mrr_at_1000
	value: 57.704
	- type: mrr_at_3
	value: 53.848
	- type: mrr_at_5
	value: 55.915000000000006
	- type: ndcg_at_1
	value: 43.308
	- type: ndcg_at_10
	value: 62.33800000000001
	- type: ndcg_at_100
	value: 65.61099999999999
	- type: ndcg_at_1000
	value: 65.995
	- type: ndcg_at_3
	value: 54.723
	- type: ndcg_at_5
	value: 59.026
	- type: precision_at_1
	value: 43.308
	- type: precision_at_10
	value: 9.803
	- type: precision_at_100
	value: 1.167
	- type: precision_at_1000
	value: 0.121
	- type: precision_at_3
	value: 24.334
	- type: precision_at_5
	value: 17.144000000000002
	- type: recall_at_1
	value: 39.021
	- type: recall_at_10
	value: 82.37299999999999
	- type: recall_at_100
	value: 96.21499999999999
	- type: recall_at_1000
	value: 99.02499999999999
	- type: recall_at_3
	value: 63.031000000000006
	- type: recall_at_5
	value: 72.856
	- task:
	type: Classification
	dataset:
	type: ag_news
	name: MTEB NewsClassification
	config: default
	split: test
	revision: eb185aade064a813bc0b7f42de02595523103ca4
	metrics:
	- type: accuracy
	value: 78.03289473684211
	- type: f1
	value: 77.89323745730803
	- task:
	type: PairClassification
	dataset:
	type: GEM/opusparcus
	name: MTEB OpusparcusPC (en)
	config: en
	split: test
	revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
	metrics:
	- type: cos_sim_accuracy
	value: 99.89816700610999
	- type: cos_sim_ap
	value: 100.0
	- type: cos_sim_f1
	value: 99.9490575649516
	- type: cos_sim_precision
	value: 100.0
	- type: cos_sim_recall
	value: 99.89816700610999
	- type: dot_accuracy
	value: 99.89816700610999
	- type: dot_ap
	value: 100.0
	- type: dot_f1
	value: 99.9490575649516
	- type: dot_precision
	value: 100.0
	- type: dot_recall
	value: 99.89816700610999
	- type: euclidean_accuracy
	value: 99.89816700610999
	- type: euclidean_ap
	value: 100.0
	- type: euclidean_f1
	value: 99.9490575649516
	- type: euclidean_precision
	value: 100.0
	- type: euclidean_recall
	value: 99.89816700610999
	- type: manhattan_accuracy
	value: 99.89816700610999
	- type: manhattan_ap
	value: 100.0
	- type: manhattan_f1
	value: 99.9490575649516
	- type: manhattan_precision
	value: 100.0
	- type: manhattan_recall
	value: 99.89816700610999
	- type: max_accuracy
	value: 99.89816700610999
	- type: max_ap
	value: 100.0
	- type: max_f1
	value: 99.9490575649516
	- task:
	type: PairClassification
	dataset:
	type: paws-x
	name: MTEB PawsX (en)
	config: en
	split: test
	revision: 8a04d940a42cd40658986fdd8e3da561533a3646
	metrics:
	- type: cos_sim_accuracy
	value: 61.75000000000001
	- type: cos_sim_ap
	value: 59.578879568280385
	- type: cos_sim_f1
	value: 62.50861474844934
	- type: cos_sim_precision
	value: 45.46365914786967
	- type: cos_sim_recall
	value: 100.0
	- type: dot_accuracy
	value: 61.75000000000001
	- type: dot_ap
	value: 59.57893088951573
	- type: dot_f1
	value: 62.50861474844934
	- type: dot_precision
	value: 45.46365914786967
	- type: dot_recall
	value: 100.0
	- type: euclidean_accuracy
	value: 61.75000000000001
	- type: euclidean_ap
	value: 59.578755624671686
	- type: euclidean_f1
	value: 62.50861474844934
	- type: euclidean_precision
	value: 45.46365914786967
	- type: euclidean_recall
	value: 100.0
	- type: manhattan_accuracy
	value: 61.75000000000001
	- type: manhattan_ap
	value: 59.58504334461159
	- type: manhattan_f1
	value: 62.50861474844934
	- type: manhattan_precision
	value: 45.46365914786967
	- type: manhattan_recall
	value: 100.0
	- type: max_accuracy
	value: 61.75000000000001
	- type: max_ap
	value: 59.58504334461159
	- type: max_f1
	value: 62.50861474844934
	- task:
	type: Retrieval
	dataset:
	type: mteb/quora
	name: MTEB QuoraRetrieval
	config: default
	split: test
	revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
	metrics:
	- type: map_at_1
	value: 70.186
	- type: map_at_10
	value: 83.875
	- type: map_at_100
	value: 84.514
	- type: map_at_1000
	value: 84.53500000000001
	- type: map_at_3
	value: 80.926
	- type: map_at_5
	value: 82.797
	- type: mrr_at_1
	value: 80.82000000000001
	- type: mrr_at_10
	value: 87.068
	- type: mrr_at_100
	value: 87.178
	- type: mrr_at_1000
	value: 87.18
	- type: mrr_at_3
	value: 86.055
	- type: mrr_at_5
	value: 86.763
	- type: ndcg_at_1
	value: 80.84
	- type: ndcg_at_10
	value: 87.723
	- type: ndcg_at_100
	value: 88.98700000000001
	- type: ndcg_at_1000
	value: 89.13499999999999
	- type: ndcg_at_3
	value: 84.821
	- type: ndcg_at_5
	value: 86.441
	- type: precision_at_1
	value: 80.84
	- type: precision_at_10
	value: 13.270000000000001
	- type: precision_at_100
	value: 1.516
	- type: precision_at_1000
	value: 0.156
	- type: precision_at_3
	value: 37.013
	- type: precision_at_5
	value: 24.37
	- type: recall_at_1
	value: 70.186
	- type: recall_at_10
	value: 94.948
	- type: recall_at_100
	value: 99.223
	- type: recall_at_1000
	value: 99.932
	- type: recall_at_3
	value: 86.57000000000001
	- type: recall_at_5
	value: 91.157
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering
	name: MTEB RedditClustering
	config: default
	split: test
	revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
	metrics:
	- type: v_measure
	value: 50.24198927949519
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering-p2p
	name: MTEB RedditClusteringP2P
	config: default
	split: test
	revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
	metrics:
	- type: v_measure
	value: 61.452073078765544
	- task:
	type: Retrieval
	dataset:
	type: mteb/scidocs
	name: MTEB SCIDOCS
	config: default
	split: test
	revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
	metrics:
	- type: map_at_1
	value: 4.972
	- type: map_at_10
	value: 12.314
	- type: map_at_100
	value: 14.333000000000002
	- type: map_at_1000
	value: 14.628
	- type: map_at_3
	value: 8.972
	- type: map_at_5
	value: 10.724
	- type: mrr_at_1
	value: 24.4
	- type: mrr_at_10
	value: 35.257
	- type: mrr_at_100
	value: 36.297000000000004
	- type: mrr_at_1000
	value: 36.363
	- type: mrr_at_3
	value: 32.267
	- type: mrr_at_5
	value: 33.942
	- type: ndcg_at_1
	value: 24.4
	- type: ndcg_at_10
	value: 20.47
	- type: ndcg_at_100
	value: 28.111000000000004
	- type: ndcg_at_1000
	value: 33.499
	- type: ndcg_at_3
	value: 19.975
	- type: ndcg_at_5
	value: 17.293
	- type: precision_at_1
	value: 24.4
	- type: precision_at_10
	value: 10.440000000000001
	- type: precision_at_100
	value: 2.136
	- type: precision_at_1000
	value: 0.34299999999999997
	- type: precision_at_3
	value: 18.733
	- type: precision_at_5
	value: 15.120000000000001
	- type: recall_at_1
	value: 4.972
	- type: recall_at_10
	value: 21.157
	- type: recall_at_100
	value: 43.335
	- type: recall_at_1000
	value: 69.652
	- type: recall_at_3
	value: 11.417
	- type: recall_at_5
	value: 15.317
	- task:
	type: STS
	dataset:
	type: mteb/sickr-sts
	name: MTEB SICK-R
	config: default
	split: test
	revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
	metrics:
	- type: cos_sim_pearson
	value: 76.70295978506286
	- type: cos_sim_spearman
	value: 70.91162732446628
	- type: euclidean_pearson
	value: 73.25693688746031
	- type: euclidean_spearman
	value: 70.91162556180127
	- type: manhattan_pearson
	value: 73.27735004735767
	- type: manhattan_spearman
	value: 70.8856787022704
	- task:
	type: STS
	dataset:
	type: mteb/sts12-sts
	name: MTEB STS12
	config: default
	split: test
	revision: a0d554a64d88156834ff5ae9920b964011b16384
	metrics:
	- type: cos_sim_pearson
	value: 67.55878682646774
	- type: cos_sim_spearman
	value: 66.10824660353681
	- type: euclidean_pearson
	value: 64.93937270068541
	- type: euclidean_spearman
	value: 66.10824660353681
	- type: manhattan_pearson
	value: 64.96325555978984
	- type: manhattan_spearman
	value: 66.12052481638577
	- task:
	type: STS
	dataset:
	type: mteb/sts13-sts
	name: MTEB STS13
	config: default
	split: test
	revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
	metrics:
	- type: cos_sim_pearson
	value: 79.79979774019496
	- type: cos_sim_spearman
	value: 79.82293444619499
	- type: euclidean_pearson
	value: 79.4830436509311
	- type: euclidean_spearman
	value: 79.82293444619499
	- type: manhattan_pearson
	value: 79.49785594799296
	- type: manhattan_spearman
	value: 79.8280390479434
	- task:
	type: STS
	dataset:
	type: mteb/sts14-sts
	name: MTEB STS14
	config: default
	split: test
	revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
	metrics:
	- type: cos_sim_pearson
	value: 76.36839628231121
	- type: cos_sim_spearman
	value: 73.63809739428072
	- type: euclidean_pearson
	value: 74.93718121215906
	- type: euclidean_spearman
	value: 73.63810227650436
	- type: manhattan_pearson
	value: 74.8737197659424
	- type: manhattan_spearman
	value: 73.57534688126572
	- task:
	type: STS
	dataset:
	type: mteb/sts15-sts
	name: MTEB STS15
	config: default
	split: test
	revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
	metrics:
	- type: cos_sim_pearson
	value: 82.67482138157656
	- type: cos_sim_spearman
	value: 83.23485786963107
	- type: euclidean_pearson
	value: 82.50847772197369
	- type: euclidean_spearman
	value: 83.23485786963107
	- type: manhattan_pearson
	value: 82.48916218377576
	- type: manhattan_spearman
	value: 83.19756483500014
	- task:
	type: STS
	dataset:
	type: mteb/sts16-sts
	name: MTEB STS16
	config: default
	split: test
	revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
	metrics:
	- type: cos_sim_pearson
	value: 81.11626268793967
	- type: cos_sim_spearman
	value: 81.58184691061507
	- type: euclidean_pearson
	value: 80.65900869004938
	- type: euclidean_spearman
	value: 81.58184691061507
	- type: manhattan_pearson
	value: 80.67912306966772
	- type: manhattan_spearman
	value: 81.59957593393145
	- 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: 80.3140990821409
	- type: cos_sim_spearman
	value: 80.59196586367551
	- type: euclidean_pearson
	value: 80.73014029317672
	- type: euclidean_spearman
	value: 80.59196586367551
	- type: manhattan_pearson
	value: 80.5774325136987
	- type: manhattan_spearman
	value: 80.35102610546238
	- 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: 68.34450491529164
	- type: cos_sim_spearman
	value: 68.79451793414492
	- type: euclidean_pearson
	value: 68.75619738499324
	- type: euclidean_spearman
	value: 68.79451793414492
	- type: manhattan_pearson
	value: 68.75256119543882
	- type: manhattan_spearman
	value: 68.81836416978547
	- task:
	type: STS
	dataset:
	type: mteb/stsbenchmark-sts
	name: MTEB STSBenchmark
	config: default
	split: test
	revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
	metrics:
	- type: cos_sim_pearson
	value: 77.95580414975612
	- type: cos_sim_spearman
	value: 77.89671867168987
	- type: euclidean_pearson
	value: 77.61352097720862
	- type: euclidean_spearman
	value: 77.89671867168987
	- type: manhattan_pearson
	value: 77.65282228135632
	- type: manhattan_spearman
	value: 77.91730533156762
	- task:
	type: STS
	dataset:
	type: PhilipMay/stsb_multi_mt
	name: MTEB STSBenchmarkMultilingualSTS (en)
	config: en
	split: test
	revision: 93d57ef91790589e3ce9c365164337a8a78b7632
	metrics:
	- type: cos_sim_pearson
	value: 77.95580421496413
	- type: cos_sim_spearman
	value: 77.89671867168987
	- type: euclidean_pearson
	value: 77.61352107168794
	- type: euclidean_spearman
	value: 77.89671867168987
	- type: manhattan_pearson
	value: 77.65282237231794
	- type: manhattan_spearman
	value: 77.91730533156762
	- task:
	type: Reranking
	dataset:
	type: mteb/scidocs-reranking
	name: MTEB SciDocsRR
	config: default
	split: test
	revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
	metrics:
	- type: map
	value: 79.22928110092924
	- type: mrr
	value: 94.46700902583257
	- task:
	type: Retrieval
	dataset:
	type: mteb/scifact
	name: MTEB SciFact
	config: default
	split: test
	revision: 0228b52cf27578f30900b9e5271d331663a030d7
	metrics:
	- type: map_at_1
	value: 56.011
	- type: map_at_10
	value: 65.544
	- type: map_at_100
	value: 66.034
	- type: map_at_1000
	value: 66.065
	- type: map_at_3
	value: 63.077000000000005
	- type: map_at_5
	value: 64.354
	- type: mrr_at_1
	value: 59.0
	- type: mrr_at_10
	value: 66.74900000000001
	- type: mrr_at_100
	value: 67.176
	- type: mrr_at_1000
	value: 67.203
	- type: mrr_at_3
	value: 65.056
	- type: mrr_at_5
	value: 65.956
	- type: ndcg_at_1
	value: 59.0
	- type: ndcg_at_10
	value: 69.95599999999999
	- type: ndcg_at_100
	value: 72.27
	- type: ndcg_at_1000
	value: 73.066
	- type: ndcg_at_3
	value: 65.837
	- type: ndcg_at_5
	value: 67.633
	- type: precision_at_1
	value: 59.0
	- type: precision_at_10
	value: 9.333
	- type: precision_at_100
	value: 1.053
	- type: precision_at_1000
	value: 0.11199999999999999
	- type: precision_at_3
	value: 26.0
	- type: precision_at_5
	value: 16.866999999999997
	- type: recall_at_1
	value: 56.011
	- type: recall_at_10
	value: 82.133
	- type: recall_at_100
	value: 92.767
	- type: recall_at_1000
	value: 99.0
	- type: recall_at_3
	value: 70.95
	- type: recall_at_5
	value: 75.556
	- task:
	type: PairClassification
	dataset:
	type: mteb/sprintduplicatequestions-pairclassification
	name: MTEB SprintDuplicateQuestions
	config: default
	split: test
	revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
	metrics:
	- type: cos_sim_accuracy
	value: 99.81584158415842
	- type: cos_sim_ap
	value: 94.67482871230736
	- type: cos_sim_f1
	value: 90.67201604814443
	- type: cos_sim_precision
	value: 90.94567404426559
	- type: cos_sim_recall
	value: 90.4
	- type: dot_accuracy
	value: 99.81584158415842
	- type: dot_ap
	value: 94.67482871230737
	- type: dot_f1
	value: 90.67201604814443
	- type: dot_precision
	value: 90.94567404426559
	- type: dot_recall
	value: 90.4
	- type: euclidean_accuracy
	value: 99.81584158415842
	- type: euclidean_ap
	value: 94.67482871230737
	- type: euclidean_f1
	value: 90.67201604814443
	- type: euclidean_precision
	value: 90.94567404426559
	- type: euclidean_recall
	value: 90.4
	- type: manhattan_accuracy
	value: 99.81188118811882
	- type: manhattan_ap
	value: 94.6409082219286
	- type: manhattan_f1
	value: 90.50949050949052
	- type: manhattan_precision
	value: 90.41916167664671
	- type: manhattan_recall
	value: 90.60000000000001
	- type: max_accuracy
	value: 99.81584158415842
	- type: max_ap
	value: 94.67482871230737
	- type: max_f1
	value: 90.67201604814443
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering
	name: MTEB StackExchangeClustering
	config: default
	split: test
	revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
	metrics:
	- type: v_measure
	value: 62.63494511649264
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering-p2p
	name: MTEB StackExchangeClusteringP2P
	config: default
	split: test
	revision: 815ca46b2622cec33ccafc3735d572c266efdb44
	metrics:
	- type: v_measure
	value: 37.165838327685755
	- task:
	type: Reranking
	dataset:
	type: mteb/stackoverflowdupquestions-reranking
	name: MTEB StackOverflowDupQuestions
	config: default
	split: test
	revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
	metrics:
	- type: map
	value: 51.384873075208084
	- type: mrr
	value: 52.196439181733304
	- task:
	type: Summarization
	dataset:
	type: mteb/summeval
	name: MTEB SummEval
	config: default
	split: test
	revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
	metrics:
	- type: cos_sim_pearson
	value: 32.13690355567596
	- type: cos_sim_spearman
	value: 31.38349778638125
	- type: dot_pearson
	value: 32.13689596691593
	- type: dot_spearman
	value: 31.38349778638125
	- task:
	type: Retrieval
	dataset:
	type: mteb/trec-covid
	name: MTEB TRECCOVID
	config: default
	split: test
	revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
	metrics:
	- type: map_at_1
	value: 0.26
	- type: map_at_10
	value: 2.08
	- type: map_at_100
	value: 12.598
	- type: map_at_1000
	value: 30.119
	- type: map_at_3
	value: 0.701
	- type: map_at_5
	value: 1.11
	- type: mrr_at_1
	value: 96.0
	- type: mrr_at_10
	value: 97.167
	- type: mrr_at_100
	value: 97.167
	- type: mrr_at_1000
	value: 97.167
	- type: mrr_at_3
	value: 96.667
	- type: mrr_at_5
	value: 97.167
	- type: ndcg_at_1
	value: 91.0
	- type: ndcg_at_10
	value: 81.69800000000001
	- type: ndcg_at_100
	value: 62.9
	- type: ndcg_at_1000
	value: 55.245999999999995
	- type: ndcg_at_3
	value: 86.397
	- type: ndcg_at_5
	value: 84.286
	- type: precision_at_1
	value: 96.0
	- type: precision_at_10
	value: 87.0
	- type: precision_at_100
	value: 64.86
	- type: precision_at_1000
	value: 24.512
	- type: precision_at_3
	value: 90.667
	- type: precision_at_5
	value: 88.8
	- type: recall_at_1
	value: 0.26
	- type: recall_at_10
	value: 2.238
	- type: recall_at_100
	value: 15.488
	- type: recall_at_1000
	value: 51.6
	- type: recall_at_3
	value: 0.716
	- type: recall_at_5
	value: 1.151
	- task:
	type: Retrieval
	dataset:
	type: mteb/touche2020
	name: MTEB Touche2020
	config: default
	split: test
	revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
	metrics:
	- type: map_at_1
	value: 3.376
	- type: map_at_10
	value: 13.142000000000001
	- type: map_at_100
	value: 19.763
	- type: map_at_1000
	value: 21.319
	- type: map_at_3
	value: 6.805999999999999
	- type: map_at_5
	value: 8.952
	- type: mrr_at_1
	value: 46.939
	- type: mrr_at_10
	value: 61.082
	- type: mrr_at_100
	value: 61.45
	- type: mrr_at_1000
	value: 61.468999999999994
	- type: mrr_at_3
	value: 57.483
	- type: mrr_at_5
	value: 59.931999999999995
	- type: ndcg_at_1
	value: 44.897999999999996
	- type: ndcg_at_10
	value: 32.35
	- type: ndcg_at_100
	value: 42.719
	- type: ndcg_at_1000
	value: 53.30200000000001
	- type: ndcg_at_3
	value: 37.724999999999994
	- type: ndcg_at_5
	value: 34.79
	- type: precision_at_1
	value: 46.939
	- type: precision_at_10
	value: 28.366999999999997
	- type: precision_at_100
	value: 8.429
	- type: precision_at_1000
	value: 1.557
	- type: precision_at_3
	value: 38.095
	- type: precision_at_5
	value: 33.469
	- type: recall_at_1
	value: 3.376
	- type: recall_at_10
	value: 20.164
	- type: recall_at_100
	value: 50.668
	- type: recall_at_1000
	value: 83.159
	- type: recall_at_3
	value: 8.155
	- type: recall_at_5
	value: 11.872
	- task:
	type: Classification
	dataset:
	type: mteb/toxic_conversations_50k
	name: MTEB ToxicConversationsClassification
	config: default
	split: test
	revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
	metrics:
	- type: accuracy
	value: 66.739
	- type: ap
	value: 12.17931839228834
	- type: f1
	value: 51.05383188624636
	- task:
	type: Classification
	dataset:
	type: mteb/tweet_sentiment_extraction
	name: MTEB TweetSentimentExtractionClassification
	config: default
	split: test
	revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
	metrics:
	- type: accuracy
	value: 56.72891907187323
	- type: f1
	value: 56.997614557150946
	- task:
	type: Clustering
	dataset:
	type: mteb/twentynewsgroups-clustering
	name: MTEB TwentyNewsgroupsClustering
	config: default
	split: test
	revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
	metrics:
	- type: v_measure
	value: 39.825318429345224
	- task:
	type: PairClassification
	dataset:
	type: mteb/twittersemeval2015-pairclassification
	name: MTEB TwitterSemEval2015
	config: default
	split: test
	revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
	metrics:
	- type: cos_sim_accuracy
	value: 83.65619598259522
	- type: cos_sim_ap
	value: 66.17412885183877
	- type: cos_sim_f1
	value: 63.09125656951745
	- type: cos_sim_precision
	value: 57.63858577040594
	- type: cos_sim_recall
	value: 69.68337730870712
	- type: dot_accuracy
	value: 83.65619598259522
	- type: dot_ap
	value: 66.17413621964548
	- type: dot_f1
	value: 63.09125656951745
	- type: dot_precision
	value: 57.63858577040594
	- type: dot_recall
	value: 69.68337730870712
	- type: euclidean_accuracy
	value: 83.65619598259522
	- type: euclidean_ap
	value: 66.17412836413126
	- type: euclidean_f1
	value: 63.09125656951745
	- type: euclidean_precision
	value: 57.63858577040594
	- type: euclidean_recall
	value: 69.68337730870712
	- type: manhattan_accuracy
	value: 83.5548667819038
	- type: manhattan_ap
	value: 66.07998834521334
	- type: manhattan_f1
	value: 62.96433419721092
	- type: manhattan_precision
	value: 59.14676559239509
	- type: manhattan_recall
	value: 67.30870712401055
	- type: max_accuracy
	value: 83.65619598259522
	- type: max_ap
	value: 66.17413621964548
	- type: max_f1
	value: 63.09125656951745
	- task:
	type: PairClassification
	dataset:
	type: mteb/twitterurlcorpus-pairclassification
	name: MTEB TwitterURLCorpus
	config: default
	split: test
	revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
	metrics:
	- type: cos_sim_accuracy
	value: 88.55706911941631
	- type: cos_sim_ap
	value: 85.20971331546805
	- type: cos_sim_f1
	value: 77.28446050593702
	- type: cos_sim_precision
	value: 74.16135881104033
	- type: cos_sim_recall
	value: 80.6821681552202
	- type: dot_accuracy
	value: 88.55706911941631
	- type: dot_ap
	value: 85.2097154112633
	- type: dot_f1
	value: 77.28446050593702
	- type: dot_precision
	value: 74.16135881104033
	- type: dot_recall
	value: 80.6821681552202
	- type: euclidean_accuracy
	value: 88.55706911941631
	- type: euclidean_ap
	value: 85.20971719214488
	- type: euclidean_f1
	value: 77.28446050593702
	- type: euclidean_precision
	value: 74.16135881104033
	- type: euclidean_recall
	value: 80.6821681552202
	- type: manhattan_accuracy
	value: 88.52020025614158
	- type: manhattan_ap
	value: 85.17569799117058
	- type: manhattan_f1
	value: 77.27157773040933
	- type: manhattan_precision
	value: 72.79286638077734
	- type: manhattan_recall
	value: 82.33754234678165
	- type: max_accuracy
	value: 88.55706911941631
	- type: max_ap
	value: 85.20971719214488
	- type: max_f1
	value: 77.28446050593702
	- task:
	type: Clustering
	dataset:
	type: jinaai/cities_wiki_clustering
	name: MTEB WikiCitiesClustering
	config: default
	split: test
	revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
	metrics:
	- type: v_measure
	value: 85.63474850264893
	---
	<h1 align="center">Snowflake's Arctic-embed-m-long</h1>
	<h4 align="center">
	<p>
	<a href=#news>News</a> \|
	<a href=#models>Models</a> \|
	<a href=#usage>Usage</a> \|
	<a href="#evaluation">Evaluation</a> \|
	<a href="#contact">Contact</a> \|
	<a href="#faq">FAQ</a>
	<a href="#license">License</a> \|
	<a href="#acknowledgement">Acknowledgement</a>
	<p>
	</h4>


	## News


	04/16/2024: Release the snowflake-arctic-embed family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/arctic-embed).


	## Models


	snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.


	The `snowflake-arctic-embedding` models achieve state-of-the-art performance on the MTEB/BEIR leaderboard for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.


	The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report will be available shortly.


	\| Name \| MTEB Retrieval Score (NDCG @ 10) \| Parameters (Millions) \| Embedding Dimension \|
	\| ----------------------------------------------------------------------- \| -------------------------------- \| --------------------- \| ------------------- \|
	\| [snowflake-arctic-embed-xs](https://huggingface.co./Snowflake/snowflake-arctic-embed-xs/) \| 50.15 \| 22 \| 384 \|
	\| [snowflake-arctic-embed-s](https://huggingface.co./Snowflake/snowflake-arctic-embed-s/) \| 51.98 \| 33 \| 384 \|
	\| [snowflake-arctic-embed-m](https://huggingface.co./Snowflake/snowflake-arctic-embed-m/) \| 54.90 \| 110 \| 768 \|
	\| [snowflake-arctic-embed-m-long](https://huggingface.co./Snowflake/snowflake-arctic-embed-m-long/) \| 54.83 \| 137 \| 768 \|
	\| [snowflake-arctic-embed-l](https://huggingface.co./Snowflake/snowflake-arctic-embed-l/) \| 55.98 \| 335 \| 1024 \|


	Aside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co./Snowflake/snowflake-arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below.


	\| Model Name \| MTEB Retrieval Score (NDCG @ 10) \|
	\| ------------------------------------------------------------------ \| -------------------------------- \|
	\| [snowflake-arctic-embed-l](https://huggingface.co./Snowflake/snowflake-arctic-embed-l/) \| 55.98 \|
	\| Google-gecko-text-embedding \| 55.7 \|
	\| text-embedding-3-large \| 55.44 \|
	\| Cohere-embed-english-v3.0 \| 55.00 \|
	\| bge-large-en-v1.5 \| 54.29 \|


	### [snowflake-arctic-embed-xs](https://huggingface.co./Snowflake/snowflake-arctic-embed-xs)


	This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers.


	\| Model Name \| MTEB Retrieval Score (NDCG @ 10) \|
	\| ------------------------------------------------------------------- \| -------------------------------- \|
	\| [snowflake-arctic-embed-xs](https://huggingface.co./Snowflake/snowflake-arctic-embed-xs/) \| 50.15 \|
	\| GIST-all-MiniLM-L6-v2 \| 45.12 \|
	\| gte-tiny \| 44.92 \|
	\| all-MiniLM-L6-v2 \| 41.95 \|
	\| bge-micro-v2 \| 42.56 \|


	### [snowflake-arctic-embed-s](https://huggingface.co./Snowflake/snowflake-arctic-embed-s)


	Based on the [intfloat/e5-small-unsupervised](https://huggingface.co./intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.


	\| Model Name \| MTEB Retrieval Score (NDCG @ 10) \|
	\| ------------------------------------------------------------------ \| -------------------------------- \|
	\| [snowflake-arctic-embed-s](https://huggingface.co./Snowflake/snowflake-arctic-embed-s/) \| 51.98 \|
	\| bge-small-en-v1.5 \| 51.68 \|
	\| Cohere-embed-english-light-v3.0 \| 51.34 \|
	\| text-embedding-3-small \| 51.08 \|
	\| e5-small-v2 \| 49.04 \|


	### [snowflake-arctic-embed-m](https://huggingface.co./Snowflake/snowflake-arctic-embed-m/)


	Based on the [intfloat/e5-base-unsupervised](https://huggingface.co./intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference.


	\| Model Name \| MTEB Retrieval Score (NDCG @ 10) \|
	\| ------------------------------------------------------------------ \| -------------------------------- \|
	\| [snowflake-arctic-embed-m](https://huggingface.co./Snowflake/snowflake-arctic-embed-m/) \| 54.90 \|
	\| bge-base-en-v1.5 \| 53.25 \|
	\| nomic-embed-text-v1.5 \| 53.25 \|
	\| GIST-Embedding-v0 \| 52.31 \|
	\| gte-base \| 52.31 \|

	### [snowflake-arctic-embed-m-long](https://huggingface.co./Snowflake/snowflake-arctic-embed-m-long/)


	Based on the [nomic-ai/nomic-embed-text-v1-unsupervised](https://huggingface.co./nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!


	\| Model Name \| MTEB Retrieval Score (NDCG @ 10) \|
	\| ------------------------------------------------------------------ \| -------------------------------- \|
	\| [snowflake-arctic-embed-m-long](https://huggingface.co./Snowflake/snowflake-arctic-embed-m-long/) \| 54.83 \|
	\| nomic-embed-text-v1.5 \| 53.01 \|
	\| nomic-embed-text-v1 \| 52.81 \|




	### [snowflake-arctic-embed-l](https://huggingface.co./Snowflake/snowflake-arctic-embed-l/)


	Based on the [intfloat/e5-large-unsupervised](https://huggingface.co./intfloat/e5-large-unsupervised) model, this large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience.


	\| Model Name \| MTEB Retrieval Score (NDCG @ 10) \|
	\| ------------------------------------------------------------------ \| -------------------------------- \|
	\| [snowflake-arctic-embed-l](https://huggingface.co./Snowflake/snowflake-arctic-embed-l/) \| 55.98 \|
	\| UAE-Large-V1 \| 54.66 \|
	\| bge-large-en-v1.5 \| 54.29 \|
	\| mxbai-embed-large-v1 \| 54.39 \|
	\| e5-Large-v2 \| 50.56 \|


	## Usage


	### Using Sentence Transformers

	You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below.

	```python
	from sentence_transformers import SentenceTransformer

	model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m-long", trust_remote_code=True)

	queries = ['what is snowflake?', 'Where can I get the best tacos?']
	documents = ['The Data Cloud!', 'Mexico City of Course!']

	query_embeddings = model.encode(queries, prompt_name="query")
	document_embeddings = model.encode(documents)

	scores = query_embeddings @ document_embeddings.T
	for query, query_scores in zip(queries, scores):
	doc_score_pairs = list(zip(documents, query_scores))
	doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
	# Output passages & scores
	print("Query:", query)
	for document, score in doc_score_pairs:
	print(score, document)
	```
	```
	Query: what is snowflake?
	0.46484852 The Data Cloud!
	0.3758855 Mexico City of Course!
	Query: Where can I get the best tacos?
	0.42407742 Mexico City of Course!
	0.36740506 The Data Cloud!
	```

	### Using Huggingface transformers


	You can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).



	```python
	import torch
	from transformers import AutoModel, AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-m-long')
	model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', trust_remote_code=True, add_pooling_layer=False, safe_serialization=True)
	model.eval()

	query_prefix = 'Represent this sentence for searching relevant passages: '
	queries = ['what is snowflake?', 'Where can I get the best tacos?']
	queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
	query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)

	documents = ['The Data Cloud!', 'Mexico City of Course!']
	document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)

	# Compute token embeddings
	with torch.no_grad():
	query_embeddings = model(**query_tokens)[0][:, 0]
	doument_embeddings = model(**document_tokens)[0][:, 0]


	# normalize embeddings
	query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
	doument_embeddings = torch.nn.functional.normalize(doument_embeddings, p=2, dim=1)

	scores = torch.mm(query_embeddings, doument_embeddings.transpose(0, 1))
	for query, query_scores in zip(queries, scores):
	doc_score_pairs = list(zip(documents, query_scores))
	doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
	#Output passages & scores
	print("Query:", query)
	for document, score in doc_score_pairs:
	print(score, document)
	```


	If you use the long context model with more than 2048 tokens, ensure that you initialize the model like below instead. This will use [RPE](https://arxiv.org/abs/2104.09864) to allow up to 8192 tokens.


	``` py
	model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', trust_remote_code=True, safe_serialization=True, rotary_scaling_factor=2)
	```

	### Using Transformers.js

	If you haven't already, you can install the [Transformers.js](https://huggingface.co./docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) by running:
	```bash
	npm i @xenova/transformers
	```

	You can then use the model to compute embeddings as follows:

	```js
	import { pipeline, dot } from '@xenova/transformers';

	// Create feature extraction pipeline
	const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-long', {
	quantized: false, // Comment out this line to use the quantized version
	});

	// Generate sentence embeddings
	const sentences = [
	'Represent this sentence for searching relevant passages: Where can I get the best tacos?',
	'The Data Cloud!',
	'Mexico City of Course!',
	]
	const output = await extractor(sentences, { normalize: true, pooling: 'cls' });

	// Compute similarity scores
	const [source_embeddings, ...document_embeddings ] = output.tolist();
	const similarities = document_embeddings.map(x => dot(source_embeddings, x));
	console.log(similarities); // [0.36740492125676116, 0.42407774292046635]
	```


	## FAQ


	TBD


	## Contact


	Feel free to open an issue or pull request if you have any questions or suggestions about this project.
	You also can email Daniel Campos([email protected]).


	## License


	Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge.


	## Acknowledgement


	We want to thank the open-source community, which has provided the great building blocks upon which we could make our models.
	We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible.
	We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work.
	We also thank the open-source community for producing the great models we could build on top of and making these releases possible.
	Finally, we thank the researchers who created BEIR and MTEB benchmarks.
	It is largely thanks to their tireless work to define what better looks like that we could improve model performance.