---
license: apache-2.0
tags:
- text-embedding
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: Gouzi3618/LENS-8000
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 93.6865671641791
- type: ap
value: 74.44778735403261
- type: ap_weighted
value: 74.44778735403261
- type: f1
value: 90.57338628851295
- type: f1_weighted
value: 93.87207694461506
- type: main_score
value: 93.6865671641791
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.06832499999999
- type: ap
value: 95.71019538629211
- type: ap_weighted
value: 95.71019538629211
- type: f1
value: 97.06781792337515
- type: f1_weighted
value: 97.06781792337515
- type: main_score
value: 97.06832499999999
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 63.608
- type: f1
value: 62.41274991021244
- type: f1_weighted
value: 62.41274991021244
- type: main_score
value: 63.608
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
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- type: map_at_10
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- type: map_at_100
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- type: map_at_1000
value: 70.159
- type: map_at_20
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- type: map_at_3
value: 67.378
- type: map_at_5
value: 69.138
- type: mrr_at_1
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- type: mrr_at_10
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: mrr_at_20
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- type: mrr_at_3
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- type: mrr_at_5
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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: recall_at_1
value: 55.903000000000006
- type: recall_at_10
value: 94.879
- type: recall_at_100
value: 99.431
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 97.795
- type: recall_at_3
value: 81.57900000000001
- type: recall_at_5
value: 89.403
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P (default)
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: main_score
value: 54.809064728970625
- type: v_measure
value: 54.809064728970625
- type: v_measure_std
value: 14.497861425102215
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S (default)
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: main_score
value: 50.144159631474416
- type: v_measure
value: 50.144159631474416
- type: v_measure_std
value: 14.596959041091187
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
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value: 65.74396432331054
- type: map
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- type: mrr
value: 77.89418722244206
- type: nAUC_map_diff1
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value: 30.670095862543384
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value: 34.51981156443003
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- task:
type: STS
dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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- type: cosine_spearman
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- type: euclidean_spearman
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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: Classification
dataset:
name: MTEB Banking77Classification (default)
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
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- type: f1
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- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P (default)
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
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type: Clustering
dataset:
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type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
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- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval (default)
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
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type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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type: Classification
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name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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type: mteb/amazon_massive_scenario
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revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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value: 21.38187908816387
- type: nauc_recall_at_1_max
value: 24.99538128197334
- type: nauc_recall_at_1_std
value: 9.118883517128223
- type: nauc_recall_at_20_diff1
value: 0.9133987668726881
- type: nauc_recall_at_20_max
value: 29.13398902376736
- type: nauc_recall_at_20_std
value: 24.28650862967556
- type: nauc_recall_at_3_diff1
value: 13.795646221056213
- type: nauc_recall_at_3_max
value: 28.458736997372156
- type: nauc_recall_at_3_std
value: 13.590754437517683
- type: nauc_recall_at_5_diff1
value: 10.27964083807708
- type: nauc_recall_at_5_max
value: 30.18285511857152
- type: nauc_recall_at_5_std
value: 15.981641598553725
- type: ndcg_at_1
value: 32.800000000000004
- type: ndcg_at_10
value: 28.538000000000004
- type: ndcg_at_100
value: 39.253
- type: ndcg_at_1000
value: 44.690000000000005
- type: ndcg_at_20
value: 32.523
- type: ndcg_at_3
value: 27.296
- type: ndcg_at_5
value: 23.615
- type: precision_at_1
value: 32.800000000000004
- type: precision_at_10
value: 14.77
- type: precision_at_100
value: 3.01
- type: precision_at_1000
value: 0.43
- type: precision_at_20
value: 9.69
- type: precision_at_3
value: 25.6
- type: precision_at_5
value: 20.66
- type: recall_at_1
value: 6.643000000000001
- type: recall_at_10
value: 29.992
- type: recall_at_100
value: 61.095
- type: recall_at_1000
value: 87.272
- type: recall_at_20
value: 39.33
- type: recall_at_3
value: 15.573
- type: recall_at_5
value: 20.948
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 84.37281363343187
- type: cosine_spearman
value: 83.30200195593044
- type: euclidean_pearson
value: 81.67701335794368
- type: euclidean_spearman
value: 83.3019997653498
- type: main_score
value: 83.30200195593044
- type: manhattan_pearson
value: 81.80922925216795
- type: manhattan_spearman
value: 82.45409932874257
- type: pearson
value: 84.37281363343187
- type: spearman
value: 83.30200195593044
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 86.82824905521925
- type: cosine_spearman
value: 80.98590815911939
- type: euclidean_pearson
value: 81.89218840695128
- type: euclidean_spearman
value: 80.98590725274755
- type: main_score
value: 80.98590815911939
- type: manhattan_pearson
value: 79.38582949943776
- type: manhattan_spearman
value: 77.30421542625247
- type: pearson
value: 86.82824905521925
- type: spearman
value: 80.98590815911939
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 87.19722316157294
- type: cosine_spearman
value: 87.34287142701457
- type: euclidean_pearson
value: 86.95469976545654
- type: euclidean_spearman
value: 87.34287142701457
- type: main_score
value: 87.34287142701457
- type: manhattan_pearson
value: 85.00802640790108
- type: manhattan_spearman
value: 84.8446065481803
- type: pearson
value: 87.19722316157294
- type: spearman
value: 87.34287142701457
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 84.82646675904164
- type: cosine_spearman
value: 84.38843815801556
- type: euclidean_pearson
value: 83.62102244440854
- type: euclidean_spearman
value: 84.38846972126379
- type: main_score
value: 84.38843815801556
- type: manhattan_pearson
value: 82.56864079042991
- type: manhattan_spearman
value: 82.88684800532234
- type: pearson
value: 84.82646675904164
- type: spearman
value: 84.38843815801556
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 89.69909533656704
- type: cosine_spearman
value: 89.74723322749233
- type: euclidean_pearson
value: 88.7169991211476
- type: euclidean_spearman
value: 89.7472332075485
- type: main_score
value: 89.74723322749233
- type: manhattan_pearson
value: 87.37922931937202
- type: manhattan_spearman
value: 87.47352246770794
- type: pearson
value: 89.69909533656704
- type: spearman
value: 89.74723322749233
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 86.84947603401746
- type: cosine_spearman
value: 87.63022743056388
- type: euclidean_pearson
value: 86.74752511965002
- type: euclidean_spearman
value: 87.63022743056388
- type: main_score
value: 87.63022743056388
- type: manhattan_pearson
value: 86.1770646766385
- type: manhattan_spearman
value: 86.43792690343828
- type: pearson
value: 86.84947603401746
- type: spearman
value: 87.63022743056388
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 91.43391567649913
- type: cosine_spearman
value: 90.86953801008369
- type: euclidean_pearson
value: 91.24907274014495
- type: euclidean_spearman
value: 90.86953801008369
- type: main_score
value: 90.86953801008369
- type: manhattan_pearson
value: 92.27103413151777
- type: manhattan_spearman
value: 91.70510079315306
- type: pearson
value: 91.43391567649913
- type: spearman
value: 90.86953801008369
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 68.81338409687908
- type: cosine_spearman
value: 68.09215270009086
- type: euclidean_pearson
value: 68.64603879303111
- type: euclidean_spearman
value: 68.09215270009086
- type: main_score
value: 68.09215270009086
- type: manhattan_pearson
value: 69.46795022258881
- type: manhattan_spearman
value: 68.27576057587602
- type: pearson
value: 68.81338409687908
- type: spearman
value: 68.09215270009086
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 87.93191595794555
- type: cosine_spearman
value: 88.46646307403641
- type: euclidean_pearson
value: 87.3793925878407
- type: euclidean_spearman
value: 88.46646307403641
- type: main_score
value: 88.46646307403641
- type: manhattan_pearson
value: 86.42857012902716
- type: manhattan_spearman
value: 86.76733082091621
- type: pearson
value: 87.93191595794555
- type: spearman
value: 88.46646307403641
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 87.62672056519489
- type: map
value: 87.62672056519489
- type: mrr
value: 96.75288491464961
- type: nAUC_map_diff1
value: -10.979336734379478
- type: nAUC_map_max
value: 50.98762609235208
- type: nAUC_map_std
value: 68.765950990151
- type: nAUC_mrr_diff1
value: 26.032783373787915
- type: nAUC_mrr_max
value: 82.4844792677926
- type: nAUC_mrr_std
value: 82.0357865297397
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 79.745
- type: map_at_1
value: 64.328
- type: map_at_10
value: 75.139
- type: map_at_100
value: 75.384
- type: map_at_1000
value: 75.397
- type: map_at_20
value: 75.297
- type: map_at_3
value: 72.222
- type: map_at_5
value: 74.079
- type: mrr_at_1
value: 67.66666666666666
- type: mrr_at_10
value: 76.05383597883596
- type: mrr_at_100
value: 76.22027125405623
- type: mrr_at_1000
value: 76.23312943054236
- type: mrr_at_20
value: 76.14538114391056
- type: mrr_at_3
value: 74.11111111111113
- type: mrr_at_5
value: 75.31111111111112
- type: nauc_map_at_1000_diff1
value: 78.43909223692039
- type: nauc_map_at_1000_max
value: 61.93783649790126
- type: nauc_map_at_1000_std
value: 4.252485546213312
- type: nauc_map_at_100_diff1
value: 78.43784839609923
- type: nauc_map_at_100_max
value: 61.95484128615342
- type: nauc_map_at_100_std
value: 4.2853337017858335
- type: nauc_map_at_10_diff1
value: 78.39217968458001
- type: nauc_map_at_10_max
value: 62.11071146014176
- type: nauc_map_at_10_std
value: 4.2617060402705995
- type: nauc_map_at_1_diff1
value: 79.46268237346375
- type: nauc_map_at_1_max
value: 51.87919162971395
- type: nauc_map_at_1_std
value: -7.9819599317263155
- type: nauc_map_at_20_diff1
value: 78.34137192881398
- type: nauc_map_at_20_max
value: 62.04478567534677
- type: nauc_map_at_20_std
value: 4.44986243492092
- type: nauc_map_at_3_diff1
value: 78.93188320960228
- type: nauc_map_at_3_max
value: 59.05645306883896
- type: nauc_map_at_3_std
value: -0.7631750269436595
- type: nauc_map_at_5_diff1
value: 78.24090341300327
- type: nauc_map_at_5_max
value: 60.40223081576741
- type: nauc_map_at_5_std
value: 2.3140404965702412
- type: nauc_mrr_at_1000_diff1
value: 77.83067231715107
- type: nauc_mrr_at_1000_max
value: 62.87956349977423
- type: nauc_mrr_at_1000_std
value: 6.261294250064705
- type: nauc_mrr_at_100_diff1
value: 77.82977924127196
- type: nauc_mrr_at_100_max
value: 62.896357638733605
- type: nauc_mrr_at_100_std
value: 6.29361759938427
- type: nauc_mrr_at_10_diff1
value: 77.67673337207816
- type: nauc_mrr_at_10_max
value: 63.029402394904096
- type: nauc_mrr_at_10_std
value: 6.4376801339784056
- type: nauc_mrr_at_1_diff1
value: 79.44451752755394
- type: nauc_mrr_at_1_max
value: 60.551707570965306
- type: nauc_mrr_at_1_std
value: 2.128815653258172
- type: nauc_mrr_at_20_diff1
value: 77.73758868156405
- type: nauc_mrr_at_20_max
value: 62.963931403619334
- type: nauc_mrr_at_20_std
value: 6.407753752007869
- type: nauc_mrr_at_3_diff1
value: 77.98942478932196
- type: nauc_mrr_at_3_max
value: 62.99215580076013
- type: nauc_mrr_at_3_std
value: 5.461908127857269
- type: nauc_mrr_at_5_diff1
value: 77.37126419303483
- type: nauc_mrr_at_5_max
value: 62.33931482696964
- type: nauc_mrr_at_5_std
value: 5.6849973918884364
- type: nauc_ndcg_at_1000_diff1
value: 77.84639188057717
- type: nauc_ndcg_at_1000_max
value: 63.315777298558665
- type: nauc_ndcg_at_1000_std
value: 6.713565158302629
- type: nauc_ndcg_at_100_diff1
value: 77.863198515294
- type: nauc_ndcg_at_100_max
value: 63.74184406752551
- type: nauc_ndcg_at_100_std
value: 7.570839930103332
- type: nauc_ndcg_at_10_diff1
value: 77.18552551495168
- type: nauc_ndcg_at_10_max
value: 64.67747343020477
- type: nauc_ndcg_at_10_std
value: 8.101265554115662
- type: nauc_ndcg_at_1_diff1
value: 79.44451752755394
- type: nauc_ndcg_at_1_max
value: 60.551707570965306
- type: nauc_ndcg_at_1_std
value: 2.128815653258172
- type: nauc_ndcg_at_20_diff1
value: 77.17994663867256
- type: nauc_ndcg_at_20_max
value: 64.43647467030159
- type: nauc_ndcg_at_20_std
value: 8.622247271863376
- type: nauc_ndcg_at_3_diff1
value: 77.88539508493001
- type: nauc_ndcg_at_3_max
value: 62.66984272127964
- type: nauc_ndcg_at_3_std
value: 2.742594418074899
- type: nauc_ndcg_at_5_diff1
value: 76.53757494977143
- type: nauc_ndcg_at_5_max
value: 61.49129748643147
- type: nauc_ndcg_at_5_std
value: 4.380236291870192
- type: nauc_precision_at_1000_diff1
value: -29.19719579231251
- type: nauc_precision_at_1000_max
value: 23.47044780637479
- type: nauc_precision_at_1000_std
value: 52.84101463334091
- type: nauc_precision_at_100_diff1
value: -19.027484143763136
- type: nauc_precision_at_100_max
value: 29.260915389748522
- type: nauc_precision_at_100_std
value: 52.8204456584782
- type: nauc_precision_at_10_diff1
value: -4.391032114724305
- type: nauc_precision_at_10_max
value: 42.8334824133652
- type: nauc_precision_at_10_std
value: 51.92832616787376
- type: nauc_precision_at_1_diff1
value: 79.44451752755394
- type: nauc_precision_at_1_max
value: 60.551707570965306
- type: nauc_precision_at_1_std
value: 2.128815653258172
- type: nauc_precision_at_20_diff1
value: -10.485053838019777
- type: nauc_precision_at_20_max
value: 36.682622883716725
- type: nauc_precision_at_20_std
value: 52.86245437696848
- type: nauc_precision_at_3_diff1
value: 37.47158426353003
- type: nauc_precision_at_3_max
value: 57.209349751980824
- type: nauc_precision_at_3_std
value: 27.18493771030643
- type: nauc_precision_at_5_diff1
value: 14.132687328859591
- type: nauc_precision_at_5_max
value: 46.577764510703226
- type: nauc_precision_at_5_std
value: 37.8320197016056
- 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: 79.55849006269133
- type: nauc_recall_at_100_max
value: 85.39415766306497
- type: nauc_recall_at_100_std
value: 52.62104841936836
- type: nauc_recall_at_10_diff1
value: 67.56888802032422
- type: nauc_recall_at_10_max
value: 80.90895272837815
- type: nauc_recall_at_10_std
value: 31.151065077193458
- type: nauc_recall_at_1_diff1
value: 79.46268237346375
- type: nauc_recall_at_1_max
value: 51.87919162971395
- type: nauc_recall_at_1_std
value: -7.9819599317263155
- type: nauc_recall_at_20_diff1
value: 66.80955210366969
- type: nauc_recall_at_20_max
value: 83.85111620405739
- type: nauc_recall_at_20_std
value: 43.14008431655496
- type: nauc_recall_at_3_diff1
value: 73.487621203687
- type: nauc_recall_at_3_max
value: 60.736184806494144
- type: nauc_recall_at_3_std
value: -0.05276306802210151
- type: nauc_recall_at_5_diff1
value: 67.32384089471962
- type: nauc_recall_at_5_max
value: 60.88484924058943
- type: nauc_recall_at_5_std
value: 7.53695946791339
- type: ndcg_at_1
value: 67.667
- type: ndcg_at_10
value: 79.745
- type: ndcg_at_100
value: 80.803
- type: ndcg_at_1000
value: 81.109
- type: ndcg_at_20
value: 80.21000000000001
- type: ndcg_at_3
value: 75.288
- type: ndcg_at_5
value: 77.739
- type: precision_at_1
value: 67.667
- type: precision_at_10
value: 10.5
- type: precision_at_100
value: 1.107
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.367
- type: precision_at_3
value: 29.555999999999997
- type: precision_at_5
value: 19.467000000000002
- type: recall_at_1
value: 64.328
- type: recall_at_10
value: 92.833
- type: recall_at_100
value: 97.667
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 94.5
- type: recall_at_3
value: 81.072
- type: recall_at_5
value: 87.339
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cosine_accuracy
value: 99.86039603960396
- type: cosine_accuracy_threshold
value: 72.46292233467102
- type: cosine_ap
value: 96.9963756118777
- type: cosine_f1
value: 92.8895612708018
- type: cosine_f1_threshold
value: 72.46292233467102
- type: cosine_precision
value: 93.69277721261444
- type: cosine_recall
value: 92.10000000000001
- type: dot_accuracy
value: 99.86039603960396
- type: dot_accuracy_threshold
value: 72.46291637420654
- type: dot_ap
value: 96.99637561187771
- type: dot_f1
value: 92.8895612708018
- type: dot_f1_threshold
value: 72.46291637420654
- type: dot_precision
value: 93.69277721261444
- type: dot_recall
value: 92.10000000000001
- type: euclidean_accuracy
value: 99.86039603960396
- type: euclidean_accuracy_threshold
value: 74.21196699142456
- type: euclidean_ap
value: 96.99637561187768
- type: euclidean_f1
value: 92.8895612708018
- type: euclidean_f1_threshold
value: 74.21196699142456
- type: euclidean_precision
value: 93.69277721261444
- type: euclidean_recall
value: 92.10000000000001
- type: main_score
value: 96.99637561187771
- type: manhattan_accuracy
value: 99.81980198019802
- type: manhattan_accuracy_threshold
value: 2608.760452270508
- type: manhattan_ap
value: 96.18773334150895
- type: manhattan_f1
value: 90.74262461851477
- type: manhattan_f1_threshold
value: 2608.760452270508
- type: manhattan_precision
value: 92.33954451345755
- type: manhattan_recall
value: 89.2
- type: max_accuracy
value: 99.86039603960396
- type: max_ap
value: 96.99637561187771
- type: max_f1
value: 92.8895612708018
- type: max_precision
value: 93.69277721261444
- type: max_recall
value: 92.10000000000001
- type: similarity_accuracy
value: 99.86039603960396
- type: similarity_accuracy_threshold
value: 72.46292233467102
- type: similarity_ap
value: 96.9963756118777
- type: similarity_f1
value: 92.8895612708018
- type: similarity_f1_threshold
value: 72.46292233467102
- type: similarity_precision
value: 93.69277721261444
- type: similarity_recall
value: 92.10000000000001
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
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type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
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dataset:
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type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
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type: mteb/summeval
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revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
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revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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- type: precision_at_20
value: 19.694
- type: precision_at_3
value: 34.694
- type: precision_at_5
value: 31.429000000000002
- type: recall_at_1
value: 3.0220000000000002
- type: recall_at_10
value: 18.838
- type: recall_at_100
value: 51.471999999999994
- type: recall_at_1000
value: 83.012
- type: recall_at_20
value: 27.165
- type: recall_at_3
value: 7.868
- type: recall_at_5
value: 11.413
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 92.3681640625
- type: ap
value: 43.441682015979104
- type: ap_weighted
value: 43.441682015979104
- type: f1
value: 79.35383898838042
- type: f1_weighted
value: 93.14474638528736
- type: main_score
value: 92.3681640625
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 80.42161856253539
- type: f1
value: 80.69309707938646
- type: f1_weighted
value: 80.3228654725881
- type: main_score
value: 80.42161856253539
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 68.78385330772423
- type: v_measure
value: 68.78385330772423
- type: v_measure_std
value: 1.4814035017480702
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 87.96566728258925
- type: cosine_accuracy_threshold
value: 74.39426183700562
- type: cosine_ap
value: 79.55550776766384
- type: cosine_f1
value: 72.25950782997764
- type: cosine_f1_threshold
value: 71.66670560836792
- type: cosine_precision
value: 68.30357142857143
- type: cosine_recall
value: 76.7018469656992
- type: dot_accuracy
value: 87.96566728258925
- type: dot_accuracy_threshold
value: 74.39426183700562
- type: dot_ap
value: 79.55551194873313
- type: dot_f1
value: 72.25950782997764
- type: dot_f1_threshold
value: 71.66670560836792
- type: dot_precision
value: 68.30357142857143
- type: dot_recall
value: 76.7018469656992
- type: euclidean_accuracy
value: 87.96566728258925
- type: euclidean_accuracy_threshold
value: 71.56219482421875
- type: euclidean_ap
value: 79.55550945424486
- type: euclidean_f1
value: 72.25950782997764
- type: euclidean_f1_threshold
value: 75.27719736099243
- type: euclidean_precision
value: 68.30357142857143
- type: euclidean_recall
value: 76.7018469656992
- type: main_score
value: 79.55707708553916
- type: manhattan_accuracy
value: 87.88221970554928
- type: manhattan_accuracy_threshold
value: 3136.1982345581055
- type: manhattan_ap
value: 79.55707708553916
- type: manhattan_f1
value: 72.5288998571243
- type: manhattan_f1_threshold
value: 3327.1324157714844
- type: manhattan_precision
value: 71.42491685853159
- type: manhattan_recall
value: 73.66754617414249
- type: max_accuracy
value: 87.96566728258925
- type: max_ap
value: 79.55707708553916
- type: max_f1
value: 72.5288998571243
- type: max_precision
value: 71.42491685853159
- type: max_recall
value: 76.7018469656992
- type: similarity_accuracy
value: 87.96566728258925
- type: similarity_accuracy_threshold
value: 74.39426183700562
- type: similarity_ap
value: 79.55550776766384
- type: similarity_f1
value: 72.25950782997764
- type: similarity_f1_threshold
value: 71.66670560836792
- type: similarity_precision
value: 68.30357142857143
- type: similarity_recall
value: 76.7018469656992
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 89.48073116777273
- type: cosine_accuracy_threshold
value: 73.83834719657898
- type: cosine_ap
value: 87.04248262089361
- type: cosine_f1
value: 79.40769846467293
- type: cosine_f1_threshold
value: 70.96807956695557
- type: cosine_precision
value: 75.27245137260311
- type: cosine_recall
value: 84.02371419772098
- type: dot_accuracy
value: 89.48073116777273
- type: dot_accuracy_threshold
value: 73.83835315704346
- type: dot_ap
value: 87.04248357792484
- type: dot_f1
value: 79.40769846467293
- type: dot_f1_threshold
value: 70.96806764602661
- type: dot_precision
value: 75.27245137260311
- type: dot_recall
value: 84.02371419772098
- type: euclidean_accuracy
value: 89.48073116777273
- type: euclidean_accuracy_threshold
value: 72.33483791351318
- type: euclidean_ap
value: 87.04247988177627
- type: euclidean_f1
value: 79.40769846467293
- type: euclidean_f1_threshold
value: 76.19963884353638
- type: euclidean_precision
value: 75.27245137260311
- type: euclidean_recall
value: 84.02371419772098
- type: main_score
value: 87.37453072241573
- type: manhattan_accuracy
value: 89.67283735009897
- type: manhattan_accuracy_threshold
value: 3345.201873779297
- type: manhattan_ap
value: 87.37453072241573
- type: manhattan_f1
value: 79.9237656873674
- type: manhattan_f1_threshold
value: 3592.564010620117
- type: manhattan_precision
value: 74.98144524660955
- type: manhattan_recall
value: 85.56359716661534
- type: max_accuracy
value: 89.67283735009897
- type: max_ap
value: 87.37453072241573
- type: max_f1
value: 79.9237656873674
- type: max_precision
value: 75.27245137260311
- type: max_recall
value: 85.56359716661534
- type: similarity_accuracy
value: 89.48073116777273
- type: similarity_accuracy_threshold
value: 73.83834719657898
- type: similarity_ap
value: 87.04248262089361
- type: similarity_f1
value: 79.40769846467293
- type: similarity_f1_threshold
value: 70.96807956695557
- type: similarity_precision
value: 75.27245137260311
- type: similarity_recall
value: 84.02371419772098
---
LENS Embeddings
LENS is a model that produces **L**exicon-based **E**mbeddi**N**g**S** (LENS) leveraging large language models. Each dimension of the embeddings is designed to correspond to a token cluster where semantically similar tokens are grouped together. These embeddings have a similar feature size as dense embeddings, with LENS-d8000 offering 8000-dimensional representations.
The technical report of **LENS** is available in [Enhancing Lexicon-Based Text Embeddings with Large Language Models](https://arxiv.org/abs/2501.09749).
## Usage
```
git clone https://huggingface.co/yibinlei/LENS-d8000
cd LENS-d8000
```
```python
import torch
from torch import Tensor
import torch.nn.functional as F
from transformers import AutoTokenizer
from bidirectional_mistral import MistralBiForCausalLM
def get_detailed_instruct(task_instruction: str, query: str) -> str:
return f'{task_instruction}\n{query}'
def pooling_func(vecs: Tensor, pooling_mask: Tensor) -> Tensor:
# We use max-pooling for LENS.
return torch.max(torch.log(1 + torch.relu(vecs)) * pooling_mask.unsqueeze(-1), dim=1).values
# Prepare the data
instruction = "Given a web search query, retrieve relevant passages that answer the query."
queries = ["what is rba",
"what is oilskin fabric"]
instructed_queries = [get_detailed_instruct(instruction, query) for query in queries]
docs = ["Since 2007, the RBA's outstanding reputation has been affected by the 'Securency' or NPA scandal.",
"Today's oilskins (or oilies) typically come in two parts, jackets and trousers. Oilskin jackets are generally similar to common rubberized waterproofs."]
# Load the model and tokenizer
model = MistralBiForCausalLM.from_pretrained("yibinlei/LENS-d8000", ignore_mismatched_sizes=True)
model.lm_head = torch.load('lm_head.pth')
tokenizer = AutoTokenizer.from_pretrained("yibinlei/LENS-d8000")
# Preprocess the data
query_max_len, doc_max_len = 512, 512
instructed_query_inputs = tokenizer(
instructed_queries,
padding=True,
truncation=True,
return_tensors='pt',
max_length=query_max_len,
add_special_tokens=True
)
doc_inputs = tokenizer(
docs,
padding=True,
truncation=True,
return_tensors='pt',
max_length=doc_max_len,
add_special_tokens=True
)
# We perform pooling exclusively on the outputs of the query tokens, excluding outputs from the instruction.
query_only_mask = torch.zeros_like(instructed_query_inputs['input_ids'], dtype=instructed_query_inputs['attention_mask'].dtype)
special_token_id = tokenizer.convert_tokens_to_ids('')
for idx, seq in enumerate(instructed_query_inputs['input_ids']):
special_pos = (seq == special_token_id).nonzero()
if len(special_pos) > 0:
query_start_pos = special_pos[-1].item()
query_only_mask[idx, query_start_pos:-2] = 1
else:
raise ValueError("No special token found")
# Obtain the embeddings
with torch.no_grad():
instructed_query_outputs = model(**instructed_query_inputs)
query_embeddings = pooling_func(instructed_query_outputs, query_only_mask)
doc_outputs = model(**doc_inputs)
# As the output of each token is used for predicting the next token, the pooling mask is shifted left by 1. The output of the final token EOS token is also excluded.
doc_inputs['attention_mask'][:, -2:] = 0
doc_embeddings = pooling_func(doc_outputs, doc_inputs['attention_mask'])
# Normalize the embeddings
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
# Compute the similarity
similarity = torch.matmul(query_embeddings, doc_embeddings.T)
```