基于Sentence Transformers的文本嵌入模型，在分类、检索、聚类等任务中表现优异，提供精准的句子相似度计算与特征提取能力。【此简介由AI生成】
library_name: sentence-transformers pipeline_tag: sentence-similarity tags:
- feature-extraction
- sentence-similarity
- mteb
- transformers
- transformers.js model-index:
- name: epoch_0_model
results:
-
task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics:
- type: accuracy value: 76.8507462686567
- type: ap value: 40.592189159090495
- type: f1 value: 71.01634655512476
-
task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics:
- type: accuracy value: 91.51892500000001
- type: ap value: 88.50346762975335
- type: f1 value: 91.50342077459624
-
task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
- type: accuracy value: 47.364
- type: f1 value: 46.72708080922794
-
task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics:
- type: map_at_1 value: 25.178
- type: map_at_10 value: 40.244
- type: map_at_100 value: 41.321999999999996
- type: map_at_1000 value: 41.331
- type: map_at_3 value: 35.016999999999996
- type: map_at_5 value: 37.99
- type: mrr_at_1 value: 25.605
- type: mrr_at_10 value: 40.422000000000004
- type: mrr_at_100 value: 41.507
- type: mrr_at_1000 value: 41.516
- type: mrr_at_3 value: 35.23
- type: mrr_at_5 value: 38.15
- type: ndcg_at_1 value: 25.178
- type: ndcg_at_10 value: 49.258
- type: ndcg_at_100 value: 53.776
- type: ndcg_at_1000 value: 53.995000000000005
- type: ndcg_at_3 value: 38.429
- type: ndcg_at_5 value: 43.803
- type: precision_at_1 value: 25.178
- type: precision_at_10 value: 7.831
- type: precision_at_100 value: 0.979
- type: precision_at_1000 value: 0.1
- type: precision_at_3 value: 16.121
- type: precision_at_5 value: 12.29
- type: recall_at_1 value: 25.178
- type: recall_at_10 value: 78.307
- type: recall_at_100 value: 97.866
- type: recall_at_1000 value: 99.57300000000001
- type: recall_at_3 value: 48.364000000000004
- type: recall_at_5 value: 61.451
-
task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics:
- type: v_measure value: 45.93034494751465
-
task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics:
- type: v_measure value: 36.64579480054327
-
task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics:
- type: map value: 60.601310529222054
- type: mrr value: 75.04484896451656
-
task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics:
- type: cos_sim_pearson value: 88.57797718095814
- type: cos_sim_spearman value: 86.47064499110101
- type: euclidean_pearson value: 87.4559602783142
- type: euclidean_spearman value: 86.47064499110101
- type: manhattan_pearson value: 87.7232764230245
- type: manhattan_spearman value: 86.91222131777742
-
task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics:
- type: accuracy value: 84.5422077922078
- type: f1 value: 84.47657456950589
-
task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics:
- type: v_measure value: 38.48953561974464
-
task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics:
- type: v_measure value: 32.75995857510105
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 30.008000000000003
- type: map_at_10 value: 39.51
- type: map_at_100 value: 40.841
- type: map_at_1000 value: 40.973
- type: map_at_3 value: 36.248999999999995
- type: map_at_5 value: 38.096999999999994
- type: mrr_at_1 value: 36.481
- type: mrr_at_10 value: 44.818000000000005
- type: mrr_at_100 value: 45.64
- type: mrr_at_1000 value: 45.687
- type: mrr_at_3 value: 42.036
- type: mrr_at_5 value: 43.782
- type: ndcg_at_1 value: 36.481
- type: ndcg_at_10 value: 45.152
- type: ndcg_at_100 value: 50.449
- type: ndcg_at_1000 value: 52.76499999999999
- type: ndcg_at_3 value: 40.161
- type: ndcg_at_5 value: 42.577999999999996
- type: precision_at_1 value: 36.481
- type: precision_at_10 value: 8.369
- type: precision_at_100 value: 1.373
- type: precision_at_1000 value: 0.186
- type: precision_at_3 value: 18.693
- type: precision_at_5 value: 13.533999999999999
- type: recall_at_1 value: 30.008000000000003
- type: recall_at_10 value: 56.108999999999995
- type: recall_at_100 value: 78.55499999999999
- type: recall_at_1000 value: 93.659
- type: recall_at_3 value: 41.754999999999995
- type: recall_at_5 value: 48.296
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 30.262
- type: map_at_10 value: 40.139
- type: map_at_100 value: 41.394
- type: map_at_1000 value: 41.526
- type: map_at_3 value: 37.155
- type: map_at_5 value: 38.785
- type: mrr_at_1 value: 38.153
- type: mrr_at_10 value: 46.369
- type: mrr_at_100 value: 47.072
- type: mrr_at_1000 value: 47.111999999999995
- type: mrr_at_3 value: 44.268
- type: mrr_at_5 value: 45.389
- type: ndcg_at_1 value: 38.153
- type: ndcg_at_10 value: 45.925
- type: ndcg_at_100 value: 50.394000000000005
- type: ndcg_at_1000 value: 52.37500000000001
- type: ndcg_at_3 value: 41.754000000000005
- type: ndcg_at_5 value: 43.574
- type: precision_at_1 value: 38.153
- type: precision_at_10 value: 8.796
- type: precision_at_100 value: 1.432
- type: precision_at_1000 value: 0.189
- type: precision_at_3 value: 20.318
- type: precision_at_5 value: 14.395
- type: recall_at_1 value: 30.262
- type: recall_at_10 value: 55.72200000000001
- type: recall_at_100 value: 74.97500000000001
- type: recall_at_1000 value: 87.342
- type: recall_at_3 value: 43.129
- type: recall_at_5 value: 48.336
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 39.951
- type: map_at_10 value: 51.248000000000005
- type: map_at_100 value: 52.188
- type: map_at_1000 value: 52.247
- type: map_at_3 value: 48.211
- type: map_at_5 value: 49.797000000000004
- type: mrr_at_1 value: 45.329
- type: mrr_at_10 value: 54.749
- type: mrr_at_100 value: 55.367999999999995
- type: mrr_at_1000 value: 55.400000000000006
- type: mrr_at_3 value: 52.382
- type: mrr_at_5 value: 53.649
- type: ndcg_at_1 value: 45.329
- type: ndcg_at_10 value: 56.847
- type: ndcg_at_100 value: 60.738
- type: ndcg_at_1000 value: 61.976
- type: ndcg_at_3 value: 51.59
- type: ndcg_at_5 value: 53.915
- type: precision_at_1 value: 45.329
- type: precision_at_10 value: 8.959
- type: precision_at_100 value: 1.187
- type: precision_at_1000 value: 0.134
- type: precision_at_3 value: 22.612
- type: precision_at_5 value: 15.273
- type: recall_at_1 value: 39.951
- type: recall_at_10 value: 70.053
- type: recall_at_100 value: 86.996
- type: recall_at_1000 value: 95.707
- type: recall_at_3 value: 56.032000000000004
- type: recall_at_5 value: 61.629999999999995
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 25.566
- type: map_at_10 value: 33.207
- type: map_at_100 value: 34.166000000000004
- type: map_at_1000 value: 34.245
- type: map_at_3 value: 30.94
- type: map_at_5 value: 32.01
- type: mrr_at_1 value: 27.345000000000002
- type: mrr_at_10 value: 35.193000000000005
- type: mrr_at_100 value: 35.965
- type: mrr_at_1000 value: 36.028999999999996
- type: mrr_at_3 value: 32.806000000000004
- type: mrr_at_5 value: 34.021
- type: ndcg_at_1 value: 27.345000000000002
- type: ndcg_at_10 value: 37.891999999999996
- type: ndcg_at_100 value: 42.664
- type: ndcg_at_1000 value: 44.757000000000005
- type: ndcg_at_3 value: 33.123000000000005
- type: ndcg_at_5 value: 35.035
- type: precision_at_1 value: 27.345000000000002
- type: precision_at_10 value: 5.763
- type: precision_at_100 value: 0.859
- type: precision_at_1000 value: 0.108
- type: precision_at_3 value: 13.71
- type: precision_at_5 value: 9.401
- type: recall_at_1 value: 25.566
- type: recall_at_10 value: 50.563
- type: recall_at_100 value: 72.86399999999999
- type: recall_at_1000 value: 88.68599999999999
- type: recall_at_3 value: 37.43
- type: recall_at_5 value: 41.894999999999996
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 16.663
- type: map_at_10 value: 23.552
- type: map_at_100 value: 24.538
- type: map_at_1000 value: 24.661
- type: map_at_3 value: 21.085
- type: map_at_5 value: 22.391
- type: mrr_at_1 value: 20.025000000000002
- type: mrr_at_10 value: 27.643
- type: mrr_at_100 value: 28.499999999999996
- type: mrr_at_1000 value: 28.582
- type: mrr_at_3 value: 25.083
- type: mrr_at_5 value: 26.544
- type: ndcg_at_1 value: 20.025000000000002
- type: ndcg_at_10 value: 28.272000000000002
- type: ndcg_at_100 value: 33.353
- type: ndcg_at_1000 value: 36.454
- type: ndcg_at_3 value: 23.579
- type: ndcg_at_5 value: 25.685000000000002
- type: precision_at_1 value: 20.025000000000002
- type: precision_at_10 value: 5.187
- type: precision_at_100 value: 0.897
- type: precision_at_1000 value: 0.13
- type: precision_at_3 value: 10.987
- type: precision_at_5 value: 8.06
- type: recall_at_1 value: 16.663
- type: recall_at_10 value: 38.808
- type: recall_at_100 value: 61.305
- type: recall_at_1000 value: 83.571
- type: recall_at_3 value: 25.907999999999998
- type: recall_at_5 value: 31.214
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 27.695999999999998
- type: map_at_10 value: 37.018
- type: map_at_100 value: 38.263000000000005
- type: map_at_1000 value: 38.371
- type: map_at_3 value: 34.226
- type: map_at_5 value: 35.809999999999995
- type: mrr_at_1 value: 32.916000000000004
- type: mrr_at_10 value: 42.067
- type: mrr_at_100 value: 42.925000000000004
- type: mrr_at_1000 value: 42.978
- type: mrr_at_3 value: 39.637
- type: mrr_at_5 value: 41.134
- type: ndcg_at_1 value: 32.916000000000004
- type: ndcg_at_10 value: 42.539
- type: ndcg_at_100 value: 47.873
- type: ndcg_at_1000 value: 50.08200000000001
- type: ndcg_at_3 value: 37.852999999999994
- type: ndcg_at_5 value: 40.201
- type: precision_at_1 value: 32.916000000000004
- type: precision_at_10 value: 7.5840000000000005
- type: precision_at_100 value: 1.199
- type: precision_at_1000 value: 0.155
- type: precision_at_3 value: 17.485
- type: precision_at_5 value: 12.512
- type: recall_at_1 value: 27.695999999999998
- type: recall_at_10 value: 53.638
- type: recall_at_100 value: 76.116
- type: recall_at_1000 value: 91.069
- type: recall_at_3 value: 41.13
- type: recall_at_5 value: 46.872
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 24.108
- type: map_at_10 value: 33.372
- type: map_at_100 value: 34.656
- type: map_at_1000 value: 34.768
- type: map_at_3 value: 30.830999999999996
- type: map_at_5 value: 32.204
- type: mrr_at_1 value: 29.110000000000003
- type: mrr_at_10 value: 37.979
- type: mrr_at_100 value: 38.933
- type: mrr_at_1000 value: 38.988
- type: mrr_at_3 value: 35.731
- type: mrr_at_5 value: 36.963
- type: ndcg_at_1 value: 29.110000000000003
- type: ndcg_at_10 value: 38.635000000000005
- type: ndcg_at_100 value: 44.324999999999996
- type: ndcg_at_1000 value: 46.747
- type: ndcg_at_3 value: 34.37
- type: ndcg_at_5 value: 36.228
- type: precision_at_1 value: 29.110000000000003
- type: precision_at_10 value: 6.963
- type: precision_at_100 value: 1.146
- type: precision_at_1000 value: 0.152
- type: precision_at_3 value: 16.400000000000002
- type: precision_at_5 value: 11.552999999999999
- type: recall_at_1 value: 24.108
- type: recall_at_10 value: 49.597
- type: recall_at_100 value: 73.88900000000001
- type: recall_at_1000 value: 90.62400000000001
- type: recall_at_3 value: 37.662
- type: recall_at_5 value: 42.565
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 25.00791666666667
- type: map_at_10 value: 33.287749999999996
- type: map_at_100 value: 34.41141666666667
- type: map_at_1000 value: 34.52583333333333
- type: map_at_3 value: 30.734416666666668
- type: map_at_5 value: 32.137166666666666
- type: mrr_at_1 value: 29.305666666666664
- type: mrr_at_10 value: 37.22966666666666
- type: mrr_at_100 value: 38.066583333333334
- type: mrr_at_1000 value: 38.12616666666667
- type: mrr_at_3 value: 34.92275
- type: mrr_at_5 value: 36.23333333333334
- type: ndcg_at_1 value: 29.305666666666664
- type: ndcg_at_10 value: 38.25533333333333
- type: ndcg_at_100 value: 43.25266666666666
- type: ndcg_at_1000 value: 45.63583333333334
- type: ndcg_at_3 value: 33.777166666666666
- type: ndcg_at_5 value: 35.85
- type: precision_at_1 value: 29.305666666666664
- type: precision_at_10 value: 6.596416666666667
- type: precision_at_100 value: 1.0784166666666668
- type: precision_at_1000 value: 0.14666666666666664
- type: precision_at_3 value: 15.31075
- type: precision_at_5 value: 10.830916666666667
- type: recall_at_1 value: 25.00791666666667
- type: recall_at_10 value: 49.10933333333333
- type: recall_at_100 value: 71.09216666666667
- type: recall_at_1000 value: 87.77725000000001
- type: recall_at_3 value: 36.660916666666665
- type: recall_at_5 value: 41.94149999999999
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 23.521
- type: map_at_10 value: 30.043
- type: map_at_100 value: 30.936000000000003
- type: map_at_1000 value: 31.022
- type: map_at_3 value: 27.926000000000002
- type: map_at_5 value: 29.076999999999998
- type: mrr_at_1 value: 26.227
- type: mrr_at_10 value: 32.822
- type: mrr_at_100 value: 33.61
- type: mrr_at_1000 value: 33.672000000000004
- type: mrr_at_3 value: 30.776999999999997
- type: mrr_at_5 value: 31.866
- type: ndcg_at_1 value: 26.227
- type: ndcg_at_10 value: 34.041
- type: ndcg_at_100 value: 38.394
- type: ndcg_at_1000 value: 40.732
- type: ndcg_at_3 value: 30.037999999999997
- type: ndcg_at_5 value: 31.845000000000002
- type: precision_at_1 value: 26.227
- type: precision_at_10 value: 5.244999999999999
- type: precision_at_100 value: 0.808
- type: precision_at_1000 value: 0.107
- type: precision_at_3 value: 12.679000000000002
- type: precision_at_5 value: 8.773
- type: recall_at_1 value: 23.521
- type: recall_at_10 value: 43.633
- type: recall_at_100 value: 63.126000000000005
- type: recall_at_1000 value: 80.765
- type: recall_at_3 value: 32.614
- type: recall_at_5 value: 37.15
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 16.236
- type: map_at_10 value: 22.898
- type: map_at_100 value: 23.878
- type: map_at_1000 value: 24.009
- type: map_at_3 value: 20.87
- type: map_at_5 value: 22.025
- type: mrr_at_1 value: 19.339000000000002
- type: mrr_at_10 value: 26.382
- type: mrr_at_100 value: 27.245
- type: mrr_at_1000 value: 27.33
- type: mrr_at_3 value: 24.386
- type: mrr_at_5 value: 25.496000000000002
- type: ndcg_at_1 value: 19.339000000000002
- type: ndcg_at_10 value: 27.139999999999997
- type: ndcg_at_100 value: 31.944
- type: ndcg_at_1000 value: 35.077999999999996
- type: ndcg_at_3 value: 23.424
- type: ndcg_at_5 value: 25.188
- type: precision_at_1 value: 19.339000000000002
- type: precision_at_10 value: 4.8309999999999995
- type: precision_at_100 value: 0.845
- type: precision_at_1000 value: 0.128
- type: precision_at_3 value: 10.874
- type: precision_at_5 value: 7.825
- type: recall_at_1 value: 16.236
- type: recall_at_10 value: 36.513
- type: recall_at_100 value: 57.999
- type: recall_at_1000 value: 80.512
- type: recall_at_3 value: 26.179999999999996
- type: recall_at_5 value: 30.712
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 24.11
- type: map_at_10 value: 31.566
- type: map_at_100 value: 32.647
- type: map_at_1000 value: 32.753
- type: map_at_3 value: 29.24
- type: map_at_5 value: 30.564999999999998
- type: mrr_at_1 value: 28.265
- type: mrr_at_10 value: 35.504000000000005
- type: mrr_at_100 value: 36.436
- type: mrr_at_1000 value: 36.503
- type: mrr_at_3 value: 33.349000000000004
- type: mrr_at_5 value: 34.622
- type: ndcg_at_1 value: 28.265
- type: ndcg_at_10 value: 36.192
- type: ndcg_at_100 value: 41.388000000000005
- type: ndcg_at_1000 value: 43.948
- type: ndcg_at_3 value: 31.959
- type: ndcg_at_5 value: 33.998
- type: precision_at_1 value: 28.265
- type: precision_at_10 value: 5.989
- type: precision_at_100 value: 0.9650000000000001
- type: precision_at_1000 value: 0.13
- type: precision_at_3 value: 14.335
- type: precision_at_5 value: 10.112
- type: recall_at_1 value: 24.11
- type: recall_at_10 value: 46.418
- type: recall_at_100 value: 69.314
- type: recall_at_1000 value: 87.397
- type: recall_at_3 value: 34.724
- type: recall_at_5 value: 39.925
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 22.091
- type: map_at_10 value: 29.948999999999998
- type: map_at_100 value: 31.502000000000002
- type: map_at_1000 value: 31.713
- type: map_at_3 value: 27.464
- type: map_at_5 value: 28.968
- type: mrr_at_1 value: 26.482
- type: mrr_at_10 value: 34.009
- type: mrr_at_100 value: 35.081
- type: mrr_at_1000 value: 35.138000000000005
- type: mrr_at_3 value: 31.785000000000004
- type: mrr_at_5 value: 33.178999999999995
- type: ndcg_at_1 value: 26.482
- type: ndcg_at_10 value: 35.008
- type: ndcg_at_100 value: 41.272999999999996
- type: ndcg_at_1000 value: 43.972
- type: ndcg_at_3 value: 30.804
- type: ndcg_at_5 value: 33.046
- type: precision_at_1 value: 26.482
- type: precision_at_10 value: 6.462
- type: precision_at_100 value: 1.431
- type: precision_at_1000 value: 0.22899999999999998
- type: precision_at_3 value: 14.360999999999999
- type: precision_at_5 value: 10.474
- type: recall_at_1 value: 22.091
- type: recall_at_10 value: 45.125
- type: recall_at_100 value: 72.313
- type: recall_at_1000 value: 89.503
- type: recall_at_3 value: 33.158
- type: recall_at_5 value: 39.086999999999996
-
task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 19.883
- type: map_at_10 value: 26.951000000000004
- type: map_at_100 value: 27.927999999999997
- type: map_at_1000 value: 28.022000000000002
- type: map_at_3 value: 24.616
- type: map_at_5 value: 25.917
- type: mrr_at_1 value: 21.996
- type: mrr_at_10 value: 29.221000000000004
- type: mrr_at_100 value: 30.024
- type: mrr_at_1000 value: 30.095
- type: mrr_at_3 value: 26.833000000000002
- type: mrr_at_5 value: 28.155
- type: ndcg_at_1 value: 21.996
- type: ndcg_at_10 value: 31.421
- type: ndcg_at_100 value: 36.237
- type: ndcg_at_1000 value: 38.744
- type: ndcg_at_3 value: 26.671
- type: ndcg_at_5 value: 28.907
- type: precision_at_1 value: 21.996
- type: precision_at_10 value: 5.009
- type: precision_at_100 value: 0.799
- type: precision_at_1000 value: 0.11199999999999999
- type: precision_at_3 value: 11.275
- type: precision_at_5 value: 8.059
- type: recall_at_1 value: 19.883
- type: recall_at_10 value: 43.132999999999996
- type: recall_at_100 value: 65.654
- type: recall_at_1000 value: 84.492
- type: recall_at_3 value: 30.209000000000003
- type: recall_at_5 value: 35.616
-
task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics:
- type: map_at_1 value: 17.756
- type: map_at_10 value: 30.378
- type: map_at_100 value: 32.537
- type: map_at_1000 value: 32.717
- type: map_at_3 value: 25.599
- type: map_at_5 value: 28.372999999999998
- type: mrr_at_1 value: 41.303
- type: mrr_at_10 value: 53.483999999999995
- type: mrr_at_100 value: 54.106
- type: mrr_at_1000 value: 54.127
- type: mrr_at_3 value: 50.315
- type: mrr_at_5 value: 52.396
- type: ndcg_at_1 value: 41.303
- type: ndcg_at_10 value: 40.503
- type: ndcg_at_100 value: 47.821000000000005
- type: ndcg_at_1000 value: 50.788
- type: ndcg_at_3 value: 34.364
- type: ndcg_at_5 value: 36.818
- type: precision_at_1 value: 41.303
- type: precision_at_10 value: 12.463000000000001
- type: precision_at_100 value: 2.037
- type: precision_at_1000 value: 0.26
- type: precision_at_3 value: 25.798
- type: precision_at_5 value: 19.896
- type: recall_at_1 value: 17.756
- type: recall_at_10 value: 46.102
- type: recall_at_100 value: 70.819
- type: recall_at_1000 value: 87.21799999999999
- type: recall_at_3 value: 30.646
- type: recall_at_5 value: 38.022
-
task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics:
- type: map_at_1 value: 9.033
- type: map_at_10 value: 20.584
- type: map_at_100 value: 29.518
- type: map_at_1000 value: 31.186000000000003
- type: map_at_3 value: 14.468
- type: map_at_5 value: 17.177
- type: mrr_at_1 value: 69.75
- type: mrr_at_10 value: 77.025
- type: mrr_at_100 value: 77.36699999999999
- type: mrr_at_1000 value: 77.373
- type: mrr_at_3 value: 75.583
- type: mrr_at_5 value: 76.396
- type: ndcg_at_1 value: 58.5
- type: ndcg_at_10 value: 45.033
- type: ndcg_at_100 value: 49.071
- type: ndcg_at_1000 value: 56.056
- type: ndcg_at_3 value: 49.936
- type: ndcg_at_5 value: 47.471999999999994
- type: precision_at_1 value: 69.75
- type: precision_at_10 value: 35.775
- type: precision_at_100 value: 11.594999999999999
- type: precision_at_1000 value: 2.062
- type: precision_at_3 value: 52.5
- type: precision_at_5 value: 45.300000000000004
- type: recall_at_1 value: 9.033
- type: recall_at_10 value: 26.596999999999998
- type: recall_at_100 value: 54.607000000000006
- type: recall_at_1000 value: 76.961
- type: recall_at_3 value: 15.754999999999999
- type: recall_at_5 value: 20.033
-
task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics:
- type: accuracy value: 48.345000000000006
- type: f1 value: 43.4514918068706
-
task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics:
- type: map_at_1 value: 71.29100000000001
- type: map_at_10 value: 81.059
- type: map_at_100 value: 81.341
- type: map_at_1000 value: 81.355
- type: map_at_3 value: 79.74799999999999
- type: map_at_5 value: 80.612
- type: mrr_at_1 value: 76.40299999999999
- type: mrr_at_10 value: 84.615
- type: mrr_at_100 value: 84.745
- type: mrr_at_1000 value: 84.748
- type: mrr_at_3 value: 83.776
- type: mrr_at_5 value: 84.343
- type: ndcg_at_1 value: 76.40299999999999
- type: ndcg_at_10 value: 84.981
- type: ndcg_at_100 value: 86.00999999999999
- type: ndcg_at_1000 value: 86.252
- type: ndcg_at_3 value: 82.97
- type: ndcg_at_5 value: 84.152
- type: precision_at_1 value: 76.40299999999999
- type: precision_at_10 value: 10.446
- type: precision_at_100 value: 1.1199999999999999
- type: precision_at_1000 value: 0.116
- type: precision_at_3 value: 32.147999999999996
- type: precision_at_5 value: 20.135
- type: recall_at_1 value: 71.29100000000001
- type: recall_at_10 value: 93.232
- type: recall_at_100 value: 97.363
- type: recall_at_1000 value: 98.905
- type: recall_at_3 value: 87.893
- type: recall_at_5 value: 90.804
-
task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics:
- type: map_at_1 value: 18.667
- type: map_at_10 value: 30.853
- type: map_at_100 value: 32.494
- type: map_at_1000 value: 32.677
- type: map_at_3 value: 26.91
- type: map_at_5 value: 29.099000000000004
- type: mrr_at_1 value: 37.191
- type: mrr_at_10 value: 46.171
- type: mrr_at_100 value: 47.056
- type: mrr_at_1000 value: 47.099000000000004
- type: mrr_at_3 value: 44.059
- type: mrr_at_5 value: 45.147
- type: ndcg_at_1 value: 37.191
- type: ndcg_at_10 value: 38.437
- type: ndcg_at_100 value: 44.62
- type: ndcg_at_1000 value: 47.795
- type: ndcg_at_3 value: 35.003
- type: ndcg_at_5 value: 36.006
- type: precision_at_1 value: 37.191
- type: precision_at_10 value: 10.586
- type: precision_at_100 value: 1.688
- type: precision_at_1000 value: 0.22699999999999998
- type: precision_at_3 value: 23.302
- type: precision_at_5 value: 17.006
- type: recall_at_1 value: 18.667
- type: recall_at_10 value: 45.367000000000004
- type: recall_at_100 value: 68.207
- type: recall_at_1000 value: 87.072
- type: recall_at_3 value: 32.129000000000005
- type: recall_at_5 value: 37.719
-
task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics:
- type: map_at_1 value: 39.494
- type: map_at_10 value: 66.223
- type: map_at_100 value: 67.062
- type: map_at_1000 value: 67.11500000000001
- type: map_at_3 value: 62.867
- type: map_at_5 value: 64.994
- type: mrr_at_1 value: 78.987
- type: mrr_at_10 value: 84.585
- type: mrr_at_100 value: 84.773
- type: mrr_at_1000 value: 84.77900000000001
- type: mrr_at_3 value: 83.592
- type: mrr_at_5 value: 84.235
- type: ndcg_at_1 value: 78.987
- type: ndcg_at_10 value: 73.64
- type: ndcg_at_100 value: 76.519
- type: ndcg_at_1000 value: 77.51
- type: ndcg_at_3 value: 68.893
- type: ndcg_at_5 value: 71.585
- type: precision_at_1 value: 78.987
- type: precision_at_10 value: 15.529000000000002
- type: precision_at_100 value: 1.7770000000000001
- type: precision_at_1000 value: 0.191
- type: precision_at_3 value: 44.808
- type: precision_at_5 value: 29.006999999999998
- type: recall_at_1 value: 39.494
- type: recall_at_10 value: 77.643
- type: recall_at_100 value: 88.825
- type: recall_at_1000 value: 95.321
- type: recall_at_3 value: 67.211
- type: recall_at_5 value: 72.519
-
task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics:
- type: accuracy value: 85.55959999999999
- type: ap value: 80.7246500384617
- type: f1 value: 85.52336485065454
-
task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics:
- type: map_at_1 value: 23.631
- type: map_at_10 value: 36.264
- type: map_at_100 value: 37.428
- type: map_at_1000 value: 37.472
- type: map_at_3 value: 32.537
- type: map_at_5 value: 34.746
- type: mrr_at_1 value: 24.312
- type: mrr_at_10 value: 36.858000000000004
- type: mrr_at_100 value: 37.966
- type: mrr_at_1000 value: 38.004
- type: mrr_at_3 value: 33.188
- type: mrr_at_5 value: 35.367
- type: ndcg_at_1 value: 24.312
- type: ndcg_at_10 value: 43.126999999999995
- type: ndcg_at_100 value: 48.642
- type: ndcg_at_1000 value: 49.741
- type: ndcg_at_3 value: 35.589
- type: ndcg_at_5 value: 39.515
- type: precision_at_1 value: 24.312
- type: precision_at_10 value: 6.699
- type: precision_at_100 value: 0.9450000000000001
- type: precision_at_1000 value: 0.104
- type: precision_at_3 value: 15.153
- type: precision_at_5 value: 11.065999999999999
- type: recall_at_1 value: 23.631
- type: recall_at_10 value: 64.145
- type: recall_at_100 value: 89.41
- type: recall_at_1000 value: 97.83500000000001
- type: recall_at_3 value: 43.769000000000005
- type: recall_at_5 value: 53.169
-
task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
- type: accuracy value: 93.4108527131783
- type: f1 value: 93.1415880261038
-
task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
- type: accuracy value: 77.24806201550388
- type: f1 value: 60.531916308197175
-
task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
- type: accuracy value: 73.71553463349024
- type: f1 value: 71.70753174900791
-
task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
- type: accuracy value: 77.79757901815736
- type: f1 value: 77.83719850433258
-
task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics:
- type: v_measure value: 33.74193296622113
-
task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics:
- type: v_measure value: 30.64257594108566
-
task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics:
- type: map value: 30.811018518883625
- type: mrr value: 31.910376577445003
-
task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics:
-
type: map_at_1 value: 5.409
-
type: map_at_10 value: 13.093
-
type: map_at_100 value: 16.256999999999998
-
type: map_at_1000 value: 17.617
-
type: map_at_3 value: 9.555
-
type: map_at_5 value: 11.428
-
type: mrr_at_1 value: 45.201
-
type: mrr_at_10 value: 54.179
-
type: mrr_at_100 value: 54.812000000000005
-
type: mrr_at_1000 value: 54.840999999999994
-
type: mrr_at_3 value: 51.909000000000006
-
type: mrr_at_5 value: 53.519000000000005
-
type: ndcg_at_1 value: 43.189
-
type: ndcg_at_10 value: 35.028
-
type: ndcg_at_100 value: 31.226
-
type: ndcg_at_1000 value: 39.678000000000004
-
type: ndcg_at_3 value: 40.596
-
type: ndcg_at_5 value: 38.75
-
type: precision_at_1 value: 44.582
-
type: precision_at_10 value: 25.974999999999998
-
type: precision_at_100 value: 7.793
-
type: precision_at_1000 value: 2.036
-
type: precision_at_3 value: 38.493
-
type: precision_at_5 value: 33.994
-
type: recall_at_1 value: 5.409
-
type: recall_at_10 value: 16.875999999999998
-
type: recall_at_100 value: 30.316
-
type: recall_at_1000 value: 60.891
-
type: recall_at_3 value: 10.688
-
type: recall_at_5 value: 13.832
-
-
task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics:
- type: map_at_1 value: 36.375
- type: map_at_10 value: 51.991
- type: map_at_100 value: 52.91400000000001
- type: map_at_1000 value: 52.93600000000001
- type: map_at_3 value: 48.014
- type: map_at_5 value: 50.381
- type: mrr_at_1 value: 40.759
- type: mrr_at_10 value: 54.617000000000004
- type: mrr_at_100 value: 55.301
- type: mrr_at_1000 value: 55.315000000000005
- type: mrr_at_3 value: 51.516
- type: mrr_at_5 value: 53.435
- type: ndcg_at_1 value: 40.759
- type: ndcg_at_10 value: 59.384
- type: ndcg_at_100 value: 63.157
- type: ndcg_at_1000 value: 63.654999999999994
- type: ndcg_at_3 value: 52.114000000000004
- type: ndcg_at_5 value: 55.986000000000004
- type: precision_at_1 value: 40.759
- type: precision_at_10 value: 9.411999999999999
- type: precision_at_100 value: 1.153
- type: precision_at_1000 value: 0.12
- type: precision_at_3 value: 23.329
- type: precision_at_5 value: 16.256999999999998
- type: recall_at_1 value: 36.375
- type: recall_at_10 value: 79.053
- type: recall_at_100 value: 95.167
- type: recall_at_1000 value: 98.82
- type: recall_at_3 value: 60.475
- type: recall_at_5 value: 69.327
-
task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics:
- type: map_at_1 value: 70.256
- type: map_at_10 value: 83.8
- type: map_at_100 value: 84.425
- type: map_at_1000 value: 84.444
- type: map_at_3 value: 80.906
- type: map_at_5 value: 82.717
- type: mrr_at_1 value: 80.97999999999999
- type: mrr_at_10 value: 87.161
- type: mrr_at_100 value: 87.262
- type: mrr_at_1000 value: 87.263
- type: mrr_at_3 value: 86.175
- type: mrr_at_5 value: 86.848
- type: ndcg_at_1 value: 80.97999999999999
- type: ndcg_at_10 value: 87.697
- type: ndcg_at_100 value: 88.959
- type: ndcg_at_1000 value: 89.09899999999999
- type: ndcg_at_3 value: 84.83800000000001
- type: ndcg_at_5 value: 86.401
- type: precision_at_1 value: 80.97999999999999
- type: precision_at_10 value: 13.261000000000001
- type: precision_at_100 value: 1.5150000000000001
- type: precision_at_1000 value: 0.156
- type: precision_at_3 value: 37.01
- type: precision_at_5 value: 24.298000000000002
- type: recall_at_1 value: 70.256
- type: recall_at_10 value: 94.935
- type: recall_at_100 value: 99.274
- type: recall_at_1000 value: 99.928
- type: recall_at_3 value: 86.602
- type: recall_at_5 value: 91.133
-
task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics:
- type: v_measure value: 56.322692497613104
-
task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics:
- type: v_measure value: 61.895813503775074
-
task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics:
- type: map_at_1 value: 4.338
- type: map_at_10 value: 10.767
- type: map_at_100 value: 12.537999999999998
- type: map_at_1000 value: 12.803999999999998
- type: map_at_3 value: 7.788
- type: map_at_5 value: 9.302000000000001
- type: mrr_at_1 value: 21.4
- type: mrr_at_10 value: 31.637999999999998
- type: mrr_at_100 value: 32.688
- type: mrr_at_1000 value: 32.756
- type: mrr_at_3 value: 28.433000000000003
- type: mrr_at_5 value: 30.178
- type: ndcg_at_1 value: 21.4
- type: ndcg_at_10 value: 18.293
- type: ndcg_at_100 value: 25.274
- type: ndcg_at_1000 value: 30.284
- type: ndcg_at_3 value: 17.391000000000002
- type: ndcg_at_5 value: 15.146999999999998
- type: precision_at_1 value: 21.4
- type: precision_at_10 value: 9.48
- type: precision_at_100 value: 1.949
- type: precision_at_1000 value: 0.316
- type: precision_at_3 value: 16.167
- type: precision_at_5 value: 13.22
- type: recall_at_1 value: 4.338
- type: recall_at_10 value: 19.213
- type: recall_at_100 value: 39.562999999999995
- type: recall_at_1000 value: 64.08
- type: recall_at_3 value: 9.828000000000001
- type: recall_at_5 value: 13.383000000000001
-
task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics:
- type: cos_sim_pearson value: 82.42568163642142
- type: cos_sim_spearman value: 78.5797159641342
- type: euclidean_pearson value: 80.22151260811604
- type: euclidean_spearman value: 78.5797151953878
- type: manhattan_pearson value: 80.21224215864788
- type: manhattan_spearman value: 78.55641478381344
-
task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics:
- type: cos_sim_pearson value: 85.44020710812569
- type: cos_sim_spearman value: 78.91631735081286
- type: euclidean_pearson value: 81.64188964182102
- type: euclidean_spearman value: 78.91633286881678
- type: manhattan_pearson value: 81.69294748512496
- type: manhattan_spearman value: 78.93438558002656
-
task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics:
- type: cos_sim_pearson value: 84.27165426412311
- type: cos_sim_spearman value: 85.40429140249618
- type: euclidean_pearson value: 84.7509580724893
- type: euclidean_spearman value: 85.40429140249618
- type: manhattan_pearson value: 84.76488289321308
- type: manhattan_spearman value: 85.4256793698708
-
task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics:
- type: cos_sim_pearson value: 83.138851760732
- type: cos_sim_spearman value: 81.64101363896586
- type: euclidean_pearson value: 82.55165038934942
- type: euclidean_spearman value: 81.64105257080502
- type: manhattan_pearson value: 82.52802949883335
- type: manhattan_spearman value: 81.61255430718158
-
task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics:
- type: cos_sim_pearson value: 86.0654695484029
- type: cos_sim_spearman value: 87.20408521902229
- type: euclidean_pearson value: 86.8110651362115
- type: euclidean_spearman value: 87.20408521902229
- type: manhattan_pearson value: 86.77984656478691
- type: manhattan_spearman value: 87.1719947099227
-
task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics:
- type: cos_sim_pearson value: 83.77823915496512
- type: cos_sim_spearman value: 85.43566325729779
- type: euclidean_pearson value: 84.5396956658821
- type: euclidean_spearman value: 85.43566325729779
- type: manhattan_pearson value: 84.5665398848169
- type: manhattan_spearman value: 85.44375870303232
-
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: 87.20030208471798
- type: cos_sim_spearman value: 87.20485505076539
- type: euclidean_pearson value: 88.10588324368722
- type: euclidean_spearman value: 87.20485505076539
- type: manhattan_pearson value: 87.92324770415183
- type: manhattan_spearman value: 87.0571314561877
-
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: 63.06093161604453
- type: cos_sim_spearman value: 64.2163140357722
- type: euclidean_pearson value: 65.27589680994006
- type: euclidean_spearman value: 64.2163140357722
- type: manhattan_pearson value: 65.45904383711101
- type: manhattan_spearman value: 64.55404716679305
-
task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics:
- type: cos_sim_pearson value: 84.32976164578706
- type: cos_sim_spearman value: 85.54302197678368
- type: euclidean_pearson value: 85.26307149193056
- type: euclidean_spearman value: 85.54302197678368
- type: manhattan_pearson value: 85.26647282029371
- type: manhattan_spearman value: 85.5316135265568
-
task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics:
- type: map value: 81.44675968318754
- type: mrr value: 94.92741826075158
-
task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics:
- type: map_at_1 value: 56.34400000000001
- type: map_at_10 value: 65.927
- type: map_at_100 value: 66.431
- type: map_at_1000 value: 66.461
- type: map_at_3 value: 63.529
- type: map_at_5 value: 64.818
- type: mrr_at_1 value: 59.333000000000006
- type: mrr_at_10 value: 67.54599999999999
- type: mrr_at_100 value: 67.892
- type: mrr_at_1000 value: 67.917
- type: mrr_at_3 value: 65.778
- type: mrr_at_5 value: 66.794
- type: ndcg_at_1 value: 59.333000000000006
- type: ndcg_at_10 value: 70.5
- type: ndcg_at_100 value: 72.688
- type: ndcg_at_1000 value: 73.483
- type: ndcg_at_3 value: 66.338
- type: ndcg_at_5 value: 68.265
- type: precision_at_1 value: 59.333000000000006
- type: precision_at_10 value: 9.3
- type: precision_at_100 value: 1.053
- type: precision_at_1000 value: 0.11199999999999999
- type: precision_at_3 value: 25.889
- type: precision_at_5 value: 16.866999999999997
- type: recall_at_1 value: 56.34400000000001
- type: recall_at_10 value: 82.789
- type: recall_at_100 value: 92.767
- type: recall_at_1000 value: 99
- type: recall_at_3 value: 71.64399999999999
- type: recall_at_5 value: 76.322
-
task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics:
- type: cos_sim_accuracy value: 99.75742574257426
- type: cos_sim_ap value: 93.52081548447406
- type: cos_sim_f1 value: 87.33850129198966
- type: cos_sim_precision value: 90.37433155080214
- type: cos_sim_recall value: 84.5
- type: dot_accuracy value: 99.75742574257426
- type: dot_ap value: 93.52081548447406
- type: dot_f1 value: 87.33850129198966
- type: dot_precision value: 90.37433155080214
- type: dot_recall value: 84.5
- type: euclidean_accuracy value: 99.75742574257426
- type: euclidean_ap value: 93.52081548447406
- type: euclidean_f1 value: 87.33850129198966
- type: euclidean_precision value: 90.37433155080214
- type: euclidean_recall value: 84.5
- type: manhattan_accuracy value: 99.75841584158415
- type: manhattan_ap value: 93.4975678585854
- type: manhattan_f1 value: 87.26708074534162
- type: manhattan_precision value: 90.45064377682404
- type: manhattan_recall value: 84.3
- type: max_accuracy value: 99.75841584158415
- type: max_ap value: 93.52081548447406
- type: max_f1 value: 87.33850129198966
-
task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics:
- type: v_measure value: 64.31437036686651
-
task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics:
- type: v_measure value: 33.25569319007206
-
task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics:
- type: map value: 49.90474939720706
- type: mrr value: 50.568115503777264
-
task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics:
- type: cos_sim_pearson value: 29.866828641244712
- type: cos_sim_spearman value: 30.077555055873866
- type: dot_pearson value: 29.866832988572266
- type: dot_spearman value: 30.077555055873866
-
task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics:
- type: map_at_1 value: 0.232
- type: map_at_10 value: 2.094
- type: map_at_100 value: 11.971
- type: map_at_1000 value: 28.158
- type: map_at_3 value: 0.688
- type: map_at_5 value: 1.114
- type: mrr_at_1 value: 88
- type: mrr_at_10 value: 93.4
- type: mrr_at_100 value: 93.4
- type: mrr_at_1000 value: 93.4
- type: mrr_at_3 value: 93
- type: mrr_at_5 value: 93.4
- type: ndcg_at_1 value: 84
- type: ndcg_at_10 value: 79.923
- type: ndcg_at_100 value: 61.17
- type: ndcg_at_1000 value: 53.03
- type: ndcg_at_3 value: 84.592
- type: ndcg_at_5 value: 82.821
- type: precision_at_1 value: 88
- type: precision_at_10 value: 85
- type: precision_at_100 value: 63.019999999999996
- type: precision_at_1000 value: 23.554
- type: precision_at_3 value: 89.333
- type: precision_at_5 value: 87.2
- type: recall_at_1 value: 0.232
- type: recall_at_10 value: 2.255
- type: recall_at_100 value: 14.823
- type: recall_at_1000 value: 49.456
- type: recall_at_3 value: 0.718
- type: recall_at_5 value: 1.175
-
task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics:
- type: map_at_1 value: 2.547
- type: map_at_10 value: 11.375
- type: map_at_100 value: 18.194
- type: map_at_1000 value: 19.749
- type: map_at_3 value: 5.825
- type: map_at_5 value: 8.581
- type: mrr_at_1 value: 32.653
- type: mrr_at_10 value: 51.32
- type: mrr_at_100 value: 51.747
- type: mrr_at_1000 value: 51.747
- type: mrr_at_3 value: 47.278999999999996
- type: mrr_at_5 value: 48.605
- type: ndcg_at_1 value: 29.592000000000002
- type: ndcg_at_10 value: 28.151
- type: ndcg_at_100 value: 39.438
- type: ndcg_at_1000 value: 50.769
- type: ndcg_at_3 value: 30.758999999999997
- type: ndcg_at_5 value: 30.366
- type: precision_at_1 value: 32.653
- type: precision_at_10 value: 25.714
- type: precision_at_100 value: 8.041
- type: precision_at_1000 value: 1.555
- type: precision_at_3 value: 33.333
- type: precision_at_5 value: 31.837
- type: recall_at_1 value: 2.547
- type: recall_at_10 value: 18.19
- type: recall_at_100 value: 49.538
- type: recall_at_1000 value: 83.86
- type: recall_at_3 value: 7.329
- type: recall_at_5 value: 11.532
-
task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics:
- type: accuracy value: 71.4952
- type: ap value: 14.793362635531409
- type: f1 value: 55.204635551516915
-
task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics:
- type: accuracy value: 61.5365025466893
- type: f1 value: 61.81742556334845
-
task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics:
- type: v_measure value: 49.05531070301185
-
task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics:
-
type: cos_sim_accuracy value: 86.51725576682364
-
type: cos_sim_ap value: 75.2292304265163
-
type: cos_sim_f1 value: 69.54022988505749
-
type: cos_sim_precision value: 63.65629110039457
-
type: cos_sim_recall
value: 76.62269129287598
-
type: dot_accuracy value: 86.51725576682364
-
type: dot_ap value: 75.22922386081054
-
type: dot_f1 value: 69.54022988505749
-
type: dot_precision value: 63.65629110039457
-
type: dot_recall value: 76.62269129287598
-
type: euclidean_accuracy value: 86.51725576682364
-
type: euclidean_ap value: 75.22925730473472
-
type: euclidean_f1 value: 69.54022988505749
-
type: euclidean_precision value: 63.65629110039457
-
type: euclidean_recall value: 76.62269129287598
-
type: manhattan_accuracy value: 86.52321630804077
-
type: manhattan_ap value: 75.20608115037336
-
type: manhattan_f1 value: 69.60000000000001
-
type: manhattan_precision value: 64.37219730941705
-
type: manhattan_recall value: 75.75197889182058
-
type: max_accuracy value: 86.52321630804077
-
type: max_ap value: 75.22925730473472
-
type: max_f1 value: 69.60000000000001
-
-
task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics:
- type: cos_sim_accuracy value: 89.34877944657896
- type: cos_sim_ap value: 86.71257569277373
- type: cos_sim_f1 value: 79.10386355986088
- type: cos_sim_precision value: 76.91468470434214
- type: cos_sim_recall value: 81.4213119802895
- type: dot_accuracy value: 89.34877944657896
- type: dot_ap value: 86.71257133133368
- type: dot_f1 value: 79.10386355986088
- type: dot_precision value: 76.91468470434214
- type: dot_recall value: 81.4213119802895
- type: euclidean_accuracy value: 89.34877944657896
- type: euclidean_ap value: 86.71257651501476
- type: euclidean_f1 value: 79.10386355986088
- type: euclidean_precision value: 76.91468470434214
- type: euclidean_recall value: 81.4213119802895
- type: manhattan_accuracy value: 89.35848177901967
- type: manhattan_ap value: 86.69330615469126
- type: manhattan_f1 value: 79.13867741453949
- type: manhattan_precision value: 76.78881807647741
- type: manhattan_recall value: 81.63689559593472
- type: max_accuracy value: 89.35848177901967
- type: max_ap value: 86.71257651501476
- type: max_f1 value: 79.13867741453949 license: apache-2.0 language:
-
- en new_version: nomic-ai/nomic-embed-text-v1.5
nomic-embed-text-v1：一款可复现的长上下文（8192）文本嵌入模型
博客 | 技术报告 | AWS SageMaker | Atlas 嵌入与非结构化数据分析平台
nomic-embed-text-v1 是一款上下文长度为 8192 的文本编码器，在短上下文和长上下文任务上的性能均超越了 OpenAI 的 text-embedding-ada-002 和 text-embedding-3-small。
性能基准测试
| 名称 | 序列长度 | MTEB | LoCo | Jina 长上下文 | 开放权重 | 开放训练代码 | 开放数据 |
|---|---|---|---|---|---|---|---|
| nomic-embed-text-v1 | 8192 | 62.39 | 85.53 | 54.16 | ✅ | ✅ | ✅ |
| jina-embeddings-v2-base-en | 8192 | 60.39 | 85.45 | 51.90 | ✅ | ❌ | ❌ |
| text-embedding-3-small | 8191 | 62.26 | 82.40 | 58.20 | ❌ | ❌ | ❌ |
| text-embedding-ada-002 | 8191 | 60.99 | 52.7 | 55.25 | ❌ | ❌ | ❌ |
重大更新！：nomic-embed-text-v1 现已支持多模态！nomic-embed-vision-v1 与 nomic-embed-text-v1 的嵌入空间保持一致，这意味着任何文本嵌入都具备多模态能力！
使用方法
重要提示：文本提示必须包含一个任务指令前缀，用于告知模型当前执行的任务。
例如，如果你正在实现一个 RAG 应用，应以 search_document: <文本内容> 的格式嵌入文档，并以 search_query: <文本内容> 的格式嵌入用户查询。
注意：从 transformers v5.5.0 和 sentence transformers v5.3.0 版本开始，将不再需要 trust_remote_code=True。目前这仅适用于纯文本系列模型。
任务指令前缀
search_document
用途：将文本作为数据集中的文档进行嵌入
此前缀用于将文本作为文档进行嵌入，例如作为RAG索引的文档。
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1")
sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten']
embeddings = model.encode(sentences)
print(embeddings)
search_query
用途：将文本嵌入为待回答的问题
此前缀用于将文本嵌入为数据集文档可解答的问题，例如作为RAG应用程序需要回答的查询。
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1")
sentences = ['search_query: Who is Laurens van Der Maaten?']
embeddings = model.encode(sentences)
print(embeddings)
clustering
用途：将文本嵌入以分组形成聚类
此前缀用于对文本进行嵌入，以便将其分组形成聚类、发现共同主题或去除语义重复内容。
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1")
sentences = ['clustering: the quick brown fox']
embeddings = model.encode(sentences)
print(embeddings)
classification
用途：将文本嵌入以进行分类
此前缀用于将文本嵌入为向量，这些向量将用作分类模型的特征。
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1")
sentences = ['classification: the quick brown fox']
embeddings = model.encode(sentences)
print(embeddings)
句子转换器
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1")
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
embeddings = model.encode(sentences)
print(embeddings)
转换器
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1')
model.eval()
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
该模型原生支持将序列长度扩展到 2048 个 token 以上。具体操作如下：
- tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)
- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1')
+ rope_parameters = {"rope_theta": 1000.0, "rope_type": "dynamic", "factor": 2.0}
+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', rope_parameters=rope_parameters)
Transformers.js
import { pipeline } from '@xenova/transformers';
// Create a feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-text-v1', {
quantized: false, // Comment out this line to use the quantized version
});
// Compute sentence embeddings
const texts = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'];
const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
console.log(embeddings);
Nomic API
开始使用 Nomic Embed 最简单的方式是通过 Nomic 嵌入 API。
使用 nomic Python 客户端生成嵌入向量就像这样简单
from nomic import embed
output = embed.text(
texts=['Nomic Embedding API', '#keepAIOpen'],
model='nomic-embed-text-v1',
task_type='search_document'
)
print(output)
如需了解更多信息，请参阅 API 参考文档
训练
点击下方的 Nomic Atlas 地图，可视化查看我们对比预训练数据的 500 万样本！
我们的嵌入模型采用多阶段训练流程进行训练。从一个长上下文 BERT 模型 开始，第一阶段是无监督对比训练，使用由弱相关文本对生成的数据集，例如来自 StackExchange 和 Quora 等论坛的问答对、亚马逊评论的标题-正文对以及新闻文章的摘要。
在第二阶段的微调中，我们利用了更高质量的标记数据集，例如网络搜索中的搜索查询和答案。此阶段中，数据整理和难例挖掘至关重要。
有关更多详细信息，请参阅 Nomic Embed 技术报告 及相应的 博客文章。
用于训练模型的训练数据已完整发布。有关更多详细信息，请参阅 contrastors 代码库
加入 Nomic 社区
- Nomic：https://nomic.ai
- Discord：https://discord.gg/myY5YDR8z8
- Twitter：https://twitter.com/nomic_ai
引用
如果您发现本模型、数据集或训练代码对您的工作有所帮助，请引用我们的研究成果
@misc{nussbaum2024nomic,
title={Nomic Embed: Training a Reproducible Long Context Text Embedder},
author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
year={2024},
eprint={2402.01613},
archivePrefix={arXiv},
primaryClass={cs.CL}
}