metadata
model-index:
- name: stella_en_400M_v5
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
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value: 92.35820895522387
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value: 70.81322736988783
- type: ap_weighted
value: 70.81322736988783
- type: f1
value: 88.9505466159595
- type: f1_weighted
value: 92.68630932872613
- type: main_score
value: 92.35820895522387
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
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value: 97.1945
- type: ap
value: 96.08192192244094
- type: ap_weighted
value: 96.08192192244094
- type: f1
value: 97.1936887167346
- type: f1_weighted
value: 97.1936887167346
- type: main_score
value: 97.1945
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
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- type: f1
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- type: f1_weighted
value: 59.21016819840188
- type: main_score
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task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
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- type: recall_at_1
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task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: main_score
value: 55.1564333205451
- type: v_measure
value: 55.1564333205451
- type: v_measure_std
value: 14.696883012214512
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: main_score
value: 49.823698316694795
- type: v_measure
value: 49.823698316694795
- type: v_measure_std
value: 14.951660654298186
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: main_score
value: 66.15294503553424
- type: map
value: 66.15294503553424
- type: mrr
value: 78.53438420612935
- type: nAUC_map_diff1
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value: 15.590272897361238
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value: 34.96072022474653
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task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
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value: 85.7824546319275
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- type: manhattan_spearman
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- type: pearson
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- type: spearman
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task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
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value: 89.30194805194806
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- type: f1_weighted
value: 89.26182507266391
- type: main_score
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task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
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- type: v_measure
value: 50.67972171889736
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task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
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task:
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- dataset:
config: default
name: MTEB CQADupstackRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: mteb/cqadupstack
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name: MTEB HotpotQA
revision: ab518f4d6fcca38d87c25209f94beba119d02014
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type: mteb/hotpotqa
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task:
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config: default
name: MTEB ImdbClassification
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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task:
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config: default
name: MTEB MSMARCO
revision: c5a29a104738b98a9e76336939199e264163d4a0
split: dev
type: mteb/msmarco
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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
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task:
type: Classification
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config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
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task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
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task:
type: Classification
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config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
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task:
type: Classification
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config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
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task:
type: Clustering
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config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
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task:
type: Clustering
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config: default
name: MTEB MindSmallReranking
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
split: test
type: mteb/mind_small
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task:
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config: default
name: MTEB NFCorpus
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
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task:
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config: default
name: MTEB NQ
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
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task:
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name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
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task:
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name: MTEB SICK-R
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value: 84.15902154703083
- type: manhattan_spearman
value: 82.19506027155957
- type: pearson
value: 86.57284584320382
- type: spearman
value: 82.20531642680812
task:
type: STS
- dataset:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cosine_pearson
value: 86.28047602146931
- type: cosine_spearman
value: 79.51504881448884
- type: euclidean_pearson
value: 83.10545189967856
- type: euclidean_spearman
value: 79.50586960492797
- type: main_score
value: 79.51504881448884
- type: manhattan_pearson
value: 83.44244457500889
- type: manhattan_spearman
value: 79.730303339846
- type: pearson
value: 86.28047602146931
- type: spearman
value: 79.51504881448884
task:
type: STS
- dataset:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cosine_pearson
value: 88.74723553048702
- type: cosine_spearman
value: 89.18936052329725
- type: euclidean_pearson
value: 88.90400878928668
- type: euclidean_spearman
value: 89.19174821431281
- type: main_score
value: 89.18936052329725
- type: manhattan_pearson
value: 88.81504628424054
- type: manhattan_spearman
value: 89.18063294142597
- type: pearson
value: 88.74723553048702
- type: spearman
value: 89.18936052329725
task:
type: STS
- dataset:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cosine_pearson
value: 86.45403437836023
- type: cosine_spearman
value: 85.14654611519086
- type: euclidean_pearson
value: 85.87509624462743
- type: euclidean_spearman
value: 85.1391108856681
- type: main_score
value: 85.14654611519086
- type: manhattan_pearson
value: 85.96635794953866
- type: manhattan_spearman
value: 85.3271371527667
- type: pearson
value: 86.45403437836023
- type: spearman
value: 85.14654611519086
task:
type: STS
- dataset:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cosine_pearson
value: 87.84742260009705
- type: cosine_spearman
value: 89.10215217191254
- type: euclidean_pearson
value: 88.97393286325477
- type: euclidean_spearman
value: 89.1014105509662
- type: main_score
value: 89.10215217191254
- type: manhattan_pearson
value: 89.31698781090151
- type: manhattan_spearman
value: 89.53000001764433
- type: pearson
value: 87.84742260009705
- type: spearman
value: 89.10215217191254
task:
type: STS
- dataset:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cosine_pearson
value: 85.22397535461835
- type: cosine_spearman
value: 87.14066355879785
- type: euclidean_pearson
value: 86.31393364087295
- type: euclidean_spearman
value: 87.14018892702765
- type: main_score
value: 87.14066355879785
- type: manhattan_pearson
value: 86.36366855248434
- type: manhattan_spearman
value: 87.20858630423012
- type: pearson
value: 85.22397535461835
- type: spearman
value: 87.14066355879785
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 90.66131612061355
- type: cosine_spearman
value: 90.97082650129164
- type: euclidean_pearson
value: 90.98181906744969
- type: euclidean_spearman
value: 90.99008476850047
- type: main_score
value: 90.97082650129164
- type: manhattan_pearson
value: 90.75245040709021
- type: manhattan_spearman
value: 90.6199877691265
- type: pearson
value: 90.66131612061355
- type: spearman
value: 90.97082650129164
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 67.270656447085
- type: cosine_spearman
value: 67.82870469746828
- type: euclidean_pearson
value: 69.03857775285664
- type: euclidean_spearman
value: 67.74455108773341
- type: main_score
value: 67.82870469746828
- type: manhattan_pearson
value: 69.25304172245812
- type: manhattan_spearman
value: 68.00987097916055
- type: pearson
value: 67.270656447085
- type: spearman
value: 67.82870469746828
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 87.17245205384889
- type: cosine_spearman
value: 87.7360146030987
- type: euclidean_pearson
value: 87.48919412794656
- type: euclidean_spearman
value: 87.7312047878383
- type: main_score
value: 87.7360146030987
- type: manhattan_pearson
value: 87.61476224354806
- type: manhattan_spearman
value: 87.95220889254693
- type: pearson
value: 87.17245205384889
- type: spearman
value: 87.7360146030987
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: main_score
value: 88.43547871921146
- type: map
value: 88.43547871921146
- type: mrr
value: 96.5564473652709
- type: nAUC_map_diff1
value: -13.66029392579231
- type: nAUC_map_max
value: 50.325613574053506
- type: nAUC_map_std
value: 60.02986231275796
- type: nAUC_mrr_diff1
value: 23.83821476411125
- type: nAUC_mrr_max
value: 86.72643311769906
- type: nAUC_mrr_std
value: 72.12741063469213
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: main_score
value: 78.233
- type: map_at_1
value: 61.49400000000001
- type: map_at_10
value: 73.30600000000001
- type: map_at_100
value: 73.719
- type: map_at_1000
value: 73.724
- type: map_at_20
value: 73.611
- type: map_at_3
value: 70.626
- type: map_at_5
value: 72.417
- type: mrr_at_1
value: 64.66666666666666
- type: mrr_at_10
value: 74.30357142857143
- type: mrr_at_100
value: 74.56950898079988
- type: mrr_at_1000
value: 74.57295833098681
- type: mrr_at_20
value: 74.46165223665226
- type: mrr_at_3
value: 72.3888888888889
- type: mrr_at_5
value: 73.60555555555557
- type: nauc_map_at_1000_diff1
value: 76.51524604780636
- type: nauc_map_at_1000_max
value: 53.48521938401881
- type: nauc_map_at_1000_std
value: -7.347799382158861
- type: nauc_map_at_100_diff1
value: 76.5122888096236
- type: nauc_map_at_100_max
value: 53.49221847471618
- type: nauc_map_at_100_std
value: -7.329683735681086
- type: nauc_map_at_10_diff1
value: 76.30928630674504
- type: nauc_map_at_10_max
value: 53.00102977185941
- type: nauc_map_at_10_std
value: -7.7467740085108705
- type: nauc_map_at_1_diff1
value: 79.54189281784247
- type: nauc_map_at_1_max
value: 46.630071622109526
- type: nauc_map_at_1_std
value: -14.395943134644112
- type: nauc_map_at_20_diff1
value: 76.41604361947962
- type: nauc_map_at_20_max
value: 53.578883876146875
- type: nauc_map_at_20_std
value: -7.403103451288041
- type: nauc_map_at_3_diff1
value: 76.25911617571941
- type: nauc_map_at_3_max
value: 49.140287380513605
- type: nauc_map_at_3_std
value: -11.35992449218983
- type: nauc_map_at_5_diff1
value: 76.35122077770336
- type: nauc_map_at_5_max
value: 52.1744367901208
- type: nauc_map_at_5_std
value: -7.85753955055384
- type: nauc_mrr_at_1000_diff1
value: 76.97223309515867
- type: nauc_mrr_at_1000_max
value: 57.263787498613326
- type: nauc_mrr_at_1000_std
value: -4.884090708840035
- type: nauc_mrr_at_100_diff1
value: 76.97312970894603
- type: nauc_mrr_at_100_max
value: 57.26850730446478
- type: nauc_mrr_at_100_std
value: -4.875200894216617
- type: nauc_mrr_at_10_diff1
value: 76.65927674223613
- type: nauc_mrr_at_10_max
value: 57.30979763941454
- type: nauc_mrr_at_10_std
value: -4.863331094022142
- type: nauc_mrr_at_1_diff1
value: 80.0454932568644
- type: nauc_mrr_at_1_max
value: 56.76038421319305
- type: nauc_mrr_at_1_std
value: -4.101939392632653
- type: nauc_mrr_at_20_diff1
value: 76.87237970440503
- type: nauc_mrr_at_20_max
value: 57.33843605225869
- type: nauc_mrr_at_20_std
value: -4.96248984417978
- type: nauc_mrr_at_3_diff1
value: 76.74130186666727
- type: nauc_mrr_at_3_max
value: 56.19313244846155
- type: nauc_mrr_at_3_std
value: -5.684365934009136
- type: nauc_mrr_at_5_diff1
value: 76.66406918799962
- type: nauc_mrr_at_5_max
value: 57.56110093228628
- type: nauc_mrr_at_5_std
value: -3.7464413085588073
- type: nauc_ndcg_at_1000_diff1
value: 76.19194173971773
- type: nauc_ndcg_at_1000_max
value: 55.57464600170693
- type: nauc_ndcg_at_1000_std
value: -6.0761689532372625
- type: nauc_ndcg_at_100_diff1
value: 76.14631273843654
- type: nauc_ndcg_at_100_max
value: 55.72246565373382
- type: nauc_ndcg_at_100_std
value: -5.595160698860595
- type: nauc_ndcg_at_10_diff1
value: 75.0108223611192
- type: nauc_ndcg_at_10_max
value: 55.27894212877493
- type: nauc_ndcg_at_10_std
value: -6.968331740214591
- type: nauc_ndcg_at_1_diff1
value: 80.0454932568644
- type: nauc_ndcg_at_1_max
value: 56.76038421319305
- type: nauc_ndcg_at_1_std
value: -4.101939392632653
- type: nauc_ndcg_at_20_diff1
value: 75.54887755702472
- type: nauc_ndcg_at_20_max
value: 56.406879417251496
- type: nauc_ndcg_at_20_std
value: -6.495231061329629
- type: nauc_ndcg_at_3_diff1
value: 75.03620356688509
- type: nauc_ndcg_at_3_max
value: 52.147381077773424
- type: nauc_ndcg_at_3_std
value: -8.448005688956199
- type: nauc_ndcg_at_5_diff1
value: 75.1195898074229
- type: nauc_ndcg_at_5_max
value: 54.2321033861173
- type: nauc_ndcg_at_5_std
value: -5.882690780895338
- type: nauc_precision_at_1000_diff1
value: -28.081979732100532
- type: nauc_precision_at_1000_max
value: 35.055348014832916
- type: nauc_precision_at_1000_std
value: 59.61280468927384
- type: nauc_precision_at_100_diff1
value: -25.112740730587458
- type: nauc_precision_at_100_max
value: 38.26331300116496
- type: nauc_precision_at_100_std
value: 62.46316222328831
- type: nauc_precision_at_10_diff1
value: -2.6766206473658833
- type: nauc_precision_at_10_max
value: 45.95321867204845
- type: nauc_precision_at_10_std
value: 45.07212468670564
- type: nauc_precision_at_1_diff1
value: 80.0454932568644
- type: nauc_precision_at_1_max
value: 56.76038421319305
- type: nauc_precision_at_1_std
value: -4.101939392632653
- type: nauc_precision_at_20_diff1
value: -10.698911116738385
- type: nauc_precision_at_20_max
value: 43.467275950182994
- type: nauc_precision_at_20_std
value: 48.00467321991766
- type: nauc_precision_at_3_diff1
value: 33.6344708541193
- type: nauc_precision_at_3_max
value: 49.309242331670504
- type: nauc_precision_at_3_std
value: 21.02940391379915
- type: nauc_precision_at_5_diff1
value: 13.560415600596318
- type: nauc_precision_at_5_max
value: 48.918726500100085
- type: nauc_precision_at_5_std
value: 39.940930429172184
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_100_diff1
value: 70.82166199813196
- type: nauc_recall_at_100_max
value: 76.6106442577042
- type: nauc_recall_at_100_std
value: 66.47992530345513
- type: nauc_recall_at_10_diff1
value: 62.68908885556092
- type: nauc_recall_at_10_max
value: 58.14262437741839
- type: nauc_recall_at_10_std
value: -12.946717875063369
- type: nauc_recall_at_1_diff1
value: 79.54189281784247
- type: nauc_recall_at_1_max
value: 46.630071622109526
- type: nauc_recall_at_1_std
value: -14.395943134644112
- type: nauc_recall_at_20_diff1
value: 65.79470497876567
- type: nauc_recall_at_20_max
value: 71.68308183488456
- type: nauc_recall_at_20_std
value: -12.556850697268453
- type: nauc_recall_at_3_diff1
value: 68.3240211318129
- type: nauc_recall_at_3_max
value: 45.05998217275036
- type: nauc_recall_at_3_std
value: -14.23179772593869
- type: nauc_recall_at_5_diff1
value: 67.53366869904056
- type: nauc_recall_at_5_max
value: 53.57935627081027
- type: nauc_recall_at_5_std
value: -3.3271112904853393
- type: ndcg_at_1
value: 64.667
- type: ndcg_at_10
value: 78.233
- type: ndcg_at_100
value: 79.806
- type: ndcg_at_1000
value: 79.92099999999999
- type: ndcg_at_20
value: 79.006
- type: ndcg_at_3
value: 74.018
- type: ndcg_at_5
value: 76.334
- type: precision_at_1
value: 64.667
- type: precision_at_10
value: 10.4
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.383
- type: precision_at_3
value: 29.444
- type: precision_at_5
value: 19.467000000000002
- type: recall_at_1
value: 61.49400000000001
- type: recall_at_10
value: 92.156
- type: recall_at_100
value: 99.167
- type: recall_at_1000
value: 100
- type: recall_at_20
value: 94.833
- type: recall_at_3
value: 80.833
- type: recall_at_5
value: 86.6
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cosine_accuracy
value: 99.8039603960396
- type: cosine_accuracy_threshold
value: 84.54211950302124
- type: cosine_ap
value: 95.59056372734358
- type: cosine_f1
value: 90.1394422310757
- type: cosine_f1_threshold
value: 84.54211950302124
- type: cosine_precision
value: 89.78174603174604
- type: cosine_recall
value: 90.5
- type: dot_accuracy
value: 99.80594059405941
- type: dot_accuracy_threshold
value: 85.57180166244507
- type: dot_ap
value: 95.53453431914399
- type: dot_f1
value: 90.10442565887618
- type: dot_f1_threshold
value: 84.59715843200684
- type: dot_precision
value: 89.61424332344214
- type: dot_recall
value: 90.60000000000001
- type: euclidean_accuracy
value: 99.8039603960396
- type: euclidean_accuracy_threshold
value: 53.253382444381714
- type: euclidean_ap
value: 95.5850992402159
- type: euclidean_f1
value: 90.09457441513192
- type: euclidean_f1_threshold
value: 55.725520849227905
- type: euclidean_precision
value: 89.69276511397423
- type: euclidean_recall
value: 90.5
- type: main_score
value: 95.7485189884476
- type: manhattan_accuracy
value: 99.81485148514851
- type: manhattan_accuracy_threshold
value: 3491.29638671875
- type: manhattan_ap
value: 95.7485189884476
- type: manhattan_f1
value: 90.464048954615
- type: manhattan_f1_threshold
value: 3491.29638671875
- type: manhattan_precision
value: 92.2996878251821
- type: manhattan_recall
value: 88.7
- type: max_ap
value: 95.7485189884476
- type: max_f1
value: 90.464048954615
- type: max_precision
value: 92.2996878251821
- type: max_recall
value: 90.60000000000001
- type: similarity_accuracy
value: 99.8039603960396
- type: similarity_accuracy_threshold
value: 84.54211950302124
- type: similarity_ap
value: 95.59056372734358
- type: similarity_f1
value: 90.1394422310757
- type: similarity_f1_threshold
value: 84.54211950302124
- type: similarity_precision
value: 89.78174603174604
- type: similarity_recall
value: 90.5
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: main_score
value: 78.49205191950675
- type: v_measure
value: 78.49205191950675
- type: v_measure_std
value: 2.84869550699959
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: main_score
value: 48.90421736513028
- type: v_measure
value: 48.90421736513028
- type: v_measure_std
value: 1.6875865714471023
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: main_score
value: 52.9874730481696
- type: map
value: 52.9874730481696
- type: mrr
value: 53.85867604617604
- type: nAUC_map_diff1
value: 39.633429293407616
- type: nAUC_map_max
value: 10.236807988858546
- type: nAUC_map_std
value: 10.276522217929674
- type: nAUC_mrr_diff1
value: 40.0543079218377
- type: nAUC_mrr_max
value: 10.96209807382042
- type: nAUC_mrr_std
value: 10.524400196109918
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cosine_pearson
value: 30.727801109114232
- type: cosine_spearman
value: 31.66058223980157
- type: dot_pearson
value: 30.78818248622866
- type: dot_spearman
value: 31.525158776890265
- type: main_score
value: 31.66058223980157
- type: pearson
value: 30.727801109114232
- type: spearman
value: 31.66058223980157
task:
type: Summarization
- dataset:
config: default
name: MTEB TRECCOVID
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
split: test
type: mteb/trec-covid
metrics:
- type: main_score
value: 85.206
- type: map_at_1
value: 0.246
- type: map_at_10
value: 2.1950000000000003
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task:
type: Retrieval
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config: default
name: MTEB ToxicConversationsClassification
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
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task:
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name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
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task:
type: Classification
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config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
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task:
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name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
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type: mteb/twittersemeval2015-pairclassification
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value: 83.84793996810913
- type: dot_ap
value: 80.18603932881858
- type: dot_f1
value: 73.07643714466204
- type: dot_f1_threshold
value: 80.87586164474487
- type: dot_precision
value: 70.10909090909091
- type: dot_recall
value: 76.3060686015831
- type: euclidean_accuracy
value: 88.1385229778864
- type: euclidean_accuracy_threshold
value: 56.77661895751953
- type: euclidean_ap
value: 80.1784070881624
- type: euclidean_f1
value: 73.04830369529574
- type: euclidean_f1_threshold
value: 61.91838979721069
- type: euclidean_precision
value: 69.96859144720948
- type: euclidean_recall
value: 76.41160949868075
- type: main_score
value: 80.18603932881858
- type: manhattan_accuracy
value: 88.0431543184121
- type: manhattan_accuracy_threshold
value: 3755.6137084960938
- type: manhattan_ap
value: 79.98270453664578
- type: manhattan_f1
value: 72.68242015061023
- type: manhattan_f1_threshold
value: 3892.494583129883
- type: manhattan_precision
value: 71.54907975460122
- type: manhattan_recall
value: 73.85224274406332
- type: max_ap
value: 80.18603932881858
- type: max_f1
value: 73.07643714466204
- type: max_precision
value: 71.54907975460122
- type: max_recall
value: 76.56992084432717
- type: similarity_accuracy
value: 88.1385229778864
- type: similarity_accuracy_threshold
value: 83.86307954788208
- type: similarity_ap
value: 80.17965893449055
- type: similarity_f1
value: 73.0614300100705
- type: similarity_f1_threshold
value: 80.7942807674408
- type: similarity_precision
value: 69.8603755416466
- type: similarity_recall
value: 76.56992084432717
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cosine_accuracy
value: 89.7892653393876
- type: cosine_accuracy_threshold
value: 79.69566583633423
- type: cosine_ap
value: 87.4579867302024
- type: cosine_f1
value: 79.91620843152658
- type: cosine_f1_threshold
value: 78.53609323501587
- type: cosine_precision
value: 77.7155329210622
- type: cosine_recall
value: 82.24514936864799
- type: dot_accuracy
value: 89.78732487289945
- type: dot_accuracy_threshold
value: 80.05315661430359
- type: dot_ap
value: 87.44916182456272
- type: dot_f1
value: 79.90419878751591
- type: dot_f1_threshold
value: 78.57890725135803
- type: dot_precision
value: 77.73409057812728
- type: dot_recall
value: 82.19895287958116
- type: euclidean_accuracy
value: 89.78538440641131
- type: euclidean_accuracy_threshold
value: 62.29925751686096
- type: euclidean_ap
value: 87.45904868911386
- type: euclidean_f1
value: 79.93127404474657
- type: euclidean_f1_threshold
value: 65.61101078987122
- type: euclidean_precision
value: 77.62060210373595
- type: euclidean_recall
value: 82.38373883584848
- type: main_score
value: 87.46554314325058
- type: manhattan_accuracy
value: 89.76597974152986
- type: manhattan_accuracy_threshold
value: 3988.5299682617188
- type: manhattan_ap
value: 87.46554314325058
- type: manhattan_f1
value: 79.97181740645973
- type: manhattan_f1_threshold
value: 4235.905838012695
- type: manhattan_precision
value: 77.13713427283783
- type: manhattan_recall
value: 83.02279026793964
- type: max_ap
value: 87.46554314325058
- type: max_f1
value: 79.97181740645973
- type: max_precision
value: 77.73409057812728
- type: max_recall
value: 83.02279026793964
- type: similarity_accuracy
value: 89.7892653393876
- type: similarity_accuracy_threshold
value: 79.69566583633423
- type: similarity_ap
value: 87.4579867302024
- type: similarity_f1
value: 79.91620843152658
- type: similarity_f1_threshold
value: 78.53609323501587
- type: similarity_precision
value: 77.7155329210622
- type: similarity_recall
value: 82.24514936864799
task:
type: PairClassification
tags:
- mteb
- sentence-transformers
- transformers
- sentence-similarity
- transformers.js
license: mit
Updates
New open-source models and ToDoList will be listed on https://github.com/DunZhang/Stella/blob/main/news_and_todo.md.
You can also find these models on my homepage.
Introduction
The models are trained based on Alibaba-NLP/gte-large-en-v1.5 and Alibaba-NLP/gte-Qwen2-1.5B-instruct. Thanks for
their contributions!
We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.
Prompt of s2p task(e.g. retrieve task):
Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: {query}
Prompt of s2s task(e.g. semantic textual similarity task):
Instruct: Retrieve semantically similar text.\nQuery: {query}
The models are finally trained by MRL, so they have multiple dimensions: 512, 768, 1024, 2048, 4096, 6144 and 8192.
The higher the dimension, the better the performance. Generally speaking, 1024d is good enough. The MTEB score of 1024d is only 0.001 lower than 8192d.
Model directory structure
The model directory structure is very simple, it is a standard SentenceTransformer directory with a series
of 2_Dense_{dims}
folders, where dims represents the final vector dimension.
For example, the 2_Dense_256 folder stores Linear weights that convert vector dimensions to 256 dimensions.
Please refer to the following chapters for specific instructions on how to use them.
Usage
You can use SentenceTransformers or transformers library to encode text.
Sentence Transformers
from sentence_transformers import SentenceTransformer
# This model supports two prompts: "s2p_query" and "s2s_query" for sentence-to-passage and sentence-to-sentence tasks, respectively.
# They are defined in `config_sentence_transformers.json`
prompt_name = "s2p_query"
queries = [
"What are some ways to reduce stress?",
"What are the benefits of drinking green tea?",
]
# docs do not need any prompts
docs = [
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
# ！The default dimension is 1024, if you need other dimensions, please clone the model and modify `modules.json` to replace `2_Dense_1024` with another dimension, e.g. `2_Dense_256` or `2_Dense_8192` !
model = SentenceTransformer("infgrad/stella_en_400M_v5", trust_remote_code=True).cuda()
query_embeddings = model.encode(queries, prompt_name=query_prompt_name)
doc_embeddings = model.encode(docs)
print(query_embeddings.shape, doc_embeddings.shape)
# (2, 1024) (2, 1024)
similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.8398, 0.2990],
# [0.3282, 0.8095]])
Transformers
import os
import torch
from transformers import AutoModel, AutoTokenizer
from sklearn.preprocessing import normalize
query_prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "
queries = [
"What are some ways to reduce stress?",
"What are the benefits of drinking green tea?",
]
queries = [query_prompt + query for query in queries]
# docs do not need any prompts
docs = [
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
# The path of your model after cloning it
model_dir = "{Your MODEL_PATH}"
vector_dim = 1024
vector_linear_directory = f"2_Dense_{vector_dim}"
model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
vector_linear = torch.nn.Linear(in_features=model.config.hidden_size, out_features=vector_dim)
vector_linear_dict = {
k.replace("linear.", ""): v for k, v in
torch.load(os.path.join(model_dir, f"{vector_linear_directory}/pytorch_model.bin")).items()
}
vector_linear.load_state_dict(vector_linear_dict)
vector_linear.cuda()
# Embed the queries
with torch.no_grad():
input_data = tokenizer(queries, padding="longest", truncation=True, max_length=512, return_tensors="pt")
input_data = {k: v.cuda() for k, v in input_data.items()}
attention_mask = input_data["attention_mask"]
last_hidden_state = model(**input_data)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
query_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
query_vectors = normalize(vector_linear(query_vectors).cpu().numpy())
# Embed the documents
with torch.no_grad():
input_data = tokenizer(docs, padding="longest", truncation=True, max_length=512, return_tensors="pt")
input_data = {k: v.cuda() for k, v in input_data.items()}
attention_mask = input_data["attention_mask"]
last_hidden_state = model(**input_data)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
docs_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
docs_vectors = normalize(vector_linear(docs_vectors).cpu().numpy())
print(query_vectors.shape, docs_vectors.shape)
# (2, 1024) (2, 1024)
similarities = query_vectors @ docs_vectors.T
print(similarities)
# [[0.8397531 0.29900077]
# [0.32818374 0.80954516]]
Transformers.js
You can also use the transformers.js library to run the model directly in JavaScript (in-browser, Node.js, Deno, etc.)!
If you haven't already, you can install the Transformers.js JavaScript library (v3) using:
npm i xenova/transformers.js#v3
Then, you can use the model as follows:
import { AutoModel, AutoTokenizer, matmul } from '@xenova/transformers';
// Load model and tokenizer
const model_id = 'dunzhang/stella_en_400M_v5';
const model = await AutoModel.from_pretrained(model_id);
const tokenizer = await AutoTokenizer.from_pretrained(model_id);
// Embed the queries
const query_prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: ";
const queries = [
"What are some ways to reduce stress?",
"What are the benefits of drinking green tea?",
].map(x => query_prompt + x);
const query_inputs = tokenizer(queries, { padding: true, truncation: true });
const query_outputs = await model(query_inputs);
const normalized_query_vectors = query_outputs.sentence_embedding.normalize();
// Embed the documents
const docs = [
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
];
const docs_inputs = tokenizer(docs, { padding: true, truncation: true });
const docs_outputs = await model(docs_inputs);
const normalized_docs_vectors = docs_outputs.sentence_embedding.normalize();
// Compute similarity scores
const similarities = await matmul(normalized_query_vectors, normalized_docs_vectors.transpose(1, 0));
console.log(similarities.tolist());
// [
// [ 0.768531322479248, 0.3589509129524231 ],
// [ 0.37918326258659363, 0.8047795295715332 ]
// ]
FAQ
Q: The details of training?
A: The training method and datasets will be released in the future. (specific time unknown, may be provided in a paper)
Q: How to choose a suitable prompt for my own task?
A: In most cases, please use the s2p and s2s prompts. These two prompts account for the vast majority of the training data.
Q: How to reproduce MTEB results?
A: Please use evaluation scripts in Alibaba-NLP/gte-Qwen2-1.5B-instruct or intfloat/e5-mistral-7b-instruct
Q: Why each dimension has a linear weight?
A: MRL has multiple training methods, we choose this method which has the best performance.
Q: What is the sequence length of models?
A: 512 is recommended, in our experiments, almost all models perform poorly on specialized long text retrieval datasets. Besides, the model is trained on datasets of 512 length. This may be an optimization term.
If you have any questions, please start a discussion on community.