transformers onnx bert feature-extraction mteb sparse sparsity quantized onnx embeddings int8 en license:mit model-index text-embeddings-inference endpoints_compatible region:us
---
language:
- en
license: mit
tags:
- mteb
- sparse sparsity quantized onnx embeddings int8
model-index:
- name: bge-small-en-v1.5-sparse
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      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:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      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:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 40.988
    - type: f1
      value: 40.776679545648506
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: 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:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 43.10651535441929
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 34.41095293826606
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 56.96575970919239
    - type: mrr
      value: 69.92503187794047
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      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:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 81.25
    - type: f1
      value: 81.20841448916138
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 34.69545244587236
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 28.84301739171936
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 41.665
    - type: f1
      value: 37.601137843331244
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      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:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 91.2220702234382
    - type: f1
      value: 90.81687856852439
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 69.39124487004105
    - type: f1
      value: 51.8350043424968
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 69.80497646267652
    - type: f1
      value: 67.34213899244814
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 74.54270342972428
    - type: f1
      value: 74.02802500235784
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 30.488580544269002
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 28.80426879476371
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 31.37970068676043
    - type: mrr
      value: 32.48523694064166
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 42.862710845031565
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 54.270000736385626
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      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:
      name: MTEB STS12
      type: mteb/sts12-sts
      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:
      name: MTEB STS13
      type: mteb/sts13-sts
      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:
      name: MTEB STS14
      type: mteb/sts14-sts
      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:
      name: MTEB STS15
      type: mteb/sts15-sts
      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:
      name: MTEB STS16
      type: mteb/sts16-sts
      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:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      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:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      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:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      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:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 79.45753132898741
    - type: mrr
      value: 93.84029822755313
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      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:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 53.088941385715735
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 33.146129414825744
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 48.7511362739003
    - type: mrr
      value: 49.61682210763093
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      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:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 57.475947934352
    - type: f1
      value: 57.77676730676238
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 38.3463456299738
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      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:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      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
---

# 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).