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---
tags:
- mteb
- sparse sparsity quantized onnx embeddings int8
model-index:
- name: bge-small-en-v1.5-sparse
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 70.71641791044776
- type: ap
value: 32.850850647310004
- type: f1
value: 64.48101916414805
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 83.33962500000001
- type: ap
value: 78.28706349240106
- type: f1
value: 83.27426715603062
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.988
- type: f1
value: 40.776679545648506
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.101999999999997
- type: map_at_10
value: 40.754000000000005
- type: map_at_100
value: 41.83
- type: map_at_1000
value: 41.845
- type: map_at_3
value: 36.178
- type: map_at_5
value: 38.646
- type: mrr_at_1
value: 26.6
- type: mrr_at_10
value: 40.934
- type: mrr_at_100
value: 42.015
- type: mrr_at_1000
value: 42.03
- type: mrr_at_3
value: 36.344
- type: mrr_at_5
value: 38.848
- type: ndcg_at_1
value: 26.101999999999997
- type: ndcg_at_10
value: 49.126999999999995
- type: ndcg_at_100
value: 53.815999999999995
- type: ndcg_at_1000
value: 54.178000000000004
- type: ndcg_at_3
value: 39.607
- type: ndcg_at_5
value: 44.086999999999996
- type: precision_at_1
value: 26.101999999999997
- type: precision_at_10
value: 7.596
- type: precision_at_100
value: 0.967
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 16.524
- type: precision_at_5
value: 12.105
- type: recall_at_1
value: 26.101999999999997
- type: recall_at_10
value: 75.96000000000001
- type: recall_at_100
value: 96.65700000000001
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 49.573
- type: recall_at_5
value: 60.526
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 43.10651535441929
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 34.41095293826606
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 56.96575970919239
- type: mrr
value: 69.92503187794047
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 79.64892774481326
- type: cos_sim_spearman
value: 78.953003817029
- type: euclidean_pearson
value: 78.92456838230683
- type: euclidean_spearman
value: 78.56504316985354
- type: manhattan_pearson
value: 79.21436359014227
- type: manhattan_spearman
value: 78.66263575501259
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 81.25
- type: f1
value: 81.20841448916138
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 34.69545244587236
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 28.84301739171936
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.401
- type: map_at_10
value: 32.451
- type: map_at_100
value: 33.891
- type: map_at_1000
value: 34.01
- type: map_at_3
value: 29.365999999999996
- type: map_at_5
value: 31.240000000000002
- type: mrr_at_1
value: 29.9
- type: mrr_at_10
value: 38.590999999999994
- type: mrr_at_100
value: 39.587
- type: mrr_at_1000
value: 39.637
- type: mrr_at_3
value: 36.028
- type: mrr_at_5
value: 37.673
- type: ndcg_at_1
value: 29.9
- type: ndcg_at_10
value: 38.251000000000005
- type: ndcg_at_100
value: 44.354
- type: ndcg_at_1000
value: 46.642
- type: ndcg_at_3
value: 33.581
- type: ndcg_at_5
value: 35.96
- type: precision_at_1
value: 29.9
- type: precision_at_10
value: 7.439
- type: precision_at_100
value: 1.28
- type: precision_at_1000
value: 0.17700000000000002
- type: precision_at_3
value: 16.404
- type: precision_at_5
value: 12.046
- type: recall_at_1
value: 23.401
- type: recall_at_10
value: 49.305
- type: recall_at_100
value: 75.885
- type: recall_at_1000
value: 90.885
- type: recall_at_3
value: 35.341
- type: recall_at_5
value: 42.275
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.103
- type: map_at_10
value: 29.271
- type: map_at_100
value: 30.151
- type: map_at_1000
value: 30.276999999999997
- type: map_at_3
value: 27.289
- type: map_at_5
value: 28.236
- type: mrr_at_1
value: 26.943
- type: mrr_at_10
value: 33.782000000000004
- type: mrr_at_100
value: 34.459
- type: mrr_at_1000
value: 34.525
- type: mrr_at_3
value: 31.985000000000003
- type: mrr_at_5
value: 32.909
- type: ndcg_at_1
value: 26.943
- type: ndcg_at_10
value: 33.616
- type: ndcg_at_100
value: 37.669000000000004
- type: ndcg_at_1000
value: 40.247
- type: ndcg_at_3
value: 30.482
- type: ndcg_at_5
value: 31.615
- type: precision_at_1
value: 26.943
- type: precision_at_10
value: 6.146
- type: precision_at_100
value: 1.038
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 14.521999999999998
- type: precision_at_5
value: 10.038
- type: recall_at_1
value: 22.103
- type: recall_at_10
value: 41.754999999999995
- type: recall_at_100
value: 59.636
- type: recall_at_1000
value: 76.801
- type: recall_at_3
value: 32.285000000000004
- type: recall_at_5
value: 35.684
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.565
- type: map_at_10
value: 43.07
- type: map_at_100
value: 44.102999999999994
- type: map_at_1000
value: 44.175
- type: map_at_3
value: 40.245
- type: map_at_5
value: 41.71
- type: mrr_at_1
value: 37.429
- type: mrr_at_10
value: 46.358
- type: mrr_at_100
value: 47.146
- type: mrr_at_1000
value: 47.187
- type: mrr_at_3
value: 44.086
- type: mrr_at_5
value: 45.318000000000005
- type: ndcg_at_1
value: 37.429
- type: ndcg_at_10
value: 48.398
- type: ndcg_at_100
value: 52.90899999999999
- type: ndcg_at_1000
value: 54.478
- type: ndcg_at_3
value: 43.418
- type: ndcg_at_5
value: 45.578
- type: precision_at_1
value: 37.429
- type: precision_at_10
value: 7.856000000000001
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 19.331
- type: precision_at_5
value: 13.191
- type: recall_at_1
value: 32.565
- type: recall_at_10
value: 61.021
- type: recall_at_100
value: 81.105
- type: recall_at_1000
value: 92.251
- type: recall_at_3
value: 47.637
- type: recall_at_5
value: 52.871
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.108
- type: map_at_10
value: 24.613
- type: map_at_100
value: 25.624000000000002
- type: map_at_1000
value: 25.721
- type: map_at_3
value: 22.271
- type: map_at_5
value: 23.681
- type: mrr_at_1
value: 19.435
- type: mrr_at_10
value: 26.124000000000002
- type: mrr_at_100
value: 27.07
- type: mrr_at_1000
value: 27.145999999999997
- type: mrr_at_3
value: 23.748
- type: mrr_at_5
value: 25.239
- type: ndcg_at_1
value: 19.435
- type: ndcg_at_10
value: 28.632
- type: ndcg_at_100
value: 33.988
- type: ndcg_at_1000
value: 36.551
- type: ndcg_at_3
value: 24.035999999999998
- type: ndcg_at_5
value: 26.525
- type: precision_at_1
value: 19.435
- type: precision_at_10
value: 4.565
- type: precision_at_100
value: 0.771
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_3
value: 10.169
- type: precision_at_5
value: 7.571
- type: recall_at_1
value: 18.108
- type: recall_at_10
value: 39.533
- type: recall_at_100
value: 64.854
- type: recall_at_1000
value: 84.421
- type: recall_at_3
value: 27.500000000000004
- type: recall_at_5
value: 33.314
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.087
- type: map_at_10
value: 17.323
- type: map_at_100
value: 18.569
- type: map_at_1000
value: 18.694
- type: map_at_3
value: 15.370000000000001
- type: map_at_5
value: 16.538
- type: mrr_at_1
value: 13.557
- type: mrr_at_10
value: 21.041
- type: mrr_at_100
value: 22.134
- type: mrr_at_1000
value: 22.207
- type: mrr_at_3
value: 18.843
- type: mrr_at_5
value: 20.236
- type: ndcg_at_1
value: 13.557
- type: ndcg_at_10
value: 21.571
- type: ndcg_at_100
value: 27.678000000000004
- type: ndcg_at_1000
value: 30.8
- type: ndcg_at_3
value: 17.922
- type: ndcg_at_5
value: 19.826
- type: precision_at_1
value: 13.557
- type: precision_at_10
value: 4.1290000000000004
- type: precision_at_100
value: 0.8370000000000001
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 8.914
- type: precision_at_5
value: 6.691999999999999
- type: recall_at_1
value: 11.087
- type: recall_at_10
value: 30.94
- type: recall_at_100
value: 57.833999999999996
- type: recall_at_1000
value: 80.365
- type: recall_at_3
value: 20.854
- type: recall_at_5
value: 25.695
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.708
- type: map_at_10
value: 30.422
- type: map_at_100
value: 31.713
- type: map_at_1000
value: 31.842
- type: map_at_3
value: 27.424
- type: map_at_5
value: 29.17
- type: mrr_at_1
value: 26.756
- type: mrr_at_10
value: 35.304
- type: mrr_at_100
value: 36.296
- type: mrr_at_1000
value: 36.359
- type: mrr_at_3
value: 32.692
- type: mrr_at_5
value: 34.288999999999994
- type: ndcg_at_1
value: 26.756
- type: ndcg_at_10
value: 35.876000000000005
- type: ndcg_at_100
value: 41.708
- type: ndcg_at_1000
value: 44.359
- type: ndcg_at_3
value: 30.946
- type: ndcg_at_5
value: 33.404
- type: precision_at_1
value: 26.756
- type: precision_at_10
value: 6.795
- type: precision_at_100
value: 1.138
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 15.046999999999999
- type: precision_at_5
value: 10.972
- type: recall_at_1
value: 21.708
- type: recall_at_10
value: 47.315000000000005
- type: recall_at_100
value: 72.313
- type: recall_at_1000
value: 90.199
- type: recall_at_3
value: 33.528999999999996
- type: recall_at_5
value: 39.985
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.902
- type: map_at_10
value: 26.166
- type: map_at_100
value: 27.368
- type: map_at_1000
value: 27.493000000000002
- type: map_at_3
value: 23.505000000000003
- type: map_at_5
value: 25.019000000000002
- type: mrr_at_1
value: 23.402
- type: mrr_at_10
value: 30.787
- type: mrr_at_100
value: 31.735000000000003
- type: mrr_at_1000
value: 31.806
- type: mrr_at_3
value: 28.33
- type: mrr_at_5
value: 29.711
- type: ndcg_at_1
value: 23.402
- type: ndcg_at_10
value: 30.971
- type: ndcg_at_100
value: 36.61
- type: ndcg_at_1000
value: 39.507999999999996
- type: ndcg_at_3
value: 26.352999999999998
- type: ndcg_at_5
value: 28.488000000000003
- type: precision_at_1
value: 23.402
- type: precision_at_10
value: 5.799
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 12.633
- type: precision_at_5
value: 9.269
- type: recall_at_1
value: 18.902
- type: recall_at_10
value: 40.929
- type: recall_at_100
value: 65.594
- type: recall_at_1000
value: 85.961
- type: recall_at_3
value: 28.121000000000002
- type: recall_at_5
value: 33.638
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.168
- type: map_at_10
value: 25.142999999999997
- type: map_at_100
value: 25.993
- type: map_at_1000
value: 26.076
- type: map_at_3
value: 23.179
- type: map_at_5
value: 24.322
- type: mrr_at_1
value: 21.933
- type: mrr_at_10
value: 27.72
- type: mrr_at_100
value: 28.518
- type: mrr_at_1000
value: 28.582
- type: mrr_at_3
value: 25.791999999999998
- type: mrr_at_5
value: 26.958
- type: ndcg_at_1
value: 21.933
- type: ndcg_at_10
value: 28.866999999999997
- type: ndcg_at_100
value: 33.285
- type: ndcg_at_1000
value: 35.591
- type: ndcg_at_3
value: 25.202999999999996
- type: ndcg_at_5
value: 27.045
- type: precision_at_1
value: 21.933
- type: precision_at_10
value: 4.632
- type: precision_at_100
value: 0.733
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 10.992
- type: precision_at_5
value: 7.853000000000001
- type: recall_at_1
value: 19.168
- type: recall_at_10
value: 37.899
- type: recall_at_100
value: 58.54899999999999
- type: recall_at_1000
value: 75.666
- type: recall_at_3
value: 27.831
- type: recall_at_5
value: 32.336
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.764000000000001
- type: map_at_10
value: 17.757
- type: map_at_100
value: 18.677
- type: map_at_1000
value: 18.813
- type: map_at_3
value: 16.151
- type: map_at_5
value: 16.946
- type: mrr_at_1
value: 15.726
- type: mrr_at_10
value: 21.019
- type: mrr_at_100
value: 21.856
- type: mrr_at_1000
value: 21.954
- type: mrr_at_3
value: 19.282
- type: mrr_at_5
value: 20.189
- type: ndcg_at_1
value: 15.726
- type: ndcg_at_10
value: 21.259
- type: ndcg_at_100
value: 25.868999999999996
- type: ndcg_at_1000
value: 29.425
- type: ndcg_at_3
value: 18.204
- type: ndcg_at_5
value: 19.434
- type: precision_at_1
value: 15.726
- type: precision_at_10
value: 3.8920000000000003
- type: precision_at_100
value: 0.741
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 8.58
- type: precision_at_5
value: 6.132
- type: recall_at_1
value: 12.764000000000001
- type: recall_at_10
value: 28.639
- type: recall_at_100
value: 49.639
- type: recall_at_1000
value: 75.725
- type: recall_at_3
value: 19.883
- type: recall_at_5
value: 23.141000000000002
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.98
- type: map_at_10
value: 25.2
- type: map_at_100
value: 26.279000000000003
- type: map_at_1000
value: 26.399
- type: map_at_3
value: 23.399
- type: map_at_5
value: 24.284
- type: mrr_at_1
value: 22.015
- type: mrr_at_10
value: 28.555000000000003
- type: mrr_at_100
value: 29.497
- type: mrr_at_1000
value: 29.574
- type: mrr_at_3
value: 26.788
- type: mrr_at_5
value: 27.576
- type: ndcg_at_1
value: 22.015
- type: ndcg_at_10
value: 29.266
- type: ndcg_at_100
value: 34.721000000000004
- type: ndcg_at_1000
value: 37.659
- type: ndcg_at_3
value: 25.741000000000003
- type: ndcg_at_5
value: 27.044
- type: precision_at_1
value: 22.015
- type: precision_at_10
value: 4.897
- type: precision_at_100
value: 0.8540000000000001
- type: precision_at_1000
value: 0.122
- type: precision_at_3
value: 11.567
- type: precision_at_5
value: 7.9479999999999995
- type: recall_at_1
value: 18.98
- type: recall_at_10
value: 38.411
- type: recall_at_100
value: 63.164
- type: recall_at_1000
value: 84.292
- type: recall_at_3
value: 28.576
- type: recall_at_5
value: 31.789
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.372
- type: map_at_10
value: 27.161
- type: map_at_100
value: 28.364
- type: map_at_1000
value: 28.554000000000002
- type: map_at_3
value: 25.135
- type: map_at_5
value: 26.200000000000003
- type: mrr_at_1
value: 24.704
- type: mrr_at_10
value: 31.219
- type: mrr_at_100
value: 32.092
- type: mrr_at_1000
value: 32.181
- type: mrr_at_3
value: 29.282000000000004
- type: mrr_at_5
value: 30.359
- type: ndcg_at_1
value: 24.704
- type: ndcg_at_10
value: 31.622
- type: ndcg_at_100
value: 36.917
- type: ndcg_at_1000
value: 40.357
- type: ndcg_at_3
value: 28.398
- type: ndcg_at_5
value: 29.764000000000003
- type: precision_at_1
value: 24.704
- type: precision_at_10
value: 5.81
- type: precision_at_100
value: 1.208
- type: precision_at_1000
value: 0.209
- type: precision_at_3
value: 13.241
- type: precision_at_5
value: 9.407
- type: recall_at_1
value: 20.372
- type: recall_at_10
value: 40.053
- type: recall_at_100
value: 64.71000000000001
- type: recall_at_1000
value: 87.607
- type: recall_at_3
value: 29.961
- type: recall_at_5
value: 34.058
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.424000000000001
- type: map_at_10
value: 20.541999999999998
- type: map_at_100
value: 21.495
- type: map_at_1000
value: 21.604
- type: map_at_3
value: 18.608
- type: map_at_5
value: 19.783
- type: mrr_at_1
value: 15.895999999999999
- type: mrr_at_10
value: 22.484
- type: mrr_at_100
value: 23.376
- type: mrr_at_1000
value: 23.467
- type: mrr_at_3
value: 20.548
- type: mrr_at_5
value: 21.731
- type: ndcg_at_1
value: 15.895999999999999
- type: ndcg_at_10
value: 24.343
- type: ndcg_at_100
value: 29.181
- type: ndcg_at_1000
value: 32.330999999999996
- type: ndcg_at_3
value: 20.518
- type: ndcg_at_5
value: 22.561999999999998
- type: precision_at_1
value: 15.895999999999999
- type: precision_at_10
value: 3.9739999999999998
- type: precision_at_100
value: 0.6799999999999999
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 9.057
- type: precision_at_5
value: 6.654
- type: recall_at_1
value: 14.424000000000001
- type: recall_at_10
value: 34.079
- type: recall_at_100
value: 56.728
- type: recall_at_1000
value: 80.765
- type: recall_at_3
value: 23.993000000000002
- type: recall_at_5
value: 28.838
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 41.665
- type: f1
value: 37.601137843331244
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 74.8052
- type: ap
value: 68.92588517572685
- type: f1
value: 74.66801685854456
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.2220702234382
- type: f1
value: 90.81687856852439
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 69.39124487004105
- type: f1
value: 51.8350043424968
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.80497646267652
- type: f1
value: 67.34213899244814
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.54270342972428
- type: f1
value: 74.02802500235784
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.488580544269002
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.80426879476371
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.37970068676043
- type: mrr
value: 32.48523694064166
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 42.862710845031565
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 54.270000736385626
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 80.89215288990194
- type: cos_sim_spearman
value: 74.386413188675
- type: euclidean_pearson
value: 78.83679563989534
- type: euclidean_spearman
value: 74.29328198771996
- type: manhattan_pearson
value: 78.77968796707641
- type: manhattan_spearman
value: 74.20887429784696
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 78.31858821914498
- type: cos_sim_spearman
value: 72.2217008523832
- type: euclidean_pearson
value: 75.38901061978429
- type: euclidean_spearman
value: 71.81255767675184
- type: manhattan_pearson
value: 75.49472202181288
- type: manhattan_spearman
value: 71.96322588726144
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 79.48334648997455
- type: cos_sim_spearman
value: 80.99654029572798
- type: euclidean_pearson
value: 80.46546523970035
- type: euclidean_spearman
value: 80.90646216980744
- type: manhattan_pearson
value: 80.35474057857608
- type: manhattan_spearman
value: 80.8141299909659
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 79.73826970784727
- type: cos_sim_spearman
value: 76.9926870133034
- type: euclidean_pearson
value: 79.6386542120984
- type: euclidean_spearman
value: 77.05041986942253
- type: manhattan_pearson
value: 79.61799508502459
- type: manhattan_spearman
value: 77.07169617647067
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 83.93999019426069
- type: cos_sim_spearman
value: 85.21166521594695
- type: euclidean_pearson
value: 84.97207676326357
- type: euclidean_spearman
value: 85.40726578482739
- type: manhattan_pearson
value: 85.0386693192183
- type: manhattan_spearman
value: 85.49230945586409
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 80.8133974034008
- type: cos_sim_spearman
value: 82.82919022688844
- type: euclidean_pearson
value: 81.92587923760179
- type: euclidean_spearman
value: 82.86629450518863
- type: manhattan_pearson
value: 81.98232365999253
- type: manhattan_spearman
value: 82.94313939920296
- 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: 86.12872422642363
- type: cos_sim_spearman
value: 87.77672179979807
- type: euclidean_pearson
value: 87.76172961705947
- type: euclidean_spearman
value: 87.9891393339215
- type: manhattan_pearson
value: 87.78863663568221
- type: manhattan_spearman
value: 88.08297053203866
- 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: 58.82824030232733
- type: cos_sim_spearman
value: 64.17079382633538
- type: euclidean_pearson
value: 61.31505225602925
- type: euclidean_spearman
value: 64.05080034530694
- type: manhattan_pearson
value: 61.77095758943306
- type: manhattan_spearman
value: 64.14475973774933
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 81.39239803497064
- type: cos_sim_spearman
value: 81.76637354520439
- type: euclidean_pearson
value: 82.98008209033587
- type: euclidean_spearman
value: 82.18662536188657
- type: manhattan_pearson
value: 82.9630328314908
- type: manhattan_spearman
value: 82.13726553603003
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.45753132898741
- type: mrr
value: 93.84029822755313
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8019801980198
- type: cos_sim_ap
value: 94.58629018512772
- type: cos_sim_f1
value: 89.84771573604061
- type: cos_sim_precision
value: 91.23711340206185
- type: cos_sim_recall
value: 88.5
- type: dot_accuracy
value: 99.74950495049505
- type: dot_ap
value: 92.5761214576951
- type: dot_f1
value: 87.09841917389087
- type: dot_precision
value: 88.86576482830385
- type: dot_recall
value: 85.39999999999999
- type: euclidean_accuracy
value: 99.80495049504951
- type: euclidean_ap
value: 94.56231673602272
- type: euclidean_f1
value: 90.02531645569621
- type: euclidean_precision
value: 91.17948717948718
- type: euclidean_recall
value: 88.9
- type: manhattan_accuracy
value: 99.8009900990099
- type: manhattan_ap
value: 94.5775591647447
- type: manhattan_f1
value: 89.86384266263238
- type: manhattan_precision
value: 90.64089521871821
- type: manhattan_recall
value: 89.1
- type: max_accuracy
value: 99.80495049504951
- type: max_ap
value: 94.58629018512772
- type: max_f1
value: 90.02531645569621
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 53.088941385715735
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.146129414825744
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 48.7511362739003
- type: mrr
value: 49.61682210763093
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 67.43820000000001
- type: ap
value: 12.899489312331003
- type: f1
value: 52.03468121072981
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 57.475947934352
- type: f1
value: 57.77676730676238
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 38.3463456299738
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 83.94230196101806
- type: cos_sim_ap
value: 67.00916556336148
- type: cos_sim_f1
value: 63.046014257939085
- type: cos_sim_precision
value: 61.961783439490446
- type: cos_sim_recall
value: 64.16886543535621
- type: dot_accuracy
value: 83.18531322644095
- type: dot_ap
value: 63.112896030267066
- type: dot_f1
value: 59.06565656565657
- type: dot_precision
value: 56.63438256658596
- type: dot_recall
value: 61.715039577836414
- type: euclidean_accuracy
value: 83.94230196101806
- type: euclidean_ap
value: 67.19856676674463
- type: euclidean_f1
value: 63.08428413691571
- type: euclidean_precision
value: 58.9543682641596
- type: euclidean_recall
value: 67.83641160949868
- type: manhattan_accuracy
value: 83.91845979614949
- type: manhattan_ap
value: 66.9845327263072
- type: manhattan_f1
value: 62.693323274236135
- type: manhattan_precision
value: 59.884698534710544
- type: manhattan_recall
value: 65.77836411609499
- type: max_accuracy
value: 83.94230196101806
- type: max_ap
value: 67.19856676674463
- type: max_f1
value: 63.08428413691571
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.0777738968448
- type: cos_sim_ap
value: 84.19747786536
- type: cos_sim_f1
value: 75.91830995817077
- type: cos_sim_precision
value: 69.84671107949033
- type: cos_sim_recall
value: 83.14598090545118
- type: dot_accuracy
value: 87.14246904955951
- type: dot_ap
value: 82.37528804640529
- type: dot_f1
value: 74.40963166732163
- type: dot_precision
value: 69.4127841098447
- type: dot_recall
value: 80.18170619032954
- type: euclidean_accuracy
value: 88.08359529630924
- type: euclidean_ap
value: 84.22633217661986
- type: euclidean_f1
value: 76.09190339866403
- type: euclidean_precision
value: 72.70304390517605
- type: euclidean_recall
value: 79.81213427779488
- type: manhattan_accuracy
value: 88.08359529630924
- type: manhattan_ap
value: 84.18362004611083
- type: manhattan_f1
value: 76.08789625360231
- type: manhattan_precision
value: 71.49336582724072
- type: manhattan_recall
value: 81.3135201724669
- type: max_accuracy
value: 88.08359529630924
- type: max_ap
value: 84.22633217661986
- type: max_f1
value: 76.09190339866403
license: mit
language:
- en
---
# bge-small-en-v1.5-sparse
## Usage
This is the sparse ONNX variant of the [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) embeddings model accelerated with [Sparsify](https://github.com/neuralmagic/sparsify) for quantization/pruning and [DeepSparseSentenceTransformers](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers) for inference.
```bash
pip install -U deepsparse-nightly[sentence_transformers]
```
```python
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
model = DeepSparseSentenceTransformer('neuralmagic/bge-small-en-v1.5-sparse', export=False)
# Our sentences we like to encode
sentences = ['This framework generates embeddings for each input sentence',
'Sentences are passed as a list of string.',
'The quick brown fox jumps over the lazy dog.']
# Sentences are encoded by calling model.encode()
embeddings = model.encode(sentences)
# Print the embeddings
for sentence, embedding in zip(sentences, embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding.shape)
print("")
```
For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). |