Upload jinaai/jina-embedding-l-en-v1 ctranslate2 weights

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- ctranslate2
+- int8
+- float16
+  - finetuner
+  - mteb
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+datasets:
+  - jinaai/negation-dataset
+language: en
+license: apache-2.0
+model-index:
+- name: jina-triplets-large
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      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:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      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:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 31.384
+    - type: f1
+      value: 30.016752348953723
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB 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:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 40.51285742156528
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 31.5825964077496
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 62.830281630546835
+    - type: mrr
+      value: 75.93072593765115
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      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:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 85.3409090909091
+    - type: f1
+      value: 85.25262617676835
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 33.560193912974974
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 28.4426572644577
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack      
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - 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:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      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:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      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:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 45.835
+    - type: f1
+      value: 41.66455981281837
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB 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:
+      type: fiqa
+      name: MTEB FiQA2018
+      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:
+      type: hotpotqa
+      name: MTEB 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:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      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:
+      type: msmarco
+      name: MTEB 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:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 92.83401732786137
+    - type: f1
+      value: 92.47678691291068
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 76.08983128134975
+    - type: f1
+      value: 59.782936393820904
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 72.73032952252858
+    - type: f1
+      value: 70.72684765888265
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 77.08473436449226
+    - type: f1
+      value: 77.31457411257054
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 30.11980959210532
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 25.2587629106119
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 31.48268319779204
+    - type: mrr
+      value: 32.501885728964304
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB 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:
+      type: nq
+      name: MTEB 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:
+      type: quora
+      name: MTEB QuoraRetrieval
+      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:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 45.049073872893494
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 55.13810914528368
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB 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:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      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:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      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:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      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:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      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:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      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:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      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:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+      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:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      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:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      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:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 80.96736259172798
+    - type: mrr
+      value: 94.48378781712114
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB 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:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      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:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 45.23573510003245
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 33.37478638401161
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 50.375920467392476
+    - type: mrr
+      value: 51.17302223919871
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: 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:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      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:
+      type: webis-touche2020
+      name: MTEB 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:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      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:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 58.16072439162422
+    - type: f1
+      value: 58.29152785435414
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 40.421119289825924
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      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:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      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}
+}
+```

config.json

@@ -0,0 +1,68 @@
+{
+    "_name_or_path": "tmp/",
+    "architectures": [
+        "T5EncoderModel"
+    ],
+    "auto_map": {
+        "AutoModel": "modeling_t5.T5EncoderModel"
+    },
+    "d_ff": 4096,
+    "d_kv": 64,
+    "d_model": 1024,
+    "decoder_start_token_id": 0,
+    "dense_act_fn": "relu",
+    "dropout_rate": 0.1,
+    "eos_token_id": 1,
+    "feed_forward_proj": "relu",
+    "initializer_factor": 1.0,
+    "is_encoder_decoder": true,
+    "is_gated_act": false,
+    "layer_norm_epsilon": null,
+    "model_type": "t5",
+    "n_positions": 512,
+    "num_decoder_layers": 24,
+    "num_heads": 16,
+    "num_layers": 24,
+    "output_past": true,
+    "pad_token_id": 0,
+    "relative_attention_max_distance": 128,
+    "relative_attention_num_buckets": 32,
+    "task_specific_params": {
+        "summarization": {
+            "early_stopping": true,
+            "length_penalty": 2.0,
+            "max_length": 200,
+            "min_length": 30,
+            "no_repeat_ngram_size": 3,
+            "num_beams": 4,
+            "prefix": "summarize: "
+        },
+        "translation_en_to_de": {
+            "early_stopping": true,
+            "max_length": 300,
+            "num_beams": 4,
+            "prefix": "translate English to German: "
+        },
+        "translation_en_to_fr": {
+            "early_stopping": true,
+            "max_length": 300,
+            "num_beams": 4,
+            "prefix": "translate English to French: "
+        },
+        "translation_en_to_ro": {
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