metadata
tags:
- mteb
- sparse
- sparsity
- quantized
- onnx
- embeddings
- int8
- deepsparse
model-index:
- name: bge-small-en-v1.5-quant
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.19402985074626
- type: ap
value: 37.562368912364036
- type: f1
value: 68.47046663470138
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.89432499999998
- type: ap
value: 88.64572979375352
- type: f1
value: 91.87171177424113
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.71799999999999
- type: f1
value: 46.25791412217894
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 34.424
- type: map_at_10
value: 49.63
- type: map_at_100
value: 50.477000000000004
- type: map_at_1000
value: 50.483
- type: map_at_3
value: 45.389
- type: map_at_5
value: 47.888999999999996
- type: mrr_at_1
value: 34.78
- type: mrr_at_10
value: 49.793
- type: mrr_at_100
value: 50.632999999999996
- type: mrr_at_1000
value: 50.638000000000005
- type: mrr_at_3
value: 45.531
- type: mrr_at_5
value: 48.010000000000005
- type: ndcg_at_1
value: 34.424
- type: ndcg_at_10
value: 57.774
- type: ndcg_at_100
value: 61.248000000000005
- type: ndcg_at_1000
value: 61.378
- type: ndcg_at_3
value: 49.067
- type: ndcg_at_5
value: 53.561
- type: precision_at_1
value: 34.424
- type: precision_at_10
value: 8.364
- type: precision_at_100
value: 0.985
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.915
- type: precision_at_5
value: 14.124999999999998
- type: recall_at_1
value: 34.424
- type: recall_at_10
value: 83.64200000000001
- type: recall_at_100
value: 98.506
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 59.744
- type: recall_at_5
value: 70.626
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.91874634333147
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 39.1201020016146
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.40334669601722
- type: mrr
value: 75.33175042870333
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.00433892980047
- type: cos_sim_spearman
value: 86.65558896421105
- type: euclidean_pearson
value: 85.98927300398377
- type: euclidean_spearman
value: 86.0905158476729
- type: manhattan_pearson
value: 86.0272425017433
- type: manhattan_spearman
value: 85.8929209838941
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.1038961038961
- type: f1
value: 85.06851570045757
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.42637694389153
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 33.89440321125906
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.111000000000004
- type: map_at_10
value: 39.067
- type: map_at_100
value: 40.519
- type: map_at_1000
value: 40.652
- type: map_at_3
value: 35.571999999999996
- type: map_at_5
value: 37.708999999999996
- type: mrr_at_1
value: 34.335
- type: mrr_at_10
value: 44.868
- type: mrr_at_100
value: 45.607
- type: mrr_at_1000
value: 45.655
- type: mrr_at_3
value: 41.798
- type: mrr_at_5
value: 43.786
- type: ndcg_at_1
value: 34.335
- type: ndcg_at_10
value: 45.513
- type: ndcg_at_100
value: 51.037
- type: ndcg_at_1000
value: 53.171
- type: ndcg_at_3
value: 40.131
- type: ndcg_at_5
value: 43.027
- type: precision_at_1
value: 34.335
- type: precision_at_10
value: 8.784
- type: precision_at_100
value: 1.4460000000000002
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 19.361
- type: precision_at_5
value: 14.249
- type: recall_at_1
value: 28.111000000000004
- type: recall_at_10
value: 58.372
- type: recall_at_100
value: 81.631
- type: recall_at_1000
value: 95.192
- type: recall_at_3
value: 42.863
- type: recall_at_5
value: 50.924
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.437
- type: map_at_10
value: 37.942
- type: map_at_100
value: 39.108
- type: map_at_1000
value: 39.242
- type: map_at_3
value: 35.419
- type: map_at_5
value: 36.825
- type: mrr_at_1
value: 35.35
- type: mrr_at_10
value: 43.855
- type: mrr_at_100
value: 44.543
- type: mrr_at_1000
value: 44.588
- type: mrr_at_3
value: 41.826
- type: mrr_at_5
value: 42.937
- type: ndcg_at_1
value: 35.35
- type: ndcg_at_10
value: 43.32
- type: ndcg_at_100
value: 47.769
- type: ndcg_at_1000
value: 49.979
- type: ndcg_at_3
value: 39.709
- type: ndcg_at_5
value: 41.316
- type: precision_at_1
value: 35.35
- type: precision_at_10
value: 7.994
- type: precision_at_100
value: 1.323
- type: precision_at_1000
value: 0.182
- type: precision_at_3
value: 18.96
- type: precision_at_5
value: 13.236
- type: recall_at_1
value: 28.437
- type: recall_at_10
value: 52.531000000000006
- type: recall_at_100
value: 71.79299999999999
- type: recall_at_1000
value: 85.675
- type: recall_at_3
value: 41.605
- type: recall_at_5
value: 46.32
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.364999999999995
- type: map_at_10
value: 49.324
- type: map_at_100
value: 50.458999999999996
- type: map_at_1000
value: 50.512
- type: map_at_3
value: 45.96
- type: map_at_5
value: 47.934
- type: mrr_at_1
value: 43.009
- type: mrr_at_10
value: 52.946000000000005
- type: mrr_at_100
value: 53.74100000000001
- type: mrr_at_1000
value: 53.76800000000001
- type: mrr_at_3
value: 50.554
- type: mrr_at_5
value: 51.964
- type: ndcg_at_1
value: 43.009
- type: ndcg_at_10
value: 55.143
- type: ndcg_at_100
value: 59.653999999999996
- type: ndcg_at_1000
value: 60.805
- type: ndcg_at_3
value: 49.605
- type: ndcg_at_5
value: 52.437
- type: precision_at_1
value: 43.009
- type: precision_at_10
value: 8.984
- type: precision_at_100
value: 1.209
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.09
- type: precision_at_5
value: 15.423
- type: recall_at_1
value: 37.364999999999995
- type: recall_at_10
value: 68.657
- type: recall_at_100
value: 88.155
- type: recall_at_1000
value: 96.48400000000001
- type: recall_at_3
value: 54.186
- type: recall_at_5
value: 60.848
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.827
- type: map_at_10
value: 31.721
- type: map_at_100
value: 32.812999999999995
- type: map_at_1000
value: 32.89
- type: map_at_3
value: 29.238999999999997
- type: map_at_5
value: 30.584
- type: mrr_at_1
value: 25.650000000000002
- type: mrr_at_10
value: 33.642
- type: mrr_at_100
value: 34.595
- type: mrr_at_1000
value: 34.650999999999996
- type: mrr_at_3
value: 31.205
- type: mrr_at_5
value: 32.499
- type: ndcg_at_1
value: 25.650000000000002
- type: ndcg_at_10
value: 36.366
- type: ndcg_at_100
value: 41.766
- type: ndcg_at_1000
value: 43.735
- type: ndcg_at_3
value: 31.447000000000003
- type: ndcg_at_5
value: 33.701
- type: precision_at_1
value: 25.650000000000002
- type: precision_at_10
value: 5.582
- type: precision_at_100
value: 0.872
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 13.107
- type: precision_at_5
value: 9.198
- type: recall_at_1
value: 23.827
- type: recall_at_10
value: 48.9
- type: recall_at_100
value: 73.917
- type: recall_at_1000
value: 88.787
- type: recall_at_3
value: 35.498000000000005
- type: recall_at_5
value: 40.929
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.47
- type: map_at_10
value: 22.679
- type: map_at_100
value: 23.823
- type: map_at_1000
value: 23.94
- type: map_at_3
value: 20.535999999999998
- type: map_at_5
value: 21.61
- type: mrr_at_1
value: 18.781
- type: mrr_at_10
value: 26.979
- type: mrr_at_100
value: 27.945999999999998
- type: mrr_at_1000
value: 28.016000000000002
- type: mrr_at_3
value: 24.648
- type: mrr_at_5
value: 25.947
- type: ndcg_at_1
value: 18.781
- type: ndcg_at_10
value: 27.55
- type: ndcg_at_100
value: 33.176
- type: ndcg_at_1000
value: 36.150999999999996
- type: ndcg_at_3
value: 23.456
- type: ndcg_at_5
value: 25.16
- type: precision_at_1
value: 18.781
- type: precision_at_10
value: 5.050000000000001
- type: precision_at_100
value: 0.9039999999999999
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 11.235000000000001
- type: precision_at_5
value: 8.01
- type: recall_at_1
value: 15.47
- type: recall_at_10
value: 38.446000000000005
- type: recall_at_100
value: 63.199000000000005
- type: recall_at_1000
value: 84.719
- type: recall_at_3
value: 26.687
- type: recall_at_5
value: 31.196
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.285999999999998
- type: map_at_10
value: 35.701
- type: map_at_100
value: 37.062
- type: map_at_1000
value: 37.175999999999995
- type: map_at_3
value: 32.65
- type: map_at_5
value: 34.129
- type: mrr_at_1
value: 32.05
- type: mrr_at_10
value: 41.105000000000004
- type: mrr_at_100
value: 41.996
- type: mrr_at_1000
value: 42.047000000000004
- type: mrr_at_3
value: 38.466
- type: mrr_at_5
value: 39.766
- type: ndcg_at_1
value: 32.05
- type: ndcg_at_10
value: 41.516999999999996
- type: ndcg_at_100
value: 47.083999999999996
- type: ndcg_at_1000
value: 49.309
- type: ndcg_at_3
value: 36.254999999999995
- type: ndcg_at_5
value: 38.346999999999994
- type: precision_at_1
value: 32.05
- type: precision_at_10
value: 7.536
- type: precision_at_100
value: 1.202
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 17.004
- type: precision_at_5
value: 11.973
- type: recall_at_1
value: 26.285999999999998
- type: recall_at_10
value: 53.667
- type: recall_at_100
value: 76.97
- type: recall_at_1000
value: 91.691
- type: recall_at_3
value: 38.571
- type: recall_at_5
value: 44.131
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.595000000000002
- type: map_at_10
value: 31.352000000000004
- type: map_at_100
value: 32.652
- type: map_at_1000
value: 32.774
- type: map_at_3
value: 28.238000000000003
- type: map_at_5
value: 30.178
- type: mrr_at_1
value: 27.626
- type: mrr_at_10
value: 36.351
- type: mrr_at_100
value: 37.297000000000004
- type: mrr_at_1000
value: 37.362
- type: mrr_at_3
value: 33.885
- type: mrr_at_5
value: 35.358000000000004
- type: ndcg_at_1
value: 27.626
- type: ndcg_at_10
value: 36.795
- type: ndcg_at_100
value: 42.808
- type: ndcg_at_1000
value: 45.417
- type: ndcg_at_3
value: 31.744
- type: ndcg_at_5
value: 34.407
- type: precision_at_1
value: 27.626
- type: precision_at_10
value: 6.781
- type: precision_at_100
value: 1.159
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 15.221000000000002
- type: precision_at_5
value: 11.279
- type: recall_at_1
value: 22.595000000000002
- type: recall_at_10
value: 48.126000000000005
- type: recall_at_100
value: 74.24300000000001
- type: recall_at_1000
value: 92.276
- type: recall_at_3
value: 34.346
- type: recall_at_5
value: 41.065000000000005
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.237000000000002
- type: map_at_10
value: 28.626
- type: map_at_100
value: 29.494999999999997
- type: map_at_1000
value: 29.587999999999997
- type: map_at_3
value: 26.747
- type: map_at_5
value: 27.903
- type: mrr_at_1
value: 24.847
- type: mrr_at_10
value: 31.091
- type: mrr_at_100
value: 31.91
- type: mrr_at_1000
value: 31.977
- type: mrr_at_3
value: 29.218
- type: mrr_at_5
value: 30.391000000000002
- type: ndcg_at_1
value: 24.847
- type: ndcg_at_10
value: 32.452999999999996
- type: ndcg_at_100
value: 37.009
- type: ndcg_at_1000
value: 39.425
- type: ndcg_at_3
value: 28.848000000000003
- type: ndcg_at_5
value: 30.752000000000002
- type: precision_at_1
value: 24.847
- type: precision_at_10
value: 4.968999999999999
- type: precision_at_100
value: 0.8009999999999999
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 12.321
- type: precision_at_5
value: 8.62
- type: recall_at_1
value: 22.237000000000002
- type: recall_at_10
value: 41.942
- type: recall_at_100
value: 62.907000000000004
- type: recall_at_1000
value: 81.035
- type: recall_at_3
value: 32.05
- type: recall_at_5
value: 36.695
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.835
- type: map_at_10
value: 21.124000000000002
- type: map_at_100
value: 22.133
- type: map_at_1000
value: 22.258
- type: map_at_3
value: 19.076999999999998
- type: map_at_5
value: 20.18
- type: mrr_at_1
value: 17.791
- type: mrr_at_10
value: 24.438
- type: mrr_at_100
value: 25.332
- type: mrr_at_1000
value: 25.417
- type: mrr_at_3
value: 22.425
- type: mrr_at_5
value: 23.524
- type: ndcg_at_1
value: 17.791
- type: ndcg_at_10
value: 25.27
- type: ndcg_at_100
value: 30.362000000000002
- type: ndcg_at_1000
value: 33.494
- type: ndcg_at_3
value: 21.474
- type: ndcg_at_5
value: 23.189999999999998
- type: precision_at_1
value: 17.791
- type: precision_at_10
value: 4.58
- type: precision_at_100
value: 0.839
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 10.071
- type: precision_at_5
value: 7.337000000000001
- type: recall_at_1
value: 14.835
- type: recall_at_10
value: 34.534
- type: recall_at_100
value: 57.812
- type: recall_at_1000
value: 80.467
- type: recall_at_3
value: 23.938000000000002
- type: recall_at_5
value: 28.269
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.400000000000002
- type: map_at_10
value: 31.55
- type: map_at_100
value: 32.72
- type: map_at_1000
value: 32.830999999999996
- type: map_at_3
value: 28.942
- type: map_at_5
value: 30.403000000000002
- type: mrr_at_1
value: 27.705000000000002
- type: mrr_at_10
value: 35.778
- type: mrr_at_100
value: 36.705
- type: mrr_at_1000
value: 36.773
- type: mrr_at_3
value: 33.458
- type: mrr_at_5
value: 34.778
- type: ndcg_at_1
value: 27.705000000000002
- type: ndcg_at_10
value: 36.541000000000004
- type: ndcg_at_100
value: 42.016999999999996
- type: ndcg_at_1000
value: 44.571
- type: ndcg_at_3
value: 31.845000000000002
- type: ndcg_at_5
value: 34.056
- type: precision_at_1
value: 27.705000000000002
- type: precision_at_10
value: 6.166
- type: precision_at_100
value: 0.993
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 14.302999999999999
- type: precision_at_5
value: 10.187
- type: recall_at_1
value: 23.400000000000002
- type: recall_at_10
value: 47.61
- type: recall_at_100
value: 71.69200000000001
- type: recall_at_1000
value: 89.652
- type: recall_at_3
value: 35.026
- type: recall_at_5
value: 40.48
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.409
- type: map_at_10
value: 29.642000000000003
- type: map_at_100
value: 31.213
- type: map_at_1000
value: 31.418000000000003
- type: map_at_3
value: 26.811
- type: map_at_5
value: 28.433999999999997
- type: mrr_at_1
value: 25.494
- type: mrr_at_10
value: 33.735
- type: mrr_at_100
value: 34.791
- type: mrr_at_1000
value: 34.848
- type: mrr_at_3
value: 31.225
- type: mrr_at_5
value: 32.688
- type: ndcg_at_1
value: 25.494
- type: ndcg_at_10
value: 35.038000000000004
- type: ndcg_at_100
value: 41.499
- type: ndcg_at_1000
value: 44.183
- type: ndcg_at_3
value: 30.305
- type: ndcg_at_5
value: 32.607
- type: precision_at_1
value: 25.494
- type: precision_at_10
value: 6.739000000000001
- type: precision_at_100
value: 1.439
- type: precision_at_1000
value: 0.233
- type: precision_at_3
value: 14.163
- type: precision_at_5
value: 10.474
- type: recall_at_1
value: 21.409
- type: recall_at_10
value: 46.033
- type: recall_at_100
value: 74.932
- type: recall_at_1000
value: 92.35600000000001
- type: recall_at_3
value: 32.858
- type: recall_at_5
value: 38.675
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.145
- type: map_at_10
value: 24.712
- type: map_at_100
value: 25.813000000000002
- type: map_at_1000
value: 25.935000000000002
- type: map_at_3
value: 22.33
- type: map_at_5
value: 23.524
- type: mrr_at_1
value: 19.224
- type: mrr_at_10
value: 26.194
- type: mrr_at_100
value: 27.208
- type: mrr_at_1000
value: 27.3
- type: mrr_at_3
value: 23.906
- type: mrr_at_5
value: 24.988
- type: ndcg_at_1
value: 19.224
- type: ndcg_at_10
value: 29.015
- type: ndcg_at_100
value: 34.224
- type: ndcg_at_1000
value: 37.235
- type: ndcg_at_3
value: 24.22
- type: ndcg_at_5
value: 26.176
- type: precision_at_1
value: 19.224
- type: precision_at_10
value: 4.713
- type: precision_at_100
value: 0.787
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 10.290000000000001
- type: precision_at_5
value: 7.32
- type: recall_at_1
value: 18.145
- type: recall_at_10
value: 40.875
- type: recall_at_100
value: 64.371
- type: recall_at_1000
value: 86.67399999999999
- type: recall_at_3
value: 27.717000000000002
- type: recall_at_5
value: 32.381
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.845
- type: f1
value: 41.70045120106269
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 89.3476
- type: ap
value: 85.26891728027032
- type: f1
value: 89.33488973832894
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.67441860465115
- type: f1
value: 92.48821366022861
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.02872777017784
- type: f1
value: 57.28822860484337
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.01479488903833
- type: f1
value: 71.83716204573571
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.95897780766644
- type: f1
value: 77.80380046125542
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.897956840478948
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.71493744677591
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.279419910393734
- type: mrr
value: 32.41989483774563
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 50.49612915002382
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 60.29912718965653
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.86793477948164
- type: cos_sim_spearman
value: 79.43675709317894
- type: euclidean_pearson
value: 81.42564463337872
- type: euclidean_spearman
value: 79.39138648510273
- type: manhattan_pearson
value: 81.31167449689285
- type: manhattan_spearman
value: 79.28411420758785
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.43490408077298
- type: cos_sim_spearman
value: 76.16878340109265
- type: euclidean_pearson
value: 80.6016219080782
- type: euclidean_spearman
value: 75.67063072565917
- type: manhattan_pearson
value: 80.7238920179759
- type: manhattan_spearman
value: 75.85631683403953
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.03882477767792
- type: cos_sim_spearman
value: 84.15171505206217
- type: euclidean_pearson
value: 84.11692506470922
- type: euclidean_spearman
value: 84.78589046217311
- type: manhattan_pearson
value: 83.98651139454486
- type: manhattan_spearman
value: 84.64928563751276
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.11158600428418
- type: cos_sim_spearman
value: 81.48561519933875
- type: euclidean_pearson
value: 83.21025907155807
- type: euclidean_spearman
value: 81.68699235487654
- type: manhattan_pearson
value: 83.16704771658094
- type: manhattan_spearman
value: 81.7133110412898
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.1514510686502
- type: cos_sim_spearman
value: 88.11449450494452
- type: euclidean_pearson
value: 87.75854949349939
- type: euclidean_spearman
value: 88.4055148221637
- type: manhattan_pearson
value: 87.71487828059706
- type: manhattan_spearman
value: 88.35301381116254
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.36838640113687
- type: cos_sim_spearman
value: 84.98776974283366
- type: euclidean_pearson
value: 84.0617526427129
- type: euclidean_spearman
value: 85.04234805662242
- type: manhattan_pearson
value: 83.87433162971784
- type: manhattan_spearman
value: 84.87174280390242
- 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: 87.72465270691285
- type: cos_sim_spearman
value: 87.97672332532184
- type: euclidean_pearson
value: 88.78764701492182
- type: euclidean_spearman
value: 88.3509718074474
- type: manhattan_pearson
value: 88.73024739256215
- type: manhattan_spearman
value: 88.24149566970154
- 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: 64.65195562203238
- type: cos_sim_spearman
value: 65.0726777678982
- type: euclidean_pearson
value: 65.84698245675273
- type: euclidean_spearman
value: 65.13121502162804
- type: manhattan_pearson
value: 65.96149904857049
- type: manhattan_spearman
value: 65.39983948112955
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.2642818050049
- type: cos_sim_spearman
value: 86.30633382439257
- type: euclidean_pearson
value: 86.46510435905633
- type: euclidean_spearman
value: 86.62650496446
- type: manhattan_pearson
value: 86.2546330637872
- type: manhattan_spearman
value: 86.46309860938591
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.009977767778
- type: mrr
value: 95.59795143128476
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.84257425742574
- type: cos_sim_ap
value: 96.25445889914926
- type: cos_sim_f1
value: 92.03805708562844
- type: cos_sim_precision
value: 92.1765295887663
- type: cos_sim_recall
value: 91.9
- type: dot_accuracy
value: 99.83069306930693
- type: dot_ap
value: 96.00517778550396
- type: dot_f1
value: 91.27995920448751
- type: dot_precision
value: 93.1321540062435
- type: dot_recall
value: 89.5
- type: euclidean_accuracy
value: 99.84455445544555
- type: euclidean_ap
value: 96.14761524546034
- type: euclidean_f1
value: 91.97751660705163
- type: euclidean_precision
value: 94.04388714733543
- type: euclidean_recall
value: 90
- type: manhattan_accuracy
value: 99.84158415841584
- type: manhattan_ap
value: 96.17014673429341
- type: manhattan_f1
value: 91.93790686029043
- type: manhattan_precision
value: 92.07622868605817
- type: manhattan_recall
value: 91.8
- type: max_accuracy
value: 99.84455445544555
- type: max_ap
value: 96.25445889914926
- type: max_f1
value: 92.03805708562844
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 59.26454683321409
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.75520575713765
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.74607778008495
- type: mrr
value: 53.55101699770818
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.5008
- type: ap
value: 13.64158304183089
- type: f1
value: 53.50073331072236
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.01980758347483
- type: f1
value: 60.35679678249753
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 45.09419243325077
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.68874053764081
- type: cos_sim_ap
value: 73.26334732095694
- type: cos_sim_f1
value: 68.01558376272465
- type: cos_sim_precision
value: 64.93880489560834
- type: cos_sim_recall
value: 71.39841688654354
- type: dot_accuracy
value: 84.71121177802945
- type: dot_ap
value: 70.33606362522605
- type: dot_f1
value: 65.0887573964497
- type: dot_precision
value: 63.50401606425703
- type: dot_recall
value: 66.75461741424802
- type: euclidean_accuracy
value: 85.80795136198367
- type: euclidean_ap
value: 73.43201285001163
- type: euclidean_f1
value: 68.33166833166834
- type: euclidean_precision
value: 64.86486486486487
- type: euclidean_recall
value: 72.18997361477572
- type: manhattan_accuracy
value: 85.62317458425225
- type: manhattan_ap
value: 73.21212085536185
- type: manhattan_f1
value: 68.01681314482232
- type: manhattan_precision
value: 65.74735286875153
- type: manhattan_recall
value: 70.44854881266491
- type: max_accuracy
value: 85.80795136198367
- type: max_ap
value: 73.43201285001163
- type: max_f1
value: 68.33166833166834
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.81709162882757
- type: cos_sim_ap
value: 85.63540257309367
- type: cos_sim_f1
value: 77.9091382258904
- type: cos_sim_precision
value: 75.32710280373833
- type: cos_sim_recall
value: 80.67446874037573
- type: dot_accuracy
value: 88.04478596654636
- type: dot_ap
value: 84.16371725220706
- type: dot_f1
value: 76.45949643213666
- type: dot_precision
value: 73.54719396827655
- type: dot_recall
value: 79.61194949183862
- type: euclidean_accuracy
value: 88.9296386851399
- type: euclidean_ap
value: 85.71894615274715
- type: euclidean_f1
value: 78.12952767313823
- type: euclidean_precision
value: 73.7688098495212
- type: euclidean_recall
value: 83.03818909762857
- type: manhattan_accuracy
value: 88.89276982186519
- type: manhattan_ap
value: 85.6838514059479
- type: manhattan_f1
value: 78.06861875184856
- type: manhattan_precision
value: 75.09246088193457
- type: manhattan_recall
value: 81.29042192793348
- type: max_accuracy
value: 88.9296386851399
- type: max_ap
value: 85.71894615274715
- type: max_f1
value: 78.12952767313823
license: mit
language:
- en
bge-small-en-v1.5-quant
DeepSparse is able to improve latency performance on a 10 core laptop by 3X and up to 5X on a 16 core AWS instance.
Usage
This is the quantized (INT8) ONNX variant of the bge-small-en-v1.5 embeddings model accelerated with Sparsify for quantization and DeepSparseSentenceTransformers for inference.
pip install -U deepsparse-nightly[sentence_transformers]
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
model = DeepSparseSentenceTransformer('neuralmagic/bge-small-en-v1.5-quant', 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.