sentence-transformers pytorch bert feature-extraction sentence-similarity transformers mteb license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: cai-lunaris-text-embeddings
  results:
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 17.07
    - type: map_at_10
      value: 29.372999999999998
    - type: map_at_100
      value: 30.79
    - type: map_at_1000
      value: 30.819999999999997
    - type: map_at_3
      value: 24.395
    - type: map_at_5
      value: 27.137
    - type: mrr_at_1
      value: 17.923000000000002
    - type: mrr_at_10
      value: 29.695
    - type: mrr_at_100
      value: 31.098
    - type: mrr_at_1000
      value: 31.128
    - type: mrr_at_3
      value: 24.704
    - type: mrr_at_5
      value: 27.449
    - type: ndcg_at_1
      value: 17.07
    - type: ndcg_at_10
      value: 37.269000000000005
    - type: ndcg_at_100
      value: 43.716
    - type: ndcg_at_1000
      value: 44.531
    - type: ndcg_at_3
      value: 26.839000000000002
    - type: ndcg_at_5
      value: 31.845000000000002
    - type: precision_at_1
      value: 17.07
    - type: precision_at_10
      value: 6.3020000000000005
    - type: precision_at_100
      value: 0.922
    - type: precision_at_1000
      value: 0.099
    - type: precision_at_3
      value: 11.309
    - type: precision_at_5
      value: 9.246
    - type: recall_at_1
      value: 17.07
    - type: recall_at_10
      value: 63.016000000000005
    - type: recall_at_100
      value: 92.24799999999999
    - type: recall_at_1000
      value: 98.72
    - type: recall_at_3
      value: 33.926
    - type: recall_at_5
      value: 46.23
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 53.44266265900711
    - type: mrr
      value: 66.54695950402322
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 75.9652953730204
    - type: cos_sim_spearman
      value: 73.96554077670989
    - type: euclidean_pearson
      value: 75.68477255792381
    - type: euclidean_spearman
      value: 74.59447076995703
    - type: manhattan_pearson
      value: 75.94984623881341
    - type: manhattan_spearman
      value: 74.72218452337502
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 14.119000000000002
    - type: map_at_10
      value: 19.661
    - type: map_at_100
      value: 20.706
    - type: map_at_1000
      value: 20.848
    - type: map_at_3
      value: 17.759
    - type: map_at_5
      value: 18.645
    - type: mrr_at_1
      value: 17.166999999999998
    - type: mrr_at_10
      value: 23.313
    - type: mrr_at_100
      value: 24.263
    - type: mrr_at_1000
      value: 24.352999999999998
    - type: mrr_at_3
      value: 21.412
    - type: mrr_at_5
      value: 22.313
    - type: ndcg_at_1
      value: 17.166999999999998
    - type: ndcg_at_10
      value: 23.631
    - type: ndcg_at_100
      value: 28.427000000000003
    - type: ndcg_at_1000
      value: 31.862000000000002
    - type: ndcg_at_3
      value: 20.175
    - type: ndcg_at_5
      value: 21.397
    - type: precision_at_1
      value: 17.166999999999998
    - type: precision_at_10
      value: 4.549
    - type: precision_at_100
      value: 0.8370000000000001
    - type: precision_at_1000
      value: 0.136
    - type: precision_at_3
      value: 9.68
    - type: precision_at_5
      value: 6.981
    - type: recall_at_1
      value: 14.119000000000002
    - type: recall_at_10
      value: 32.147999999999996
    - type: recall_at_100
      value: 52.739999999999995
    - type: recall_at_1000
      value: 76.67
    - type: recall_at_3
      value: 22.019
    - type: recall_at_5
      value: 25.361
    - type: map_at_1
      value: 16.576
    - type: map_at_10
      value: 22.281000000000002
    - type: map_at_100
      value: 23.066
    - type: map_at_1000
      value: 23.166
    - type: map_at_3
      value: 20.385
    - type: map_at_5
      value: 21.557000000000002
    - type: mrr_at_1
      value: 20.892
    - type: mrr_at_10
      value: 26.605
    - type: mrr_at_100
      value: 27.229
    - type: mrr_at_1000
      value: 27.296
    - type: mrr_at_3
      value: 24.809
    - type: mrr_at_5
      value: 25.927
    - type: ndcg_at_1
      value: 20.892
    - type: ndcg_at_10
      value: 26.092
    - type: ndcg_at_100
      value: 29.398999999999997
    - type: ndcg_at_1000
      value: 31.884
    - type: ndcg_at_3
      value: 23.032
    - type: ndcg_at_5
      value: 24.634
    - type: precision_at_1
      value: 20.892
    - type: precision_at_10
      value: 4.885
    - type: precision_at_100
      value: 0.818
    - type: precision_at_1000
      value: 0.126
    - type: precision_at_3
      value: 10.977
    - type: precision_at_5
      value: 8.013
    - type: recall_at_1
      value: 16.576
    - type: recall_at_10
      value: 32.945
    - type: recall_at_100
      value: 47.337
    - type: recall_at_1000
      value: 64.592
    - type: recall_at_3
      value: 24.053
    - type: recall_at_5
      value: 28.465
    - type: map_at_1
      value: 20.604
    - type: map_at_10
      value: 28.754999999999995
    - type: map_at_100
      value: 29.767
    - type: map_at_1000
      value: 29.852
    - type: map_at_3
      value: 26.268
    - type: map_at_5
      value: 27.559
    - type: mrr_at_1
      value: 24.326
    - type: mrr_at_10
      value: 31.602000000000004
    - type: mrr_at_100
      value: 32.46
    - type: mrr_at_1000
      value: 32.521
    - type: mrr_at_3
      value: 29.415000000000003
    - type: mrr_at_5
      value: 30.581000000000003
    - type: ndcg_at_1
      value: 24.326
    - type: ndcg_at_10
      value: 33.335
    - type: ndcg_at_100
      value: 38.086
    - type: ndcg_at_1000
      value: 40.319
    - type: ndcg_at_3
      value: 28.796
    - type: ndcg_at_5
      value: 30.758999999999997
    - type: precision_at_1
      value: 24.326
    - type: precision_at_10
      value: 5.712
    - type: precision_at_100
      value: 0.893
    - type: precision_at_1000
      value: 0.11499999999999999
    - type: precision_at_3
      value: 13.208
    - type: precision_at_5
      value: 9.329
    - type: recall_at_1
      value: 20.604
    - type: recall_at_10
      value: 44.505
    - type: recall_at_100
      value: 65.866
    - type: recall_at_1000
      value: 82.61800000000001
    - type: recall_at_3
      value: 31.794
    - type: recall_at_5
      value: 36.831
    - type: map_at_1
      value: 8.280999999999999
    - type: map_at_10
      value: 11.636000000000001
    - type: map_at_100
      value: 12.363
    - type: map_at_1000
      value: 12.469
    - type: map_at_3
      value: 10.415000000000001
    - type: map_at_5
      value: 11.144
    - type: mrr_at_1
      value: 9.266
    - type: mrr_at_10
      value: 12.838
    - type: mrr_at_100
      value: 13.608999999999998
    - type: mrr_at_1000
      value: 13.700999999999999
    - type: mrr_at_3
      value: 11.507000000000001
    - type: mrr_at_5
      value: 12.343
    - type: ndcg_at_1
      value: 9.266
    - type: ndcg_at_10
      value: 13.877
    - type: ndcg_at_100
      value: 18.119
    - type: ndcg_at_1000
      value: 21.247
    - type: ndcg_at_3
      value: 11.376999999999999
    - type: ndcg_at_5
      value: 12.675
    - type: precision_at_1
      value: 9.266
    - type: precision_at_10
      value: 2.226
    - type: precision_at_100
      value: 0.47200000000000003
    - type: precision_at_1000
      value: 0.077
    - type: precision_at_3
      value: 4.859
    - type: precision_at_5
      value: 3.6380000000000003
    - type: recall_at_1
      value: 8.280999999999999
    - type: recall_at_10
      value: 19.872999999999998
    - type: recall_at_100
      value: 40.585
    - type: recall_at_1000
      value: 65.225
    - type: recall_at_3
      value: 13.014000000000001
    - type: recall_at_5
      value: 16.147
    - type: map_at_1
      value: 4.1209999999999996
    - type: map_at_10
      value: 7.272
    - type: map_at_100
      value: 8.079
    - type: map_at_1000
      value: 8.199
    - type: map_at_3
      value: 6.212
    - type: map_at_5
      value: 6.736000000000001
    - type: mrr_at_1
      value: 5.721
    - type: mrr_at_10
      value: 9.418
    - type: mrr_at_100
      value: 10.281
    - type: mrr_at_1000
      value: 10.385
    - type: mrr_at_3
      value: 8.126
    - type: mrr_at_5
      value: 8.779
    - type: ndcg_at_1
      value: 5.721
    - type: ndcg_at_10
      value: 9.673
    - type: ndcg_at_100
      value: 13.852999999999998
    - type: ndcg_at_1000
      value: 17.546999999999997
    - type: ndcg_at_3
      value: 7.509
    - type: ndcg_at_5
      value: 8.373
    - type: precision_at_1
      value: 5.721
    - type: precision_at_10
      value: 2.04
    - type: precision_at_100
      value: 0.48
    - type: precision_at_1000
      value: 0.093
    - type: precision_at_3
      value: 4.022
    - type: precision_at_5
      value: 3.06
    - type: recall_at_1
      value: 4.1209999999999996
    - type: recall_at_10
      value: 15.201
    - type: recall_at_100
      value: 33.922999999999995
    - type: recall_at_1000
      value: 61.529999999999994
    - type: recall_at_3
      value: 8.869
    - type: recall_at_5
      value: 11.257
    - type: map_at_1
      value: 14.09
    - type: map_at_10
      value: 19.573999999999998
    - type: map_at_100
      value: 20.580000000000002
    - type: map_at_1000
      value: 20.704
    - type: map_at_3
      value: 17.68
    - type: map_at_5
      value: 18.64
    - type: mrr_at_1
      value: 17.227999999999998
    - type: mrr_at_10
      value: 23.152
    - type: mrr_at_100
      value: 24.056
    - type: mrr_at_1000
      value: 24.141000000000002
    - type: mrr_at_3
      value: 21.142
    - type: mrr_at_5
      value: 22.201
    - type: ndcg_at_1
      value: 17.227999999999998
    - type: ndcg_at_10
      value: 23.39
    - type: ndcg_at_100
      value: 28.483999999999998
    - type: ndcg_at_1000
      value: 31.709
    - type: ndcg_at_3
      value: 19.883
    - type: ndcg_at_5
      value: 21.34
    - type: precision_at_1
      value: 17.227999999999998
    - type: precision_at_10
      value: 4.3790000000000004
    - type: precision_at_100
      value: 0.826
    - type: precision_at_1000
      value: 0.128
    - type: precision_at_3
      value: 9.496
    - type: precision_at_5
      value: 6.872
    - type: recall_at_1
      value: 14.09
    - type: recall_at_10
      value: 31.580000000000002
    - type: recall_at_100
      value: 54.074
    - type: recall_at_1000
      value: 77.092
    - type: recall_at_3
      value: 21.601
    - type: recall_at_5
      value: 25.333
    - type: map_at_1
      value: 10.538
    - type: map_at_10
      value: 15.75
    - type: map_at_100
      value: 16.71
    - type: map_at_1000
      value: 16.838
    - type: map_at_3
      value: 13.488
    - type: map_at_5
      value: 14.712
    - type: mrr_at_1
      value: 13.813
    - type: mrr_at_10
      value: 19.08
    - type: mrr_at_100
      value: 19.946
    - type: mrr_at_1000
      value: 20.044
    - type: mrr_at_3
      value: 16.838
    - type: mrr_at_5
      value: 17.951
    - type: ndcg_at_1
      value: 13.813
    - type: ndcg_at_10
      value: 19.669
    - type: ndcg_at_100
      value: 24.488
    - type: ndcg_at_1000
      value: 27.87
    - type: ndcg_at_3
      value: 15.479000000000001
    - type: ndcg_at_5
      value: 17.229
    - type: precision_at_1
      value: 13.813
    - type: precision_at_10
      value: 3.916
    - type: precision_at_100
      value: 0.743
    - type: precision_at_1000
      value: 0.122
    - type: precision_at_3
      value: 7.534000000000001
    - type: precision_at_5
      value: 5.822
    - type: recall_at_1
      value: 10.538
    - type: recall_at_10
      value: 28.693
    - type: recall_at_100
      value: 50.308
    - type: recall_at_1000
      value: 74.44
    - type: recall_at_3
      value: 16.866999999999997
    - type: recall_at_5
      value: 21.404999999999998
    - type: map_at_1
      value: 11.044583333333332
    - type: map_at_10
      value: 15.682833333333335
    - type: map_at_100
      value: 16.506500000000003
    - type: map_at_1000
      value: 16.623833333333334
    - type: map_at_3
      value: 14.130833333333333
    - type: map_at_5
      value: 14.963583333333332
    - type: mrr_at_1
      value: 13.482833333333332
    - type: mrr_at_10
      value: 18.328500000000002
    - type: mrr_at_100
      value: 19.095416666666665
    - type: mrr_at_1000
      value: 19.18241666666666
    - type: mrr_at_3
      value: 16.754749999999998
    - type: mrr_at_5
      value: 17.614749999999997
    - type: ndcg_at_1
      value: 13.482833333333332
    - type: ndcg_at_10
      value: 18.81491666666667
    - type: ndcg_at_100
      value: 22.946833333333334
    - type: ndcg_at_1000
      value: 26.061083333333336
    - type: ndcg_at_3
      value: 15.949333333333332
    - type: ndcg_at_5
      value: 17.218333333333334
    - type: precision_at_1
      value: 13.482833333333332
    - type: precision_at_10
      value: 3.456583333333333
    - type: precision_at_100
      value: 0.6599166666666666
    - type: precision_at_1000
      value: 0.109
    - type: precision_at_3
      value: 7.498833333333332
    - type: precision_at_5
      value: 5.477166666666667
    - type: recall_at_1
      value: 11.044583333333332
    - type: recall_at_10
      value: 25.737750000000005
    - type: recall_at_100
      value: 44.617916666666666
    - type: recall_at_1000
      value: 67.56524999999999
    - type: recall_at_3
      value: 17.598249999999997
    - type: recall_at_5
      value: 20.9035
    - type: map_at_1
      value: 9.362
    - type: map_at_10
      value: 13.414000000000001
    - type: map_at_100
      value: 14.083000000000002
    - type: map_at_1000
      value: 14.168
    - type: map_at_3
      value: 12.098
    - type: map_at_5
      value: 12.803999999999998
    - type: mrr_at_1
      value: 11.043
    - type: mrr_at_10
      value: 15.158
    - type: mrr_at_100
      value: 15.845999999999998
    - type: mrr_at_1000
      value: 15.916
    - type: mrr_at_3
      value: 13.88
    - type: mrr_at_5
      value: 14.601
    - type: ndcg_at_1
      value: 11.043
    - type: ndcg_at_10
      value: 16.034000000000002
    - type: ndcg_at_100
      value: 19.686
    - type: ndcg_at_1000
      value: 22.188
    - type: ndcg_at_3
      value: 13.530000000000001
    - type: ndcg_at_5
      value: 14.704
    - type: precision_at_1
      value: 11.043
    - type: precision_at_10
      value: 2.791
    - type: precision_at_100
      value: 0.5
    - type: precision_at_1000
      value: 0.077
    - type: precision_at_3
      value: 6.237
    - type: precision_at_5
      value: 4.5089999999999995
    - type: recall_at_1
      value: 9.362
    - type: recall_at_10
      value: 22.396
    - type: recall_at_100
      value: 39.528999999999996
    - type: recall_at_1000
      value: 58.809
    - type: recall_at_3
      value: 15.553
    - type: recall_at_5
      value: 18.512
    - type: map_at_1
      value: 5.657
    - type: map_at_10
      value: 8.273
    - type: map_at_100
      value: 8.875
    - type: map_at_1000
      value: 8.977
    - type: map_at_3
      value: 7.32
    - type: map_at_5
      value: 7.792000000000001
    - type: mrr_at_1
      value: 7.02
    - type: mrr_at_10
      value: 9.966999999999999
    - type: mrr_at_100
      value: 10.636
    - type: mrr_at_1000
      value: 10.724
    - type: mrr_at_3
      value: 8.872
    - type: mrr_at_5
      value: 9.461
    - type: ndcg_at_1
      value: 7.02
    - type: ndcg_at_10
      value: 10.199
    - type: ndcg_at_100
      value: 13.642000000000001
    - type: ndcg_at_1000
      value: 16.643
    - type: ndcg_at_3
      value: 8.333
    - type: ndcg_at_5
      value: 9.103
    - type: precision_at_1
      value: 7.02
    - type: precision_at_10
      value: 1.8929999999999998
    - type: precision_at_100
      value: 0.43
    - type: precision_at_1000
      value: 0.08099999999999999
    - type: precision_at_3
      value: 3.843
    - type: precision_at_5
      value: 2.884
    - type: recall_at_1
      value: 5.657
    - type: recall_at_10
      value: 14.563
    - type: recall_at_100
      value: 30.807000000000002
    - type: recall_at_1000
      value: 53.251000000000005
    - type: recall_at_3
      value: 9.272
    - type: recall_at_5
      value: 11.202
    - type: map_at_1
      value: 10.671999999999999
    - type: map_at_10
      value: 14.651
    - type: map_at_100
      value: 15.406
    - type: map_at_1000
      value: 15.525
    - type: map_at_3
      value: 13.461
    - type: map_at_5
      value: 14.163
    - type: mrr_at_1
      value: 12.407
    - type: mrr_at_10
      value: 16.782
    - type: mrr_at_100
      value: 17.562
    - type: mrr_at_1000
      value: 17.653
    - type: mrr_at_3
      value: 15.47
    - type: mrr_at_5
      value: 16.262
    - type: ndcg_at_1
      value: 12.407
    - type: ndcg_at_10
      value: 17.251
    - type: ndcg_at_100
      value: 21.378
    - type: ndcg_at_1000
      value: 24.689
    - type: ndcg_at_3
      value: 14.915000000000001
    - type: ndcg_at_5
      value: 16.1
    - type: precision_at_1
      value: 12.407
    - type: precision_at_10
      value: 2.91
    - type: precision_at_100
      value: 0.573
    - type: precision_at_1000
      value: 0.096
    - type: precision_at_3
      value: 6.779
    - type: precision_at_5
      value: 4.888
    - type: recall_at_1
      value: 10.671999999999999
    - type: recall_at_10
      value: 23.099
    - type: recall_at_100
      value: 41.937999999999995
    - type: recall_at_1000
      value: 66.495
    - type: recall_at_3
      value: 16.901
    - type: recall_at_5
      value: 19.807
    - type: map_at_1
      value: 13.364
    - type: map_at_10
      value: 17.772
    - type: map_at_100
      value: 18.659
    - type: map_at_1000
      value: 18.861
    - type: map_at_3
      value: 16.659
    - type: map_at_5
      value: 17.174
    - type: mrr_at_1
      value: 16.996
    - type: mrr_at_10
      value: 21.687
    - type: mrr_at_100
      value: 22.313
    - type: mrr_at_1000
      value: 22.422
    - type: mrr_at_3
      value: 20.652
    - type: mrr_at_5
      value: 21.146
    - type: ndcg_at_1
      value: 16.996
    - type: ndcg_at_10
      value: 21.067
    - type: ndcg_at_100
      value: 24.829
    - type: ndcg_at_1000
      value: 28.866999999999997
    - type: ndcg_at_3
      value: 19.466
    - type: ndcg_at_5
      value: 19.993
    - type: precision_at_1
      value: 16.996
    - type: precision_at_10
      value: 4.071000000000001
    - type: precision_at_100
      value: 0.9329999999999999
    - type: precision_at_1000
      value: 0.183
    - type: precision_at_3
      value: 9.223
    - type: precision_at_5
      value: 6.4030000000000005
    - type: recall_at_1
      value: 13.364
    - type: recall_at_10
      value: 25.976
    - type: recall_at_100
      value: 44.134
    - type: recall_at_1000
      value: 73.181
    - type: recall_at_3
      value: 20.503
    - type: recall_at_5
      value: 22.409000000000002
    - type: map_at_1
      value: 5.151
    - type: map_at_10
      value: 9.155000000000001
    - type: map_at_100
      value: 9.783999999999999
    - type: map_at_1000
      value: 9.879
    - type: map_at_3
      value: 7.825
    - type: map_at_5
      value: 8.637
    - type: mrr_at_1
      value: 5.915
    - type: mrr_at_10
      value: 10.34
    - type: mrr_at_100
      value: 10.943999999999999
    - type: mrr_at_1000
      value: 11.033
    - type: mrr_at_3
      value: 8.934000000000001
    - type: mrr_at_5
      value: 9.812
    - type: ndcg_at_1
      value: 5.915
    - type: ndcg_at_10
      value: 11.561
    - type: ndcg_at_100
      value: 14.971
    - type: ndcg_at_1000
      value: 17.907999999999998
    - type: ndcg_at_3
      value: 8.896999999999998
    - type: ndcg_at_5
      value: 10.313
    - type: precision_at_1
      value: 5.915
    - type: precision_at_10
      value: 2.1069999999999998
    - type: precision_at_100
      value: 0.414
    - type: precision_at_1000
      value: 0.074
    - type: precision_at_3
      value: 4.128
    - type: precision_at_5
      value: 3.327
    - type: recall_at_1
      value: 5.151
    - type: recall_at_10
      value: 17.874000000000002
    - type: recall_at_100
      value: 34.174
    - type: recall_at_1000
      value: 56.879999999999995
    - type: recall_at_3
      value: 10.732999999999999
    - type: recall_at_5
      value: 14.113000000000001
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 3.101
    - type: map_at_10
      value: 5.434
    - type: map_at_100
      value: 6.267
    - type: map_at_1000
      value: 6.418
    - type: map_at_3
      value: 4.377000000000001
    - type: map_at_5
      value: 4.841
    - type: mrr_at_1
      value: 7.166
    - type: mrr_at_10
      value: 12.012
    - type: mrr_at_100
      value: 13.144
    - type: mrr_at_1000
      value: 13.229
    - type: mrr_at_3
      value: 9.826
    - type: mrr_at_5
      value: 10.921
    - type: ndcg_at_1
      value: 7.166
    - type: ndcg_at_10
      value: 8.687000000000001
    - type: ndcg_at_100
      value: 13.345
    - type: ndcg_at_1000
      value: 16.915
    - type: ndcg_at_3
      value: 6.276
    - type: ndcg_at_5
      value: 7.013
    - type: precision_at_1
      value: 7.166
    - type: precision_at_10
      value: 2.9250000000000003
    - type: precision_at_100
      value: 0.771
    - type: precision_at_1000
      value: 0.13999999999999999
    - type: precision_at_3
      value: 4.734
    - type: precision_at_5
      value: 3.8830000000000005
    - type: recall_at_1
      value: 3.101
    - type: recall_at_10
      value: 11.774999999999999
    - type: recall_at_100
      value: 28.819
    - type: recall_at_1000
      value: 49.886
    - type: recall_at_3
      value: 5.783
    - type: recall_at_5
      value: 7.692
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 2.758
    - type: map_at_10
      value: 5.507
    - type: map_at_100
      value: 7.1819999999999995
    - type: map_at_1000
      value: 7.652
    - type: map_at_3
      value: 4.131
    - type: map_at_5
      value: 4.702
    - type: mrr_at_1
      value: 28.499999999999996
    - type: mrr_at_10
      value: 37.693
    - type: mrr_at_100
      value: 38.657000000000004
    - type: mrr_at_1000
      value: 38.704
    - type: mrr_at_3
      value: 34.792
    - type: mrr_at_5
      value: 36.417
    - type: ndcg_at_1
      value: 20.625
    - type: ndcg_at_10
      value: 14.771999999999998
    - type: ndcg_at_100
      value: 16.821
    - type: ndcg_at_1000
      value: 21.546000000000003
    - type: ndcg_at_3
      value: 16.528000000000002
    - type: ndcg_at_5
      value: 15.573
    - type: precision_at_1
      value: 28.499999999999996
    - type: precision_at_10
      value: 12.25
    - type: precision_at_100
      value: 3.7600000000000002
    - type: precision_at_1000
      value: 0.86
    - type: precision_at_3
      value: 19.167
    - type: precision_at_5
      value: 16.25
    - type: recall_at_1
      value: 2.758
    - type: recall_at_10
      value: 9.164
    - type: recall_at_100
      value: 21.022
    - type: recall_at_1000
      value: 37.053999999999995
    - type: recall_at_3
      value: 5.112
    - type: recall_at_5
      value: 6.413
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 28.53554681148413
    - type: mrr
      value: 29.290078704990325
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 76.52926207453477
    - type: cos_sim_spearman
      value: 68.98528351149498
    - type: euclidean_pearson
      value: 73.7744559091218
    - type: euclidean_spearman
      value: 69.03481995814735
    - type: manhattan_pearson
      value: 73.72818267270651
    - type: manhattan_spearman
      value: 69.00576442086793
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 61.71540153163407
    - type: cos_sim_spearman
      value: 58.502746406116614
    - type: euclidean_pearson
      value: 60.82817999438477
    - type: euclidean_spearman
      value: 58.988494433752756
    - type: manhattan_pearson
      value: 60.87147859170236
    - type: manhattan_spearman
      value: 59.03527382025516
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 72.89990498692094
    - type: cos_sim_spearman
      value: 74.03028513377879
    - type: euclidean_pearson
      value: 73.8252088833803
    - type: euclidean_spearman
      value: 74.15554246478399
    - type: manhattan_pearson
      value: 73.80947397334666
    - type: manhattan_spearman
      value: 74.13117958176566
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 70.67974206005906
    - type: cos_sim_spearman
      value: 66.18263558486296
    - type: euclidean_pearson
      value: 69.5048876024341
    - type: euclidean_spearman
      value: 66.36380457878391
    - type: manhattan_pearson
      value: 69.4895372451589
    - type: manhattan_spearman
      value: 66.36941569935124
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 73.99856913569187
    - type: cos_sim_spearman
      value: 75.54712054246464
    - type: euclidean_pearson
      value: 74.55692573876115
    - type: euclidean_spearman
      value: 75.34499056740096
    - type: manhattan_pearson
      value: 74.59342318869683
    - type: manhattan_spearman
      value: 75.35708317926819
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 72.3343670787494
    - type: cos_sim_spearman
      value: 73.7136650302399
    - type: euclidean_pearson
      value: 73.86004257913046
    - type: euclidean_spearman
      value: 73.9557418048638
    - type: manhattan_pearson
      value: 73.78919091538661
    - type: manhattan_spearman
      value: 73.86316425954108
  - 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: 79.08159601556619
    - type: cos_sim_spearman
      value: 80.13910828685532
    - type: euclidean_pearson
      value: 79.39197806617453
    - type: euclidean_spearman
      value: 79.85692277871196
    - type: manhattan_pearson
      value: 79.32452246324705
    - type: manhattan_spearman
      value: 79.70120373587193
  - 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: 62.29720207747786
    - type: cos_sim_spearman
      value: 65.65260681394685
    - type: euclidean_pearson
      value: 64.49002165983158
    - type: euclidean_spearman
      value: 65.25917651158736
    - type: manhattan_pearson
      value: 64.49981108236335
    - type: manhattan_spearman
      value: 65.20426825202405
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 71.1871068550574
    - type: cos_sim_spearman
      value: 71.40167034949341
    - type: euclidean_pearson
      value: 72.2373684855404
    - type: euclidean_spearman
      value: 71.90255429812984
    - type: manhattan_pearson
      value: 72.23173532049509
    - type: manhattan_spearman
      value: 71.87843489689064
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 68.65000574464773
    - type: mrr
      value: 88.29363084265044
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 40.76107749144358
    - type: mrr
      value: 41.03689202953908
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 28.68520527813894
    - type: cos_sim_spearman
      value: 29.017620841627433
    - type: dot_pearson
      value: 29.25380949876322
    - type: dot_spearman
      value: 29.33885250837327
---

# {MODEL_NAME}

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```



## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```
