---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js
license: apache-2.0
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ky
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- pa
- pl
- pt
- qu
- ro
- ru
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- yo
- zh
model-index:
- name: snowflake-arctic-embed-l-v2.0
results:
- dataset:
config: en-ext
name: MTEB AmazonCounterfactualClassification (en-ext)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 67.039
- type: f1
value: 55.1806
- type: f1_weighted
value: 73.41149999999999
- type: ap
value: 17.9914
- type: ap_weighted
value: 17.9914
- type: main_score
value: 67.039
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 65.59700000000001
- type: f1
value: 60.244299999999996
- type: f1_weighted
value: 68.9975
- type: ap
value: 29.762100000000004
- type: ap_weighted
value: 29.762100000000004
- type: main_score
value: 65.59700000000001
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 74.2565
- type: f1
value: 74.0291
- type: f1_weighted
value: 74.0291
- type: ap
value: 68.7595
- type: ap_weighted
value: 68.7595
- type: main_score
value: 74.2565
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 34.946
- type: f1
value: 34.2853
- type: f1_weighted
value: 34.2853
- type: main_score
value: 34.946
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
- type: ndcg_at_1
value: 33.286
- type: ndcg_at_3
value: 49.051
- type: ndcg_at_5
value: 54.107000000000006
- type: ndcg_at_10
value: 59.146
- type: ndcg_at_20
value: 60.897999999999996
- type: ndcg_at_100
value: 61.78399999999999
- type: ndcg_at_1000
value: 61.845000000000006
- type: map_at_1
value: 33.286
- type: map_at_3
value: 45.14
- type: map_at_5
value: 47.939
- type: map_at_10
value: 50.046
- type: map_at_20
value: 50.56
- type: map_at_100
value: 50.708
- type: map_at_1000
value: 50.712
- type: recall_at_1
value: 33.286
- type: recall_at_3
value: 60.38400000000001
- type: recall_at_5
value: 72.688
- type: recall_at_10
value: 88.122
- type: recall_at_20
value: 94.808
- type: recall_at_100
value: 99.21799999999999
- type: recall_at_1000
value: 99.644
- type: precision_at_1
value: 33.286
- type: precision_at_3
value: 20.128
- type: precision_at_5
value: 14.538
- type: precision_at_10
value: 8.812000000000001
- type: precision_at_20
value: 4.74
- type: precision_at_100
value: 0.992
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 33.926
- type: mrr_at_3
value: 45.3414
- type: mrr_at_5
value: 48.1828
- type: mrr_at_10
value: 50.270700000000005
- type: mrr_at_20
value: 50.7844
- type: mrr_at_100
value: 50.9259
- type: mrr_at_1000
value: 50.9294
- type: nauc_ndcg_at_1_max
value: -10.305
- type: nauc_ndcg_at_1_std
value: -15.674199999999999
- type: nauc_ndcg_at_1_diff1
value: 18.6355
- type: nauc_ndcg_at_3_max
value: -7.744
- type: nauc_ndcg_at_3_std
value: -16.894000000000002
- type: nauc_ndcg_at_3_diff1
value: 15.4469
- type: nauc_ndcg_at_5_max
value: -6.4887
- type: nauc_ndcg_at_5_std
value: -16.1382
- type: nauc_ndcg_at_5_diff1
value: 13.8214
- type: nauc_ndcg_at_10_max
value: -7.616499999999999
- type: nauc_ndcg_at_10_std
value: -15.8073
- type: nauc_ndcg_at_10_diff1
value: 13.7678
- type: nauc_ndcg_at_20_max
value: -6.9801
- type: nauc_ndcg_at_20_std
value: -15.068699999999998
- type: nauc_ndcg_at_20_diff1
value: 14.2013
- type: nauc_ndcg_at_100_max
value: -7.5221
- type: nauc_ndcg_at_100_std
value: -15.417200000000001
- type: nauc_ndcg_at_100_diff1
value: 15.1072
- type: nauc_ndcg_at_1000_max
value: -7.6931
- type: nauc_ndcg_at_1000_std
value: -15.5367
- type: nauc_ndcg_at_1000_diff1
value: 15.001700000000001
- type: nauc_map_at_1_max
value: -10.305
- type: nauc_map_at_1_std
value: -15.674199999999999
- type: nauc_map_at_1_diff1
value: 18.6355
- type: nauc_map_at_3_max
value: -8.4505
- type: nauc_map_at_3_std
value: -16.5487
- type: nauc_map_at_3_diff1
value: 15.965599999999998
- type: nauc_map_at_5_max
value: -7.8429
- type: nauc_map_at_5_std
value: -16.1332
- type: nauc_map_at_5_diff1
value: 15.0893
- type: nauc_map_at_10_max
value: -8.3186
- type: nauc_map_at_10_std
value: -15.979399999999998
- type: nauc_map_at_10_diff1
value: 15.136199999999999
- type: nauc_map_at_20_max
value: -8.1697
- type: nauc_map_at_20_std
value: -15.8241
- type: nauc_map_at_20_diff1
value: 15.260599999999998
- type: nauc_map_at_100_max
value: -8.2285
- type: nauc_map_at_100_std
value: -15.8624
- type: nauc_map_at_100_diff1
value: 15.412600000000001
- type: nauc_map_at_1000_max
value: -8.2359
- type: nauc_map_at_1000_std
value: -15.867
- type: nauc_map_at_1000_diff1
value: 15.408
- type: nauc_recall_at_1_max
value: -10.305
- type: nauc_recall_at_1_std
value: -15.674199999999999
- type: nauc_recall_at_1_diff1
value: 18.6355
- type: nauc_recall_at_3_max
value: -5.5097
- type: nauc_recall_at_3_std
value: -17.9896
- type: nauc_recall_at_3_diff1
value: 13.9525
- type: nauc_recall_at_5_max
value: -0.9383
- type: nauc_recall_at_5_std
value: -16.035
- type: nauc_recall_at_5_diff1
value: 8.8431
- type: nauc_recall_at_10_max
value: -2.8548
- type: nauc_recall_at_10_std
value: -14.1203
- type: nauc_recall_at_10_diff1
value: 3.2265
- type: nauc_recall_at_20_max
value: 14.2043
- type: nauc_recall_at_20_std
value: 2.1298999999999997
- type: nauc_recall_at_20_diff1
value: -1.9900000000000002
- type: nauc_recall_at_100_max
value: 44.0173
- type: nauc_recall_at_100_std
value: 42.131800000000005
- type: nauc_recall_at_100_diff1
value: 29.9983
- type: nauc_recall_at_1000_max
value: 25.9434
- type: nauc_recall_at_1000_std
value: 53.9252
- type: nauc_recall_at_1000_diff1
value: -0.9778
- type: nauc_precision_at_1_max
value: -10.305
- type: nauc_precision_at_1_std
value: -15.674199999999999
- type: nauc_precision_at_1_diff1
value: 18.6355
- type: nauc_precision_at_3_max
value: -5.5097
- type: nauc_precision_at_3_std
value: -17.9896
- type: nauc_precision_at_3_diff1
value: 13.9525
- type: nauc_precision_at_5_max
value: -0.9383
- type: nauc_precision_at_5_std
value: -16.035
- type: nauc_precision_at_5_diff1
value: 8.8431
- type: nauc_precision_at_10_max
value: -2.8548
- type: nauc_precision_at_10_std
value: -14.1203
- type: nauc_precision_at_10_diff1
value: 3.2265
- type: nauc_precision_at_20_max
value: 14.2043
- type: nauc_precision_at_20_std
value: 2.1298999999999997
- type: nauc_precision_at_20_diff1
value: -1.9900000000000002
- type: nauc_precision_at_100_max
value: 44.0173
- type: nauc_precision_at_100_std
value: 42.131800000000005
- type: nauc_precision_at_100_diff1
value: 29.9983
- type: nauc_precision_at_1000_max
value: 25.9434
- type: nauc_precision_at_1000_std
value: 53.9252
- type: nauc_precision_at_1000_diff1
value: -0.9778
- type: nauc_mrr_at_1_max
value: -9.833
- type: nauc_mrr_at_1_std
value: -14.8351
- type: nauc_mrr_at_1_diff1
value: 16.7604
- type: nauc_mrr_at_3_max
value: -9.0116
- type: nauc_mrr_at_3_std
value: -16.296
- type: nauc_mrr_at_3_diff1
value: 14.178199999999999
- type: nauc_mrr_at_5_max
value: -8.308300000000001
- type: nauc_mrr_at_5_std
value: -15.751999999999999
- type: nauc_mrr_at_5_diff1
value: 13.306299999999998
- type: nauc_mrr_at_10_max
value: -8.7962
- type: nauc_mrr_at_10_std
value: -15.688099999999999
- type: nauc_mrr_at_10_diff1
value: 13.2589
- type: nauc_mrr_at_20_max
value: -8.6773
- type: nauc_mrr_at_20_std
value: -15.479499999999998
- type: nauc_mrr_at_20_diff1
value: 13.354
- type: nauc_mrr_at_100_max
value: -8.7533
- type: nauc_mrr_at_100_std
value: -15.553600000000001
- type: nauc_mrr_at_100_diff1
value: 13.4796
- type: nauc_mrr_at_1000_max
value: -8.7608
- type: nauc_mrr_at_1000_std
value: -15.5582
- type: nauc_mrr_at_1000_diff1
value: 13.4748
- type: main_score
value: 59.146
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P (default)
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 43.9715
- type: v_measure_std
value: 13.4325
- type: main_score
value: 43.9715
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S (default)
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 34.775800000000004
- type: v_measure_std
value: 13.922799999999999
- type: main_score
value: 34.775800000000004
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 63.3521
- type: mrr
value: 77.5965
- type: nAUC_map_max
value: 21.2353
- type: nAUC_map_std
value: 17.002100000000002
- type: nAUC_map_diff1
value: 3.8135000000000003
- type: nAUC_mrr_max
value: 35.058299999999996
- type: nAUC_mrr_std
value: 20.432
- type: nAUC_mrr_diff1
value: 9.2584
- type: main_score
value: 63.3521
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: pearson
value: 89.8072
- type: spearman
value: 87.2875
- type: cosine_pearson
value: 89.8072
- type: cosine_spearman
value: 87.2875
- type: manhattan_pearson
value: 87.9173
- type: manhattan_spearman
value: 86.7327
- type: euclidean_pearson
value: 88.21600000000001
- type: euclidean_spearman
value: 87.2875
- type: main_score
value: 87.2875
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 81.8149
- type: f1
value: 81.2226
- type: f1_weighted
value: 81.2226
- type: main_score
value: 81.8149
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P (default)
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 35.0927
- type: v_measure_std
value: 0.7048
- type: main_score
value: 35.0927
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S (default)
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 30.220999999999997
- type: v_measure_std
value: 1.107
- type: main_score
value: 30.220999999999997
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval (default)
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: mteb/cqadupstack-android
metrics:
- type: ndcg_at_1
value: 44.349
- type: ndcg_at_3
value: 50.109
- type: ndcg_at_5
value: 52.88699999999999
- type: ndcg_at_10
value: 55.799
- type: ndcg_at_20
value: 57.589999999999996
- type: ndcg_at_100
value: 60.539
- type: ndcg_at_1000
value: 61.897000000000006
- type: map_at_1
value: 36.230000000000004
- type: map_at_3
value: 44.929
- type: map_at_5
value: 47.191
- type: map_at_10
value: 48.88
- type: map_at_20
value: 49.685
- type: map_at_100
value: 50.327
- type: map_at_1000
value: 50.431000000000004
- type: recall_at_1
value: 36.230000000000004
- type: recall_at_3
value: 53.173
- type: recall_at_5
value: 60.35
- type: recall_at_10
value: 69.07
- type: recall_at_20
value: 75.371
- type: recall_at_100
value: 88.736
- type: recall_at_1000
value: 96.75399999999999
- type: precision_at_1
value: 44.349
- type: precision_at_3
value: 23.748
- type: precision_at_5
value: 17.368
- type: precision_at_10
value: 10.629
- type: precision_at_20
value: 6.152
- type: precision_at_100
value: 1.6150000000000002
- type: precision_at_1000
value: 0.201
- type: mrr_at_1
value: 44.3491
- type: mrr_at_3
value: 52.0744
- type: mrr_at_5
value: 53.9628
- type: mrr_at_10
value: 54.9072
- type: mrr_at_20
value: 55.19539999999999
- type: mrr_at_100
value: 55.4537
- type: mrr_at_1000
value: 55.4787
- type: nauc_ndcg_at_1_max
value: 36.404599999999995
- type: nauc_ndcg_at_1_std
value: -4.5556
- type: nauc_ndcg_at_1_diff1
value: 57.4025
- type: nauc_ndcg_at_3_max
value: 38.0347
- type: nauc_ndcg_at_3_std
value: -2.2339
- type: nauc_ndcg_at_3_diff1
value: 50.9146
- type: nauc_ndcg_at_5_max
value: 38.2927
- type: nauc_ndcg_at_5_std
value: -2.3645
- type: nauc_ndcg_at_5_diff1
value: 51.638
- type: nauc_ndcg_at_10_max
value: 38.4619
- type: nauc_ndcg_at_10_std
value: -2.8955
- type: nauc_ndcg_at_10_diff1
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- type: nauc_ndcg_at_20_max
value: 38.2122
- type: nauc_ndcg_at_20_std
value: -1.9339
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value: 50.4981
- type: nauc_ndcg_at_100_max
value: 39.380900000000004
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value: 51.5696
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value: 38.9069
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value: -0.8251
- type: nauc_ndcg_at_1000_diff1
value: 51.605500000000006
- type: nauc_map_at_1_max
value: 31.694
- type: nauc_map_at_1_std
value: -4.2857
- type: nauc_map_at_1_diff1
value: 57.991400000000006
- type: nauc_map_at_3_max
value: 36.115399999999994
- type: nauc_map_at_3_std
value: -3.9859999999999998
- type: nauc_map_at_3_diff1
value: 52.394
- type: nauc_map_at_5_max
value: 36.896499999999996
- type: nauc_map_at_5_std
value: -3.6282
- type: nauc_map_at_5_diff1
value: 52.7023
- type: nauc_map_at_10_max
value: 37.2695
- type: nauc_map_at_10_std
value: -3.7142
- type: nauc_map_at_10_diff1
value: 52.6081
- type: nauc_map_at_20_max
value: 37.4097
- type: nauc_map_at_20_std
value: -3.0479
- type: nauc_map_at_20_diff1
value: 52.2999
- type: nauc_map_at_100_max
value: 37.6608
- type: nauc_map_at_100_std
value: -2.7363999999999997
- type: nauc_map_at_100_diff1
value: 52.5068
- type: nauc_map_at_1000_max
value: 37.6406
- type: nauc_map_at_1000_std
value: -2.7695000000000003
- type: nauc_map_at_1000_diff1
value: 52.5091
- type: nauc_recall_at_1_max
value: 31.694
- type: nauc_recall_at_1_std
value: -4.2857
- type: nauc_recall_at_1_diff1
value: 57.991400000000006
- type: nauc_recall_at_3_max
value: 35.9705
- type: nauc_recall_at_3_std
value: -2.78
- type: nauc_recall_at_3_diff1
value: 44.2342
- type: nauc_recall_at_5_max
value: 36.3608
- type: nauc_recall_at_5_std
value: -1.8541999999999998
- type: nauc_recall_at_5_diff1
value: 45.0955
- type: nauc_recall_at_10_max
value: 35.7364
- type: nauc_recall_at_10_std
value: -3.2479
- type: nauc_recall_at_10_diff1
value: 42.3031
- type: nauc_recall_at_20_max
value: 34.7814
- type: nauc_recall_at_20_std
value: 0.7642
- type: nauc_recall_at_20_diff1
value: 37.3357
- type: nauc_recall_at_100_max
value: 49.1721
- type: nauc_recall_at_100_std
value: 27.8334
- type: nauc_recall_at_100_diff1
value: 39.549
- type: nauc_recall_at_1000_max
value: 59.516400000000004
- type: nauc_recall_at_1000_std
value: 66.1089
- type: nauc_recall_at_1000_diff1
value: 31.4818
- type: nauc_precision_at_1_max
value: 36.404599999999995
- type: nauc_precision_at_1_std
value: -4.5556
- type: nauc_precision_at_1_diff1
value: 57.4025
- type: nauc_precision_at_3_max
value: 35.7954
- type: nauc_precision_at_3_std
value: 0.6122
- type: nauc_precision_at_3_diff1
value: 29.4346
- type: nauc_precision_at_5_max
value: 31.322699999999998
- type: nauc_precision_at_5_std
value: 2.2124
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task:
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revision: 5003b3064772da1887988e05400cf3806fe491f2
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revision: CQADupstackRetrieval_is_a_combined_dataset
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type: CQADupstackRetrieval_is_a_combined_dataset
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task:
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revision: 46989137a86843e03a6195de44b09deda022eec7
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value: 37.8126
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task:
type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 93.5317
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value: 93.1956
- type: f1_weighted
value: 93.5431
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value: 93.5317
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 67.7907
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value: 48.2877
- type: f1_weighted
value: 70.3225
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value: 67.7907
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 71.456
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value: 68.2268
- type: f1_weighted
value: 70.4722
- type: main_score
value: 71.456
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
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value: 76.21719999999999
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value: 75.14189999999999
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value: 76.0733
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value: 76.21719999999999
task:
type: Classification
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config: default
name: MTEB MedrxivClusteringP2P (default)
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
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value: 31.3917
- type: v_measure_std
value: 1.4778
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value: 31.3917
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S (default)
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
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value: 28.2408
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value: 1.1622999999999999
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value: 28.2408
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking (default)
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
split: test
type: mteb/mind_small
metrics:
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value: 30.3081
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value: -24.9194
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value: -9.042
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value: 12.1611
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value: -19.3867
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value: -6.3873
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value: 11.8078
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value: 29.5796
task:
type: Reranking
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config: default
name: MTEB NFCorpus (default)
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
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task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ (default)
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
metrics:
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task:
type: Retrieval
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config: default
name: MTEB QuoraRetrieval (default)
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
split: test
type: mteb/quora
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task:
type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering (default)
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
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- type: v_measure_std
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value: 51.208600000000004
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P (default)
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
split: test
type: mteb/reddit-clustering-p2p
metrics:
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value: 12.0829
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value: 60.372899999999994
task:
type: Clustering
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config: default
name: MTEB SCIDOCS (default)
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
split: test
type: mteb/scidocs
metrics:
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task:
type: Retrieval
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config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
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task:
type: STS
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config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
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task:
type: STS
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config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
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value: 77.8946
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value: 80.4405
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value: 79.6856
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value: 80.1236
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value: 80.0315
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value: 80.44059999999999
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value: 80.4405
task:
type: STS
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config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
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value: 76.2196
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value: 75.10419999999999
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value: 75.4647
- type: manhattan_spearman
value: 74.81179999999999
- type: euclidean_pearson
value: 75.8091
- type: euclidean_spearman
value: 75.10419999999999
- type: main_score
value: 75.10419999999999
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: pearson
value: 81.2455
- type: spearman
value: 82.8681
- type: cosine_pearson
value: 81.2455
- type: cosine_spearman
value: 82.8681
- type: manhattan_pearson
value: 82.4327
- type: manhattan_spearman
value: 82.7513
- type: euclidean_pearson
value: 82.5635
- type: euclidean_spearman
value: 82.8681
- type: main_score
value: 82.8681
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: pearson
value: 81.6322
- type: spearman
value: 83.487
- type: cosine_pearson
value: 81.6322
- type: cosine_spearman
value: 83.487
- type: manhattan_pearson
value: 83.0048
- type: manhattan_spearman
value: 83.4064
- type: euclidean_pearson
value: 83.0938
- type: euclidean_spearman
value: 83.487
- type: main_score
value: 83.487
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 81.1124
- type: spearman
value: 84.5436
- type: cosine_pearson
value: 81.1124
- type: cosine_spearman
value: 84.5436
- type: manhattan_pearson
value: 83.5158
- type: manhattan_spearman
value: 84.596
- type: euclidean_pearson
value: 83.4429
- type: euclidean_spearman
value: 84.5436
- type: main_score
value: 84.5436
task:
type: STS
- dataset:
config: en-tr
name: MTEB STS17 (en-tr)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 62.0001
- type: spearman
value: 63.631099999999996
- type: cosine_pearson
value: 62.0001
- type: cosine_spearman
value: 63.631099999999996
- type: manhattan_pearson
value: 62.239599999999996
- type: manhattan_spearman
value: 62.892199999999995
- type: euclidean_pearson
value: 62.9809
- type: euclidean_spearman
value: 63.631099999999996
- type: main_score
value: 63.631099999999996
task:
type: STS
- dataset:
config: it-en
name: MTEB STS17 (it-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 75.1556
- type: spearman
value: 76.8807
- type: cosine_pearson
value: 75.1556
- type: cosine_spearman
value: 76.8807
- type: manhattan_pearson
value: 76.2428
- type: manhattan_spearman
value: 76.8101
- type: euclidean_pearson
value: 76.107
- type: euclidean_spearman
value: 76.8807
- type: main_score
value: 76.8807
task:
type: STS
- dataset:
config: es-en
name: MTEB STS17 (es-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 69.85719999999999
- type: spearman
value: 71.0489
- type: cosine_pearson
value: 69.85719999999999
- type: cosine_spearman
value: 71.0489
- type: manhattan_pearson
value: 71.08449999999999
- type: manhattan_spearman
value: 71.0051
- type: euclidean_pearson
value: 71.19760000000001
- type: euclidean_spearman
value: 71.0489
- type: main_score
value: 71.0489
task:
type: STS
- dataset:
config: nl-en
name: MTEB STS17 (nl-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 76.1131
- type: spearman
value: 78.2714
- type: cosine_pearson
value: 76.1131
- type: cosine_spearman
value: 78.2714
- type: manhattan_pearson
value: 76.70270000000001
- type: manhattan_spearman
value: 77.7803
- type: euclidean_pearson
value: 77.14269999999999
- type: euclidean_spearman
value: 78.2714
- type: main_score
value: 78.2714
task:
type: STS
- dataset:
config: fr-en
name: MTEB STS17 (fr-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 74.49719999999999
- type: spearman
value: 76.2747
- type: cosine_pearson
value: 74.49719999999999
- type: cosine_spearman
value: 76.2747
- type: manhattan_pearson
value: 75.071
- type: manhattan_spearman
value: 75.8969
- type: euclidean_pearson
value: 75.289
- type: euclidean_spearman
value: 76.2747
- type: main_score
value: 76.2747
task:
type: STS
- dataset:
config: en-de
name: MTEB STS17 (en-de)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 76.7073
- type: spearman
value: 79.3107
- type: cosine_pearson
value: 76.7073
- type: cosine_spearman
value: 79.3107
- type: manhattan_pearson
value: 77.9578
- type: manhattan_spearman
value: 79.3195
- type: euclidean_pearson
value: 77.7386
- type: euclidean_spearman
value: 79.3107
- type: main_score
value: 79.3107
task:
type: STS
- dataset:
config: en-ar
name: MTEB STS17 (en-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 60.5826
- type: spearman
value: 61.0502
- type: cosine_pearson
value: 60.5826
- type: cosine_spearman
value: 61.0502
- type: manhattan_pearson
value: 61.202
- type: manhattan_spearman
value: 61.2039
- type: euclidean_pearson
value: 61.1915
- type: euclidean_spearman
value: 61.0502
- type: main_score
value: 61.0502
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 69.2521
- type: spearman
value: 68.06219999999999
- type: cosine_pearson
value: 69.2521
- type: cosine_spearman
value: 68.06219999999999
- type: manhattan_pearson
value: 70.5115
- type: manhattan_spearman
value: 67.8705
- type: euclidean_pearson
value: 70.68480000000001
- type: euclidean_spearman
value: 68.06219999999999
- type: main_score
value: 68.06219999999999
task:
type: STS
- dataset:
config: pl-en
name: MTEB STS22 (pl-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 77.97500000000001
- type: spearman
value: 76.848
- type: cosine_pearson
value: 77.97500000000001
- type: cosine_spearman
value: 76.848
- type: manhattan_pearson
value: 76.4098
- type: manhattan_spearman
value: 76.6188
- type: euclidean_pearson
value: 77.17500000000001
- type: euclidean_spearman
value: 76.848
- type: main_score
value: 76.848
task:
type: STS
- dataset:
config: zh-en
name: MTEB STS22 (zh-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 71.3604
- type: spearman
value: 70.7891
- type: cosine_pearson
value: 71.3604
- type: cosine_spearman
value: 70.7891
- type: manhattan_pearson
value: 73.0185
- type: manhattan_spearman
value: 70.79299999999999
- type: euclidean_pearson
value: 73.17620000000001
- type: euclidean_spearman
value: 70.7891
- type: main_score
value: 70.7891
task:
type: STS
- dataset:
config: es-en
name: MTEB STS22 (es-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 77.58239999999999
- type: spearman
value: 78.5907
- type: cosine_pearson
value: 77.58239999999999
- type: cosine_spearman
value: 78.5907
- type: manhattan_pearson
value: 79.25720000000001
- type: manhattan_spearman
value: 78.6249
- type: euclidean_pearson
value: 79.3724
- type: euclidean_spearman
value: 78.5907
- type: main_score
value: 78.5907
task:
type: STS
- dataset:
config: de-en
name: MTEB STS22 (de-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 63.324000000000005
- type: spearman
value: 55.1099
- type: cosine_pearson
value: 63.324000000000005
- type: cosine_spearman
value: 55.1099
- type: manhattan_pearson
value: 67.3128
- type: manhattan_spearman
value: 56.340199999999996
- type: euclidean_pearson
value: 67.12089999999999
- type: euclidean_spearman
value: 55.1099
- type: main_score
value: 55.1099
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: pearson
value: 78.02329999999999
- type: spearman
value: 79.1887
- type: cosine_pearson
value: 78.02329999999999
- type: cosine_spearman
value: 79.1887
- type: manhattan_pearson
value: 78.8951
- type: manhattan_spearman
value: 78.9444
- type: euclidean_pearson
value: 79.1499
- type: euclidean_spearman
value: 79.1888
- type: main_score
value: 79.1887
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 78.7501
- type: mrr
value: 93.9748
- type: nAUC_map_max
value: 54.495599999999996
- type: nAUC_map_std
value: 70.0377
- type: nAUC_map_diff1
value: 6.0146999999999995
- type: nAUC_mrr_max
value: 81.1486
- type: nAUC_mrr_std
value: 78.3478
- type: nAUC_mrr_diff1
value: 50.7613
- type: main_score
value: 78.7501
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: ndcg_at_1
value: 58.667
- type: ndcg_at_3
value: 66.022
- type: ndcg_at_5
value: 68.508
- type: ndcg_at_10
value: 70.586
- type: ndcg_at_20
value: 71.714
- type: ndcg_at_100
value: 72.81
- type: ndcg_at_1000
value: 73.482
- type: map_at_1
value: 55.594
- type: map_at_3
value: 63.2
- type: map_at_5
value: 64.996
- type: map_at_10
value: 65.988
- type: map_at_20
value: 66.347
- type: map_at_100
value: 66.526
- type: map_at_1000
value: 66.547
- type: recall_at_1
value: 55.594
- type: recall_at_3
value: 71.22800000000001
- type: recall_at_5
value: 77.078
- type: recall_at_10
value: 83.172
- type: recall_at_20
value: 87.422
- type: recall_at_100
value: 93.167
- type: recall_at_1000
value: 98.667
- type: precision_at_1
value: 58.667
- type: precision_at_3
value: 25.778000000000002
- type: precision_at_5
value: 17.333000000000002
- type: precision_at_10
value: 9.433
- type: precision_at_20
value: 4.967
- type: precision_at_100
value: 1.06
- type: precision_at_1000
value: 0.11199999999999999
- type: mrr_at_1
value: 58.666700000000006
- type: mrr_at_3
value: 65.3889
- type: mrr_at_5
value: 66.62219999999999
- type: mrr_at_10
value: 67.3364
- type: mrr_at_20
value: 67.6046
- type: mrr_at_100
value: 67.73320000000001
- type: mrr_at_1000
value: 67.7526
- type: nauc_ndcg_at_1_max
value: 60.2511
- type: nauc_ndcg_at_1_std
value: 12.422
- type: nauc_ndcg_at_1_diff1
value: 74.4289
- type: nauc_ndcg_at_3_max
value: 60.2109
- type: nauc_ndcg_at_3_std
value: 11.0152
- type: nauc_ndcg_at_3_diff1
value: 71.0436
- type: nauc_ndcg_at_5_max
value: 62.690999999999995
- type: nauc_ndcg_at_5_std
value: 13.585700000000001
- type: nauc_ndcg_at_5_diff1
value: 70.4007
- type: nauc_ndcg_at_10_max
value: 62.740899999999996
- type: nauc_ndcg_at_10_std
value: 13.980400000000001
- type: nauc_ndcg_at_10_diff1
value: 70.0506
- type: nauc_ndcg_at_20_max
value: 62.271699999999996
- type: nauc_ndcg_at_20_std
value: 15.9756
- type: nauc_ndcg_at_20_diff1
value: 70.3237
- type: nauc_ndcg_at_100_max
value: 62.125
- type: nauc_ndcg_at_100_std
value: 15.5809
- type: nauc_ndcg_at_100_diff1
value: 70.4151
- type: nauc_ndcg_at_1000_max
value: 61.9259
- type: nauc_ndcg_at_1000_std
value: 15.3462
- type: nauc_ndcg_at_1000_diff1
value: 70.7346
- type: nauc_map_at_1_max
value: 53.6767
- type: nauc_map_at_1_std
value: 3.7751
- type: nauc_map_at_1_diff1
value: 74.60329999999999
- type: nauc_map_at_3_max
value: 57.0403
- type: nauc_map_at_3_std
value: 8.2272
- type: nauc_map_at_3_diff1
value: 71.7906
- type: nauc_map_at_5_max
value: 59.6713
- type: nauc_map_at_5_std
value: 10.8346
- type: nauc_map_at_5_diff1
value: 71.3356
- type: nauc_map_at_10_max
value: 60.0086
- type: nauc_map_at_10_std
value: 11.4394
- type: nauc_map_at_10_diff1
value: 71.14869999999999
- type: nauc_map_at_20_max
value: 59.940599999999996
- type: nauc_map_at_20_std
value: 12.0728
- type: nauc_map_at_20_diff1
value: 71.31
- type: nauc_map_at_100_max
value: 59.95589999999999
- type: nauc_map_at_100_std
value: 12.148299999999999
- type: nauc_map_at_100_diff1
value: 71.2142
- type: nauc_map_at_1000_max
value: 59.9486
- type: nauc_map_at_1000_std
value: 12.139
- type: nauc_map_at_1000_diff1
value: 71.2225
- type: nauc_recall_at_1_max
value: 53.6767
- type: nauc_recall_at_1_std
value: 3.7751
- type: nauc_recall_at_1_diff1
value: 74.60329999999999
- type: nauc_recall_at_3_max
value: 60.4078
- type: nauc_recall_at_3_std
value: 9.038300000000001
- type: nauc_recall_at_3_diff1
value: 67.60119999999999
- type: nauc_recall_at_5_max
value: 68.0179
- type: nauc_recall_at_5_std
value: 16.061600000000002
- type: nauc_recall_at_5_diff1
value: 65.54759999999999
- type: nauc_recall_at_10_max
value: 68.7372
- type: nauc_recall_at_10_std
value: 16.8637
- type: nauc_recall_at_10_diff1
value: 62.7613
- type: nauc_recall_at_20_max
value: 67.1403
- type: nauc_recall_at_20_std
value: 31.3919
- type: nauc_recall_at_20_diff1
value: 62.66929999999999
- type: nauc_recall_at_100_max
value: 68.6366
- type: nauc_recall_at_100_std
value: 32.4577
- type: nauc_recall_at_100_diff1
value: 64.52029999999999
- type: nauc_recall_at_1000_max
value: 70.7166
- type: nauc_recall_at_1000_std
value: 70.47149999999999
- type: nauc_recall_at_1000_diff1
value: 85.58590000000001
- type: nauc_precision_at_1_max
value: 60.2511
- type: nauc_precision_at_1_std
value: 12.422
- type: nauc_precision_at_1_diff1
value: 74.4289
- type: nauc_precision_at_3_max
value: 58.75280000000001
- type: nauc_precision_at_3_std
value: 27.605400000000003
- type: nauc_precision_at_3_diff1
value: 49.1523
- type: nauc_precision_at_5_max
value: 56.4694
- type: nauc_precision_at_5_std
value: 39.080799999999996
- type: nauc_precision_at_5_diff1
value: 28.8162
- type: nauc_precision_at_10_max
value: 48.8894
- type: nauc_precision_at_10_std
value: 43.8149
- type: nauc_precision_at_10_diff1
value: 15.0093
- type: nauc_precision_at_20_max
value: 41.4059
- type: nauc_precision_at_20_std
value: 50.7143
- type: nauc_precision_at_20_diff1
value: 8.3552
- type: nauc_precision_at_100_max
value: 33.5064
- type: nauc_precision_at_100_std
value: 52.8775
- type: nauc_precision_at_100_diff1
value: -5.0870999999999995
- type: nauc_precision_at_1000_max
value: 23.9064
- type: nauc_precision_at_1000_std
value: 57.784800000000004
- type: nauc_precision_at_1000_diff1
value: -20.1246
- type: nauc_mrr_at_1_max
value: 60.2511
- type: nauc_mrr_at_1_std
value: 12.422
- type: nauc_mrr_at_1_diff1
value: 74.4289
- type: nauc_mrr_at_3_max
value: 62.663199999999996
- type: nauc_mrr_at_3_std
value: 14.7348
- type: nauc_mrr_at_3_diff1
value: 72.1185
- type: nauc_mrr_at_5_max
value: 63.3871
- type: nauc_mrr_at_5_std
value: 15.773000000000001
- type: nauc_mrr_at_5_diff1
value: 71.6722
- type: nauc_mrr_at_10_max
value: 62.8474
- type: nauc_mrr_at_10_std
value: 15.1896
- type: nauc_mrr_at_10_diff1
value: 71.64110000000001
- type: nauc_mrr_at_20_max
value: 62.699400000000004
- type: nauc_mrr_at_20_std
value: 15.554499999999999
- type: nauc_mrr_at_20_diff1
value: 71.6049
- type: nauc_mrr_at_100_max
value: 62.6665
- type: nauc_mrr_at_100_std
value: 15.4586
- type: nauc_mrr_at_100_diff1
value: 71.6217
- type: nauc_mrr_at_1000_max
value: 62.6641
- type: nauc_mrr_at_1000_std
value: 15.4535
- type: nauc_mrr_at_1000_diff1
value: 71.6307
- type: main_score
value: 70.586
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: similarity_accuracy
value: 99.8416
- type: similarity_accuracy_threshold
value: 74.52069999999999
- type: similarity_f1
value: 92.008
- type: similarity_f1_threshold
value: 74.4529
- type: similarity_precision
value: 91.9162
- type: similarity_recall
value: 92.10000000000001
- type: similarity_ap
value: 96.54209999999999
- type: cosine_accuracy
value: 99.8416
- type: cosine_accuracy_threshold
value: 74.52069999999999
- type: cosine_f1
value: 92.008
- type: cosine_f1_threshold
value: 74.4529
- type: cosine_precision
value: 91.9162
- type: cosine_recall
value: 92.10000000000001
- type: cosine_ap
value: 96.54209999999999
- type: manhattan_accuracy
value: 99.8446
- type: manhattan_accuracy_threshold
value: 1784.866
- type: manhattan_f1
value: 92.1539
- type: manhattan_f1_threshold
value: 1787.6774
- type: manhattan_precision
value: 92.1079
- type: manhattan_recall
value: 92.2
- type: manhattan_ap
value: 96.5207
- type: euclidean_accuracy
value: 99.8416
- type: euclidean_accuracy_threshold
value: 71.3853
- type: euclidean_f1
value: 92.008
- type: euclidean_f1_threshold
value: 71.4803
- type: euclidean_precision
value: 91.9162
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value: 99.8416
- type: dot_accuracy_threshold
value: 74.52069999999999
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value: 92.008
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value: 74.4528
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value: 91.9162
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- type: max_accuracy
value: 99.8446
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value: 92.1539
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value: 92.1079
- type: max_recall
value: 92.2
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value: 96.54209999999999
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value: 96.54209999999999
task:
type: PairClassification
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config: default
name: MTEB StackExchangeClustering (default)
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
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value: 63.4035
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value: 4.758
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value: 63.4035
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P (default)
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
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value: 1.3107
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task:
type: Clustering
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config: default
name: MTEB StackOverflowDupQuestions (default)
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
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task:
type: Reranking
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config: default
name: MTEB SummEval (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
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task:
type: Summarization
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config: default
name: MTEB TRECCOVID (default)
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
split: test
type: mteb/trec-covid
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task:
type: Retrieval
- dataset:
config: default
name: MTEB Touche2020 (default)
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
metrics:
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- type: precision_at_1
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- type: precision_at_10
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- type: precision_at_1000
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- type: mrr_at_3
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- type: mrr_at_5
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- type: mrr_at_10
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- type: mrr_at_20
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- type: mrr_at_1000
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- type: nauc_ndcg_at_1_max
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task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification (default)
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
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- type: f1
value: 50.6764
- type: f1_weighted
value: 73.2472
- type: ap
value: 12.2658
- type: ap_weighted
value: 12.2658
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value: 65.8643
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification (default)
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
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value: 59.6633
- type: f1
value: 59.935700000000004
- type: f1_weighted
value: 59.0249
- type: main_score
value: 59.6633
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering (default)
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 43.2311
- type: v_measure_std
value: 2.3994999999999997
- type: main_score
value: 43.2311
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015 (default)
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: similarity_accuracy
value: 83.8469
- type: similarity_accuracy_threshold
value: 77.6695
- type: similarity_f1
value: 62.3159
- type: similarity_f1_threshold
value: 71.6554
- type: similarity_precision
value: 59.114599999999996
- type: similarity_recall
value: 65.8839
- type: similarity_ap
value: 67.00930000000001
- type: cosine_accuracy
value: 83.8469
- type: cosine_accuracy_threshold
value: 77.6695
- type: cosine_f1
value: 62.3159
- type: cosine_f1_threshold
value: 71.6554
- type: cosine_precision
value: 59.114599999999996
- type: cosine_recall
value: 65.8839
- type: cosine_ap
value: 67.00930000000001
- type: manhattan_accuracy
value: 83.7694
- type: manhattan_accuracy_threshold
value: 1677.8293999999999
- type: manhattan_f1
value: 62.1324
- type: manhattan_f1_threshold
value: 1848.6641
- type: manhattan_precision
value: 61.839999999999996
- type: manhattan_recall
value: 62.4274
- type: manhattan_ap
value: 66.8849
- type: euclidean_accuracy
value: 83.8469
- type: euclidean_accuracy_threshold
value: 66.8288
- type: euclidean_f1
value: 62.3159
- type: euclidean_f1_threshold
value: 75.2922
- type: euclidean_precision
value: 59.114599999999996
- type: euclidean_recall
value: 65.8839
- type: euclidean_ap
value: 67.00930000000001
- type: dot_accuracy
value: 83.8469
- type: dot_accuracy_threshold
value: 77.6695
- type: dot_f1
value: 62.3159
- type: dot_f1_threshold
value: 71.6554
- type: dot_precision
value: 59.114599999999996
- type: dot_recall
value: 65.8839
- type: dot_ap
value: 67.00930000000001
- type: max_accuracy
value: 83.8469
- type: max_f1
value: 62.3159
- type: max_precision
value: 61.839999999999996
- type: max_recall
value: 65.8839
- type: max_ap
value: 67.00930000000001
- type: main_score
value: 67.00930000000001
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus (default)
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: similarity_accuracy
value: 88.8811
- type: similarity_accuracy_threshold
value: 71.1053
- type: similarity_f1
value: 77.9005
- type: similarity_f1_threshold
value: 67.5068
- type: similarity_precision
value: 75.5609
- type: similarity_recall
value: 80.3896
- type: similarity_ap
value: 85.459
- type: cosine_accuracy
value: 88.8811
- type: cosine_accuracy_threshold
value: 71.1053
- type: cosine_f1
value: 77.9005
- type: cosine_f1_threshold
value: 67.5068
- type: cosine_precision
value: 75.5609
- type: cosine_recall
value: 80.3896
- type: cosine_ap
value: 85.459
- type: manhattan_accuracy
value: 88.8598
- type: manhattan_accuracy_threshold
value: 1928.9173
- type: manhattan_f1
value: 77.9172
- type: manhattan_f1_threshold
value: 2007.8883999999998
- type: manhattan_precision
value: 76.29310000000001
- type: manhattan_recall
value: 79.6119
- type: manhattan_ap
value: 85.4464
- type: euclidean_accuracy
value: 88.8811
- type: euclidean_accuracy_threshold
value: 76.0193
- type: euclidean_f1
value: 77.9005
- type: euclidean_f1_threshold
value: 80.6141
- type: euclidean_precision
value: 75.5609
- type: euclidean_recall
value: 80.3896
- type: euclidean_ap
value: 85.459
- type: dot_accuracy
value: 88.8811
- type: dot_accuracy_threshold
value: 71.1053
- type: dot_f1
value: 77.9005
- type: dot_f1_threshold
value: 67.5068
- type: dot_precision
value: 75.5609
- type: dot_recall
value: 80.3896
- type: dot_ap
value: 85.459
- type: max_accuracy
value: 88.8811
- type: max_f1
value: 77.9172
- type: max_precision
value: 76.29310000000001
- type: max_recall
value: 80.3896
- type: max_ap
value: 85.459
- type: main_score
value: 85.459
task:
type: PairClassification
---
<h1 align="center">Snowflake's Arctic-embed-l-v2.0</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
12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind.
## Models
Snowflake arctic-embed-l-v2.0 is the newest addition to the suite of embedding models Snowflake has released optimizing for retrieval performance and inference efficiency.
Arctic Embed 2.0 introduces a new standard for multilingual embedding models, combining high-quality multilingual text retrieval without sacrificing performance in English.
Released under the permissive Apache 2.0 license, Arctic Embed 2.0 is ideal for applications that demand reliable, enterprise-grade multilingual search and retrieval at scale.
Key Features:
1. Multilingual without compromise: Excels in English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL.
2. Inference efficiency: Its 303m non-embedding parameters inference is fast and efficient for any scale.
3. Compression-friendly: Achieves high-quality retrieval with embeddings as small as 128 bytes/vector using Matryoshka Representation Learning (MRL) and quantization-aware embedding training.
4. Drop-In Replacement: arctic-embed-l-v2.0 builds on BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) which allows direct drop-in inference replacement with any form of new libraries, kernels, inference engines etc.
5. Long Context Support: arctic-embed-l-v2.0 builds on [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) which can support a context window of up to 8192 via the use of RoPE.
### Quality Benchmarks
Unlike most other open-source models, Arctic-embed-l-v2.0 excels across English (via MTEB Retrieval) and multilingual (via MIRACL and CLEF).
You no longer need to support models to empower high-quality English and multilingual retrieval. All numbers mentioned below are the average NDCG@10 across the dataset being discussed.
| Model Name | # params | # non-emb params | # dimensions | BEIR (15) | MIRACL (4) | CLEF (Focused) | CLEF (Full) |
|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| **snowflake-arctic-l-v2.0** | 568M | 303M | 1024 | **55.6** | 55.8 | **52.9** | **54.3** |
| snowflake-arctic-m | 109M | 86M | 768 | 54.9 | 24.9 | 34.4 | 29.1 |
| snowflake-arctic-l | 335M | 303M | 1024 | 56.0 | 34.8 | 38.2 | 33.7 |
| me5 base | 560M | 303M | 1024 | 51.4 | 54.0 | 43.0 | 34.6 |
| bge-m3 (BAAI) | 568M | 303M | 1024 | 48.8 | **56.8** | 40.8 | 41.3 |
| gte (Alibaba) | 305M | 113M | 768 | 51.1 | 52.3 | 47.7 | 53.1 |
Aside from high-quality retrieval arctic delivers embeddings that are easily compressible. Leverage vector truncation via MRL to decrease vector size by 4x with less than 3% degredation in quality.
Combine MRLed vectors with vector compression (Int4) to power retrieval in 128 bytes per doc.
| Model | | BEIR (15) | Relative Performance | MIRACL (4) | Relative Performance | CLEF (5) | Relative Performance | CLEF (Full) | Relative Performance |
|---|---|:---:|:---:|:---:|:---:|:---:|---|---|---|
| snowflake-arctic-l-v2.0 | 1024 | 55.6 | N/A | 55.8 | N/A | 52.9 | N/A | 54.3 | N/A |
| snowflake-arctic-l-v2.0 | 256 | 54.3 | -0.18% | 54.3 | -2.70% | 51.9 | -1.81% | 53.4 | -1.53% |
## Usage
### Using Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
# Load the model
model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0'
model = SentenceTransformer(model_name)
# Define the queries and documents
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']
# Compute embeddings: use `prompt_name="query"` to encode queries!
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
# Compute cosine similarity scores
scores = model.similarity(query_embeddings, document_embeddings)
# Output the results
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)
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
### Using Huggingface Transformers
You can use the transformers package to use Snowflake's 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
model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name, add_pooling_layer=False)
model.eval()
query_prefix = 'query: '
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=8192)
documents = ['The Data Cloud!', 'Mexico City of Course!']
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=8192)
# Compute token embeddings
with torch.no_grad():
query_embeddings = model(**query_tokens)[0][:, 0]
document_embeddings = model(**document_tokens)[0][:, 0]
# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)
scores = torch.mm(query_embeddings, document_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)
```
This should produce the following scores
```
Query: what is snowflake?
tensor(0.2715) The Data Cloud!
tensor(0.0661) Mexico City of Course!
Query: Where can I get the best tacos?
tensor(0.2797) Mexico City of Course!
tensor(0.1250) The Data Cloud!
```
### Using Huggingface 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/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
You can then use the model for retrieval, as follows:
```js
import { pipeline, dot } from '@huggingface/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v2.0', {
dtype: 'q8',
});
// Generate sentence embeddings
const sentences = [
'query: what is snowflake?',
'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); // [0.24783534471401417, 0.05313122704326892]
```
## 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(daniel.campos@snowflake.com).
## 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.