onnx bert feature-extraction sentence-similarity mteb teradata en license:mit model-index region:us
---
language:
- en
license: mit
tags:
- feature-extraction
- sentence-similarity
- mteb
- onnx
- teradata
model-index:
- name: bge-large-en-v1.5
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 75.8507462686567
    - type: ap
      value: 38.566457320228245
    - type: f1
      value: 69.69386648043475
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 92.416675
    - type: ap
      value: 89.1928861155922
    - type: f1
      value: 92.39477019574215
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 48.175999999999995
    - type: f1
      value: 47.80712792870253
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 40.184999999999995
    - type: map_at_10
      value: 55.654
    - type: map_at_100
      value: 56.25
    - type: map_at_1000
      value: 56.255
    - type: map_at_3
      value: 51.742999999999995
    - type: map_at_5
      value: 54.129000000000005
    - type: mrr_at_1
      value: 40.967
    - type: mrr_at_10
      value: 55.96
    - type: mrr_at_100
      value: 56.54900000000001
    - type: mrr_at_1000
      value: 56.554
    - type: mrr_at_3
      value: 51.980000000000004
    - type: mrr_at_5
      value: 54.44
    - type: ndcg_at_1
      value: 40.184999999999995
    - type: ndcg_at_10
      value: 63.542
    - type: ndcg_at_100
      value: 65.96499999999999
    - type: ndcg_at_1000
      value: 66.08699999999999
    - type: ndcg_at_3
      value: 55.582
    - type: ndcg_at_5
      value: 59.855000000000004
    - type: precision_at_1
      value: 40.184999999999995
    - type: precision_at_10
      value: 8.841000000000001
    - type: precision_at_100
      value: 0.987
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 22.238
    - type: precision_at_5
      value: 15.405
    - type: recall_at_1
      value: 40.184999999999995
    - type: recall_at_10
      value: 88.407
    - type: recall_at_100
      value: 98.72
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_3
      value: 66.714
    - type: recall_at_5
      value: 77.027
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 48.567077926750066
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 43.19453389182364
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 64.46555939623092
    - type: mrr
      value: 77.82361605768807
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 84.9554128814735
    - type: cos_sim_spearman
      value: 84.65373612172036
    - type: euclidean_pearson
      value: 83.2905059954138
    - type: euclidean_spearman
      value: 84.52240782811128
    - type: manhattan_pearson
      value: 82.99533802997436
    - type: manhattan_spearman
      value: 84.20673798475734
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 87.78896103896103
    - type: f1
      value: 87.77189310964883
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 39.714538337650495
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 36.90108349284447
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 32.795
    - type: map_at_10
      value: 43.669000000000004
    - type: map_at_100
      value: 45.151
    - type: map_at_1000
      value: 45.278
    - type: map_at_3
      value: 40.006
    - type: map_at_5
      value: 42.059999999999995
    - type: mrr_at_1
      value: 39.771
    - type: mrr_at_10
      value: 49.826
    - type: mrr_at_100
      value: 50.504000000000005
    - type: mrr_at_1000
      value: 50.549
    - type: mrr_at_3
      value: 47.115
    - type: mrr_at_5
      value: 48.832
    - type: ndcg_at_1
      value: 39.771
    - type: ndcg_at_10
      value: 50.217999999999996
    - type: ndcg_at_100
      value: 55.454
    - type: ndcg_at_1000
      value: 57.37
    - type: ndcg_at_3
      value: 44.885000000000005
    - type: ndcg_at_5
      value: 47.419
    - type: precision_at_1
      value: 39.771
    - type: precision_at_10
      value: 9.642000000000001
    - type: precision_at_100
      value: 1.538
    - type: precision_at_1000
      value: 0.198
    - type: precision_at_3
      value: 21.268
    - type: precision_at_5
      value: 15.536
    - type: recall_at_1
      value: 32.795
    - type: recall_at_10
      value: 62.580999999999996
    - type: recall_at_100
      value: 84.438
    - type: recall_at_1000
      value: 96.492
    - type: recall_at_3
      value: 47.071000000000005
    - type: recall_at_5
      value: 54.079
    - type: map_at_1
      value: 32.671
    - type: map_at_10
      value: 43.334
    - type: map_at_100
      value: 44.566
    - type: map_at_1000
      value: 44.702999999999996
    - type: map_at_3
      value: 40.343
    - type: map_at_5
      value: 41.983
    - type: mrr_at_1
      value: 40.764
    - type: mrr_at_10
      value: 49.382
    - type: mrr_at_100
      value: 49.988
    - type: mrr_at_1000
      value: 50.03300000000001
    - type: mrr_at_3
      value: 47.293
    - type: mrr_at_5
      value: 48.51
    - type: ndcg_at_1
      value: 40.764
    - type: ndcg_at_10
      value: 49.039
    - type: ndcg_at_100
      value: 53.259
    - type: ndcg_at_1000
      value: 55.253
    - type: ndcg_at_3
      value: 45.091
    - type: ndcg_at_5
      value: 46.839999999999996
    - type: precision_at_1
      value: 40.764
    - type: precision_at_10
      value: 9.191
    - type: precision_at_100
      value: 1.476
    - type: precision_at_1000
      value: 0.19499999999999998
    - type: precision_at_3
      value: 21.72
    - type: precision_at_5
      value: 15.299
    - type: recall_at_1
      value: 32.671
    - type: recall_at_10
      value: 58.816
    - type: recall_at_100
      value: 76.654
    - type: recall_at_1000
      value: 89.05999999999999
    - type: recall_at_3
      value: 46.743
    - type: recall_at_5
      value: 51.783
    - type: map_at_1
      value: 40.328
    - type: map_at_10
      value: 53.32599999999999
    - type: map_at_100
      value: 54.37499999999999
    - type: map_at_1000
      value: 54.429
    - type: map_at_3
      value: 49.902
    - type: map_at_5
      value: 52.002
    - type: mrr_at_1
      value: 46.332
    - type: mrr_at_10
      value: 56.858
    - type: mrr_at_100
      value: 57.522
    - type: mrr_at_1000
      value: 57.54899999999999
    - type: mrr_at_3
      value: 54.472
    - type: mrr_at_5
      value: 55.996
    - type: ndcg_at_1
      value: 46.332
    - type: ndcg_at_10
      value: 59.313
    - type: ndcg_at_100
      value: 63.266999999999996
    - type: ndcg_at_1000
      value: 64.36
    - type: ndcg_at_3
      value: 53.815000000000005
    - type: ndcg_at_5
      value: 56.814
    - type: precision_at_1
      value: 46.332
    - type: precision_at_10
      value: 9.53
    - type: precision_at_100
      value: 1.238
    - type: precision_at_1000
      value: 0.13699999999999998
    - type: precision_at_3
      value: 24.054000000000002
    - type: precision_at_5
      value: 16.589000000000002
    - type: recall_at_1
      value: 40.328
    - type: recall_at_10
      value: 73.421
    - type: recall_at_100
      value: 90.059
    - type: recall_at_1000
      value: 97.81
    - type: recall_at_3
      value: 59.009
    - type: recall_at_5
      value: 66.352
    - type: map_at_1
      value: 27.424
    - type: map_at_10
      value: 36.332
    - type: map_at_100
      value: 37.347
    - type: map_at_1000
      value: 37.422
    - type: map_at_3
      value: 33.743
    - type: map_at_5
      value: 35.176
    - type: mrr_at_1
      value: 29.153000000000002
    - type: mrr_at_10
      value: 38.233
    - type: mrr_at_100
      value: 39.109
    - type: mrr_at_1000
      value: 39.164
    - type: mrr_at_3
      value: 35.876000000000005
    - type: mrr_at_5
      value: 37.169000000000004
    - type: ndcg_at_1
      value: 29.153000000000002
    - type: ndcg_at_10
      value: 41.439
    - type: ndcg_at_100
      value: 46.42
    - type: ndcg_at_1000
      value: 48.242000000000004
    - type: ndcg_at_3
      value: 36.362
    - type: ndcg_at_5
      value: 38.743
    - type: precision_at_1
      value: 29.153000000000002
    - type: precision_at_10
      value: 6.315999999999999
    - type: precision_at_100
      value: 0.927
    - type: precision_at_1000
      value: 0.11199999999999999
    - type: precision_at_3
      value: 15.443000000000001
    - type: precision_at_5
      value: 10.644
    - type: recall_at_1
      value: 27.424
    - type: recall_at_10
      value: 55.364000000000004
    - type: recall_at_100
      value: 78.211
    - type: recall_at_1000
      value: 91.74600000000001
    - type: recall_at_3
      value: 41.379
    - type: recall_at_5
      value: 47.14
    - type: map_at_1
      value: 19.601
    - type: map_at_10
      value: 27.826
    - type: map_at_100
      value: 29.017
    - type: map_at_1000
      value: 29.137
    - type: map_at_3
      value: 25.125999999999998
    - type: map_at_5
      value: 26.765
    - type: mrr_at_1
      value: 24.005000000000003
    - type: mrr_at_10
      value: 32.716
    - type: mrr_at_100
      value: 33.631
    - type: mrr_at_1000
      value: 33.694
    - type: mrr_at_3
      value: 29.934
    - type: mrr_at_5
      value: 31.630999999999997
    - type: ndcg_at_1
      value: 24.005000000000003
    - type: ndcg_at_10
      value: 33.158
    - type: ndcg_at_100
      value: 38.739000000000004
    - type: ndcg_at_1000
      value: 41.495
    - type: ndcg_at_3
      value: 28.185
    - type: ndcg_at_5
      value: 30.796
    - type: precision_at_1
      value: 24.005000000000003
    - type: precision_at_10
      value: 5.908
    - type: precision_at_100
      value: 1.005
    - type: precision_at_1000
      value: 0.13899999999999998
    - type: precision_at_3
      value: 13.391
    - type: precision_at_5
      value: 9.876
    - type: recall_at_1
      value: 19.601
    - type: recall_at_10
      value: 44.746
    - type: recall_at_100
      value: 68.82300000000001
    - type: recall_at_1000
      value: 88.215
    - type: recall_at_3
      value: 31.239
    - type: recall_at_5
      value: 37.695
    - type: map_at_1
      value: 30.130000000000003
    - type: map_at_10
      value: 40.96
    - type: map_at_100
      value: 42.282
    - type: map_at_1000
      value: 42.392
    - type: map_at_3
      value: 37.889
    - type: map_at_5
      value: 39.661
    - type: mrr_at_1
      value: 36.958999999999996
    - type: mrr_at_10
      value: 46.835
    - type: mrr_at_100
      value: 47.644
    - type: mrr_at_1000
      value: 47.688
    - type: mrr_at_3
      value: 44.562000000000005
    - type: mrr_at_5
      value: 45.938
    - type: ndcg_at_1
      value: 36.958999999999996
    - type: ndcg_at_10
      value: 47.06
    - type: ndcg_at_100
      value: 52.345
    - type: ndcg_at_1000
      value: 54.35
    - type: ndcg_at_3
      value: 42.301
    - type: ndcg_at_5
      value: 44.635999999999996
    - type: precision_at_1
      value: 36.958999999999996
    - type: precision_at_10
      value: 8.479000000000001
    - type: precision_at_100
      value: 1.284
    - type: precision_at_1000
      value: 0.163
    - type: precision_at_3
      value: 20.244
    - type: precision_at_5
      value: 14.224999999999998
    - type: recall_at_1
      value: 30.130000000000003
    - type: recall_at_10
      value: 59.27
    - type: recall_at_100
      value: 81.195
    - type: recall_at_1000
      value: 94.21199999999999
    - type: recall_at_3
      value: 45.885
    - type: recall_at_5
      value: 52.016
    - type: map_at_1
      value: 26.169999999999998
    - type: map_at_10
      value: 36.451
    - type: map_at_100
      value: 37.791000000000004
    - type: map_at_1000
      value: 37.897
    - type: map_at_3
      value: 33.109
    - type: map_at_5
      value: 34.937000000000005
    - type: mrr_at_1
      value: 32.877
    - type: mrr_at_10
      value: 42.368
    - type: mrr_at_100
      value: 43.201
    - type: mrr_at_1000
      value: 43.259
    - type: mrr_at_3
      value: 39.763999999999996
    - type: mrr_at_5
      value: 41.260000000000005
    - type: ndcg_at_1
      value: 32.877
    - type: ndcg_at_10
      value: 42.659000000000006
    - type: ndcg_at_100
      value: 48.161
    - type: ndcg_at_1000
      value: 50.345
    - type: ndcg_at_3
      value: 37.302
    - type: ndcg_at_5
      value: 39.722
    - type: precision_at_1
      value: 32.877
    - type: precision_at_10
      value: 7.9
    - type: precision_at_100
      value: 1.236
    - type: precision_at_1000
      value: 0.158
    - type: precision_at_3
      value: 17.846
    - type: precision_at_5
      value: 12.9
    - type: recall_at_1
      value: 26.169999999999998
    - type: recall_at_10
      value: 55.35
    - type: recall_at_100
      value: 78.755
    - type: recall_at_1000
      value: 93.518
    - type: recall_at_3
      value: 40.176
    - type: recall_at_5
      value: 46.589000000000006
    - type: map_at_1
      value: 27.15516666666667
    - type: map_at_10
      value: 36.65741666666667
    - type: map_at_100
      value: 37.84991666666666
    - type: map_at_1000
      value: 37.96316666666667
    - type: map_at_3
      value: 33.74974999999999
    - type: map_at_5
      value: 35.3765
    - type: mrr_at_1
      value: 32.08233333333334
    - type: mrr_at_10
      value: 41.033833333333334
    - type: mrr_at_100
      value: 41.84524999999999
    - type: mrr_at_1000
      value: 41.89983333333333
    - type: mrr_at_3
      value: 38.62008333333333
    - type: mrr_at_5
      value: 40.03441666666666
    - type: ndcg_at_1
      value: 32.08233333333334
    - type: ndcg_at_10
      value: 42.229
    - type: ndcg_at_100
      value: 47.26716666666667
    - type: ndcg_at_1000
      value: 49.43466666666667
    - type: ndcg_at_3
      value: 37.36408333333333
    - type: ndcg_at_5
      value: 39.6715
    - type: precision_at_1
      value: 32.08233333333334
    - type: precision_at_10
      value: 7.382583333333334
    - type: precision_at_100
      value: 1.16625
    - type: precision_at_1000
      value: 0.15408333333333332
    - type: precision_at_3
      value: 17.218
    - type: precision_at_5
      value: 12.21875
    - type: recall_at_1
      value: 27.15516666666667
    - type: recall_at_10
      value: 54.36683333333333
    - type: recall_at_100
      value: 76.37183333333333
    - type: recall_at_1000
      value: 91.26183333333333
    - type: recall_at_3
      value: 40.769916666666674
    - type: recall_at_5
      value: 46.702333333333335
    - type: map_at_1
      value: 25.749
    - type: map_at_10
      value: 33.001999999999995
    - type: map_at_100
      value: 33.891
    - type: map_at_1000
      value: 33.993
    - type: map_at_3
      value: 30.703999999999997
    - type: map_at_5
      value: 31.959
    - type: mrr_at_1
      value: 28.834
    - type: mrr_at_10
      value: 35.955
    - type: mrr_at_100
      value: 36.709
    - type: mrr_at_1000
      value: 36.779
    - type: mrr_at_3
      value: 33.947
    - type: mrr_at_5
      value: 35.089
    - type: ndcg_at_1
      value: 28.834
    - type: ndcg_at_10
      value: 37.329
    - type: ndcg_at_100
      value: 41.79
    - type: ndcg_at_1000
      value: 44.169000000000004
    - type: ndcg_at_3
      value: 33.184999999999995
    - type: ndcg_at_5
      value: 35.107
    - type: precision_at_1
      value: 28.834
    - type: precision_at_10
      value: 5.7669999999999995
    - type: precision_at_100
      value: 0.876
    - type: precision_at_1000
      value: 0.11399999999999999
    - type: precision_at_3
      value: 14.213000000000001
    - type: precision_at_5
      value: 9.754999999999999
    - type: recall_at_1
      value: 25.749
    - type: recall_at_10
      value: 47.791
    - type: recall_at_100
      value: 68.255
    - type: recall_at_1000
      value: 85.749
    - type: recall_at_3
      value: 36.199
    - type: recall_at_5
      value: 41.071999999999996
    - type: map_at_1
      value: 17.777
    - type: map_at_10
      value: 25.201
    - type: map_at_100
      value: 26.423999999999996
    - type: map_at_1000
      value: 26.544
    - type: map_at_3
      value: 22.869
    - type: map_at_5
      value: 24.023
    - type: mrr_at_1
      value: 21.473
    - type: mrr_at_10
      value: 29.12
    - type: mrr_at_100
      value: 30.144
    - type: mrr_at_1000
      value: 30.215999999999998
    - type: mrr_at_3
      value: 26.933
    - type: mrr_at_5
      value: 28.051
    - type: ndcg_at_1
      value: 21.473
    - type: ndcg_at_10
      value: 30.003
    - type: ndcg_at_100
      value: 35.766
    - type: ndcg_at_1000
      value: 38.501000000000005
    - type: ndcg_at_3
      value: 25.773000000000003
    - type: ndcg_at_5
      value: 27.462999999999997
    - type: precision_at_1
      value: 21.473
    - type: precision_at_10
      value: 5.482
    - type: precision_at_100
      value: 0.975
    - type: precision_at_1000
      value: 0.13799999999999998
    - type: precision_at_3
      value: 12.205
    - type: precision_at_5
      value: 8.692
    - type: recall_at_1
      value: 17.777
    - type: recall_at_10
      value: 40.582
    - type: recall_at_100
      value: 66.305
    - type: recall_at_1000
      value: 85.636
    - type: recall_at_3
      value: 28.687
    - type: recall_at_5
      value: 33.089
    - type: map_at_1
      value: 26.677
    - type: map_at_10
      value: 36.309000000000005
    - type: map_at_100
      value: 37.403999999999996
    - type: map_at_1000
      value: 37.496
    - type: map_at_3
      value: 33.382
    - type: map_at_5
      value: 34.98
    - type: mrr_at_1
      value: 31.343
    - type: mrr_at_10
      value: 40.549
    - type: mrr_at_100
      value: 41.342
    - type: mrr_at_1000
      value: 41.397
    - type: mrr_at_3
      value: 38.029
    - type: mrr_at_5
      value: 39.451
    - type: ndcg_at_1
      value: 31.343
    - type: ndcg_at_10
      value: 42.1
    - type: ndcg_at_100
      value: 47.089999999999996
    - type: ndcg_at_1000
      value: 49.222
    - type: ndcg_at_3
      value: 36.836999999999996
    - type: ndcg_at_5
      value: 39.21
    - type: precision_at_1
      value: 31.343
    - type: precision_at_10
      value: 7.164
    - type: precision_at_100
      value: 1.0959999999999999
    - type: precision_at_1000
      value: 0.13899999999999998
    - type: precision_at_3
      value: 16.915
    - type: precision_at_5
      value: 11.940000000000001
    - type: recall_at_1
      value: 26.677
    - type: recall_at_10
      value: 55.54599999999999
    - type: recall_at_100
      value: 77.094
    - type: recall_at_1000
      value: 92.01
    - type: recall_at_3
      value: 41.191
    - type: recall_at_5
      value: 47.006
    - type: map_at_1
      value: 24.501
    - type: map_at_10
      value: 33.102
    - type: map_at_100
      value: 34.676
    - type: map_at_1000
      value: 34.888000000000005
    - type: map_at_3
      value: 29.944
    - type: map_at_5
      value: 31.613999999999997
    - type: mrr_at_1
      value: 29.447000000000003
    - type: mrr_at_10
      value: 37.996
    - type: mrr_at_100
      value: 38.946
    - type: mrr_at_1000
      value: 38.995000000000005
    - type: mrr_at_3
      value: 35.079
    - type: mrr_at_5
      value: 36.69
    - type: ndcg_at_1
      value: 29.447000000000003
    - type: ndcg_at_10
      value: 39.232
    - type: ndcg_at_100
      value: 45.247
    - type: ndcg_at_1000
      value: 47.613
    - type: ndcg_at_3
      value: 33.922999999999995
    - type: ndcg_at_5
      value: 36.284
    - type: precision_at_1
      value: 29.447000000000003
    - type: precision_at_10
      value: 7.648000000000001
    - type: precision_at_100
      value: 1.516
    - type: precision_at_1000
      value: 0.23900000000000002
    - type: precision_at_3
      value: 16.008
    - type: precision_at_5
      value: 11.779
    - type: recall_at_1
      value: 24.501
    - type: recall_at_10
      value: 51.18899999999999
    - type: recall_at_100
      value: 78.437
    - type: recall_at_1000
      value: 92.842
    - type: recall_at_3
      value: 35.808
    - type: recall_at_5
      value: 42.197
    - type: map_at_1
      value: 22.039
    - type: map_at_10
      value: 30.377
    - type: map_at_100
      value: 31.275
    - type: map_at_1000
      value: 31.379
    - type: map_at_3
      value: 27.98
    - type: map_at_5
      value: 29.358
    - type: mrr_at_1
      value: 24.03
    - type: mrr_at_10
      value: 32.568000000000005
    - type: mrr_at_100
      value: 33.403
    - type: mrr_at_1000
      value: 33.475
    - type: mrr_at_3
      value: 30.436999999999998
    - type: mrr_at_5
      value: 31.796000000000003
    - type: ndcg_at_1
      value: 24.03
    - type: ndcg_at_10
      value: 35.198
    - type: ndcg_at_100
      value: 39.668
    - type: ndcg_at_1000
      value: 42.296
    - type: ndcg_at_3
      value: 30.709999999999997
    - type: ndcg_at_5
      value: 33.024
    - type: precision_at_1
      value: 24.03
    - type: precision_at_10
      value: 5.564
    - type: precision_at_100
      value: 0.828
    - type: precision_at_1000
      value: 0.117
    - type: precision_at_3
      value: 13.309000000000001
    - type: precision_at_5
      value: 9.39
    - type: recall_at_1
      value: 22.039
    - type: recall_at_10
      value: 47.746
    - type: recall_at_100
      value: 68.23599999999999
    - type: recall_at_1000
      value: 87.852
    - type: recall_at_3
      value: 35.852000000000004
    - type: recall_at_5
      value: 41.410000000000004
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 15.692999999999998
    - type: map_at_10
      value: 26.903
    - type: map_at_100
      value: 28.987000000000002
    - type: map_at_1000
      value: 29.176999999999996
    - type: map_at_3
      value: 22.137
    - type: map_at_5
      value: 24.758
    - type: mrr_at_1
      value: 35.57
    - type: mrr_at_10
      value: 47.821999999999996
    - type: mrr_at_100
      value: 48.608000000000004
    - type: mrr_at_1000
      value: 48.638999999999996
    - type: mrr_at_3
      value: 44.452000000000005
    - type: mrr_at_5
      value: 46.546
    - type: ndcg_at_1
      value: 35.57
    - type: ndcg_at_10
      value: 36.567
    - type: ndcg_at_100
      value: 44.085
    - type: ndcg_at_1000
      value: 47.24
    - type: ndcg_at_3
      value: 29.964000000000002
    - type: ndcg_at_5
      value: 32.511
    - type: precision_at_1
      value: 35.57
    - type: precision_at_10
      value: 11.485
    - type: precision_at_100
      value: 1.9619999999999997
    - type: precision_at_1000
      value: 0.256
    - type: precision_at_3
      value: 22.237000000000002
    - type: precision_at_5
      value: 17.471999999999998
    - type: recall_at_1
      value: 15.692999999999998
    - type: recall_at_10
      value: 43.056
    - type: recall_at_100
      value: 68.628
    - type: recall_at_1000
      value: 86.075
    - type: recall_at_3
      value: 26.918999999999997
    - type: recall_at_5
      value: 34.14
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 9.53
    - type: map_at_10
      value: 20.951
    - type: map_at_100
      value: 30.136000000000003
    - type: map_at_1000
      value: 31.801000000000002
    - type: map_at_3
      value: 15.021
    - type: map_at_5
      value: 17.471999999999998
    - type: mrr_at_1
      value: 71
    - type: mrr_at_10
      value: 79.176
    - type: mrr_at_100
      value: 79.418
    - type: mrr_at_1000
      value: 79.426
    - type: mrr_at_3
      value: 78.125
    - type: mrr_at_5
      value: 78.61200000000001
    - type: ndcg_at_1
      value: 58.5
    - type: ndcg_at_10
      value: 44.106
    - type: ndcg_at_100
      value: 49.268
    - type: ndcg_at_1000
      value: 56.711999999999996
    - type: ndcg_at_3
      value: 48.934
    - type: ndcg_at_5
      value: 45.826
    - type: precision_at_1
      value: 71
    - type: precision_at_10
      value: 35
    - type: precision_at_100
      value: 11.360000000000001
    - type: precision_at_1000
      value: 2.046
    - type: precision_at_3
      value: 52.833
    - type: precision_at_5
      value: 44.15
    - type: recall_at_1
      value: 9.53
    - type: recall_at_10
      value: 26.811
    - type: recall_at_100
      value: 55.916999999999994
    - type: recall_at_1000
      value: 79.973
    - type: recall_at_3
      value: 16.413
    - type: recall_at_5
      value: 19.980999999999998
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 51.519999999999996
    - type: f1
      value: 46.36601294761231
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 74.413
    - type: map_at_10
      value: 83.414
    - type: map_at_100
      value: 83.621
    - type: map_at_1000
      value: 83.635
    - type: map_at_3
      value: 82.337
    - type: map_at_5
      value: 83.039
    - type: mrr_at_1
      value: 80.19800000000001
    - type: mrr_at_10
      value: 87.715
    - type: mrr_at_100
      value: 87.778
    - type: mrr_at_1000
      value: 87.779
    - type: mrr_at_3
      value: 87.106
    - type: mrr_at_5
      value: 87.555
    - type: ndcg_at_1
      value: 80.19800000000001
    - type: ndcg_at_10
      value: 87.182
    - type: ndcg_at_100
      value: 87.90299999999999
    - type: ndcg_at_1000
      value: 88.143
    - type: ndcg_at_3
      value: 85.60600000000001
    - type: ndcg_at_5
      value: 86.541
    - type: precision_at_1
      value: 80.19800000000001
    - type: precision_at_10
      value: 10.531
    - type: precision_at_100
      value: 1.113
    - type: precision_at_1000
      value: 0.11499999999999999
    - type: precision_at_3
      value: 32.933
    - type: precision_at_5
      value: 20.429
    - type: recall_at_1
      value: 74.413
    - type: recall_at_10
      value: 94.363
    - type: recall_at_100
      value: 97.165
    - type: recall_at_1000
      value: 98.668
    - type: recall_at_3
      value: 90.108
    - type: recall_at_5
      value: 92.52
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 22.701
    - type: map_at_10
      value: 37.122
    - type: map_at_100
      value: 39.178000000000004
    - type: map_at_1000
      value: 39.326
    - type: map_at_3
      value: 32.971000000000004
    - type: map_at_5
      value: 35.332
    - type: mrr_at_1
      value: 44.753
    - type: mrr_at_10
      value: 53.452
    - type: mrr_at_100
      value: 54.198
    - type: mrr_at_1000
      value: 54.225
    - type: mrr_at_3
      value: 50.952
    - type: mrr_at_5
      value: 52.464
    - type: ndcg_at_1
      value: 44.753
    - type: ndcg_at_10
      value: 45.021
    - type: ndcg_at_100
      value: 52.028
    - type: ndcg_at_1000
      value: 54.596000000000004
    - type: ndcg_at_3
      value: 41.622
    - type: ndcg_at_5
      value: 42.736000000000004
    - type: precision_at_1
      value: 44.753
    - type: precision_at_10
      value: 12.284
    - type: precision_at_100
      value: 1.955
    - type: precision_at_1000
      value: 0.243
    - type: precision_at_3
      value: 27.828999999999997
    - type: precision_at_5
      value: 20.061999999999998
    - type: recall_at_1
      value: 22.701
    - type: recall_at_10
      value: 51.432
    - type: recall_at_100
      value: 77.009
    - type: recall_at_1000
      value: 92.511
    - type: recall_at_3
      value: 37.919000000000004
    - type: recall_at_5
      value: 44.131
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 40.189
    - type: map_at_10
      value: 66.24600000000001
    - type: map_at_100
      value: 67.098
    - type: map_at_1000
      value: 67.149
    - type: map_at_3
      value: 62.684
    - type: map_at_5
      value: 64.974
    - type: mrr_at_1
      value: 80.378
    - type: mrr_at_10
      value: 86.127
    - type: mrr_at_100
      value: 86.29299999999999
    - type: mrr_at_1000
      value: 86.297
    - type: mrr_at_3
      value: 85.31400000000001
    - type: mrr_at_5
      value: 85.858
    - type: ndcg_at_1
      value: 80.378
    - type: ndcg_at_10
      value: 74.101
    - type: ndcg_at_100
      value: 76.993
    - type: ndcg_at_1000
      value: 77.948
    - type: ndcg_at_3
      value: 69.232
    - type: ndcg_at_5
      value: 72.04599999999999
    - type: precision_at_1
      value: 80.378
    - type: precision_at_10
      value: 15.595999999999998
    - type: precision_at_100
      value: 1.7840000000000003
    - type: precision_at_1000
      value: 0.191
    - type: precision_at_3
      value: 44.884
    - type: precision_at_5
      value: 29.145
    - type: recall_at_1
      value: 40.189
    - type: recall_at_10
      value: 77.981
    - type: recall_at_100
      value: 89.21
    - type: recall_at_1000
      value: 95.48299999999999
    - type: recall_at_3
      value: 67.326
    - type: recall_at_5
      value: 72.863
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 92.84599999999999
    - type: ap
      value: 89.4710787567357
    - type: f1
      value: 92.83752676932258
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: msmarco
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 23.132
    - type: map_at_10
      value: 35.543
    - type: map_at_100
      value: 36.702
    - type: map_at_1000
      value: 36.748999999999995
    - type: map_at_3
      value: 31.737
    - type: map_at_5
      value: 33.927
    - type: mrr_at_1
      value: 23.782
    - type: mrr_at_10
      value: 36.204
    - type: mrr_at_100
      value: 37.29
    - type: mrr_at_1000
      value: 37.330999999999996
    - type: mrr_at_3
      value: 32.458999999999996
    - type: mrr_at_5
      value: 34.631
    - type: ndcg_at_1
      value: 23.782
    - type: ndcg_at_10
      value: 42.492999999999995
    - type: ndcg_at_100
      value: 47.985
    - type: ndcg_at_1000
      value: 49.141
    - type: ndcg_at_3
      value: 34.748000000000005
    - type: ndcg_at_5
      value: 38.651
    - type: precision_at_1
      value: 23.782
    - type: precision_at_10
      value: 6.665
    - type: precision_at_100
      value: 0.941
    - type: precision_at_1000
      value: 0.104
    - type: precision_at_3
      value: 14.776
    - type: precision_at_5
      value: 10.84
    - type: recall_at_1
      value: 23.132
    - type: recall_at_10
      value: 63.794
    - type: recall_at_100
      value: 89.027
    - type: recall_at_1000
      value: 97.807
    - type: recall_at_3
      value: 42.765
    - type: recall_at_5
      value: 52.11
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 94.59188326493388
    - type: f1
      value: 94.3842594786827
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 79.49384404924761
    - type: f1
      value: 59.7580539534629
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 77.56220578345663
    - type: f1
      value: 75.27228165561478
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 80.53463349024884
    - type: f1
      value: 80.4893958236536
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 32.56100273484962
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 31.470380028839607
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 32.06102792457849
    - type: mrr
      value: 33.30709199672238
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 6.776999999999999
    - type: map_at_10
      value: 14.924000000000001
    - type: map_at_100
      value: 18.955
    - type: map_at_1000
      value: 20.538999999999998
    - type: map_at_3
      value: 10.982
    - type: map_at_5
      value: 12.679000000000002
    - type: mrr_at_1
      value: 47.988
    - type: mrr_at_10
      value: 57.232000000000006
    - type: mrr_at_100
      value: 57.818999999999996
    - type: mrr_at_1000
      value: 57.847
    - type: mrr_at_3
      value: 54.901999999999994
    - type: mrr_at_5
      value: 56.481
    - type: ndcg_at_1
      value: 46.594
    - type: ndcg_at_10
      value: 38.129000000000005
    - type: ndcg_at_100
      value: 35.54
    - type: ndcg_at_1000
      value: 44.172
    - type: ndcg_at_3
      value: 43.025999999999996
    - type: ndcg_at_5
      value: 41.052
    - type: precision_at_1
      value: 47.988
    - type: precision_at_10
      value: 28.111000000000004
    - type: precision_at_100
      value: 8.929
    - type: precision_at_1000
      value: 2.185
    - type: precision_at_3
      value: 40.144000000000005
    - type: precision_at_5
      value: 35.232
    - type: recall_at_1
      value: 6.776999999999999
    - type: recall_at_10
      value: 19.289
    - type: recall_at_100
      value: 36.359
    - type: recall_at_1000
      value: 67.54
    - type: recall_at_3
      value: 11.869
    - type: recall_at_5
      value: 14.999
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: nq
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 31.108000000000004
    - type: map_at_10
      value: 47.126000000000005
    - type: map_at_100
      value: 48.171
    - type: map_at_1000
      value: 48.199
    - type: map_at_3
      value: 42.734
    - type: map_at_5
      value: 45.362
    - type: mrr_at_1
      value: 34.936
    - type: mrr_at_10
      value: 49.571
    - type: mrr_at_100
      value: 50.345
    - type: mrr_at_1000
      value: 50.363
    - type: mrr_at_3
      value: 45.959
    - type: mrr_at_5
      value: 48.165
    - type: ndcg_at_1
      value: 34.936
    - type: ndcg_at_10
      value: 55.028999999999996
    - type: ndcg_at_100
      value: 59.244
    - type: ndcg_at_1000
      value: 59.861
    - type: ndcg_at_3
      value: 46.872
    - type: ndcg_at_5
      value: 51.217999999999996
    - type: precision_at_1
      value: 34.936
    - type: precision_at_10
      value: 9.099
    - type: precision_at_100
      value: 1.145
    - type: precision_at_1000
      value: 0.12
    - type: precision_at_3
      value: 21.456
    - type: precision_at_5
      value: 15.411
    - type: recall_at_1
      value: 31.108000000000004
    - type: recall_at_10
      value: 76.53999999999999
    - type: recall_at_100
      value: 94.39
    - type: recall_at_1000
      value: 98.947
    - type: recall_at_3
      value: 55.572
    - type: recall_at_5
      value: 65.525
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 71.56400000000001
    - type: map_at_10
      value: 85.482
    - type: map_at_100
      value: 86.114
    - type: map_at_1000
      value: 86.13
    - type: map_at_3
      value: 82.607
    - type: map_at_5
      value: 84.405
    - type: mrr_at_1
      value: 82.42
    - type: mrr_at_10
      value: 88.304
    - type: mrr_at_100
      value: 88.399
    - type: mrr_at_1000
      value: 88.399
    - type: mrr_at_3
      value: 87.37
    - type: mrr_at_5
      value: 88.024
    - type: ndcg_at_1
      value: 82.45
    - type: ndcg_at_10
      value: 89.06500000000001
    - type: ndcg_at_100
      value: 90.232
    - type: ndcg_at_1000
      value: 90.305
    - type: ndcg_at_3
      value: 86.375
    - type: ndcg_at_5
      value: 87.85300000000001
    - type: precision_at_1
      value: 82.45
    - type: precision_at_10
      value: 13.486999999999998
    - type: precision_at_100
      value: 1.534
    - type: precision_at_1000
      value: 0.157
    - type: precision_at_3
      value: 37.813
    - type: precision_at_5
      value: 24.773999999999997
    - type: recall_at_1
      value: 71.56400000000001
    - type: recall_at_10
      value: 95.812
    - type: recall_at_100
      value: 99.7
    - type: recall_at_1000
      value: 99.979
    - type: recall_at_3
      value: 87.966
    - type: recall_at_5
      value: 92.268
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 57.241876648614145
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 64.66212576446223
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 5.308
    - type: map_at_10
      value: 13.803
    - type: map_at_100
      value: 16.176
    - type: map_at_1000
      value: 16.561
    - type: map_at_3
      value: 9.761000000000001
    - type: map_at_5
      value: 11.802
    - type: mrr_at_1
      value: 26.200000000000003
    - type: mrr_at_10
      value: 37.621
    - type: mrr_at_100
      value: 38.767
    - type: mrr_at_1000
      value: 38.815
    - type: mrr_at_3
      value: 34.117
    - type: mrr_at_5
      value: 36.107
    - type: ndcg_at_1
      value: 26.200000000000003
    - type: ndcg_at_10
      value: 22.64
    - type: ndcg_at_100
      value: 31.567
    - type: ndcg_at_1000
      value: 37.623
    - type: ndcg_at_3
      value: 21.435000000000002
    - type: ndcg_at_5
      value: 18.87
    - type: precision_at_1
      value: 26.200000000000003
    - type: precision_at_10
      value: 11.74
    - type: precision_at_100
      value: 2.465
    - type: precision_at_1000
      value: 0.391
    - type: precision_at_3
      value: 20.033
    - type: precision_at_5
      value: 16.64
    - type: recall_at_1
      value: 5.308
    - type: recall_at_10
      value: 23.794999999999998
    - type: recall_at_100
      value: 50.015
    - type: recall_at_1000
      value: 79.283
    - type: recall_at_3
      value: 12.178
    - type: recall_at_5
      value: 16.882
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 84.93231134675553
    - type: cos_sim_spearman
      value: 81.68319292603205
    - type: euclidean_pearson
      value: 81.8396814380367
    - type: euclidean_spearman
      value: 81.24641903349945
    - type: manhattan_pearson
      value: 81.84698799204274
    - type: manhattan_spearman
      value: 81.24269997904105
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 86.73241671587446
    - type: cos_sim_spearman
      value: 79.05091082971826
    - type: euclidean_pearson
      value: 83.91146869578044
    - type: euclidean_spearman
      value: 79.87978465370936
    - type: manhattan_pearson
      value: 83.90888338917678
    - type: manhattan_spearman
      value: 79.87482848584241
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 85.14970731146177
    - type: cos_sim_spearman
      value: 86.37363490084627
    - type: euclidean_pearson
      value: 83.02154218530433
    - type: euclidean_spearman
      value: 83.80258761957367
    - type: manhattan_pearson
      value: 83.01664495119347
    - type: manhattan_spearman
      value: 83.77567458007952
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 83.40474139886784
    - type: cos_sim_spearman
      value: 82.77768789165984
    - type: euclidean_pearson
      value: 80.7065877443695
    - type: euclidean_spearman
      value: 81.375940662505
    - type: manhattan_pearson
      value: 80.6507552270278
    - type: manhattan_spearman
      value: 81.32782179098741
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 87.08585968722274
    - type: cos_sim_spearman
      value: 88.03110031451399
    - type: euclidean_pearson
      value: 85.74012019602384
    - type: euclidean_spearman
      value: 86.13592849438209
    - type: manhattan_pearson
      value: 85.74404842369206
    - type: manhattan_spearman
      value: 86.14492318960154
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 84.95069052788875
    - type: cos_sim_spearman
      value: 86.4867991595147
    - type: euclidean_pearson
      value: 84.31013325754635
    - type: euclidean_spearman
      value: 85.01529258006482
    - type: manhattan_pearson
      value: 84.26995570085374
    - type: manhattan_spearman
      value: 84.96982104986162
  - 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: 87.54617647971897
    - type: cos_sim_spearman
      value: 87.49834181751034
    - type: euclidean_pearson
      value: 86.01015322577122
    - type: euclidean_spearman
      value: 84.63362652063199
    - type: manhattan_pearson
      value: 86.13807574475706
    - type: manhattan_spearman
      value: 84.7772370721132
  - 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: 67.20047755786615
    - type: cos_sim_spearman
      value: 67.05324077987636
    - type: euclidean_pearson
      value: 66.91930642976601
    - type: euclidean_spearman
      value: 65.21491856099105
    - type: manhattan_pearson
      value: 66.78756851976624
    - type: manhattan_spearman
      value: 65.12356257740728
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 86.19852871539686
    - type: cos_sim_spearman
      value: 87.5161895296395
    - type: euclidean_pearson
      value: 84.59848645207485
    - type: euclidean_spearman
      value: 85.26427328757919
    - type: manhattan_pearson
      value: 84.59747366996524
    - type: manhattan_spearman
      value: 85.24045855146915
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 87.63320317811032
    - type: mrr
      value: 96.26242947321379
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 60.928000000000004
    - type: map_at_10
      value: 70.112
    - type: map_at_100
      value: 70.59299999999999
    - type: map_at_1000
      value: 70.623
    - type: map_at_3
      value: 66.846
    - type: map_at_5
      value: 68.447
    - type: mrr_at_1
      value: 64
    - type: mrr_at_10
      value: 71.212
    - type: mrr_at_100
      value: 71.616
    - type: mrr_at_1000
      value: 71.64500000000001
    - type: mrr_at_3
      value: 68.77799999999999
    - type: mrr_at_5
      value: 70.094
    - type: ndcg_at_1
      value: 64
    - type: ndcg_at_10
      value: 74.607
    - type: ndcg_at_100
      value: 76.416
    - type: ndcg_at_1000
      value: 77.102
    - type: ndcg_at_3
      value: 69.126
    - type: ndcg_at_5
      value: 71.41300000000001
    - type: precision_at_1
      value: 64
    - type: precision_at_10
      value: 9.933
    - type: precision_at_100
      value: 1.077
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 26.556
    - type: precision_at_5
      value: 17.467
    - type: recall_at_1
      value: 60.928000000000004
    - type: recall_at_10
      value: 87.322
    - type: recall_at_100
      value: 94.833
    - type: recall_at_1000
      value: 100
    - type: recall_at_3
      value: 72.628
    - type: recall_at_5
      value: 78.428
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.86237623762376
    - type: cos_sim_ap
      value: 96.72586477206649
    - type: cos_sim_f1
      value: 93.01858362631845
    - type: cos_sim_precision
      value: 93.4409687184662
    - type: cos_sim_recall
      value: 92.60000000000001
    - type: dot_accuracy
      value: 99.78019801980199
    - type: dot_ap
      value: 93.72748205246228
    - type: dot_f1
      value: 89.04109589041096
    - type: dot_precision
      value: 87.16475095785441
    - type: dot_recall
      value: 91
    - type: euclidean_accuracy
      value: 99.85445544554456
    - type: euclidean_ap
      value: 96.6661459876145
    - type: euclidean_f1
      value: 92.58337481333997
    - type: euclidean_precision
      value: 92.17046580773042
    - type: euclidean_recall
      value: 93
    - type: manhattan_accuracy
      value: 99.85445544554456
    - type: manhattan_ap
      value: 96.6883549244056
    - type: manhattan_f1
      value: 92.57598405580468
    - type: manhattan_precision
      value: 92.25422045680239
    - type: manhattan_recall
      value: 92.9
    - type: max_accuracy
      value: 99.86237623762376
    - type: max_ap
      value: 96.72586477206649
    - type: max_f1
      value: 93.01858362631845
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 66.39930057069995
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 34.96398659903402
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 55.946944700355395
    - type: mrr
      value: 56.97151398438164
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 31.541657650692905
    - type: cos_sim_spearman
      value: 31.605804192286303
    - type: dot_pearson
      value: 28.26905996736398
    - type: dot_spearman
      value: 27.864801765851187
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.22599999999999998
    - type: map_at_10
      value: 1.8870000000000002
    - type: map_at_100
      value: 9.78
    - type: map_at_1000
      value: 22.514
    - type: map_at_3
      value: 0.6669999999999999
    - type: map_at_5
      value: 1.077
    - type: mrr_at_1
      value: 82
    - type: mrr_at_10
      value: 89.86699999999999
    - type: mrr_at_100
      value: 89.86699999999999
    - type: mrr_at_1000
      value: 89.86699999999999
    - type: mrr_at_3
      value: 89.667
    - type: mrr_at_5
      value: 89.667
    - type: ndcg_at_1
      value: 79
    - type: ndcg_at_10
      value: 74.818
    - type: ndcg_at_100
      value: 53.715999999999994
    - type: ndcg_at_1000
      value: 47.082
    - type: ndcg_at_3
      value: 82.134
    - type: ndcg_at_5
      value: 79.81899999999999
    - type: precision_at_1
      value: 82
    - type: precision_at_10
      value: 78
    - type: precision_at_100
      value: 54.48
    - type: precision_at_1000
      value: 20.518
    - type: precision_at_3
      value: 87.333
    - type: precision_at_5
      value: 85.2
    - type: recall_at_1
      value: 0.22599999999999998
    - type: recall_at_10
      value: 2.072
    - type: recall_at_100
      value: 13.013
    - type: recall_at_1000
      value: 43.462
    - type: recall_at_3
      value: 0.695
    - type: recall_at_5
      value: 1.139
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: webis-touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 2.328
    - type: map_at_10
      value: 9.795
    - type: map_at_100
      value: 15.801000000000002
    - type: map_at_1000
      value: 17.23
    - type: map_at_3
      value: 4.734
    - type: map_at_5
      value: 6.644
    - type: mrr_at_1
      value: 30.612000000000002
    - type: mrr_at_10
      value: 46.902
    - type: mrr_at_100
      value: 47.495
    - type: mrr_at_1000
      value: 47.495
    - type: mrr_at_3
      value: 41.156
    - type: mrr_at_5
      value: 44.218
    - type: ndcg_at_1
      value: 28.571
    - type: ndcg_at_10
      value: 24.806
    - type: ndcg_at_100
      value: 36.419000000000004
    - type: ndcg_at_1000
      value: 47.272999999999996
    - type: ndcg_at_3
      value: 25.666
    - type: ndcg_at_5
      value: 25.448999999999998
    - type: precision_at_1
      value: 30.612000000000002
    - type: precision_at_10
      value: 23.061
    - type: precision_at_100
      value: 7.714
    - type: precision_at_1000
      value: 1.484
    - type: precision_at_3
      value: 26.531
    - type: precision_at_5
      value: 26.122
    - type: recall_at_1
      value: 2.328
    - type: recall_at_10
      value: 16.524
    - type: recall_at_100
      value: 47.179
    - type: recall_at_1000
      value: 81.22200000000001
    - type: recall_at_3
      value: 5.745
    - type: recall_at_5
      value: 9.339
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 70.9142
    - type: ap
      value: 14.335574772555415
    - type: f1
      value: 54.62839595194111
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 59.94340690435768
    - type: f1
      value: 60.286487936731916
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 51.26597708987974
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 87.48882398521786
    - type: cos_sim_ap
      value: 79.04326607602204
    - type: cos_sim_f1
      value: 71.64566826860633
    - type: cos_sim_precision
      value: 70.55512918905092
    - type: cos_sim_recall
      value: 72.77044854881267
    - type: dot_accuracy
      value: 84.19264469213805
    - type: dot_ap
      value: 67.96360043562528
    - type: dot_f1
      value: 64.06418393006827
    - type: dot_precision
      value: 58.64941898706424
    - type: dot_recall
      value: 70.58047493403694
    - type: euclidean_accuracy
      value: 87.45902127913214
    - type: euclidean_ap
      value: 78.9742237648272
    - type: euclidean_f1
      value: 71.5553235908142
    - type: euclidean_precision
      value: 70.77955601445535
    - type: euclidean_recall
      value: 72.34828496042216
    - type: manhattan_accuracy
      value: 87.41729749061214
    - type: manhattan_ap
      value: 78.90073137580596
    - type: manhattan_f1
      value: 71.3942611553533
    - type: manhattan_precision
      value: 68.52705653967483
    - type: manhattan_recall
      value: 74.51187335092348
    - type: max_accuracy
      value: 87.48882398521786
    - type: max_ap
      value: 79.04326607602204
    - type: max_f1
      value: 71.64566826860633
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.68125897465751
    - type: cos_sim_ap
      value: 85.6003454431979
    - type: cos_sim_f1
      value: 77.6957163958641
    - type: cos_sim_precision
      value: 73.0110366307807
    - type: cos_sim_recall
      value: 83.02279026793964
    - type: dot_accuracy
      value: 87.7672992587418
    - type: dot_ap
      value: 82.4971301112899
    - type: dot_f1
      value: 75.90528233151184
    - type: dot_precision
      value: 72.0370626469368
    - type: dot_recall
      value: 80.21250384970742
    - type: euclidean_accuracy
      value: 88.4503434625684
    - type: euclidean_ap
      value: 84.91949884748384
    - type: euclidean_f1
      value: 76.92365018444684
    - type: euclidean_precision
      value: 74.53245721712759
    - type: euclidean_recall
      value: 79.47336002463813
    - type: manhattan_accuracy
      value: 88.47556952691427
    - type: manhattan_ap
      value: 84.8963689101517
    - type: manhattan_f1
      value: 76.85901249256395
    - type: manhattan_precision
      value: 74.31693989071039
    - type: manhattan_recall
      value: 79.58115183246073
    - type: max_accuracy
      value: 88.68125897465751
    - type: max_ap
      value: 85.6003454431979
    - type: max_f1
      value: 77.6957163958641
---

 
***See Disclaimer below***

----

# A Teradata Vantage compatible Embeddings Model

# BAAI/bge-large-en-v1.5

## Overview of this Model

An Embedding Model which maps text (sentence/ paragraphs) into a vector.  The [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) model well known for its effectiveness in capturing semantic meanings in text data. It's a state-of-the-art model trained on a large corpus, capable of generating high-quality text embeddings.

- 335.14M params (Sizes in ONNX format - "fp32": 1275.11MB, "int8": 320.63MB, "uint8": 320.63MB)
- 512 maximum input tokens 
- 1024 dimensions of output vector
- Licence: mit. The released models can be used for commercial purposes free of charge.
- Reference to Original Model: https://huggingface.co/BAAI/bge-large-en-v1.5


## Quickstart: Deploying this Model in Teradata Vantage

We have pre-converted the model into the ONNX format compatible with BYOM 6.0, eliminating the need for manual conversion. 

**Note:** Ensure you have access to a Teradata Database with BYOM 6.0 installed.

To get started, clone the pre-converted model directly from the Teradata HuggingFace repository.


```python

import teradataml as tdml
import getpass
from huggingface_hub import hf_hub_download

model_name = "bge-large-en-v1.5"
number_dimensions_output = 1024
model_file_name = "model.onnx"

# Step 1: Download Model from Teradata HuggingFace Page

hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./")
hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./")

# Step 2: Create Connection to Vantage

tdml.create_context(host = input('enter your hostname'), 
                    username=input('enter your username'), 
                    password = getpass.getpass("enter your password"))

# Step 3: Load Models into Vantage
# a) Embedding model
tdml.save_byom(model_id = model_name, # must be unique in the models table
               model_file = f"onnx/{model_file_name}",
               table_name = 'embeddings_models' )
# b) Tokenizer
tdml.save_byom(model_id = model_name, # must be unique in the models table
              model_file = 'tokenizer.json',
              table_name = 'embeddings_tokenizers') 

# Step 4: Test ONNXEmbeddings Function
# Note that ONNXEmbeddings expects the 'payload' column to be 'txt'. 
# If it has got a different name, just rename it in a subquery/CTE.
input_table = "emails.emails"
embeddings_query = f"""
SELECT 
        *
from mldb.ONNXEmbeddings(
        on {input_table} as InputTable
        on (select * from embeddings_models where model_id = '{model_name}') as ModelTable DIMENSION
        on (select model as tokenizer from embeddings_tokenizers where model_id = '{model_name}') as TokenizerTable DIMENSION
        using
            Accumulate('id', 'txt') 
            ModelOutputTensor('sentence_embedding')
            EnableMemoryCheck('false')
            OutputFormat('FLOAT32({number_dimensions_output})')
            OverwriteCachedModel('true')
    ) a 
"""
DF_embeddings = tdml.DataFrame.from_query(embeddings_query)
DF_embeddings
```



## What Can I Do with the Embeddings?

Teradata Vantage includes pre-built in-database functions to process embeddings further. Explore the following examples:

- **Semantic Clustering with TD_KMeans:** [Semantic Clustering Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Clustering_Python.ipynb)
- **Semantic Distance with TD_VectorDistance:** [Semantic Similarity Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Similarity_Python.ipynb)
- **RAG-Based Application with TD_VectorDistance:** [RAG and Bedrock Query PDF Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/RAG_and_Bedrock_QueryPDF.ipynb)


## Deep Dive into Model Conversion to ONNX

**The steps below outline how we converted the open-source Hugging Face model into an ONNX file compatible with the in-database ONNXEmbeddings function.** 

You do not need to perform these steps—they are provided solely for documentation and transparency. However, they may be helpful if you wish to convert another model to the required format.


### Part 1. Importing and Converting Model using optimum

We start by importing the pre-trained [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) model from Hugging Face.

To enhance performance and ensure compatibility with various execution environments, we'll use the [Optimum](https://github.com/huggingface/optimum) utility to convert the model into the ONNX (Open Neural Network Exchange) format. 

After conversion to ONNX, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage

We are generating ONNX files for multiple different precisions: fp32, int8, uint8

You can find the detailed conversion steps in the file [convert.py](./convert.py)

### Part 2. Running the model in Python with onnxruntime & compare results

Once the fixes are applied, we proceed to test the correctness of the ONNX model by calculating cosine similarity between two texts using native SentenceTransformers and ONNX runtime, comparing the results.

If the results are identical, it confirms that the ONNX model gives the same result as the native models, validating its correctness and suitability for further use in the database.


```python
import onnxruntime as rt

from sentence_transformers.util import cos_sim
from sentence_transformers import SentenceTransformer

import transformers


sentences_1 = 'How is the weather today?'
sentences_2 = 'What is the current weather like today?'

# Calculate ONNX result
tokenizer = transformers.AutoTokenizer.from_pretrained("BAAI/bge-large-en-v1.5")
predef_sess = rt.InferenceSession("onnx/model.onnx")

enc1 = tokenizer(sentences_1)
embeddings_1_onnx = predef_sess.run(None,     {"input_ids": [enc1.input_ids], 
     "attention_mask": [enc1.attention_mask]})

enc2 = tokenizer(sentences_2)
embeddings_2_onnx = predef_sess.run(None,     {"input_ids": [enc2.input_ids], 
     "attention_mask": [enc2.attention_mask]})


# Calculate embeddings with SentenceTransformer
model = SentenceTransformer(model_id, trust_remote_code=True)
embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True)
embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True)

# Compare results
print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(embeddings_1_onnx[1][0], embeddings_2_onnx[1][0])))
print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))
```

You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py)



-----

DISCLAIMER: The content herein (“Content”) is provided “AS IS” and is not covered by any Teradata Operations, Inc. and its affiliates (“Teradata”) agreements. Its listing here does not constitute certification or endorsement by Teradata. 

To the extent any of the Content contains or is related to any artificial intelligence (“AI”) or other language learning models (“Models”) that interoperate with the products and services of Teradata, by accessing, bringing, deploying or using such Models, you acknowledge and agree that you are solely responsible for ensuring compliance with all applicable laws, regulations, and restrictions governing the use, deployment, and distribution of AI technologies. This includes, but is not limited to, AI Diffusion Rules, European Union AI Act, AI-related laws and regulations, privacy laws, export controls, and financial or sector-specific regulations.

While Teradata may provide support, guidance, or assistance in the deployment or implementation of Models to interoperate with Teradata’s products and/or services, you remain fully responsible for ensuring that your Models, data, and applications comply with all relevant legal and regulatory obligations. Our assistance does not constitute legal or regulatory approval, and Teradata disclaims any liability arising from non-compliance with applicable laws.

You must determine the suitability of the Models for any purpose. Given the probabilistic nature of machine learning and modeling, the use of the Models may in some situations result in incorrect output that does not accurately reflect the action generated. You should evaluate the accuracy of any output as appropriate for your use case, including by using human review of the output.