sentence-transformers safetensors bert mteb sentence-similarity model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- mteb
model-index:
- name: b1ade_embed_kd
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification
      type: mteb/amazon_counterfactual
      config: default
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 75.81709145427287
    - type: ap
      value: 23.581082591688467
    - type: f1
      value: 62.54637626017967
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 80.300125
    - type: ap
      value: 74.26836190039964
    - type: f1
      value: 80.2158066692679
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification
      type: mteb/amazon_reviews_multi
      config: default
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 43.084
    - type: f1
      value: 42.66774553366831
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: mteb/arguana
      config: default
      split: test
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
    metrics:
    - type: map_at_1
      value: 29.232000000000003
    - type: map_at_10
      value: 45.777
    - type: map_at_100
      value: 46.634
    - type: map_at_1000
      value: 46.64
    - type: map_at_20
      value: 46.489000000000004
    - type: map_at_3
      value: 40.861
    - type: map_at_5
      value: 43.659
    - type: mrr_at_1
      value: 30.156
    - type: mrr_at_10
      value: 46.141
    - type: mrr_at_100
      value: 46.983999999999995
    - type: mrr_at_1000
      value: 46.989999999999995
    - type: mrr_at_20
      value: 46.839
    - type: mrr_at_3
      value: 41.157
    - type: mrr_at_5
      value: 44.013000000000005
    - type: ndcg_at_1
      value: 29.232000000000003
    - type: ndcg_at_10
      value: 54.832
    - type: ndcg_at_100
      value: 58.303000000000004
    - type: ndcg_at_1000
      value: 58.451
    - type: ndcg_at_20
      value: 57.328
    - type: ndcg_at_3
      value: 44.685
    - type: ndcg_at_5
      value: 49.756
    - type: precision_at_1
      value: 29.232000000000003
    - type: precision_at_10
      value: 8.371
    - type: precision_at_100
      value: 0.985
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_20
      value: 4.6690000000000005
    - type: precision_at_3
      value: 18.587
    - type: precision_at_5
      value: 13.627
    - type: recall_at_1
      value: 29.232000000000003
    - type: recall_at_10
      value: 83.71300000000001
    - type: recall_at_100
      value: 98.506
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_20
      value: 93.38499999999999
    - type: recall_at_3
      value: 55.761
    - type: recall_at_5
      value: 68.137
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 45.801946024895756
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 37.639210206045206
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 57.589359041891576
    - type: mrr
      value: 70.88334872268389
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 86.63594177060354
    - type: cos_sim_spearman
      value: 84.75132870687939
    - type: euclidean_pearson
      value: 85.05646621990854
    - type: euclidean_spearman
      value: 84.68686940680522
    - type: manhattan_pearson
      value: 85.08705700579426
    - type: manhattan_spearman
      value: 84.83446313127413
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 85.1948051948052
    - type: f1
      value: 85.13229898343104
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 38.68616898014911
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 34.45376891835619
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: mteb/cqadupstack-android
      config: default
      split: test
      revision: f46a197baaae43b4f621051089b82a364682dfeb
    metrics:
    - type: map_at_1
      value: 26.340000000000003
    - type: map_at_10
      value: 36.513
    - type: map_at_100
      value: 37.968
    - type: map_at_1000
      value: 38.107
    - type: map_at_20
      value: 37.355
    - type: map_at_3
      value: 33.153
    - type: map_at_5
      value: 34.899
    - type: mrr_at_1
      value: 33.763
    - type: mrr_at_10
      value: 42.778
    - type: mrr_at_100
      value: 43.667
    - type: mrr_at_1000
      value: 43.724000000000004
    - type: mrr_at_20
      value: 43.349
    - type: mrr_at_3
      value: 40.32
    - type: mrr_at_5
      value: 41.657
    - type: ndcg_at_1
      value: 33.763
    - type: ndcg_at_10
      value: 42.783
    - type: ndcg_at_100
      value: 48.209999999999994
    - type: ndcg_at_1000
      value: 50.678999999999995
    - type: ndcg_at_20
      value: 45.073
    - type: ndcg_at_3
      value: 37.841
    - type: ndcg_at_5
      value: 39.818999999999996
    - type: precision_at_1
      value: 33.763
    - type: precision_at_10
      value: 8.398
    - type: precision_at_100
      value: 1.396
    - type: precision_at_1000
      value: 0.188
    - type: precision_at_20
      value: 5.0569999999999995
    - type: precision_at_3
      value: 18.503
    - type: precision_at_5
      value: 13.219
    - type: recall_at_1
      value: 26.340000000000003
    - type: recall_at_10
      value: 54.603
    - type: recall_at_100
      value: 77.264
    - type: recall_at_1000
      value: 93.882
    - type: recall_at_20
      value: 63.101
    - type: recall_at_3
      value: 39.6
    - type: recall_at_5
      value: 45.651
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackEnglishRetrieval
      type: mteb/cqadupstack-english
      config: default
      split: test
      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
    metrics:
    - type: map_at_1
      value: 24.313000000000002
    - type: map_at_10
      value: 33.225
    - type: map_at_100
      value: 34.293
    - type: map_at_1000
      value: 34.421
    - type: map_at_20
      value: 33.818
    - type: map_at_3
      value: 30.545
    - type: map_at_5
      value: 32.144
    - type: mrr_at_1
      value: 31.083
    - type: mrr_at_10
      value: 39.199
    - type: mrr_at_100
      value: 39.835
    - type: mrr_at_1000
      value: 39.892
    - type: mrr_at_20
      value: 39.57
    - type: mrr_at_3
      value: 36.879
    - type: mrr_at_5
      value: 38.245000000000005
    - type: ndcg_at_1
      value: 31.083
    - type: ndcg_at_10
      value: 38.553
    - type: ndcg_at_100
      value: 42.685
    - type: ndcg_at_1000
      value: 45.144
    - type: ndcg_at_20
      value: 40.116
    - type: ndcg_at_3
      value: 34.608
    - type: ndcg_at_5
      value: 36.551
    - type: precision_at_1
      value: 31.083
    - type: precision_at_10
      value: 7.28
    - type: precision_at_100
      value: 1.183
    - type: precision_at_1000
      value: 0.168
    - type: precision_at_20
      value: 4.322
    - type: precision_at_3
      value: 16.858
    - type: precision_at_5
      value: 12.127
    - type: recall_at_1
      value: 24.313000000000002
    - type: recall_at_10
      value: 48.117
    - type: recall_at_100
      value: 65.768
    - type: recall_at_1000
      value: 81.935
    - type: recall_at_20
      value: 53.689
    - type: recall_at_3
      value: 36.335
    - type: recall_at_5
      value: 41.803000000000004
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackGamingRetrieval
      type: mteb/cqadupstack-gaming
      config: default
      split: test
      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
    metrics:
    - type: map_at_1
      value: 33.013999999999996
    - type: map_at_10
      value: 44.567
    - type: map_at_100
      value: 45.664
    - type: map_at_1000
      value: 45.732
    - type: map_at_20
      value: 45.190000000000005
    - type: map_at_3
      value: 41.393
    - type: map_at_5
      value: 43.147000000000006
    - type: mrr_at_1
      value: 37.806
    - type: mrr_at_10
      value: 47.841
    - type: mrr_at_100
      value: 48.597
    - type: mrr_at_1000
      value: 48.638
    - type: mrr_at_20
      value: 48.262
    - type: mrr_at_3
      value: 45.361000000000004
    - type: mrr_at_5
      value: 46.803
    - type: ndcg_at_1
      value: 37.806
    - type: ndcg_at_10
      value: 50.412
    - type: ndcg_at_100
      value: 55.019
    - type: ndcg_at_1000
      value: 56.52
    - type: ndcg_at_20
      value: 52.23100000000001
    - type: ndcg_at_3
      value: 44.944
    - type: ndcg_at_5
      value: 47.535
    - type: precision_at_1
      value: 37.806
    - type: precision_at_10
      value: 8.351
    - type: precision_at_100
      value: 1.163
    - type: precision_at_1000
      value: 0.134
    - type: precision_at_20
      value: 4.727
    - type: precision_at_3
      value: 20.376
    - type: precision_at_5
      value: 14.056
    - type: recall_at_1
      value: 33.013999999999996
    - type: recall_at_10
      value: 64.35600000000001
    - type: recall_at_100
      value: 84.748
    - type: recall_at_1000
      value: 95.525
    - type: recall_at_20
      value: 71.137
    - type: recall_at_3
      value: 49.726
    - type: recall_at_5
      value: 56.054
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackGisRetrieval
      type: mteb/cqadupstack-gis
      config: default
      split: test
      revision: 5003b3064772da1887988e05400cf3806fe491f2
    metrics:
    - type: map_at_1
      value: 18.476
    - type: map_at_10
      value: 24.715999999999998
    - type: map_at_100
      value: 25.72
    - type: map_at_1000
      value: 25.826999999999998
    - type: map_at_20
      value: 25.276
    - type: map_at_3
      value: 22.656000000000002
    - type: map_at_5
      value: 23.737
    - type: mrr_at_1
      value: 20.113
    - type: mrr_at_10
      value: 26.423999999999996
    - type: mrr_at_100
      value: 27.328000000000003
    - type: mrr_at_1000
      value: 27.418
    - type: mrr_at_20
      value: 26.936
    - type: mrr_at_3
      value: 24.275
    - type: mrr_at_5
      value: 25.501
    - type: ndcg_at_1
      value: 20.113
    - type: ndcg_at_10
      value: 28.626
    - type: ndcg_at_100
      value: 33.649
    - type: ndcg_at_1000
      value: 36.472
    - type: ndcg_at_20
      value: 30.581999999999997
    - type: ndcg_at_3
      value: 24.490000000000002
    - type: ndcg_at_5
      value: 26.394000000000002
    - type: precision_at_1
      value: 20.113
    - type: precision_at_10
      value: 4.52
    - type: precision_at_100
      value: 0.739
    - type: precision_at_1000
      value: 0.10200000000000001
    - type: precision_at_20
      value: 2.706
    - type: precision_at_3
      value: 10.433
    - type: precision_at_5
      value: 7.48
    - type: recall_at_1
      value: 18.476
    - type: recall_at_10
      value: 39.129000000000005
    - type: recall_at_100
      value: 62.44
    - type: recall_at_1000
      value: 83.95700000000001
    - type: recall_at_20
      value: 46.611999999999995
    - type: recall_at_3
      value: 27.772000000000002
    - type: recall_at_5
      value: 32.312000000000005
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackMathematicaRetrieval
      type: mteb/cqadupstack-mathematica
      config: default
      split: test
      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
    metrics:
    - type: map_at_1
      value: 10.126
    - type: map_at_10
      value: 15.916
    - type: map_at_100
      value: 17.049
    - type: map_at_1000
      value: 17.19
    - type: map_at_20
      value: 16.569
    - type: map_at_3
      value: 13.986
    - type: map_at_5
      value: 15.052999999999999
    - type: mrr_at_1
      value: 13.059999999999999
    - type: mrr_at_10
      value: 19.52
    - type: mrr_at_100
      value: 20.599999999999998
    - type: mrr_at_1000
      value: 20.693
    - type: mrr_at_20
      value: 20.177999999999997
    - type: mrr_at_3
      value: 17.496000000000002
    - type: mrr_at_5
      value: 18.541
    - type: ndcg_at_1
      value: 13.059999999999999
    - type: ndcg_at_10
      value: 19.987
    - type: ndcg_at_100
      value: 25.602000000000004
    - type: ndcg_at_1000
      value: 29.171999999999997
    - type: ndcg_at_20
      value: 22.31
    - type: ndcg_at_3
      value: 16.286
    - type: ndcg_at_5
      value: 17.931
    - type: precision_at_1
      value: 13.059999999999999
    - type: precision_at_10
      value: 3.9050000000000002
    - type: precision_at_100
      value: 0.771
    - type: precision_at_1000
      value: 0.123
    - type: precision_at_20
      value: 2.606
    - type: precision_at_3
      value: 8.167
    - type: precision_at_5
      value: 6.045
    - type: recall_at_1
      value: 10.126
    - type: recall_at_10
      value: 29.137
    - type: recall_at_100
      value: 53.824000000000005
    - type: recall_at_1000
      value: 79.373
    - type: recall_at_20
      value: 37.475
    - type: recall_at_3
      value: 18.791
    - type: recall_at_5
      value: 22.993
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackPhysicsRetrieval
      type: mteb/cqadupstack-physics
      config: default
      split: test
      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
    metrics:
    - type: map_at_1
      value: 25.281
    - type: map_at_10
      value: 34.875
    - type: map_at_100
      value: 36.268
    - type: map_at_1000
      value: 36.385
    - type: map_at_20
      value: 35.711999999999996
    - type: map_at_3
      value: 31.808999999999997
    - type: map_at_5
      value: 33.550999999999995
    - type: mrr_at_1
      value: 31.28
    - type: mrr_at_10
      value: 40.489000000000004
    - type: mrr_at_100
      value: 41.434
    - type: mrr_at_1000
      value: 41.491
    - type: mrr_at_20
      value: 41.088
    - type: mrr_at_3
      value: 38.033
    - type: mrr_at_5
      value: 39.621
    - type: ndcg_at_1
      value: 31.28
    - type: ndcg_at_10
      value: 40.716
    - type: ndcg_at_100
      value: 46.45
    - type: ndcg_at_1000
      value: 48.851
    - type: ndcg_at_20
      value: 43.216
    - type: ndcg_at_3
      value: 35.845
    - type: ndcg_at_5
      value: 38.251000000000005
    - type: precision_at_1
      value: 31.28
    - type: precision_at_10
      value: 7.623
    - type: precision_at_100
      value: 1.214
    - type: precision_at_1000
      value: 0.159
    - type: precision_at_20
      value: 4.625
    - type: precision_at_3
      value: 17.26
    - type: precision_at_5
      value: 12.435
    - type: recall_at_1
      value: 25.281
    - type: recall_at_10
      value: 52.476
    - type: recall_at_100
      value: 76.535
    - type: recall_at_1000
      value: 92.658
    - type: recall_at_20
      value: 61.211000000000006
    - type: recall_at_3
      value: 38.805
    - type: recall_at_5
      value: 45.053
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackProgrammersRetrieval
      type: mteb/cqadupstack-programmers
      config: default
      split: test
      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
    metrics:
    - type: map_at_1
      value: 20.092
    - type: map_at_10
      value: 27.805999999999997
    - type: map_at_100
      value: 29.137999999999998
    - type: map_at_1000
      value: 29.266
    - type: map_at_20
      value: 28.587
    - type: map_at_3
      value: 25.112000000000002
    - type: map_at_5
      value: 26.551000000000002
    - type: mrr_at_1
      value: 24.315
    - type: mrr_at_10
      value: 32.068000000000005
    - type: mrr_at_100
      value: 33.039
    - type: mrr_at_1000
      value: 33.114
    - type: mrr_at_20
      value: 32.66
    - type: mrr_at_3
      value: 29.49
    - type: mrr_at_5
      value: 30.906
    - type: ndcg_at_1
      value: 24.315
    - type: ndcg_at_10
      value: 32.9
    - type: ndcg_at_100
      value: 38.741
    - type: ndcg_at_1000
      value: 41.657
    - type: ndcg_at_20
      value: 35.338
    - type: ndcg_at_3
      value: 28.069
    - type: ndcg_at_5
      value: 30.169
    - type: precision_at_1
      value: 24.315
    - type: precision_at_10
      value: 6.2330000000000005
    - type: precision_at_100
      value: 1.072
    - type: precision_at_1000
      value: 0.15
    - type: precision_at_20
      value: 3.8580000000000005
    - type: precision_at_3
      value: 13.318
    - type: precision_at_5
      value: 9.748999999999999
    - type: recall_at_1
      value: 20.092
    - type: recall_at_10
      value: 43.832
    - type: recall_at_100
      value: 68.75099999999999
    - type: recall_at_1000
      value: 89.25
    - type: recall_at_20
      value: 52.445
    - type: recall_at_3
      value: 30.666
    - type: recall_at_5
      value: 35.873
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackRetrieval
      type: mteb/cqadupstack
      config: default
      split: test
      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
    metrics:
    - type: map_at_1
      value: 19.317
    - type: map_at_10
      value: 26.653
    - type: map_at_100
      value: 28.011999999999997
    - type: map_at_1000
      value: 28.231
    - type: map_at_20
      value: 27.301
    - type: map_at_3
      value: 23.763
    - type: map_at_5
      value: 25.391000000000002
    - type: mrr_at_1
      value: 24.506
    - type: mrr_at_10
      value: 31.991999999999997
    - type: mrr_at_100
      value: 32.924
    - type: mrr_at_1000
      value: 32.993
    - type: mrr_at_20
      value: 32.521
    - type: mrr_at_3
      value: 29.48
    - type: mrr_at_5
      value: 30.982
    - type: ndcg_at_1
      value: 24.506
    - type: ndcg_at_10
      value: 32.202999999999996
    - type: ndcg_at_100
      value: 37.797
    - type: ndcg_at_1000
      value: 40.859
    - type: ndcg_at_20
      value: 34.098
    - type: ndcg_at_3
      value: 27.552
    - type: ndcg_at_5
      value: 29.781000000000002
    - type: precision_at_1
      value: 24.506
    - type: precision_at_10
      value: 6.462
    - type: precision_at_100
      value: 1.35
    - type: precision_at_1000
      value: 0.22499999999999998
    - type: precision_at_20
      value: 4.071000000000001
    - type: precision_at_3
      value: 13.241
    - type: precision_at_5
      value: 9.921000000000001
    - type: recall_at_1
      value: 19.317
    - type: recall_at_10
      value: 42.296
    - type: recall_at_100
      value: 68.2
    - type: recall_at_1000
      value: 88.565
    - type: recall_at_20
      value: 49.883
    - type: recall_at_3
      value: 28.608
    - type: recall_at_5
      value: 34.854
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackStatsRetrieval
      type: mteb/cqadupstack-stats
      config: default
      split: test
      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
    metrics:
    - type: map_at_1
      value: 18.0
    - type: map_at_10
      value: 24.444
    - type: map_at_100
      value: 25.205
    - type: map_at_1000
      value: 25.291000000000004
    - type: map_at_20
      value: 24.834
    - type: map_at_3
      value: 22.311
    - type: map_at_5
      value: 23.442
    - type: mrr_at_1
      value: 20.552
    - type: mrr_at_10
      value: 27.028999999999996
    - type: mrr_at_100
      value: 27.706999999999997
    - type: mrr_at_1000
      value: 27.775
    - type: mrr_at_20
      value: 27.366
    - type: mrr_at_3
      value: 25.051000000000002
    - type: mrr_at_5
      value: 26.063
    - type: ndcg_at_1
      value: 20.552
    - type: ndcg_at_10
      value: 28.519
    - type: ndcg_at_100
      value: 32.580999999999996
    - type: ndcg_at_1000
      value: 34.99
    - type: ndcg_at_20
      value: 29.848000000000003
    - type: ndcg_at_3
      value: 24.46
    - type: ndcg_at_5
      value: 26.273000000000003
    - type: precision_at_1
      value: 20.552
    - type: precision_at_10
      value: 4.801
    - type: precision_at_100
      value: 0.729
    - type: precision_at_1000
      value: 0.101
    - type: precision_at_20
      value: 2.715
    - type: precision_at_3
      value: 10.940999999999999
    - type: precision_at_5
      value: 7.761
    - type: recall_at_1
      value: 18.0
    - type: recall_at_10
      value: 38.425
    - type: recall_at_100
      value: 57.885
    - type: recall_at_1000
      value: 75.945
    - type: recall_at_20
      value: 43.472
    - type: recall_at_3
      value: 27.483
    - type: recall_at_5
      value: 31.866
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackTexRetrieval
      type: mteb/cqadupstack-tex
      config: default
      split: test
      revision: 46989137a86843e03a6195de44b09deda022eec7
    metrics:
    - type: map_at_1
      value: 10.014000000000001
    - type: map_at_10
      value: 14.462
    - type: map_at_100
      value: 15.364
    - type: map_at_1000
      value: 15.482999999999999
    - type: map_at_20
      value: 14.931
    - type: map_at_3
      value: 12.842
    - type: map_at_5
      value: 13.697999999999999
    - type: mrr_at_1
      value: 12.526000000000002
    - type: mrr_at_10
      value: 17.433
    - type: mrr_at_100
      value: 18.296
    - type: mrr_at_1000
      value: 18.383
    - type: mrr_at_20
      value: 17.897
    - type: mrr_at_3
      value: 15.703
    - type: mrr_at_5
      value: 16.627
    - type: ndcg_at_1
      value: 12.526000000000002
    - type: ndcg_at_10
      value: 17.697
    - type: ndcg_at_100
      value: 22.33
    - type: ndcg_at_1000
      value: 25.587
    - type: ndcg_at_20
      value: 19.302
    - type: ndcg_at_3
      value: 14.606
    - type: ndcg_at_5
      value: 15.946
    - type: precision_at_1
      value: 12.526000000000002
    - type: precision_at_10
      value: 3.383
    - type: precision_at_100
      value: 0.6799999999999999
    - type: precision_at_1000
      value: 0.11199999999999999
    - type: precision_at_20
      value: 2.147
    - type: precision_at_3
      value: 7.02
    - type: precision_at_5
      value: 5.196
    - type: recall_at_1
      value: 10.014000000000001
    - type: recall_at_10
      value: 24.623
    - type: recall_at_100
      value: 45.795
    - type: recall_at_1000
      value: 69.904
    - type: recall_at_20
      value: 30.534
    - type: recall_at_3
      value: 15.955
    - type: recall_at_5
      value: 19.394
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackUnixRetrieval
      type: mteb/cqadupstack-unix
      config: default
      split: test
      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
    metrics:
    - type: map_at_1
      value: 19.156000000000002
    - type: map_at_10
      value: 26.144000000000002
    - type: map_at_100
      value: 27.157999999999998
    - type: map_at_1000
      value: 27.288
    - type: map_at_20
      value: 26.689
    - type: map_at_3
      value: 24.125
    - type: map_at_5
      value: 25.369000000000003
    - type: mrr_at_1
      value: 22.854
    - type: mrr_at_10
      value: 29.874000000000002
    - type: mrr_at_100
      value: 30.738
    - type: mrr_at_1000
      value: 30.826999999999998
    - type: mrr_at_20
      value: 30.354
    - type: mrr_at_3
      value: 27.689999999999998
    - type: mrr_at_5
      value: 29.131
    - type: ndcg_at_1
      value: 22.854
    - type: ndcg_at_10
      value: 30.469
    - type: ndcg_at_100
      value: 35.475
    - type: ndcg_at_1000
      value: 38.59
    - type: ndcg_at_20
      value: 32.333
    - type: ndcg_at_3
      value: 26.674999999999997
    - type: ndcg_at_5
      value: 28.707
    - type: precision_at_1
      value: 22.854
    - type: precision_at_10
      value: 5.1209999999999996
    - type: precision_at_100
      value: 0.8500000000000001
    - type: precision_at_1000
      value: 0.123
    - type: precision_at_20
      value: 3.0460000000000003
    - type: precision_at_3
      value: 12.127
    - type: precision_at_5
      value: 8.75
    - type: recall_at_1
      value: 19.156000000000002
    - type: recall_at_10
      value: 40.009
    - type: recall_at_100
      value: 62.419999999999995
    - type: recall_at_1000
      value: 84.585
    - type: recall_at_20
      value: 46.912
    - type: recall_at_3
      value: 29.733999999999998
    - type: recall_at_5
      value: 34.741
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackWebmastersRetrieval
      type: mteb/cqadupstack-webmasters
      config: default
      split: test
      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
    metrics:
    - type: map_at_1
      value: 19.317
    - type: map_at_10
      value: 26.653
    - type: map_at_100
      value: 28.011999999999997
    - type: map_at_1000
      value: 28.231
    - type: map_at_20
      value: 27.301
    - type: map_at_3
      value: 23.763
    - type: map_at_5
      value: 25.391000000000002
    - type: mrr_at_1
      value: 24.506
    - type: mrr_at_10
      value: 31.991999999999997
    - type: mrr_at_100
      value: 32.924
    - type: mrr_at_1000
      value: 32.993
    - type: mrr_at_20
      value: 32.521
    - type: mrr_at_3
      value: 29.48
    - type: mrr_at_5
      value: 30.982
    - type: ndcg_at_1
      value: 24.506
    - type: ndcg_at_10
      value: 32.202999999999996
    - type: ndcg_at_100
      value: 37.797
    - type: ndcg_at_1000
      value: 40.859
    - type: ndcg_at_20
      value: 34.098
    - type: ndcg_at_3
      value: 27.552
    - type: ndcg_at_5
      value: 29.781000000000002
    - type: precision_at_1
      value: 24.506
    - type: precision_at_10
      value: 6.462
    - type: precision_at_100
      value: 1.35
    - type: precision_at_1000
      value: 0.22499999999999998
    - type: precision_at_20
      value: 4.071000000000001
    - type: precision_at_3
      value: 13.241
    - type: precision_at_5
      value: 9.921000000000001
    - type: recall_at_1
      value: 19.317
    - type: recall_at_10
      value: 42.296
    - type: recall_at_100
      value: 68.2
    - type: recall_at_1000
      value: 88.565
    - type: recall_at_20
      value: 49.883
    - type: recall_at_3
      value: 28.608
    - type: recall_at_5
      value: 34.854
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackWordpressRetrieval
      type: mteb/cqadupstack-wordpress
      config: default
      split: test
      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
    metrics:
    - type: map_at_1
      value: 12.822
    - type: map_at_10
      value: 18.055
    - type: map_at_100
      value: 18.942
    - type: map_at_1000
      value: 19.057
    - type: map_at_20
      value: 18.544
    - type: map_at_3
      value: 15.964
    - type: map_at_5
      value: 16.833000000000002
    - type: mrr_at_1
      value: 14.048
    - type: mrr_at_10
      value: 19.489
    - type: mrr_at_100
      value: 20.392
    - type: mrr_at_1000
      value: 20.49
    - type: mrr_at_20
      value: 19.979
    - type: mrr_at_3
      value: 17.344
    - type: mrr_at_5
      value: 18.287
    - type: ndcg_at_1
      value: 14.048
    - type: ndcg_at_10
      value: 21.737000000000002
    - type: ndcg_at_100
      value: 26.383000000000003
    - type: ndcg_at_1000
      value: 29.555
    - type: ndcg_at_20
      value: 23.463
    - type: ndcg_at_3
      value: 17.29
    - type: ndcg_at_5
      value: 18.829
    - type: precision_at_1
      value: 14.048
    - type: precision_at_10
      value: 3.6229999999999998
    - type: precision_at_100
      value: 0.641
    - type: precision_at_1000
      value: 0.099
    - type: precision_at_20
      value: 2.1999999999999997
    - type: precision_at_3
      value: 7.2090000000000005
    - type: precision_at_5
      value: 5.213
    - type: recall_at_1
      value: 12.822
    - type: recall_at_10
      value: 32.123000000000005
    - type: recall_at_100
      value: 53.657999999999994
    - type: recall_at_1000
      value: 77.72200000000001
    - type: recall_at_20
      value: 38.66
    - type: recall_at_3
      value: 19.814999999999998
    - type: recall_at_5
      value: 23.432
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: mteb/climate-fever
      config: default
      split: test
      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
    metrics:
    - type: map_at_1
      value: 13.119
    - type: map_at_10
      value: 22.999
    - type: map_at_100
      value: 25.108000000000004
    - type: map_at_1000
      value: 25.306
    - type: map_at_20
      value: 24.141000000000002
    - type: map_at_3
      value: 19.223000000000003
    - type: map_at_5
      value: 21.181
    - type: mrr_at_1
      value: 30.554
    - type: mrr_at_10
      value: 42.553000000000004
    - type: mrr_at_100
      value: 43.498
    - type: mrr_at_1000
      value: 43.527
    - type: mrr_at_20
      value: 43.193
    - type: mrr_at_3
      value: 39.283
    - type: mrr_at_5
      value: 41.143
    - type: ndcg_at_1
      value: 30.554
    - type: ndcg_at_10
      value: 31.946
    - type: ndcg_at_100
      value: 39.934999999999995
    - type: ndcg_at_1000
      value: 43.256
    - type: ndcg_at_20
      value: 35.101
    - type: ndcg_at_3
      value: 26.489
    - type: ndcg_at_5
      value: 28.272000000000002
    - type: precision_at_1
      value: 30.554
    - type: precision_at_10
      value: 10.039
    - type: precision_at_100
      value: 1.864
    - type: precision_at_1000
      value: 0.248
    - type: precision_at_20
      value: 6.371
    - type: precision_at_3
      value: 20.174
    - type: precision_at_5
      value: 15.296000000000001
    - type: recall_at_1
      value: 13.119
    - type: recall_at_10
      value: 37.822
    - type: recall_at_100
      value: 65.312
    - type: recall_at_1000
      value: 83.817
    - type: recall_at_20
      value: 46.760000000000005
    - type: recall_at_3
      value: 23.858999999999998
    - type: recall_at_5
      value: 29.609999999999996
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: mteb/dbpedia
      config: default
      split: test
      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
    metrics:
    - type: map_at_1
      value: 8.176
    - type: map_at_10
      value: 19.594
    - type: map_at_100
      value: 28.081
    - type: map_at_1000
      value: 29.864
    - type: map_at_20
      value: 22.983999999999998
    - type: map_at_3
      value: 13.923
    - type: map_at_5
      value: 16.597
    - type: mrr_at_1
      value: 66.75
    - type: mrr_at_10
      value: 75.82600000000001
    - type: mrr_at_100
      value: 76.145
    - type: mrr_at_1000
      value: 76.14999999999999
    - type: mrr_at_20
      value: 76.074
    - type: mrr_at_3
      value: 74.333
    - type: mrr_at_5
      value: 75.25800000000001
    - type: ndcg_at_1
      value: 54.50000000000001
    - type: ndcg_at_10
      value: 41.806
    - type: ndcg_at_100
      value: 47.067
    - type: ndcg_at_1000
      value: 54.397
    - type: ndcg_at_20
      value: 41.727
    - type: ndcg_at_3
      value: 46.92
    - type: ndcg_at_5
      value: 44.381
    - type: precision_at_1
      value: 66.75
    - type: precision_at_10
      value: 33.35
    - type: precision_at_100
      value: 10.92
    - type: precision_at_1000
      value: 2.222
    - type: precision_at_20
      value: 25.862000000000002
    - type: precision_at_3
      value: 51.417
    - type: precision_at_5
      value: 43.65
    - type: recall_at_1
      value: 8.176
    - type: recall_at_10
      value: 26.029000000000003
    - type: recall_at_100
      value: 53.872
    - type: recall_at_1000
      value: 76.895
    - type: recall_at_20
      value: 34.192
    - type: recall_at_3
      value: 15.789
    - type: recall_at_5
      value: 20.255000000000003
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 48.22
    - type: f1
      value: 43.59074485488622
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: mteb/fever
      config: default
      split: test
      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
    metrics:
    - type: map_at_1
      value: 40.872
    - type: map_at_10
      value: 55.178000000000004
    - type: map_at_100
      value: 55.859
    - type: map_at_1000
      value: 55.881
    - type: map_at_20
      value: 55.66
    - type: map_at_3
      value: 51.4
    - type: map_at_5
      value: 53.754000000000005
    - type: mrr_at_1
      value: 43.744
    - type: mrr_at_10
      value: 58.36900000000001
    - type: mrr_at_100
      value: 58.911
    - type: mrr_at_1000
      value: 58.916999999999994
    - type: mrr_at_20
      value: 58.779
    - type: mrr_at_3
      value: 54.653
    - type: mrr_at_5
      value: 56.987
    - type: ndcg_at_1
      value: 43.744
    - type: ndcg_at_10
      value: 62.936
    - type: ndcg_at_100
      value: 65.666
    - type: ndcg_at_1000
      value: 66.08699999999999
    - type: ndcg_at_20
      value: 64.548
    - type: ndcg_at_3
      value: 55.543
    - type: ndcg_at_5
      value: 59.646
    - type: precision_at_1
      value: 43.744
    - type: precision_at_10
      value: 9.191
    - type: precision_at_100
      value: 1.072
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_20
      value: 4.967
    - type: precision_at_3
      value: 23.157
    - type: precision_at_5
      value: 16.115
    - type: recall_at_1
      value: 40.872
    - type: recall_at_10
      value: 83.818
    - type: recall_at_100
      value: 95.14200000000001
    - type: recall_at_1000
      value: 97.897
    - type: recall_at_20
      value: 89.864
    - type: recall_at_3
      value: 64.19200000000001
    - type: recall_at_5
      value: 74.029
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: mteb/fiqa
      config: default
      split: test
      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
    metrics:
    - type: map_at_1
      value: 14.804999999999998
    - type: map_at_10
      value: 22.86
    - type: map_at_100
      value: 24.823999999999998
    - type: map_at_1000
      value: 25.041000000000004
    - type: map_at_20
      value: 23.881
    - type: map_at_3
      value: 20.09
    - type: map_at_5
      value: 21.39
    - type: mrr_at_1
      value: 29.938
    - type: mrr_at_10
      value: 37.041000000000004
    - type: mrr_at_100
      value: 38.196000000000005
    - type: mrr_at_1000
      value: 38.256
    - type: mrr_at_20
      value: 37.693
    - type: mrr_at_3
      value: 34.721999999999994
    - type: mrr_at_5
      value: 35.787
    - type: ndcg_at_1
      value: 29.938
    - type: ndcg_at_10
      value: 29.358
    - type: ndcg_at_100
      value: 37.544
    - type: ndcg_at_1000
      value: 41.499
    - type: ndcg_at_20
      value: 32.354
    - type: ndcg_at_3
      value: 26.434
    - type: ndcg_at_5
      value: 26.93
    - type: precision_at_1
      value: 29.938
    - type: precision_at_10
      value: 8.117
    - type: precision_at_100
      value: 1.611
    - type: precision_at_1000
      value: 0.232
    - type: precision_at_20
      value: 5.255
    - type: precision_at_3
      value: 17.49
    - type: precision_at_5
      value: 12.747
    - type: recall_at_1
      value: 14.804999999999998
    - type: recall_at_10
      value: 34.776
    - type: recall_at_100
      value: 66.279
    - type: recall_at_1000
      value: 89.96600000000001
    - type: recall_at_20
      value: 44.31
    - type: recall_at_3
      value: 23.623
    - type: recall_at_5
      value: 27.194000000000003
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: mteb/hotpotqa
      config: default
      split: test
      revision: ab518f4d6fcca38d87c25209f94beba119d02014
    metrics:
    - type: map_at_1
      value: 38.555
    - type: map_at_10
      value: 54.20700000000001
    - type: map_at_100
      value: 55.177
    - type: map_at_1000
      value: 55.254999999999995
    - type: map_at_20
      value: 54.788000000000004
    - type: map_at_3
      value: 51.034
    - type: map_at_5
      value: 52.998
    - type: mrr_at_1
      value: 77.11
    - type: mrr_at_10
      value: 82.93199999999999
    - type: mrr_at_100
      value: 83.14200000000001
    - type: mrr_at_1000
      value: 83.15
    - type: mrr_at_20
      value: 83.062
    - type: mrr_at_3
      value: 81.95599999999999
    - type: mrr_at_5
      value: 82.586
    - type: ndcg_at_1
      value: 77.11
    - type: ndcg_at_10
      value: 63.853
    - type: ndcg_at_100
      value: 67.18499999999999
    - type: ndcg_at_1000
      value: 68.676
    - type: ndcg_at_20
      value: 65.279
    - type: ndcg_at_3
      value: 59.301
    - type: ndcg_at_5
      value: 61.822
    - type: precision_at_1
      value: 77.11
    - type: precision_at_10
      value: 13.044
    - type: precision_at_100
      value: 1.5630000000000002
    - type: precision_at_1000
      value: 0.17600000000000002
    - type: precision_at_20
      value: 6.979
    - type: precision_at_3
      value: 36.759
    - type: precision_at_5
      value: 24.054000000000002
    - type: recall_at_1
      value: 38.555
    - type: recall_at_10
      value: 65.21900000000001
    - type: recall_at_100
      value: 78.16300000000001
    - type: recall_at_1000
      value: 88.02799999999999
    - type: recall_at_20
      value: 69.791
    - type: recall_at_3
      value: 55.138
    - type: recall_at_5
      value: 60.135000000000005
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 69.8728
    - type: ap
      value: 63.98214492125858
    - type: f1
      value: 69.59975497754624
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification
      type: mteb/mtop_domain
      config: default
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 94.76288189694483
    - type: f1
      value: 94.52150972672682
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification
      type: mteb/mtop_intent
      config: default
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 76.83994528043777
    - type: f1
      value: 57.95571154189732
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification
      type: mteb/amazon_massive_intent
      config: default
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
    - type: accuracy
      value: 46.1163416274378
    - type: f1
      value: 45.425692244093064
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification
      type: mteb/amazon_massive_scenario
      config: default
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
    - type: accuracy
      value: 45.57834566240753
    - type: f1
      value: 43.84840097785479
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 32.86396397182615
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 34.018965727588565
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
    metrics:
    - type: map
      value: 31.286618059824573
    - type: mrr
      value: 32.481830769278965
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: mteb/nfcorpus
      config: default
      split: test
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
    metrics:
    - type: map_at_1
      value: 4.236
    - type: map_at_10
      value: 9.352
    - type: map_at_100
      value: 12.382
    - type: map_at_1000
      value: 13.828999999999999
    - type: map_at_20
      value: 10.619
    - type: map_at_3
      value: 6.814000000000001
    - type: map_at_5
      value: 7.887
    - type: mrr_at_1
      value: 37.152
    - type: mrr_at_10
      value: 47.055
    - type: mrr_at_100
      value: 47.82
    - type: mrr_at_1000
      value: 47.86
    - type: mrr_at_20
      value: 47.605
    - type: mrr_at_3
      value: 44.118
    - type: mrr_at_5
      value: 46.115
    - type: ndcg_at_1
      value: 34.365
    - type: ndcg_at_10
      value: 28.473
    - type: ndcg_at_100
      value: 27.311999999999998
    - type: ndcg_at_1000
      value: 36.671
    - type: ndcg_at_20
      value: 27.137
    - type: ndcg_at_3
      value: 31.939
    - type: ndcg_at_5
      value: 30.428
    - type: precision_at_1
      value: 36.223
    - type: precision_at_10
      value: 21.858
    - type: precision_at_100
      value: 7.417999999999999
    - type: precision_at_1000
      value: 2.0709999999999997
    - type: precision_at_20
      value: 16.502
    - type: precision_at_3
      value: 30.857
    - type: precision_at_5
      value: 26.997
    - type: recall_at_1
      value: 4.236
    - type: recall_at_10
      value: 13.489
    - type: recall_at_100
      value: 29.580000000000002
    - type: recall_at_1000
      value: 62.726000000000006
    - type: recall_at_20
      value: 18.346999999999998
    - type: recall_at_3
      value: 7.811
    - type: recall_at_5
      value: 10.086
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: mteb/nq
      config: default
      split: test
      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
    metrics:
    - type: map_at_1
      value: 21.123
    - type: map_at_10
      value: 34.429
    - type: map_at_100
      value: 35.803000000000004
    - type: map_at_1000
      value: 35.853
    - type: map_at_20
      value: 35.308
    - type: map_at_3
      value: 30.095
    - type: map_at_5
      value: 32.435
    - type: mrr_at_1
      value: 23.841
    - type: mrr_at_10
      value: 36.864999999999995
    - type: mrr_at_100
      value: 37.935
    - type: mrr_at_1000
      value: 37.97
    - type: mrr_at_20
      value: 37.566
    - type: mrr_at_3
      value: 32.918
    - type: mrr_at_5
      value: 35.11
    - type: ndcg_at_1
      value: 23.841
    - type: ndcg_at_10
      value: 42.043
    - type: ndcg_at_100
      value: 48.015
    - type: ndcg_at_1000
      value: 49.152
    - type: ndcg_at_20
      value: 44.936
    - type: ndcg_at_3
      value: 33.513999999999996
    - type: ndcg_at_5
      value: 37.541999999999994
    - type: precision_at_1
      value: 23.841
    - type: precision_at_10
      value: 7.454
    - type: precision_at_100
      value: 1.081
    - type: precision_at_1000
      value: 0.11900000000000001
    - type: precision_at_20
      value: 4.413
    - type: precision_at_3
      value: 15.672
    - type: precision_at_5
      value: 11.657
    - type: recall_at_1
      value: 21.123
    - type: recall_at_10
      value: 63.096
    - type: recall_at_100
      value: 89.27199999999999
    - type: recall_at_1000
      value: 97.69
    - type: recall_at_20
      value: 73.873
    - type: recall_at_3
      value: 40.588
    - type: recall_at_5
      value: 49.928
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: mteb/quora
      config: default
      split: test
      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
    metrics:
    - type: map_at_1
      value: 70.255
    - type: map_at_10
      value: 84.387
    - type: map_at_100
      value: 85.027
    - type: map_at_1000
      value: 85.043
    - type: map_at_20
      value: 84.809
    - type: map_at_3
      value: 81.5
    - type: map_at_5
      value: 83.286
    - type: mrr_at_1
      value: 80.85
    - type: mrr_at_10
      value: 87.25699999999999
    - type: mrr_at_100
      value: 87.363
    - type: mrr_at_1000
      value: 87.363
    - type: mrr_at_20
      value: 87.336
    - type: mrr_at_3
      value: 86.357
    - type: mrr_at_5
      value: 86.939
    - type: ndcg_at_1
      value: 80.86
    - type: ndcg_at_10
      value: 88.151
    - type: ndcg_at_100
      value: 89.381
    - type: ndcg_at_1000
      value: 89.47800000000001
    - type: ndcg_at_20
      value: 88.82100000000001
    - type: ndcg_at_3
      value: 85.394
    - type: ndcg_at_5
      value: 86.855
    - type: precision_at_1
      value: 80.86
    - type: precision_at_10
      value: 13.397
    - type: precision_at_100
      value: 1.5310000000000001
    - type: precision_at_1000
      value: 0.157
    - type: precision_at_20
      value: 7.106999999999999
    - type: precision_at_3
      value: 37.46
    - type: precision_at_5
      value: 24.568
    - type: recall_at_1
      value: 70.255
    - type: recall_at_10
      value: 95.405
    - type: recall_at_100
      value: 99.56
    - type: recall_at_1000
      value: 99.98599999999999
    - type: recall_at_20
      value: 97.544
    - type: recall_at_3
      value: 87.414
    - type: recall_at_5
      value: 91.598
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 54.7557403999403
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
    metrics:
    - type: v_measure
      value: 56.2773308957202
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: mteb/scidocs
      config: default
      split: test
      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
    metrics:
    - type: map_at_1
      value: 4.123
    - type: map_at_10
      value: 9.940999999999999
    - type: map_at_100
      value: 11.928999999999998
    - type: map_at_1000
      value: 12.257
    - type: map_at_20
      value: 10.866000000000001
    - type: map_at_3
      value: 7.091
    - type: map_at_5
      value: 8.393
    - type: mrr_at_1
      value: 20.3
    - type: mrr_at_10
      value: 30.068
    - type: mrr_at_100
      value: 31.296000000000003
    - type: mrr_at_1000
      value: 31.36
    - type: mrr_at_20
      value: 30.756
    - type: mrr_at_3
      value: 26.667
    - type: mrr_at_5
      value: 28.616999999999997
    - type: ndcg_at_1
      value: 20.3
    - type: ndcg_at_10
      value: 17.305
    - type: ndcg_at_100
      value: 25.529000000000003
    - type: ndcg_at_1000
      value: 31.41
    - type: ndcg_at_20
      value: 19.967
    - type: ndcg_at_3
      value: 16.022
    - type: ndcg_at_5
      value: 14.12
    - type: precision_at_1
      value: 20.3
    - type: precision_at_10
      value: 9.06
    - type: precision_at_100
      value: 2.103
    - type: precision_at_1000
      value: 0.35200000000000004
    - type: precision_at_20
      value: 6.075
    - type: precision_at_3
      value: 14.832999999999998
    - type: precision_at_5
      value: 12.36
    - type: recall_at_1
      value: 4.123
    - type: recall_at_10
      value: 18.383
    - type: recall_at_100
      value: 42.67
    - type: recall_at_1000
      value: 71.44800000000001
    - type: recall_at_20
      value: 24.64
    - type: recall_at_3
      value: 9.043
    - type: recall_at_5
      value: 12.543000000000001
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
    metrics:
    - type: cos_sim_pearson
      value: 84.37101718384514
    - type: cos_sim_spearman
      value: 80.73657031880697
    - type: euclidean_pearson
      value: 81.42351850520845
    - type: euclidean_spearman
      value: 80.81452496851979
    - type: manhattan_pearson
      value: 81.47676252115669
    - type: manhattan_spearman
      value: 80.87566944708885
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 84.79559176971591
    - type: cos_sim_spearman
      value: 75.41866597445552
    - type: euclidean_pearson
      value: 83.20287101163838
    - type: euclidean_spearman
      value: 75.54564777571143
    - type: manhattan_pearson
      value: 83.24622548900163
    - type: manhattan_spearman
      value: 75.63826258190343
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 84.63322096299294
    - type: cos_sim_spearman
      value: 85.48272638914783
    - type: euclidean_pearson
      value: 85.57327707819331
    - type: euclidean_spearman
      value: 85.90735298172922
    - type: manhattan_pearson
      value: 85.5744191274933
    - type: manhattan_spearman
      value: 85.90828008488766
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 82.05530140566407
    - type: cos_sim_spearman
      value: 78.85454907951474
    - type: euclidean_pearson
      value: 81.4307311680376
    - type: euclidean_spearman
      value: 78.99131623529348
    - type: manhattan_pearson
      value: 81.46870892683134
    - type: manhattan_spearman
      value: 79.05473823658481
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 83.66620817683379
    - type: cos_sim_spearman
      value: 85.23347998035328
    - type: euclidean_pearson
      value: 84.59001637865366
    - type: euclidean_spearman
      value: 85.0081410316597
    - type: manhattan_pearson
      value: 84.59742325369818
    - type: manhattan_spearman
      value: 85.01721329704324
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 79.86344730144208
    - type: cos_sim_spearman
      value: 82.15966778685441
    - type: euclidean_pearson
      value: 81.85580574642779
    - type: euclidean_spearman
      value: 82.06482873417123
    - type: manhattan_pearson
      value: 81.82971046102377
    - type: manhattan_spearman
      value: 82.04185436355144
  - task:
      type: STS
    dataset:
      name: MTEB STS17
      type: mteb/sts17-crosslingual-sts
      config: default
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: cos_sim_pearson
      value: 31.440481026661672
    - type: cos_sim_spearman
      value: 31.592743544965913
    - type: euclidean_pearson
      value: 31.15111049327518
    - type: euclidean_spearman
      value: 30.555124184361464
    - type: manhattan_pearson
      value: 31.724139249295654
    - type: manhattan_spearman
      value: 30.483389245793504
  - task:
      type: STS
    dataset:
      name: MTEB STS22
      type: mteb/sts22-crosslingual-sts
      config: default
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: cos_sim_pearson
      value: 34.51489724275415
    - type: cos_sim_spearman
      value: 47.06532141601629
    - type: euclidean_pearson
      value: 33.28904737503036
    - type: euclidean_spearman
      value: 45.111172981641865
    - type: manhattan_pearson
      value: 33.36374172942392
    - type: manhattan_spearman
      value: 45.100940945158534
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 82.09996292950329
    - type: cos_sim_spearman
      value: 82.69376206796092
    - type: euclidean_pearson
      value: 82.83254956369134
    - type: euclidean_spearman
      value: 82.34202999843637
    - type: manhattan_pearson
      value: 82.8048494319632
    - type: manhattan_spearman
      value: 82.34713123336984
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 82.1402269601644
    - type: mrr
      value: 94.84447197682492
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: mteb/scifact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
    metrics:
    - type: map_at_1
      value: 49.138999999999996
    - type: map_at_10
      value: 60.288
    - type: map_at_100
      value: 61.082
    - type: map_at_1000
      value: 61.11
    - type: map_at_20
      value: 60.831999999999994
    - type: map_at_3
      value: 57.106
    - type: map_at_5
      value: 58.857000000000006
    - type: mrr_at_1
      value: 51.333
    - type: mrr_at_10
      value: 61.364
    - type: mrr_at_100
      value: 62.029999999999994
    - type: mrr_at_1000
      value: 62.056
    - type: mrr_at_20
      value: 61.85000000000001
    - type: mrr_at_3
      value: 58.721999999999994
    - type: mrr_at_5
      value: 60.221999999999994
    - type: ndcg_at_1
      value: 51.333
    - type: ndcg_at_10
      value: 65.71900000000001
    - type: ndcg_at_100
      value: 69.036
    - type: ndcg_at_1000
      value: 69.626
    - type: ndcg_at_20
      value: 67.571
    - type: ndcg_at_3
      value: 60.019
    - type: ndcg_at_5
      value: 62.733000000000004
    - type: precision_at_1
      value: 51.333
    - type: precision_at_10
      value: 9.067
    - type: precision_at_100
      value: 1.083
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_20
      value: 4.95
    - type: precision_at_3
      value: 23.889
    - type: precision_at_5
      value: 16.0
    - type: recall_at_1
      value: 49.138999999999996
    - type: recall_at_10
      value: 81.256
    - type: recall_at_100
      value: 95.6
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_20
      value: 88.289
    - type: recall_at_3
      value: 66.078
    - type: recall_at_5
      value: 72.661
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.73762376237623
    - type: cos_sim_ap
      value: 93.02149432690442
    - type: cos_sim_f1
      value: 86.59079663532904
    - type: cos_sim_precision
      value: 85.70029382957884
    - type: cos_sim_recall
      value: 87.5
    - type: dot_accuracy
      value: 99.73267326732673
    - type: dot_ap
      value: 92.38661051842968
    - type: dot_f1
      value: 85.92283628779978
    - type: dot_precision
      value: 89.76034858387798
    - type: dot_recall
      value: 82.39999999999999
    - type: euclidean_accuracy
      value: 99.73960396039604
    - type: euclidean_ap
      value: 92.99557708360517
    - type: euclidean_f1
      value: 86.49183572488866
    - type: euclidean_precision
      value: 85.60235063663075
    - type: euclidean_recall
      value: 87.4
    - type: manhattan_accuracy
      value: 99.74059405940594
    - type: manhattan_ap
      value: 93.24237279644005
    - type: manhattan_f1
      value: 86.77727501256913
    - type: manhattan_precision
      value: 87.25985844287159
    - type: manhattan_recall
      value: 86.3
    - type: max_accuracy
      value: 99.74059405940594
    - type: max_ap
      value: 93.24237279644005
    - type: max_f1
      value: 86.77727501256913
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 63.94924261127149
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 32.22297034902405
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 46.12948438780115
    - type: mrr
      value: 46.77186783804431
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 30.02235612863601
    - type: cos_sim_spearman
      value: 30.567504287706598
    - type: dot_pearson
      value: 28.943978981614897
    - type: dot_spearman
      value: 29.905635915797358
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: mteb/trec-covid
      config: default
      split: test
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
    metrics:
    - type: map_at_1
      value: 0.173
    - type: map_at_10
      value: 1.124
    - type: map_at_100
      value: 5.645
    - type: map_at_1000
      value: 14.965
    - type: map_at_20
      value: 1.876
    - type: map_at_3
      value: 0.45599999999999996
    - type: map_at_5
      value: 0.699
    - type: mrr_at_1
      value: 70.0
    - type: mrr_at_10
      value: 81.786
    - type: mrr_at_100
      value: 81.786
    - type: mrr_at_1000
      value: 81.786
    - type: mrr_at_20
      value: 81.786
    - type: mrr_at_3
      value: 80.0
    - type: mrr_at_5
      value: 81.5
    - type: ndcg_at_1
      value: 65.0
    - type: ndcg_at_10
      value: 53.88699999999999
    - type: ndcg_at_100
      value: 38.028
    - type: ndcg_at_1000
      value: 37.183
    - type: ndcg_at_20
      value: 49.286
    - type: ndcg_at_3
      value: 63.05
    - type: ndcg_at_5
      value: 59.49100000000001
    - type: precision_at_1
      value: 70.0
    - type: precision_at_10
      value: 55.400000000000006
    - type: precision_at_100
      value: 38.800000000000004
    - type: precision_at_1000
      value: 17.082
    - type: precision_at_20
      value: 50.7
    - type: precision_at_3
      value: 66.667
    - type: precision_at_5
      value: 62.4
    - type: recall_at_1
      value: 0.173
    - type: recall_at_10
      value: 1.353
    - type: recall_at_100
      value: 8.887
    - type: recall_at_1000
      value: 36.012
    - type: recall_at_20
      value: 2.476
    - type: recall_at_3
      value: 0.508
    - type: recall_at_5
      value: 0.795
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: mteb/touche2020
      config: default
      split: test
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
    metrics:
    - type: map_at_1
      value: 2.614
    - type: map_at_10
      value: 6.651999999999999
    - type: map_at_100
      value: 11.59
    - type: map_at_1000
      value: 13.044
    - type: map_at_20
      value: 8.702
    - type: map_at_3
      value: 4.159
    - type: map_at_5
      value: 5.327
    - type: mrr_at_1
      value: 30.612000000000002
    - type: mrr_at_10
      value: 42.664
    - type: mrr_at_100
      value: 43.957
    - type: mrr_at_1000
      value: 43.957
    - type: mrr_at_20
      value: 43.193
    - type: mrr_at_3
      value: 40.476
    - type: mrr_at_5
      value: 42.007
    - type: ndcg_at_1
      value: 27.551
    - type: ndcg_at_10
      value: 18.098
    - type: ndcg_at_100
      value: 30.019000000000002
    - type: ndcg_at_1000
      value: 42.179
    - type: ndcg_at_20
      value: 19.552
    - type: ndcg_at_3
      value: 21.22
    - type: ndcg_at_5
      value: 19.774
    - type: precision_at_1
      value: 30.612000000000002
    - type: precision_at_10
      value: 15.101999999999999
    - type: precision_at_100
      value: 6.510000000000001
    - type: precision_at_1000
      value: 1.4569999999999999
    - type: precision_at_20
      value: 12.449
    - type: precision_at_3
      value: 22.448999999999998
    - type: precision_at_5
      value: 19.592000000000002
    - type: recall_at_1
      value: 2.614
    - type: recall_at_10
      value: 11.068
    - type: recall_at_100
      value: 42.317
    - type: recall_at_1000
      value: 79.063
    - type: recall_at_20
      value: 18.589
    - type: recall_at_3
      value: 5.06
    - type: recall_at_5
      value: 7.356
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
    metrics:
    - type: accuracy
      value: 75.0146484375
    - type: ap
      value: 16.80191476928431
    - type: f1
      value: 58.08037205204817
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 61.80249009620826
    - type: f1
      value: 62.24155926661914
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 47.074846780747094
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 85.21785778148656
    - type: cos_sim_ap
      value: 71.06584074764645
    - type: cos_sim_f1
      value: 65.81720166625826
    - type: cos_sim_precision
      value: 61.43641354071363
    - type: cos_sim_recall
      value: 70.87071240105541
    - type: dot_accuracy
      value: 84.30589497526375
    - type: dot_ap
      value: 68.85872202019365
    - type: dot_f1
      value: 64.20295157946092
    - type: dot_precision
      value: 59.69607620775687
    - type: dot_recall
      value: 69.44591029023746
    - type: euclidean_accuracy
      value: 85.21189724026942
    - type: euclidean_ap
      value: 71.18847194129523
    - type: euclidean_f1
      value: 66.00049962528105
    - type: euclidean_precision
      value: 62.66603415559773
    - type: euclidean_recall
      value: 69.70976253298153
    - type: manhattan_accuracy
      value: 85.25958157000656
    - type: manhattan_ap
      value: 71.12967638566641
    - type: manhattan_f1
      value: 65.77477594492791
    - type: manhattan_precision
      value: 64.77359938603223
    - type: manhattan_recall
      value: 66.80738786279683
    - type: max_accuracy
      value: 85.25958157000656
    - type: max_ap
      value: 71.18847194129523
    - type: max_f1
      value: 66.00049962528105
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.22330888345559
    - type: cos_sim_ap
      value: 84.40304506741951
    - type: cos_sim_f1
      value: 76.46823520855303
    - type: cos_sim_precision
      value: 72.45537867824409
    - type: cos_sim_recall
      value: 80.95164767477672
    - type: dot_accuracy
      value: 87.9400007761866
    - type: dot_ap
      value: 83.63499141834609
    - type: dot_f1
      value: 75.98620939938304
    - type: dot_precision
      value: 71.86792064254823
    - type: dot_recall
      value: 80.60517400677548
    - type: euclidean_accuracy
      value: 88.21166608452671
    - type: euclidean_ap
      value: 84.40463988450605
    - type: euclidean_f1
      value: 76.52312831312177
    - type: euclidean_precision
      value: 72.40621135083138
    - type: euclidean_recall
      value: 81.13643363104404
    - type: manhattan_accuracy
      value: 88.24659448131331
    - type: manhattan_ap
      value: 84.42287495905447
    - type: manhattan_f1
      value: 76.54849595413475
    - type: manhattan_precision
      value: 72.39036442248302
    - type: manhattan_recall
      value: 81.21342777948875
    - type: max_accuracy
      value: 88.24659448131331
    - type: max_ap
      value: 84.42287495905447
    - type: max_f1
      value: 76.54849595413475
---

# b1ade-embed-kd

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)
```



## Evaluation Results

<!--- Describe how your model was evaluated -->

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})


## Training
The model was distilled with teacher model as 


and student model as b1ade-embed

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 275105 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.MSELoss.MSELoss` 

Parameters of the fit()-Method:
```
{
    "epochs": 3,
    "evaluation_steps": 5000,
    "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "eps": 1e-06,
        "lr": 5e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 1000,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Results:

Good agreement with teacher model, at least on STS:

Teacher:
```
2024-05-20 16:29:07 - Teacher Performance:
2024-05-20 16:29:07 - EmbeddingSimilarityEvaluator: Evaluating the model on the sts-dev dataset:
2024-05-20 16:29:12 - Cosine-Similarity :	Pearson: 0.8561	Spearman: 0.8597
2024-05-20 16:29:12 - Manhattan-Distance:	Pearson: 0.8569	Spearman: 0.8567
2024-05-20 16:29:12 - Euclidean-Distance:	Pearson: 0.8575	Spearman: 0.8571
2024-05-20 16:29:12 - Dot-Product-Similarity:	Pearson: 0.8624	Spearman: 0.8662
```

Student: 
```
2024-05-20 16:29:12 - Student Performance:
2024-05-20 16:29:12 - EmbeddingSimilarityEvaluator: Evaluating the model on the sts-dev dataset:
2024-05-20 16:29:17 - Cosine-Similarity :	Pearson: 0.8561	Spearman: 0.8597
2024-05-20 16:29:17 - Manhattan-Distance:	Pearson: 0.8569	Spearman: 0.8567
2024-05-20 16:29:17 - Euclidean-Distance:	Pearson: 0.8575	Spearman: 0.8571
2024-05-20 16:29:17 - Dot-Product-Similarity:	Pearson: 0.8624	Spearman: 0.8662
```