---
tags:
- mteb
model-index:
- name: cde-small-v1
  results:
  - dataset:
      config: en
      name: MTEB AmazonCounterfactualClassification (en)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    metrics:
    - type: accuracy
      value: 87.02985074626866
    - type: ap
      value: 56.706190238632956
    - type: ap_weighted
      value: 56.706190238632956
    - type: f1
      value: 81.93161953007674
    - type: f1_weighted
      value: 87.7650174177188
    - type: main_score
      value: 87.02985074626866
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB AmazonPolarityClassification (default)
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
      split: test
      type: mteb/amazon_polarity
    metrics:
    - type: accuracy
      value: 94.664175
    - type: ap
      value: 91.68668057762052
    - type: ap_weighted
      value: 91.68668057762052
    - type: f1
      value: 94.65859470333152
    - type: f1_weighted
      value: 94.65859470333152
    - type: main_score
      value: 94.664175
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB AmazonReviewsClassification (en)
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
      split: test
      type: mteb/amazon_reviews_multi
    metrics:
    - type: accuracy
      value: 55.762
    - type: f1
      value: 55.06427827477677
    - type: f1_weighted
      value: 55.06427827477677
    - type: main_score
      value: 55.762
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB ArguAna (default)
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
      split: test
      type: mteb/arguana
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      value: 49.004
    - type: map_at_10
      value: 64.741
    - type: map_at_100
      value: 65.045
    - type: map_at_1000
      value: 65.048
    - type: map_at_20
      value: 64.999
    - type: map_at_3
      value: 61.344
    - type: map_at_5
      value: 63.595
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      value: 50.71123755334281
    - type: mrr_at_10
      value: 65.32688703741336
    - type: mrr_at_100
      value: 65.63793917015693
    - type: mrr_at_1000
      value: 65.64038101143724
    - type: mrr_at_20
      value: 65.59178002869953
    - type: mrr_at_3
      value: 61.960644855381695
    - type: mrr_at_5
      value: 64.12636320531058
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      value: -16.496874845739885
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      value: 12.049863016253427
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      value: -11.968579522299635
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      value: 0.7775458885343522
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      value: 9.373350504221595
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      value: -9.131259877846116
    - type: ndcg_at_1
      value: 49.004
    - type: ndcg_at_10
      value: 71.99600000000001
    - type: ndcg_at_100
      value: 73.173
    - type: ndcg_at_1000
      value: 73.214
    - type: ndcg_at_20
      value: 72.91
    - type: ndcg_at_3
      value: 65.21900000000001
    - type: ndcg_at_5
      value: 69.284
    - type: precision_at_1
      value: 49.004
    - type: precision_at_10
      value: 9.452
    - type: precision_at_100
      value: 0.9939999999999999
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_20
      value: 4.904
    - type: precision_at_3
      value: 25.462
    - type: precision_at_5
      value: 17.255000000000003
    - type: recall_at_1
      value: 49.004
    - type: recall_at_10
      value: 94.523
    - type: recall_at_100
      value: 99.36
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_20
      value: 98.08
    - type: recall_at_3
      value: 76.387
    - type: recall_at_5
      value: 86.273
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ArxivClusteringP2P (default)
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
      split: test
      type: mteb/arxiv-clustering-p2p
    metrics:
    - type: main_score
      value: 48.629569816593516
    - type: v_measure
      value: 48.629569816593516
    - type: v_measure_std
      value: 14.01810149072028
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB ArxivClusteringS2S (default)
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
      split: test
      type: mteb/arxiv-clustering-s2s
    metrics:
    - type: main_score
      value: 40.52366904677561
    - type: v_measure
      value: 40.52366904677561
    - type: v_measure_std
      value: 14.375876773823757
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB AskUbuntuDupQuestions (default)
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
      split: test
      type: mteb/askubuntudupquestions-reranking
    metrics:
    - type: main_score
      value: 61.27347206107508
    - type: map
      value: 61.27347206107508
    - type: mrr
      value: 74.49105219188321
    - type: nAUC_map_diff1
      value: 13.442645655149457
    - type: nAUC_map_max
      value: 25.013363268430027
    - type: nAUC_map_std
      value: 17.60175231611674
    - type: nAUC_mrr_diff1
      value: 25.217675209249435
    - type: nAUC_mrr_max
      value: 32.37381560372622
    - type: nAUC_mrr_std
      value: 22.584922632508412
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB BIOSSES (default)
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
      split: test
      type: mteb/biosses-sts
    metrics:
    - type: cosine_pearson
      value: 89.09452267906886
    - type: cosine_spearman
      value: 86.73450642504955
    - type: euclidean_pearson
      value: 87.1275130552617
    - type: euclidean_spearman
      value: 86.93812552248012
    - type: main_score
      value: 86.73450642504955
    - type: manhattan_pearson
      value: 86.79403606129864
    - type: manhattan_spearman
      value: 86.76824213349957
    - type: pearson
      value: 89.09452267906886
    - type: spearman
      value: 86.73450642504955
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB Banking77Classification (default)
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
      split: test
      type: mteb/banking77
    metrics:
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      value: 88.58116883116884
    - type: f1
      value: 88.54536316207125
    - type: f1_weighted
      value: 88.54536316207125
    - type: main_score
      value: 88.58116883116884
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB BiorxivClusteringP2P (default)
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
      split: test
      type: mteb/biorxiv-clustering-p2p
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      value: 44.89554099528695
    - type: v_measure
      value: 44.89554099528695
    - type: v_measure_std
      value: 0.6101675839696261
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB BiorxivClusteringS2S (default)
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
      split: test
      type: mteb/biorxiv-clustering-s2s
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    - type: v_measure
      value: 37.89775676199564
    - type: v_measure_std
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    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB CQADupstackAndroidRetrieval (default)
      revision: f46a197baaae43b4f621051089b82a364682dfeb
      split: test
      type: mteb/cqadupstack-android
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    task:
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  - dataset:
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      name: MTEB ImdbClassification (default)
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
      split: test
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    task:
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  - dataset:
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      revision: c5a29a104738b98a9e76336939199e264163d4a0
      split: dev
      type: mteb/msmarco
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    - type: recall_at_1000
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    task:
      type: Retrieval
  - dataset:
      config: en
      name: MTEB MTOPDomainClassification (en)
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
      split: test
      type: mteb/mtop_domain
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    - type: f1_weighted
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    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MTOPIntentClassification (en)
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
      split: test
      type: mteb/mtop_intent
    metrics:
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      value: 89.1655266757866
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveIntentClassification (en)
      revision: 4672e20407010da34463acc759c162ca9734bca6
      split: test
      type: mteb/amazon_massive_intent
    metrics:
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      value: 76.60390047074647
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveScenarioClassification (en)
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
      split: test
      type: mteb/amazon_massive_scenario
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    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB MedrxivClusteringP2P (default)
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
      split: test
      type: mteb/medrxiv-clustering-p2p
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      value: 37.66500681777215
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      value: 1.4953449515069268
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB MedrxivClusteringS2S (default)
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
      split: test
      type: mteb/medrxiv-clustering-s2s
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      value: 35.51021437644991
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      value: 1.3321174913629759
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB MindSmallReranking (default)
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
      split: test
      type: mteb/mind_small
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    task:
      type: Reranking
  - dataset:
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      name: MTEB NFCorpus (default)
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
      split: test
      type: mteb/nfcorpus
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      revision: a0d554a64d88156834ff5ae9920b964011b16384
      split: test
      type: mteb/sts12-sts
    metrics:
    - type: cosine_pearson
      value: 81.36071984442052
    - type: cosine_spearman
      value: 74.2212823495219
    - type: euclidean_pearson
      value: 78.31139429452078
    - type: euclidean_spearman
      value: 74.02790834412275
    - type: main_score
      value: 74.2212823495219
    - type: manhattan_pearson
      value: 78.26141328104697
    - type: manhattan_spearman
      value: 74.02545007676329
    - type: pearson
      value: 81.36071984442052
    - type: spearman
      value: 74.2212823495219
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS13 (default)
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
      split: test
      type: mteb/sts13-sts
    metrics:
    - type: cosine_pearson
      value: 85.49925337918731
    - type: cosine_spearman
      value: 86.12368715292688
    - type: euclidean_pearson
      value: 85.71147581542367
    - type: euclidean_spearman
      value: 86.64112317821541
    - type: main_score
      value: 86.12368715292688
    - type: manhattan_pearson
      value: 85.58242941611371
    - type: manhattan_spearman
      value: 86.51041533466731
    - type: pearson
      value: 85.49925337918731
    - type: spearman
      value: 86.12368715292688
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS14 (default)
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
      split: test
      type: mteb/sts14-sts
    metrics:
    - type: cosine_pearson
      value: 82.24735192639226
    - type: cosine_spearman
      value: 78.88155361224834
    - type: euclidean_pearson
      value: 80.52048132030517
    - type: euclidean_spearman
      value: 78.1335955670817
    - type: main_score
      value: 78.88155361224834
    - type: manhattan_pearson
      value: 80.48178866605353
    - type: manhattan_spearman
      value: 78.08994918255844
    - type: pearson
      value: 82.24735192639226
    - type: spearman
      value: 78.88155361224834
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS15 (default)
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
      split: test
      type: mteb/sts15-sts
    metrics:
    - type: cosine_pearson
      value: 86.27381322229758
    - type: cosine_spearman
      value: 87.5038962579188
    - type: euclidean_pearson
      value: 86.7575259976948
    - type: euclidean_spearman
      value: 87.3358778981031
    - type: main_score
      value: 87.5038962579188
    - type: manhattan_pearson
      value: 86.72177109814491
    - type: manhattan_spearman
      value: 87.30593609243358
    - type: pearson
      value: 86.27381322229758
    - type: spearman
      value: 87.5038962579188
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS16 (default)
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
      split: test
      type: mteb/sts16-sts
    metrics:
    - type: cosine_pearson
      value: 82.90364706517789
    - type: cosine_spearman
      value: 84.25854334490232
    - type: euclidean_pearson
      value: 83.30065780824273
    - type: euclidean_spearman
      value: 84.17467271748362
    - type: main_score
      value: 84.25854334490232
    - type: manhattan_pearson
      value: 83.21239264085494
    - type: manhattan_spearman
      value: 84.05456832118482
    - type: pearson
      value: 82.90364706517789
    - type: spearman
      value: 84.25854334490232
    task:
      type: STS
  - dataset:
      config: en-en
      name: MTEB STS17 (en-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 88.88258729094343
    - type: cosine_spearman
      value: 89.68436656381257
    - type: euclidean_pearson
      value: 88.23417725579127
    - type: euclidean_spearman
      value: 87.96688277361433
    - type: main_score
      value: 89.68436656381257
    - type: manhattan_pearson
      value: 88.07673471897155
    - type: manhattan_spearman
      value: 87.7976329721765
    - type: pearson
      value: 88.88258729094343
    - type: spearman
      value: 89.68436656381257
    task:
      type: STS
  - dataset:
      config: en
      name: MTEB STS22 (en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 65.24627744968292
    - type: cosine_spearman
      value: 65.96283849168346
    - type: euclidean_pearson
      value: 66.2111925054528
    - type: euclidean_spearman
      value: 65.83563143944401
    - type: main_score
      value: 65.96283849168346
    - type: manhattan_pearson
      value: 66.25664281582083
    - type: manhattan_spearman
      value: 65.8830797513158
    - type: pearson
      value: 65.24627744968292
    - type: spearman
      value: 65.96283849168346
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STSBenchmark (default)
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
      split: test
      type: mteb/stsbenchmark-sts
    metrics:
    - type: cosine_pearson
      value: 85.57515090752183
    - type: cosine_spearman
      value: 85.54441587714372
    - type: euclidean_pearson
      value: 85.53938106211463
    - type: euclidean_spearman
      value: 85.28473579067878
    - type: main_score
      value: 85.54441587714372
    - type: manhattan_pearson
      value: 85.51025100057596
    - type: manhattan_spearman
      value: 85.260887707662
    - type: pearson
      value: 85.57515090752183
    - type: spearman
      value: 85.54441587714372
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SciDocsRR (default)
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
      split: test
      type: mteb/scidocs-reranking
    metrics:
    - type: main_score
      value: 82.9058801876062
    - type: map
      value: 82.9058801876062
    - type: mrr
      value: 95.256220721907
    - type: nAUC_map_diff1
      value: 0.13078953297011875
    - type: nAUC_map_max
      value: 59.173980738758026
    - type: nAUC_map_std
      value: 73.35735418975649
    - type: nAUC_mrr_diff1
      value: 46.534353907114514
    - type: nAUC_mrr_max
      value: 89.56255914950661
    - type: nAUC_mrr_std
      value: 85.6716185155955
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SciFact (default)
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
      split: test
      type: mteb/scifact
    metrics:
    - type: main_score
      value: 71.844
    - type: map_at_1
      value: 57.278
    - type: map_at_10
      value: 67.109
    - type: map_at_100
      value: 67.66499999999999
    - type: map_at_1000
      value: 67.685
    - type: map_at_20
      value: 67.482
    - type: map_at_3
      value: 64.16199999999999
    - type: map_at_5
      value: 65.82900000000001
    - type: mrr_at_1
      value: 60.0
    - type: mrr_at_10
      value: 68.19960317460317
    - type: mrr_at_100
      value: 68.62748949394921
    - type: mrr_at_1000
      value: 68.64515905414915
    - type: mrr_at_20
      value: 68.472601010101
    - type: mrr_at_3
      value: 66.0
    - type: mrr_at_5
      value: 67.21666666666667
    - type: nauc_map_at_1000_diff1
      value: 70.04313292027558
    - type: nauc_map_at_1000_max
      value: 57.24529193476731
    - type: nauc_map_at_1000_std
      value: -4.8888921470785585
    - type: nauc_map_at_100_diff1
      value: 70.04624674117014
    - type: nauc_map_at_100_max
      value: 57.25302539508853
    - type: nauc_map_at_100_std
      value: -4.907703072069842
    - type: nauc_map_at_10_diff1
      value: 70.06943109940849
    - type: nauc_map_at_10_max
      value: 57.39452715929109
    - type: nauc_map_at_10_std
      value: -4.743417671263566
    - type: nauc_map_at_1_diff1
      value: 76.61111479875207
    - type: nauc_map_at_1_max
      value: 52.822124992902374
    - type: nauc_map_at_1_std
      value: -7.6071857283495445
    - type: nauc_map_at_20_diff1
      value: 69.95251393140202
    - type: nauc_map_at_20_max
      value: 57.328356768833146
    - type: nauc_map_at_20_std
      value: -4.871357691032887
    - type: nauc_map_at_3_diff1
      value: 69.71499509001714
    - type: nauc_map_at_3_max
      value: 53.645107897260026
    - type: nauc_map_at_3_std
      value: -7.908850295935557
    - type: nauc_map_at_5_diff1
      value: 69.7531280646943
    - type: nauc_map_at_5_max
      value: 55.71038914997073
    - type: nauc_map_at_5_std
      value: -6.7813041970848476
    - type: nauc_mrr_at_1000_diff1
      value: 69.61840192382927
    - type: nauc_mrr_at_1000_max
      value: 58.419734360225696
    - type: nauc_mrr_at_1000_std
      value: -1.8503761885586425
    - type: nauc_mrr_at_100_diff1
      value: 69.6153571701724
    - type: nauc_mrr_at_100_max
      value: 58.422378816414565
    - type: nauc_mrr_at_100_std
      value: -1.8731915889302972
    - type: nauc_mrr_at_10_diff1
      value: 69.5874772943516
    - type: nauc_mrr_at_10_max
      value: 58.78121978366665
    - type: nauc_mrr_at_10_std
      value: -1.2843146465927913
    - type: nauc_mrr_at_1_diff1
      value: 74.35688136934793
    - type: nauc_mrr_at_1_max
      value: 57.487384980706416
    - type: nauc_mrr_at_1_std
      value: -1.3005837538340144
    - type: nauc_mrr_at_20_diff1
      value: 69.53988639045606
    - type: nauc_mrr_at_20_max
      value: 58.49631860342686
    - type: nauc_mrr_at_20_std
      value: -1.7220227513588833
    - type: nauc_mrr_at_3_diff1
      value: 68.94320178615871
    - type: nauc_mrr_at_3_max
      value: 56.60856449749424
    - type: nauc_mrr_at_3_std
      value: -3.3432894595086866
    - type: nauc_mrr_at_5_diff1
      value: 68.94240340867633
    - type: nauc_mrr_at_5_max
      value: 58.27068018852665
    - type: nauc_mrr_at_5_std
      value: -2.320192066949136
    - type: nauc_ndcg_at_1000_diff1
      value: 69.15093538086137
    - type: nauc_ndcg_at_1000_max
      value: 58.6801221127507
    - type: nauc_ndcg_at_1000_std
      value: -3.002038837722594
    - type: nauc_ndcg_at_100_diff1
      value: 69.11507044508373
    - type: nauc_ndcg_at_100_max
      value: 58.843490113137605
    - type: nauc_ndcg_at_100_std
      value: -3.2810475322338566
    - type: nauc_ndcg_at_10_diff1
      value: 68.71920945656667
    - type: nauc_ndcg_at_10_max
      value: 60.13600198034469
    - type: nauc_ndcg_at_10_std
      value: -1.6190106644777749
    - type: nauc_ndcg_at_1_diff1
      value: 74.35688136934793
    - type: nauc_ndcg_at_1_max
      value: 57.487384980706416
    - type: nauc_ndcg_at_1_std
      value: -1.3005837538340144
    - type: nauc_ndcg_at_20_diff1
      value: 68.33714726670162
    - type: nauc_ndcg_at_20_max
      value: 59.45907982196103
    - type: nauc_ndcg_at_20_std
      value: -2.5953063304797754
    - type: nauc_ndcg_at_3_diff1
      value: 67.33605891922716
    - type: nauc_ndcg_at_3_max
      value: 55.01142849375101
    - type: nauc_ndcg_at_3_std
      value: -6.5632981093508205
    - type: nauc_ndcg_at_5_diff1
      value: 67.59450950578172
    - type: nauc_ndcg_at_5_max
      value: 57.50106057747294
    - type: nauc_ndcg_at_5_std
      value: -5.415038422866616
    - type: nauc_precision_at_1000_diff1
      value: -33.21156082089814
    - type: nauc_precision_at_1000_max
      value: 19.132732038554398
    - type: nauc_precision_at_1000_std
      value: 44.091281225705714
    - type: nauc_precision_at_100_diff1
      value: -20.015823755259245
    - type: nauc_precision_at_100_max
      value: 26.507243354636085
    - type: nauc_precision_at_100_std
      value: 37.87274756817076
    - type: nauc_precision_at_10_diff1
      value: 8.35057694800983
    - type: nauc_precision_at_10_max
      value: 49.60611953844157
    - type: nauc_precision_at_10_std
      value: 32.18410475820039
    - type: nauc_precision_at_1_diff1
      value: 74.35688136934793
    - type: nauc_precision_at_1_max
      value: 57.487384980706416
    - type: nauc_precision_at_1_std
      value: -1.3005837538340144
    - type: nauc_precision_at_20_diff1
      value: -3.0872665961524612
    - type: nauc_precision_at_20_max
      value: 40.5565038905005
    - type: nauc_precision_at_20_std
      value: 32.15291813716766
    - type: nauc_precision_at_3_diff1
      value: 34.627722605371545
    - type: nauc_precision_at_3_max
      value: 49.65219072739979
    - type: nauc_precision_at_3_std
      value: 7.7588985130719434
    - type: nauc_precision_at_5_diff1
      value: 22.06911561993657
    - type: nauc_precision_at_5_max
      value: 49.09578970278826
    - type: nauc_precision_at_5_std
      value: 16.038789872070705
    - 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: 64.77257569694551
    - type: nauc_recall_at_100_max
      value: 65.07269574496497
    - type: nauc_recall_at_100_std
      value: -10.979947534569218
    - type: nauc_recall_at_10_diff1
      value: 62.14297161941494
    - type: nauc_recall_at_10_max
      value: 70.41353364022896
    - type: nauc_recall_at_10_std
      value: 9.172932719542075
    - type: nauc_recall_at_1_diff1
      value: 76.61111479875207
    - type: nauc_recall_at_1_max
      value: 52.822124992902374
    - type: nauc_recall_at_1_std
      value: -7.6071857283495445
    - type: nauc_recall_at_20_diff1
      value: 57.631464811333224
    - type: nauc_recall_at_20_max
      value: 67.83558221740536
    - type: nauc_recall_at_20_std
      value: 3.110691973832695
    - type: nauc_recall_at_3_diff1
      value: 60.39078444139112
    - type: nauc_recall_at_3_max
      value: 51.122425596651574
    - type: nauc_recall_at_3_std
      value: -10.307895490015559
    - type: nauc_recall_at_5_diff1
      value: 59.703727953513145
    - type: nauc_recall_at_5_max
      value: 59.81893786534298
    - type: nauc_recall_at_5_std
      value: -6.231017907901268
    - type: ndcg_at_1
      value: 60.0
    - type: ndcg_at_10
      value: 71.844
    - type: ndcg_at_100
      value: 74.278
    - type: ndcg_at_1000
      value: 74.74199999999999
    - type: ndcg_at_20
      value: 72.99
    - type: ndcg_at_3
      value: 66.721
    - type: ndcg_at_5
      value: 69.137
    - type: precision_at_1
      value: 60.0
    - type: precision_at_10
      value: 9.6
    - type: precision_at_100
      value: 1.093
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_20
      value: 5.067
    - type: precision_at_3
      value: 26.111
    - type: precision_at_5
      value: 17.267
    - type: recall_at_1
      value: 57.278
    - type: recall_at_10
      value: 85.344
    - type: recall_at_100
      value: 96.5
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_20
      value: 89.589
    - type: recall_at_3
      value: 71.45
    - type: recall_at_5
      value: 77.361
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SprintDuplicateQuestions (default)
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
      split: test
      type: mteb/sprintduplicatequestions-pairclassification
    metrics:
    - type: cosine_accuracy
      value: 99.8019801980198
    - type: cosine_accuracy_threshold
      value: 74.77510571479797
    - type: cosine_ap
      value: 95.30006120252773
    - type: cosine_f1
      value: 89.75265017667844
    - type: cosine_f1_threshold
      value: 72.93492555618286
    - type: cosine_precision
      value: 90.62181447502549
    - type: cosine_recall
      value: 88.9
    - type: dot_accuracy
      value: 99.74554455445545
    - type: dot_accuracy_threshold
      value: 794.2790985107422
    - type: dot_ap
      value: 93.33073289508414
    - type: dot_f1
      value: 87.11779448621553
    - type: dot_f1_threshold
      value: 793.5191631317139
    - type: dot_precision
      value: 87.33668341708542
    - type: dot_recall
      value: 86.9
    - type: euclidean_accuracy
      value: 99.7960396039604
    - type: euclidean_accuracy_threshold
      value: 238.72876167297363
    - type: euclidean_ap
      value: 95.04815354196363
    - type: euclidean_f1
      value: 89.53252032520325
    - type: euclidean_f1_threshold
      value: 241.42813682556152
    - type: euclidean_precision
      value: 91.01239669421489
    - type: euclidean_recall
      value: 88.1
    - type: main_score
      value: 95.30006120252773
    - type: manhattan_accuracy
      value: 99.7960396039604
    - type: manhattan_accuracy_threshold
      value: 5224.44953918457
    - type: manhattan_ap
      value: 95.02798265540767
    - type: manhattan_f1
      value: 89.4552723638181
    - type: manhattan_f1_threshold
      value: 5434.450531005859
    - type: manhattan_precision
      value: 89.41058941058941
    - type: manhattan_recall
      value: 89.5
    - type: max_accuracy
      value: 99.8019801980198
    - type: max_ap
      value: 95.30006120252773
    - type: max_f1
      value: 89.75265017667844
    - type: max_precision
      value: 91.01239669421489
    - type: max_recall
      value: 89.5
    - type: similarity_accuracy
      value: 99.8019801980198
    - type: similarity_accuracy_threshold
      value: 74.77510571479797
    - type: similarity_ap
      value: 95.30006120252773
    - type: similarity_f1
      value: 89.75265017667844
    - type: similarity_f1_threshold
      value: 72.93492555618286
    - type: similarity_precision
      value: 90.62181447502549
    - type: similarity_recall
      value: 88.9
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB StackExchangeClustering (default)
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
      split: test
      type: mteb/stackexchange-clustering
    metrics:
    - type: main_score
      value: 66.76593843797666
    - type: v_measure
      value: 66.76593843797666
    - type: v_measure_std
      value: 3.5421488096435416
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB StackExchangeClusteringP2P (default)
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
      split: test
      type: mteb/stackexchange-clustering-p2p
    metrics:
    - type: main_score
      value: 38.90007255920144
    - type: v_measure
      value: 38.90007255920144
    - type: v_measure_std
      value: 1.440894289494648
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB StackOverflowDupQuestions (default)
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
      split: test
      type: mteb/stackoverflowdupquestions-reranking
    metrics:
    - type: main_score
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    - type: ndcg_at_100
      value: 34.660000000000004
    - type: ndcg_at_1000
      value: 45.798
    - type: ndcg_at_20
      value: 24.988
    - type: ndcg_at_3
      value: 29.273
    - type: ndcg_at_5
      value: 25.453
    - type: precision_at_1
      value: 28.571
    - type: precision_at_10
      value: 21.02
    - type: precision_at_100
      value: 7.122000000000001
    - type: precision_at_1000
      value: 1.435
    - type: precision_at_20
      value: 16.326999999999998
    - type: precision_at_3
      value: 31.293
    - type: precision_at_5
      value: 24.898
    - type: recall_at_1
      value: 2.355
    - type: recall_at_10
      value: 15.397
    - type: recall_at_100
      value: 43.647000000000006
    - type: recall_at_1000
      value: 77.089
    - type: recall_at_20
      value: 22.792
    - type: recall_at_3
      value: 6.847
    - type: recall_at_5
      value: 9.136
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ToxicConversationsClassification (default)
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
      split: test
      type: mteb/toxic_conversations_50k
    metrics:
    - type: accuracy
      value: 72.7734375
    - type: ap
      value: 15.655230461083708
    - type: ap_weighted
      value: 15.655230461083708
    - type: f1
      value: 56.31497978454638
    - type: f1_weighted
      value: 78.70509613747345
    - type: main_score
      value: 72.7734375
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TweetSentimentExtractionClassification (default)
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
      split: test
      type: mteb/tweet_sentiment_extraction
    metrics:
    - type: accuracy
      value: 72.56366723259762
    - type: f1
      value: 72.90413275122202
    - type: f1_weighted
      value: 72.19948169084057
    - type: main_score
      value: 72.56366723259762
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TwentyNewsgroupsClustering (default)
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
      split: test
      type: mteb/twentynewsgroups-clustering
    metrics:
    - type: main_score
      value: 56.90970017457857
    - type: v_measure
      value: 56.90970017457857
    - type: v_measure_std
      value: 1.5885885070403738
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB TwitterSemEval2015 (default)
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
      split: test
      type: mteb/twittersemeval2015-pairclassification
    metrics:
    - type: cosine_accuracy
      value: 85.7006616200751
    - type: cosine_accuracy_threshold
      value: 75.78572630882263
    - type: cosine_ap
      value: 72.87577990245127
    - type: cosine_f1
      value: 67.36422521175885
    - type: cosine_f1_threshold
      value: 70.15678882598877
    - type: cosine_precision
      value: 63.80368098159509
    - type: cosine_recall
      value: 71.34564643799473
    - type: dot_accuracy
      value: 83.60851165285807
    - type: dot_accuracy_threshold
      value: 744.7918891906738
    - type: dot_ap
      value: 64.82619159813649
    - type: dot_f1
      value: 62.62379263968699
    - type: dot_f1_threshold
      value: 696.7735290527344
    - type: dot_precision
      value: 58.350421508316245
    - type: dot_recall
      value: 67.57255936675462
    - type: euclidean_accuracy
      value: 85.84371460928652
    - type: euclidean_accuracy_threshold
      value: 220.4747200012207
    - type: euclidean_ap
      value: 72.47837433257799
    - type: euclidean_f1
      value: 67.2811059907834
    - type: euclidean_f1_threshold
      value: 240.81902503967285
    - type: euclidean_precision
      value: 65.34062655395326
    - type: euclidean_recall
      value: 69.34036939313984
    - type: main_score
      value: 72.87577990245127
    - type: manhattan_accuracy
      value: 85.83179352685224
    - type: manhattan_accuracy_threshold
      value: 4910.404205322266
    - type: manhattan_ap
      value: 72.44111617709422
    - type: manhattan_f1
      value: 67.09989806320081
    - type: manhattan_f1_threshold
      value: 5333.793640136719
    - type: manhattan_precision
      value: 64.88417939871857
    - type: manhattan_recall
      value: 69.47229551451187
    - type: max_accuracy
      value: 85.84371460928652
    - type: max_ap
      value: 72.87577990245127
    - type: max_f1
      value: 67.36422521175885
    - type: max_precision
      value: 65.34062655395326
    - type: max_recall
      value: 71.34564643799473
    - type: similarity_accuracy
      value: 85.7006616200751
    - type: similarity_accuracy_threshold
      value: 75.78572630882263
    - type: similarity_ap
      value: 72.87577990245127
    - type: similarity_f1
      value: 67.36422521175885
    - type: similarity_f1_threshold
      value: 70.15678882598877
    - type: similarity_precision
      value: 63.80368098159509
    - type: similarity_recall
      value: 71.34564643799473
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB TwitterURLCorpus (default)
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
      split: test
      type: mteb/twitterurlcorpus-pairclassification
    metrics:
    - type: cosine_accuracy
      value: 88.88112702293631
    - type: cosine_accuracy_threshold
      value: 71.48405313491821
    - type: cosine_ap
      value: 85.88088882163336
    - type: cosine_f1
      value: 78.2251744598276
    - type: cosine_f1_threshold
      value: 70.09605169296265
    - type: cosine_precision
      value: 75.8997755087262
    - type: cosine_recall
      value: 80.69756698490914
    - type: dot_accuracy
      value: 88.04672643303451
    - type: dot_accuracy_threshold
      value: 700.6264686584473
    - type: dot_ap
      value: 83.52072844458456
    - type: dot_f1
      value: 76.24239256244634
    - type: dot_f1_threshold
      value: 664.9115562438965
    - type: dot_precision
      value: 74.0123233055455
    - type: dot_recall
      value: 78.61102556205728
    - type: euclidean_accuracy
      value: 88.72588970388482
    - type: euclidean_accuracy_threshold
      value: 226.53303146362305
    - type: euclidean_ap
      value: 85.51788295919707
    - type: euclidean_f1
      value: 77.73453426739316
    - type: euclidean_f1_threshold
      value: 238.7503147125244
    - type: euclidean_precision
      value: 74.94818097348296
    - type: euclidean_recall
      value: 80.73606405913151
    - type: main_score
      value: 85.88088882163336
    - type: manhattan_accuracy
      value: 88.68902084061008
    - type: manhattan_accuracy_threshold
      value: 5034.079742431641
    - type: manhattan_ap
      value: 85.49952903626239
    - type: manhattan_f1
      value: 77.74326743888625
    - type: manhattan_f1_threshold
      value: 5334.531021118164
    - type: manhattan_precision
      value: 73.98289171708741
    - type: manhattan_recall
      value: 81.90637511549123
    - type: max_accuracy
      value: 88.88112702293631
    - type: max_ap
      value: 85.88088882163336
    - type: max_f1
      value: 78.2251744598276
    - type: max_precision
      value: 75.8997755087262
    - type: max_recall
      value: 81.90637511549123
    - type: similarity_accuracy
      value: 88.88112702293631
    - type: similarity_accuracy_threshold
      value: 71.48405313491821
    - type: similarity_ap
      value: 85.88088882163336
    - type: similarity_f1
      value: 78.2251744598276
    - type: similarity_f1_threshold
      value: 70.09605169296265
    - type: similarity_precision
      value: 75.8997755087262
    - type: similarity_recall
      value: 80.69756698490914
    task:
      type: PairClassification
---
# Contextual Document Embeddings (CDE)

Our new model that naturally integrates "context tokens" into the embedding process. As of October 1st, 2024, `cde-small-v1` is the best small model (under 400M params) on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for text embedding models, with an average score of 65.00.

👉  <b><a href="https://colab.research.google.com/drive/1r8xwbp7_ySL9lP-ve4XMJAHjidB9UkbL?usp=sharing">Try on Colab</a></b>
<br>
👉  <b><a href="https://arxiv.org/abs/2410.02525">Contextual Document Embeddings (ArXiv)</a></b>

![CDE Overview Figure](https://i.imgur.com/LyXJZjM.png)

<br>
<hr>

# How to use `cde-small-v1`

Our embedding model needs to be used in *two stages*. The first stage is to gather some dataset information by embedding a subset of the corpus using our "first-stage" model. The second stage is to actually embed queries and documents, conditioning on the corpus information from the first stage. Note that we can do the first stage part offline and only use the second-stage weights at inference time.


## Loading the model

Our model can be loaded using `transformers` out-of-the-box with "trust remote code" enabled. We use the default BERT uncased tokenizer:
```python
import transformers

model = transformers.AutoModel.from_pretrained("jxm/cde-small-v1", trust_remote_code=True)
tokenizer = transformers.AutoTokenizer.from_pretrained("bert-base-uncased")
```

#### Note on prefixes

*Nota bene*: Like all state-of-the-art embedding models, our model was trained with task-specific prefixes. To do retrieval, you can prepend the following strings to queries & documents:

```python
query_prefix = "search_query: "
document_prefix = "search_document: "
```

## First stage

```python
minicorpus_size = model.config.transductive_corpus_size
minicorpus_docs = [ ... ] # Put some strings here that are representative of your corpus, for example by calling random.sample(corpus, k=minicorpus_size)
assert len(minicorpus_docs) == minicorpus_size # You must use exactly this many documents in the minicorpus. You can oversample if your corpus is smaller.
minicorpus_docs = tokenizer(
    [document_prefix + doc for doc in minicorpus_docs],
    truncation=True,
    padding=True,
    max_length=512,
    return_tensors="pt"
)
import torch
from tqdm.autonotebook import tqdm

batch_size = 32

dataset_embeddings = []
for i in tqdm(range(0, len(minicorpus_docs["input_ids"]), batch_size)):
    minicorpus_docs_batch = {k: v[i:i+batch_size] for k,v in minicorpus_docs.items()}
    with torch.no_grad():
        dataset_embeddings.append(
            model.first_stage_model(**minicorpus_docs_batch)
        )

dataset_embeddings = torch.cat(dataset_embeddings)
```

## Running the second stage

Now that we have obtained "dataset embeddings" we can embed documents and queries like normal. Remember to use the document prefix for documents:
```python
docs = tokenizer(
    [document_prefix + doc for doc in docs],
    truncation=True,
    padding=True,
    max_length=512,
    return_tensors="pt"
).to(device)

with torch.no_grad():
  doc_embeddings = model.second_stage_model(
      input_ids=docs["input_ids"],
      attention_mask=docs["attention_mask"],
      dataset_embeddings=dataset_embeddings,
  )
doc_embeddings /= doc_embeddings.norm(p=2, dim=1, keepdim=True)
```

and the query prefix for queries:
```python
queries = queries.select(range(16))["text"]
queries = tokenizer(
    [query_prefix + query for query in queries],
    truncation=True,
    padding=True,
    max_length=512,
    return_tensors="pt"
).to(device)

with torch.no_grad():
  query_embeddings = model.second_stage_model(
      input_ids=queries["input_ids"],
      attention_mask=queries["attention_mask"],
      dataset_embeddings=dataset_embeddings,
  )
query_embeddings /= query_embeddings.norm(p=2, dim=1, keepdim=True)
```

these embeddings can be compared using dot product, since they're normalized.

### What if I don't know what my corpus will be ahead of time?

If you can't obtain corpus information ahead of time, you still have to pass *something* as the dataset embeddings; our model will work fine in this case, but not quite as well; without corpus information, our model performance drops from 65.0 to 63.8 on MTEB. We provide [some random strings](https://huggingface.co/jxm/cde-small-v1/resolve/main/random_strings.txt) that worked well for us that can be used as a substitute for corpus sampling.

### Colab demo

We've set up a short demo in a Colab notebook showing how you might use our model:
[Try our model in Colab:](https://colab.research.google.com/drive/1r8xwbp7_ySL9lP-ve4XMJAHjidB9UkbL?usp=sharing)

### Acknowledgments

Early experiments on CDE were done with support from [Nomic](https://www.nomic.ai/) and [Hyperbolic](https://hyperbolic.xyz/). We're especially indebted to Nomic for [open-sourcing their efficient BERT implementation and contrastive pre-training data](https://www.nomic.ai/blog/posts/nomic-embed-text-v1), which proved vital in the development of CDE.

### Cite us

Used our model, method, or architecture? Want to cite us? Here's the ArXiv citation information:
```
@misc{morris2024contextualdocumentembeddings,
      title={Contextual Document Embeddings}, 
      author={John X. Morris and Alexander M. Rush},
      year={2024},
      eprint={2410.02525},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.02525}, 
}
```