sentence-transformers safetensors mistral feature-extraction mteb transformers en license:mit model-index autotrain_compatible text-generation-inference text-embeddings-inference endpoints_compatible region:us
---
language:
- en
license: mit
tags:
- mteb
- transformers
- sentence-transformers
model-index:
- name: Zeta-Alpha-E5-Mistral
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 77.76119402985074
    - type: ap
      value: 39.97673988468886
    - type: ap_weighted
      value: 39.97673988468886
    - type: f1
      value: 71.23171737695898
    - type: f1_weighted
      value: 79.55230307558237
    - type: main_score
      value: 77.76119402985074
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 96.61810000000001
    - type: ap
      value: 94.99559013902017
    - type: ap_weighted
      value: 94.99559013902017
    - type: f1
      value: 96.61758649480731
    - type: f1_weighted
      value: 96.61758649480731
    - type: main_score
      value: 96.61810000000001
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 59.26199999999999
    - type: f1
      value: 56.32963321217333
    - type: f1_weighted
      value: 56.32963321217333
    - type: main_score
      value: 59.26199999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: mteb/arguana
      config: default
      split: test
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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      value: 65.623
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      value: 41.536
    - type: map_at_10
      value: 57.485
    - type: map_at_100
      value: 58.013000000000005
    - type: map_at_1000
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    - type: map_at_20
      value: 57.957
    - type: map_at_3
      value: 53.284
    - type: map_at_5
      value: 55.837
    - type: mrr_at_1
      value: 42.17638691322902
    - type: mrr_at_10
      value: 57.7096175122492
    - type: mrr_at_100
      value: 58.23610809196743
    - type: mrr_at_1000
      value: 58.23673750573145
    - type: mrr_at_20
      value: 58.180348622747324
    - type: mrr_at_3
      value: 53.44950213371275
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    - type: ndcg_at_1
      value: 41.536
    - type: ndcg_at_10
      value: 65.623
    - type: ndcg_at_100
      value: 67.63
    - type: ndcg_at_1000
      value: 67.64099999999999
    - type: ndcg_at_20
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    - type: precision_at_1
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    - type: precision_at_100
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    - type: precision_at_3
      value: 22.641
    - type: precision_at_5
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    - type: recall_at_1
      value: 41.536
    - type: recall_at_10
      value: 91.323
    - type: recall_at_100
      value: 99.57300000000001
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_20
      value: 97.51100000000001
    - type: recall_at_3
      value: 67.923
    - type: recall_at_5
      value: 79.232
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: main_score
      value: 52.026635060007244
    - type: v_measure
      value: 52.026635060007244
    - type: v_measure_std
      value: 14.357137408692006
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: main_score
      value: 47.834914950269855
    - type: v_measure
      value: 47.834914950269855
    - type: v_measure_std
      value: 14.487028918517247
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: main_score
      value: 64.5808745066313
    - type: map
      value: 64.5808745066313
    - type: mrr
      value: 77.56540473991997
    - type: nAUC_map_diff1
      value: 23.168800779252464
    - type: nAUC_map_max
      value: 30.342203769599735
    - type: nAUC_map_std
      value: 22.562701982176833
    - type: nAUC_mrr_diff1
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    - type: nAUC_mrr_max
      value: 43.302228243606045
    - type: nAUC_mrr_std
      value: 26.432985515912673
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cosine_pearson
      value: 87.97778619889539
    - type: cosine_spearman
      value: 86.44233109293758
    - type: euclidean_pearson
      value: 86.6664224630525
    - type: euclidean_spearman
      value: 86.44233109293758
    - type: main_score
      value: 86.44233109293758
    - type: manhattan_pearson
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    - type: manhattan_spearman
      value: 86.61402175201368
    - type: pearson
      value: 87.97778619889539
    - type: spearman
      value: 86.44233109293758
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
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      value: 83.1103896103896
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      value: 82.2932953112279
    - type: f1_weighted
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    - type: main_score
      value: 83.1103896103896
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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    - type: v_measure
      value: 43.73746639290943
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      value: 0.8808902310879784
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
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    - type: v_measure
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  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackRetrieval
      type: CQADupstackRetrieval_is_a_combined_dataset
      config: default
      split: test
      revision: 46989137a86843e03a6195de44b09deda022eec7
    metrics:
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  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: mteb/climate-fever
      config: default
      split: test
      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
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  - task:
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      type: mteb/imdb
      config: default
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      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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  - task:
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    dataset:
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      type: mteb/msmarco
      config: default
      split: dev
      revision: c5a29a104738b98a9e76336939199e264163d4a0
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    - type: recall_at_1
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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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    - type: f1
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    - type: f1_weighted
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    - type: main_score
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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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    - type: f1
      value: 59.350461259612345
    - type: f1_weighted
      value: 81.51891267687044
    - type: main_score
      value: 80.51527587779297
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
    - type: accuracy
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  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
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      value: 79.97982515131137
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
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      value: 38.37635785818304
    - type: v_measure
      value: 38.37635785818304
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      value: 1.6943794496059137
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
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      value: 37.6711034083755
    - type: v_measure
      value: 37.6711034083755
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      value: 1.1408887612104992
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
    metrics:
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    - type: map
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  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: mteb/nfcorpus
      config: default
      split: test
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
    metrics:
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      value: 40.463
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    - type: map_at_10
      value: 15.699
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  - task:
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  - task:
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  - task:
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  - task:
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      value: 81.88046945336663
    - type: euclidean_pearson
      value: 82.82887106181879
    - type: euclidean_spearman
      value: 81.88047605481775
    - type: main_score
      value: 81.88046945336663
    - type: manhattan_pearson
      value: 82.7839019603756
    - type: manhattan_spearman
      value: 81.83505450284663
    - type: pearson
      value: 85.65467419133347
    - type: spearman
      value: 81.88046945336663
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cosine_pearson
      value: 85.8979872663498
    - type: cosine_spearman
      value: 78.63991285161867
    - type: euclidean_pearson
      value: 81.20243176386163
    - type: euclidean_spearman
      value: 78.64021127260493
    - type: main_score
      value: 78.63991285161867
    - type: manhattan_pearson
      value: 81.58673652635328
    - type: manhattan_spearman
      value: 79.03930665482164
    - type: pearson
      value: 85.8979872663498
    - type: spearman
      value: 78.63991285161867
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cosine_pearson
      value: 87.10598414063074
    - type: cosine_spearman
      value: 87.12110799581852
    - type: euclidean_pearson
      value: 86.52284239759508
    - type: euclidean_spearman
      value: 87.12110799581852
    - type: main_score
      value: 87.12110799581852
    - type: manhattan_pearson
      value: 86.61105352996736
    - type: manhattan_spearman
      value: 87.34100209521596
    - type: pearson
      value: 87.10598414063074
    - type: spearman
      value: 87.12110799581852
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cosine_pearson
      value: 85.66540041627184
    - type: cosine_spearman
      value: 83.55263671417923
    - type: euclidean_pearson
      value: 84.2332532036626
    - type: euclidean_spearman
      value: 83.55264421653584
    - type: main_score
      value: 83.55263671417923
    - type: manhattan_pearson
      value: 84.14418954784165
    - type: manhattan_spearman
      value: 83.58193360267302
    - type: pearson
      value: 85.66540041627184
    - type: spearman
      value: 83.55263671417923
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cosine_pearson
      value: 89.83404956912175
    - type: cosine_spearman
      value: 90.09569633194636
    - type: euclidean_pearson
      value: 89.31121256629982
    - type: euclidean_spearman
      value: 90.09569632193572
    - type: main_score
      value: 90.09569633194636
    - type: manhattan_pearson
      value: 89.30064909066367
    - type: manhattan_spearman
      value: 90.20232732019451
    - type: pearson
      value: 89.83404956912175
    - type: spearman
      value: 90.09569633194636
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cosine_pearson
      value: 86.27894370732598
    - type: cosine_spearman
      value: 87.22000226558832
    - type: euclidean_pearson
      value: 85.92822715155758
    - type: euclidean_spearman
      value: 87.22000226558832
    - type: main_score
      value: 87.22000226558832
    - type: manhattan_pearson
      value: 85.9498561399522
    - type: manhattan_spearman
      value: 87.28837300894288
    - type: pearson
      value: 86.27894370732598
    - type: spearman
      value: 87.22000226558832
  - 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: 91.60185356782324
    - type: cosine_spearman
      value: 91.43471625912765
    - type: euclidean_pearson
      value: 91.52529087606635
    - type: euclidean_spearman
      value: 91.43471625912765
    - type: main_score
      value: 91.43471625912765
    - type: manhattan_pearson
      value: 91.34917173506308
    - type: manhattan_spearman
      value: 91.2112665439884
    - type: pearson
      value: 91.60185356782324
    - type: spearman
      value: 91.43471625912765
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: cosine_pearson
      value: 68.735098373629
    - type: cosine_spearman
      value: 67.76156085991387
    - type: euclidean_pearson
      value: 68.38053954511516
    - type: euclidean_spearman
      value: 67.76156085991387
    - type: main_score
      value: 67.76156085991387
    - type: manhattan_pearson
      value: 68.4533080173714
    - type: manhattan_spearman
      value: 67.76676959397871
    - type: pearson
      value: 68.735098373629
    - type: spearman
      value: 67.76156085991387
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cosine_pearson
      value: 87.63236624274985
    - type: cosine_spearman
      value: 88.27561759951514
    - type: euclidean_pearson
      value: 87.61137355553329
    - type: euclidean_spearman
      value: 88.27561759951514
    - type: main_score
      value: 88.27561759951514
    - type: manhattan_pearson
      value: 87.63505381780153
    - type: manhattan_spearman
      value: 88.41268943146845
    - type: pearson
      value: 87.63236624274985
    - type: spearman
      value: 88.27561759951514
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: main_score
      value: 85.16412972900244
    - type: map
      value: 85.16412972900244
    - type: mrr
      value: 96.15786628041529
    - type: nAUC_map_diff1
      value: -1.5068306084088756
    - type: nAUC_map_max
      value: 48.81296049442589
    - type: nAUC_map_std
      value: 65.0187132933644
    - type: nAUC_mrr_diff1
      value: 44.22872564939586
    - type: nAUC_mrr_max
      value: 85.19719227096536
    - type: nAUC_mrr_std
      value: 79.62669870868876
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: mteb/scifact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
    metrics:
    - type: main_score
      value: 77.377
    - type: map_at_1
      value: 62.217
    - type: map_at_10
      value: 73.115
    - type: map_at_100
      value: 73.63499999999999
    - type: map_at_1000
      value: 73.644
    - type: map_at_20
      value: 73.528
    - type: map_at_3
      value: 70.62
    - type: map_at_5
      value: 72.16
    - type: mrr_at_1
      value: 65.0
    - type: mrr_at_10
      value: 74.05542328042326
    - type: mrr_at_100
      value: 74.46295785951277
    - type: mrr_at_1000
      value: 74.47168088874803
    - type: mrr_at_20
      value: 74.35632423132421
    - type: mrr_at_3
      value: 72.55555555555556
    - type: mrr_at_5
      value: 73.38888888888887
    - type: nauc_map_at_1000_diff1
      value: 72.31754010838618
    - type: nauc_map_at_1000_max
      value: 60.59518156728312
    - type: nauc_map_at_1000_std
      value: -3.601504782295705
    - type: nauc_map_at_100_diff1
      value: 72.32057771059107
    - type: nauc_map_at_100_max
      value: 60.60481879601873
    - type: nauc_map_at_100_std
      value: -3.6030430073837167
    - type: nauc_map_at_10_diff1
      value: 72.15009895006031
    - type: nauc_map_at_10_max
      value: 60.49958178006608
    - type: nauc_map_at_10_std
      value: -4.305475753173601
    - type: nauc_map_at_1_diff1
      value: 76.32919417574946
    - type: nauc_map_at_1_max
      value: 54.77358788281581
    - type: nauc_map_at_1_std
      value: -9.773898055794557
    - type: nauc_map_at_20_diff1
      value: 72.15740734516393
    - type: nauc_map_at_20_max
      value: 60.61318821265446
    - type: nauc_map_at_20_std
      value: -3.6016854193910803
    - type: nauc_map_at_3_diff1
      value: 72.07435404889445
    - type: nauc_map_at_3_max
      value: 56.93970890047747
    - type: nauc_map_at_3_std
      value: -8.697324220121793
    - type: nauc_map_at_5_diff1
      value: 72.42599960854554
    - type: nauc_map_at_5_max
      value: 60.12535137001906
    - type: nauc_map_at_5_std
      value: -4.437892354037166
    - type: nauc_mrr_at_1000_diff1
      value: 72.75103842052889
    - type: nauc_mrr_at_1000_max
      value: 62.72341811793062
    - type: nauc_mrr_at_1000_std
      value: -0.7759889099766357
    - type: nauc_mrr_at_100_diff1
      value: 72.75396801842608
    - type: nauc_mrr_at_100_max
      value: 62.73241247525427
    - type: nauc_mrr_at_100_std
      value: -0.7786866224468205
    - type: nauc_mrr_at_10_diff1
      value: 72.5942754009733
    - type: nauc_mrr_at_10_max
      value: 62.895066542256664
    - type: nauc_mrr_at_10_std
      value: -0.9018200301159104
    - type: nauc_mrr_at_1_diff1
      value: 77.63311362465076
    - type: nauc_mrr_at_1_max
      value: 62.42059294219759
    - type: nauc_mrr_at_1_std
      value: -1.3182520953698476
    - type: nauc_mrr_at_20_diff1
      value: 72.58522880943326
    - type: nauc_mrr_at_20_max
      value: 62.73063935403417
    - type: nauc_mrr_at_20_std
      value: -0.7910003366564456
    - type: nauc_mrr_at_3_diff1
      value: 72.70751722757556
    - type: nauc_mrr_at_3_max
      value: 62.38218933726893
    - type: nauc_mrr_at_3_std
      value: -1.7126398606397155
    - type: nauc_mrr_at_5_diff1
      value: 72.57550761997256
    - type: nauc_mrr_at_5_max
      value: 62.70945847818393
    - type: nauc_mrr_at_5_std
      value: -0.30886077098332143
    - type: nauc_ndcg_at_1000_diff1
      value: 71.6036105202873
    - type: nauc_ndcg_at_1000_max
      value: 61.99911514670603
    - type: nauc_ndcg_at_1000_std
      value: -2.050470755577302
    - type: nauc_ndcg_at_100_diff1
      value: 71.70345074974581
    - type: nauc_ndcg_at_100_max
      value: 62.374525611545714
    - type: nauc_ndcg_at_100_std
      value: -1.922345118135967
    - type: nauc_ndcg_at_10_diff1
      value: 70.40027928749286
    - type: nauc_ndcg_at_10_max
      value: 62.36595526966657
    - type: nauc_ndcg_at_10_std
      value: -3.862278807246422
    - type: nauc_ndcg_at_1_diff1
      value: 77.63311362465076
    - type: nauc_ndcg_at_1_max
      value: 62.42059294219759
    - type: nauc_ndcg_at_1_std
      value: -1.3182520953698476
    - type: nauc_ndcg_at_20_diff1
      value: 70.21719291674641
    - type: nauc_ndcg_at_20_max
      value: 62.356711760569404
    - type: nauc_ndcg_at_20_std
      value: -2.240360396463778
    - type: nauc_ndcg_at_3_diff1
      value: 70.72483260039468
    - type: nauc_ndcg_at_3_max
      value: 59.465348910073445
    - type: nauc_ndcg_at_3_std
      value: -6.379991854598364
    - type: nauc_ndcg_at_5_diff1
      value: 70.91296936044013
    - type: nauc_ndcg_at_5_max
      value: 61.5986283773017
    - type: nauc_ndcg_at_5_std
      value: -3.064893399445654
    - type: nauc_precision_at_1000_diff1
      value: -25.399544557043956
    - type: nauc_precision_at_1000_max
      value: 17.838641101318792
    - type: nauc_precision_at_1000_std
      value: 54.531382221213185
    - type: nauc_precision_at_100_diff1
      value: -15.78139909072201
    - type: nauc_precision_at_100_max
      value: 24.183801380755472
    - type: nauc_precision_at_100_std
      value: 50.39320972640593
    - type: nauc_precision_at_10_diff1
      value: 4.1199958514831
    - type: nauc_precision_at_10_max
      value: 37.922630159717926
    - type: nauc_precision_at_10_std
      value: 32.94959551960178
    - type: nauc_precision_at_1_diff1
      value: 77.63311362465076
    - type: nauc_precision_at_1_max
      value: 62.42059294219759
    - type: nauc_precision_at_1_std
      value: -1.3182520953698476
    - type: nauc_precision_at_20_diff1
      value: -8.926047159112303
    - type: nauc_precision_at_20_max
      value: 29.369903951067172
    - type: nauc_precision_at_20_std
      value: 41.793379234725904
    - type: nauc_precision_at_3_diff1
      value: 36.51209832895358
    - type: nauc_precision_at_3_max
      value: 51.07398992745159
    - type: nauc_precision_at_3_std
      value: 13.831661495933623
    - type: nauc_precision_at_5_diff1
      value: 19.526084047733807
    - type: nauc_precision_at_5_max
      value: 46.67537950098273
    - type: nauc_precision_at_5_std
      value: 31.06747779005178
    - 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: 77.45764972655711
    - type: nauc_recall_at_100_max
      value: 85.69427771108462
    - type: nauc_recall_at_100_std
      value: 10.277444311057575
    - type: nauc_recall_at_10_diff1
      value: 59.14685653975806
    - type: nauc_recall_at_10_max
      value: 67.75739956082005
    - type: nauc_recall_at_10_std
      value: -12.22251646924215
    - type: nauc_recall_at_1_diff1
      value: 76.32919417574946
    - type: nauc_recall_at_1_max
      value: 54.77358788281581
    - type: nauc_recall_at_1_std
      value: -9.773898055794557
    - type: nauc_recall_at_20_diff1
      value: 49.90908644159423
    - type: nauc_recall_at_20_max
      value: 70.55383556931541
    - type: nauc_recall_at_20_std
      value: -3.7004275394368182
    - type: nauc_recall_at_3_diff1
      value: 64.34183819693267
    - type: nauc_recall_at_3_max
      value: 55.782789721196444
    - type: nauc_recall_at_3_std
      value: -13.886583892174077
    - type: nauc_recall_at_5_diff1
      value: 63.467364565196135
    - type: nauc_recall_at_5_max
      value: 62.51562390716315
    - type: nauc_recall_at_5_std
      value: -4.715416491952255
    - type: ndcg_at_1
      value: 65.0
    - type: ndcg_at_10
      value: 77.377
    - type: ndcg_at_100
      value: 79.36
    - type: ndcg_at_1000
      value: 79.644
    - type: ndcg_at_20
      value: 78.61200000000001
    - type: ndcg_at_3
      value: 73.624
    - type: ndcg_at_5
      value: 75.458
    - type: precision_at_1
      value: 65.0
    - type: precision_at_10
      value: 10.100000000000001
    - type: precision_at_100
      value: 1.107
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_20
      value: 5.333
    - type: precision_at_3
      value: 28.778
    - type: precision_at_5
      value: 18.8
    - type: recall_at_1
      value: 62.217
    - type: recall_at_10
      value: 89.156
    - type: recall_at_100
      value: 97.667
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_20
      value: 93.667
    - type: recall_at_3
      value: 79.183
    - type: recall_at_5
      value: 83.672
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.8019801980198
    - type: cos_sim_ap
      value: 95.25139396923107
    - type: dot_sim_accuracy
      value: 0.998019801980198
    - type: dot_sim_ap
      value: 95.25139396923107
    - type: max_accuracy
      value: 99.8019801980198
    - type: max_ap
      value: 95.43878917155146
    - type: max_f1
      value: 90.0398406374502
    - type: main_score
      value: 95.43878917155146
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: main_score
      value: 75.91311883393888
    - type: v_measure
      value: 75.91311883393888
    - type: v_measure_std
      value: 3.286198100593212
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: main_score
      value: 47.171049215275694
    - type: v_measure
      value: 47.171049215275694
    - type: v_measure_std
      value: 1.6586563477857534
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: main_score
      value: 54.15041943470163
    - type: map
      value: 54.15041943470163
    - type: mrr
      value: 55.03112798149563
    - type: nAUC_map_diff1
      value: 39.50144777017669
    - type: nAUC_map_max
      value: 14.024793174481395
    - type: nAUC_map_std
      value: 6.533766502190137
    - type: nAUC_mrr_diff1
      value: 39.72560651870919
    - type: nAUC_mrr_max
      value: 14.807887392821616
    - type: nAUC_mrr_std
      value: 7.270272018791473
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cosine_pearson
      value: 31.57515534177576
    - type: cosine_spearman
      value: 31.415247541636194
    - type: dot_pearson
      value: 31.575170220667488
    - type: dot_spearman
      value: 31.415247541636194
    - type: main_score
      value: 31.415247541636194
    - type: pearson
      value: 31.57515534177576
    - type: spearman
      value: 31.415247541636194
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: mteb/trec-covid
      config: default
      split: test
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
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      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
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      value: 16.4629793865873
    - type: ap_weighted
      value: 16.4629793865873
    - type: f1
      value: 58.32993265544471
    - type: f1_weighted
      value: 80.94360012442658
    - type: main_score
      value: 75.87890625
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 65.21788341822298
    - type: f1
      value: 65.00914562845475
    - type: f1_weighted
      value: 63.672388825903845
    - type: main_score
      value: 65.21788341822298
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: main_score
      value: 57.152337838073485
    - type: v_measure
      value: 57.152337838073485
    - type: v_measure_std
      value: 0.8799366494028795
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: max_accuracy
      value: 88.63920844012637
    - type: max_ap
      value: 81.41444048232692
    - type: max_f1
      value: 74.84396892115653
    - type: accuracy
      value: 88.63920844012637
    - type: accuracy_threshold
      value: 84.5294713973999
    - type: ap
      value: 81.41443623323144
    - type: f1
      value: 74.84396892115653
    - type: f1_threshold
      value: 82.87262320518494
    - type: precision
      value: 72.34671263235656
    - type: recall
      value: 77.5197889182058
    - type: main_score
      value: 81.41444048232692
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: max_accuracy
      value: 89.90569332867622
    - type: max_ap
      value: 87.91825594329686
    - type: max_f1
      value: 80.35081439054949
    - type: accuracy
      value: 89.90569332867622
    - type: accuracy_threshold
      value: 81.01733326911926
    - type: ap
      value: 87.91824445175028
    - type: f1
      value: 80.35081439054949
    - type: f1_threshold
      value: 78.65387201309204
    - type: precision
      value: 75.0853013982739
    - type: recall
      value: 86.41053279950724
    - type: main_score
      value: 87.91825594329686
---
## Zeta-Alpha-E5-Mistral

We introduce Zeta Alpha's first public embedding model, a retrieval-specialized, 7B parameter embedding model trained on top of [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct).
This model marks the first published model from Zeta Alpha's open science embedding models. 

Check out our blog post for a complete breakdown of the training set we used and all the training details: [Zeta Alpha blog](https://www.zeta-alpha.com/post/fine-tuning-an-llm-for-state-of-the-art-retrieval-zeta-alpha-s-top-10-submission-to-the-the-mteb-be)

We are also making available our internal evaluation set, called [NanoBEIR](https://huggingface.co/collections/zeta-alpha-ai/nanobeir-66e1a0af21dfd93e620cd9f6), a collection of Nano (i.e., 50 queries+~10k documents) per BEIR dataset.

### Lora Weights

The lora weights are also available, so there is no need to download the full model. 
## How to Run

The model was trained with the same instruction-tuning strategy as the original [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) model. Therefore, queries should be formatted as follows:

```
Instruct: <task description>\nQuery: <query>
```

### Sentence Transformers
```python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("zeta-alpha-ai/Zeta-Alpha-E5-Mistral")

def get_detailed_instruct(task_description: str, query: str) -> str:
    return f'Instruct: {task_description}\nQuery: {query}'

task = "Given a claim about climate change, retrieve documents that support or refute the claim"
queries = [
    get_detailed_instruct(task, "In Alaska, brown bears are changing their feeding habits to eat elderberries that ripen earlier."),
    get_detailed_instruct(task, "Local and regional sea levels continue to exhibit typical natural variability—in some places rising and in others falling.")
]

passages = [
  "The brown bear ( Ursus arctos ) is a large bear with the widest distribution of any living ursid . The species is distributed across much of northern Eurasia and North America . It is one of the two largest terrestrial carnivorans alive today , rivaled in body size only by its close cousin , the polar bear ( Ursus maritimus ) , which is much less variable in size and averages larger due to this . There are several recognized subspecies , many of which are quite well-known within their native ranges , found in the brown bear species .   The brown bear 's principal range includes parts of Russia , Central Asia , China , Canada , the United States ( mostly Alaska ) , Scandinavia and the Carpathian region ( especially Romania ) , Anatolia , and Caucasus . The brown bear is recognized as a national and state animal in several European countries .   While the brown bear 's range has shrunk and it has faced local extinctions , it remains listed as a least concern species by the International Union for Conservation of Nature ( IUCN ) with a total population of approximately 200,000 . As of 2012 , this and the American black bear are the only bear species not classified as threatened by the IUCN . However , the Californian , North African ( Atlas bear ) , and Mexican subspecies were hunted to extinction in the nineteenth and early twentieth centuries , and many of the southern Asian subspecies are highly endangered . One of the smaller-bodied subspecies , the Himalayan brown bear , is critically endangered , occupying only 2 % of its former range and threatened by uncontrolled poaching for its parts . The Marsican brown bear , one of several currently isolated populations of the main Eurasian brown bear race , in central Italy is believed to have a population of just 30 to 40 bears .",
  "ean sea level ( MSL ) ( abbreviated simply sea level ) is an average level of the surface of one or more of Earth 's oceans from which heights such as elevations may be measured . MSL is a type of vertical datuma standardised geodetic reference pointthat is used , for example , as a chart datum in cartography and marine navigation , or , in aviation , as the standard sea level at which atmospheric pressure is measured in order to calibrate altitude and , consequently , aircraft flight levels . A common and relatively straightforward mean sea-level standard is the midpoint between a mean low and mean high tide at a particular location .   Sea levels can be affected by many factors and are known to have varied greatly over geological time scales . The careful measurement of variations in MSL can offer insights into ongoing climate change , and sea level rise has been widely quoted as evidence of ongoing global warming .   The term above sea level generally refers to above mean sea level ( AMSL ) ."
]

embeddings = model.encode(queries + passages)
scores = model.similarity(embeddings[:2], embeddings[2:]) * 100
print(scores.tolist())
# [[66.12603759765625, 43.760101318359375], [47.67058563232422, 63.7889518737793]]
```

### Transformers
``` python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel

def last_token_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
    if left_padding:
        return last_hidden_states[:, -1]
    else:
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = last_hidden_states.shape[0]
        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
    return f'Instruct: {task_description}\nQuery: {query}'

task = "Given a claim about climate change, retrieve documents that support or refute the claim"
queries = [
    get_detailed_instruct(task, "In Alaska, brown bears are changing their feeding habits to eat elderberries that ripen earlier."),
    get_detailed_instruct(task, "Local and regional sea levels continue to exhibit typical natural variability—in some places rising and in others falling.")
]

passages = [
  "The brown bear ( Ursus arctos ) is a large bear with the widest distribution of any living ursid . The species is distributed across much of northern Eurasia and North America . It is one of the two largest terrestrial carnivorans alive today , rivaled in body size only by its close cousin , the polar bear ( Ursus maritimus ) , which is much less variable in size and averages larger due to this . There are several recognized subspecies , many of which are quite well-known within their native ranges , found in the brown bear species .   The brown bear 's principal range includes parts of Russia , Central Asia , China , Canada , the United States ( mostly Alaska ) , Scandinavia and the Carpathian region ( especially Romania ) , Anatolia , and Caucasus . The brown bear is recognized as a national and state animal in several European countries .   While the brown bear 's range has shrunk and it has faced local extinctions , it remains listed as a least concern species by the International Union for Conservation of Nature ( IUCN ) with a total population of approximately 200,000 . As of 2012 , this and the American black bear are the only bear species not classified as threatened by the IUCN . However , the Californian , North African ( Atlas bear ) , and Mexican subspecies were hunted to extinction in the nineteenth and early twentieth centuries , and many of the southern Asian subspecies are highly endangered . One of the smaller-bodied subspecies , the Himalayan brown bear , is critically endangered , occupying only 2 % of its former range and threatened by uncontrolled poaching for its parts . The Marsican brown bear , one of several currently isolated populations of the main Eurasian brown bear race , in central Italy is believed to have a population of just 30 to 40 bears .",
  "ean sea level ( MSL ) ( abbreviated simply sea level ) is an average level of the surface of one or more of Earth 's oceans from which heights such as elevations may be measured . MSL is a type of vertical datuma standardised geodetic reference pointthat is used , for example , as a chart datum in cartography and marine navigation , or , in aviation , as the standard sea level at which atmospheric pressure is measured in order to calibrate altitude and , consequently , aircraft flight levels . A common and relatively straightforward mean sea-level standard is the midpoint between a mean low and mean high tide at a particular location .   Sea levels can be affected by many factors and are known to have varied greatly over geological time scales . The careful measurement of variations in MSL can offer insights into ongoing climate change , and sea level rise has been widely quoted as evidence of ongoing global warming .   The term above sea level generally refers to above mean sea level ( AMSL ) ."
]

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("zeta-alpha-ai/Zeta-Alpha-E5-Mistral")
model = AutoModel.from_pretrained("zeta-alpha-ai/Zeta-Alpha-E5-Mistral")

# get the embeddings
max_length = 4096
input_texts = queries + passages
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors="pt")
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
[[66.15530395507812, 43.65541458129883], [47.681705474853516, 63.67986297607422]]

``` 

### Zeta Alpha
Zeta Alpha is the premier Neural Discovery Platform for AI and more. We are an Amsterdam-based R&D and product lab with a passion for AI technology, with offices on the Science Park campus of the University of Amsterdam. and in San Francisco. 


The Zeta Alpha Research team:
- Arthur Câmara
- Dinos Papakostas
- Mathias Parisot
- Fernando Rejon Barrera
- Jakub Zavrel
