---
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-m-v2.0
results:
- dataset:
config: en-ext
name: MTEB AmazonCounterfactualClassification (en-ext)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 66.6867
- type: f1
value: 55.0373
- type: f1_weighted
value: 73.07430000000001
- type: ap
value: 18.077399999999997
- type: ap_weighted
value: 18.077399999999997
- type: main_score
value: 66.6867
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 66.194
- type: f1
value: 60.854299999999995
- type: f1_weighted
value: 69.57339999999999
- type: ap
value: 30.279099999999996
- type: ap_weighted
value: 30.279099999999996
- type: main_score
value: 66.194
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 70.3589
- type: f1
value: 70.0409
- type: f1_weighted
value: 70.0409
- type: ap
value: 64.81949999999999
- type: ap_weighted
value: 64.81949999999999
- type: main_score
value: 70.3589
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 33.766
- type: f1
value: 33.3656
- type: f1_weighted
value: 33.3656
- type: main_score
value: 33.766
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
- type: ndcg_at_1
value: 33.144
- type: ndcg_at_3
value: 47.909
- type: ndcg_at_5
value: 52.932
- type: ndcg_at_10
value: 58.011
- type: ndcg_at_20
value: 60.168
- type: ndcg_at_100
value: 60.928000000000004
- type: ndcg_at_1000
value: 61.046
- type: map_at_1
value: 33.144
- type: map_at_3
value: 44.156
- type: map_at_5
value: 46.951
- type: map_at_10
value: 49.071999999999996
- type: map_at_20
value: 49.692
- type: map_at_100
value: 49.809
- type: map_at_1000
value: 49.815
- type: recall_at_1
value: 33.144
- type: recall_at_3
value: 58.819
- type: recall_at_5
value: 70.982
- type: recall_at_10
value: 86.558
- type: recall_at_20
value: 94.879
- type: recall_at_100
value: 98.791
- type: recall_at_1000
value: 99.644
- type: precision_at_1
value: 33.144
- type: precision_at_3
value: 19.606
- type: precision_at_5
value: 14.196
- type: precision_at_10
value: 8.656
- type: precision_at_20
value: 4.744000000000001
- type: precision_at_100
value: 0.988
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 33.4993
- type: mrr_at_3
value: 44.393100000000004
- type: mrr_at_5
value: 47.131299999999996
- type: mrr_at_10
value: 49.264599999999994
- type: mrr_at_20
value: 49.8707
- type: mrr_at_100
value: 49.987700000000004
- type: mrr_at_1000
value: 49.993700000000004
- type: nauc_ndcg_at_1_max
value: -10.8287
- type: nauc_ndcg_at_1_std
value: -17.1177
- type: nauc_ndcg_at_1_diff1
value: 14.4508
- type: nauc_ndcg_at_3_max
value: -7.7004
- type: nauc_ndcg_at_3_std
value: -16.6705
- type: nauc_ndcg_at_3_diff1
value: 10.0448
- type: nauc_ndcg_at_5_max
value: -7.0436
- type: nauc_ndcg_at_5_std
value: -15.8744
- type: nauc_ndcg_at_5_diff1
value: 9.1132
- type: nauc_ndcg_at_10_max
value: -7.4729
- type: nauc_ndcg_at_10_std
value: -14.9349
- type: nauc_ndcg_at_10_diff1
value: 8.527700000000001
- type: nauc_ndcg_at_20_max
value: -6.997000000000001
- type: nauc_ndcg_at_20_std
value: -14.688399999999998
- type: nauc_ndcg_at_20_diff1
value: 9.7605
- type: nauc_ndcg_at_100_max
value: -7.5599
- type: nauc_ndcg_at_100_std
value: -15.0565
- type: nauc_ndcg_at_100_diff1
value: 10.2688
- type: nauc_ndcg_at_1000_max
value: -7.675800000000001
- type: nauc_ndcg_at_1000_std
value: -15.223500000000001
- type: nauc_ndcg_at_1000_diff1
value: 10.32
- type: nauc_map_at_1_max
value: -10.8287
- type: nauc_map_at_1_std
value: -17.1177
- type: nauc_map_at_1_diff1
value: 14.4508
- type: nauc_map_at_3_max
value: -8.5473
- type: nauc_map_at_3_std
value: -16.6674
- type: nauc_map_at_3_diff1
value: 11.1004
- type: nauc_map_at_5_max
value: -8.1927
- type: nauc_map_at_5_std
value: -16.2275
- type: nauc_map_at_5_diff1
value: 10.678600000000001
- type: nauc_map_at_10_max
value: -8.3855
- type: nauc_map_at_10_std
value: -15.8309
- type: nauc_map_at_10_diff1
value: 10.5414
- type: nauc_map_at_20_max
value: -8.277700000000001
- type: nauc_map_at_20_std
value: -15.824
- type: nauc_map_at_20_diff1
value: 10.8494
- type: nauc_map_at_100_max
value: -8.3178
- type: nauc_map_at_100_std
value: -15.848300000000002
- type: nauc_map_at_100_diff1
value: 10.9384
- type: nauc_map_at_1000_max
value: -8.319799999999999
- type: nauc_map_at_1000_std
value: -15.8522
- type: nauc_map_at_1000_diff1
value: 10.9401
- type: nauc_recall_at_1_max
value: -10.8287
- type: nauc_recall_at_1_std
value: -17.1177
- type: nauc_recall_at_1_diff1
value: 14.4508
- type: nauc_recall_at_3_max
value: -5.0587
- type: nauc_recall_at_3_std
value: -16.730800000000002
- type: nauc_recall_at_3_diff1
value: 6.8079
- type: nauc_recall_at_5_max
value: -2.6783
- type: nauc_recall_at_5_std
value: -14.5046
- type: nauc_recall_at_5_diff1
value: 3.096
- type: nauc_recall_at_10_max
value: -1.5855000000000001
- type: nauc_recall_at_10_std
value: -8.2276
- type: nauc_recall_at_10_diff1
value: -6.1741
- type: nauc_recall_at_20_max
value: 15.754299999999999
- type: nauc_recall_at_20_std
value: 8.1974
- type: nauc_recall_at_20_diff1
value: -4.9207
- type: nauc_recall_at_100_max
value: 20.4574
- type: nauc_recall_at_100_std
value: 36.3741
- type: nauc_recall_at_100_diff1
value: -7.9483
- type: nauc_recall_at_1000_max
value: 21.6023
- type: nauc_recall_at_1000_std
value: 68.7296
- type: nauc_recall_at_1000_diff1
value: -24.9261
- type: nauc_precision_at_1_max
value: -10.8287
- type: nauc_precision_at_1_std
value: -17.1177
- type: nauc_precision_at_1_diff1
value: 14.4508
- type: nauc_precision_at_3_max
value: -5.0587
- type: nauc_precision_at_3_std
value: -16.730800000000002
- type: nauc_precision_at_3_diff1
value: 6.8079
- type: nauc_precision_at_5_max
value: -2.6783
- type: nauc_precision_at_5_std
value: -14.5046
- type: nauc_precision_at_5_diff1
value: 3.096
- type: nauc_precision_at_10_max
value: -1.5855000000000001
- type: nauc_precision_at_10_std
value: -8.2276
- type: nauc_precision_at_10_diff1
value: -6.1741
- type: nauc_precision_at_20_max
value: 15.754299999999999
- type: nauc_precision_at_20_std
value: 8.1974
- type: nauc_precision_at_20_diff1
value: -4.9207
- type: nauc_precision_at_100_max
value: 20.4574
- type: nauc_precision_at_100_std
value: 36.3741
- type: nauc_precision_at_100_diff1
value: -7.9483
- type: nauc_precision_at_1000_max
value: 21.6023
- type: nauc_precision_at_1000_std
value: 68.7296
- type: nauc_precision_at_1000_diff1
value: -24.9261
- type: nauc_mrr_at_1_max
value: -11.251999999999999
- type: nauc_mrr_at_1_std
value: -17.4386
- type: nauc_mrr_at_1_diff1
value: 13.414200000000001
- type: nauc_mrr_at_3_max
value: -9.7985
- type: nauc_mrr_at_3_std
value: -16.650000000000002
- type: nauc_mrr_at_3_diff1
value: 9.5099
- type: nauc_mrr_at_5_max
value: -9.064
- type: nauc_mrr_at_5_std
value: -16.4409
- type: nauc_mrr_at_5_diff1
value: 9.4773
- type: nauc_mrr_at_10_max
value: -9.310400000000001
- type: nauc_mrr_at_10_std
value: -16.0546
- type: nauc_mrr_at_10_diff1
value: 9.2528
- type: nauc_mrr_at_20_max
value: -9.223099999999999
- type: nauc_mrr_at_20_std
value: -16.0659
- type: nauc_mrr_at_20_diff1
value: 9.5259
- type: nauc_mrr_at_100_max
value: -9.2678
- type: nauc_mrr_at_100_std
value: -16.0911
- type: nauc_mrr_at_100_diff1
value: 9.608600000000001
- type: nauc_mrr_at_1000_max
value: -9.2699
- type: nauc_mrr_at_1000_std
value: -16.095100000000002
- type: nauc_mrr_at_1000_diff1
value: 9.6099
- type: main_score
value: 58.011
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P (default)
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 44.684400000000004
- type: v_measure_std
value: 13.5064
- type: main_score
value: 44.684400000000004
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S (default)
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 35.0503
- type: v_measure_std
value: 13.9543
- type: main_score
value: 35.0503
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 60.648500000000006
- type: mrr
value: 74.528
- type: nAUC_map_max
value: 19.4239
- type: nAUC_map_std
value: 20.0729
- type: nAUC_map_diff1
value: 10.0382
- type: nAUC_mrr_max
value: 30.693199999999997
- type: nAUC_mrr_std
value: 27.1279
- type: nAUC_mrr_diff1
value: 23.0291
- type: main_score
value: 60.648500000000006
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: pearson
value: 89.5081
- type: spearman
value: 87.0568
- type: cosine_pearson
value: 89.5081
- type: cosine_spearman
value: 87.0568
- type: manhattan_pearson
value: 88.1247
- type: manhattan_spearman
value: 87.2556
- type: euclidean_pearson
value: 88.3266
- type: euclidean_spearman
value: 87.0568
- type: main_score
value: 87.0568
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 80.18180000000001
- type: f1
value: 79.5538
- type: f1_weighted
value: 79.5538
- type: main_score
value: 80.18180000000001
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P (default)
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 36.0126
- type: v_measure_std
value: 0.47019999999999995
- type: main_score
value: 36.0126
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S (default)
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 28.6331
- type: v_measure_std
value: 0.8607999999999999
- type: main_score
value: 28.6331
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval (default)
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: mteb/cqadupstack-android
metrics:
- type: ndcg_at_1
value: 45.207
- type: ndcg_at_3
value: 51.31400000000001
- type: ndcg_at_5
value: 54.093999999999994
- type: ndcg_at_10
value: 56.31
- type: ndcg_at_20
value: 58.378
- type: ndcg_at_100
value: 61.307
- type: ndcg_at_1000
value: 62.724999999999994
- type: map_at_1
value: 37.732
- type: map_at_3
value: 46.263
- type: map_at_5
value: 48.553000000000004
- type: map_at_10
value: 49.984
- type: map_at_20
value: 50.888999999999996
- type: map_at_100
value: 51.568999999999996
- type: map_at_1000
value: 51.666999999999994
- type: recall_at_1
value: 37.732
- type: recall_at_3
value: 53.736
- type: recall_at_5
value: 60.95399999999999
- type: recall_at_10
value: 68.062
- type: recall_at_20
value: 75.149
- type: recall_at_100
value: 88.075
- type: recall_at_1000
value: 96.878
- type: precision_at_1
value: 45.207
- type: precision_at_3
value: 24.368000000000002
- type: precision_at_5
value: 17.854
- type: precision_at_10
value: 10.558
- type: precision_at_20
value: 6.23
- type: precision_at_100
value: 1.614
- type: precision_at_1000
value: 0.202
- type: mrr_at_1
value: 45.2074
- type: mrr_at_3
value: 52.9804
- type: mrr_at_5
value: 54.718599999999995
- type: mrr_at_10
value: 55.5713
- type: mrr_at_20
value: 55.94
- type: mrr_at_100
value: 56.21699999999999
- type: mrr_at_1000
value: 56.2504
- type: nauc_ndcg_at_1_max
value: 43.7697
- type: nauc_ndcg_at_1_std
value: -3.9530000000000003
- type: nauc_ndcg_at_1_diff1
value: 57.75320000000001
- type: nauc_ndcg_at_3_max
value: 42.7238
- type: nauc_ndcg_at_3_std
value: -3.5654
- type: nauc_ndcg_at_3_diff1
value: 53.552299999999995
- type: nauc_ndcg_at_5_max
value: 43.115500000000004
- type: nauc_ndcg_at_5_std
value: -2.1444
- type: nauc_ndcg_at_5_diff1
value: 53.130500000000005
- type: nauc_ndcg_at_10_max
value: 43.0188
- type: nauc_ndcg_at_10_std
value: -3.1515
- type: nauc_ndcg_at_10_diff1
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value: -1.1407
- type: nauc_ndcg_at_1000_diff1
value: 53.8728
- type: nauc_map_at_1_max
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value: -5.8014
- type: nauc_map_at_1_diff1
value: 59.0186
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- type: nauc_map_at_3_std
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- type: nauc_map_at_3_diff1
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- type: nauc_map_at_5_max
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- type: nauc_map_at_5_diff1
value: 54.8805
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- type: nauc_map_at_10_diff1
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- type: nauc_map_at_20_max
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- type: nauc_map_at_20_diff1
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- type: nauc_map_at_100_max
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- type: nauc_map_at_1000_std
value: -3.4715999999999996
- type: nauc_map_at_1000_diff1
value: 55.0117
- type: nauc_recall_at_1_max
value: 36.613800000000005
- type: nauc_recall_at_1_std
value: -5.8014
- type: nauc_recall_at_1_diff1
value: 59.0186
- type: nauc_recall_at_3_max
value: 39.3588
- type: nauc_recall_at_3_std
value: -3.29
- type: nauc_recall_at_3_diff1
value: 50.1633
- type: nauc_recall_at_5_max
value: 39.7596
- type: nauc_recall_at_5_std
value: 0.4483
- type: nauc_recall_at_5_diff1
value: 47.598600000000005
- type: nauc_recall_at_10_max
value: 37.5367
- type: nauc_recall_at_10_std
value: -2.5935
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- type: nauc_recall_at_20_max
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value: -2.5774
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value: 44.099
- type: nauc_recall_at_100_max
value: 44.043
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- type: nauc_recall_at_1000_std
value: 52.512
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value: 45.2841
- type: nauc_precision_at_1_max
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- type: nauc_precision_at_1_std
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- type: nauc_precision_at_1_diff1
value: 57.75320000000001
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value: 37.486000000000004
- type: nauc_precision_at_3_std
value: -1.0619
- type: nauc_precision_at_3_diff1
value: 28.264699999999998
- type: nauc_precision_at_5_max
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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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task:
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revision: 46989137a86843e03a6195de44b09deda022eec7
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task:
type: Retrieval
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name: MTEB CQADupstackUnixRetrieval (default)
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
split: test
type: mteb/cqadupstack-unix
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task:
type: Retrieval
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config: default
name: MTEB CQADupstackWebmastersRetrieval (default)
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
split: test
type: mteb/cqadupstack-webmasters
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task:
type: Retrieval
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config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
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value: 92.08619999999999
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value: 91.8074
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value: 92.0765
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value: 92.08619999999999
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
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value: 65.2668
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value: 44.499
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value: 67.9193
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value: 65.2668
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 68.0128
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value: 64.4011
- type: f1_weighted
value: 67.4705
- type: main_score
value: 68.0128
task:
type: Classification
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config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
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value: 72.67320000000001
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value: 71.7881
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value: 72.9092
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value: 72.67320000000001
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: 31.5764
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value: 1.3743999999999998
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value: 31.5764
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: 28.006999999999998
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value: 28.006999999999998
task:
type: Clustering
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config: default
name: MTEB MindSmallReranking (default)
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
split: test
type: mteb/mind_small
metrics:
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value: 30.3039
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
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config: default
name: MTEB NQ (default)
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
metrics:
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name: MTEB QuoraRetrieval (default)
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
split: test
type: mteb/quora
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task:
type: Retrieval
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config: default
name: MTEB RedditClustering (default)
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
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task:
type: Clustering
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config: default
name: MTEB RedditClusteringP2P (default)
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
split: test
type: mteb/reddit-clustering-p2p
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task:
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config: default
name: MTEB SCIDOCS (default)
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
split: test
type: mteb/scidocs
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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
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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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- type: main_score
value: 77.4225
task:
type: STS
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config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: pearson
value: 74.46340000000001
- type: spearman
value: 72.9162
- type: cosine_pearson
value: 74.46340000000001
- type: cosine_spearman
value: 72.9162
- type: manhattan_pearson
value: 73.8079
- type: manhattan_spearman
value: 72.8704
- type: euclidean_pearson
value: 73.8244
- type: euclidean_spearman
value: 72.9162
- type: main_score
value: 72.9162
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: pearson
value: 80.1161
- type: spearman
value: 81.83200000000001
- type: cosine_pearson
value: 80.1161
- type: cosine_spearman
value: 81.83200000000001
- type: manhattan_pearson
value: 81.573
- type: manhattan_spearman
value: 81.807
- type: euclidean_pearson
value: 81.59490000000001
- type: euclidean_spearman
value: 81.83200000000001
- type: main_score
value: 81.83200000000001
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: pearson
value: 78.8244
- type: spearman
value: 81.2262
- type: cosine_pearson
value: 78.8244
- type: cosine_spearman
value: 81.2262
- type: manhattan_pearson
value: 80.6177
- type: manhattan_spearman
value: 81.1361
- type: euclidean_pearson
value: 80.7347
- type: euclidean_spearman
value: 81.2262
- type: main_score
value: 81.2262
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: 67.9751
- type: spearman
value: 68.92099999999999
- type: cosine_pearson
value: 67.9751
- type: cosine_spearman
value: 68.92099999999999
- type: manhattan_pearson
value: 68.9355
- type: manhattan_spearman
value: 68.777
- type: euclidean_pearson
value: 69.11410000000001
- type: euclidean_spearman
value: 68.92099999999999
- type: main_score
value: 68.92099999999999
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: 72.08449999999999
- type: spearman
value: 74.6931
- type: cosine_pearson
value: 72.08449999999999
- type: cosine_spearman
value: 74.6931
- type: manhattan_pearson
value: 73.52
- type: manhattan_spearman
value: 74.7097
- type: euclidean_pearson
value: 73.62180000000001
- type: euclidean_spearman
value: 74.6931
- type: main_score
value: 74.6931
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: 80.528
- type: spearman
value: 84.10459999999999
- type: cosine_pearson
value: 80.528
- type: cosine_spearman
value: 84.10459999999999
- type: manhattan_pearson
value: 83.1537
- type: manhattan_spearman
value: 84.0952
- type: euclidean_pearson
value: 83.337
- type: euclidean_spearman
value: 84.10459999999999
- type: main_score
value: 84.10459999999999
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: 49.641400000000004
- type: spearman
value: 48.9413
- type: cosine_pearson
value: 49.641400000000004
- type: cosine_spearman
value: 48.9413
- type: manhattan_pearson
value: 51.434000000000005
- type: manhattan_spearman
value: 49.1595
- type: euclidean_pearson
value: 50.867799999999995
- type: euclidean_spearman
value: 48.9413
- type: main_score
value: 48.9413
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: 71.2577
- type: spearman
value: 73.82419999999999
- type: cosine_pearson
value: 71.2577
- type: cosine_spearman
value: 73.82419999999999
- type: manhattan_pearson
value: 71.9329
- type: manhattan_spearman
value: 73.4651
- type: euclidean_pearson
value: 72.2771
- type: euclidean_spearman
value: 73.82419999999999
- type: main_score
value: 73.82419999999999
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: 64.1562
- type: spearman
value: 64.8766
- type: cosine_pearson
value: 64.1562
- type: cosine_spearman
value: 64.8766
- type: manhattan_pearson
value: 64.16579999999999
- type: manhattan_spearman
value: 64.1931
- type: euclidean_pearson
value: 64.6169
- type: euclidean_spearman
value: 64.8766
- type: main_score
value: 64.8766
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: 42.257400000000004
- type: spearman
value: 43.2176
- type: cosine_pearson
value: 42.257400000000004
- type: cosine_spearman
value: 43.2176
- type: manhattan_pearson
value: 43.5359
- type: manhattan_spearman
value: 42.4143
- type: euclidean_pearson
value: 43.6717
- type: euclidean_spearman
value: 43.2176
- type: main_score
value: 43.2176
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: 74.0088
- type: spearman
value: 75.8687
- type: cosine_pearson
value: 74.0088
- type: cosine_spearman
value: 75.8687
- type: manhattan_pearson
value: 74.8505
- type: manhattan_spearman
value: 75.6101
- type: euclidean_pearson
value: 75.1303
- type: euclidean_spearman
value: 75.8687
- type: main_score
value: 75.8687
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: 68.0842
- type: spearman
value: 69.4346
- type: cosine_pearson
value: 68.0842
- type: cosine_spearman
value: 69.4346
- type: manhattan_pearson
value: 69.9982
- type: manhattan_spearman
value: 69.8952
- type: euclidean_pearson
value: 69.6375
- type: euclidean_spearman
value: 69.4346
- type: main_score
value: 69.4346
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: 76.3695
- type: spearman
value: 78.88730000000001
- type: cosine_pearson
value: 76.3695
- type: cosine_spearman
value: 78.88730000000001
- type: manhattan_pearson
value: 79.0721
- type: manhattan_spearman
value: 79.1151
- type: euclidean_pearson
value: 78.783
- type: euclidean_spearman
value: 78.88730000000001
- type: main_score
value: 78.88730000000001
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: 60.59139999999999
- type: spearman
value: 52.692099999999996
- type: cosine_pearson
value: 60.59139999999999
- type: cosine_spearman
value: 52.692099999999996
- type: manhattan_pearson
value: 64.66499999999999
- type: manhattan_spearman
value: 53.09009999999999
- type: euclidean_pearson
value: 64.5541
- type: euclidean_spearman
value: 52.692099999999996
- type: main_score
value: 52.692099999999996
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: 77.8405
- type: spearman
value: 76.6188
- type: cosine_pearson
value: 77.8405
- type: cosine_spearman
value: 76.6188
- type: manhattan_pearson
value: 76.6598
- type: manhattan_spearman
value: 76.3583
- type: euclidean_pearson
value: 77.1442
- type: euclidean_spearman
value: 76.6188
- type: main_score
value: 76.6188
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 69.8017
- type: spearman
value: 68.7734
- type: cosine_pearson
value: 69.8017
- type: cosine_spearman
value: 68.7734
- type: manhattan_pearson
value: 70.6884
- type: manhattan_spearman
value: 68.2974
- type: euclidean_pearson
value: 70.7968
- type: euclidean_spearman
value: 68.7734
- type: main_score
value: 68.7734
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: pearson
value: 73.3293
- type: spearman
value: 76.00919999999999
- type: cosine_pearson
value: 73.3293
- type: cosine_spearman
value: 76.00919999999999
- type: manhattan_pearson
value: 75.0184
- type: manhattan_spearman
value: 75.8014
- type: euclidean_pearson
value: 75.2638
- type: euclidean_spearman
value: 76.00919999999999
- type: main_score
value: 76.00919999999999
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 77.3669
- type: mrr
value: 93.5985
- type: nAUC_map_max
value: 50.2355
- type: nAUC_map_std
value: 65.5401
- type: nAUC_map_diff1
value: 9.6333
- type: nAUC_mrr_max
value: 76.5201
- type: nAUC_mrr_std
value: 74.7401
- type: nAUC_mrr_diff1
value: 53.170899999999996
- type: main_score
value: 77.3669
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: ndcg_at_1
value: 61.0
- type: ndcg_at_3
value: 67.589
- type: ndcg_at_5
value: 68.948
- type: ndcg_at_10
value: 71.8
- type: ndcg_at_20
value: 72.595
- type: ndcg_at_100
value: 74.138
- type: ndcg_at_1000
value: 74.83800000000001
- type: map_at_1
value: 57.74399999999999
- type: map_at_3
value: 64.866
- type: map_at_5
value: 66.018
- type: map_at_10
value: 67.535
- type: map_at_20
value: 67.77
- type: map_at_100
value: 68.011
- type: map_at_1000
value: 68.042
- type: recall_at_1
value: 57.74399999999999
- type: recall_at_3
value: 71.906
- type: recall_at_5
value: 75.344
- type: recall_at_10
value: 83.2
- type: recall_at_20
value: 86.26700000000001
- type: recall_at_100
value: 94.333
- type: recall_at_1000
value: 99.667
- type: precision_at_1
value: 61.0
- type: precision_at_3
value: 26.111
- type: precision_at_5
value: 16.8
- type: precision_at_10
value: 9.5
- type: precision_at_20
value: 4.933
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.11299999999999999
- type: mrr_at_1
value: 61.0
- type: mrr_at_3
value: 67.4444
- type: mrr_at_5
value: 68.0778
- type: mrr_at_10
value: 69.0483
- type: mrr_at_20
value: 69.2333
- type: mrr_at_100
value: 69.4403
- type: mrr_at_1000
value: 69.4708
- type: nauc_ndcg_at_1_max
value: 53.481500000000004
- type: nauc_ndcg_at_1_std
value: 8.227
- type: nauc_ndcg_at_1_diff1
value: 72.0771
- type: nauc_ndcg_at_3_max
value: 57.0147
- type: nauc_ndcg_at_3_std
value: 5.2435
- type: nauc_ndcg_at_3_diff1
value: 68.8841
- type: nauc_ndcg_at_5_max
value: 57.4675
- type: nauc_ndcg_at_5_std
value: 8.4709
- type: nauc_ndcg_at_5_diff1
value: 67.2977
- type: nauc_ndcg_at_10_max
value: 60.3957
- type: nauc_ndcg_at_10_std
value: 11.3174
- type: nauc_ndcg_at_10_diff1
value: 67.8332
- type: nauc_ndcg_at_20_max
value: 60.3607
- type: nauc_ndcg_at_20_std
value: 11.9948
- type: nauc_ndcg_at_20_diff1
value: 68.1122
- type: nauc_ndcg_at_100_max
value: 59.5293
- type: nauc_ndcg_at_100_std
value: 11.697799999999999
- type: nauc_ndcg_at_100_diff1
value: 68.453
- type: nauc_ndcg_at_1000_max
value: 58.8931
- type: nauc_ndcg_at_1000_std
value: 10.876199999999999
- type: nauc_ndcg_at_1000_diff1
value: 68.5746
- type: nauc_map_at_1_max
value: 49.762299999999996
- type: nauc_map_at_1_std
value: -0.2785
- type: nauc_map_at_1_diff1
value: 71.9072
- type: nauc_map_at_3_max
value: 54.108599999999996
- type: nauc_map_at_3_std
value: 2.0995
- type: nauc_map_at_3_diff1
value: 69.3459
- type: nauc_map_at_5_max
value: 55.257
- type: nauc_map_at_5_std
value: 5.5776
- type: nauc_map_at_5_diff1
value: 68.3314
- type: nauc_map_at_10_max
value: 57.1506
- type: nauc_map_at_10_std
value: 7.4561
- type: nauc_map_at_10_diff1
value: 68.8482
- type: nauc_map_at_20_max
value: 57.126200000000004
- type: nauc_map_at_20_std
value: 7.6833
- type: nauc_map_at_20_diff1
value: 68.9132
- type: nauc_map_at_100_max
value: 56.9874
- type: nauc_map_at_100_std
value: 7.7405
- type: nauc_map_at_100_diff1
value: 68.9371
- type: nauc_map_at_1000_max
value: 56.959199999999996
- type: nauc_map_at_1000_std
value: 7.709499999999999
- type: nauc_map_at_1000_diff1
value: 68.9444
- type: nauc_recall_at_1_max
value: 49.762299999999996
- type: nauc_recall_at_1_std
value: -0.2785
- type: nauc_recall_at_1_diff1
value: 71.9072
- type: nauc_recall_at_3_max
value: 58.22580000000001
- type: nauc_recall_at_3_std
value: 2.3135
- type: nauc_recall_at_3_diff1
value: 65.5868
- type: nauc_recall_at_5_max
value: 60.4096
- type: nauc_recall_at_5_std
value: 11.7662
- type: nauc_recall_at_5_diff1
value: 61.5815
- type: nauc_recall_at_10_max
value: 72.74629999999999
- type: nauc_recall_at_10_std
value: 22.148
- type: nauc_recall_at_10_diff1
value: 62.2401
- type: nauc_recall_at_20_max
value: 74.9625
- type: nauc_recall_at_20_std
value: 28.1358
- type: nauc_recall_at_20_diff1
value: 63.240700000000004
- type: nauc_recall_at_100_max
value: 79.15910000000001
- type: nauc_recall_at_100_std
value: 39.4162
- type: nauc_recall_at_100_diff1
value: 65.733
- type: nauc_recall_at_1000_max
value: 100.0
- type: nauc_recall_at_1000_std
value: 72.2222
- type: nauc_recall_at_1000_diff1
value: 72.2222
- type: nauc_precision_at_1_max
value: 53.481500000000004
- type: nauc_precision_at_1_std
value: 8.227
- type: nauc_precision_at_1_diff1
value: 72.0771
- type: nauc_precision_at_3_max
value: 55.675799999999995
- type: nauc_precision_at_3_std
value: 23.9615
- type: nauc_precision_at_3_diff1
value: 48.1199
- type: nauc_precision_at_5_max
value: 50.503299999999996
- type: nauc_precision_at_5_std
value: 36.9259
- type: nauc_precision_at_5_diff1
value: 31.769399999999997
- type: nauc_precision_at_10_max
value: 45.4878
- type: nauc_precision_at_10_std
value: 44.0469
- type: nauc_precision_at_10_diff1
value: 16.666900000000002
- type: nauc_precision_at_20_max
value: 40.2908
- type: nauc_precision_at_20_std
value: 47.330600000000004
- type: nauc_precision_at_20_diff1
value: 11.0043
- type: nauc_precision_at_100_max
value: 27.4643
- type: nauc_precision_at_100_std
value: 53.0014
- type: nauc_precision_at_100_diff1
value: -4.8238
- type: nauc_precision_at_1000_max
value: 15.755099999999999
- type: nauc_precision_at_1000_std
value: 56.634499999999996
- type: nauc_precision_at_1000_diff1
value: -21.124100000000002
- type: nauc_mrr_at_1_max
value: 53.481500000000004
- type: nauc_mrr_at_1_std
value: 8.227
- type: nauc_mrr_at_1_diff1
value: 72.0771
- type: nauc_mrr_at_3_max
value: 57.6662
- type: nauc_mrr_at_3_std
value: 9.2816
- type: nauc_mrr_at_3_diff1
value: 69.8276
- type: nauc_mrr_at_5_max
value: 57.6565
- type: nauc_mrr_at_5_std
value: 10.422099999999999
- type: nauc_mrr_at_5_diff1
value: 69.0964
- type: nauc_mrr_at_10_max
value: 58.000099999999996
- type: nauc_mrr_at_10_std
value: 10.957600000000001
- type: nauc_mrr_at_10_diff1
value: 69.0098
- type: nauc_mrr_at_20_max
value: 58.0066
- type: nauc_mrr_at_20_std
value: 11.0139
- type: nauc_mrr_at_20_diff1
value: 69.1278
- type: nauc_mrr_at_100_max
value: 57.9072
- type: nauc_mrr_at_100_std
value: 10.9621
- type: nauc_mrr_at_100_diff1
value: 69.1925
- type: nauc_mrr_at_1000_max
value: 57.87949999999999
- type: nauc_mrr_at_1000_std
value: 10.934199999999999
- type: nauc_mrr_at_1000_diff1
value: 69.2004
- type: main_score
value: 71.8
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: similarity_accuracy
value: 99.8248
- type: similarity_accuracy_threshold
value: 74.6155
- type: similarity_f1
value: 91.12780000000001
- type: similarity_f1_threshold
value: 74.2422
- type: similarity_precision
value: 91.3568
- type: similarity_recall
value: 90.9
- type: similarity_ap
value: 96.00319999999999
- type: cosine_accuracy
value: 99.8248
- type: cosine_accuracy_threshold
value: 74.6155
- type: cosine_f1
value: 91.12780000000001
- type: cosine_f1_threshold
value: 74.2422
- type: cosine_precision
value: 91.3568
- type: cosine_recall
value: 90.9
- type: cosine_ap
value: 96.00319999999999
- type: manhattan_accuracy
value: 99.8257
- type: manhattan_accuracy_threshold
value: 1574.1653
- type: manhattan_f1
value: 91.1531
- type: manhattan_f1_threshold
value: 1595.7924
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value: 90.6126
- type: manhattan_recall
value: 91.7
- type: manhattan_ap
value: 95.9848
- type: euclidean_accuracy
value: 99.8248
- type: euclidean_accuracy_threshold
value: 71.2523
- type: euclidean_f1
value: 91.12780000000001
- type: euclidean_f1_threshold
value: 71.7744
- type: euclidean_precision
value: 91.3568
- type: euclidean_recall
value: 90.9
- type: euclidean_ap
value: 96.00319999999999
- type: dot_accuracy
value: 99.8248
- type: dot_accuracy_threshold
value: 74.6155
- type: dot_f1
value: 91.12780000000001
- type: dot_f1_threshold
value: 74.2422
- type: dot_precision
value: 91.3568
- type: dot_recall
value: 90.9
- type: dot_ap
value: 96.00319999999999
- type: max_accuracy
value: 99.8257
- type: max_f1
value: 91.1531
- type: max_precision
value: 91.3568
- type: max_recall
value: 91.7
- type: max_ap
value: 96.00319999999999
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value: 96.00319999999999
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: 61.3985
- type: v_measure_std
value: 5.2151000000000005
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value: 61.3985
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: 36.1433
- type: v_measure_std
value: 1.5853
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value: 36.1433
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions (default)
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
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value: 51.221399999999996
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value: 10.1311
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value: 6.239999999999999
- type: nAUC_map_diff1
value: 36.3486
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value: 6.7909
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value: 36.5536
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value: 50.47580000000001
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
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- type: cosine_pearson
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value: 29.391099999999998
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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- type: mrr_at_1000
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- type: nauc_precision_at_3_std
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- type: nauc_precision_at_5_std
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- type: nauc_precision_at_100_std
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- type: nauc_precision_at_1000_diff1
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- type: nauc_mrr_at_3_max
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value: 25.0584
- type: nauc_mrr_at_5_max
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- type: nauc_mrr_at_20_max
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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: ndcg_at_5
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- type: ndcg_at_10
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- type: ndcg_at_100
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- type: ndcg_at_1000
value: 51.937
- type: map_at_1
value: 2.64
- type: map_at_3
value: 6.428000000000001
- type: map_at_5
value: 8.792
- type: map_at_10
value: 11.882
- type: map_at_20
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- type: map_at_100
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- type: recall_at_1
value: 2.64
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- type: recall_at_5
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- type: recall_at_10
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- type: recall_at_20
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- type: recall_at_100
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- type: recall_at_1000
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- type: precision_at_1
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- type: precision_at_3
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- type: precision_at_5
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- type: precision_at_10
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- type: precision_at_20
value: 19.898
- type: precision_at_100
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- type: precision_at_1000
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- type: mrr_at_1
value: 36.7347
- type: mrr_at_3
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- type: mrr_at_5
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- type: mrr_at_10
value: 55.8868
- type: mrr_at_20
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: nauc_ndcg_at_1_max
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- type: nauc_ndcg_at_1_std
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task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification (default)
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 64.8096
- type: f1
value: 49.844300000000004
- type: f1_weighted
value: 72.5251
- type: ap
value: 11.7519
- type: ap_weighted
value: 11.7519
- type: main_score
value: 64.8096
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification (default)
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 58.1692
- type: f1
value: 58.4408
- type: f1_weighted
value: 57.565599999999996
- type: main_score
value: 58.1692
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering (default)
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 39.293
- type: v_measure_std
value: 1.5684
- type: main_score
value: 39.293
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015 (default)
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: similarity_accuracy
value: 83.29260000000001
- type: similarity_accuracy_threshold
value: 78.2732
- type: similarity_f1
value: 60.656600000000005
- type: similarity_f1_threshold
value: 73.4961
- type: similarity_precision
value: 59.007
- type: similarity_recall
value: 62.4011
- type: similarity_ap
value: 64.7501
- type: cosine_accuracy
value: 83.29260000000001
- type: cosine_accuracy_threshold
value: 78.2732
- type: cosine_f1
value: 60.656600000000005
- type: cosine_f1_threshold
value: 73.4961
- type: cosine_precision
value: 59.007
- type: cosine_recall
value: 62.4011
- type: cosine_ap
value: 64.7501
- type: manhattan_accuracy
value: 83.2986
- type: manhattan_accuracy_threshold
value: 1476.7148
- type: manhattan_f1
value: 60.7459
- type: manhattan_f1_threshold
value: 1607.9180000000001
- type: manhattan_precision
value: 59.0581
- type: manhattan_recall
value: 62.53300000000001
- type: manhattan_ap
value: 64.76859999999999
- type: euclidean_accuracy
value: 83.29260000000001
- type: euclidean_accuracy_threshold
value: 65.9194
- type: euclidean_f1
value: 60.656600000000005
- type: euclidean_f1_threshold
value: 72.8065
- type: euclidean_precision
value: 59.007
- type: euclidean_recall
value: 62.4011
- type: euclidean_ap
value: 64.7501
- type: dot_accuracy
value: 83.29260000000001
- type: dot_accuracy_threshold
value: 78.2731
- type: dot_f1
value: 60.656600000000005
- type: dot_f1_threshold
value: 73.4961
- type: dot_precision
value: 59.007
- type: dot_recall
value: 62.4011
- type: dot_ap
value: 64.7501
- type: max_accuracy
value: 83.2986
- type: max_f1
value: 60.7459
- type: max_precision
value: 59.0581
- type: max_recall
value: 62.53300000000001
- type: max_ap
value: 64.76859999999999
- type: main_score
value: 64.76859999999999
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus (default)
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: similarity_accuracy
value: 89.0247
- type: similarity_accuracy_threshold
value: 69.271
- type: similarity_f1
value: 78.24419999999999
- type: similarity_f1_threshold
value: 66.2183
- type: similarity_precision
value: 76.616
- type: similarity_recall
value: 79.943
- type: similarity_ap
value: 85.9494
- type: cosine_accuracy
value: 89.0247
- type: cosine_accuracy_threshold
value: 69.271
- type: cosine_f1
value: 78.24419999999999
- type: cosine_f1_threshold
value: 66.2183
- type: cosine_precision
value: 76.616
- type: cosine_recall
value: 79.943
- type: cosine_ap
value: 85.9494
- type: manhattan_accuracy
value: 89.0267
- type: manhattan_accuracy_threshold
value: 1750.3544000000002
- type: manhattan_f1
value: 78.2188
- type: manhattan_f1_threshold
value: 1837.7304
- type: manhattan_precision
value: 75.1472
- type: manhattan_recall
value: 81.5522
- type: manhattan_ap
value: 85.9496
- type: euclidean_accuracy
value: 89.0247
- type: euclidean_accuracy_threshold
value: 78.3951
- type: euclidean_f1
value: 78.24419999999999
- type: euclidean_f1_threshold
value: 82.197
- type: euclidean_precision
value: 76.616
- type: euclidean_recall
value: 79.943
- type: euclidean_ap
value: 85.9494
- type: dot_accuracy
value: 89.0247
- type: dot_accuracy_threshold
value: 69.271
- type: dot_f1
value: 78.24419999999999
- type: dot_f1_threshold
value: 66.2183
- type: dot_precision
value: 76.616
- type: dot_recall
value: 79.943
- type: dot_ap
value: 85.9494
- type: max_accuracy
value: 89.0267
- type: max_f1
value: 78.24419999999999
- type: max_precision
value: 76.616
- type: max_recall
value: 81.5522
- type: max_ap
value: 85.9496
- type: main_score
value: 85.9496
task:
type: PairClassification
---
Snowflake's Arctic-embed-m-v2.0
News |
Models |
Usage |
Evaluation |
Contact |
FAQ
License |
Acknowledgement
## News
- 12/11/2024: Release of [Technical Report](https://arxiv.org/abs/2412.04506)
- 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-m-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 113m 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. Long Context Support: arctic-embed-m-v2.0 builds on [GTE-multilingual-base](https://huggingface.co./Alibaba-NLP/gte-multilingual-base) 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-m-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-m-v2.0** | 305M | 113M | 768 | **55.4** | 55.2 | **51.7** | **53.9** |
| snowflake-arctic-m | 109M | 86M | 768 | 54.9 | 24.9 | 34.4 | 29.1 |
| 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. By leveraging vector truncation via MRL to decrease vector size by 3x with about 3% degradation 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-m-v2.0 | 768 | 55.4 | N/A | 55.2 | N/A | 51.7 | N/A | 53.9 | N/A |
| snowflake-arctic-m-v2.0 | 256 | 54.4 | -1.81% | 54.0 | -2.17% | 50.6 | -2.13% | 52.3 | -3.06% |
## Usage
### Using Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
# Load the model
model_name = 'Snowflake/snowflake-arctic-embed-m-v2.0'
model = SentenceTransformer(model_name, trust_remote_code=True)
# 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-m-v2.0'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name, add_pooling_layer=False, trust_remote_code=True)
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)
```
### 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');
// 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); // [0.32719788157046004, 0.06960141111667434]
```
## 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.