transformers safetensors bert feature-extraction mteb model-index text-embeddings-inference endpoints_compatible region:us
---
tags:
- mteb
model-index:
- name: sf_model_e5
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 70.85074626865672
    - type: ap
      value: 33.779217850079206
    - type: f1
      value: 64.96977487239377
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 91.80945
    - type: ap
      value: 88.22978189506895
    - type: f1
      value: 91.7858219911604
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 48.94200000000001
    - type: f1
      value: 47.911934405973895
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 39.616
    - type: map_at_10
      value: 55.938
    - type: map_at_100
      value: 56.552
    - type: map_at_1000
      value: 56.556
    - type: map_at_3
      value: 51.754
    - type: map_at_5
      value: 54.623999999999995
    - type: mrr_at_1
      value: 40.967
    - type: mrr_at_10
      value: 56.452999999999996
    - type: mrr_at_100
      value: 57.053
    - type: mrr_at_1000
      value: 57.057
    - type: mrr_at_3
      value: 52.312000000000005
    - type: mrr_at_5
      value: 55.1
    - type: ndcg_at_1
      value: 39.616
    - type: ndcg_at_10
      value: 64.067
    - type: ndcg_at_100
      value: 66.384
    - type: ndcg_at_1000
      value: 66.468
    - type: ndcg_at_3
      value: 55.74
    - type: ndcg_at_5
      value: 60.889
    - type: precision_at_1
      value: 39.616
    - type: precision_at_10
      value: 8.953999999999999
    - type: precision_at_100
      value: 0.9900000000000001
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 22.428
    - type: precision_at_5
      value: 15.946
    - type: recall_at_1
      value: 39.616
    - type: recall_at_10
      value: 89.545
    - type: recall_at_100
      value: 99.004
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_3
      value: 67.283
    - type: recall_at_5
      value: 79.73
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 48.72923923743124
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 42.87449955203238
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 64.3214434754065
    - type: mrr
      value: 77.87879787187265
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 88.82418607751953
    - type: cos_sim_spearman
      value: 86.74535004562274
    - type: euclidean_pearson
      value: 86.58792166831103
    - type: euclidean_spearman
      value: 86.74535004562274
    - type: manhattan_pearson
      value: 86.23957813056677
    - type: manhattan_spearman
      value: 86.41522204150452
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 84.61363636363636
    - type: f1
      value: 83.98373241136187
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 39.73148995791471
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 37.23723038699733
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 32.217
    - type: map_at_10
      value: 43.453
    - type: map_at_100
      value: 45.038
    - type: map_at_1000
      value: 45.162
    - type: map_at_3
      value: 39.589
    - type: map_at_5
      value: 41.697
    - type: mrr_at_1
      value: 39.628
    - type: mrr_at_10
      value: 49.698
    - type: mrr_at_100
      value: 50.44
    - type: mrr_at_1000
      value: 50.482000000000006
    - type: mrr_at_3
      value: 46.781
    - type: mrr_at_5
      value: 48.548
    - type: ndcg_at_1
      value: 39.628
    - type: ndcg_at_10
      value: 50.158
    - type: ndcg_at_100
      value: 55.687
    - type: ndcg_at_1000
      value: 57.499
    - type: ndcg_at_3
      value: 44.594
    - type: ndcg_at_5
      value: 47.198
    - type: precision_at_1
      value: 39.628
    - type: precision_at_10
      value: 9.828000000000001
    - type: precision_at_100
      value: 1.591
    - type: precision_at_1000
      value: 0.20600000000000002
    - type: precision_at_3
      value: 21.507
    - type: precision_at_5
      value: 15.765
    - type: recall_at_1
      value: 32.217
    - type: recall_at_10
      value: 62.717999999999996
    - type: recall_at_100
      value: 85.992
    - type: recall_at_1000
      value: 97.271
    - type: recall_at_3
      value: 46.694
    - type: recall_at_5
      value: 53.952
    - type: map_at_1
      value: 30.862000000000002
    - type: map_at_10
      value: 41.287
    - type: map_at_100
      value: 42.526
    - type: map_at_1000
      value: 42.653999999999996
    - type: map_at_3
      value: 38.055
    - type: map_at_5
      value: 40.022000000000006
    - type: mrr_at_1
      value: 38.408
    - type: mrr_at_10
      value: 46.943
    - type: mrr_at_100
      value: 47.597
    - type: mrr_at_1000
      value: 47.64
    - type: mrr_at_3
      value: 44.607
    - type: mrr_at_5
      value: 46.079
    - type: ndcg_at_1
      value: 38.408
    - type: ndcg_at_10
      value: 46.936
    - type: ndcg_at_100
      value: 51.307
    - type: ndcg_at_1000
      value: 53.312000000000005
    - type: ndcg_at_3
      value: 42.579
    - type: ndcg_at_5
      value: 44.877
    - type: precision_at_1
      value: 38.408
    - type: precision_at_10
      value: 8.885
    - type: precision_at_100
      value: 1.4449999999999998
    - type: precision_at_1000
      value: 0.192
    - type: precision_at_3
      value: 20.616
    - type: precision_at_5
      value: 14.841
    - type: recall_at_1
      value: 30.862000000000002
    - type: recall_at_10
      value: 56.994
    - type: recall_at_100
      value: 75.347
    - type: recall_at_1000
      value: 87.911
    - type: recall_at_3
      value: 44.230000000000004
    - type: recall_at_5
      value: 50.625
    - type: map_at_1
      value: 39.076
    - type: map_at_10
      value: 52.535
    - type: map_at_100
      value: 53.537
    - type: map_at_1000
      value: 53.591
    - type: map_at_3
      value: 48.961
    - type: map_at_5
      value: 50.96000000000001
    - type: mrr_at_1
      value: 44.765
    - type: mrr_at_10
      value: 55.615
    - type: mrr_at_100
      value: 56.24
    - type: mrr_at_1000
      value: 56.264
    - type: mrr_at_3
      value: 52.925999999999995
    - type: mrr_at_5
      value: 54.493
    - type: ndcg_at_1
      value: 44.765
    - type: ndcg_at_10
      value: 58.777
    - type: ndcg_at_100
      value: 62.574
    - type: ndcg_at_1000
      value: 63.624
    - type: ndcg_at_3
      value: 52.81
    - type: ndcg_at_5
      value: 55.657999999999994
    - type: precision_at_1
      value: 44.765
    - type: precision_at_10
      value: 9.693
    - type: precision_at_100
      value: 1.248
    - type: precision_at_1000
      value: 0.13799999999999998
    - type: precision_at_3
      value: 23.866
    - type: precision_at_5
      value: 16.489
    - type: recall_at_1
      value: 39.076
    - type: recall_at_10
      value: 74.01299999999999
    - type: recall_at_100
      value: 90.363
    - type: recall_at_1000
      value: 97.782
    - type: recall_at_3
      value: 58.056
    - type: recall_at_5
      value: 65.029
    - type: map_at_1
      value: 26.357000000000003
    - type: map_at_10
      value: 35.492000000000004
    - type: map_at_100
      value: 36.504999999999995
    - type: map_at_1000
      value: 36.578
    - type: map_at_3
      value: 32.696999999999996
    - type: map_at_5
      value: 34.388999999999996
    - type: mrr_at_1
      value: 28.136
    - type: mrr_at_10
      value: 37.383
    - type: mrr_at_100
      value: 38.271
    - type: mrr_at_1000
      value: 38.324999999999996
    - type: mrr_at_3
      value: 34.782999999999994
    - type: mrr_at_5
      value: 36.416
    - type: ndcg_at_1
      value: 28.136
    - type: ndcg_at_10
      value: 40.741
    - type: ndcg_at_100
      value: 45.803
    - type: ndcg_at_1000
      value: 47.637
    - type: ndcg_at_3
      value: 35.412
    - type: ndcg_at_5
      value: 38.251000000000005
    - type: precision_at_1
      value: 28.136
    - type: precision_at_10
      value: 6.315999999999999
    - type: precision_at_100
      value: 0.931
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 15.254000000000001
    - type: precision_at_5
      value: 10.757
    - type: recall_at_1
      value: 26.357000000000003
    - type: recall_at_10
      value: 55.021
    - type: recall_at_100
      value: 78.501
    - type: recall_at_1000
      value: 92.133
    - type: recall_at_3
      value: 40.798
    - type: recall_at_5
      value: 47.591
    - type: map_at_1
      value: 17.302
    - type: map_at_10
      value: 26.365
    - type: map_at_100
      value: 27.581
    - type: map_at_1000
      value: 27.705999999999996
    - type: map_at_3
      value: 23.682
    - type: map_at_5
      value: 25.304
    - type: mrr_at_1
      value: 21.891
    - type: mrr_at_10
      value: 31.227
    - type: mrr_at_100
      value: 32.22
    - type: mrr_at_1000
      value: 32.282
    - type: mrr_at_3
      value: 28.711
    - type: mrr_at_5
      value: 30.314999999999998
    - type: ndcg_at_1
      value: 21.891
    - type: ndcg_at_10
      value: 31.965
    - type: ndcg_at_100
      value: 37.869
    - type: ndcg_at_1000
      value: 40.642
    - type: ndcg_at_3
      value: 27.184
    - type: ndcg_at_5
      value: 29.686
    - type: precision_at_1
      value: 21.891
    - type: precision_at_10
      value: 5.9830000000000005
    - type: precision_at_100
      value: 1.0250000000000001
    - type: precision_at_1000
      value: 0.14100000000000001
    - type: precision_at_3
      value: 13.391
    - type: precision_at_5
      value: 9.801
    - type: recall_at_1
      value: 17.302
    - type: recall_at_10
      value: 44.312000000000005
    - type: recall_at_100
      value: 70.274
    - type: recall_at_1000
      value: 89.709
    - type: recall_at_3
      value: 31.117
    - type: recall_at_5
      value: 37.511
    - type: map_at_1
      value: 29.404000000000003
    - type: map_at_10
      value: 40.571
    - type: map_at_100
      value: 42.049
    - type: map_at_1000
      value: 42.156
    - type: map_at_3
      value: 37.413000000000004
    - type: map_at_5
      value: 39.206
    - type: mrr_at_1
      value: 36.285000000000004
    - type: mrr_at_10
      value: 46.213
    - type: mrr_at_100
      value: 47.129
    - type: mrr_at_1000
      value: 47.168
    - type: mrr_at_3
      value: 43.84
    - type: mrr_at_5
      value: 45.226
    - type: ndcg_at_1
      value: 36.285000000000004
    - type: ndcg_at_10
      value: 46.809
    - type: ndcg_at_100
      value: 52.615
    - type: ndcg_at_1000
      value: 54.538
    - type: ndcg_at_3
      value: 41.91
    - type: ndcg_at_5
      value: 44.224999999999994
    - type: precision_at_1
      value: 36.285000000000004
    - type: precision_at_10
      value: 8.527
    - type: precision_at_100
      value: 1.3259999999999998
    - type: precision_at_1000
      value: 0.167
    - type: precision_at_3
      value: 20.083000000000002
    - type: precision_at_5
      value: 14.071
    - type: recall_at_1
      value: 29.404000000000003
    - type: recall_at_10
      value: 59.611999999999995
    - type: recall_at_100
      value: 83.383
    - type: recall_at_1000
      value: 95.703
    - type: recall_at_3
      value: 45.663
    - type: recall_at_5
      value: 51.971999999999994
    - type: map_at_1
      value: 25.317
    - type: map_at_10
      value: 35.217999999999996
    - type: map_at_100
      value: 36.665
    - type: map_at_1000
      value: 36.768
    - type: map_at_3
      value: 31.924000000000003
    - type: map_at_5
      value: 33.591
    - type: mrr_at_1
      value: 31.507
    - type: mrr_at_10
      value: 40.671
    - type: mrr_at_100
      value: 41.609
    - type: mrr_at_1000
      value: 41.657
    - type: mrr_at_3
      value: 38.261
    - type: mrr_at_5
      value: 39.431
    - type: ndcg_at_1
      value: 31.507
    - type: ndcg_at_10
      value: 41.375
    - type: ndcg_at_100
      value: 47.426
    - type: ndcg_at_1000
      value: 49.504
    - type: ndcg_at_3
      value: 35.989
    - type: ndcg_at_5
      value: 38.068000000000005
    - type: precision_at_1
      value: 31.507
    - type: precision_at_10
      value: 7.8420000000000005
    - type: precision_at_100
      value: 1.257
    - type: precision_at_1000
      value: 0.16199999999999998
    - type: precision_at_3
      value: 17.352
    - type: precision_at_5
      value: 12.328999999999999
    - type: recall_at_1
      value: 25.317
    - type: recall_at_10
      value: 54.254999999999995
    - type: recall_at_100
      value: 80.184
    - type: recall_at_1000
      value: 94.07
    - type: recall_at_3
      value: 39.117000000000004
    - type: recall_at_5
      value: 44.711
    - type: map_at_1
      value: 25.813000000000002
    - type: map_at_10
      value: 35.47183333333334
    - type: map_at_100
      value: 36.71775
    - type: map_at_1000
      value: 36.833000000000006
    - type: map_at_3
      value: 32.449916666666674
    - type: map_at_5
      value: 34.1235
    - type: mrr_at_1
      value: 30.766750000000005
    - type: mrr_at_10
      value: 39.77508333333334
    - type: mrr_at_100
      value: 40.64233333333333
    - type: mrr_at_1000
      value: 40.69658333333333
    - type: mrr_at_3
      value: 37.27349999999999
    - type: mrr_at_5
      value: 38.723416666666665
    - type: ndcg_at_1
      value: 30.766750000000005
    - type: ndcg_at_10
      value: 41.141416666666665
    - type: ndcg_at_100
      value: 46.42016666666666
    - type: ndcg_at_1000
      value: 48.61916666666667
    - type: ndcg_at_3
      value: 36.06883333333333
    - type: ndcg_at_5
      value: 38.43966666666666
    - type: precision_at_1
      value: 30.766750000000005
    - type: precision_at_10
      value: 7.340000000000001
    - type: precision_at_100
      value: 1.1796666666666666
    - type: precision_at_1000
      value: 0.15625
    - type: precision_at_3
      value: 16.763833333333334
    - type: precision_at_5
      value: 11.972166666666666
    - type: recall_at_1
      value: 25.813000000000002
    - type: recall_at_10
      value: 53.62741666666667
    - type: recall_at_100
      value: 76.70125000000002
    - type: recall_at_1000
      value: 91.85566666666666
    - type: recall_at_3
      value: 39.55075
    - type: recall_at_5
      value: 45.645250000000004
    - type: map_at_1
      value: 23.249
    - type: map_at_10
      value: 31.095
    - type: map_at_100
      value: 32.056000000000004
    - type: map_at_1000
      value: 32.163000000000004
    - type: map_at_3
      value: 29.275000000000002
    - type: map_at_5
      value: 30.333
    - type: mrr_at_1
      value: 26.687
    - type: mrr_at_10
      value: 34.122
    - type: mrr_at_100
      value: 34.958
    - type: mrr_at_1000
      value: 35.039
    - type: mrr_at_3
      value: 32.541
    - type: mrr_at_5
      value: 33.43
    - type: ndcg_at_1
      value: 26.687
    - type: ndcg_at_10
      value: 35.248000000000005
    - type: ndcg_at_100
      value: 39.933
    - type: ndcg_at_1000
      value: 42.616
    - type: ndcg_at_3
      value: 31.980999999999998
    - type: ndcg_at_5
      value: 33.583
    - type: precision_at_1
      value: 26.687
    - type: precision_at_10
      value: 5.445
    - type: precision_at_100
      value: 0.848
    - type: precision_at_1000
      value: 0.11499999999999999
    - type: precision_at_3
      value: 13.957
    - type: precision_at_5
      value: 9.479
    - type: recall_at_1
      value: 23.249
    - type: recall_at_10
      value: 45.005
    - type: recall_at_100
      value: 66.175
    - type: recall_at_1000
      value: 86.116
    - type: recall_at_3
      value: 36.03
    - type: recall_at_5
      value: 40.037
    - type: map_at_1
      value: 17.592
    - type: map_at_10
      value: 25.003999999999998
    - type: map_at_100
      value: 26.208
    - type: map_at_1000
      value: 26.333000000000002
    - type: map_at_3
      value: 22.479
    - type: map_at_5
      value: 23.712
    - type: mrr_at_1
      value: 21.37
    - type: mrr_at_10
      value: 28.951999999999998
    - type: mrr_at_100
      value: 29.915999999999997
    - type: mrr_at_1000
      value: 29.99
    - type: mrr_at_3
      value: 26.503
    - type: mrr_at_5
      value: 27.728
    - type: ndcg_at_1
      value: 21.37
    - type: ndcg_at_10
      value: 29.944
    - type: ndcg_at_100
      value: 35.632000000000005
    - type: ndcg_at_1000
      value: 38.393
    - type: ndcg_at_3
      value: 25.263999999999996
    - type: ndcg_at_5
      value: 27.115000000000002
    - type: precision_at_1
      value: 21.37
    - type: precision_at_10
      value: 5.568
    - type: precision_at_100
      value: 0.992
    - type: precision_at_1000
      value: 0.13999999999999999
    - type: precision_at_3
      value: 11.895
    - type: precision_at_5
      value: 8.61
    - type: recall_at_1
      value: 17.592
    - type: recall_at_10
      value: 40.976
    - type: recall_at_100
      value: 66.487
    - type: recall_at_1000
      value: 85.954
    - type: recall_at_3
      value: 27.797
    - type: recall_at_5
      value: 32.553
    - type: map_at_1
      value: 25.173000000000002
    - type: map_at_10
      value: 34.611999999999995
    - type: map_at_100
      value: 35.735
    - type: map_at_1000
      value: 35.842
    - type: map_at_3
      value: 31.345
    - type: map_at_5
      value: 33.123000000000005
    - type: mrr_at_1
      value: 29.570999999999998
    - type: mrr_at_10
      value: 38.775999999999996
    - type: mrr_at_100
      value: 39.621
    - type: mrr_at_1000
      value: 39.684000000000005
    - type: mrr_at_3
      value: 35.992000000000004
    - type: mrr_at_5
      value: 37.586999999999996
    - type: ndcg_at_1
      value: 29.570999999999998
    - type: ndcg_at_10
      value: 40.388000000000005
    - type: ndcg_at_100
      value: 45.59
    - type: ndcg_at_1000
      value: 47.948
    - type: ndcg_at_3
      value: 34.497
    - type: ndcg_at_5
      value: 37.201
    - type: precision_at_1
      value: 29.570999999999998
    - type: precision_at_10
      value: 6.931
    - type: precision_at_100
      value: 1.082
    - type: precision_at_1000
      value: 0.13999999999999999
    - type: precision_at_3
      value: 15.609
    - type: precision_at_5
      value: 11.286999999999999
    - type: recall_at_1
      value: 25.173000000000002
    - type: recall_at_10
      value: 53.949000000000005
    - type: recall_at_100
      value: 76.536
    - type: recall_at_1000
      value: 92.979
    - type: recall_at_3
      value: 37.987
    - type: recall_at_5
      value: 44.689
    - type: map_at_1
      value: 24.224
    - type: map_at_10
      value: 32.903
    - type: map_at_100
      value: 34.65
    - type: map_at_1000
      value: 34.873
    - type: map_at_3
      value: 29.673
    - type: map_at_5
      value: 31.361
    - type: mrr_at_1
      value: 30.435000000000002
    - type: mrr_at_10
      value: 38.677
    - type: mrr_at_100
      value: 39.805
    - type: mrr_at_1000
      value: 39.851
    - type: mrr_at_3
      value: 35.935
    - type: mrr_at_5
      value: 37.566
    - type: ndcg_at_1
      value: 30.435000000000002
    - type: ndcg_at_10
      value: 39.012
    - type: ndcg_at_100
      value: 45.553
    - type: ndcg_at_1000
      value: 47.919
    - type: ndcg_at_3
      value: 33.809
    - type: ndcg_at_5
      value: 36.120999999999995
    - type: precision_at_1
      value: 30.435000000000002
    - type: precision_at_10
      value: 7.628
    - type: precision_at_100
      value: 1.5810000000000002
    - type: precision_at_1000
      value: 0.243
    - type: precision_at_3
      value: 15.744
    - type: precision_at_5
      value: 11.66
    - type: recall_at_1
      value: 24.224
    - type: recall_at_10
      value: 50.009
    - type: recall_at_100
      value: 78.839
    - type: recall_at_1000
      value: 93.71300000000001
    - type: recall_at_3
      value: 35.512
    - type: recall_at_5
      value: 41.541
    - type: map_at_1
      value: 18.983
    - type: map_at_10
      value: 27.127000000000002
    - type: map_at_100
      value: 28.063
    - type: map_at_1000
      value: 28.17
    - type: map_at_3
      value: 24.306
    - type: map_at_5
      value: 25.784000000000002
    - type: mrr_at_1
      value: 20.518
    - type: mrr_at_10
      value: 29.024
    - type: mrr_at_100
      value: 29.902
    - type: mrr_at_1000
      value: 29.976999999999997
    - type: mrr_at_3
      value: 26.401999999999997
    - type: mrr_at_5
      value: 27.862
    - type: ndcg_at_1
      value: 20.518
    - type: ndcg_at_10
      value: 32.344
    - type: ndcg_at_100
      value: 37.053000000000004
    - type: ndcg_at_1000
      value: 39.798
    - type: ndcg_at_3
      value: 26.796999999999997
    - type: ndcg_at_5
      value: 29.293000000000003
    - type: precision_at_1
      value: 20.518
    - type: precision_at_10
      value: 5.434
    - type: precision_at_100
      value: 0.83
    - type: precision_at_1000
      value: 0.11800000000000001
    - type: precision_at_3
      value: 11.892
    - type: precision_at_5
      value: 8.577
    - type: recall_at_1
      value: 18.983
    - type: recall_at_10
      value: 46.665
    - type: recall_at_100
      value: 68.33399999999999
    - type: recall_at_1000
      value: 88.927
    - type: recall_at_3
      value: 31.608000000000004
    - type: recall_at_5
      value: 37.532
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 11.200000000000001
    - type: map_at_10
      value: 20.241999999999997
    - type: map_at_100
      value: 22.357
    - type: map_at_1000
      value: 22.556
    - type: map_at_3
      value: 16.564999999999998
    - type: map_at_5
      value: 18.443
    - type: mrr_at_1
      value: 25.277
    - type: mrr_at_10
      value: 37.582
    - type: mrr_at_100
      value: 38.525999999999996
    - type: mrr_at_1000
      value: 38.564
    - type: mrr_at_3
      value: 33.898
    - type: mrr_at_5
      value: 36.191
    - type: ndcg_at_1
      value: 25.277
    - type: ndcg_at_10
      value: 28.74
    - type: ndcg_at_100
      value: 36.665
    - type: ndcg_at_1000
      value: 40.08
    - type: ndcg_at_3
      value: 22.888
    - type: ndcg_at_5
      value: 25.081999999999997
    - type: precision_at_1
      value: 25.277
    - type: precision_at_10
      value: 9.251
    - type: precision_at_100
      value: 1.773
    - type: precision_at_1000
      value: 0.241
    - type: precision_at_3
      value: 17.329
    - type: precision_at_5
      value: 13.746
    - type: recall_at_1
      value: 11.200000000000001
    - type: recall_at_10
      value: 35.419
    - type: recall_at_100
      value: 62.41
    - type: recall_at_1000
      value: 81.467
    - type: recall_at_3
      value: 21.275
    - type: recall_at_5
      value: 27.201999999999998
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 9.396
    - type: map_at_10
      value: 20.735
    - type: map_at_100
      value: 30.098000000000003
    - type: map_at_1000
      value: 31.866
    - type: map_at_3
      value: 14.71
    - type: map_at_5
      value: 17.259
    - type: mrr_at_1
      value: 70.25
    - type: mrr_at_10
      value: 77.09700000000001
    - type: mrr_at_100
      value: 77.398
    - type: mrr_at_1000
      value: 77.40899999999999
    - type: mrr_at_3
      value: 75.542
    - type: mrr_at_5
      value: 76.354
    - type: ndcg_at_1
      value: 57.75
    - type: ndcg_at_10
      value: 42.509
    - type: ndcg_at_100
      value: 48.94
    - type: ndcg_at_1000
      value: 56.501000000000005
    - type: ndcg_at_3
      value: 46.827000000000005
    - type: ndcg_at_5
      value: 44.033
    - type: precision_at_1
      value: 70.25
    - type: precision_at_10
      value: 33.85
    - type: precision_at_100
      value: 11.373
    - type: precision_at_1000
      value: 2.136
    - type: precision_at_3
      value: 50.917
    - type: precision_at_5
      value: 42.8
    - type: recall_at_1
      value: 9.396
    - type: recall_at_10
      value: 26.472
    - type: recall_at_100
      value: 57.30800000000001
    - type: recall_at_1000
      value: 80.983
    - type: recall_at_3
      value: 15.859000000000002
    - type: recall_at_5
      value: 19.758
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 54.900000000000006
    - type: f1
      value: 48.14707395235448
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 66.369
    - type: map_at_10
      value: 76.708
    - type: map_at_100
      value: 76.981
    - type: map_at_1000
      value: 76.995
    - type: map_at_3
      value: 75.114
    - type: map_at_5
      value: 76.116
    - type: mrr_at_1
      value: 71.557
    - type: mrr_at_10
      value: 80.95
    - type: mrr_at_100
      value: 81.075
    - type: mrr_at_1000
      value: 81.07900000000001
    - type: mrr_at_3
      value: 79.728
    - type: mrr_at_5
      value: 80.522
    - type: ndcg_at_1
      value: 71.557
    - type: ndcg_at_10
      value: 81.381
    - type: ndcg_at_100
      value: 82.421
    - type: ndcg_at_1000
      value: 82.709
    - type: ndcg_at_3
      value: 78.671
    - type: ndcg_at_5
      value: 80.17
    - type: precision_at_1
      value: 71.557
    - type: precision_at_10
      value: 10.159
    - type: precision_at_100
      value: 1.089
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 30.668
    - type: precision_at_5
      value: 19.337
    - type: recall_at_1
      value: 66.369
    - type: recall_at_10
      value: 91.482
    - type: recall_at_100
      value: 95.848
    - type: recall_at_1000
      value: 97.749
    - type: recall_at_3
      value: 84.185
    - type: recall_at_5
      value: 87.908
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 20.902
    - type: map_at_10
      value: 34.554
    - type: map_at_100
      value: 36.632
    - type: map_at_1000
      value: 36.811
    - type: map_at_3
      value: 30.264000000000003
    - type: map_at_5
      value: 32.714999999999996
    - type: mrr_at_1
      value: 42.13
    - type: mrr_at_10
      value: 51.224000000000004
    - type: mrr_at_100
      value: 52.044999999999995
    - type: mrr_at_1000
      value: 52.075
    - type: mrr_at_3
      value: 48.842999999999996
    - type: mrr_at_5
      value: 50.108
    - type: ndcg_at_1
      value: 42.13
    - type: ndcg_at_10
      value: 42.643
    - type: ndcg_at_100
      value: 49.806
    - type: ndcg_at_1000
      value: 52.583
    - type: ndcg_at_3
      value: 38.927
    - type: ndcg_at_5
      value: 40.071
    - type: precision_at_1
      value: 42.13
    - type: precision_at_10
      value: 11.928999999999998
    - type: precision_at_100
      value: 1.931
    - type: precision_at_1000
      value: 0.243
    - type: precision_at_3
      value: 26.337
    - type: precision_at_5
      value: 19.29
    - type: recall_at_1
      value: 20.902
    - type: recall_at_10
      value: 49.527
    - type: recall_at_100
      value: 75.754
    - type: recall_at_1000
      value: 92.171
    - type: recall_at_3
      value: 35.024
    - type: recall_at_5
      value: 41.207
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 39.831
    - type: map_at_10
      value: 63.958999999999996
    - type: map_at_100
      value: 64.869
    - type: map_at_1000
      value: 64.924
    - type: map_at_3
      value: 60.25
    - type: map_at_5
      value: 62.572
    - type: mrr_at_1
      value: 79.662
    - type: mrr_at_10
      value: 85.57900000000001
    - type: mrr_at_100
      value: 85.744
    - type: mrr_at_1000
      value: 85.748
    - type: mrr_at_3
      value: 84.718
    - type: mrr_at_5
      value: 85.312
    - type: ndcg_at_1
      value: 79.662
    - type: ndcg_at_10
      value: 72.366
    - type: ndcg_at_100
      value: 75.42999999999999
    - type: ndcg_at_1000
      value: 76.469
    - type: ndcg_at_3
      value: 67.258
    - type: ndcg_at_5
      value: 70.14099999999999
    - type: precision_at_1
      value: 79.662
    - type: precision_at_10
      value: 15.254999999999999
    - type: precision_at_100
      value: 1.763
    - type: precision_at_1000
      value: 0.19
    - type: precision_at_3
      value: 43.358000000000004
    - type: precision_at_5
      value: 28.288999999999998
    - type: recall_at_1
      value: 39.831
    - type: recall_at_10
      value: 76.273
    - type: recall_at_100
      value: 88.163
    - type: recall_at_1000
      value: 95.017
    - type: recall_at_3
      value: 65.037
    - type: recall_at_5
      value: 70.722
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 93.13879999999999
    - type: ap
      value: 89.94638859649079
    - type: f1
      value: 93.13371537570421
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: msmarco
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 21.482
    - type: map_at_10
      value: 33.635999999999996
    - type: map_at_100
      value: 34.792
    - type: map_at_1000
      value: 34.839999999999996
    - type: map_at_3
      value: 29.553
    - type: map_at_5
      value: 31.892
    - type: mrr_at_1
      value: 22.076999999999998
    - type: mrr_at_10
      value: 34.247
    - type: mrr_at_100
      value: 35.337
    - type: mrr_at_1000
      value: 35.38
    - type: mrr_at_3
      value: 30.208000000000002
    - type: mrr_at_5
      value: 32.554
    - type: ndcg_at_1
      value: 22.092
    - type: ndcg_at_10
      value: 40.657
    - type: ndcg_at_100
      value: 46.251999999999995
    - type: ndcg_at_1000
      value: 47.466
    - type: ndcg_at_3
      value: 32.353
    - type: ndcg_at_5
      value: 36.532
    - type: precision_at_1
      value: 22.092
    - type: precision_at_10
      value: 6.5040000000000004
    - type: precision_at_100
      value: 0.9329999999999999
    - type: precision_at_1000
      value: 0.104
    - type: precision_at_3
      value: 13.719999999999999
    - type: precision_at_5
      value: 10.344000000000001
    - type: recall_at_1
      value: 21.482
    - type: recall_at_10
      value: 62.316
    - type: recall_at_100
      value: 88.283
    - type: recall_at_1000
      value: 97.554
    - type: recall_at_3
      value: 39.822
    - type: recall_at_5
      value: 49.805
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 93.63657090743274
    - type: f1
      value: 93.49355466580484
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 66.01459188326493
    - type: f1
      value: 48.48386472180784
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 73.49024882313383
    - type: f1
      value: 71.8750196914349
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 77.38063214525891
    - type: f1
      value: 76.87364042122763
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 34.30572302322684
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 32.18418556367587
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 32.268707296386154
    - type: mrr
      value: 33.481925531215055
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 6.586
    - type: map_at_10
      value: 14.954999999999998
    - type: map_at_100
      value: 19.03
    - type: map_at_1000
      value: 20.653
    - type: map_at_3
      value: 10.859
    - type: map_at_5
      value: 12.577
    - type: mrr_at_1
      value: 47.988
    - type: mrr_at_10
      value: 57.57
    - type: mrr_at_100
      value: 58.050000000000004
    - type: mrr_at_1000
      value: 58.083
    - type: mrr_at_3
      value: 55.212
    - type: mrr_at_5
      value: 56.713
    - type: ndcg_at_1
      value: 45.975
    - type: ndcg_at_10
      value: 38.432
    - type: ndcg_at_100
      value: 35.287
    - type: ndcg_at_1000
      value: 44.35
    - type: ndcg_at_3
      value: 43.077
    - type: ndcg_at_5
      value: 40.952
    - type: precision_at_1
      value: 47.368
    - type: precision_at_10
      value: 28.483000000000004
    - type: precision_at_100
      value: 8.882
    - type: precision_at_1000
      value: 2.217
    - type: precision_at_3
      value: 40.144000000000005
    - type: precision_at_5
      value: 35.17
    - type: recall_at_1
      value: 6.586
    - type: recall_at_10
      value: 19.688
    - type: recall_at_100
      value: 35.426
    - type: recall_at_1000
      value: 68.09100000000001
    - type: recall_at_3
      value: 12.234
    - type: recall_at_5
      value: 14.937000000000001
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: nq
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 27.322000000000003
    - type: map_at_10
      value: 43.224000000000004
    - type: map_at_100
      value: 44.275999999999996
    - type: map_at_1000
      value: 44.308
    - type: map_at_3
      value: 38.239000000000004
    - type: map_at_5
      value: 41.244
    - type: mrr_at_1
      value: 31.025000000000002
    - type: mrr_at_10
      value: 45.635
    - type: mrr_at_100
      value: 46.425
    - type: mrr_at_1000
      value: 46.445
    - type: mrr_at_3
      value: 41.42
    - type: mrr_at_5
      value: 44.038
    - type: ndcg_at_1
      value: 30.997000000000003
    - type: ndcg_at_10
      value: 51.55499999999999
    - type: ndcg_at_100
      value: 55.964999999999996
    - type: ndcg_at_1000
      value: 56.657000000000004
    - type: ndcg_at_3
      value: 42.185
    - type: ndcg_at_5
      value: 47.229
    - type: precision_at_1
      value: 30.997000000000003
    - type: precision_at_10
      value: 8.885
    - type: precision_at_100
      value: 1.1360000000000001
    - type: precision_at_1000
      value: 0.12
    - type: precision_at_3
      value: 19.457
    - type: precision_at_5
      value: 14.554
    - type: recall_at_1
      value: 27.322000000000003
    - type: recall_at_10
      value: 74.59400000000001
    - type: recall_at_100
      value: 93.699
    - type: recall_at_1000
      value: 98.76599999999999
    - type: recall_at_3
      value: 50.43
    - type: recall_at_5
      value: 62.073
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 71.109
    - type: map_at_10
      value: 85.137
    - type: map_at_100
      value: 85.759
    - type: map_at_1000
      value: 85.774
    - type: map_at_3
      value: 82.25200000000001
    - type: map_at_5
      value: 84.031
    - type: mrr_at_1
      value: 82.01
    - type: mrr_at_10
      value: 87.97
    - type: mrr_at_100
      value: 88.076
    - type: mrr_at_1000
      value: 88.076
    - type: mrr_at_3
      value: 87.06
    - type: mrr_at_5
      value: 87.694
    - type: ndcg_at_1
      value: 81.99
    - type: ndcg_at_10
      value: 88.738
    - type: ndcg_at_100
      value: 89.928
    - type: ndcg_at_1000
      value: 90.01400000000001
    - type: ndcg_at_3
      value: 86.042
    - type: ndcg_at_5
      value: 87.505
    - type: precision_at_1
      value: 81.99
    - type: precision_at_10
      value: 13.468
    - type: precision_at_100
      value: 1.534
    - type: precision_at_1000
      value: 0.157
    - type: precision_at_3
      value: 37.702999999999996
    - type: precision_at_5
      value: 24.706
    - type: recall_at_1
      value: 71.109
    - type: recall_at_10
      value: 95.58
    - type: recall_at_100
      value: 99.62299999999999
    - type: recall_at_1000
      value: 99.98899999999999
    - type: recall_at_3
      value: 87.69
    - type: recall_at_5
      value: 91.982
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 59.43361510023748
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 64.53582642500159
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.2299999999999995
    - type: map_at_10
      value: 11.802
    - type: map_at_100
      value: 14.454
    - type: map_at_1000
      value: 14.865
    - type: map_at_3
      value: 7.911
    - type: map_at_5
      value: 9.912
    - type: mrr_at_1
      value: 21.0
    - type: mrr_at_10
      value: 32.722
    - type: mrr_at_100
      value: 33.989000000000004
    - type: mrr_at_1000
      value: 34.026
    - type: mrr_at_3
      value: 28.65
    - type: mrr_at_5
      value: 31.075000000000003
    - type: ndcg_at_1
      value: 21.0
    - type: ndcg_at_10
      value: 20.161
    - type: ndcg_at_100
      value: 30.122
    - type: ndcg_at_1000
      value: 36.399
    - type: ndcg_at_3
      value: 17.881
    - type: ndcg_at_5
      value: 16.439999999999998
    - type: precision_at_1
      value: 21.0
    - type: precision_at_10
      value: 10.94
    - type: precision_at_100
      value: 2.5340000000000003
    - type: precision_at_1000
      value: 0.402
    - type: precision_at_3
      value: 17.067
    - type: precision_at_5
      value: 15.120000000000001
    - type: recall_at_1
      value: 4.2299999999999995
    - type: recall_at_10
      value: 22.163
    - type: recall_at_100
      value: 51.42
    - type: recall_at_1000
      value: 81.652
    - type: recall_at_3
      value: 10.353
    - type: recall_at_5
      value: 15.323
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 86.44056731476951
    - type: cos_sim_spearman
      value: 82.32974396072802
    - type: euclidean_pearson
      value: 83.63616080755894
    - type: euclidean_spearman
      value: 82.32974071069209
    - type: manhattan_pearson
      value: 83.64149958303744
    - type: manhattan_spearman
      value: 82.32161014878858
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 85.65083720426293
    - type: cos_sim_spearman
      value: 77.60786500521749
    - type: euclidean_pearson
      value: 81.8149634918642
    - type: euclidean_spearman
      value: 77.60637450428892
    - type: manhattan_pearson
      value: 81.83507575657566
    - type: manhattan_spearman
      value: 77.613220311151
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 87.35683624595698
    - type: cos_sim_spearman
      value: 87.94550696434106
    - type: euclidean_pearson
      value: 87.50272679030367
    - type: euclidean_spearman
      value: 87.94550696434106
    - type: manhattan_pearson
      value: 87.4759786099497
    - type: manhattan_spearman
      value: 87.90226811166427
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 86.27438743391316
    - type: cos_sim_spearman
      value: 83.85378984594779
    - type: euclidean_pearson
      value: 85.25840635223642
    - type: euclidean_spearman
      value: 83.85378983163673
    - type: manhattan_pearson
      value: 85.24936075631025
    - type: manhattan_spearman
      value: 83.85052479958138
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 87.4783814521557
    - type: cos_sim_spearman
      value: 88.473284566453
    - type: euclidean_pearson
      value: 87.94757741870404
    - type: euclidean_spearman
      value: 88.47327698999878
    - type: manhattan_pearson
      value: 87.93617414057984
    - type: manhattan_spearman
      value: 88.45889274229359
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 84.68359147631057
    - type: cos_sim_spearman
      value: 86.46426572535646
    - type: euclidean_pearson
      value: 85.98303971468599
    - type: euclidean_spearman
      value: 86.46426572535646
    - type: manhattan_pearson
      value: 85.95109710640726
    - type: manhattan_spearman
      value: 86.43282632541583
  - 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: 88.88758959688604
    - type: cos_sim_spearman
      value: 88.70384784133324
    - type: euclidean_pearson
      value: 89.27293800474978
    - type: euclidean_spearman
      value: 88.70384784133324
    - type: manhattan_pearson
      value: 89.41494348093664
    - type: manhattan_spearman
      value: 88.8330050824941
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 67.66759812551814
    - type: cos_sim_spearman
      value: 68.02368115471576
    - type: euclidean_pearson
      value: 69.52859542757353
    - type: euclidean_spearman
      value: 68.02368115471576
    - type: manhattan_pearson
      value: 69.50332399468952
    - type: manhattan_spearman
      value: 67.91228681203849
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 87.75891320010409
    - type: cos_sim_spearman
      value: 88.33063922402347
    - type: euclidean_pearson
      value: 88.02964654543274
    - type: euclidean_spearman
      value: 88.33063922402347
    - type: manhattan_pearson
      value: 88.03029440701458
    - type: manhattan_spearman
      value: 88.3158691488696
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 87.46897310470844
    - type: mrr
      value: 96.29042072669523
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 62.261
    - type: map_at_10
      value: 71.023
    - type: map_at_100
      value: 71.5
    - type: map_at_1000
      value: 71.518
    - type: map_at_3
      value: 67.857
    - type: map_at_5
      value: 69.44500000000001
    - type: mrr_at_1
      value: 65.0
    - type: mrr_at_10
      value: 72.11
    - type: mrr_at_100
      value: 72.479
    - type: mrr_at_1000
      value: 72.49600000000001
    - type: mrr_at_3
      value: 69.722
    - type: mrr_at_5
      value: 71.02199999999999
    - type: ndcg_at_1
      value: 65.0
    - type: ndcg_at_10
      value: 75.40599999999999
    - type: ndcg_at_100
      value: 77.41
    - type: ndcg_at_1000
      value: 77.83200000000001
    - type: ndcg_at_3
      value: 69.95599999999999
    - type: ndcg_at_5
      value: 72.296
    - type: precision_at_1
      value: 65.0
    - type: precision_at_10
      value: 9.966999999999999
    - type: precision_at_100
      value: 1.097
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 26.667
    - type: precision_at_5
      value: 17.666999999999998
    - type: recall_at_1
      value: 62.261
    - type: recall_at_10
      value: 87.822
    - type: recall_at_100
      value: 96.833
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_3
      value: 73.06099999999999
    - type: recall_at_5
      value: 78.88300000000001
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.86138613861387
    - type: cos_sim_ap
      value: 96.7851799601876
    - type: cos_sim_f1
      value: 92.94354838709677
    - type: cos_sim_precision
      value: 93.69918699186992
    - type: cos_sim_recall
      value: 92.2
    - type: dot_accuracy
      value: 99.86138613861387
    - type: dot_ap
      value: 96.78517996018759
    - type: dot_f1
      value: 92.94354838709677
    - type: dot_precision
      value: 93.69918699186992
    - type: dot_recall
      value: 92.2
    - type: euclidean_accuracy
      value: 99.86138613861387
    - type: euclidean_ap
      value: 96.78517996018759
    - type: euclidean_f1
      value: 92.94354838709677
    - type: euclidean_precision
      value: 93.69918699186992
    - type: euclidean_recall
      value: 92.2
    - type: manhattan_accuracy
      value: 99.86336633663366
    - type: manhattan_ap
      value: 96.79790073128503
    - type: manhattan_f1
      value: 93.0930930930931
    - type: manhattan_precision
      value: 93.18637274549098
    - type: manhattan_recall
      value: 93.0
    - type: max_accuracy
      value: 99.86336633663366
    - type: max_ap
      value: 96.79790073128503
    - type: max_f1
      value: 93.0930930930931
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 65.07696952556874
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 35.51701116515262
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 55.40099299306496
    - type: mrr
      value: 56.411316420507596
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 30.940008734510055
    - type: cos_sim_spearman
      value: 31.606997026865212
    - type: dot_pearson
      value: 30.940010256206353
    - type: dot_spearman
      value: 31.62194110302714
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.197
    - type: map_at_10
      value: 1.6549999999999998
    - type: map_at_100
      value: 8.939
    - type: map_at_1000
      value: 22.402
    - type: map_at_3
      value: 0.587
    - type: map_at_5
      value: 0.931
    - type: mrr_at_1
      value: 74.0
    - type: mrr_at_10
      value: 84.667
    - type: mrr_at_100
      value: 84.667
    - type: mrr_at_1000
      value: 84.667
    - type: mrr_at_3
      value: 83.667
    - type: mrr_at_5
      value: 84.667
    - type: ndcg_at_1
      value: 69.0
    - type: ndcg_at_10
      value: 66.574
    - type: ndcg_at_100
      value: 51.074
    - type: ndcg_at_1000
      value: 47.263
    - type: ndcg_at_3
      value: 71.95
    - type: ndcg_at_5
      value: 70.52000000000001
    - type: precision_at_1
      value: 74.0
    - type: precision_at_10
      value: 70.39999999999999
    - type: precision_at_100
      value: 52.580000000000005
    - type: precision_at_1000
      value: 20.93
    - type: precision_at_3
      value: 76.667
    - type: precision_at_5
      value: 75.6
    - type: recall_at_1
      value: 0.197
    - type: recall_at_10
      value: 1.92
    - type: recall_at_100
      value: 12.655
    - type: recall_at_1000
      value: 44.522
    - type: recall_at_3
      value: 0.639
    - type: recall_at_5
      value: 1.03
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: webis-touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 1.735
    - type: map_at_10
      value: 9.064
    - type: map_at_100
      value: 15.021999999999998
    - type: map_at_1000
      value: 16.596
    - type: map_at_3
      value: 4.188
    - type: map_at_5
      value: 6.194999999999999
    - type: mrr_at_1
      value: 26.531
    - type: mrr_at_10
      value: 44.413000000000004
    - type: mrr_at_100
      value: 45.433
    - type: mrr_at_1000
      value: 45.452999999999996
    - type: mrr_at_3
      value: 41.497
    - type: mrr_at_5
      value: 42.925000000000004
    - type: ndcg_at_1
      value: 22.448999999999998
    - type: ndcg_at_10
      value: 22.597
    - type: ndcg_at_100
      value: 34.893
    - type: ndcg_at_1000
      value: 46.763
    - type: ndcg_at_3
      value: 24.366
    - type: ndcg_at_5
      value: 23.959
    - type: precision_at_1
      value: 26.531
    - type: precision_at_10
      value: 21.02
    - type: precision_at_100
      value: 7.51
    - type: precision_at_1000
      value: 1.541
    - type: precision_at_3
      value: 27.211000000000002
    - type: precision_at_5
      value: 25.306
    - type: recall_at_1
      value: 1.735
    - type: recall_at_10
      value: 15.870999999999999
    - type: recall_at_100
      value: 47.385
    - type: recall_at_1000
      value: 83.55
    - type: recall_at_3
      value: 5.813
    - type: recall_at_5
      value: 9.707
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 71.19
    - type: ap
      value: 15.106812062408629
    - type: f1
      value: 55.254852511954255
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 61.553480475382
    - type: f1
      value: 61.697424438626435
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 53.12092298453447
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 87.35173153722357
    - type: cos_sim_ap
      value: 78.22985044080261
    - type: cos_sim_f1
      value: 71.23356926188069
    - type: cos_sim_precision
      value: 68.36487142163999
    - type: cos_sim_recall
      value: 74.35356200527704
    - type: dot_accuracy
      value: 87.35173153722357
    - type: dot_ap
      value: 78.22985958574529
    - type: dot_f1
      value: 71.23356926188069
    - type: dot_precision
      value: 68.36487142163999
    - type: dot_recall
      value: 74.35356200527704
    - type: euclidean_accuracy
      value: 87.35173153722357
    - type: euclidean_ap
      value: 78.22985909816191
    - type: euclidean_f1
      value: 71.23356926188069
    - type: euclidean_precision
      value: 68.36487142163999
    - type: euclidean_recall
      value: 74.35356200527704
    - type: manhattan_accuracy
      value: 87.36365261965786
    - type: manhattan_ap
      value: 78.18108280854142
    - type: manhattan_f1
      value: 71.19958634953466
    - type: manhattan_precision
      value: 69.79219462747086
    - type: manhattan_recall
      value: 72.66490765171504
    - type: max_accuracy
      value: 87.36365261965786
    - type: max_ap
      value: 78.22985958574529
    - type: max_f1
      value: 71.23356926188069
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.71424690495596
    - type: cos_sim_ap
      value: 85.53000600450122
    - type: cos_sim_f1
      value: 77.95508274231679
    - type: cos_sim_precision
      value: 74.92189718829879
    - type: cos_sim_recall
      value: 81.24422543886665
    - type: dot_accuracy
      value: 88.71424690495596
    - type: dot_ap
      value: 85.53000387261983
    - type: dot_f1
      value: 77.95508274231679
    - type: dot_precision
      value: 74.92189718829879
    - type: dot_recall
      value: 81.24422543886665
    - type: euclidean_accuracy
      value: 88.71424690495596
    - type: euclidean_ap
      value: 85.53000527321076
    - type: euclidean_f1
      value: 77.95508274231679
    - type: euclidean_precision
      value: 74.92189718829879
    - type: euclidean_recall
      value: 81.24422543886665
    - type: manhattan_accuracy
      value: 88.7297706368611
    - type: manhattan_ap
      value: 85.49670114967172
    - type: manhattan_f1
      value: 77.91265729089562
    - type: manhattan_precision
      value: 75.01425313568986
    - type: manhattan_recall
      value: 81.04404065291038
    - type: max_accuracy
      value: 88.7297706368611
    - type: max_ap
      value: 85.53000600450122
    - type: max_f1
      value: 77.95508274231679
---

# {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)
```



## 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 trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 1196 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 5,
    "evaluation_steps": 50,
    "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 598,
    "weight_decay": 0.01
}
```


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

## Citing & Authors

<!--- Describe where people can find more information -->