WhereIsAI_UAE-Large-V1

---

tags:

• mteb
• sentence_embedding
• feature_extraction
• sentence-transformers
• transformers
• transformers.js model-index:
• name: UAE-Large-V1 results:
  • task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics:
    • type: accuracy value: 75.55223880597015
    • type: ap value: 38.264070815317794
    • type: f1 value: 69.40977934769845
  • task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics:
    • type: accuracy value: 92.84267499999999
    • type: ap value: 89.57568507997713
    • type: f1 value: 92.82590734337774
  • task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
    • type: accuracy value: 48.292
    • type: f1 value: 47.90257816032778
  • task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics:
    • type: map_at_1 value: 42.105
    • type: map_at_10 value: 58.181000000000004
    • type: map_at_100 value: 58.653999999999996
    • type: map_at_1000 value: 58.657000000000004
    • type: map_at_3 value: 54.386
    • type: map_at_5 value: 56.757999999999996
    • type: mrr_at_1 value: 42.745
    • type: mrr_at_10 value: 58.437
    • type: mrr_at_100 value: 58.894999999999996
    • type: mrr_at_1000 value: 58.897999999999996
    • type: mrr_at_3 value: 54.635
    • type: mrr_at_5 value: 56.99999999999999
    • type: ndcg_at_1 value: 42.105
    • type: ndcg_at_10 value: 66.14999999999999
    • type: ndcg_at_100 value: 68.048
    • type: ndcg_at_1000 value: 68.11399999999999
    • type: ndcg_at_3 value: 58.477000000000004
    • type: ndcg_at_5 value: 62.768
    • type: precision_at_1 value: 42.105
    • type: precision_at_10 value: 9.110999999999999
    • type: precision_at_100 value: 0.991
    • type: precision_at_1000 value: 0.1
    • type: precision_at_3 value: 23.447000000000003
    • type: precision_at_5 value: 16.159000000000002
    • type: recall_at_1 value: 42.105
    • type: recall_at_10 value: 91.11
    • type: recall_at_100 value: 99.14699999999999
    • type: recall_at_1000 value: 99.644
    • type: recall_at_3 value: 70.341
    • type: recall_at_5 value: 80.797
  • task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics:
    • type: v_measure value: 49.02580759154173
  • task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics:
    • type: v_measure value: 43.093601280163554
  • task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics:
    • type: map value: 64.19590406875427
    • type: mrr value: 77.09547992788991
  • task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics:
    • type: cos_sim_pearson value: 87.86678362843676
    • type: cos_sim_spearman value: 86.1423242570783
    • type: euclidean_pearson value: 85.98994198511751
    • type: euclidean_spearman value: 86.48209103503942
    • type: manhattan_pearson value: 85.6446436316182
    • type: manhattan_spearman value: 86.21039809734357
  • task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics:
    • type: accuracy value: 87.69155844155844
    • type: f1 value: 87.68109381943547
  • task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics:
    • type: v_measure value: 39.37501687500394
  • task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics:
    • type: v_measure value: 37.23401405155885
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 30.232
    • type: map_at_10 value: 41.404999999999994
    • type: map_at_100 value: 42.896
    • type: map_at_1000 value: 43.028
    • type: map_at_3 value: 37.925
    • type: map_at_5 value: 39.865
    • type: mrr_at_1 value: 36.338
    • type: mrr_at_10 value: 46.969
    • type: mrr_at_100 value: 47.684
    • type: mrr_at_1000 value: 47.731
    • type: mrr_at_3 value: 44.063
    • type: mrr_at_5 value: 45.908
    • type: ndcg_at_1 value: 36.338
    • type: ndcg_at_10 value: 47.887
    • type: ndcg_at_100 value: 53.357
    • type: ndcg_at_1000 value: 55.376999999999995
    • type: ndcg_at_3 value: 42.588
    • type: ndcg_at_5 value: 45.132
    • type: precision_at_1 value: 36.338
    • type: precision_at_10 value: 9.17
    • type: precision_at_100 value: 1.4909999999999999
    • type: precision_at_1000 value: 0.196
    • type: precision_at_3 value: 20.315
    • type: precision_at_5 value: 14.793000000000001
    • type: recall_at_1 value: 30.232
    • type: recall_at_10 value: 60.67399999999999
    • type: recall_at_100 value: 83.628
    • type: recall_at_1000 value: 96.209
    • type: recall_at_3 value: 45.48
    • type: recall_at_5 value: 52.354
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 32.237
    • type: map_at_10 value: 42.829
    • type: map_at_100 value: 44.065
    • type: map_at_1000 value: 44.199
    • type: map_at_3 value: 39.885999999999996
    • type: map_at_5 value: 41.55
    • type: mrr_at_1 value: 40.064
    • type: mrr_at_10 value: 48.611
    • type: mrr_at_100 value: 49.245
    • type: mrr_at_1000 value: 49.29
    • type: mrr_at_3 value: 46.561
    • type: mrr_at_5 value: 47.771
    • type: ndcg_at_1 value: 40.064
    • type: ndcg_at_10 value: 48.388
    • type: ndcg_at_100 value: 52.666999999999994
    • type: ndcg_at_1000 value: 54.67100000000001
    • type: ndcg_at_3 value: 44.504
    • type: ndcg_at_5 value: 46.303
    • type: precision_at_1 value: 40.064
    • type: precision_at_10 value: 9.051
    • type: precision_at_100 value: 1.4500000000000002
    • type: precision_at_1000 value: 0.193
    • type: precision_at_3 value: 21.444
    • type: precision_at_5 value: 15.045
    • type: recall_at_1 value: 32.237
    • type: recall_at_10 value: 57.943999999999996
    • type: recall_at_100 value: 75.98700000000001
    • type: recall_at_1000 value: 88.453
    • type: recall_at_3 value: 46.268
    • type: recall_at_5 value: 51.459999999999994
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 38.797
    • type: map_at_10 value: 51.263000000000005
    • type: map_at_100 value: 52.333
    • type: map_at_1000 value: 52.393
    • type: map_at_3 value: 47.936
    • type: map_at_5 value: 49.844
    • type: mrr_at_1 value: 44.389
    • type: mrr_at_10 value: 54.601
    • type: mrr_at_100 value: 55.300000000000004
    • type: mrr_at_1000 value: 55.333
    • type: mrr_at_3 value: 52.068999999999996
    • type: mrr_at_5 value: 53.627
    • type: ndcg_at_1 value: 44.389
    • type: ndcg_at_10 value: 57.193000000000005
    • type: ndcg_at_100 value: 61.307
    • type: ndcg_at_1000 value: 62.529
    • type: ndcg_at_3 value: 51.607
    • type: ndcg_at_5 value: 54.409
    • type: precision_at_1 value: 44.389
    • type: precision_at_10 value: 9.26
    • type: precision_at_100 value: 1.222
    • type: precision_at_1000 value: 0.13699999999999998
    • type: precision_at_3 value: 23.03
    • type: precision_at_5 value: 15.887
    • type: recall_at_1 value: 38.797
    • type: recall_at_10 value: 71.449
    • type: recall_at_100 value: 88.881
    • type: recall_at_1000 value: 97.52
    • type: recall_at_3 value: 56.503
    • type: recall_at_5 value: 63.392
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 27.291999999999998
    • type: map_at_10 value: 35.65
    • type: map_at_100 value: 36.689
    • type: map_at_1000 value: 36.753
    • type: map_at_3 value: 32.995000000000005
    • type: map_at_5 value: 34.409
    • type: mrr_at_1 value: 29.04
    • type: mrr_at_10 value: 37.486000000000004
    • type: mrr_at_100 value: 38.394
    • type: mrr_at_1000 value: 38.445
    • type: mrr_at_3 value: 35.028
    • type: mrr_at_5 value: 36.305
    • type: ndcg_at_1 value: 29.04
    • type: ndcg_at_10 value: 40.613
    • type: ndcg_at_100 value: 45.733000000000004
    • type: ndcg_at_1000 value: 47.447
    • type: ndcg_at_3 value: 35.339999999999996
    • type: ndcg_at_5 value: 37.706
    • type: precision_at_1 value: 29.04
    • type: precision_at_10 value: 6.192
    • type: precision_at_100 value: 0.9249999999999999
    • type: precision_at_1000 value: 0.11
    • type: precision_at_3 value: 14.802000000000001
    • type: precision_at_5 value: 10.305
    • type: recall_at_1 value: 27.291999999999998
    • type: recall_at_10 value: 54.25299999999999
    • type: recall_at_100 value: 77.773
    • type: recall_at_1000 value: 90.795
    • type: recall_at_3 value: 39.731
    • type: recall_at_5 value: 45.403999999999996
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 18.326
    • type: map_at_10 value: 26.290999999999997
    • type: map_at_100 value: 27.456999999999997
    • type: map_at_1000 value: 27.583000000000002
    • type: map_at_3 value: 23.578
    • type: map_at_5 value: 25.113000000000003
    • type: mrr_at_1 value: 22.637
    • type: mrr_at_10 value: 31.139
    • type: mrr_at_100 value: 32.074999999999996
    • type: mrr_at_1000 value: 32.147
    • type: mrr_at_3 value: 28.483000000000004
    • type: mrr_at_5 value: 29.963
    • type: ndcg_at_1 value: 22.637
    • type: ndcg_at_10 value: 31.717000000000002
    • type: ndcg_at_100 value: 37.201
    • type: ndcg_at_1000 value: 40.088
    • type: ndcg_at_3 value: 26.686
    • type: ndcg_at_5 value: 29.076999999999998
    • type: precision_at_1 value: 22.637
    • type: precision_at_10 value: 5.7090000000000005
    • type: precision_at_100 value: 0.979
    • type: precision_at_1000 value: 0.13799999999999998
    • type: precision_at_3 value: 12.894
    • type: precision_at_5 value: 9.328
    • type: recall_at_1 value: 18.326
    • type: recall_at_10 value: 43.824999999999996
    • type: recall_at_100 value: 67.316
    • type: recall_at_1000 value: 87.481
    • type: recall_at_3 value: 29.866999999999997
    • type: recall_at_5 value: 35.961999999999996
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 29.875
    • type: map_at_10 value: 40.458
    • type: map_at_100 value: 41.772
    • type: map_at_1000 value: 41.882999999999996
    • type: map_at_3 value: 37.086999999999996
    • type: map_at_5 value: 39.153
    • type: mrr_at_1 value: 36.381
    • type: mrr_at_10 value: 46.190999999999995
    • type: mrr_at_100 value: 46.983999999999995
    • type: mrr_at_1000 value: 47.032000000000004
    • type: mrr_at_3 value: 43.486999999999995
    • type: mrr_at_5 value: 45.249
    • type: ndcg_at_1 value: 36.381
    • type: ndcg_at_10 value: 46.602
    • type: ndcg_at_100 value: 51.885999999999996
    • type: ndcg_at_1000 value: 53.895
    • type: ndcg_at_3 value: 41.155
    • type: ndcg_at_5 value: 44.182
    • type: precision_at_1 value: 36.381
    • type: precision_at_10 value: 8.402
    • type: precision_at_100 value: 1.278
    • type: precision_at_1000 value: 0.16199999999999998
    • type: precision_at_3 value: 19.346
    • type: precision_at_5 value: 14.09
    • type: recall_at_1 value: 29.875
    • type: recall_at_10 value: 59.065999999999995
    • type: recall_at_100 value: 80.923
    • type: recall_at_1000 value: 93.927
    • type: recall_at_3 value: 44.462
    • type: recall_at_5 value: 51.89
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 24.94
    • type: map_at_10 value: 35.125
    • type: map_at_100 value: 36.476
    • type: map_at_1000 value: 36.579
    • type: map_at_3 value: 31.840000000000003
    • type: map_at_5 value: 33.647
    • type: mrr_at_1 value: 30.936000000000003
    • type: mrr_at_10 value: 40.637
    • type: mrr_at_100 value: 41.471000000000004
    • type: mrr_at_1000 value: 41.525
    • type: mrr_at_3 value: 38.013999999999996
    • type: mrr_at_5 value: 39.469
    • type: ndcg_at_1 value: 30.936000000000003
    • type: ndcg_at_10 value: 41.295
    • type: ndcg_at_100 value: 46.92
    • type: ndcg_at_1000 value: 49.183
    • type: ndcg_at_3 value: 35.811
    • type: ndcg_at_5 value: 38.306000000000004
    • type: precision_at_1 value: 30.936000000000003
    • type: precision_at_10 value: 7.728
    • type: precision_at_100 value: 1.226
    • type: precision_at_1000 value: 0.158
    • type: precision_at_3 value: 17.237
    • type: precision_at_5 value: 12.42
    • type: recall_at_1 value: 24.94
    • type: recall_at_10 value: 54.235
    • type: recall_at_100 value: 78.314
    • type: recall_at_1000 value: 93.973
    • type: recall_at_3 value: 38.925
    • type: recall_at_5 value: 45.505
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 26.250833333333333
    • type: map_at_10 value: 35.46875
    • type: map_at_100 value: 36.667
    • type: map_at_1000 value: 36.78025
    • type: map_at_3 value: 32.56733333333334
    • type: map_at_5 value: 34.20333333333333
    • type: mrr_at_1 value: 30.8945
    • type: mrr_at_10 value: 39.636833333333335
    • type: mrr_at_100 value: 40.46508333333333
    • type: mrr_at_1000 value: 40.521249999999995
    • type: mrr_at_3 value: 37.140166666666666
    • type: mrr_at_5 value: 38.60999999999999
    • type: ndcg_at_1 value: 30.8945
    • type: ndcg_at_10 value: 40.93441666666667
    • type: ndcg_at_100 value: 46.062416666666664
    • type: ndcg_at_1000 value: 48.28341666666667
    • type: ndcg_at_3 value: 35.97575
    • type: ndcg_at_5 value: 38.3785
    • type: precision_at_1 value: 30.8945
    • type: precision_at_10 value: 7.180250000000001
    • type: precision_at_100 value: 1.1468333333333334
    • type: precision_at_1000 value: 0.15283333333333332
    • type: precision_at_3 value: 16.525583333333334
    • type: precision_at_5 value: 11.798333333333332
    • type: recall_at_1 value: 26.250833333333333
    • type: recall_at_10 value: 52.96108333333333
    • type: recall_at_100 value: 75.45908333333334
    • type: recall_at_1000 value: 90.73924999999998
    • type: recall_at_3 value: 39.25483333333333
    • type: recall_at_5 value: 45.37950000000001
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 24.595
    • type: map_at_10 value: 31.747999999999998
    • type: map_at_100 value: 32.62
    • type: map_at_1000 value: 32.713
    • type: map_at_3 value: 29.48
    • type: map_at_5 value: 30.635
    • type: mrr_at_1 value: 27.607
    • type: mrr_at_10 value: 34.449000000000005
    • type: mrr_at_100 value: 35.182
    • type: mrr_at_1000 value: 35.254000000000005
    • type: mrr_at_3 value: 32.413
    • type: mrr_at_5 value: 33.372
    • type: ndcg_at_1 value: 27.607
    • type: ndcg_at_10 value: 36.041000000000004
    • type: ndcg_at_100 value: 40.514
    • type: ndcg_at_1000 value: 42.851
    • type: ndcg_at_3 value: 31.689
    • type: ndcg_at_5 value: 33.479
    • type: precision_at_1 value: 27.607
    • type: precision_at_10 value: 5.66
    • type: precision_at_100 value: 0.868
    • type: precision_at_1000 value: 0.11299999999999999
    • type: precision_at_3 value: 13.446
    • type: precision_at_5 value: 9.264
    • type: recall_at_1 value: 24.595
    • type: recall_at_10 value: 46.79
    • type: recall_at_100 value: 67.413
    • type: recall_at_1000 value: 84.753
    • type: recall_at_3 value: 34.644999999999996
    • type: recall_at_5 value: 39.09
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 17.333000000000002
    • type: map_at_10 value: 24.427
    • type: map_at_100 value: 25.576
    • type: map_at_1000 value: 25.692999999999998
    • type: map_at_3 value: 22.002
    • type: map_at_5 value: 23.249
    • type: mrr_at_1 value: 20.716
    • type: mrr_at_10 value: 28.072000000000003
    • type: mrr_at_100 value: 29.067
    • type: mrr_at_1000 value: 29.137
    • type: mrr_at_3 value: 25.832
    • type: mrr_at_5 value: 27.045
    • type: ndcg_at_1 value: 20.716
    • type: ndcg_at_10 value: 29.109
    • type: ndcg_at_100 value: 34.797
    • type: ndcg_at_1000 value: 37.503
    • type: ndcg_at_3 value: 24.668
    • type: ndcg_at_5 value: 26.552999999999997
    • type: precision_at_1 value: 20.716
    • type: precision_at_10 value: 5.351
    • type: precision_at_100 value: 0.955
    • type: precision_at_1000 value: 0.136
    • type: precision_at_3 value: 11.584999999999999
    • type: precision_at_5 value: 8.362
    • type: recall_at_1 value: 17.333000000000002
    • type: recall_at_10 value: 39.604
    • type: recall_at_100 value: 65.525
    • type: recall_at_1000 value: 84.651
    • type: recall_at_3 value: 27.199
    • type: recall_at_5 value: 32.019
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 26.342
    • type: map_at_10 value: 35.349000000000004
    • type: map_at_100 value: 36.443
    • type: map_at_1000 value: 36.548
    • type: map_at_3 value: 32.307
    • type: map_at_5 value: 34.164
    • type: mrr_at_1 value: 31.063000000000002
    • type: mrr_at_10 value: 39.703
    • type: mrr_at_100 value: 40.555
    • type: mrr_at_1000 value: 40.614
    • type: mrr_at_3 value: 37.141999999999996
    • type: mrr_at_5 value: 38.812000000000005
    • type: ndcg_at_1 value: 31.063000000000002
    • type: ndcg_at_10 value: 40.873
    • type: ndcg_at_100 value: 45.896
    • type: ndcg_at_1000 value: 48.205999999999996
    • type: ndcg_at_3 value: 35.522
    • type: ndcg_at_5 value: 38.419
    • type: precision_at_1 value: 31.063000000000002
    • type: precision_at_10 value: 6.866
    • type: precision_at_100 value: 1.053
    • type: precision_at_1000 value: 0.13699999999999998
    • type: precision_at_3 value: 16.014
    • type: precision_at_5 value: 11.604000000000001
    • type: recall_at_1 value: 26.342
    • type: recall_at_10 value: 53.40200000000001
    • type: recall_at_100 value: 75.251
    • type: recall_at_1000 value: 91.13799999999999
    • type: recall_at_3 value: 39.103
    • type: recall_at_5 value: 46.357
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 23.71
    • type: map_at_10 value: 32.153999999999996
    • type: map_at_100 value: 33.821
    • type: map_at_1000 value: 34.034
    • type: map_at_3 value: 29.376
    • type: map_at_5 value: 30.878
    • type: mrr_at_1 value: 28.458
    • type: mrr_at_10 value: 36.775999999999996
    • type: mrr_at_100 value: 37.804
    • type: mrr_at_1000 value: 37.858999999999995
    • type: mrr_at_3 value: 34.123999999999995
    • type: mrr_at_5 value: 35.596
    • type: ndcg_at_1 value: 28.458
    • type: ndcg_at_10 value: 37.858999999999995
    • type: ndcg_at_100 value: 44.194
    • type: ndcg_at_1000 value: 46.744
    • type: ndcg_at_3 value: 33.348
    • type: ndcg_at_5 value: 35.448
    • type: precision_at_1 value: 28.458
    • type: precision_at_10 value: 7.4510000000000005
    • type: precision_at_100 value: 1.5
    • type: precision_at_1000 value: 0.23700000000000002
    • type: precision_at_3 value: 15.809999999999999
    • type: precision_at_5 value: 11.462
    • type: recall_at_1 value: 23.71
    • type: recall_at_10 value: 48.272999999999996
    • type: recall_at_100 value: 77.134
    • type: recall_at_1000 value: 93.001
    • type: recall_at_3 value: 35.480000000000004
    • type: recall_at_5 value: 41.19
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 21.331
    • type: map_at_10 value: 28.926000000000002
    • type: map_at_100 value: 29.855999999999998
    • type: map_at_1000 value: 29.957
    • type: map_at_3 value: 26.395999999999997
    • type: map_at_5 value: 27.933000000000003
    • type: mrr_at_1 value: 23.105
    • type: mrr_at_10 value: 31.008000000000003
    • type: mrr_at_100 value: 31.819999999999997
    • type: mrr_at_1000 value: 31.887999999999998
    • type: mrr_at_3 value: 28.466
    • type: mrr_at_5 value: 30.203000000000003
    • type: ndcg_at_1 value: 23.105
    • type: ndcg_at_10 value: 33.635999999999996
    • type: ndcg_at_100 value: 38.277
    • type: ndcg_at_1000 value: 40.907
    • type: ndcg_at_3 value: 28.791
    • type: ndcg_at_5 value: 31.528
    • type: precision_at_1 value: 23.105
    • type: precision_at_10 value: 5.323
    • type: precision_at_100 value: 0.815
    • type: precision_at_1000 value: 0.117
    • type: precision_at_3 value: 12.384
    • type: precision_at_5 value: 9.02
    • type: recall_at_1 value: 21.331
    • type: recall_at_10 value: 46.018
    • type: recall_at_100 value: 67.364
    • type: recall_at_1000 value: 86.97
    • type: recall_at_3 value: 33.395
    • type: recall_at_5 value: 39.931
  • task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics:
    • type: map_at_1 value: 17.011000000000003
    • type: map_at_10 value: 28.816999999999997
    • type: map_at_100 value: 30.761
    • type: map_at_1000 value: 30.958000000000002
    • type: map_at_3 value: 24.044999999999998
    • type: map_at_5 value: 26.557
    • type: mrr_at_1 value: 38.696999999999996
    • type: mrr_at_10 value: 50.464
    • type: mrr_at_100 value: 51.193999999999996
    • type: mrr_at_1000 value: 51.219
    • type: mrr_at_3 value: 47.339999999999996
    • type: mrr_at_5 value: 49.346000000000004
    • type: ndcg_at_1 value: 38.696999999999996
    • type: ndcg_at_10 value: 38.53
    • type: ndcg_at_100 value: 45.525
    • type: ndcg_at_1000 value: 48.685
    • type: ndcg_at_3 value: 32.282
    • type: ndcg_at_5 value: 34.482
    • type: precision_at_1 value: 38.696999999999996
    • type: precision_at_10 value: 11.895999999999999
    • type: precision_at_100 value: 1.95
    • type: precision_at_1000 value: 0.254
    • type: precision_at_3 value: 24.038999999999998
    • type: precision_at_5 value: 18.332
    • type: recall_at_1 value: 17.011000000000003
    • type: recall_at_10 value: 44.452999999999996
    • type: recall_at_100 value: 68.223
    • type: recall_at_1000 value: 85.653
    • type: recall_at_3 value: 28.784
    • type: recall_at_5 value: 35.66
  • task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics:
    • type: map_at_1 value: 9.516
    • type: map_at_10 value: 21.439
    • type: map_at_100 value: 31.517
    • type: map_at_1000 value: 33.267
    • type: map_at_3 value: 15.004999999999999
    • type: map_at_5 value: 17.793999999999997
    • type: mrr_at_1 value: 71.25
    • type: mrr_at_10 value: 79.071
    • type: mrr_at_100 value: 79.325
    • type: mrr_at_1000 value: 79.33
    • type: mrr_at_3 value: 77.708
    • type: mrr_at_5 value: 78.546
    • type: ndcg_at_1 value: 58.62500000000001
    • type: ndcg_at_10 value: 44.889
    • type: ndcg_at_100 value: 50.536
    • type: ndcg_at_1000 value: 57.724
    • type: ndcg_at_3 value: 49.32
    • type: ndcg_at_5 value: 46.775
    • type: precision_at_1 value: 71.25
    • type: precision_at_10 value: 36.175000000000004
    • type: precision_at_100 value: 11.940000000000001
    • type: precision_at_1000 value: 2.178
    • type: precision_at_3 value: 53.583000000000006
    • type: precision_at_5 value: 45.550000000000004
    • type: recall_at_1 value: 9.516
    • type: recall_at_10 value: 27.028000000000002
    • type: recall_at_100 value: 57.581
    • type: recall_at_1000 value: 80.623
    • type: recall_at_3 value: 16.313
    • type: recall_at_5 value: 20.674
  • task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics:
    • type: accuracy value: 51.74999999999999
    • type: f1 value: 46.46706502669774
  • task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics:
    • type: map_at_1 value: 77.266
    • type: map_at_10 value: 84.89999999999999
    • type: map_at_100 value: 85.109
    • type: map_at_1000 value: 85.123
    • type: map_at_3 value: 83.898
    • type: map_at_5 value: 84.541
    • type: mrr_at_1 value: 83.138
    • type: mrr_at_10 value: 89.37
    • type: mrr_at_100 value: 89.432
    • type: mrr_at_1000 value: 89.43299999999999
    • type: mrr_at_3 value: 88.836
    • type: mrr_at_5 value: 89.21
    • type: ndcg_at_1 value: 83.138
    • type: ndcg_at_10 value: 88.244
    • type: ndcg_at_100 value: 88.98700000000001
    • type: ndcg_at_1000 value: 89.21900000000001
    • type: ndcg_at_3 value: 86.825
    • type: ndcg_at_5 value: 87.636
    • type: precision_at_1 value: 83.138
    • type: precision_at_10 value: 10.47
    • type: precision_at_100 value: 1.1079999999999999
    • type: precision_at_1000 value: 0.11499999999999999
    • type: precision_at_3 value: 32.933
    • type: precision_at_5 value: 20.36
    • type: recall_at_1 value: 77.266
    • type: recall_at_10 value: 94.063
    • type: recall_at_100 value: 96.993
    • type: recall_at_1000 value: 98.414
    • type: recall_at_3 value: 90.228
    • type: recall_at_5 value: 92.328
  • task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics:
    • type: map_at_1 value: 22.319
    • type: map_at_10 value: 36.943
    • type: map_at_100 value: 38.951
    • type: map_at_1000 value: 39.114
    • type: map_at_3 value: 32.82
    • type: map_at_5 value: 34.945
    • type: mrr_at_1 value: 44.135999999999996
    • type: mrr_at_10 value: 53.071999999999996
    • type: mrr_at_100 value: 53.87
    • type: mrr_at_1000 value: 53.90200000000001
    • type: mrr_at_3 value: 50.77199999999999
    • type: mrr_at_5 value: 52.129999999999995
    • type: ndcg_at_1 value: 44.135999999999996
    • type: ndcg_at_10 value: 44.836
    • type: ndcg_at_100 value: 51.754
    • type: ndcg_at_1000 value: 54.36
    • type: ndcg_at_3 value: 41.658
    • type: ndcg_at_5 value: 42.354
    • type: precision_at_1 value: 44.135999999999996
    • type: precision_at_10 value: 12.284
    • type: precision_at_100 value: 1.952
    • type: precision_at_1000 value: 0.242
    • type: precision_at_3 value: 27.828999999999997
    • type: precision_at_5 value: 20.093
    • type: recall_at_1 value: 22.319
    • type: recall_at_10 value: 51.528
    • type: recall_at_100 value: 76.70700000000001
    • type: recall_at_1000 value: 92.143
    • type: recall_at_3 value: 38.641
    • type: recall_at_5 value: 43.653999999999996
  • task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics:
    • type: map_at_1 value: 40.182
    • type: map_at_10 value: 65.146
    • type: map_at_100 value: 66.023
    • type: map_at_1000 value: 66.078
    • type: map_at_3 value: 61.617999999999995
    • type: map_at_5 value: 63.82299999999999
    • type: mrr_at_1 value: 80.365
    • type: mrr_at_10 value: 85.79
    • type: mrr_at_100 value: 85.963
    • type: mrr_at_1000 value: 85.968
    • type: mrr_at_3 value: 84.952
    • type: mrr_at_5 value: 85.503
    • type: ndcg_at_1 value: 80.365
    • type: ndcg_at_10 value: 73.13499999999999
    • type: ndcg_at_100 value: 76.133
    • type: ndcg_at_1000 value: 77.151
    • type: ndcg_at_3 value: 68.255
    • type: ndcg_at_5 value: 70.978
    • type: precision_at_1 value: 80.365
    • type: precision_at_10 value: 15.359
    • type: precision_at_100 value: 1.7690000000000001
    • type: precision_at_1000 value: 0.19
    • type: precision_at_3 value: 44.024
    • type: precision_at_5 value: 28.555999999999997
    • type: recall_at_1 value: 40.182
    • type: recall_at_10 value: 76.793
    • type: recall_at_100 value: 88.474
    • type: recall_at_1000 value: 95.159
    • type: recall_at_3 value: 66.036
    • type: recall_at_5 value: 71.391
  • task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics:
    • type: accuracy value: 92.7796
    • type: ap value: 89.24883716810874
    • type: f1 value: 92.7706903433313
  • task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics:
    • type: map_at_1 value: 22.016
    • type: map_at_10 value: 34.408
    • type: map_at_100 value: 35.592
    • type: map_at_1000 value: 35.64
    • type: map_at_3 value: 30.459999999999997
    • type: map_at_5 value: 32.721000000000004
    • type: mrr_at_1 value: 22.593
    • type: mrr_at_10 value: 34.993
    • type: mrr_at_100 value: 36.113
    • type: mrr_at_1000 value: 36.156
    • type: mrr_at_3 value: 31.101
    • type: mrr_at_5 value: 33.364
    • type: ndcg_at_1 value: 22.579
    • type: ndcg_at_10 value: 41.404999999999994
    • type: ndcg_at_100 value: 47.018
    • type: ndcg_at_1000 value: 48.211999999999996
    • type: ndcg_at_3 value: 33.389
    • type: ndcg_at_5 value: 37.425000000000004
    • type: precision_at_1 value: 22.579
    • type: precision_at_10 value: 6.59
    • type: precision_at_100 value: 0.938
    • type: precision_at_1000 value: 0.104
    • type: precision_at_3 value: 14.241000000000001
    • type: precision_at_5 value: 10.59
    • type: recall_at_1 value: 22.016
    • type: recall_at_10 value: 62.927
    • type: recall_at_100 value: 88.72
    • type: recall_at_1000 value: 97.80799999999999
    • type: recall_at_3 value: 41.229
    • type: recall_at_5 value: 50.88
  • task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
    • type: accuracy value: 94.01732786137711
    • type: f1 value: 93.76353126402202
  • task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
    • type: accuracy value: 76.91746466028272
    • type: f1 value: 57.715651682646765
  • task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
    • type: accuracy value: 76.5030262273033
    • type: f1 value: 74.6693629986121
  • task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
    • type: accuracy value: 79.74781439139207
    • type: f1 value: 79.96684171018774
  • task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics:
    • type: v_measure value: 33.2156206892017
  • task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics:
    • type: v_measure value: 31.180539484816137
  • task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics:
    • type: map value: 32.51125957874274
    • type: mrr value: 33.777037359249995
  • task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics:
    • type: map_at_1 value: 7.248
    • type: map_at_10 value: 15.340000000000002
    • type: map_at_100 value: 19.591
    • type: map_at_1000 value: 21.187
    • type: map_at_3 value: 11.329
    • type: map_at_5 value: 13.209999999999999
    • type: mrr_at_1 value: 47.678
    • type: mrr_at_10 value: 57.493
    • type: mrr_at_100 value: 58.038999999999994
    • type: mrr_at_1000 value: 58.07
    • type: mrr_at_3 value: 55.36600000000001
    • type: mrr_at_5 value: 56.635999999999996
    • type: ndcg_at_1 value: 46.129999999999995
    • type: ndcg_at_10 value: 38.653999999999996
    • type: ndcg_at_100 value: 36.288
    • type: ndcg_at_1000 value: 44.765
    • type: ndcg_at_3 value: 43.553
    • type: ndcg_at_5 value: 41.317
    • type: precision_at_1 value: 47.368
    • type: precision_at_10 value: 28.669
    • type: precision_at_100 value: 9.158
    • type: precision_at_1000 value: 2.207
    • type: precision_at_3 value: 40.97
    • type: precision_at_5 value: 35.604
    • type: recall_at_1 value: 7.248
    • type: recall_at_10 value: 19.46
    • type: recall_at_100 value: 37.214000000000006
    • type: recall_at_1000 value: 67.64099999999999
    • type: recall_at_3 value: 12.025
    • type: recall_at_5 value: 15.443999999999999
  • task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics:
    • type: map_at_1 value: 31.595000000000002
    • type: map_at_10 value: 47.815999999999995
    • type: map_at_100 value: 48.811
    • type: map_at_1000 value: 48.835
    • type: map_at_3 value: 43.225
    • type: map_at_5 value: 46.017
    • type: mrr_at_1 value: 35.689
    • type: mrr_at_10 value: 50.341
    • type: mrr_at_100 value: 51.044999999999995
    • type: mrr_at_1000 value: 51.062
    • type: mrr_at_3 value: 46.553
    • type: mrr_at_5 value: 48.918
    • type: ndcg_at_1 value: 35.66
    • type: ndcg_at_10 value: 55.859
    • type: ndcg_at_100 value: 59.864
    • type: ndcg_at_1000 value: 60.419999999999995
    • type: ndcg_at_3 value: 47.371
    • type: ndcg_at_5 value: 51.995000000000005
    • type: precision_at_1 value: 35.66
    • type: precision_at_10 value: 9.27
    • type: precision_at_100 value: 1.1520000000000001
    • type: precision_at_1000 value: 0.12
    • type: precision_at_3 value: 21.63
    • type: precision_at_5 value: 15.655
    • type: recall_at_1 value: 31.595000000000002
    • type: recall_at_10 value: 77.704
    • type: recall_at_100 value: 94.774
    • type: recall_at_1000 value: 98.919
    • type: recall_at_3 value: 56.052
    • type: recall_at_5 value: 66.623
  • task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 71.489
    • type: map_at_10 value: 85.411
    • type: map_at_100 value: 86.048
    • type: map_at_1000 value: 86.064
    • type: map_at_3 value: 82.587
    • type: map_at_5 value: 84.339
    • type: mrr_at_1 value: 82.28
    • type: mrr_at_10 value: 88.27199999999999
    • type: mrr_at_100 value: 88.362
    • type: mrr_at_1000 value: 88.362
    • type: mrr_at_3 value: 87.372
    • type: mrr_at_5 value: 87.995
    • type: ndcg_at_1 value: 82.27
    • type: ndcg_at_10 value: 89.023
    • type: ndcg_at_100 value: 90.191
    • type: ndcg_at_1000 value: 90.266
    • type: ndcg_at_3 value: 86.37
    • type: ndcg_at_5 value: 87.804
    • type: precision_at_1 value: 82.27
    • type: precision_at_10 value: 13.469000000000001
    • type: precision_at_100 value: 1.533
    • type: precision_at_1000 value: 0.157
    • type: precision_at_3 value: 37.797
    • type: precision_at_5 value: 24.734
    • type: recall_at_1 value: 71.489
    • type: recall_at_10 value: 95.824
    • type: recall_at_100 value: 99.70599999999999
    • type: recall_at_1000 value: 99.979
    • type: recall_at_3 value: 88.099
    • type: recall_at_5 value: 92.285
  • task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics:
    • type: v_measure value: 60.52398807444541
  • task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics:
    • type: v_measure value: 65.34855891507871
  • task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics:
    • type: map_at_1 value: 5.188000000000001
    • type: map_at_10 value: 13.987
    • type: map_at_100 value: 16.438
    • type: map_at_1000 value: 16.829
    • type: map_at_3 value: 9.767000000000001
    • type: map_at_5 value: 11.912
    • type: mrr_at_1 value: 25.6
    • type: mrr_at_10 value: 37.744
    • type: mrr_at_100 value: 38.847
    • type: mrr_at_1000 value: 38.894
    • type: mrr_at_3 value: 34.166999999999994
    • type: mrr_at_5 value: 36.207
    • type: ndcg_at_1 value: 25.6
    • type: ndcg_at_10 value: 22.980999999999998
    • type: ndcg_at_100 value: 32.039
    • type: ndcg_at_1000 value: 38.157000000000004
    • type: ndcg_at_3 value: 21.567
    • type: ndcg_at_5 value: 19.070999999999998
    • type: precision_at_1 value: 25.6
    • type: precision_at_10 value: 12.02
    • type: precision_at_100 value: 2.5100000000000002
    • type: precision_at_1000 value: 0.396
    • type: precision_at_3 value: 20.333000000000002
    • type: precision_at_5 value: 16.98
    • type: recall_at_1 value: 5.188000000000001
    • type: recall_at_10 value: 24.372
    • type: recall_at_100 value: 50.934999999999995
    • type: recall_at_1000 value: 80.477
    • type: recall_at_3 value: 12.363
    • type: recall_at_5 value: 17.203
  • task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics:
    • type: cos_sim_pearson value: 87.24286275535398
    • type: cos_sim_spearman value: 82.62333770991818
    • type: euclidean_pearson value: 84.60353717637284
    • type: euclidean_spearman value: 82.32990108810047
    • type: manhattan_pearson value: 84.6089049738196
    • type: manhattan_spearman value: 82.33361785438936
  • task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics:
    • type: cos_sim_pearson value: 87.87428858503165
    • type: cos_sim_spearman value: 79.09145886519929
    • type: euclidean_pearson value: 86.42669231664036
    • type: euclidean_spearman value: 80.03127375435449
    • type: manhattan_pearson value: 86.41330338305022
    • type: manhattan_spearman value: 80.02492538673368
  • task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics:
    • type: cos_sim_pearson value: 88.67912277322645
    • type: cos_sim_spearman value: 89.6171319711762
    • type: euclidean_pearson value: 86.56571917398725
    • type: euclidean_spearman value: 87.71216907898948
    • type: manhattan_pearson value: 86.57459050182473
    • type: manhattan_spearman value: 87.71916648349993
  • task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics:
    • type: cos_sim_pearson value: 86.71957379085862
    • type: cos_sim_spearman value: 85.01784075851465
    • type: euclidean_pearson value: 84.7407848472801
    • type: euclidean_spearman value: 84.61063091345538
    • type: manhattan_pearson value: 84.71494352494403
    • type: manhattan_spearman value: 84.58772077604254
  • task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics:
    • type: cos_sim_pearson value: 88.40508326325175
    • type: cos_sim_spearman value: 89.50912897763186
    • type: euclidean_pearson value: 87.82349070086627
    • type: euclidean_spearman value: 88.44179162727521
    • type: manhattan_pearson value: 87.80181927025595
    • type: manhattan_spearman value: 88.43205129636243
  • task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics:
    • type: cos_sim_pearson value: 85.35846741715478
    • type: cos_sim_spearman value: 86.61172476741842
    • type: euclidean_pearson value: 84.60123125491637
    • type: euclidean_spearman value: 85.3001948141827
    • type: manhattan_pearson value: 84.56231142658329
    • type: manhattan_spearman value: 85.23579900798813
  • task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
    • type: cos_sim_pearson value: 88.94539129818824
    • type: cos_sim_spearman value: 88.99349064256742
    • type: euclidean_pearson value: 88.7142444640351
    • type: euclidean_spearman value: 88.34120813505011
    • type: manhattan_pearson value: 88.70363008238084
    • type: manhattan_spearman value: 88.31952816956954
  • task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics:
    • type: cos_sim_pearson value: 68.29910260369893
    • type: cos_sim_spearman value: 68.79263346213466
    • type: euclidean_pearson value: 68.41627521422252
    • type: euclidean_spearman value: 66.61602587398579
    • type: manhattan_pearson value: 68.49402183447361
    • type: manhattan_spearman value: 66.80157792354453
  • task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics:
    • type: cos_sim_pearson value: 87.43703906343708
    • type: cos_sim_spearman value: 89.06081805093662
    • type: euclidean_pearson value: 87.48311456299662
    • type: euclidean_spearman value: 88.07417597580013
    • type: manhattan_pearson value: 87.48202249768894
    • type: manhattan_spearman value: 88.04758031111642
  • task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics:
    • type: map value: 87.49080620485203
    • type: mrr value: 96.19145378949301
  • task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics:
    • type: map_at_1 value: 59.317
    • type: map_at_10 value: 69.296
    • type: map_at_100 value: 69.738
    • type: map_at_1000 value: 69.759
    • type: map_at_3 value: 66.12599999999999
    • type: map_at_5 value: 67.532
    • type: mrr_at_1 value: 62
    • type: mrr_at_10 value: 70.176
    • type: mrr_at_100 value: 70.565
    • type: mrr_at_1000 value: 70.583
    • type: mrr_at_3 value: 67.833
    • type: mrr_at_5 value: 68.93299999999999
    • type: ndcg_at_1 value: 62
    • type: ndcg_at_10 value: 74.069
    • type: ndcg_at_100 value: 76.037
    • type: ndcg_at_1000 value: 76.467
    • type: ndcg_at_3 value: 68.628
    • type: ndcg_at_5 value: 70.57600000000001
    • type: precision_at_1 value: 62
    • type: precision_at_10 value: 10
    • type: precision_at_100 value: 1.097
    • type: precision_at_1000 value: 0.11299999999999999
    • type: precision_at_3 value: 26.667
    • type: precision_at_5 value: 17.4
    • type: recall_at_1 value: 59.317
    • type: recall_at_10 value: 87.822
    • type: recall_at_100 value: 96.833
    • type: recall_at_1000 value: 100
    • type: recall_at_3 value: 73.06099999999999
    • type: recall_at_5 value: 77.928
  • task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics:
    • type: cos_sim_accuracy value: 99.88910891089108
    • type: cos_sim_ap value: 97.236958456951
    • type: cos_sim_f1 value: 94.39999999999999
    • type: cos_sim_precision value: 94.39999999999999
    • type: cos_sim_recall value: 94.39999999999999
    • type: dot_accuracy value: 99.82574257425742
    • type: dot_ap value: 94.94344759441888
    • type: dot_f1 value: 91.17352056168507
    • type: dot_precision value: 91.44869215291752
    • type: dot_recall value: 90.9
    • type: euclidean_accuracy value: 99.88415841584158
    • type: euclidean_ap value: 97.2044250782305
    • type: euclidean_f1 value: 94.210786739238
    • type: euclidean_precision value: 93.24191968658178
    • type: euclidean_recall value: 95.19999999999999
    • type: manhattan_accuracy value: 99.88613861386139
    • type: manhattan_ap value: 97.20683205497689
    • type: manhattan_f1 value: 94.2643391521197
    • type: manhattan_precision value: 94.02985074626866
    • type: manhattan_recall value: 94.5
    • type: max_accuracy value: 99.88910891089108
    • type: max_ap value: 97.236958456951
    • type: max_f1 value: 94.39999999999999
  • task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics:
    • type: v_measure value: 66.53940781726187
  • task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics:
    • type: v_measure value: 36.71865011295108
  • task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics:
    • type: map value: 55.3218674533331
    • type: mrr value: 56.28279910449028
  • task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics:
    • type: cos_sim_pearson value: 30.723915667479673
    • type: cos_sim_spearman value: 32.029070449745234
    • type: dot_pearson value: 28.864944212481454
    • type: dot_spearman value: 27.939266999596725
  • task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics:
    • type: map_at_1 value: 0.231
    • type: map_at_10 value: 1.949
    • type: map_at_100 value: 10.023
    • type: map_at_1000 value: 23.485
    • type: map_at_3 value: 0.652
    • type: map_at_5 value: 1.054
    • type: mrr_at_1 value: 86
    • type: mrr_at_10 value: 92.067
    • type: mrr_at_100 value: 92.067
    • type: mrr_at_1000 value: 92.067
    • type: mrr_at_3 value: 91.667
    • type: mrr_at_5 value: 92.067
    • type: ndcg_at_1 value: 83
    • type: ndcg_at_10 value: 76.32900000000001
    • type: ndcg_at_100 value: 54.662
    • type: ndcg_at_1000 value: 48.062
    • type: ndcg_at_3 value: 81.827
    • type: ndcg_at_5 value: 80.664
    • type: precision_at_1 value: 86
    • type: precision_at_10 value: 80
    • type: precision_at_100 value: 55.48
    • type: precision_at_1000 value: 20.938000000000002
    • type: precision_at_3 value: 85.333
    • type: precision_at_5 value: 84.39999999999999
    • type: recall_at_1 value: 0.231
    • type: recall_at_10 value: 2.158
    • type: recall_at_100 value: 13.344000000000001
    • type: recall_at_1000 value: 44.31
    • type: recall_at_3 value: 0.6779999999999999
    • type: recall_at_5 value: 1.13
  • task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics:
    • type: map_at_1 value: 2.524
    • type: map_at_10 value: 10.183
    • type: map_at_100 value: 16.625
    • type: map_at_1000 value: 18.017
    • type: map_at_3 value: 5.169
    • type: map_at_5 value: 6.772
    • type: mrr_at_1 value: 32.653
    • type: mrr_at_10 value: 47.128
    • type: mrr_at_100 value: 48.458
    • type: mrr_at_1000 value: 48.473
    • type: mrr_at_3 value: 44.897999999999996
    • type: mrr_at_5 value: 45.306000000000004
    • type: ndcg_at_1 value: 30.612000000000002
    • type: ndcg_at_10 value: 24.928
    • type: ndcg_at_100 value: 37.613
    • type: ndcg_at_1000 value: 48.528
    • type: ndcg_at_3 value: 28.829
    • type: ndcg_at_5 value: 25.237
    • type: precision_at_1 value: 32.653
    • type: precision_at_10 value: 22.448999999999998
    • type: precision_at_100 value: 8.02
    • type: precision_at_1000 value: 1.537
    • type: precision_at_3 value: 30.612000000000002
    • type: precision_at_5 value: 24.490000000000002
    • type: recall_at_1 value: 2.524
    • type: recall_at_10 value: 16.38
    • type: recall_at_100 value: 49.529
    • type: recall_at_1000 value: 83.598
    • type: recall_at_3 value: 6.411
    • type: recall_at_5 value: 8.932
  • task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics:
    • type: accuracy value: 71.09020000000001
    • type: ap value: 14.451710060978993
    • type: f1 value: 54.7874410609049
  • task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics:
    • type: accuracy value: 59.745331069609506
    • type: f1 value: 60.08387848592697
  • task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics:
    • type: v_measure value: 51.71549485462037
  • task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics:
    • type: cos_sim_accuracy value: 87.39345532574357
    • type: cos_sim_ap value: 78.16796549696478
    • type: cos_sim_f1 value: 71.27713276123171
    • type: cos_sim_precision value: 68.3115626511853
    • type: cos_sim_recall value: 74.51187335092348
    • type: dot_accuracy value: 85.12248912201228
    • type: dot_ap value: 69.26039256107077
    • type: dot_f1 value: 65.04294321240867
    • type: dot_precision value: 63.251059586138126
    • type: dot_recall value: 66.93931398416886
    • type: euclidean_accuracy value: 87.07754664123503
    • type: euclidean_ap value: 77.7872176038945
    • type: euclidean_f1 value: 70.85587801278899
    • type: euclidean_precision value: 66.3519115614924
    • type: euclidean_recall value: 76.01583113456465
    • type: manhattan_accuracy value: 87.07754664123503
    • type: manhattan_ap value: 77.7341400185556
    • type: manhattan_f1 value: 70.80310880829015
    • type: manhattan_precision value: 69.54198473282443
    • type: manhattan_recall value: 72.1108179419525
    • type: max_accuracy value: 87.39345532574357
    • type: max_ap value: 78.16796549696478
    • type: max_f1 value: 71.27713276123171
  • task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics:
    • type: cos_sim_accuracy value: 89.09457833663213
    • type: cos_sim_ap value: 86.33024314706873
    • type: cos_sim_f1 value: 78.59623733719248
    • type: cos_sim_precision value: 74.13322413322413
    • type: cos_sim_recall value: 83.63104404065291
    • type: dot_accuracy value: 88.3086894089339
    • type: dot_ap value: 83.92225241805097
    • type: dot_f1 value: 76.8721826377781
    • type: dot_precision value: 72.8168044077135
    • type: dot_recall value: 81.40591315060055
    • type: euclidean_accuracy value: 88.77052043311213
    • type: euclidean_ap value: 85.7410710218755
    • type: euclidean_f1 value: 77.97705489398781
    • type: euclidean_precision value: 73.77713657598241
    • type: euclidean_recall value: 82.68401601478288
    • type: manhattan_accuracy value: 88.73753250281368
    • type: manhattan_ap value: 85.72867199072802
    • type: manhattan_f1 value: 77.89774182922812
    • type: manhattan_precision value: 74.23787931635857
    • type: manhattan_recall value: 81.93717277486911
    • type: max_accuracy value: 89.09457833663213
    • type: max_ap value: 86.33024314706873
    • type: max_f1 value: 78.59623733719248

license: mit language:

• en

---

Universal AnglE Embedding

📢 WhereIsAI/UAE-Large-V1 is licensed under MIT. Feel free to use it in any scenario. If you use it for academic papers, you could cite us via 👉 citation info.

🤝 Follow us on:

• GitHub: https://github.com/SeanLee97/AnglE.
• Preprint Paper: AnglE-optimized Text Embeddings
• Conference Paper: AoE: Angle-optimized Embeddings for Semantic Textual Similarity (ACL24)
• 📘 Documentation: https://angle.readthedocs.io/en/latest/index.html

Welcome to using AnglE to train and infer powerful sentence embeddings.

🏆 Achievements

• 📅 May 16, 2024 | AnglE's paper is accepted by ACL 2024 Main Conference
• 📅 Dec 4, 2023 | 🔥 Our universal English sentence embedding WhereIsAI/UAE-Large-V1 achieves SOTA on the MTEB Leaderboard with an average score of 64.64!

🧑‍🤝‍🧑 Siblings:

• WhereIsAI/UAE-Code-Large-V1: This model can be used for code or GitHub issue similarity measurement.

Usage

1. angle_emb

python -m pip install -U angle-emb

1. Non-Retrieval Tasks

There is no need to specify any prompts.

from angle_emb import AnglE
from angle_emb.utils import cosine_similarity

angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()
doc_vecs = angle.encode([
    'The weather is great!',
    'The weather is very good!',
    'i am going to bed'
], normalize_embedding=True)

for i, dv1 in enumerate(doc_vecs):
    for dv2 in doc_vecs[i+1:]:
        print(cosine_similarity(dv1, dv2))

1. Retrieval Tasks

For retrieval purposes, please use the prompt Prompts.C for query (not for document).

from angle_emb import AnglE, Prompts
from angle_emb.utils import cosine_similarity

angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()
qv = angle.encode(Prompts.C.format(text='what is the weather?'))
doc_vecs = angle.encode([
    'The weather is great!',
    'it is rainy today.',
    'i am going to bed'
])

for dv in doc_vecs:
    print(cosine_similarity(qv[0], dv))

2. sentence transformer

from angle_emb import Prompts
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("WhereIsAI/UAE-Large-V1").cuda()

qv = model.encode(Prompts.C.format(text='what is the weather?'))
doc_vecs = model.encode([
    'The weather is great!',
    'it is rainy today.',
    'i am going to bed'
])

for dv in doc_vecs:
    print(1 - spatial.distance.cosine(qv, dv))

3. Infinity

Infinity is a MIT licensed server for OpenAI-compatible deployment.

docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:latest \
v2 --model-id WhereIsAI/UAE-Large-V1 --revision "369c368f70f16a613f19f5598d4f12d9f44235d4" --dtype float16 --batch-size 32 --device cuda --engine torch --port 7997

Citation

If you use our pre-trained models, welcome to support us by citing our work:

@article{li2023angle,
  title={AnglE-optimized Text Embeddings},
  author={Li, Xianming and Li, Jing},
  journal={arXiv preprint arXiv:2309.12871},
  year={2023}
}