transformers safetensors mistral feature-extraction text-embedding sentence-similarity mteb arxiv:2501.09749 license:apache-2.0 model-index text-generation-inference text-embeddings-inference endpoints_compatible region:us
---
license: apache-2.0
tags:
- text-embedding
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: Gouzi3618/LENS-4000
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
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      value: 93.61194029850746
    - type: ap
      value: 73.89383804438975
    - type: ap_weighted
      value: 73.89383804438975
    - type: f1
      value: 90.31690759629414
    - type: f1_weighted
      value: 93.75647989786705
    - type: main_score
      value: 93.61194029850746
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification (default)
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 97.05455
    - type: ap
      value: 95.53082050876944
    - type: ap_weighted
      value: 95.53082050876944
    - type: f1
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    - type: f1_weighted
      value: 97.05405422635297
    - type: main_score
      value: 97.05455
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
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      value: 62.834
    - type: f1
      value: 61.45864309016823
    - type: f1_weighted
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    - type: main_score
      value: 62.834
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna (default)
      type: mteb/arguana
      config: default
      split: test
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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    - type: map_at_20
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    - type: map_at_3
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    - type: map_at_5
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    - type: mrr_at_10
      value: 71.48944997629222
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    - type: mrr_at_1000
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    - type: mrr_at_20
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    - type: precision_at_1
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    - type: recall_at_1
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    - type: recall_at_10
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    - type: recall_at_3
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      value: 90.04299999999999
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P (default)
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: main_score
      value: 54.87086052375513
    - type: v_measure
      value: 54.87086052375513
    - type: v_measure_std
      value: 14.454097589509681
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S (default)
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
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      value: 50.24788850687535
    - type: v_measure
      value: 50.24788850687535
    - type: v_measure_std
      value: 14.477615357158207
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions (default)
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
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    - type: map
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    - type: nAUC_mrr_std
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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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  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification (default)
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P (default)
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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  - task:
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    dataset:
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      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
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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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  - task:
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      type: mteb/imdb
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  - task:
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      type: mteb/msmarco
      config: default
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      revision: c5a29a104738b98a9e76336939199e264163d4a0
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  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
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  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
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      value: 92.5923392612859
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
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  - task:
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    dataset:
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      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
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  - task:
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      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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  - task:
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      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
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  - task:
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      type: mteb/mind_small
      config: default
      split: test
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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  - task:
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      type: mteb/nfcorpus
      config: default
      split: test
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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  - task:
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      type: mteb/reddit-clustering
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  - task:
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  - task:
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  - task:
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      type: mteb/sickr-sts
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    - type: manhattan_spearman
      value: 82.3715754389135
    - type: pearson
      value: 85.22165753437119
    - type: spearman
      value: 83.8127407315392
  - task:
      type: STS
    dataset:
      name: MTEB STS12 (default)
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cosine_pearson
      value: 85.37993195563598
    - type: cosine_spearman
      value: 79.06871408688198
    - type: euclidean_pearson
      value: 80.69951951053761
    - type: euclidean_spearman
      value: 79.06873064755126
    - type: main_score
      value: 79.06871408688198
    - type: manhattan_pearson
      value: 77.95412896760531
    - type: manhattan_spearman
      value: 75.49651289323124
    - type: pearson
      value: 85.37993195563598
    - type: spearman
      value: 79.06871408688198
  - task:
      type: STS
    dataset:
      name: MTEB STS13 (default)
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cosine_pearson
      value: 86.17030892013604
    - type: cosine_spearman
      value: 86.54428612066569
    - type: euclidean_pearson
      value: 86.42021459238435
    - type: euclidean_spearman
      value: 86.54428612066569
    - type: main_score
      value: 86.54428612066569
    - type: manhattan_pearson
      value: 84.64899940139117
    - type: manhattan_spearman
      value: 84.37528077160499
    - type: pearson
      value: 86.17030892013604
    - type: spearman
      value: 86.54428612066569
  - task:
      type: STS
    dataset:
      name: MTEB STS14 (default)
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cosine_pearson
      value: 84.46097652298906
    - type: cosine_spearman
      value: 84.31789230181545
    - type: euclidean_pearson
      value: 83.53652229013105
    - type: euclidean_spearman
      value: 84.31787771751202
    - type: main_score
      value: 84.31789230181545
    - type: manhattan_pearson
      value: 82.40679358381392
    - type: manhattan_spearman
      value: 82.56529092906449
    - type: pearson
      value: 84.46097652298906
    - type: spearman
      value: 84.31789230181545
  - task:
      type: STS
    dataset:
      name: MTEB STS15 (default)
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cosine_pearson
      value: 89.580101203536
    - type: cosine_spearman
      value: 89.69254612113068
    - type: euclidean_pearson
      value: 88.78501564809129
    - type: euclidean_spearman
      value: 89.69254607130148
    - type: main_score
      value: 89.69254612113068
    - type: manhattan_pearson
      value: 87.37048209358335
    - type: manhattan_spearman
      value: 87.3836150196757
    - type: pearson
      value: 89.580101203536
    - type: spearman
      value: 89.69254612113068
  - task:
      type: STS
    dataset:
      name: MTEB STS16 (default)
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cosine_pearson
      value: 86.34409195642276
    - type: cosine_spearman
      value: 87.22893647955566
    - type: euclidean_pearson
      value: 86.47233799859978
    - type: euclidean_spearman
      value: 87.22893647955566
    - type: main_score
      value: 87.22893647955566
    - type: manhattan_pearson
      value: 86.28871397722244
    - type: manhattan_spearman
      value: 86.54681756151196
    - type: pearson
      value: 86.34409195642276
    - type: spearman
      value: 87.22893647955566
  - 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: 91.6309842235666
    - type: cosine_spearman
      value: 91.55107309513775
    - type: euclidean_pearson
      value: 91.66305652923727
    - type: euclidean_spearman
      value: 91.55107309513775
    - type: main_score
      value: 91.55107309513775
    - type: manhattan_pearson
      value: 92.34412264807419
    - type: manhattan_spearman
      value: 91.76106893098941
    - type: pearson
      value: 91.6309842235666
    - type: spearman
      value: 91.55107309513775
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: cosine_pearson
      value: 69.34620080035205
    - type: cosine_spearman
      value: 68.68546006466259
    - type: euclidean_pearson
      value: 68.92323864900831
    - type: euclidean_spearman
      value: 68.68546006466259
    - type: main_score
      value: 68.68546006466259
    - type: manhattan_pearson
      value: 69.50252696626819
    - type: manhattan_spearman
      value: 68.6026900249137
    - type: pearson
      value: 69.34620080035205
    - type: spearman
      value: 68.68546006466259
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark (default)
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cosine_pearson
      value: 87.72211485964012
    - type: cosine_spearman
      value: 88.21684368202543
    - type: euclidean_pearson
      value: 87.4152174509492
    - type: euclidean_spearman
      value: 88.21684358110474
    - type: main_score
      value: 88.21684368202543
    - type: manhattan_pearson
      value: 86.18736905144627
    - type: manhattan_spearman
      value: 86.2967005957272
    - type: pearson
      value: 87.72211485964012
    - type: spearman
      value: 88.21684368202543
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR (default)
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: main_score
      value: 87.91887485668568
    - type: map
      value: 87.91887485668568
    - type: mrr
      value: 96.79923108844677
    - type: nAUC_map_diff1
      value: -9.544019533700576
    - type: nAUC_map_max
      value: 51.305518546271486
    - type: nAUC_map_std
      value: 68.93338639531362
    - type: nAUC_mrr_diff1
      value: 28.20896050152944
    - type: nAUC_mrr_max
      value: 84.08480139020106
    - type: nAUC_mrr_std
      value: 81.66707142756775
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact (default)
      type: mteb/scifact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
    metrics:
    - type: main_score
      value: 78.385
    - type: map_at_1
      value: 65.261
    - type: map_at_10
      value: 74.265
    - type: map_at_100
      value: 74.68900000000001
    - type: map_at_1000
      value: 74.7
    - type: map_at_20
      value: 74.61
    - type: map_at_3
      value: 71.485
    - type: map_at_5
      value: 73.07000000000001
    - type: mrr_at_1
      value: 68.33333333333333
    - type: mrr_at_10
      value: 75.00052910052908
    - type: mrr_at_100
      value: 75.36791603863972
    - type: mrr_at_1000
      value: 75.37836387206892
    - type: mrr_at_20
      value: 75.28900989361513
    - type: mrr_at_3
      value: 73.16666666666667
    - type: mrr_at_5
      value: 74.06666666666666
    - type: nauc_map_at_1000_diff1
      value: 71.95557264143025
    - type: nauc_map_at_1000_max
      value: 57.668494594642375
    - type: nauc_map_at_1000_std
      value: 0.9160722241006062
    - type: nauc_map_at_100_diff1
      value: 71.96131086694861
    - type: nauc_map_at_100_max
      value: 57.67394651480537
    - type: nauc_map_at_100_std
      value: 0.9111248974804423
    - type: nauc_map_at_10_diff1
      value: 71.8005197158061
    - type: nauc_map_at_10_max
      value: 57.926225509350296
    - type: nauc_map_at_10_std
      value: 1.0289591605730695
    - type: nauc_map_at_1_diff1
      value: 74.04117350009464
    - type: nauc_map_at_1_max
      value: 46.01270356681121
    - type: nauc_map_at_1_std
      value: -12.34453479186478
    - type: nauc_map_at_20_diff1
      value: 71.79288203065293
    - type: nauc_map_at_20_max
      value: 57.748067223890466
    - type: nauc_map_at_20_std
      value: 1.0471868877436754
    - type: nauc_map_at_3_diff1
      value: 73.12655880469308
    - type: nauc_map_at_3_max
      value: 53.170175466998955
    - type: nauc_map_at_3_std
      value: -2.841120496331886
    - type: nauc_map_at_5_diff1
      value: 72.37537625825152
    - type: nauc_map_at_5_max
      value: 57.22646320702063
    - type: nauc_map_at_5_std
      value: 0.08993845130894543
    - type: nauc_mrr_at_1000_diff1
      value: 72.33151450517484
    - type: nauc_mrr_at_1000_max
      value: 59.05887764321693
    - type: nauc_mrr_at_1000_std
      value: 2.978447313200519
    - type: nauc_mrr_at_100_diff1
      value: 72.3371689393142
    - type: nauc_mrr_at_100_max
      value: 59.063748264607554
    - type: nauc_mrr_at_100_std
      value: 2.9724134206438007
    - type: nauc_mrr_at_10_diff1
      value: 72.15441848985677
    - type: nauc_mrr_at_10_max
      value: 59.323659507427315
    - type: nauc_mrr_at_10_std
      value: 3.202392266950175
    - type: nauc_mrr_at_1_diff1
      value: 74.70791175021019
    - type: nauc_mrr_at_1_max
      value: 54.890557504421224
    - type: nauc_mrr_at_1_std
      value: -3.1003391992577676
    - type: nauc_mrr_at_20_diff1
      value: 72.16447875028192
    - type: nauc_mrr_at_20_max
      value: 59.13406185965151
    - type: nauc_mrr_at_20_std
      value: 3.1032769225166454
    - type: nauc_mrr_at_3_diff1
      value: 72.74517143863574
    - type: nauc_mrr_at_3_max
      value: 58.78449780863764
    - type: nauc_mrr_at_3_std
      value: 3.1947844580560276
    - type: nauc_mrr_at_5_diff1
      value: 72.55041655786376
    - type: nauc_mrr_at_5_max
      value: 59.379628404843956
    - type: nauc_mrr_at_5_std
      value: 3.0807485088011655
    - type: nauc_ndcg_at_1000_diff1
      value: 71.3780675347069
    - type: nauc_ndcg_at_1000_max
      value: 59.48945646166557
    - type: nauc_ndcg_at_1000_std
      value: 3.4914501826426503
    - type: nauc_ndcg_at_100_diff1
      value: 71.53734704134561
    - type: nauc_ndcg_at_100_max
      value: 59.745110507117275
    - type: nauc_ndcg_at_100_std
      value: 3.783265578398072
    - type: nauc_ndcg_at_10_diff1
      value: 69.96639696430987
    - type: nauc_ndcg_at_10_max
      value: 60.93159115976958
    - type: nauc_ndcg_at_10_std
      value: 4.90530364691378
    - type: nauc_ndcg_at_1_diff1
      value: 74.70791175021019
    - type: nauc_ndcg_at_1_max
      value: 54.890557504421224
    - type: nauc_ndcg_at_1_std
      value: -3.1003391992577676
    - type: nauc_ndcg_at_20_diff1
      value: 69.89569028363886
    - type: nauc_ndcg_at_20_max
      value: 60.270211929349834
    - type: nauc_ndcg_at_20_std
      value: 4.838097933264383
    - type: nauc_ndcg_at_3_diff1
      value: 71.97085051507173
    - type: nauc_ndcg_at_3_max
      value: 57.05247760108673
    - type: nauc_ndcg_at_3_std
      value: 1.342308002922158
    - type: nauc_ndcg_at_5_diff1
      value: 71.34405011749429
    - type: nauc_ndcg_at_5_max
      value: 60.15875062308923
    - type: nauc_ndcg_at_5_std
      value: 3.0796119978456793
    - type: nauc_precision_at_1000_diff1
      value: -29.157292935130375
    - type: nauc_precision_at_1000_max
      value: 26.889021898412864
    - type: nauc_precision_at_1000_std
      value: 49.35914635404835
    - type: nauc_precision_at_100_diff1
      value: -18.882174284520445
    - type: nauc_precision_at_100_max
      value: 31.615184568467097
    - type: nauc_precision_at_100_std
      value: 47.60363461742358
    - type: nauc_precision_at_10_diff1
      value: -2.8344355852237415
    - type: nauc_precision_at_10_max
      value: 44.568061478871776
    - type: nauc_precision_at_10_std
      value: 43.421513484558055
    - type: nauc_precision_at_1_diff1
      value: 74.70791175021019
    - type: nauc_precision_at_1_max
      value: 54.890557504421224
    - type: nauc_precision_at_1_std
      value: -3.1003391992577676
    - type: nauc_precision_at_20_diff1
      value: -15.053027139874736
    - type: nauc_precision_at_20_max
      value: 36.35184411969381
    - type: nauc_precision_at_20_std
      value: 46.455671537926236
    - type: nauc_precision_at_3_diff1
      value: 36.35406984818856
    - type: nauc_precision_at_3_max
      value: 51.10573379058357
    - type: nauc_precision_at_3_std
      value: 24.525389143510285
    - type: nauc_precision_at_5_diff1
      value: 17.185063562924082
    - type: nauc_precision_at_5_max
      value: 51.988011211557364
    - type: nauc_precision_at_5_std
      value: 34.07769395557144
    - 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: 80.72562358276696
    - type: nauc_recall_at_100_max
      value: 77.43097238895595
    - type: nauc_recall_at_100_std
      value: 30.585567560357074
    - type: nauc_recall_at_10_diff1
      value: 55.594191699668386
    - type: nauc_recall_at_10_max
      value: 73.93911587623553
    - type: nauc_recall_at_10_std
      value: 22.56028848320937
    - type: nauc_recall_at_1_diff1
      value: 74.04117350009464
    - type: nauc_recall_at_1_max
      value: 46.01270356681121
    - type: nauc_recall_at_1_std
      value: -12.34453479186478
    - type: nauc_recall_at_20_diff1
      value: 44.765406162464785
    - type: nauc_recall_at_20_max
      value: 76.48517740429489
    - type: nauc_recall_at_20_std
      value: 34.141573295985225
    - type: nauc_recall_at_3_diff1
      value: 69.04055599707765
    - type: nauc_recall_at_3_max
      value: 57.037557037556965
    - type: nauc_recall_at_3_std
      value: 3.6347123303645557
    - type: nauc_recall_at_5_diff1
      value: 66.13492482259224
    - type: nauc_recall_at_5_max
      value: 67.49828930893953
    - type: nauc_recall_at_5_std
      value: 9.62641835622894
    - type: ndcg_at_1
      value: 68.333
    - type: ndcg_at_10
      value: 78.385
    - type: ndcg_at_100
      value: 80.097
    - type: ndcg_at_1000
      value: 80.382
    - type: ndcg_at_20
      value: 79.532
    - type: ndcg_at_3
      value: 73.96000000000001
    - type: ndcg_at_5
      value: 75.922
    - type: precision_at_1
      value: 68.333
    - type: precision_at_10
      value: 10.267
    - type: precision_at_100
      value: 1.107
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_20
      value: 5.383
    - type: precision_at_3
      value: 28.666999999999998
    - type: precision_at_5
      value: 18.733
    - type: recall_at_1
      value: 65.261
    - type: recall_at_10
      value: 90.333
    - type: recall_at_100
      value: 97.667
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_20
      value: 94.667
    - type: recall_at_3
      value: 78.35
    - type: recall_at_5
      value: 83.217
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions (default)
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cosine_accuracy
      value: 99.86435643564356
    - type: cosine_accuracy_threshold
      value: 75.08841156959534
    - type: cosine_ap
      value: 96.9830495771001
    - type: cosine_f1
      value: 93.24790537210448
    - type: cosine_f1_threshold
      value: 73.8122820854187
    - type: cosine_precision
      value: 91.93391642371235
    - type: cosine_recall
      value: 94.6
    - type: dot_accuracy
      value: 99.86435643564356
    - type: dot_accuracy_threshold
      value: 75.08841156959534
    - type: dot_ap
      value: 96.9830495771001
    - type: dot_f1
      value: 93.24790537210448
    - type: dot_f1_threshold
      value: 73.81229400634766
    - type: dot_precision
      value: 91.93391642371235
    - type: dot_recall
      value: 94.6
    - type: euclidean_accuracy
      value: 99.86435643564356
    - type: euclidean_accuracy_threshold
      value: 70.58552503585815
    - type: euclidean_ap
      value: 96.9830495771001
    - type: euclidean_f1
      value: 93.24790537210448
    - type: euclidean_f1_threshold
      value: 72.37086296081543
    - type: euclidean_precision
      value: 91.93391642371235
    - type: euclidean_recall
      value: 94.6
    - type: main_score
      value: 96.9830495771001
    - type: manhattan_accuracy
      value: 99.85544554455446
    - type: manhattan_accuracy_threshold
      value: 2102.3300170898438
    - type: manhattan_ap
      value: 96.69996535175346
    - type: manhattan_f1
      value: 92.60385005065855
    - type: manhattan_f1_threshold
      value: 2106.606674194336
    - type: manhattan_precision
      value: 93.83983572895276
    - type: manhattan_recall
      value: 91.4
    - type: max_accuracy
      value: 99.86435643564356
    - type: max_ap
      value: 96.9830495771001
    - type: max_f1
      value: 93.24790537210448
    - type: max_precision
      value: 93.83983572895276
    - type: max_recall
      value: 94.6
    - type: similarity_accuracy
      value: 99.86435643564356
    - type: similarity_accuracy_threshold
      value: 75.08841156959534
    - type: similarity_ap
      value: 96.9830495771001
    - type: similarity_f1
      value: 93.24790537210448
    - type: similarity_f1_threshold
      value: 73.8122820854187
    - type: similarity_precision
      value: 91.93391642371235
    - type: similarity_recall
      value: 94.6
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering (default)
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: main_score
      value: 81.69710673313244
    - type: v_measure
      value: 81.69710673313244
    - type: v_measure_std
      value: 2.655167436381706
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P (default)
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: main_score
      value: 43.72862700989394
    - type: v_measure
      value: 43.72862700989394
    - type: v_measure_std
      value: 1.3902399715070008
  - task:
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    - type: nauc_recall_at_5_diff1
      value: 7.188322256567744
    - type: nauc_recall_at_5_max
      value: -25.957944490064744
    - type: nauc_recall_at_5_std
      value: -17.745642764320777
    - type: ndcg_at_1
      value: 28.571
    - type: ndcg_at_10
      value: 25.855
    - type: ndcg_at_100
      value: 37.69
    - type: ndcg_at_1000
      value: 48.808
    - type: ndcg_at_20
      value: 26.883000000000003
    - type: ndcg_at_3
      value: 28.904000000000003
    - type: ndcg_at_5
      value: 27.901999999999997
    - type: precision_at_1
      value: 30.612000000000002
    - type: precision_at_10
      value: 23.265
    - type: precision_at_100
      value: 7.878
    - type: precision_at_1000
      value: 1.522
    - type: precision_at_20
      value: 17.653
    - type: precision_at_3
      value: 29.932
    - type: precision_at_5
      value: 28.163
    - type: recall_at_1
      value: 2.3720000000000003
    - type: recall_at_10
      value: 17.071
    - type: recall_at_100
      value: 48.829
    - type: recall_at_1000
      value: 81.194
    - type: recall_at_20
      value: 24.882
    - type: recall_at_3
      value: 6.578
    - type: recall_at_5
      value: 10.951
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification (default)
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
    metrics:
    - type: accuracy
      value: 92.28515625
    - type: ap
      value: 43.967425546221364
    - type: ap_weighted
      value: 43.967425546221364
    - type: f1
      value: 79.48199263483515
    - type: f1_weighted
      value: 93.11764775204445
    - type: main_score
      value: 92.28515625
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification (default)
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 80.1726089417091
    - type: f1
      value: 80.44865150205347
    - type: f1_weighted
      value: 80.01110885829492
    - type: main_score
      value: 80.1726089417091
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering (default)
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: main_score
      value: 69.43612684622691
    - type: v_measure
      value: 69.43612684622691
    - type: v_measure_std
      value: 0.65287690596996
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015 (default)
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cosine_accuracy
      value: 87.65571913929784
    - type: cosine_accuracy_threshold
      value: 75.32307505607605
    - type: cosine_ap
      value: 79.12498779374573
    - type: cosine_f1
      value: 72.19822109275731
    - type: cosine_f1_threshold
      value: 73.05474281311035
    - type: cosine_precision
      value: 69.63235294117646
    - type: cosine_recall
      value: 74.96042216358839
    - type: dot_accuracy
      value: 87.65571913929784
    - type: dot_accuracy_threshold
      value: 75.32307505607605
    - type: dot_ap
      value: 79.1249879229425
    - type: dot_f1
      value: 72.19822109275731
    - type: dot_f1_threshold
      value: 73.0547547340393
    - type: dot_precision
      value: 69.63235294117646
    - type: dot_recall
      value: 74.96042216358839
    - type: euclidean_accuracy
      value: 87.65571913929784
    - type: euclidean_accuracy_threshold
      value: 70.2522873878479
    - type: euclidean_ap
      value: 79.12498501352084
    - type: euclidean_f1
      value: 72.19822109275731
    - type: euclidean_f1_threshold
      value: 73.4101414680481
    - type: euclidean_precision
      value: 69.63235294117646
    - type: euclidean_recall
      value: 74.96042216358839
    - type: main_score
      value: 79.31400852296694
    - type: manhattan_accuracy
      value: 87.78685104607499
    - type: manhattan_accuracy_threshold
      value: 2240.8660888671875
    - type: manhattan_ap
      value: 79.31400852296694
    - type: manhattan_f1
      value: 72.46414265408968
    - type: manhattan_f1_threshold
      value: 2333.853530883789
    - type: manhattan_precision
      value: 71.00531780197518
    - type: manhattan_recall
      value: 73.98416886543535
    - type: max_accuracy
      value: 87.78685104607499
    - type: max_ap
      value: 79.31400852296694
    - type: max_f1
      value: 72.46414265408968
    - type: max_precision
      value: 71.00531780197518
    - type: max_recall
      value: 74.96042216358839
    - type: similarity_accuracy
      value: 87.65571913929784
    - type: similarity_accuracy_threshold
      value: 75.32307505607605
    - type: similarity_ap
      value: 79.12498779374573
    - type: similarity_f1
      value: 72.19822109275731
    - type: similarity_f1_threshold
      value: 73.05474281311035
    - type: similarity_precision
      value: 69.63235294117646
    - type: similarity_recall
      value: 74.96042216358839
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus (default)
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cosine_accuracy
      value: 89.58939729110878
    - type: cosine_accuracy_threshold
      value: 74.62999820709229
    - type: cosine_ap
      value: 87.2110644450708
    - type: cosine_f1
      value: 79.70716766892018
    - type: cosine_f1_threshold
      value: 72.82971739768982
    - type: cosine_precision
      value: 77.40297424737032
    - type: cosine_recall
      value: 82.15275639051433
    - type: dot_accuracy
      value: 89.58939729110878
    - type: dot_accuracy_threshold
      value: 74.62999224662781
    - type: dot_ap
      value: 87.21106174391684
    - type: dot_f1
      value: 79.70716766892018
    - type: dot_f1_threshold
      value: 72.8297233581543
    - type: dot_precision
      value: 77.40297424737032
    - type: dot_recall
      value: 82.15275639051433
    - type: euclidean_accuracy
      value: 89.58939729110878
    - type: euclidean_accuracy_threshold
      value: 71.23202085494995
    - type: euclidean_ap
      value: 87.21106341739784
    - type: euclidean_f1
      value: 79.70716766892018
    - type: euclidean_f1_threshold
      value: 73.71604442596436
    - type: euclidean_precision
      value: 77.40297424737032
    - type: euclidean_recall
      value: 82.15275639051433
    - type: main_score
      value: 87.50024399984746
    - type: manhattan_accuracy
      value: 89.72522994527885
    - type: manhattan_accuracy_threshold
      value: 2315.9988403320312
    - type: manhattan_ap
      value: 87.50024399984746
    - type: manhattan_f1
      value: 80.0
    - type: manhattan_f1_threshold
      value: 2496.2581634521484
    - type: manhattan_precision
      value: 76.38859704419696
    - type: manhattan_recall
      value: 83.96981829380967
    - type: max_accuracy
      value: 89.72522994527885
    - type: max_ap
      value: 87.50024399984746
    - type: max_f1
      value: 80.0
    - type: max_precision
      value: 77.40297424737032
    - type: max_recall
      value: 83.96981829380967
    - type: similarity_accuracy
      value: 89.58939729110878
    - type: similarity_accuracy_threshold
      value: 74.62999820709229
    - type: similarity_ap
      value: 87.2110644450708
    - type: similarity_f1
      value: 79.70716766892018
    - type: similarity_f1_threshold
      value: 72.82971739768982
    - type: similarity_precision
      value: 77.40297424737032
    - type: similarity_recall
      value: 82.15275639051433
---


<h2 align="center"> LENS Embeddings</h2>

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-d4000 offering 4000-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-d4000
cd LENS-d4000
```

```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'<instruct>{task_instruction}\n<query>{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-d4000", ignore_mismatched_sizes=True)
model.lm_head = torch.load('lm_head.pth')
tokenizer = AutoTokenizer.from_pretrained("yibinlei/LENS-d4000")

# 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('<query>')
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)
```