transformers t5 feature-extraction ctranslate2 int8 float16 - finetuner - mteb - sentence-transformers - feature-extraction - sentence-similarity sentence-similarity custom_code en dataset:jinaai/negation-dataset arxiv:2307.11224 license:apache-2.0 model-index region:us
---
datasets:
- jinaai/negation-dataset
language: en
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- ctranslate2
- int8
- float16 - finetuner - mteb - sentence-transformers - feature-extraction - sentence-similarity
model-index:
- name: jina-triplets-large
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 68.92537313432835
    - type: ap
      value: 29.723758877632513
    - type: f1
      value: 61.909704211663794
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 69.13669999999999
    - type: ap
      value: 65.30216072238086
    - type: f1
      value: 67.1890891071034
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 31.384
    - type: f1
      value: 30.016752348953723
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 23.613
    - type: map_at_10
      value: 37.897
    - type: map_at_100
      value: 39.093
    - type: map_at_1000
      value: 39.109
    - type: map_at_3
      value: 32.824
    - type: map_at_5
      value: 35.679
    - type: mrr_at_1
      value: 23.826
    - type: mrr_at_10
      value: 37.997
    - type: mrr_at_100
      value: 39.186
    - type: mrr_at_1000
      value: 39.202
    - type: mrr_at_3
      value: 32.918
    - type: mrr_at_5
      value: 35.748999999999995
    - type: ndcg_at_1
      value: 23.613
    - type: ndcg_at_10
      value: 46.482
    - type: ndcg_at_100
      value: 51.55499999999999
    - type: ndcg_at_1000
      value: 51.974
    - type: ndcg_at_3
      value: 35.964
    - type: ndcg_at_5
      value: 41.144999999999996
    - type: precision_at_1
      value: 23.613
    - type: precision_at_10
      value: 7.417999999999999
    - type: precision_at_100
      value: 0.963
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 15.031
    - type: precision_at_5
      value: 11.55
    - type: recall_at_1
      value: 23.613
    - type: recall_at_10
      value: 74.182
    - type: recall_at_100
      value: 96.30199999999999
    - type: recall_at_1000
      value: 99.57300000000001
    - type: recall_at_3
      value: 45.092
    - type: recall_at_5
      value: 57.752
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 40.51285742156528
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 31.5825964077496
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 62.830281630546835
    - type: mrr
      value: 75.93072593765115
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 87.26764516732737
    - type: cos_sim_spearman
      value: 84.42541766631741
    - type: euclidean_pearson
      value: 48.71357447655235
    - type: euclidean_spearman
      value: 49.2023259276511
    - type: manhattan_pearson
      value: 48.36366272727299
    - type: manhattan_spearman
      value: 48.457128224924354
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 85.3409090909091
    - type: f1
      value: 85.25262617676835
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 33.560193912974974
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 28.4426572644577
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 27.822999999999997
    - type: map_at_10
      value: 39.088
    - type: map_at_100
      value: 40.561
    - type: map_at_1000
      value: 40.69
    - type: map_at_3
      value: 35.701
    - type: map_at_5
      value: 37.556
    - type: mrr_at_1
      value: 33.906
    - type: mrr_at_10
      value: 44.527
    - type: mrr_at_100
      value: 45.403999999999996
    - type: mrr_at_1000
      value: 45.452
    - type: mrr_at_3
      value: 41.726
    - type: mrr_at_5
      value: 43.314
    - type: ndcg_at_1
      value: 33.906
    - type: ndcg_at_10
      value: 45.591
    - type: ndcg_at_100
      value: 51.041000000000004
    - type: ndcg_at_1000
      value: 53.1
    - type: ndcg_at_3
      value: 40.324
    - type: ndcg_at_5
      value: 42.723
    - type: precision_at_1
      value: 33.906
    - type: precision_at_10
      value: 8.655
    - type: precision_at_100
      value: 1.418
    - type: precision_at_1000
      value: 0.19499999999999998
    - type: precision_at_3
      value: 19.123
    - type: precision_at_5
      value: 13.963000000000001
    - type: recall_at_1
      value: 27.822999999999997
    - type: recall_at_10
      value: 58.63699999999999
    - type: recall_at_100
      value: 80.874
    - type: recall_at_1000
      value: 93.82000000000001
    - type: recall_at_3
      value: 44.116
    - type: recall_at_5
      value: 50.178999999999995
    - type: map_at_1
      value: 26.823999999999998
    - type: map_at_10
      value: 37.006
    - type: map_at_100
      value: 38.256
    - type: map_at_1000
      value: 38.397999999999996
    - type: map_at_3
      value: 34.011
    - type: map_at_5
      value: 35.643
    - type: mrr_at_1
      value: 34.268
    - type: mrr_at_10
      value: 43.374
    - type: mrr_at_100
      value: 44.096000000000004
    - type: mrr_at_1000
      value: 44.144
    - type: mrr_at_3
      value: 41.008
    - type: mrr_at_5
      value: 42.359
    - type: ndcg_at_1
      value: 34.268
    - type: ndcg_at_10
      value: 43.02
    - type: ndcg_at_100
      value: 47.747
    - type: ndcg_at_1000
      value: 50.019999999999996
    - type: ndcg_at_3
      value: 38.687
    - type: ndcg_at_5
      value: 40.647
    - type: precision_at_1
      value: 34.268
    - type: precision_at_10
      value: 8.261000000000001
    - type: precision_at_100
      value: 1.376
    - type: precision_at_1000
      value: 0.189
    - type: precision_at_3
      value: 19.108
    - type: precision_at_5
      value: 13.489999999999998
    - type: recall_at_1
      value: 26.823999999999998
    - type: recall_at_10
      value: 53.84100000000001
    - type: recall_at_100
      value: 73.992
    - type: recall_at_1000
      value: 88.524
    - type: recall_at_3
      value: 40.711000000000006
    - type: recall_at_5
      value: 46.477000000000004
    - type: map_at_1
      value: 34.307
    - type: map_at_10
      value: 45.144
    - type: map_at_100
      value: 46.351
    - type: map_at_1000
      value: 46.414
    - type: map_at_3
      value: 42.315000000000005
    - type: map_at_5
      value: 43.991
    - type: mrr_at_1
      value: 39.06
    - type: mrr_at_10
      value: 48.612
    - type: mrr_at_100
      value: 49.425000000000004
    - type: mrr_at_1000
      value: 49.458999999999996
    - type: mrr_at_3
      value: 46.144
    - type: mrr_at_5
      value: 47.654999999999994
    - type: ndcg_at_1
      value: 39.06
    - type: ndcg_at_10
      value: 50.647
    - type: ndcg_at_100
      value: 55.620000000000005
    - type: ndcg_at_1000
      value: 56.976000000000006
    - type: ndcg_at_3
      value: 45.705
    - type: ndcg_at_5
      value: 48.269
    - type: precision_at_1
      value: 39.06
    - type: precision_at_10
      value: 8.082
    - type: precision_at_100
      value: 1.161
    - type: precision_at_1000
      value: 0.133
    - type: precision_at_3
      value: 20.376
    - type: precision_at_5
      value: 14.069
    - type: recall_at_1
      value: 34.307
    - type: recall_at_10
      value: 63.497
    - type: recall_at_100
      value: 85.038
    - type: recall_at_1000
      value: 94.782
    - type: recall_at_3
      value: 50.209
    - type: recall_at_5
      value: 56.525000000000006
    - type: map_at_1
      value: 26.448
    - type: map_at_10
      value: 34.86
    - type: map_at_100
      value: 36.004999999999995
    - type: map_at_1000
      value: 36.081
    - type: map_at_3
      value: 32.527
    - type: map_at_5
      value: 33.955
    - type: mrr_at_1
      value: 28.701
    - type: mrr_at_10
      value: 36.909
    - type: mrr_at_100
      value: 37.89
    - type: mrr_at_1000
      value: 37.945
    - type: mrr_at_3
      value: 34.576
    - type: mrr_at_5
      value: 35.966
    - type: ndcg_at_1
      value: 28.701
    - type: ndcg_at_10
      value: 39.507999999999996
    - type: ndcg_at_100
      value: 45.056000000000004
    - type: ndcg_at_1000
      value: 47.034
    - type: ndcg_at_3
      value: 34.985
    - type: ndcg_at_5
      value: 37.384
    - type: precision_at_1
      value: 28.701
    - type: precision_at_10
      value: 5.921
    - type: precision_at_100
      value: 0.914
    - type: precision_at_1000
      value: 0.11199999999999999
    - type: precision_at_3
      value: 14.689
    - type: precision_at_5
      value: 10.237
    - type: recall_at_1
      value: 26.448
    - type: recall_at_10
      value: 51.781
    - type: recall_at_100
      value: 77.142
    - type: recall_at_1000
      value: 92.10000000000001
    - type: recall_at_3
      value: 39.698
    - type: recall_at_5
      value: 45.469
    - type: map_at_1
      value: 14.174000000000001
    - type: map_at_10
      value: 22.019
    - type: map_at_100
      value: 23.18
    - type: map_at_1000
      value: 23.304
    - type: map_at_3
      value: 19.332
    - type: map_at_5
      value: 20.816000000000003
    - type: mrr_at_1
      value: 17.785999999999998
    - type: mrr_at_10
      value: 26.233
    - type: mrr_at_100
      value: 27.254
    - type: mrr_at_1000
      value: 27.328000000000003
    - type: mrr_at_3
      value: 23.653
    - type: mrr_at_5
      value: 25.095
    - type: ndcg_at_1
      value: 17.785999999999998
    - type: ndcg_at_10
      value: 27.236
    - type: ndcg_at_100
      value: 32.932
    - type: ndcg_at_1000
      value: 36.134
    - type: ndcg_at_3
      value: 22.33
    - type: ndcg_at_5
      value: 24.573999999999998
    - type: precision_at_1
      value: 17.785999999999998
    - type: precision_at_10
      value: 5.286
    - type: precision_at_100
      value: 0.9369999999999999
    - type: precision_at_1000
      value: 0.136
    - type: precision_at_3
      value: 11.07
    - type: precision_at_5
      value: 8.308
    - type: recall_at_1
      value: 14.174000000000001
    - type: recall_at_10
      value: 39.135
    - type: recall_at_100
      value: 64.095
    - type: recall_at_1000
      value: 87.485
    - type: recall_at_3
      value: 25.496999999999996
    - type: recall_at_5
      value: 31.148999999999997
    - type: map_at_1
      value: 24.371000000000002
    - type: map_at_10
      value: 33.074999999999996
    - type: map_at_100
      value: 34.486
    - type: map_at_1000
      value: 34.608
    - type: map_at_3
      value: 30.483
    - type: map_at_5
      value: 31.972
    - type: mrr_at_1
      value: 29.548000000000002
    - type: mrr_at_10
      value: 38.431
    - type: mrr_at_100
      value: 39.347
    - type: mrr_at_1000
      value: 39.4
    - type: mrr_at_3
      value: 35.980000000000004
    - type: mrr_at_5
      value: 37.413999999999994
    - type: ndcg_at_1
      value: 29.548000000000002
    - type: ndcg_at_10
      value: 38.552
    - type: ndcg_at_100
      value: 44.598
    - type: ndcg_at_1000
      value: 47.0
    - type: ndcg_at_3
      value: 34.109
    - type: ndcg_at_5
      value: 36.263
    - type: precision_at_1
      value: 29.548000000000002
    - type: precision_at_10
      value: 6.92
    - type: precision_at_100
      value: 1.179
    - type: precision_at_1000
      value: 0.159
    - type: precision_at_3
      value: 16.137
    - type: precision_at_5
      value: 11.511000000000001
    - type: recall_at_1
      value: 24.371000000000002
    - type: recall_at_10
      value: 49.586999999999996
    - type: recall_at_100
      value: 75.15899999999999
    - type: recall_at_1000
      value: 91.06
    - type: recall_at_3
      value: 37.09
    - type: recall_at_5
      value: 42.588
    - type: map_at_1
      value: 24.517
    - type: map_at_10
      value: 32.969
    - type: map_at_100
      value: 34.199
    - type: map_at_1000
      value: 34.322
    - type: map_at_3
      value: 30.270999999999997
    - type: map_at_5
      value: 31.863000000000003
    - type: mrr_at_1
      value: 30.479
    - type: mrr_at_10
      value: 38.633
    - type: mrr_at_100
      value: 39.522
    - type: mrr_at_1000
      value: 39.583
    - type: mrr_at_3
      value: 36.454
    - type: mrr_at_5
      value: 37.744
    - type: ndcg_at_1
      value: 30.479
    - type: ndcg_at_10
      value: 38.269
    - type: ndcg_at_100
      value: 43.91
    - type: ndcg_at_1000
      value: 46.564
    - type: ndcg_at_3
      value: 34.03
    - type: ndcg_at_5
      value: 36.155
    - type: precision_at_1
      value: 30.479
    - type: precision_at_10
      value: 6.815
    - type: precision_at_100
      value: 1.138
    - type: precision_at_1000
      value: 0.158
    - type: precision_at_3
      value: 16.058
    - type: precision_at_5
      value: 11.416
    - type: recall_at_1
      value: 24.517
    - type: recall_at_10
      value: 48.559000000000005
    - type: recall_at_100
      value: 73.307
    - type: recall_at_1000
      value: 91.508
    - type: recall_at_3
      value: 36.563
    - type: recall_at_5
      value: 42.375
    - type: map_at_1
      value: 24.336166666666664
    - type: map_at_10
      value: 32.80791666666667
    - type: map_at_100
      value: 34.043416666666666
    - type: map_at_1000
      value: 34.162749999999996
    - type: map_at_3
      value: 30.187083333333337
    - type: map_at_5
      value: 31.637833333333337
    - type: mrr_at_1
      value: 28.669583333333343
    - type: mrr_at_10
      value: 36.88616666666667
    - type: mrr_at_100
      value: 37.80233333333333
    - type: mrr_at_1000
      value: 37.86141666666666
    - type: mrr_at_3
      value: 34.537416666666665
    - type: mrr_at_5
      value: 35.84275
    - type: ndcg_at_1
      value: 28.669583333333343
    - type: ndcg_at_10
      value: 37.956916666666665
    - type: ndcg_at_100
      value: 43.39475
    - type: ndcg_at_1000
      value: 45.79925
    - type: ndcg_at_3
      value: 33.43683333333334
    - type: ndcg_at_5
      value: 35.52575
    - type: precision_at_1
      value: 28.669583333333343
    - type: precision_at_10
      value: 6.603833333333335
    - type: precision_at_100
      value: 1.1079166666666667
    - type: precision_at_1000
      value: 0.15208333333333335
    - type: precision_at_3
      value: 15.338750000000001
    - type: precision_at_5
      value: 10.88775
    - type: recall_at_1
      value: 24.336166666666664
    - type: recall_at_10
      value: 49.19358333333333
    - type: recall_at_100
      value: 73.07583333333334
    - type: recall_at_1000
      value: 89.81675
    - type: recall_at_3
      value: 36.54091666666667
    - type: recall_at_5
      value: 41.919250000000005
    - type: map_at_1
      value: 23.388
    - type: map_at_10
      value: 29.408
    - type: map_at_100
      value: 30.452
    - type: map_at_1000
      value: 30.546
    - type: map_at_3
      value: 27.139000000000003
    - type: map_at_5
      value: 28.402
    - type: mrr_at_1
      value: 25.46
    - type: mrr_at_10
      value: 31.966
    - type: mrr_at_100
      value: 32.879999999999995
    - type: mrr_at_1000
      value: 32.944
    - type: mrr_at_3
      value: 29.755
    - type: mrr_at_5
      value: 30.974
    - type: ndcg_at_1
      value: 25.46
    - type: ndcg_at_10
      value: 33.449
    - type: ndcg_at_100
      value: 38.67
    - type: ndcg_at_1000
      value: 41.035
    - type: ndcg_at_3
      value: 29.048000000000002
    - type: ndcg_at_5
      value: 31.127
    - type: precision_at_1
      value: 25.46
    - type: precision_at_10
      value: 5.199
    - type: precision_at_100
      value: 0.8670000000000001
    - type: precision_at_1000
      value: 0.11399999999999999
    - type: precision_at_3
      value: 12.168
    - type: precision_at_5
      value: 8.62
    - type: recall_at_1
      value: 23.388
    - type: recall_at_10
      value: 43.428
    - type: recall_at_100
      value: 67.245
    - type: recall_at_1000
      value: 84.75399999999999
    - type: recall_at_3
      value: 31.416
    - type: recall_at_5
      value: 36.451
    - type: map_at_1
      value: 17.136000000000003
    - type: map_at_10
      value: 24.102999999999998
    - type: map_at_100
      value: 25.219
    - type: map_at_1000
      value: 25.344
    - type: map_at_3
      value: 22.004
    - type: map_at_5
      value: 23.145
    - type: mrr_at_1
      value: 20.613
    - type: mrr_at_10
      value: 27.753
    - type: mrr_at_100
      value: 28.698
    - type: mrr_at_1000
      value: 28.776000000000003
    - type: mrr_at_3
      value: 25.711000000000002
    - type: mrr_at_5
      value: 26.795
    - type: ndcg_at_1
      value: 20.613
    - type: ndcg_at_10
      value: 28.510999999999996
    - type: ndcg_at_100
      value: 33.924
    - type: ndcg_at_1000
      value: 36.849
    - type: ndcg_at_3
      value: 24.664
    - type: ndcg_at_5
      value: 26.365
    - type: precision_at_1
      value: 20.613
    - type: precision_at_10
      value: 5.069
    - type: precision_at_100
      value: 0.918
    - type: precision_at_1000
      value: 0.136
    - type: precision_at_3
      value: 11.574
    - type: precision_at_5
      value: 8.211
    - type: recall_at_1
      value: 17.136000000000003
    - type: recall_at_10
      value: 38.232
    - type: recall_at_100
      value: 62.571
    - type: recall_at_1000
      value: 83.23
    - type: recall_at_3
      value: 27.468999999999998
    - type: recall_at_5
      value: 31.852999999999998
    - type: map_at_1
      value: 25.580000000000002
    - type: map_at_10
      value: 33.449
    - type: map_at_100
      value: 34.58
    - type: map_at_1000
      value: 34.692
    - type: map_at_3
      value: 30.660999999999998
    - type: map_at_5
      value: 32.425
    - type: mrr_at_1
      value: 30.037000000000003
    - type: mrr_at_10
      value: 37.443
    - type: mrr_at_100
      value: 38.32
    - type: mrr_at_1000
      value: 38.384
    - type: mrr_at_3
      value: 34.778999999999996
    - type: mrr_at_5
      value: 36.458
    - type: ndcg_at_1
      value: 30.037000000000003
    - type: ndcg_at_10
      value: 38.46
    - type: ndcg_at_100
      value: 43.746
    - type: ndcg_at_1000
      value: 46.28
    - type: ndcg_at_3
      value: 33.52
    - type: ndcg_at_5
      value: 36.175000000000004
    - type: precision_at_1
      value: 30.037000000000003
    - type: precision_at_10
      value: 6.418
    - type: precision_at_100
      value: 1.0210000000000001
    - type: precision_at_1000
      value: 0.136
    - type: precision_at_3
      value: 15.018999999999998
    - type: precision_at_5
      value: 10.877
    - type: recall_at_1
      value: 25.580000000000002
    - type: recall_at_10
      value: 49.830000000000005
    - type: recall_at_100
      value: 73.04899999999999
    - type: recall_at_1000
      value: 90.751
    - type: recall_at_3
      value: 36.370999999999995
    - type: recall_at_5
      value: 43.104
    - type: map_at_1
      value: 24.071
    - type: map_at_10
      value: 33.384
    - type: map_at_100
      value: 35.004999999999995
    - type: map_at_1000
      value: 35.215999999999994
    - type: map_at_3
      value: 30.459000000000003
    - type: map_at_5
      value: 31.769
    - type: mrr_at_1
      value: 28.854000000000003
    - type: mrr_at_10
      value: 37.512
    - type: mrr_at_100
      value: 38.567
    - type: mrr_at_1000
      value: 38.618
    - type: mrr_at_3
      value: 35.211
    - type: mrr_at_5
      value: 36.13
    - type: ndcg_at_1
      value: 28.854000000000003
    - type: ndcg_at_10
      value: 39.216
    - type: ndcg_at_100
      value: 45.214
    - type: ndcg_at_1000
      value: 47.573
    - type: ndcg_at_3
      value: 34.597
    - type: ndcg_at_5
      value: 36.063
    - type: precision_at_1
      value: 28.854000000000003
    - type: precision_at_10
      value: 7.648000000000001
    - type: precision_at_100
      value: 1.545
    - type: precision_at_1000
      value: 0.241
    - type: precision_at_3
      value: 16.667
    - type: precision_at_5
      value: 11.818
    - type: recall_at_1
      value: 24.071
    - type: recall_at_10
      value: 50.802
    - type: recall_at_100
      value: 77.453
    - type: recall_at_1000
      value: 92.304
    - type: recall_at_3
      value: 36.846000000000004
    - type: recall_at_5
      value: 41.14
    - type: map_at_1
      value: 23.395
    - type: map_at_10
      value: 29.189999999999998
    - type: map_at_100
      value: 30.226999999999997
    - type: map_at_1000
      value: 30.337999999999997
    - type: map_at_3
      value: 27.342
    - type: map_at_5
      value: 28.116999999999997
    - type: mrr_at_1
      value: 25.323
    - type: mrr_at_10
      value: 31.241000000000003
    - type: mrr_at_100
      value: 32.225
    - type: mrr_at_1000
      value: 32.304
    - type: mrr_at_3
      value: 29.452
    - type: mrr_at_5
      value: 30.209000000000003
    - type: ndcg_at_1
      value: 25.323
    - type: ndcg_at_10
      value: 33.024
    - type: ndcg_at_100
      value: 38.279
    - type: ndcg_at_1000
      value: 41.026
    - type: ndcg_at_3
      value: 29.243000000000002
    - type: ndcg_at_5
      value: 30.564000000000004
    - type: precision_at_1
      value: 25.323
    - type: precision_at_10
      value: 4.972
    - type: precision_at_100
      value: 0.8210000000000001
    - type: precision_at_1000
      value: 0.116
    - type: precision_at_3
      value: 12.076
    - type: precision_at_5
      value: 8.133
    - type: recall_at_1
      value: 23.395
    - type: recall_at_10
      value: 42.994
    - type: recall_at_100
      value: 66.985
    - type: recall_at_1000
      value: 87.483
    - type: recall_at_3
      value: 32.505
    - type: recall_at_5
      value: 35.721000000000004
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 8.322000000000001
    - type: map_at_10
      value: 14.491000000000001
    - type: map_at_100
      value: 16.066
    - type: map_at_1000
      value: 16.238
    - type: map_at_3
      value: 12.235
    - type: map_at_5
      value: 13.422999999999998
    - type: mrr_at_1
      value: 19.479
    - type: mrr_at_10
      value: 29.38
    - type: mrr_at_100
      value: 30.520999999999997
    - type: mrr_at_1000
      value: 30.570999999999998
    - type: mrr_at_3
      value: 26.395000000000003
    - type: mrr_at_5
      value: 27.982000000000003
    - type: ndcg_at_1
      value: 19.479
    - type: ndcg_at_10
      value: 21.215
    - type: ndcg_at_100
      value: 27.966
    - type: ndcg_at_1000
      value: 31.324
    - type: ndcg_at_3
      value: 17.194000000000003
    - type: ndcg_at_5
      value: 18.593
    - type: precision_at_1
      value: 19.479
    - type: precision_at_10
      value: 6.5280000000000005
    - type: precision_at_100
      value: 1.359
    - type: precision_at_1000
      value: 0.198
    - type: precision_at_3
      value: 12.703999999999999
    - type: precision_at_5
      value: 9.655
    - type: recall_at_1
      value: 8.322000000000001
    - type: recall_at_10
      value: 26.165
    - type: recall_at_100
      value: 49.573
    - type: recall_at_1000
      value: 68.501
    - type: recall_at_3
      value: 16.179
    - type: recall_at_5
      value: 20.175
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 8.003
    - type: map_at_10
      value: 16.087
    - type: map_at_100
      value: 21.363
    - type: map_at_1000
      value: 22.64
    - type: map_at_3
      value: 12.171999999999999
    - type: map_at_5
      value: 13.866
    - type: mrr_at_1
      value: 61.25000000000001
    - type: mrr_at_10
      value: 68.626
    - type: mrr_at_100
      value: 69.134
    - type: mrr_at_1000
      value: 69.144
    - type: mrr_at_3
      value: 67.042
    - type: mrr_at_5
      value: 67.929
    - type: ndcg_at_1
      value: 49.0
    - type: ndcg_at_10
      value: 34.132
    - type: ndcg_at_100
      value: 37.545
    - type: ndcg_at_1000
      value: 44.544
    - type: ndcg_at_3
      value: 38.946999999999996
    - type: ndcg_at_5
      value: 36.317
    - type: precision_at_1
      value: 61.25000000000001
    - type: precision_at_10
      value: 26.325
    - type: precision_at_100
      value: 8.173
    - type: precision_at_1000
      value: 1.778
    - type: precision_at_3
      value: 41.667
    - type: precision_at_5
      value: 34.300000000000004
    - type: recall_at_1
      value: 8.003
    - type: recall_at_10
      value: 20.577
    - type: recall_at_100
      value: 41.884
    - type: recall_at_1000
      value: 64.36500000000001
    - type: recall_at_3
      value: 13.602
    - type: recall_at_5
      value: 16.41
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 45.835
    - type: f1
      value: 41.66455981281837
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 55.717000000000006
    - type: map_at_10
      value: 66.34100000000001
    - type: map_at_100
      value: 66.776
    - type: map_at_1000
      value: 66.794
    - type: map_at_3
      value: 64.386
    - type: map_at_5
      value: 65.566
    - type: mrr_at_1
      value: 60.141
    - type: mrr_at_10
      value: 70.928
    - type: mrr_at_100
      value: 71.29299999999999
    - type: mrr_at_1000
      value: 71.30199999999999
    - type: mrr_at_3
      value: 69.07900000000001
    - type: mrr_at_5
      value: 70.244
    - type: ndcg_at_1
      value: 60.141
    - type: ndcg_at_10
      value: 71.90100000000001
    - type: ndcg_at_100
      value: 73.836
    - type: ndcg_at_1000
      value: 74.214
    - type: ndcg_at_3
      value: 68.203
    - type: ndcg_at_5
      value: 70.167
    - type: precision_at_1
      value: 60.141
    - type: precision_at_10
      value: 9.268
    - type: precision_at_100
      value: 1.03
    - type: precision_at_1000
      value: 0.108
    - type: precision_at_3
      value: 27.028000000000002
    - type: precision_at_5
      value: 17.342
    - type: recall_at_1
      value: 55.717000000000006
    - type: recall_at_10
      value: 84.66799999999999
    - type: recall_at_100
      value: 93.28
    - type: recall_at_1000
      value: 95.887
    - type: recall_at_3
      value: 74.541
    - type: recall_at_5
      value: 79.389
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 17.744
    - type: map_at_10
      value: 29.554000000000002
    - type: map_at_100
      value: 31.180000000000003
    - type: map_at_1000
      value: 31.372
    - type: map_at_3
      value: 25.6
    - type: map_at_5
      value: 27.642
    - type: mrr_at_1
      value: 35.802
    - type: mrr_at_10
      value: 44.812999999999995
    - type: mrr_at_100
      value: 45.56
    - type: mrr_at_1000
      value: 45.606
    - type: mrr_at_3
      value: 42.181000000000004
    - type: mrr_at_5
      value: 43.516
    - type: ndcg_at_1
      value: 35.802
    - type: ndcg_at_10
      value: 37.269999999999996
    - type: ndcg_at_100
      value: 43.575
    - type: ndcg_at_1000
      value: 46.916000000000004
    - type: ndcg_at_3
      value: 33.511
    - type: ndcg_at_5
      value: 34.504000000000005
    - type: precision_at_1
      value: 35.802
    - type: precision_at_10
      value: 10.448
    - type: precision_at_100
      value: 1.7129999999999999
    - type: precision_at_1000
      value: 0.231
    - type: precision_at_3
      value: 22.531000000000002
    - type: precision_at_5
      value: 16.512
    - type: recall_at_1
      value: 17.744
    - type: recall_at_10
      value: 44.616
    - type: recall_at_100
      value: 68.51899999999999
    - type: recall_at_1000
      value: 88.495
    - type: recall_at_3
      value: 30.235
    - type: recall_at_5
      value: 35.821999999999996
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 33.315
    - type: map_at_10
      value: 45.932
    - type: map_at_100
      value: 46.708
    - type: map_at_1000
      value: 46.778999999999996
    - type: map_at_3
      value: 43.472
    - type: map_at_5
      value: 45.022
    - type: mrr_at_1
      value: 66.631
    - type: mrr_at_10
      value: 73.083
    - type: mrr_at_100
      value: 73.405
    - type: mrr_at_1000
      value: 73.421
    - type: mrr_at_3
      value: 71.756
    - type: mrr_at_5
      value: 72.616
    - type: ndcg_at_1
      value: 66.631
    - type: ndcg_at_10
      value: 54.949000000000005
    - type: ndcg_at_100
      value: 57.965
    - type: ndcg_at_1000
      value: 59.467000000000006
    - type: ndcg_at_3
      value: 51.086
    - type: ndcg_at_5
      value: 53.272
    - type: precision_at_1
      value: 66.631
    - type: precision_at_10
      value: 11.178
    - type: precision_at_100
      value: 1.3559999999999999
    - type: precision_at_1000
      value: 0.156
    - type: precision_at_3
      value: 31.582
    - type: precision_at_5
      value: 20.678
    - type: recall_at_1
      value: 33.315
    - type: recall_at_10
      value: 55.888000000000005
    - type: recall_at_100
      value: 67.812
    - type: recall_at_1000
      value: 77.839
    - type: recall_at_3
      value: 47.373
    - type: recall_at_5
      value: 51.695
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 66.424
    - type: ap
      value: 61.132235499939256
    - type: f1
      value: 66.07094958225315
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: msmarco
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 21.575
    - type: map_at_10
      value: 33.509
    - type: map_at_100
      value: 34.725
    - type: map_at_1000
      value: 34.775
    - type: map_at_3
      value: 29.673
    - type: map_at_5
      value: 31.805
    - type: mrr_at_1
      value: 22.235
    - type: mrr_at_10
      value: 34.1
    - type: mrr_at_100
      value: 35.254999999999995
    - type: mrr_at_1000
      value: 35.299
    - type: mrr_at_3
      value: 30.334
    - type: mrr_at_5
      value: 32.419
    - type: ndcg_at_1
      value: 22.235
    - type: ndcg_at_10
      value: 40.341
    - type: ndcg_at_100
      value: 46.161
    - type: ndcg_at_1000
      value: 47.400999999999996
    - type: ndcg_at_3
      value: 32.482
    - type: ndcg_at_5
      value: 36.269
    - type: precision_at_1
      value: 22.235
    - type: precision_at_10
      value: 6.422999999999999
    - type: precision_at_100
      value: 0.9329999999999999
    - type: precision_at_1000
      value: 0.104
    - type: precision_at_3
      value: 13.835
    - type: precision_at_5
      value: 10.226
    - type: recall_at_1
      value: 21.575
    - type: recall_at_10
      value: 61.448
    - type: recall_at_100
      value: 88.289
    - type: recall_at_1000
      value: 97.76899999999999
    - type: recall_at_3
      value: 39.971000000000004
    - type: recall_at_5
      value: 49.053000000000004
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 92.83401732786137
    - type: f1
      value: 92.47678691291068
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 76.08983128134975
    - type: f1
      value: 59.782936393820904
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 72.73032952252858
    - type: f1
      value: 70.72684765888265
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 77.08473436449226
    - type: f1
      value: 77.31457411257054
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 30.11980959210532
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 25.2587629106119
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 31.48268319779204
    - type: mrr
      value: 32.501885728964304
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 5.284
    - type: map_at_10
      value: 11.509
    - type: map_at_100
      value: 14.624
    - type: map_at_1000
      value: 16.035
    - type: map_at_3
      value: 8.347999999999999
    - type: map_at_5
      value: 9.919
    - type: mrr_at_1
      value: 43.344
    - type: mrr_at_10
      value: 52.303999999999995
    - type: mrr_at_100
      value: 52.994
    - type: mrr_at_1000
      value: 53.032999999999994
    - type: mrr_at_3
      value: 50.361
    - type: mrr_at_5
      value: 51.754
    - type: ndcg_at_1
      value: 41.176
    - type: ndcg_at_10
      value: 32.244
    - type: ndcg_at_100
      value: 29.916999999999998
    - type: ndcg_at_1000
      value: 38.753
    - type: ndcg_at_3
      value: 36.856
    - type: ndcg_at_5
      value: 35.394999999999996
    - type: precision_at_1
      value: 43.034
    - type: precision_at_10
      value: 24.118000000000002
    - type: precision_at_100
      value: 7.926
    - type: precision_at_1000
      value: 2.045
    - type: precision_at_3
      value: 34.675
    - type: precision_at_5
      value: 31.146
    - type: recall_at_1
      value: 5.284
    - type: recall_at_10
      value: 15.457
    - type: recall_at_100
      value: 30.914
    - type: recall_at_1000
      value: 63.788999999999994
    - type: recall_at_3
      value: 9.596
    - type: recall_at_5
      value: 12.391
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: nq
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 29.537999999999997
    - type: map_at_10
      value: 43.99
    - type: map_at_100
      value: 45.003
    - type: map_at_1000
      value: 45.04
    - type: map_at_3
      value: 39.814
    - type: map_at_5
      value: 42.166
    - type: mrr_at_1
      value: 33.256
    - type: mrr_at_10
      value: 46.487
    - type: mrr_at_100
      value: 47.264
    - type: mrr_at_1000
      value: 47.29
    - type: mrr_at_3
      value: 43.091
    - type: mrr_at_5
      value: 45.013999999999996
    - type: ndcg_at_1
      value: 33.256
    - type: ndcg_at_10
      value: 51.403
    - type: ndcg_at_100
      value: 55.706999999999994
    - type: ndcg_at_1000
      value: 56.586000000000006
    - type: ndcg_at_3
      value: 43.559
    - type: ndcg_at_5
      value: 47.426
    - type: precision_at_1
      value: 33.256
    - type: precision_at_10
      value: 8.540000000000001
    - type: precision_at_100
      value: 1.093
    - type: precision_at_1000
      value: 0.11800000000000001
    - type: precision_at_3
      value: 19.834
    - type: precision_at_5
      value: 14.143
    - type: recall_at_1
      value: 29.537999999999997
    - type: recall_at_10
      value: 71.5
    - type: recall_at_100
      value: 90.25
    - type: recall_at_1000
      value: 96.82600000000001
    - type: recall_at_3
      value: 51.108
    - type: recall_at_5
      value: 60.006
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 70.526
    - type: map_at_10
      value: 84.342
    - type: map_at_100
      value: 84.985
    - type: map_at_1000
      value: 85.003
    - type: map_at_3
      value: 81.472
    - type: map_at_5
      value: 83.292
    - type: mrr_at_1
      value: 81.17
    - type: mrr_at_10
      value: 87.33999999999999
    - type: mrr_at_100
      value: 87.445
    - type: mrr_at_1000
      value: 87.446
    - type: mrr_at_3
      value: 86.387
    - type: mrr_at_5
      value: 87.042
    - type: ndcg_at_1
      value: 81.19
    - type: ndcg_at_10
      value: 88.088
    - type: ndcg_at_100
      value: 89.35
    - type: ndcg_at_1000
      value: 89.462
    - type: ndcg_at_3
      value: 85.319
    - type: ndcg_at_5
      value: 86.858
    - type: precision_at_1
      value: 81.19
    - type: precision_at_10
      value: 13.33
    - type: precision_at_100
      value: 1.528
    - type: precision_at_1000
      value: 0.157
    - type: precision_at_3
      value: 37.31
    - type: precision_at_5
      value: 24.512
    - type: recall_at_1
      value: 70.526
    - type: recall_at_10
      value: 95.166
    - type: recall_at_100
      value: 99.479
    - type: recall_at_1000
      value: 99.984
    - type: recall_at_3
      value: 87.124
    - type: recall_at_5
      value: 91.53
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 45.049073872893494
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 55.13810914528368
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.593
    - type: map_at_10
      value: 10.907
    - type: map_at_100
      value: 12.888
    - type: map_at_1000
      value: 13.167000000000002
    - type: map_at_3
      value: 7.936
    - type: map_at_5
      value: 9.31
    - type: mrr_at_1
      value: 22.7
    - type: mrr_at_10
      value: 32.509
    - type: mrr_at_100
      value: 33.69
    - type: mrr_at_1000
      value: 33.747
    - type: mrr_at_3
      value: 29.599999999999998
    - type: mrr_at_5
      value: 31.155
    - type: ndcg_at_1
      value: 22.7
    - type: ndcg_at_10
      value: 18.445
    - type: ndcg_at_100
      value: 26.241999999999997
    - type: ndcg_at_1000
      value: 31.409
    - type: ndcg_at_3
      value: 17.864
    - type: ndcg_at_5
      value: 15.232999999999999
    - type: precision_at_1
      value: 22.7
    - type: precision_at_10
      value: 9.43
    - type: precision_at_100
      value: 2.061
    - type: precision_at_1000
      value: 0.331
    - type: precision_at_3
      value: 16.467000000000002
    - type: precision_at_5
      value: 13.08
    - type: recall_at_1
      value: 4.593
    - type: recall_at_10
      value: 19.115
    - type: recall_at_100
      value: 41.82
    - type: recall_at_1000
      value: 67.167
    - type: recall_at_3
      value: 9.983
    - type: recall_at_5
      value: 13.218
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 82.94432059816452
    - type: cos_sim_spearman
      value: 79.19993315048852
    - type: euclidean_pearson
      value: 72.43261099671753
    - type: euclidean_spearman
      value: 71.51531114998619
    - type: manhattan_pearson
      value: 71.83604124130447
    - type: manhattan_spearman
      value: 71.24460392842295
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 84.25401068481673
    - type: cos_sim_spearman
      value: 74.5249604699309
    - type: euclidean_pearson
      value: 71.1324859629043
    - type: euclidean_spearman
      value: 58.77041705276752
    - type: manhattan_pearson
      value: 71.01471521586141
    - type: manhattan_spearman
      value: 58.69949381017865
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 82.85731544223766
    - type: cos_sim_spearman
      value: 83.15607264736185
    - type: euclidean_pearson
      value: 75.8803249521361
    - type: euclidean_spearman
      value: 76.4862168799065
    - type: manhattan_pearson
      value: 75.80451454386811
    - type: manhattan_spearman
      value: 76.35986831074699
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 82.40669043798857
    - type: cos_sim_spearman
      value: 78.08686090667834
    - type: euclidean_pearson
      value: 74.48574712193803
    - type: euclidean_spearman
      value: 70.79423012045118
    - type: manhattan_pearson
      value: 74.39099211477354
    - type: manhattan_spearman
      value: 70.73135427277684
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 86.03027014209859
    - type: cos_sim_spearman
      value: 86.91082847840946
    - type: euclidean_pearson
      value: 69.13187603971996
    - type: euclidean_spearman
      value: 70.0370035340552
    - type: manhattan_pearson
      value: 69.2586635812031
    - type: manhattan_spearman
      value: 70.18638387118486
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 82.41190748361883
    - type: cos_sim_spearman
      value: 83.64850851235231
    - type: euclidean_pearson
      value: 71.60523243575282
    - type: euclidean_spearman
      value: 72.26134033805099
    - type: manhattan_pearson
      value: 71.50771482066683
    - type: manhattan_spearman
      value: 72.13707967973161
  - 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: 90.42838477648627
    - type: cos_sim_spearman
      value: 90.15798155439076
    - type: euclidean_pearson
      value: 77.09619972244516
    - type: euclidean_spearman
      value: 75.5953488548861
    - type: manhattan_pearson
      value: 77.36892406451771
    - type: manhattan_spearman
      value: 75.76625156149356
  - 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: 65.76151154879307
    - type: cos_sim_spearman
      value: 64.8846800918359
    - type: euclidean_pearson
      value: 50.23302700257155
    - type: euclidean_spearman
      value: 58.89455187289583
    - type: manhattan_pearson
      value: 50.05498582284945
    - type: manhattan_spearman
      value: 58.75893793871576
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 84.72381109169437
    - type: cos_sim_spearman
      value: 84.59820928231167
    - type: euclidean_pearson
      value: 74.85450857429493
    - type: euclidean_spearman
      value: 73.83634052565915
    - type: manhattan_pearson
      value: 74.97349743979106
    - type: manhattan_spearman
      value: 73.9636470375881
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 80.96736259172798
    - type: mrr
      value: 94.48378781712114
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 46.344
    - type: map_at_10
      value: 54.962
    - type: map_at_100
      value: 55.772
    - type: map_at_1000
      value: 55.81700000000001
    - type: map_at_3
      value: 51.832
    - type: map_at_5
      value: 53.718999999999994
    - type: mrr_at_1
      value: 49.0
    - type: mrr_at_10
      value: 56.721
    - type: mrr_at_100
      value: 57.287
    - type: mrr_at_1000
      value: 57.330000000000005
    - type: mrr_at_3
      value: 54.056000000000004
    - type: mrr_at_5
      value: 55.822
    - type: ndcg_at_1
      value: 49.0
    - type: ndcg_at_10
      value: 59.757000000000005
    - type: ndcg_at_100
      value: 63.149
    - type: ndcg_at_1000
      value: 64.43100000000001
    - type: ndcg_at_3
      value: 54.105000000000004
    - type: ndcg_at_5
      value: 57.196999999999996
    - type: precision_at_1
      value: 49.0
    - type: precision_at_10
      value: 8.200000000000001
    - type: precision_at_100
      value: 1.0070000000000001
    - type: precision_at_1000
      value: 0.11100000000000002
    - type: precision_at_3
      value: 20.889
    - type: precision_at_5
      value: 14.399999999999999
    - type: recall_at_1
      value: 46.344
    - type: recall_at_10
      value: 72.722
    - type: recall_at_100
      value: 88.167
    - type: recall_at_1000
      value: 98.333
    - type: recall_at_3
      value: 57.994
    - type: recall_at_5
      value: 65.506
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.83366336633664
    - type: cos_sim_ap
      value: 96.09329747251944
    - type: cos_sim_f1
      value: 91.66255550074001
    - type: cos_sim_precision
      value: 90.45764362220059
    - type: cos_sim_recall
      value: 92.9
    - type: dot_accuracy
      value: 99.32871287128712
    - type: dot_ap
      value: 63.95436644147969
    - type: dot_f1
      value: 60.61814556331008
    - type: dot_precision
      value: 60.437375745526836
    - type: dot_recall
      value: 60.8
    - type: euclidean_accuracy
      value: 99.66534653465347
    - type: euclidean_ap
      value: 85.85143979761818
    - type: euclidean_f1
      value: 81.57033805888769
    - type: euclidean_precision
      value: 89.68824940047962
    - type: euclidean_recall
      value: 74.8
    - type: manhattan_accuracy
      value: 99.65742574257426
    - type: manhattan_ap
      value: 85.55693926348405
    - type: manhattan_f1
      value: 81.13804004214963
    - type: manhattan_precision
      value: 85.74610244988864
    - type: manhattan_recall
      value: 77.0
    - type: max_accuracy
      value: 99.83366336633664
    - type: max_ap
      value: 96.09329747251944
    - type: max_f1
      value: 91.66255550074001
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 45.23573510003245
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 33.37478638401161
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 50.375920467392476
    - type: mrr
      value: 51.17302223919871
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 29.768864092288343
    - type: cos_sim_spearman
      value: 29.854278347043266
    - type: dot_pearson
      value: 20.51281723837505
    - type: dot_spearman
      value: 21.799102540913665
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.2
    - type: map_at_10
      value: 1.202
    - type: map_at_100
      value: 6.729
    - type: map_at_1000
      value: 15.928
    - type: map_at_3
      value: 0.492
    - type: map_at_5
      value: 0.712
    - type: mrr_at_1
      value: 76.0
    - type: mrr_at_10
      value: 84.75
    - type: mrr_at_100
      value: 84.75
    - type: mrr_at_1000
      value: 84.75
    - type: mrr_at_3
      value: 83.0
    - type: mrr_at_5
      value: 84.5
    - type: ndcg_at_1
      value: 71.0
    - type: ndcg_at_10
      value: 57.253
    - type: ndcg_at_100
      value: 44.383
    - type: ndcg_at_1000
      value: 38.666
    - type: ndcg_at_3
      value: 64.324
    - type: ndcg_at_5
      value: 60.791
    - type: precision_at_1
      value: 76.0
    - type: precision_at_10
      value: 59.599999999999994
    - type: precision_at_100
      value: 45.440000000000005
    - type: precision_at_1000
      value: 17.458000000000002
    - type: precision_at_3
      value: 69.333
    - type: precision_at_5
      value: 63.2
    - type: recall_at_1
      value: 0.2
    - type: recall_at_10
      value: 1.4949999999999999
    - type: recall_at_100
      value: 10.266
    - type: recall_at_1000
      value: 35.853
    - type: recall_at_3
      value: 0.5349999999999999
    - type: recall_at_5
      value: 0.8109999999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: webis-touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 2.0140000000000002
    - type: map_at_10
      value: 8.474
    - type: map_at_100
      value: 14.058000000000002
    - type: map_at_1000
      value: 15.381
    - type: map_at_3
      value: 4.508
    - type: map_at_5
      value: 5.87
    - type: mrr_at_1
      value: 22.448999999999998
    - type: mrr_at_10
      value: 37.242
    - type: mrr_at_100
      value: 38.291
    - type: mrr_at_1000
      value: 38.311
    - type: mrr_at_3
      value: 32.312999999999995
    - type: mrr_at_5
      value: 34.762
    - type: ndcg_at_1
      value: 20.408
    - type: ndcg_at_10
      value: 20.729
    - type: ndcg_at_100
      value: 33.064
    - type: ndcg_at_1000
      value: 44.324999999999996
    - type: ndcg_at_3
      value: 21.251
    - type: ndcg_at_5
      value: 20.28
    - type: precision_at_1
      value: 22.448999999999998
    - type: precision_at_10
      value: 18.98
    - type: precision_at_100
      value: 7.224
    - type: precision_at_1000
      value: 1.471
    - type: precision_at_3
      value: 22.448999999999998
    - type: precision_at_5
      value: 20.816000000000003
    - type: recall_at_1
      value: 2.0140000000000002
    - type: recall_at_10
      value: 13.96
    - type: recall_at_100
      value: 44.187
    - type: recall_at_1000
      value: 79.328
    - type: recall_at_3
      value: 5.345
    - type: recall_at_5
      value: 7.979
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 69.1312
    - type: ap
      value: 12.606776505497608
    - type: f1
      value: 52.4112415600534
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 58.16072439162422
    - type: f1
      value: 58.29152785435414
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 40.421119289825924
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 85.48012159504083
    - type: cos_sim_ap
      value: 72.31974877212102
    - type: cos_sim_f1
      value: 67.96846573681019
    - type: cos_sim_precision
      value: 62.89562289562289
    - type: cos_sim_recall
      value: 73.93139841688654
    - type: dot_accuracy
      value: 78.52416999463551
    - type: dot_ap
      value: 43.65271285411479
    - type: dot_f1
      value: 46.94641449960599
    - type: dot_precision
      value: 37.456774599182644
    - type: dot_recall
      value: 62.875989445910285
    - type: euclidean_accuracy
      value: 83.90057817249806
    - type: euclidean_ap
      value: 65.96278727778665
    - type: euclidean_f1
      value: 63.35733232284957
    - type: euclidean_precision
      value: 60.770535497940394
    - type: euclidean_recall
      value: 66.17414248021109
    - type: manhattan_accuracy
      value: 83.96614412588663
    - type: manhattan_ap
      value: 66.03670273156699
    - type: manhattan_f1
      value: 63.49128406579917
    - type: manhattan_precision
      value: 59.366391184573
    - type: manhattan_recall
      value: 68.23218997361478
    - type: max_accuracy
      value: 85.48012159504083
    - type: max_ap
      value: 72.31974877212102
    - type: max_f1
      value: 67.96846573681019
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.97038848139093
    - type: cos_sim_ap
      value: 85.982764495556
    - type: cos_sim_f1
      value: 78.73283281450284
    - type: cos_sim_precision
      value: 75.07857791436754
    - type: cos_sim_recall
      value: 82.7610101632276
    - type: dot_accuracy
      value: 83.21108394458028
    - type: dot_ap
      value: 70.97956937273386
    - type: dot_f1
      value: 66.53083038279111
    - type: dot_precision
      value: 58.7551622418879
    - type: dot_recall
      value: 76.67847243609486
    - type: euclidean_accuracy
      value: 84.31520937633407
    - type: euclidean_ap
      value: 74.67323411319909
    - type: euclidean_f1
      value: 67.21935410935676
    - type: euclidean_precision
      value: 65.82773636430733
    - type: euclidean_recall
      value: 68.67108099784416
    - type: manhattan_accuracy
      value: 84.35013777312066
    - type: manhattan_ap
      value: 74.66508905354597
    - type: manhattan_f1
      value: 67.28264162375038
    - type: manhattan_precision
      value: 66.19970193740686
    - type: manhattan_recall
      value: 68.40160147828766
    - type: max_accuracy
      value: 88.97038848139093
    - type: max_ap
      value: 85.982764495556
    - type: max_f1
      value: 78.73283281450284
---
# # Fast-Inference with Ctranslate2
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.

quantized version of [jinaai/jina-embedding-l-en-v1](https://huggingface.co/jinaai/jina-embedding-l-en-v1)
```bash
pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1
```

```python
# from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-jina-embedding-l-en-v1"
model_name_orig="jinaai/jina-embedding-l-en-v1"

from hf_hub_ctranslate2 import EncoderCT2fromHfHub
model = EncoderCT2fromHfHub(
        # load in int8 on CUDA
        model_name_or_path=model_name,
        device="cuda",
        compute_type="int8_float16"
)
outputs = model.generate(
    text=["I like soccer", "I like tennis", "The eiffel tower is in Paris"],
    max_length=64,
) # perform downstream tasks on outputs
outputs["pooler_output"]
outputs["last_hidden_state"]
outputs["attention_mask"]

# alternative, use SentenceTransformer Mix-In
# for end-to-end Sentence embeddings generation
# (not pulling from this CT2fast-HF repo)

from hf_hub_ctranslate2 import CT2SentenceTransformer
model = CT2SentenceTransformer(
    model_name_orig, compute_type="int8_float16", device="cuda"
)
embeddings = model.encode(
    ["I like soccer", "I like tennis", "The eiffel tower is in Paris"],
    batch_size=32,
    convert_to_numpy=True,
    normalize_embeddings=True,
)
print(embeddings.shape, embeddings)
scores = (embeddings @ embeddings.T) * 100

# Hint: you can also host this code via REST API and
# via github.com/michaelfeil/infinity  


```

Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2)
and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8`  for `device="cpu"`

Converted on 2023-10-13 using
```
LLama-2 -> removed <pad> token.
```

# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.

# Original description
    

<br><br>

<p align="center">
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>


<p align="center">
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>, <a href="https://github.com/jina-ai/finetuner"><b>Finetuner</b></a> team.</b>
</p>


## Intented Usage & Model Info

`jina-embedding-l-en-v1` is a language model that has been trained using Jina AI's Linnaeus-Clean dataset.
This dataset consists of 380 million pairs of sentences, which include both query-document pairs.
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs.

The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more.

With a size of 330 million parameters,
the model enables single-gpu inference while delivering better performance than our small and base model.
Additionally, we provide the following options:

- [`jina-embedding-t-en-v1`](https://huggingface.co/jinaai/jina-embedding-t-en-v1): 14 million parameters.
- [`jina-embedding-s-en-v1`](https://huggingface.co/jinaai/jina-embedding-s-en-v1): 35 million parameters
- [`jina-embedding-b-en-v1`](https://huggingface.co/jinaai/jina-embedding-b-en-v1): 110 million parameters.
- [`jina-embedding-l-en-v1`](https://huggingface.co/jinaai/jina-embedding-l-en-v1): 330 million parameters **(you are here)**.
- `jina-embedding-1b-en-v1`: 1.2 billion parameters, 10 times bert-base (soon).
- `jina-embedding-6b-en-v1`: 6 billion parameters, 30 times bert-base (soon).

## Data & Parameters

Please checkout our [technical blog](https://arxiv.org/abs/2307.11224).

## Metrics

We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI:

|Name|param    |dimension|
|------------------------------|-----|------|
|all-minilm-l6-v2|23m      |384|
|all-mpnet-base-v2 |110m     |768|
|ada-embedding-002|Unknown/OpenAI API  |1536|
|jina-embedding-t-en-v1|14m      |312|
|jina-embedding-s-en-v1|35m      |512|
|jina-embedding-b-en-v1|110m      |768|
|jina-embedding-l-en-v1|330m      |1024|


|Name|STS12|STS13|STS14|STS15|STS16|STS17|TRECOVID|Quora|SciFact|
|------------------------------|-----|-----|-----|-----|-----|-----|--------|-----|-----|
|all-minilm-l6-v2|0.724|0.806|0.756|0.854|0.79 |0.876|0.473   |0.876|0.645  |
|all-mpnet-base-v2|0.726|**0.835**|0.78 |0.857|0.8  |**0.906**|0.513   |0.875|0.656  |
|ada-embedding-002|0.698|0.833|0.761|0.861|**0.86** |0.903|**0.685**   |0.876|**0.726**  |
|jina-embedding-t-en-v1|0.717|0.773|0.731|0.829|0.777|0.860|0.482   |0.840|0.522  |
|jina-embedding-s-en-v1|0.743|0.786|0.738|0.837|0.80|0.875|0.523   |0.857|0.524  |
|jina-embedding-b-en-v1|**0.751**|0.809|0.761|0.856|0.812|0.890|0.606   |0.876|0.594  |
|jina-embedding-l-en-v1|0.745|0.832|**0.781**|**0.869**|0.837|0.902|0.573   |**0.881**|0.598  |

## Usage

Use with Jina AI Finetuner

```python
!pip install finetuner
import finetuner

model = finetuner.build_model('jinaai/jina-embedding-l-en-v1')
embeddings = finetuner.encode(
    model=model,
    data=['how is the weather today', 'What is the current weather like today?']
)
print(finetuner.cos_sim(embeddings[0], embeddings[1]))
```

Use with sentence-transformers:

```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

sentences = ['how is the weather today', 'What is the current weather like today?']

model = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```

## Fine-tuning

Please consider [Finetuner](https://github.com/jina-ai/finetuner).

## Plans

1. The development of `jina-embedding-s-en-v2` is currently underway with two main objectives: improving performance and increasing the maximum sequence length.
2. We are currently working on a bilingual embedding model that combines English and X language. The upcoming model will be called `jina-embedding-s/b/l-de-v1`.

## Contact

Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.

## Citation

If you find Jina Embeddings useful in your research, please cite the following paper:

``` latex
@misc{günther2023jina,
      title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models}, 
      author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
      year={2023},
      eprint={2307.11224},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```