README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- mteb
base_model: sbintuitions/modernbert-ja-310m
language:
- ja
- en
model-index:
- name: retrieva-jp/amber-large
  results:
  - dataset:
      config: en
      name: MTEB AmazonCounterfactualClassification (en)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    metrics:
    - type: accuracy
      value: 73.3433
    - type: f1
      value: 67.2899
    - type: f1_weighted
      value: 75.7948
    - type: ap
      value: 36.123
    - type: ap_weighted
      value: 36.123
    - type: main_score
      value: 73.3433
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB ArXivHierarchicalClusteringP2P (default)
      revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8
      split: test
      type: mteb/arxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 53.3936
    - type: v_measure_std
      value: 3.9726999999999997
    - type: main_score
      value: 53.3936
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB ArXivHierarchicalClusteringS2S (default)
      revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3
      split: test
      type: mteb/arxiv-clustering-s2s
    metrics:
    - type: v_measure
      value: 51.35999999999999
    - type: v_measure_std
      value: 4.9623
    - type: main_score
      value: 51.35999999999999
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB ArguAna (default)
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
      split: test
      type: mteb/arguana
    metrics:
    - type: ndcg_at_1
      value: 26.743
    - type: ndcg_at_3
      value: 40.550999999999995
    - type: ndcg_at_5
      value: 45.550000000000004
    - type: ndcg_at_10
      value: 51.317
    - type: ndcg_at_20
      value: 53.96300000000001
    - type: ndcg_at_100
      value: 55.358
    - type: ndcg_at_1000
      value: 55.596000000000004
    - type: map_at_1
      value: 26.743
    - type: map_at_3
      value: 37.162
    - type: map_at_5
      value: 39.964
    - type: map_at_10
      value: 42.355
    - type: map_at_20
      value: 43.1
    - type: map_at_100
      value: 43.313
    - type: map_at_1000
      value: 43.323
    - type: recall_at_1
      value: 26.743
    - type: recall_at_3
      value: 50.356
    - type: recall_at_5
      value: 62.376
    - type: recall_at_10
      value: 80.156
    - type: recall_at_20
      value: 90.469
    - type: recall_at_100
      value: 97.724
    - type: recall_at_1000
      value: 99.502
    - type: precision_at_1
      value: 26.743
    - type: precision_at_3
      value: 16.785
    - type: precision_at_5
      value: 12.475
    - type: precision_at_10
      value: 8.016
    - type: precision_at_20
      value: 4.523
    - type: precision_at_100
      value: 0.9769999999999999
    - type: precision_at_1000
      value: 0.1
    - type: mrr_at_1
      value: 27.169300000000003
    - type: mrr_at_3
      value: 37.411100000000005
    - type: mrr_at_5
      value: 40.1102
    - type: mrr_at_10
      value: 42.493900000000004
    - type: mrr_at_20
      value: 43.2491
    - type: mrr_at_100
      value: 43.4578
    - type: mrr_at_1000
      value: 43.4685
    - type: nauc_ndcg_at_1_max
      value: -6.2333
    - type: nauc_ndcg_at_1_std
      value: -7.9555
    - type: nauc_ndcg_at_1_diff1
      value: 14.512
    - type: nauc_ndcg_at_3_max
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    - type: nauc_ndcg_at_3_std
      value: -5.8094
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    - type: nauc_ndcg_at_5_max
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    - type: nauc_ndcg_at_10_max
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    - type: nauc_ndcg_at_10_std
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      value: 11.1
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      value: 0.47200000000000003
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    - type: nauc_map_at_1_max
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    - type: nauc_map_at_1_std
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    - type: nauc_map_at_1_diff1
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    - type: nauc_map_at_3_max
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    - type: nauc_recall_at_1_std
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    - type: nauc_recall_at_1_diff1
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    - type: nauc_recall_at_10_max
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    - type: nauc_recall_at_20_max
      value: 29.6782
    - type: nauc_recall_at_20_std
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      value: 15.8604
    - type: nauc_recall_at_100_max
      value: 28.772599999999997
    - type: nauc_recall_at_100_std
      value: 48.7738
    - type: nauc_recall_at_100_diff1
      value: 15.8629
    - type: nauc_recall_at_1000_max
      value: 31.0293
    - type: nauc_recall_at_1000_std
      value: 52.7185
    - type: nauc_recall_at_1000_diff1
      value: 14.3646
    - type: nauc_precision_at_1_max
      value: -6.2333
    - type: nauc_precision_at_1_std
      value: -7.9555
    - type: nauc_precision_at_1_diff1
      value: 14.512
    - type: nauc_precision_at_3_max
      value: 1.2414
    - type: nauc_precision_at_3_std
      value: -4.6148
    - type: nauc_precision_at_3_diff1
      value: 6.45
    - type: nauc_precision_at_5_max
      value: 2.7998
    - type: nauc_precision_at_5_std
      value: -2.6652
    - type: nauc_precision_at_5_diff1
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    - type: nauc_precision_at_10_max
      value: 17.322100000000002
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      value: 5.9032
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    - type: nauc_precision_at_20_max
      value: 29.6782
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      value: 16.4192
    - type: nauc_precision_at_20_diff1
      value: 15.8604
    - type: nauc_precision_at_100_max
      value: 28.772599999999997
    - type: nauc_precision_at_100_std
      value: 48.7738
    - type: nauc_precision_at_100_diff1
      value: 15.8629
    - type: nauc_precision_at_1000_max
      value: 31.0293
    - type: nauc_precision_at_1000_std
      value: 52.7185
    - type: nauc_precision_at_1000_diff1
      value: 14.3646
    - type: nauc_mrr_at_1_max
      value: -6.0675
    - type: nauc_mrr_at_1_std
      value: -7.0283999999999995
    - type: nauc_mrr_at_1_diff1
      value: 13.1112
    - type: nauc_mrr_at_3_max
      value: -3.8593
    - type: nauc_mrr_at_3_std
      value: -5.9281
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      value: 8.807
    - type: nauc_mrr_at_5_max
      value: -3.6332999999999998
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    - type: nauc_mrr_at_10_max
      value: -2.8869
    - type: nauc_mrr_at_10_std
      value: -4.9811000000000005
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    - type: nauc_mrr_at_20_max
      value: -2.9609
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    - type: nauc_mrr_at_100_max
      value: -3.15
    - type: nauc_mrr_at_100_std
      value: -4.9643
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      value: 9.6056
    - type: nauc_mrr_at_1000_max
      value: -3.159
    - type: nauc_mrr_at_1000_std
      value: -4.982
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      value: 9.6061
    - type: main_score
      value: 51.317
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB AskUbuntuDupQuestions (default)
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
      split: test
      type: mteb/askubuntudupquestions-reranking
    metrics:
    - type: map
      value: 58.0233
    - type: mrr
      value: 70.5882
    - type: nAUC_map_max
      value: 20.8533
    - type: nAUC_map_std
      value: 12.612300000000001
    - type: nAUC_map_diff1
      value: 1.3859
    - type: nAUC_mrr_max
      value: 33.692
    - type: nAUC_mrr_std
      value: 14.176400000000001
    - type: nAUC_mrr_diff1
      value: 14.2379
    - type: main_score
      value: 58.0233
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB BIOSSES (default)
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
      split: test
      type: mteb/biosses-sts
    metrics:
    - type: pearson
      value: 83.4314
    - type: spearman
      value: 78.7367
    - type: cosine_pearson
      value: 83.4314
    - type: cosine_spearman
      value: 78.7367
    - type: manhattan_pearson
      value: 82.1388
    - type: manhattan_spearman
      value: 78.747
    - type: euclidean_pearson
      value: 82.1716
    - type: euclidean_spearman
      value: 78.7367
    - type: main_score
      value: 78.7367
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB Banking77Classification (default)
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
      split: test
      type: mteb/banking77
    metrics:
    - type: accuracy
      value: 76.8961
    - type: f1
      value: 75.8746
    - type: f1_weighted
      value: 75.8746
    - type: main_score
      value: 76.8961
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB BiorxivClusteringP2P.v2 (default)
      revision: f5dbc242e11dd8e24def4c4268607a49e02946dc
      split: test
      type: mteb/biorxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 36.2676
    - type: v_measure_std
      value: 0.8959
    - type: main_score
      value: 36.2676
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB CQADupstackGamingRetrieval (default)
      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
      split: test
      type: mteb/cqadupstack-gaming
    metrics:
    - type: ndcg_at_1
      value: 36.489
    - type: ndcg_at_3
      value: 42.821999999999996
    - type: ndcg_at_5
      value: 44.915
    - type: ndcg_at_10
      value: 47.74
    - type: ndcg_at_20
      value: 49.613
    - type: ndcg_at_100
      value: 52.406
    - type: ndcg_at_1000
      value: 53.984
    - type: map_at_1
      value: 31.812
    - type: map_at_3
      value: 39.568
    - type: map_at_5
      value: 40.976
    - type: map_at_10
      value: 42.36
    - type: map_at_20
      value: 42.978
    - type: map_at_100
      value: 43.418
    - type: map_at_1000
      value: 43.488
    - type: recall_at_1
      value: 31.812
    - type: recall_at_3
      value: 47.199999999999996
    - type: recall_at_5
      value: 52.361999999999995
    - type: recall_at_10
      value: 60.535000000000004
    - type: recall_at_20
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    - type: recall_at_100
      value: 81.432
    - type: recall_at_1000
      value: 92.935
    - type: precision_at_1
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    - type: precision_at_3
      value: 19.269
    - type: precision_at_5
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    - type: precision_at_10
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    - type: precision_at_100
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    - type: precision_at_1000
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    - type: mrr_at_10
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    - type: nauc_ndcg_at_100_std
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    - type: nauc_recall_at_3_max
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    - type: nauc_recall_at_10_max
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    - type: nauc_recall_at_20_max
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    - type: nauc_recall_at_20_std
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    - type: nauc_precision_at_1_max
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      value: 60.8946
    - type: nauc_precision_at_3_max
      value: 38.8376
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      value: 1.5544
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      value: 39.1529
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      value: 36.391400000000004
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      value: 3.1532999999999998
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      value: 33.7108
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      value: 5.743
    - type: nauc_precision_at_10_diff1
      value: 28.9605
    - type: nauc_precision_at_20_max
      value: 32.0646
    - type: nauc_precision_at_20_std
      value: 11.411999999999999
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      value: 26.562200000000004
    - type: nauc_precision_at_100_max
      value: 39.3941
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      value: 28.2403
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      value: 26.353700000000003
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      value: 43.751400000000004
    - type: nauc_precision_at_1000_std
      value: 55.13249999999999
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      value: 10.1938
    - type: nauc_mrr_at_1_max
      value: 45.0721
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      value: -4.7617
    - type: nauc_mrr_at_1_diff1
      value: 60.8946
    - type: nauc_mrr_at_3_max
      value: 44.7879
    - type: nauc_mrr_at_3_std
      value: -5.1337
    - type: nauc_mrr_at_3_diff1
      value: 58.2349
    - type: nauc_mrr_at_5_max
      value: 44.6627
    - type: nauc_mrr_at_5_std
      value: -4.9526
    - type: nauc_mrr_at_5_diff1
      value: 57.7376
    - type: nauc_mrr_at_10_max
      value: 44.7676
    - type: nauc_mrr_at_10_std
      value: -4.7908
    - type: nauc_mrr_at_10_diff1
      value: 57.537400000000005
    - type: nauc_mrr_at_20_max
      value: 44.7882
    - type: nauc_mrr_at_20_std
      value: -4.5173
    - type: nauc_mrr_at_20_diff1
      value: 57.575900000000004
    - type: nauc_mrr_at_100_max
      value: 44.9292
    - type: nauc_mrr_at_100_std
      value: -4.4029
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      value: 57.6909
    - type: nauc_mrr_at_1000_max
      value: 44.912800000000004
    - type: nauc_mrr_at_1000_std
      value: -4.429
    - type: nauc_mrr_at_1000_diff1
      value: 57.6896
    - type: main_score
      value: 45.302
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ImdbClassification (default)
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
      split: test
      type: mteb/imdb
    metrics:
    - type: accuracy
      value: 71.792
    - type: f1
      value: 71.6599
    - type: f1_weighted
      value: 71.6599
    - type: ap
      value: 65.6717
    - type: ap_weighted
      value: 65.6717
    - type: main_score
      value: 71.792
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MTOPDomainClassification (en)
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
      split: test
      type: mteb/mtop_domain
    metrics:
    - type: accuracy
      value: 90.798
    - type: f1
      value: 90.14569999999999
    - type: f1_weighted
      value: 90.8211
    - type: main_score
      value: 90.798
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveIntentClassification (en)
      revision: 4672e20407010da34463acc759c162ca9734bca6
      split: test
      type: mteb/amazon_massive_intent
    metrics:
    - type: accuracy
      value: 66.4829
    - type: f1
      value: 64.3878
    - type: f1_weighted
      value: 65.2855
    - type: main_score
      value: 66.4829
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveScenarioClassification (en)
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
      split: test
      type: mteb/amazon_massive_scenario
    metrics:
    - type: accuracy
      value: 71.1903
    - type: f1
      value: 71.0214
    - type: f1_weighted
      value: 70.7184
    - type: main_score
      value: 71.1903
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB MedrxivClusteringP2P.v2 (default)
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
      split: test
      type: mteb/medrxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 35.781
    - type: v_measure_std
      value: 0.7404
    - type: main_score
      value: 35.781
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB MedrxivClusteringS2S.v2 (default)
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
      split: test
      type: mteb/medrxiv-clustering-s2s
    metrics:
    - type: v_measure
      value: 33.900200000000005
    - type: v_measure_std
      value: 0.8489
    - type: main_score
      value: 33.900200000000005
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB MindSmallReranking (default)
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
      split: test
      type: mteb/mind_small
    metrics:
    - type: map
      value: 29.646499999999996
    - type: mrr
      value: 30.604799999999997
    - type: nAUC_map_max
      value: -23.3675
    - type: nAUC_map_std
      value: -5.0637
    - type: nAUC_map_diff1
      value: 13.4632
    - type: nAUC_mrr_max
      value: -17.5124
    - type: nAUC_mrr_std
      value: -2.8459000000000003
    - type: nAUC_mrr_diff1
      value: 12.4125
    - type: main_score
      value: 29.646499999999996
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SCIDOCS (default)
      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
      split: test
      type: mteb/scidocs
    metrics:
    - type: ndcg_at_1
      value: 20
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      value: 15.842
    - type: ndcg_at_5
      value: 13.894
    - type: ndcg_at_10
      value: 16.926
    - type: ndcg_at_20
      value: 19.803
    - type: ndcg_at_100
      value: 25.081999999999997
    - type: ndcg_at_1000
      value: 30.864000000000004
    - type: map_at_1
      value: 4.093
    - type: map_at_3
      value: 7.091
    - type: map_at_5
      value: 8.389000000000001
    - type: map_at_10
      value: 9.831
    - type: map_at_20
      value: 10.801
    - type: map_at_100
      value: 11.815000000000001
    - type: map_at_1000
      value: 12.139999999999999
    - type: recall_at_1
      value: 4.093
    - type: recall_at_3
      value: 8.938
    - type: recall_at_5
      value: 12.323
    - type: recall_at_10
      value: 17.907
    - type: recall_at_20
      value: 24.708
    - type: recall_at_100
      value: 41.897
    - type: recall_at_1000
      value: 70.048
    - type: precision_at_1
      value: 20
    - type: precision_at_3
      value: 14.667
    - type: precision_at_5
      value: 12.120000000000001
    - type: precision_at_10
      value: 8.81
    - type: precision_at_20
      value: 6.08
    - type: precision_at_100
      value: 2.061
    - type: precision_at_1000
      value: 0.345
    - type: mrr_at_1
      value: 20
    - type: mrr_at_3
      value: 26.016699999999997
    - type: mrr_at_5
      value: 27.896700000000003
    - type: mrr_at_10
      value: 29.309800000000003
    - type: mrr_at_20
      value: 30.1817
    - type: mrr_at_100
      value: 30.642999999999997
    - type: mrr_at_1000
      value: 30.7072
    - type: nauc_ndcg_at_1_max
      value: 25.9162
    - type: nauc_ndcg_at_1_std
      value: 7.375800000000001
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      value: 21.4553
    - type: nauc_ndcg_at_3_max
      value: 29.9782
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      value: 11.0489
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      value: 17.3996
    - type: nauc_ndcg_at_5_max
      value: 31.5098
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      value: 13.3131
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      value: 18.3321
    - type: nauc_ndcg_at_10_max
      value: 33.3401
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      value: 16.1576
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      value: 16.9853
    - type: nauc_ndcg_at_20_max
      value: 34.343
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      value: 20.0335
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      value: 15.6531
    - type: nauc_ndcg_at_100_max
      value: 37.066500000000005
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      value: 26.8663
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      value: 16.4485
    - type: nauc_ndcg_at_1000_max
      value: 37.6377
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      value: 28.4086
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      value: 16.598
    - type: nauc_map_at_1_max
      value: 25.571899999999996
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      value: 7.2567
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      value: 21.1815
    - type: nauc_map_at_3_max
      value: 29.7213
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      value: 9.027000000000001
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      value: 17.6405
    - type: nauc_map_at_5_max
      value: 30.912499999999998
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      value: 10.8177
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      value: 18.2512
    - type: nauc_map_at_10_max
      value: 32.1247
    - type: nauc_map_at_10_std
      value: 13.3522
    - type: nauc_map_at_10_diff1
      value: 17.0684
    - type: nauc_map_at_20_max
      value: 32.8604
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      value: 15.534899999999999
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      value: 16.3024
    - type: nauc_map_at_100_max
      value: 33.9481
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      value: 17.9563
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      value: 16.5858
    - type: nauc_map_at_1000_max
      value: 34.104099999999995
    - type: nauc_map_at_1000_std
      value: 18.3399
    - type: nauc_map_at_1000_diff1
      value: 16.5982
    - type: nauc_recall_at_1_max
      value: 25.571899999999996
    - type: nauc_recall_at_1_std
      value: 7.2567
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      value: 21.1815
    - type: nauc_recall_at_3_max
      value: 31.102
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      value: 12.208
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      value: 15.7802
    - type: nauc_recall_at_5_max
      value: 33.0649
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      value: 15.7429
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      value: 17.3206
    - type: nauc_recall_at_10_max
      value: 34.0055
    - type: nauc_recall_at_10_std
      value: 19.4785
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      value: 13.9128
    - type: nauc_recall_at_20_max
      value: 34.4532
    - type: nauc_recall_at_20_std
      value: 26.6761
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      value: 10.6585
    - type: nauc_recall_at_100_max
      value: 36.5745
    - type: nauc_recall_at_100_std
      value: 39.6888
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      value: 11.683
    - type: nauc_recall_at_1000_max
      value: 33.799
    - type: nauc_recall_at_1000_std
      value: 44.5965
    - type: nauc_recall_at_1000_diff1
      value: 9.332699999999999
    - type: nauc_precision_at_1_max
      value: 25.9162
    - type: nauc_precision_at_1_std
      value: 7.375800000000001
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      value: 21.4553
    - type: nauc_precision_at_3_max
      value: 31.4508
    - type: nauc_precision_at_3_std
      value: 12.4827
    - type: nauc_precision_at_3_diff1
      value: 15.9863
    - type: nauc_precision_at_5_max
      value: 33.2365
    - type: nauc_precision_at_5_std
      value: 15.9467
    - type: nauc_precision_at_5_diff1
      value: 17.3246
    - type: nauc_precision_at_10_max
      value: 34.1244
    - type: nauc_precision_at_10_std
      value: 19.545
    - type: nauc_precision_at_10_diff1
      value: 14.082600000000001
    - type: nauc_precision_at_20_max
      value: 34.367399999999996
    - type: nauc_precision_at_20_std
      value: 26.530199999999997
    - type: nauc_precision_at_20_diff1
      value: 10.7493
    - type: nauc_precision_at_100_max
      value: 36.3502
    - type: nauc_precision_at_100_std
      value: 39.5794
    - type: nauc_precision_at_100_diff1
      value: 11.6971
    - type: nauc_precision_at_1000_max
      value: 32.6092
    - type: nauc_precision_at_1000_std
      value: 43.249500000000005
    - type: nauc_precision_at_1000_diff1
      value: 9.149899999999999
    - type: nauc_mrr_at_1_max
      value: 25.9162
    - type: nauc_mrr_at_1_std
      value: 7.375800000000001
    - type: nauc_mrr_at_1_diff1
      value: 21.4553
    - type: nauc_mrr_at_3_max
      value: 28.1601
    - type: nauc_mrr_at_3_std
      value: 11.7872
    - type: nauc_mrr_at_3_diff1
      value: 18.1467
    - type: nauc_mrr_at_5_max
      value: 29.1462
    - type: nauc_mrr_at_5_std
      value: 12.9036
    - type: nauc_mrr_at_5_diff1
      value: 18.834899999999998
    - type: nauc_mrr_at_10_max
      value: 29.837799999999998
    - type: nauc_mrr_at_10_std
      value: 13.2935
    - type: nauc_mrr_at_10_diff1
      value: 18.7271
    - type: nauc_mrr_at_20_max
      value: 29.808600000000002
    - type: nauc_mrr_at_20_std
      value: 13.7856
    - type: nauc_mrr_at_20_diff1
      value: 18.6675
    - type: nauc_mrr_at_100_max
      value: 29.7584
    - type: nauc_mrr_at_100_std
      value: 13.8851
    - type: nauc_mrr_at_100_diff1
      value: 18.601
    - type: nauc_mrr_at_1000_max
      value: 29.7331
    - type: nauc_mrr_at_1000_std
      value: 13.8237
    - type: nauc_mrr_at_1000_diff1
      value: 18.6124
    - type: main_score
      value: 16.926
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SICK-R (default)
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
      split: test
      type: mteb/sickr-sts
    metrics:
    - type: pearson
      value: 84.7166
    - type: spearman
      value: 80.3972
    - type: cosine_pearson
      value: 84.7166
    - type: cosine_spearman
      value: 80.3972
    - type: manhattan_pearson
      value: 81.3592
    - type: manhattan_spearman
      value: 80.4202
    - type: euclidean_pearson
      value: 81.3441
    - type: euclidean_spearman
      value: 80.3972
    - type: main_score
      value: 80.3972
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS12 (default)
      revision: a0d554a64d88156834ff5ae9920b964011b16384
      split: test
      type: mteb/sts12-sts
    metrics:
    - type: pearson
      value: 86.7684
    - type: spearman
      value: 78.7071
    - type: cosine_pearson
      value: 86.7684
    - type: cosine_spearman
      value: 78.70899999999999
    - type: manhattan_pearson
      value: 83.7029
    - type: manhattan_spearman
      value: 78.7584
    - type: euclidean_pearson
      value: 83.604
    - type: euclidean_spearman
      value: 78.70899999999999
    - type: main_score
      value: 78.70899999999999
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS13 (default)
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
      split: test
      type: mteb/sts13-sts
    metrics:
    - type: pearson
      value: 85.1773
    - type: spearman
      value: 86.1602
    - type: cosine_pearson
      value: 85.1773
    - type: cosine_spearman
      value: 86.1602
    - type: manhattan_pearson
      value: 84.7533
    - type: manhattan_spearman
      value: 86.0645
    - type: euclidean_pearson
      value: 84.8639
    - type: euclidean_spearman
      value: 86.1602
    - type: main_score
      value: 86.1602
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS14 (default)
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
      split: test
      type: mteb/sts14-sts
    metrics:
    - type: pearson
      value: 82.87780000000001
    - type: spearman
      value: 81.2081
    - type: cosine_pearson
      value: 82.87780000000001
    - type: cosine_spearman
      value: 81.2081
    - type: manhattan_pearson
      value: 81.89750000000001
    - type: manhattan_spearman
      value: 81.2182
    - type: euclidean_pearson
      value: 81.917
    - type: euclidean_spearman
      value: 81.2081
    - type: main_score
      value: 81.2081
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS15 (default)
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
      split: test
      type: mteb/sts15-sts
    metrics:
    - type: pearson
      value: 86.9104
    - type: spearman
      value: 87.5072
    - type: cosine_pearson
      value: 86.9104
    - type: cosine_spearman
      value: 87.5073
    - type: manhattan_pearson
      value: 86.74849999999999
    - type: manhattan_spearman
      value: 87.4643
    - type: euclidean_pearson
      value: 86.7938
    - type: euclidean_spearman
      value: 87.5072
    - type: main_score
      value: 87.5073
    task:
      type: STS
  - dataset:
      config: en-en
      name: MTEB STS17 (en-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 89.4941
    - type: spearman
      value: 88.9712
    - type: cosine_pearson
      value: 89.4941
    - type: cosine_spearman
      value: 88.9712
    - type: manhattan_pearson
      value: 89.04039999999999
    - type: manhattan_spearman
      value: 89.05720000000001
    - type: euclidean_pearson
      value: 89.0296
    - type: euclidean_spearman
      value: 88.9712
    - type: main_score
      value: 88.9712
    task:
      type: STS
  - dataset:
      config: en
      name: MTEB STS22.v2 (en)
      revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 66.6691
    - type: spearman
      value: 65.5503
    - type: cosine_pearson
      value: 66.6691
    - type: cosine_spearman
      value: 65.5503
    - type: manhattan_pearson
      value: 67.6732
    - type: manhattan_spearman
      value: 65.2781
    - type: euclidean_pearson
      value: 67.6466
    - type: euclidean_spearman
      value: 65.5503
    - type: main_score
      value: 65.5503
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STSBenchmark (default)
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
      split: test
      type: mteb/stsbenchmark-sts
    metrics:
    - type: pearson
      value: 85.8143
    - type: spearman
      value: 86.40339999999999
    - type: cosine_pearson
      value: 85.8143
    - type: cosine_spearman
      value: 86.40339999999999
    - type: manhattan_pearson
      value: 86.0569
    - type: manhattan_spearman
      value: 86.3744
    - type: euclidean_pearson
      value: 86.0947
    - type: euclidean_spearman
      value: 86.40339999999999
    - type: main_score
      value: 86.40339999999999
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SprintDuplicateQuestions (default)
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
      split: test
      type: mteb/sprintduplicatequestions-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 99.8
    - type: similarity_accuracy_threshold
      value: 71.084
    - type: similarity_f1
      value: 89.7462
    - type: similarity_f1_threshold
      value: 71.084
    - type: similarity_precision
      value: 91.134
    - type: similarity_recall
      value: 88.4
    - type: similarity_ap
      value: 94.32199999999999
    - type: cosine_accuracy
      value: 99.8
    - type: cosine_accuracy_threshold
      value: 71.084
    - type: cosine_f1
      value: 89.7462
    - type: cosine_f1_threshold
      value: 71.084
    - type: cosine_precision
      value: 91.134
    - type: cosine_recall
      value: 88.4
    - type: cosine_ap
      value: 94.32199999999999
    - type: manhattan_accuracy
      value: 99.7941
    - type: manhattan_accuracy_threshold
      value: 1641.3430999999998
    - type: manhattan_f1
      value: 89.6245
    - type: manhattan_f1_threshold
      value: 1705.1424000000002
    - type: manhattan_precision
      value: 88.5742
    - type: manhattan_recall
      value: 90.7
    - type: manhattan_ap
      value: 94.22840000000001
    - type: euclidean_accuracy
      value: 99.8
    - type: euclidean_accuracy_threshold
      value: 76.0474
    - type: euclidean_f1
      value: 89.7462
    - type: euclidean_f1_threshold
      value: 76.0474
    - type: euclidean_precision
      value: 91.134
    - type: euclidean_recall
      value: 88.4
    - type: euclidean_ap
      value: 94.32199999999999
    - type: dot_accuracy
      value: 99.8
    - type: dot_accuracy_threshold
      value: 71.084
    - type: dot_f1
      value: 89.7462
    - type: dot_f1_threshold
      value: 71.084
    - type: dot_precision
      value: 91.134
    - type: dot_recall
      value: 88.4
    - type: dot_ap
      value: 94.32199999999999
    - type: max_accuracy
      value: 99.8
    - type: max_f1
      value: 89.7462
    - type: max_precision
      value: 91.134
    - type: max_recall
      value: 90.7
    - type: max_ap
      value: 94.32199999999999
    - type: main_score
      value: 94.32199999999999
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB StackExchangeClustering.v2 (default)
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
      split: test
      type: mteb/stackexchange-clustering
    metrics:
    - type: v_measure
      value: 53.5198
    - type: v_measure_std
      value: 0.6015
    - type: main_score
      value: 53.5198
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB StackExchangeClusteringP2P.v2 (default)
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
      split: test
      type: mteb/stackexchange-clustering-p2p
    metrics:
    - type: v_measure
      value: 40.029399999999995
    - type: v_measure_std
      value: 0.4919
    - type: main_score
      value: 40.029399999999995
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB SummEvalSummarization.v2 (default)
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
      split: test
      type: mteb/summeval
    metrics:
    - type: pearson
      value: 33.6198
    - type: spearman
      value: 30.206699999999998
    - type: cosine_spearman
      value: 30.206699999999998
    - type: cosine_pearson
      value: 33.6198
    - type: dot_spearman
      value: 30.206699999999998
    - type: dot_pearson
      value: 33.6198
    - type: main_score
      value: 30.206699999999998
    task:
      type: Summarization
  - dataset:
      config: default
      name: MTEB TRECCOVID (default)
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
      split: test
      type: mteb/trec-covid
    metrics:
    - type: ndcg_at_1
      value: 63
    - type: ndcg_at_3
      value: 66.47999999999999
    - type: ndcg_at_5
      value: 61.090999999999994
    - type: ndcg_at_10
      value: 56.823
    - type: ndcg_at_20
      value: 53.21
    - type: ndcg_at_100
      value: 42.365
    - type: ndcg_at_1000
      value: 40.819
    - type: map_at_1
      value: 0.186
    - type: map_at_3
      value: 0.527
    - type: map_at_5
      value: 0.762
    - type: map_at_10
      value: 1.275
    - type: map_at_20
      value: 2.177
    - type: map_at_100
      value: 6.935
    - type: map_at_1000
      value: 16.973
    - type: recall_at_1
      value: 0.186
    - type: recall_at_3
      value: 0.581
    - type: recall_at_5
      value: 0.8710000000000001
    - type: recall_at_10
      value: 1.582
    - type: recall_at_20
      value: 2.897
    - type: recall_at_100
      value: 10.546
    - type: recall_at_1000
      value: 38.541
    - type: precision_at_1
      value: 68
    - type: precision_at_3
      value: 70.667
    - type: precision_at_5
      value: 63.2
    - type: precision_at_10
      value: 58.4
    - type: precision_at_20
      value: 54.400000000000006
    - type: precision_at_100
      value: 42.46
    - type: precision_at_1000
      value: 17.657999999999998
    - type: mrr_at_1
      value: 68
    - type: mrr_at_3
      value: 79
    - type: mrr_at_5
      value: 79.5
    - type: mrr_at_10
      value: 79.8333
    - type: mrr_at_20
      value: 80.0152
    - type: mrr_at_100
      value: 80.0152
    - type: mrr_at_1000
      value: 80.0152
    - type: nauc_ndcg_at_1_max
      value: -5.9922
    - type: nauc_ndcg_at_1_std
      value: 0.42110000000000003
    - type: nauc_ndcg_at_1_diff1
      value: 23.3553
    - type: nauc_ndcg_at_3_max
      value: 10.2171
    - type: nauc_ndcg_at_3_std
      value: 17.6509
    - type: nauc_ndcg_at_3_diff1
      value: 14.5183
    - type: nauc_ndcg_at_5_max
      value: 23.7407
    - type: nauc_ndcg_at_5_std
      value: 37.241
    - type: nauc_ndcg_at_5_diff1
      value: 18.1059
    - type: nauc_ndcg_at_10_max
      value: 29.640300000000003
    - type: nauc_ndcg_at_10_std
      value: 41.2782
    - type: nauc_ndcg_at_10_diff1
      value: 8.6037
    - type: nauc_ndcg_at_20_max
      value: 40.3419
    - type: nauc_ndcg_at_20_std
      value: 52.5532
    - type: nauc_ndcg_at_20_diff1
      value: 8.1576
    - type: nauc_ndcg_at_100_max
      value: 51.4533
    - type: nauc_ndcg_at_100_std
      value: 69.6289
    - type: nauc_ndcg_at_100_diff1
      value: -3.2301
    - type: nauc_ndcg_at_1000_max
      value: 56.962900000000005
    - type: nauc_ndcg_at_1000_std
      value: 74.6131
    - type: nauc_ndcg_at_1000_diff1
      value: -8.241999999999999
    - type: nauc_map_at_1_max
      value: -4.668
    - type: nauc_map_at_1_std
      value: -10.0497
    - type: nauc_map_at_1_diff1
      value: 23.029700000000002
    - type: nauc_map_at_3_max
      value: 0.6419
    - type: nauc_map_at_3_std
      value: 1.0362
    - type: nauc_map_at_3_diff1
      value: 14.8847
    - type: nauc_map_at_5_max
      value: 10.632
    - type: nauc_map_at_5_std
      value: 14.382200000000001
    - type: nauc_map_at_5_diff1
      value: 17.8863
    - type: nauc_map_at_10_max
      value: 16.8052
    - type: nauc_map_at_10_std
      value: 21.084500000000002
    - type: nauc_map_at_10_diff1
      value: 15.3248
    - type: nauc_map_at_20_max
      value: 27.3457
    - type: nauc_map_at_20_std
      value: 34.2901
    - type: nauc_map_at_20_diff1
      value: 11.4443
    - type: nauc_map_at_100_max
      value: 49.5995
    - type: nauc_map_at_100_std
      value: 65.1028
    - type: nauc_map_at_100_diff1
      value: -1.8796
    - type: nauc_map_at_1000_max
      value: 60.618399999999994
    - type: nauc_map_at_1000_std
      value: 76.28399999999999
    - type: nauc_map_at_1000_diff1
      value: -13.772100000000002
    - type: nauc_recall_at_1_max
      value: -4.668
    - type: nauc_recall_at_1_std
      value: -10.0497
    - type: nauc_recall_at_1_diff1
      value: 23.029700000000002
    - type: nauc_recall_at_3_max
      value: 0.0493
    - type: nauc_recall_at_3_std
      value: 2.2468
    - type: nauc_recall_at_3_diff1
      value: 16.5914
    - type: nauc_recall_at_5_max
      value: 9.1725
    - type: nauc_recall_at_5_std
      value: 14.597999999999999
    - type: nauc_recall_at_5_diff1
      value: 18.6063
    - type: nauc_recall_at_10_max
      value: 13.672400000000001
    - type: nauc_recall_at_10_std
      value: 15.9268
    - type: nauc_recall_at_10_diff1
      value: 16.3772
    - type: nauc_recall_at_20_max
      value: 21.4077
    - type: nauc_recall_at_20_std
      value: 27.209
    - type: nauc_recall_at_20_diff1
      value: 14.8917
    - type: nauc_recall_at_100_max
      value: 42.282799999999995
    - type: nauc_recall_at_100_std
      value: 57.6084
    - type: nauc_recall_at_100_diff1
      value: 2.6269
    - type: nauc_recall_at_1000_max
      value: 54.055
    - type: nauc_recall_at_1000_std
      value: 68.8306
    - type: nauc_recall_at_1000_diff1
      value: -9.5473
    - type: nauc_precision_at_1_max
      value: -1.8693000000000002
    - type: nauc_precision_at_1_std
      value: -5.061800000000001
    - type: nauc_precision_at_1_diff1
      value: 39.6344
    - type: nauc_precision_at_3_max
      value: 20.2643
    - type: nauc_precision_at_3_std
      value: 23.1419
    - type: nauc_precision_at_3_diff1
      value: 20.305999999999997
    - type: nauc_precision_at_5_max
      value: 35.8846
    - type: nauc_precision_at_5_std
      value: 48.295
    - type: nauc_precision_at_5_diff1
      value: 22.5559
    - type: nauc_precision_at_10_max
      value: 39.8361
    - type: nauc_precision_at_10_std
      value: 46.245000000000005
    - type: nauc_precision_at_10_diff1
      value: 6.433800000000001
    - type: nauc_precision_at_20_max
      value: 47.9467
    - type: nauc_precision_at_20_std
      value: 57.981
    - type: nauc_precision_at_20_diff1
      value: 7.721699999999999
    - type: nauc_precision_at_100_max
      value: 55.6948
    - type: nauc_precision_at_100_std
      value: 71.6681
    - type: nauc_precision_at_100_diff1
      value: -5.4666
    - type: nauc_precision_at_1000_max
      value: 49.0064
    - type: nauc_precision_at_1000_std
      value: 56.2352
    - type: nauc_precision_at_1000_diff1
      value: -17.4375
    - type: nauc_mrr_at_1_max
      value: -1.8693000000000002
    - type: nauc_mrr_at_1_std
      value: -5.061800000000001
    - type: nauc_mrr_at_1_diff1
      value: 39.6344
    - type: nauc_mrr_at_3_max
      value: 7.8541
    - type: nauc_mrr_at_3_std
      value: 7.0844000000000005
    - type: nauc_mrr_at_3_diff1
      value: 44.6714
    - type: nauc_mrr_at_5_max
      value: 7.070600000000001
    - type: nauc_mrr_at_5_std
      value: 6.2793
    - type: nauc_mrr_at_5_diff1
      value: 43.1205
    - type: nauc_mrr_at_10_max
      value: 5.829899999999999
    - type: nauc_mrr_at_10_std
      value: 4.7435
    - type: nauc_mrr_at_10_diff1
      value: 42.8864
    - type: nauc_mrr_at_20_max
      value: 4.8414
    - type: nauc_mrr_at_20_std
      value: 3.7436
    - type: nauc_mrr_at_20_diff1
      value: 42.9607
    - type: nauc_mrr_at_100_max
      value: 4.8414
    - type: nauc_mrr_at_100_std
      value: 3.7436
    - type: nauc_mrr_at_100_diff1
      value: 42.9607
    - type: nauc_mrr_at_1000_max
      value: 4.8414
    - type: nauc_mrr_at_1000_std
      value: 3.7436
    - type: nauc_mrr_at_1000_diff1
      value: 42.9607
    - type: main_score
      value: 56.823
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB Touche2020Retrieval.v3 (default)
      revision: 431886eaecc48f067a3975b70d0949ea2862463c
      split: test
      type: mteb/webis-touche2020-v3
    metrics:
    - type: ndcg_at_1
      value: 52.041000000000004
    - type: ndcg_at_3
      value: 52.178000000000004
    - type: ndcg_at_5
      value: 52.23100000000001
    - type: ndcg_at_10
      value: 47.693999999999996
    - type: ndcg_at_20
      value: 43.242999999999995
    - type: ndcg_at_100
      value: 51.503
    - type: ndcg_at_1000
      value: 63.939
    - type: map_at_1
      value: 2.407
    - type: map_at_3
      value: 6.193
    - type: map_at_5
      value: 9.617
    - type: map_at_10
      value: 15.279000000000002
    - type: map_at_20
      value: 21.498
    - type: map_at_100
      value: 30.198999999999998
    - type: map_at_1000
      value: 33.217
    - type: recall_at_1
      value: 2.407
    - type: recall_at_3
      value: 6.762
    - type: recall_at_5
      value: 11.392
    - type: recall_at_10
      value: 19.333
    - type: recall_at_20
      value: 30.013
    - type: recall_at_100
      value: 56.041
    - type: recall_at_1000
      value: 86.126
    - type: precision_at_1
      value: 61.224000000000004
    - type: precision_at_3
      value: 63.26500000000001
    - type: precision_at_5
      value: 62.449
    - type: precision_at_10
      value: 52.245
    - type: precision_at_20
      value: 42.041000000000004
    - type: precision_at_100
      value: 17.653
    - type: precision_at_1000
      value: 2.9819999999999998
    - type: mrr_at_1
      value: 61.224500000000006
    - type: mrr_at_3
      value: 74.1497
    - type: mrr_at_5
      value: 76.4966
    - type: mrr_at_10
      value: 76.7881
    - type: mrr_at_20
      value: 76.7881
    - type: mrr_at_100
      value: 76.7881
    - type: mrr_at_1000
      value: 76.7881
    - type: nauc_ndcg_at_1_max
      value: 11.4245
    - type: nauc_ndcg_at_1_std
      value: -14.1654
    - type: nauc_ndcg_at_1_diff1
      value: 8.206299999999999
    - type: nauc_ndcg_at_3_max
      value: 9.2585
    - type: nauc_ndcg_at_3_std
      value: -11.469999999999999
    - type: nauc_ndcg_at_3_diff1
      value: 16.437099999999997
    - type: nauc_ndcg_at_5_max
      value: 4.9696
    - type: nauc_ndcg_at_5_std
      value: -0.6109
    - type: nauc_ndcg_at_5_diff1
      value: 27.5214
    - type: nauc_ndcg_at_10_max
      value: -1.3538
    - type: nauc_ndcg_at_10_std
      value: -6.0539000000000005
    - type: nauc_ndcg_at_10_diff1
      value: 37.565799999999996
    - type: nauc_ndcg_at_20_max
      value: -3.3665000000000003
    - type: nauc_ndcg_at_20_std
      value: 0.364
    - type: nauc_ndcg_at_20_diff1
      value: 37.418800000000005
    - type: nauc_ndcg_at_100_max
      value: -7.1732000000000005
    - type: nauc_ndcg_at_100_std
      value: 6.9091
    - type: nauc_ndcg_at_100_diff1
      value: 31.342799999999997
    - type: nauc_ndcg_at_1000_max
      value: 4.9213
    - type: nauc_ndcg_at_1000_std
      value: 27.2304
    - type: nauc_ndcg_at_1000_diff1
      value: 26.5774
    - type: nauc_map_at_1_max
      value: -10.1278
    - type: nauc_map_at_1_std
      value: -30.9116
    - type: nauc_map_at_1_diff1
      value: 47.6006
    - type: nauc_map_at_3_max
      value: -9.9654
    - type: nauc_map_at_3_std
      value: -26.4025
    - type: nauc_map_at_3_diff1
      value: 40.3311
    - type: nauc_map_at_5_max
      value: -10.3545
    - type: nauc_map_at_5_std
      value: -21.662699999999997
    - type: nauc_map_at_5_diff1
      value: 46.1136
    - type: nauc_map_at_10_max
      value: -9.528
    - type: nauc_map_at_10_std
      value: -21.3903
    - type: nauc_map_at_10_diff1
      value: 41.5027
    - type: nauc_map_at_20_max
      value: -7.0028999999999995
    - type: nauc_map_at_20_std
      value: -15.9361
    - type: nauc_map_at_20_diff1
      value: 42.6171
    - type: nauc_map_at_100_max
      value: -2.8579
    - type: nauc_map_at_100_std
      value: -4.1692
    - type: nauc_map_at_100_diff1
      value: 35.200900000000004
    - type: nauc_map_at_1000_max
      value: -0.1717
    - type: nauc_map_at_1000_std
      value: 1.4015
    - type: nauc_map_at_1000_diff1
      value: 34.1462
    - type: nauc_recall_at_1_max
      value: -10.1278
    - type: nauc_recall_at_1_std
      value: -30.9116
    - type: nauc_recall_at_1_diff1
      value: 47.6006
    - type: nauc_recall_at_3_max
      value: -9.7092
    - type: nauc_recall_at_3_std
      value: -26.067800000000002
    - type: nauc_recall_at_3_diff1
      value: 44.094100000000005
    - type: nauc_recall_at_5_max
      value: -16.8476
    - type: nauc_recall_at_5_std
      value: -21.546799999999998
    - type: nauc_recall_at_5_diff1
      value: 51.0826
    - type: nauc_recall_at_10_max
      value: -19.3996
    - type: nauc_recall_at_10_std
      value: -23.857400000000002
    - type: nauc_recall_at_10_diff1
      value: 43.743900000000004
    - type: nauc_recall_at_20_max
      value: -17.413500000000003
    - type: nauc_recall_at_20_std
      value: -13.7552
    - type: nauc_recall_at_20_diff1
      value: 41.761900000000004
    - type: nauc_recall_at_100_max
      value: -13.270399999999999
    - type: nauc_recall_at_100_std
      value: 12.9632
    - type: nauc_recall_at_100_diff1
      value: 25.7781
    - type: nauc_recall_at_1000_max
      value: 4.5253000000000005
    - type: nauc_recall_at_1000_std
      value: 71.75280000000001
    - type: nauc_recall_at_1000_diff1
      value: 9.0837
    - type: nauc_precision_at_1_max
      value: 26.4969
    - type: nauc_precision_at_1_std
      value: -21.090600000000002
    - type: nauc_precision_at_1_diff1
      value: 25.671899999999997
    - type: nauc_precision_at_3_max
      value: 17.132
    - type: nauc_precision_at_3_std
      value: -14.341999999999999
    - type: nauc_precision_at_3_diff1
      value: 27.7326
    - type: nauc_precision_at_5_max
      value: 10.6548
    - type: nauc_precision_at_5_std
      value: 2.9193000000000002
    - type: nauc_precision_at_5_diff1
      value: 38.373400000000004
    - type: nauc_precision_at_10_max
      value: 1.3576
    - type: nauc_precision_at_10_std
      value: -3.8871
    - type: nauc_precision_at_10_diff1
      value: 33.6879
    - type: nauc_precision_at_20_max
      value: 4.9846
    - type: nauc_precision_at_20_std
      value: 16.8654
    - type: nauc_precision_at_20_diff1
      value: 25.1747
    - type: nauc_precision_at_100_max
      value: 32.9312
    - type: nauc_precision_at_100_std
      value: 50.7741
    - type: nauc_precision_at_100_diff1
      value: -19.561700000000002
    - type: nauc_precision_at_1000_max
      value: 44.7539
    - type: nauc_precision_at_1000_std
      value: 50.897800000000004
    - type: nauc_precision_at_1000_diff1
      value: -34.477999999999994
    - type: nauc_mrr_at_1_max
      value: 26.4969
    - type: nauc_mrr_at_1_std
      value: -21.090600000000002
    - type: nauc_mrr_at_1_diff1
      value: 25.671899999999997
    - type: nauc_mrr_at_3_max
      value: 36.031600000000005
    - type: nauc_mrr_at_3_std
      value: -9.915799999999999
    - type: nauc_mrr_at_3_diff1
      value: 32.4812
    - type: nauc_mrr_at_5_max
      value: 32.5212
    - type: nauc_mrr_at_5_std
      value: -10.443
    - type: nauc_mrr_at_5_diff1
      value: 31.8118
    - type: nauc_mrr_at_10_max
      value: 31.4955
    - type: nauc_mrr_at_10_std
      value: -11.698
    - type: nauc_mrr_at_10_diff1
      value: 30.974400000000003
    - type: nauc_mrr_at_20_max
      value: 31.4955
    - type: nauc_mrr_at_20_std
      value: -11.698
    - type: nauc_mrr_at_20_diff1
      value: 30.974400000000003
    - type: nauc_mrr_at_100_max
      value: 31.4955
    - type: nauc_mrr_at_100_std
      value: -11.698
    - type: nauc_mrr_at_100_diff1
      value: 30.974400000000003
    - type: nauc_mrr_at_1000_max
      value: 31.4955
    - type: nauc_mrr_at_1000_std
      value: -11.698
    - type: nauc_mrr_at_1000_diff1
      value: 30.974400000000003
    - type: main_score
      value: 47.693999999999996
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ToxicConversationsClassification (default)
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
      split: test
      type: mteb/toxic_conversations_50k
    metrics:
    - type: accuracy
      value: 65.65429999999999
    - type: f1
      value: 50.530699999999996
    - type: f1_weighted
      value: 73.3205
    - type: ap
      value: 12.0938
    - type: ap_weighted
      value: 12.0938
    - type: main_score
      value: 65.65429999999999
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TweetSentimentExtractionClassification (default)
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
      split: test
      type: mteb/tweet_sentiment_extraction
    metrics:
    - type: accuracy
      value: 61.7119
    - type: f1
      value: 61.8672
    - type: f1_weighted
      value: 60.762499999999996
    - type: main_score
      value: 61.7119
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TwentyNewsgroupsClustering.v2 (default)
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
      split: test
      type: mteb/twentynewsgroups-clustering
    metrics:
    - type: v_measure
      value: 37.4338
    - type: v_measure_std
      value: 1.5165
    - type: main_score
      value: 37.4338
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB TwitterSemEval2015 (default)
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
      split: test
      type: mteb/twittersemeval2015-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 82.8873
    - type: similarity_accuracy_threshold
      value: 67.9403
    - type: similarity_f1
      value: 60.3641
    - type: similarity_f1_threshold
      value: 60.5738
    - type: similarity_precision
      value: 55.887600000000006
    - type: similarity_recall
      value: 65.62010000000001
    - type: similarity_ap
      value: 63.522
    - type: cosine_accuracy
      value: 82.8873
    - type: cosine_accuracy_threshold
      value: 67.9403
    - type: cosine_f1
      value: 60.3641
    - type: cosine_f1_threshold
      value: 60.5738
    - type: cosine_precision
      value: 55.887600000000006
    - type: cosine_recall
      value: 65.62010000000001
    - type: cosine_ap
      value: 63.522
    - type: manhattan_accuracy
      value: 82.8098
    - type: manhattan_accuracy_threshold
      value: 1739.439
    - type: manhattan_f1
      value: 60.1751
    - type: manhattan_f1_threshold
      value: 1961.5566000000001
    - type: manhattan_precision
      value: 54.5474
    - type: manhattan_recall
      value: 67.0976
    - type: manhattan_ap
      value: 63.42100000000001
    - type: euclidean_accuracy
      value: 82.8873
    - type: euclidean_accuracy_threshold
      value: 80.07459999999999
    - type: euclidean_f1
      value: 60.3641
    - type: euclidean_f1_threshold
      value: 88.7989
    - type: euclidean_precision
      value: 55.887600000000006
    - type: euclidean_recall
      value: 65.62010000000001
    - type: euclidean_ap
      value: 63.522
    - type: dot_accuracy
      value: 82.8873
    - type: dot_accuracy_threshold
      value: 67.9403
    - type: dot_f1
      value: 60.3641
    - type: dot_f1_threshold
      value: 60.5738
    - type: dot_precision
      value: 55.887600000000006
    - type: dot_recall
      value: 65.62010000000001
    - type: dot_ap
      value: 63.522
    - type: max_accuracy
      value: 82.8873
    - type: max_f1
      value: 60.3641
    - type: max_precision
      value: 55.887600000000006
    - type: max_recall
      value: 67.0976
    - type: max_ap
      value: 63.522
    - type: main_score
      value: 63.522
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB TwitterURLCorpus (default)
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
      split: test
      type: mteb/twitterurlcorpus-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 88.7337
    - type: similarity_accuracy_threshold
      value: 62.43729999999999
    - type: similarity_f1
      value: 77.8938
    - type: similarity_f1_threshold
      value: 59.013400000000004
    - type: similarity_precision
      value: 74.31309999999999
    - type: similarity_recall
      value: 81.83709999999999
    - type: similarity_ap
      value: 85.1691
    - type: cosine_accuracy
      value: 88.7337
    - type: cosine_accuracy_threshold
      value: 62.43729999999999
    - type: cosine_f1
      value: 77.8938
    - type: cosine_f1_threshold
      value: 59.013400000000004
    - type: cosine_precision
      value: 74.31309999999999
    - type: cosine_recall
      value: 81.83709999999999
    - type: cosine_ap
      value: 85.1691
    - type: manhattan_accuracy
      value: 88.689
    - type: manhattan_accuracy_threshold
      value: 1888.1997999999999
    - type: manhattan_f1
      value: 77.8453
    - type: manhattan_f1_threshold
      value: 1974.1371000000001
    - type: manhattan_precision
      value: 74.6414
    - type: manhattan_recall
      value: 81.3366
    - type: manhattan_ap
      value: 85.0954
    - type: euclidean_accuracy
      value: 88.7337
    - type: euclidean_accuracy_threshold
      value: 86.6749
    - type: euclidean_f1
      value: 77.8938
    - type: euclidean_f1_threshold
      value: 90.53909999999999
    - type: euclidean_precision
      value: 74.31309999999999
    - type: euclidean_recall
      value: 81.83709999999999
    - type: euclidean_ap
      value: 85.1691
    - type: dot_accuracy
      value: 88.7337
    - type: dot_accuracy_threshold
      value: 62.43729999999999
    - type: dot_f1
      value: 77.8938
    - type: dot_f1_threshold
      value: 59.013400000000004
    - type: dot_precision
      value: 74.31309999999999
    - type: dot_recall
      value: 81.83709999999999
    - type: dot_ap
      value: 85.1691
    - type: max_accuracy
      value: 88.7337
    - type: max_f1
      value: 77.8938
    - type: max_precision
      value: 74.6414
    - type: max_recall
      value: 81.83709999999999
    - type: max_ap
      value: 85.1691
    - type: main_score
      value: 85.1691
    task:
      type: PairClassification
license: apache-2.0
---

# RetrievaEmbedding-01: AMBER

The **AMBER (Adaptive Multitask Bilingual Embedding Representations)** is a text embedding model trained by Retrieva, Inc.
This model is primarily designed for Japanese, but it also supports English.
We trained this model on various datasets related to Japanese and English.

This model size is 315M parameters (large size).

## Model Details

### Model Description

The AMBER model is a text embedding model based on the [sbintuitions/modernbert-ja-310m](https://huggingface.co/sbintuitions/modernbert-ja-310m) architecture, designed for Japanese text.
This model was trained on a variety of datasets related to Japanese, and also includes English datasets.
The model can be used for English text as well.
During training, prompts (instructions) in natural language were included, allowing the model to generate embeddings tailored to specific tasks.

- **Developed by:** Retrieva, Inc.
- **Model type:** Based on the [ModernBERT](https://arxiv.org/abs/2412.13663) Architecture.
- **Language(s) (NLP):** Primarily Japanese (optional support for English).
- **License:** Apache 2.0
- **Finetuned from model:** `sbintuitions/modernbert-ja-310m`
- **Model Type:** Sentence Transformer
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity

## Uses

## How to Get Started with the Model

### Install Library

First install the python library using pip:

```bash
pip install sentence-transformers sentencepiece
```

### Run Inference

Then you can load this model and run inference.

You can specify the prompt at inference time by adding an argument called `prompt` to `model.encode`.
The prompts used in the Japanese benchmark are described in `jmteb/tasks`, and the prompts used in the English benchmark are described in `mteb/models/retrieva_en.py`.

```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("retrieva-jp/amber-large")
# Run inference
queries = [
    "自然言語処理とはなんですか？",
    "株式会社レトリバについて教えて",
]
documents = [
    "自然言語処理（しぜんげんごしょり、英語: Natural language processing、略称：NLP）は、人間が日常的に使っている自然言語をコンピュータに処理させる一連の技術であり、人工知能と言語学の一分野である。",
    "株式会社レトリバは、自然言語処理と機械学習を核としたAI技術で組織の課題解決を支援するテクノロジー企業である。",
]

queries_embeddings = model.encode(queries, prompt_name="Retrieval-query")
documents_embeddings = model.encode(documents, prompt_name="Retrieval-passage")

similarities = model.similarity(queries_embeddings, documents_embeddings)
print(similarities.shape)
```

## Training Details

### Training Data

We used multiple datasets to train this model.
We selected datasets from [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval), [llm-japanese-dataset](https://github.com/masanorihirano/llm-japanese-dataset), and [hpprc/emb](https://huggingface.co/datasets/hpprc/emb) for Japanese datasets.
For English datasets, we mainly used some of the datasets utilized in [Asai et al. (2023)](https://arxiv.org/abs/2211.09260).
Additionally, we partially used the English datasets at [the sentence-transformers repository](https://huggingface.co/sentence-transformers) and [kilt-tasks](https://huggingface.co/datasets/facebook/kilt_tasks).
To consider cross-lingual between Japanese and English, we also used translation datasets between Japanese and English.

For Japanese, we used synthetic data created by LLM to prepare a sufficient amount of training data.

## Evaluation

We evaluated the model on the following benchmarks:
- Japanese Benchmark: [JMTEB](https://github.com/sbintuitions/JMTEB)
- Japanese Retrieval Tasks: [JQaRA](https://github.com/hotchpotch/JQaRA/), [JaCWIR](https://github.com/hotchpotch/JaCWIR/), [MLDR Japanese Subset](https://huggingface.co/datasets/Shitao/MLDR)
- English Benchmark: [MTEB(eng, v2)](https://github.com/embeddings-benchmark/mteb).

The scores in the table are all calculated by us unless otherwise noted.

### Japanese Benchmark: JMTEB

Note that the `Mean (TaskType)` in the following leaderboard is the same as the `Avg.` in the original JMTEB leaderboard.

The files used for evaluation are stored in the `jmteb` directory.

| Model | # Parameters | Mean (TaskType) | Mean (Task) | Retrieval | STS | Classification | Reranking | Clustering | PairClassification |
| :---  |  ---         | ---:         |  ---:            | ---:       | ---: | ---:            | ---:       | ---:        | ---:                |
| base models | < 300M  |         |             |        |  |             |       |         |                 |
| [cl-nagoya/ruri-base](https://huggingface.co/cl-nagoya/ruri-base) | 111M | 72.60 | 71.56 | 69.53 | 82.87 | 75.49 | 92.91 | 52.40 | 62.38 |
| [AMBER-base](https://huggingface.co/retrieva-jp/amber-base) | 130M | 72.12 | 72.12 | **73.40** | 77.81 | **76.14** | **93.27** | 48.05 | **64.03** |
| [pkshatech/GLuCoSE-base-ja-v2](https://huggingface.co/pkshatech/GLuCoSE-base-ja-v2) | 133M | **72.89** | **72.47** | 73.03 | **82.96** | 74.02 | 93.01 | 51.96 | 62.37 |
| [pkshatech/RoSEtta-base-ja](https://huggingface.co/pkshatech/RoSEtta-base-ja) | 190M | 72.49 | 72.05 | 73.14 | 81.39 | 72.37 | 92.69 | **53.60** | 61.74 |
| [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 278M | 71.11 | 69.72 | 69.45 | 80.45 | 69.86 | 92.90 | 51.62 | 62.35 |
| large models | 300M <  |         |             |        |  |             |       |         |                 |
| AMBER-large <br> (this model) | 315M | 72.52 | **73.22** | **75.40** | 79.32 | 77.14 | **93.54** | 48.73 | 60.97 |
| [cl-nagoya/ruri-large](https://huggingface.co/cl-nagoya/ruri-large) | 337M | **73.20** | 73.06 | 72.86 | **83.14** | **77.15** | 93.00 | 50.78 | 62.29 |
| [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 560M | 72.06 | 71.29 | 71.71 | 80.87 | 72.45 | 93.29 | **51.59** | **62.42** |


### Japanese Retrieval Tasks: JQaRA, JaCWIR, MLDR Japanese Subset

The files used for MLDR are stored in the `mldr` directory.

The prompts used in JQaRA and JaCWIR are `Retrieval-query` and `Retrieval-passage` described in `config_sentence_transformers.json`.

| Model | # Parameters | JQaRA (nDCG@10) | JaCWIR (MAP@10) | MLDR Japanese Subset (nDCG@10) |
| :---  |  ---         | ---:            |  ---:           | ---:                             |
| base models | < 300M  |                 |                 |                                  |
| [cl-nagoya/ruri-base](https://huggingface.co/cl-nagoya/ruri-base) | 111M | 58.4 | 83.3 | 32.77 |
| [AMBER-base](https://huggingface.co/retrieva-jp/amber-base) | 130M | 57.1 | 81.6 | **35.69** |
| [pkshatech/GLuCoSE-base-ja-v2](https://huggingface.co/pkshatech/GLuCoSE-base-ja-v2) | 133M | **60.6** | **85.3** | 33.99  |
| [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 278M | 47.1 | **85.3** | 25.46 |
| large models | 300M <  |                 |                 |                                  |
| AMBER-large <br> (this model) | 315M | 62.5 | 82.4 | 34.57  |
| [cl-nagoya/ruri-large](https://huggingface.co/cl-nagoya/ruri-large) | 337M | **62.8** | 82.5 | **34.78** |
| [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 560M | 55.4| **87.3** | 29.95  |

### English Benchmark: MTEB(eng, v2)

The files used for evaluation are stored in the `mteb` directory.

| Model | # Parameters | Mean (TaskType) | Mean (Task) | Retrieval | STS | Classification | Reranking | Clustering | PairClassification | Summarization |
| :---  |  ---         | ---:         |  ---:            | ---:       | ---: | ---:            | ---:       | ---:        | ---:                | ---: |
| base models | < 300M   |        |            |       | |            |       |        |                 |  |
| [AMBER-base](https://huggingface.co/retrieva-jp/amber-base) | 130M | 54.75 | 58.20 | 40.11 | **81.29** | 70.39 | 42.98 | **42.27** | 80.12 | 26.08 |
| [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 278M | **56.21** | **59.75** | **43.22** | 80.50 | **73.84** | **43.87** | 42.19 | **83.74** | **26.10** |
| large models | 300M <  |        |             |        |  |             |        |         |                | |
| AMBER-large <br> (this model) | 315M | 56.08 | 59.13 | 41.04 | **81.52** | 72.23 | 43.83 | **42.71** | 81.00 | **30.21** |
| [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 560M | **57.06** | **60.84** | **46.17** | 81.11 | **74.88** | **44.31** | 41.91 | **84.33** | 26.67 |

## More Information

TBA


## Model Card Authors

Satoru Katsumata, Daisuke Kimura, Jiro Nishitoba

## Model Card Contact

pr[at]retrieva.jp