Upload jinaai/jina-embedding-s-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-embedding-s-en-v1
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 64.82089552238806
+    - type: ap
+      value: 27.100981946230778
+    - type: f1
+      value: 58.3354886367184
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 64.282775
+    - type: ap
+      value: 60.350688924943796
+    - type: f1
+      value: 62.06346948494396
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 30.623999999999995
+    - type: f1
+      value: 29.427789186742153
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.119
+    - type: map_at_10
+      value: 35.609
+    - type: map_at_100
+      value: 36.935
+    - type: map_at_1000
+      value: 36.957
+    - type: map_at_3
+      value: 31.046000000000003
+    - type: map_at_5
+      value: 33.574
+    - type: mrr_at_1
+      value: 22.404
+    - type: mrr_at_10
+      value: 35.695
+    - type: mrr_at_100
+      value: 37.021
+    - type: mrr_at_1000
+      value: 37.043
+    - type: mrr_at_3
+      value: 31.093
+    - type: mrr_at_5
+      value: 33.635999999999996
+    - type: ndcg_at_1
+      value: 22.119
+    - type: ndcg_at_10
+      value: 43.566
+    - type: ndcg_at_100
+      value: 49.370000000000005
+    - type: ndcg_at_1000
+      value: 49.901
+    - type: ndcg_at_3
+      value: 34.06
+    - type: ndcg_at_5
+      value: 38.653999999999996
+    - type: precision_at_1
+      value: 22.119
+    - type: precision_at_10
+      value: 6.92
+    - type: precision_at_100
+      value: 0.95
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 14.272000000000002
+    - type: precision_at_5
+      value: 10.811
+    - type: recall_at_1
+      value: 22.119
+    - type: recall_at_10
+      value: 69.203
+    - type: recall_at_100
+      value: 95.021
+    - type: recall_at_1000
+      value: 99.075
+    - type: recall_at_3
+      value: 42.817
+    - type: recall_at_5
+      value: 54.054
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 34.1740289109719
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 23.985251383455463
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 60.24873612289029
+    - type: mrr
+      value: 74.65692740623489
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.22415390332444
+    - type: cos_sim_spearman
+      value: 82.9591191954711
+    - type: euclidean_pearson
+      value: 44.096317524324945
+    - type: euclidean_spearman
+      value: 42.95218351391625
+    - type: manhattan_pearson
+      value: 44.07766490545065
+    - type: manhattan_spearman
+      value: 42.78350497166606
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 74.64285714285714
+    - type: f1
+      value: 73.53680835577447
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 28.512813238490164
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 20.942214972649488
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.255999999999997
+    - type: map_at_10
+      value: 37.091
+    - type: map_at_100
+      value: 38.428000000000004
+    - type: map_at_1000
+      value: 38.559
+    - type: map_at_3
+      value: 34.073
+    - type: map_at_5
+      value: 35.739
+    - type: mrr_at_1
+      value: 34.907
+    - type: mrr_at_10
+      value: 42.769
+    - type: mrr_at_100
+      value: 43.607
+    - type: mrr_at_1000
+      value: 43.656
+    - type: mrr_at_3
+      value: 39.986
+    - type: mrr_at_5
+      value: 41.581
+    - type: ndcg_at_1
+      value: 34.907
+    - type: ndcg_at_10
+      value: 42.681000000000004
+    - type: ndcg_at_100
+      value: 48.213
+    - type: ndcg_at_1000
+      value: 50.464
+    - type: ndcg_at_3
+      value: 37.813
+    - type: ndcg_at_5
+      value: 39.936
+    - type: precision_at_1
+      value: 34.907
+    - type: precision_at_10
+      value: 7.911
+    - type: precision_at_100
+      value: 1.349
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 17.93
+    - type: precision_at_5
+      value: 12.732
+    - type: recall_at_1
+      value: 28.255999999999997
+    - type: recall_at_10
+      value: 53.49699999999999
+    - type: recall_at_100
+      value: 77.288
+    - type: recall_at_1000
+      value: 91.776
+    - type: recall_at_3
+      value: 39.18
+    - type: recall_at_5
+      value: 45.365
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.563999999999997
+    - type: map_at_10
+      value: 33.913
+    - type: map_at_100
+      value: 34.966
+    - type: map_at_1000
+      value: 35.104
+    - type: map_at_3
+      value: 31.413000000000004
+    - type: map_at_5
+      value: 32.854
+    - type: mrr_at_1
+      value: 31.72
+    - type: mrr_at_10
+      value: 39.391
+    - type: mrr_at_100
+      value: 40.02
+    - type: mrr_at_1000
+      value: 40.076
+    - type: mrr_at_3
+      value: 37.314
+    - type: mrr_at_5
+      value: 38.507999999999996
+    - type: ndcg_at_1
+      value: 31.72
+    - type: ndcg_at_10
+      value: 38.933
+    - type: ndcg_at_100
+      value: 43.024
+    - type: ndcg_at_1000
+      value: 45.556999999999995
+    - type: ndcg_at_3
+      value: 35.225
+    - type: ndcg_at_5
+      value: 36.984
+    - type: precision_at_1
+      value: 31.72
+    - type: precision_at_10
+      value: 7.248
+    - type: precision_at_100
+      value: 1.192
+    - type: precision_at_1000
+      value: 0.16999999999999998
+    - type: precision_at_3
+      value: 16.943
+    - type: precision_at_5
+      value: 11.975
+    - type: recall_at_1
+      value: 25.563999999999997
+    - type: recall_at_10
+      value: 47.808
+    - type: recall_at_100
+      value: 65.182
+    - type: recall_at_1000
+      value: 81.831
+    - type: recall_at_3
+      value: 36.889
+    - type: recall_at_5
+      value: 41.829
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 33.662
+    - type: map_at_10
+      value: 44.096999999999994
+    - type: map_at_100
+      value: 45.153999999999996
+    - type: map_at_1000
+      value: 45.223
+    - type: map_at_3
+      value: 41.377
+    - type: map_at_5
+      value: 42.935
+    - type: mrr_at_1
+      value: 38.997
+    - type: mrr_at_10
+      value: 47.675
+    - type: mrr_at_100
+      value: 48.476
+    - type: mrr_at_1000
+      value: 48.519
+    - type: mrr_at_3
+      value: 45.549
+    - type: mrr_at_5
+      value: 46.884
+    - type: ndcg_at_1
+      value: 38.997
+    - type: ndcg_at_10
+      value: 49.196
+    - type: ndcg_at_100
+      value: 53.788000000000004
+    - type: ndcg_at_1000
+      value: 55.393
+    - type: ndcg_at_3
+      value: 44.67
+    - type: ndcg_at_5
+      value: 46.991
+    - type: precision_at_1
+      value: 38.997
+    - type: precision_at_10
+      value: 7.875
+    - type: precision_at_100
+      value: 1.102
+    - type: precision_at_1000
+      value: 0.13
+    - type: precision_at_3
+      value: 19.854
+    - type: precision_at_5
+      value: 13.605
+    - type: recall_at_1
+      value: 33.662
+    - type: recall_at_10
+      value: 60.75899999999999
+    - type: recall_at_100
+      value: 81.11699999999999
+    - type: recall_at_1000
+      value: 92.805
+    - type: recall_at_3
+      value: 48.577999999999996
+    - type: recall_at_5
+      value: 54.384
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.313
+    - type: map_at_10
+      value: 29.036
+    - type: map_at_100
+      value: 29.975
+    - type: map_at_1000
+      value: 30.063000000000002
+    - type: map_at_3
+      value: 26.878999999999998
+    - type: map_at_5
+      value: 28.005999999999997
+    - type: mrr_at_1
+      value: 23.39
+    - type: mrr_at_10
+      value: 31.072
+    - type: mrr_at_100
+      value: 31.922
+    - type: mrr_at_1000
+      value: 31.995
+    - type: mrr_at_3
+      value: 28.908
+    - type: mrr_at_5
+      value: 30.104999999999997
+    - type: ndcg_at_1
+      value: 23.39
+    - type: ndcg_at_10
+      value: 33.448
+    - type: ndcg_at_100
+      value: 38.255
+    - type: ndcg_at_1000
+      value: 40.542
+    - type: ndcg_at_3
+      value: 29.060000000000002
+    - type: ndcg_at_5
+      value: 31.023
+    - type: precision_at_1
+      value: 23.39
+    - type: precision_at_10
+      value: 5.175
+    - type: precision_at_100
+      value: 0.8049999999999999
+    - type: precision_at_1000
+      value: 0.10300000000000001
+    - type: precision_at_3
+      value: 12.504999999999999
+    - type: precision_at_5
+      value: 8.61
+    - type: recall_at_1
+      value: 21.313
+    - type: recall_at_10
+      value: 45.345
+    - type: recall_at_100
+      value: 67.752
+    - type: recall_at_1000
+      value: 84.937
+    - type: recall_at_3
+      value: 33.033
+    - type: recall_at_5
+      value: 37.929
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 14.255999999999998
+    - type: map_at_10
+      value: 20.339
+    - type: map_at_100
+      value: 21.491
+    - type: map_at_1000
+      value: 21.616
+    - type: map_at_3
+      value: 18.481
+    - type: map_at_5
+      value: 19.594
+    - type: mrr_at_1
+      value: 17.413
+    - type: mrr_at_10
+      value: 24.146
+    - type: mrr_at_100
+      value: 25.188
+    - type: mrr_at_1000
+      value: 25.273
+    - type: mrr_at_3
+      value: 22.264
+    - type: mrr_at_5
+      value: 23.302
+    - type: ndcg_at_1
+      value: 17.413
+    - type: ndcg_at_10
+      value: 24.272
+    - type: ndcg_at_100
+      value: 29.82
+    - type: ndcg_at_1000
+      value: 33.072
+    - type: ndcg_at_3
+      value: 20.826
+    - type: ndcg_at_5
+      value: 22.535
+    - type: precision_at_1
+      value: 17.413
+    - type: precision_at_10
+      value: 4.366
+    - type: precision_at_100
+      value: 0.818
+    - type: precision_at_1000
+      value: 0.124
+    - type: precision_at_3
+      value: 9.866999999999999
+    - type: precision_at_5
+      value: 7.164
+    - type: recall_at_1
+      value: 14.255999999999998
+    - type: recall_at_10
+      value: 32.497
+    - type: recall_at_100
+      value: 56.592
+    - type: recall_at_1000
+      value: 80.17699999999999
+    - type: recall_at_3
+      value: 23.195
+    - type: recall_at_5
+      value: 27.392
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.709
+    - type: map_at_10
+      value: 31.377
+    - type: map_at_100
+      value: 32.536
+    - type: map_at_1000
+      value: 32.669
+    - type: map_at_3
+      value: 28.572999999999997
+    - type: map_at_5
+      value: 30.205
+    - type: mrr_at_1
+      value: 27.815
+    - type: mrr_at_10
+      value: 36.452
+    - type: mrr_at_100
+      value: 37.302
+    - type: mrr_at_1000
+      value: 37.364000000000004
+    - type: mrr_at_3
+      value: 33.75
+    - type: mrr_at_5
+      value: 35.43
+    - type: ndcg_at_1
+      value: 27.815
+    - type: ndcg_at_10
+      value: 36.84
+    - type: ndcg_at_100
+      value: 42.092
+    - type: ndcg_at_1000
+      value: 44.727
+    - type: ndcg_at_3
+      value: 31.964
+    - type: ndcg_at_5
+      value: 34.428
+    - type: precision_at_1
+      value: 27.815
+    - type: precision_at_10
+      value: 6.67
+    - type: precision_at_100
+      value: 1.093
+    - type: precision_at_1000
+      value: 0.151
+    - type: precision_at_3
+      value: 14.982000000000001
+    - type: precision_at_5
+      value: 10.857
+    - type: recall_at_1
+      value: 22.709
+    - type: recall_at_10
+      value: 48.308
+    - type: recall_at_100
+      value: 70.866
+    - type: recall_at_1000
+      value: 88.236
+    - type: recall_at_3
+      value: 34.709
+    - type: recall_at_5
+      value: 40.996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.348000000000003
+    - type: map_at_10
+      value: 29.427999999999997
+    - type: map_at_100
+      value: 30.499
+    - type: map_at_1000
+      value: 30.631999999999998
+    - type: map_at_3
+      value: 27.035999999999998
+    - type: map_at_5
+      value: 28.351
+    - type: mrr_at_1
+      value: 27.74
+    - type: mrr_at_10
+      value: 34.424
+    - type: mrr_at_100
+      value: 35.341
+    - type: mrr_at_1000
+      value: 35.419
+    - type: mrr_at_3
+      value: 32.401
+    - type: mrr_at_5
+      value: 33.497
+    - type: ndcg_at_1
+      value: 27.74
+    - type: ndcg_at_10
+      value: 34.136
+    - type: ndcg_at_100
+      value: 39.269
+    - type: ndcg_at_1000
+      value: 42.263
+    - type: ndcg_at_3
+      value: 30.171999999999997
+    - type: ndcg_at_5
+      value: 31.956
+    - type: precision_at_1
+      value: 27.74
+    - type: precision_at_10
+      value: 6.062
+    - type: precision_at_100
+      value: 1.014
+    - type: precision_at_1000
+      value: 0.146
+    - type: precision_at_3
+      value: 14.079
+    - type: precision_at_5
+      value: 9.977
+    - type: recall_at_1
+      value: 22.348000000000003
+    - type: recall_at_10
+      value: 43.477
+    - type: recall_at_100
+      value: 65.945
+    - type: recall_at_1000
+      value: 86.587
+    - type: recall_at_3
+      value: 32.107
+    - type: recall_at_5
+      value: 36.974000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.688499999999998
+    - type: map_at_10
+      value: 29.164666666666665
+    - type: map_at_100
+      value: 30.22575
+    - type: map_at_1000
+      value: 30.350833333333334
+    - type: map_at_3
+      value: 26.82025
+    - type: map_at_5
+      value: 28.14966666666667
+    - type: mrr_at_1
+      value: 25.779249999999998
+    - type: mrr_at_10
+      value: 32.969
+    - type: mrr_at_100
+      value: 33.81725
+    - type: mrr_at_1000
+      value: 33.88825
+    - type: mrr_at_3
+      value: 30.831250000000004
+    - type: mrr_at_5
+      value: 32.065000000000005
+    - type: ndcg_at_1
+      value: 25.779249999999998
+    - type: ndcg_at_10
+      value: 33.73675
+    - type: ndcg_at_100
+      value: 38.635666666666665
+    - type: ndcg_at_1000
+      value: 41.353500000000004
+    - type: ndcg_at_3
+      value: 29.66283333333333
+    - type: ndcg_at_5
+      value: 31.607249999999997
+    - type: precision_at_1
+      value: 25.779249999999998
+    - type: precision_at_10
+      value: 5.861416666666667
+    - type: precision_at_100
+      value: 0.9852500000000002
+    - type: precision_at_1000
+      value: 0.14108333333333334
+    - type: precision_at_3
+      value: 13.563583333333332
+    - type: precision_at_5
+      value: 9.630333333333335
+    - type: recall_at_1
+      value: 21.688499999999998
+    - type: recall_at_10
+      value: 43.605
+    - type: recall_at_100
+      value: 65.52366666666667
+    - type: recall_at_1000
+      value: 84.69683333333332
+    - type: recall_at_3
+      value: 32.195499999999996
+    - type: recall_at_5
+      value: 37.25325
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.279
+    - type: map_at_10
+      value: 23.238
+    - type: map_at_100
+      value: 24.026
+    - type: map_at_1000
+      value: 24.13
+    - type: map_at_3
+      value: 20.730999999999998
+    - type: map_at_5
+      value: 22.278000000000002
+    - type: mrr_at_1
+      value: 19.017999999999997
+    - type: mrr_at_10
+      value: 25.188
+    - type: mrr_at_100
+      value: 25.918999999999997
+    - type: mrr_at_1000
+      value: 25.996999999999996
+    - type: mrr_at_3
+      value: 22.776
+    - type: mrr_at_5
+      value: 24.256
+    - type: ndcg_at_1
+      value: 19.017999999999997
+    - type: ndcg_at_10
+      value: 27.171
+    - type: ndcg_at_100
+      value: 31.274
+    - type: ndcg_at_1000
+      value: 34.016000000000005
+    - type: ndcg_at_3
+      value: 22.442
+    - type: ndcg_at_5
+      value: 24.955
+    - type: precision_at_1
+      value: 19.017999999999997
+    - type: precision_at_10
+      value: 4.494
+    - type: precision_at_100
+      value: 0.712
+    - type: precision_at_1000
+      value: 0.10300000000000001
+    - type: precision_at_3
+      value: 9.611
+    - type: precision_at_5
+      value: 7.331
+    - type: recall_at_1
+      value: 17.279
+    - type: recall_at_10
+      value: 37.464999999999996
+    - type: recall_at_100
+      value: 56.458
+    - type: recall_at_1000
+      value: 76.759
+    - type: recall_at_3
+      value: 24.659
+    - type: recall_at_5
+      value: 30.672
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 14.901
+    - type: map_at_10
+      value: 20.268
+    - type: map_at_100
+      value: 21.143
+    - type: map_at_1000
+      value: 21.264
+    - type: map_at_3
+      value: 18.557000000000002
+    - type: map_at_5
+      value: 19.483
+    - type: mrr_at_1
+      value: 17.997
+    - type: mrr_at_10
+      value: 23.591
+    - type: mrr_at_100
+      value: 24.387
+    - type: mrr_at_1000
+      value: 24.471
+    - type: mrr_at_3
+      value: 21.874
+    - type: mrr_at_5
+      value: 22.797
+    - type: ndcg_at_1
+      value: 17.997
+    - type: ndcg_at_10
+      value: 23.87
+    - type: ndcg_at_100
+      value: 28.459
+    - type: ndcg_at_1000
+      value: 31.66
+    - type: ndcg_at_3
+      value: 20.779
+    - type: ndcg_at_5
+      value: 22.137
+    - type: precision_at_1
+      value: 17.997
+    - type: precision_at_10
+      value: 4.25
+    - type: precision_at_100
+      value: 0.761
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_3
+      value: 9.716
+    - type: precision_at_5
+      value: 6.909999999999999
+    - type: recall_at_1
+      value: 14.901
+    - type: recall_at_10
+      value: 31.44
+    - type: recall_at_100
+      value: 52.717000000000006
+    - type: recall_at_1000
+      value: 76.102
+    - type: recall_at_3
+      value: 22.675
+    - type: recall_at_5
+      value: 26.336
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.52
+    - type: map_at_10
+      value: 28.397
+    - type: map_at_100
+      value: 29.443
+    - type: map_at_1000
+      value: 29.56
+    - type: map_at_3
+      value: 26.501
+    - type: map_at_5
+      value: 27.375
+    - type: mrr_at_1
+      value: 25.28
+    - type: mrr_at_10
+      value: 32.102000000000004
+    - type: mrr_at_100
+      value: 33.005
+    - type: mrr_at_1000
+      value: 33.084
+    - type: mrr_at_3
+      value: 30.208000000000002
+    - type: mrr_at_5
+      value: 31.146
+    - type: ndcg_at_1
+      value: 25.28
+    - type: ndcg_at_10
+      value: 32.635
+    - type: ndcg_at_100
+      value: 37.672
+    - type: ndcg_at_1000
+      value: 40.602
+    - type: ndcg_at_3
+      value: 28.951999999999998
+    - type: ndcg_at_5
+      value: 30.336999999999996
+    - type: precision_at_1
+      value: 25.28
+    - type: precision_at_10
+      value: 5.3260000000000005
+    - type: precision_at_100
+      value: 0.8840000000000001
+    - type: precision_at_1000
+      value: 0.126
+    - type: precision_at_3
+      value: 12.687000000000001
+    - type: precision_at_5
+      value: 8.638
+    - type: recall_at_1
+      value: 21.52
+    - type: recall_at_10
+      value: 41.955
+    - type: recall_at_100
+      value: 64.21
+    - type: recall_at_1000
+      value: 85.28099999999999
+    - type: recall_at_3
+      value: 31.979999999999997
+    - type: recall_at_5
+      value: 35.406
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.296
+    - type: map_at_10
+      value: 28.449999999999996
+    - type: map_at_100
+      value: 29.847
+    - type: map_at_1000
+      value: 30.073
+    - type: map_at_3
+      value: 25.995
+    - type: map_at_5
+      value: 27.603
+    - type: mrr_at_1
+      value: 25.296000000000003
+    - type: mrr_at_10
+      value: 32.751999999999995
+    - type: mrr_at_100
+      value: 33.705
+    - type: mrr_at_1000
+      value: 33.783
+    - type: mrr_at_3
+      value: 30.731
+    - type: mrr_at_5
+      value: 32.006
+    - type: ndcg_at_1
+      value: 25.296000000000003
+    - type: ndcg_at_10
+      value: 33.555
+    - type: ndcg_at_100
+      value: 38.891999999999996
+    - type: ndcg_at_1000
+      value: 42.088
+    - type: ndcg_at_3
+      value: 29.944
+    - type: ndcg_at_5
+      value: 31.997999999999998
+    - type: precision_at_1
+      value: 25.296000000000003
+    - type: precision_at_10
+      value: 6.542000000000001
+    - type: precision_at_100
+      value: 1.354
+    - type: precision_at_1000
+      value: 0.22599999999999998
+    - type: precision_at_3
+      value: 14.360999999999999
+    - type: precision_at_5
+      value: 10.593
+    - type: recall_at_1
+      value: 20.296
+    - type: recall_at_10
+      value: 42.742000000000004
+    - type: recall_at_100
+      value: 67.351
+    - type: recall_at_1000
+      value: 88.774
+    - type: recall_at_3
+      value: 32.117000000000004
+    - type: recall_at_5
+      value: 37.788
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 18.157999999999998
+    - type: map_at_10
+      value: 24.342
+    - type: map_at_100
+      value: 25.201
+    - type: map_at_1000
+      value: 25.317
+    - type: map_at_3
+      value: 22.227
+    - type: map_at_5
+      value: 23.372999999999998
+    - type: mrr_at_1
+      value: 19.778000000000002
+    - type: mrr_at_10
+      value: 26.066
+    - type: mrr_at_100
+      value: 26.935
+    - type: mrr_at_1000
+      value: 27.022000000000002
+    - type: mrr_at_3
+      value: 24.214
+    - type: mrr_at_5
+      value: 25.268
+    - type: ndcg_at_1
+      value: 19.778000000000002
+    - type: ndcg_at_10
+      value: 28.104000000000003
+    - type: ndcg_at_100
+      value: 32.87
+    - type: ndcg_at_1000
+      value: 35.858000000000004
+    - type: ndcg_at_3
+      value: 24.107
+    - type: ndcg_at_5
+      value: 26.007
+    - type: precision_at_1
+      value: 19.778000000000002
+    - type: precision_at_10
+      value: 4.417999999999999
+    - type: precision_at_100
+      value: 0.739
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 10.228
+    - type: precision_at_5
+      value: 7.172000000000001
+    - type: recall_at_1
+      value: 18.157999999999998
+    - type: recall_at_10
+      value: 37.967
+    - type: recall_at_100
+      value: 60.806000000000004
+    - type: recall_at_1000
+      value: 83.097
+    - type: recall_at_3
+      value: 27.223999999999997
+    - type: recall_at_5
+      value: 31.968000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 7.055
+    - type: map_at_10
+      value: 11.609
+    - type: map_at_100
+      value: 12.83
+    - type: map_at_1000
+      value: 12.995000000000001
+    - type: map_at_3
+      value: 9.673
+    - type: map_at_5
+      value: 10.761999999999999
+    - type: mrr_at_1
+      value: 15.309000000000001
+    - type: mrr_at_10
+      value: 23.655
+    - type: mrr_at_100
+      value: 24.785
+    - type: mrr_at_1000
+      value: 24.856
+    - type: mrr_at_3
+      value: 20.499000000000002
+    - type: mrr_at_5
+      value: 22.425
+    - type: ndcg_at_1
+      value: 15.309000000000001
+    - type: ndcg_at_10
+      value: 17.252000000000002
+    - type: ndcg_at_100
+      value: 22.976
+    - type: ndcg_at_1000
+      value: 26.480999999999998
+    - type: ndcg_at_3
+      value: 13.418
+    - type: ndcg_at_5
+      value: 15.084
+    - type: precision_at_1
+      value: 15.309000000000001
+    - type: precision_at_10
+      value: 5.309
+    - type: precision_at_100
+      value: 1.1320000000000001
+    - type: precision_at_1000
+      value: 0.17600000000000002
+    - type: precision_at_3
+      value: 9.62
+    - type: precision_at_5
+      value: 7.883
+    - type: recall_at_1
+      value: 7.055
+    - type: recall_at_10
+      value: 21.891
+    - type: recall_at_100
+      value: 41.979
+    - type: recall_at_1000
+      value: 62.239999999999995
+    - type: recall_at_3
+      value: 12.722
+    - type: recall_at_5
+      value: 16.81
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.909
+    - type: map_at_10
+      value: 12.844
+    - type: map_at_100
+      value: 16.435
+    - type: map_at_1000
+      value: 17.262
+    - type: map_at_3
+      value: 10.131
+    - type: map_at_5
+      value: 11.269
+    - type: mrr_at_1
+      value: 54.50000000000001
+    - type: mrr_at_10
+      value: 62.202
+    - type: mrr_at_100
+      value: 62.81
+    - type: mrr_at_1000
+      value: 62.824000000000005
+    - type: mrr_at_3
+      value: 60.5
+    - type: mrr_at_5
+      value: 61.324999999999996
+    - type: ndcg_at_1
+      value: 42.125
+    - type: ndcg_at_10
+      value: 28.284
+    - type: ndcg_at_100
+      value: 30.444
+    - type: ndcg_at_1000
+      value: 36.397
+    - type: ndcg_at_3
+      value: 33.439
+    - type: ndcg_at_5
+      value: 30.473
+    - type: precision_at_1
+      value: 54.50000000000001
+    - type: precision_at_10
+      value: 21.4
+    - type: precision_at_100
+      value: 6.192
+    - type: precision_at_1000
+      value: 1.398
+    - type: precision_at_3
+      value: 36.583
+    - type: precision_at_5
+      value: 28.799999999999997
+    - type: recall_at_1
+      value: 6.909
+    - type: recall_at_10
+      value: 17.296
+    - type: recall_at_100
+      value: 33.925
+    - type: recall_at_1000
+      value: 53.786
+    - type: recall_at_3
+      value: 11.333
+    - type: recall_at_5
+      value: 13.529
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 36.08
+    - type: f1
+      value: 33.016420191943766
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 52.605000000000004
+    - type: map_at_10
+      value: 63.31400000000001
+    - type: map_at_100
+      value: 63.678000000000004
+    - type: map_at_1000
+      value: 63.699
+    - type: map_at_3
+      value: 61.141
+    - type: map_at_5
+      value: 62.517999999999994
+    - type: mrr_at_1
+      value: 56.871
+    - type: mrr_at_10
+      value: 67.915
+    - type: mrr_at_100
+      value: 68.24900000000001
+    - type: mrr_at_1000
+      value: 68.262
+    - type: mrr_at_3
+      value: 65.809
+    - type: mrr_at_5
+      value: 67.171
+    - type: ndcg_at_1
+      value: 56.871
+    - type: ndcg_at_10
+      value: 69.122
+    - type: ndcg_at_100
+      value: 70.855
+    - type: ndcg_at_1000
+      value: 71.368
+    - type: ndcg_at_3
+      value: 64.974
+    - type: ndcg_at_5
+      value: 67.318
+    - type: precision_at_1
+      value: 56.871
+    - type: precision_at_10
+      value: 9.029
+    - type: precision_at_100
+      value: 0.996
+    - type: precision_at_1000
+      value: 0.105
+    - type: precision_at_3
+      value: 25.893
+    - type: precision_at_5
+      value: 16.838
+    - type: recall_at_1
+      value: 52.605000000000004
+    - type: recall_at_10
+      value: 82.679
+    - type: recall_at_100
+      value: 90.586
+    - type: recall_at_1000
+      value: 94.38
+    - type: recall_at_3
+      value: 71.447
+    - type: recall_at_5
+      value: 77.218
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 10.759
+    - type: map_at_10
+      value: 18.877
+    - type: map_at_100
+      value: 20.498
+    - type: map_at_1000
+      value: 20.682000000000002
+    - type: map_at_3
+      value: 16.159000000000002
+    - type: map_at_5
+      value: 17.575
+    - type: mrr_at_1
+      value: 22.531000000000002
+    - type: mrr_at_10
+      value: 31.155
+    - type: mrr_at_100
+      value: 32.188
+    - type: mrr_at_1000
+      value: 32.245000000000005
+    - type: mrr_at_3
+      value: 28.781000000000002
+    - type: mrr_at_5
+      value: 30.054
+    - type: ndcg_at_1
+      value: 22.531000000000002
+    - type: ndcg_at_10
+      value: 25.189
+    - type: ndcg_at_100
+      value: 31.958
+    - type: ndcg_at_1000
+      value: 35.693999999999996
+    - type: ndcg_at_3
+      value: 22.235
+    - type: ndcg_at_5
+      value: 23.044999999999998
+    - type: precision_at_1
+      value: 22.531000000000002
+    - type: precision_at_10
+      value: 7.438000000000001
+    - type: precision_at_100
+      value: 1.418
+    - type: precision_at_1000
+      value: 0.208
+    - type: precision_at_3
+      value: 15.329
+    - type: precision_at_5
+      value: 11.451
+    - type: recall_at_1
+      value: 10.759
+    - type: recall_at_10
+      value: 31.416
+    - type: recall_at_100
+      value: 56.989000000000004
+    - type: recall_at_1000
+      value: 80.33200000000001
+    - type: recall_at_3
+      value: 20.61
+    - type: recall_at_5
+      value: 24.903
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.21
+    - type: map_at_10
+      value: 38.765
+    - type: map_at_100
+      value: 39.498
+    - type: map_at_1000
+      value: 39.568
+    - type: map_at_3
+      value: 36.699
+    - type: map_at_5
+      value: 37.925
+    - type: mrr_at_1
+      value: 58.42
+    - type: mrr_at_10
+      value: 65.137
+    - type: mrr_at_100
+      value: 65.542
+    - type: mrr_at_1000
+      value: 65.568
+    - type: mrr_at_3
+      value: 63.698
+    - type: mrr_at_5
+      value: 64.575
+    - type: ndcg_at_1
+      value: 58.42
+    - type: ndcg_at_10
+      value: 47.476
+    - type: ndcg_at_100
+      value: 50.466
+    - type: ndcg_at_1000
+      value: 52.064
+    - type: ndcg_at_3
+      value: 43.986
+    - type: ndcg_at_5
+      value: 45.824
+    - type: precision_at_1
+      value: 58.42
+    - type: precision_at_10
+      value: 9.649000000000001
+    - type: precision_at_100
+      value: 1.201
+    - type: precision_at_1000
+      value: 0.14100000000000001
+    - type: precision_at_3
+      value: 26.977
+    - type: precision_at_5
+      value: 17.642
+    - type: recall_at_1
+      value: 29.21
+    - type: recall_at_10
+      value: 48.244
+    - type: recall_at_100
+      value: 60.041
+    - type: recall_at_1000
+      value: 70.743
+    - type: recall_at_3
+      value: 40.466
+    - type: recall_at_5
+      value: 44.105
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 58.7064
+    - type: ap
+      value: 55.36326227125519
+    - type: f1
+      value: 57.46763115215848
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.889000000000001
+    - type: map_at_10
+      value: 25.979000000000003
+    - type: map_at_100
+      value: 27.21
+    - type: map_at_1000
+      value: 27.284000000000002
+    - type: map_at_3
+      value: 22.665
+    - type: map_at_5
+      value: 24.578
+    - type: mrr_at_1
+      value: 16.39
+    - type: mrr_at_10
+      value: 26.504
+    - type: mrr_at_100
+      value: 27.689999999999998
+    - type: mrr_at_1000
+      value: 27.758
+    - type: mrr_at_3
+      value: 23.24
+    - type: mrr_at_5
+      value: 25.108000000000004
+    - type: ndcg_at_1
+      value: 16.39
+    - type: ndcg_at_10
+      value: 31.799
+    - type: ndcg_at_100
+      value: 38.034
+    - type: ndcg_at_1000
+      value: 39.979
+    - type: ndcg_at_3
+      value: 25.054
+    - type: ndcg_at_5
+      value: 28.463
+    - type: precision_at_1
+      value: 16.39
+    - type: precision_at_10
+      value: 5.189
+    - type: precision_at_100
+      value: 0.835
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 10.84
+    - type: precision_at_5
+      value: 8.238
+    - type: recall_at_1
+      value: 15.889000000000001
+    - type: recall_at_10
+      value: 49.739
+    - type: recall_at_100
+      value: 79.251
+    - type: recall_at_1000
+      value: 94.298
+    - type: recall_at_3
+      value: 31.427
+    - type: recall_at_5
+      value: 39.623000000000005
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 88.81668946648426
+    - type: f1
+      value: 88.55200075528438
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 58.611491108071135
+    - type: f1
+      value: 42.12391403999353
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 64.67047747141896
+    - type: f1
+      value: 62.88410885922258
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 71.78547410894419
+    - type: f1
+      value: 71.69467869218154
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 27.23799937752035
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 23.26502601343789
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 30.680711484149832
+    - type: mrr
+      value: 31.705059795117307
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.077
+    - type: map_at_10
+      value: 8.657
+    - type: map_at_100
+      value: 10.753
+    - type: map_at_1000
+      value: 11.885
+    - type: map_at_3
+      value: 6.5089999999999995
+    - type: map_at_5
+      value: 7.405
+    - type: mrr_at_1
+      value: 38.7
+    - type: mrr_at_10
+      value: 46.065
+    - type: mrr_at_100
+      value: 46.772000000000006
+    - type: mrr_at_1000
+      value: 46.83
+    - type: mrr_at_3
+      value: 44.118
+    - type: mrr_at_5
+      value: 45.015
+    - type: ndcg_at_1
+      value: 36.997
+    - type: ndcg_at_10
+      value: 25.96
+    - type: ndcg_at_100
+      value: 23.607
+    - type: ndcg_at_1000
+      value: 32.317
+    - type: ndcg_at_3
+      value: 31.06
+    - type: ndcg_at_5
+      value: 28.921000000000003
+    - type: precision_at_1
+      value: 38.7
+    - type: precision_at_10
+      value: 19.195
+    - type: precision_at_100
+      value: 6.164
+    - type: precision_at_1000
+      value: 1.839
+    - type: precision_at_3
+      value: 28.999000000000002
+    - type: precision_at_5
+      value: 25.014999999999997
+    - type: recall_at_1
+      value: 4.077
+    - type: recall_at_10
+      value: 11.802
+    - type: recall_at_100
+      value: 24.365000000000002
+    - type: recall_at_1000
+      value: 55.277
+    - type: recall_at_3
+      value: 7.435
+    - type: recall_at_5
+      value: 8.713999999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.588
+    - type: map_at_10
+      value: 32.08
+    - type: map_at_100
+      value: 33.32
+    - type: map_at_1000
+      value: 33.377
+    - type: map_at_3
+      value: 28.166000000000004
+    - type: map_at_5
+      value: 30.383
+    - type: mrr_at_1
+      value: 22.161
+    - type: mrr_at_10
+      value: 34.121
+    - type: mrr_at_100
+      value: 35.171
+    - type: mrr_at_1000
+      value: 35.214
+    - type: mrr_at_3
+      value: 30.692000000000004
+    - type: mrr_at_5
+      value: 32.706
+    - type: ndcg_at_1
+      value: 22.131999999999998
+    - type: ndcg_at_10
+      value: 38.887
+    - type: ndcg_at_100
+      value: 44.433
+    - type: ndcg_at_1000
+      value: 45.823
+    - type: ndcg_at_3
+      value: 31.35
+    - type: ndcg_at_5
+      value: 35.144
+    - type: precision_at_1
+      value: 22.131999999999998
+    - type: precision_at_10
+      value: 6.8629999999999995
+    - type: precision_at_100
+      value: 0.993
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 14.706
+    - type: precision_at_5
+      value: 10.972999999999999
+    - type: recall_at_1
+      value: 19.588
+    - type: recall_at_10
+      value: 57.703
+    - type: recall_at_100
+      value: 82.194
+    - type: recall_at_1000
+      value: 92.623
+    - type: recall_at_3
+      value: 38.012
+    - type: recall_at_5
+      value: 46.847
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 68.038
+    - type: map_at_10
+      value: 81.572
+    - type: map_at_100
+      value: 82.25200000000001
+    - type: map_at_1000
+      value: 82.27600000000001
+    - type: map_at_3
+      value: 78.618
+    - type: map_at_5
+      value: 80.449
+    - type: mrr_at_1
+      value: 78.31
+    - type: mrr_at_10
+      value: 84.98
+    - type: mrr_at_100
+      value: 85.122
+    - type: mrr_at_1000
+      value: 85.124
+    - type: mrr_at_3
+      value: 83.852
+    - type: mrr_at_5
+      value: 84.6
+    - type: ndcg_at_1
+      value: 78.31
+    - type: ndcg_at_10
+      value: 85.693
+    - type: ndcg_at_100
+      value: 87.191
+    - type: ndcg_at_1000
+      value: 87.386
+    - type: ndcg_at_3
+      value: 82.585
+    - type: ndcg_at_5
+      value: 84.255
+    - type: precision_at_1
+      value: 78.31
+    - type: precision_at_10
+      value: 12.986
+    - type: precision_at_100
+      value: 1.505
+    - type: precision_at_1000
+      value: 0.156
+    - type: precision_at_3
+      value: 36.007
+    - type: precision_at_5
+      value: 23.735999999999997
+    - type: recall_at_1
+      value: 68.038
+    - type: recall_at_10
+      value: 93.598
+    - type: recall_at_100
+      value: 98.869
+    - type: recall_at_1000
+      value: 99.86500000000001
+    - type: recall_at_3
+      value: 84.628
+    - type: recall_at_5
+      value: 89.316
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 37.948231664922865
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 49.90597913763894
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 3.753
+    - type: map_at_10
+      value: 8.915
+    - type: map_at_100
+      value: 10.374
+    - type: map_at_1000
+      value: 10.612
+    - type: map_at_3
+      value: 6.577
+    - type: map_at_5
+      value: 7.8
+    - type: mrr_at_1
+      value: 18.4
+    - type: mrr_at_10
+      value: 27.325
+    - type: mrr_at_100
+      value: 28.419
+    - type: mrr_at_1000
+      value: 28.494000000000003
+    - type: mrr_at_3
+      value: 24.349999999999998
+    - type: mrr_at_5
+      value: 26.205000000000002
+    - type: ndcg_at_1
+      value: 18.4
+    - type: ndcg_at_10
+      value: 15.293000000000001
+    - type: ndcg_at_100
+      value: 21.592
+    - type: ndcg_at_1000
+      value: 26.473000000000003
+    - type: ndcg_at_3
+      value: 14.748
+    - type: ndcg_at_5
+      value: 12.98
+    - type: precision_at_1
+      value: 18.4
+    - type: precision_at_10
+      value: 7.779999999999999
+    - type: precision_at_100
+      value: 1.693
+    - type: precision_at_1000
+      value: 0.28800000000000003
+    - type: precision_at_3
+      value: 13.700000000000001
+    - type: precision_at_5
+      value: 11.379999999999999
+    - type: recall_at_1
+      value: 3.753
+    - type: recall_at_10
+      value: 15.806999999999999
+    - type: recall_at_100
+      value: 34.37
+    - type: recall_at_1000
+      value: 58.463
+    - type: recall_at_3
+      value: 8.338
+    - type: recall_at_5
+      value: 11.538
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.58843987639705
+    - type: cos_sim_spearman
+      value: 76.33071660715956
+    - type: euclidean_pearson
+      value: 72.8029921002978
+    - type: euclidean_spearman
+      value: 69.34534284782808
+    - type: manhattan_pearson
+      value: 72.49781034973653
+    - type: manhattan_spearman
+      value: 69.24754112621694
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.31673079903189
+    - type: cos_sim_spearman
+      value: 74.27699263517789
+    - type: euclidean_pearson
+      value: 69.4008910999579
+    - type: euclidean_spearman
+      value: 59.0716984643048
+    - type: manhattan_pearson
+      value: 68.87342686919199
+    - type: manhattan_spearman
+      value: 58.904612865335025
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 77.59122302327788
+    - type: cos_sim_spearman
+      value: 78.55383586979005
+    - type: euclidean_pearson
+      value: 68.18338642204289
+    - type: euclidean_spearman
+      value: 68.95092864180276
+    - type: manhattan_pearson
+      value: 68.08807059822706
+    - type: manhattan_spearman
+      value: 68.86135938270193
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 78.51766841424501
+    - type: cos_sim_spearman
+      value: 73.84318001499558
+    - type: euclidean_pearson
+      value: 67.2007138855177
+    - type: euclidean_spearman
+      value: 63.98672842723766
+    - type: manhattan_pearson
+      value: 67.17773810895949
+    - type: manhattan_spearman
+      value: 64.07359154832962
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.73438541570299
+    - type: cos_sim_spearman
+      value: 83.71357922283677
+    - type: euclidean_pearson
+      value: 57.50131347498546
+    - type: euclidean_spearman
+      value: 57.73623619252132
+    - type: manhattan_pearson
+      value: 58.082992079000725
+    - type: manhattan_spearman
+      value: 58.42728201167522
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 78.14794654172421
+    - type: cos_sim_spearman
+      value: 80.025736165043
+    - type: euclidean_pearson
+      value: 65.87773913985473
+    - type: euclidean_spearman
+      value: 66.69337751784794
+    - type: manhattan_pearson
+      value: 66.01039761004415
+    - type: manhattan_spearman
+      value: 66.89215027952318
+  - 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: 87.10554507136152
+    - type: cos_sim_spearman
+      value: 87.4898082140765
+    - type: euclidean_pearson
+      value: 72.19391114541367
+    - type: euclidean_spearman
+      value: 70.36647944993783
+    - type: manhattan_pearson
+      value: 72.18680758133698
+    - type: manhattan_spearman
+      value: 70.3871215447305
+  - 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: 64.54868111501618
+    - type: cos_sim_spearman
+      value: 64.25173617448473
+    - type: euclidean_pearson
+      value: 39.116088900637116
+    - type: euclidean_spearman
+      value: 53.300772929884
+    - type: manhattan_pearson
+      value: 38.3844195287959
+    - type: manhattan_spearman
+      value: 52.846675312001246
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.04396610550214
+    - type: cos_sim_spearman
+      value: 79.19504854997832
+    - type: euclidean_pearson
+      value: 66.3284657637072
+    - type: euclidean_spearman
+      value: 63.69531796729492
+    - type: manhattan_pearson
+      value: 66.82324081038026
+    - type: manhattan_spearman
+      value: 64.18254512904923
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 74.16264051781705
+    - type: mrr
+      value: 91.80864796060874
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.983000000000004
+    - type: map_at_10
+      value: 47.858000000000004
+    - type: map_at_100
+      value: 48.695
+    - type: map_at_1000
+      value: 48.752
+    - type: map_at_3
+      value: 45.444
+    - type: map_at_5
+      value: 46.906
+    - type: mrr_at_1
+      value: 41.333
+    - type: mrr_at_10
+      value: 49.935
+    - type: mrr_at_100
+      value: 50.51
+    - type: mrr_at_1000
+      value: 50.55500000000001
+    - type: mrr_at_3
+      value: 47.833
+    - type: mrr_at_5
+      value: 49.117
+    - type: ndcg_at_1
+      value: 41.333
+    - type: ndcg_at_10
+      value: 52.398999999999994
+    - type: ndcg_at_100
+      value: 56.196
+    - type: ndcg_at_1000
+      value: 57.838
+    - type: ndcg_at_3
+      value: 47.987
+    - type: ndcg_at_5
+      value: 50.356
+    - type: precision_at_1
+      value: 41.333
+    - type: precision_at_10
+      value: 7.167
+    - type: precision_at_100
+      value: 0.9299999999999999
+    - type: precision_at_1000
+      value: 0.108
+    - type: precision_at_3
+      value: 19.0
+    - type: precision_at_5
+      value: 12.8
+    - type: recall_at_1
+      value: 38.983000000000004
+    - type: recall_at_10
+      value: 64.183
+    - type: recall_at_100
+      value: 82.02199999999999
+    - type: recall_at_1000
+      value: 95.167
+    - type: recall_at_3
+      value: 52.383
+    - type: recall_at_5
+      value: 58.411
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.8019801980198
+    - type: cos_sim_ap
+      value: 94.9287554635848
+    - type: cos_sim_f1
+      value: 89.83739837398375
+    - type: cos_sim_precision
+      value: 91.32231404958677
+    - type: cos_sim_recall
+      value: 88.4
+    - type: dot_accuracy
+      value: 99.23762376237623
+    - type: dot_ap
+      value: 55.22534191245801
+    - type: dot_f1
+      value: 54.054054054054056
+    - type: dot_precision
+      value: 55.15088449531738
+    - type: dot_recall
+      value: 53.0
+    - type: euclidean_accuracy
+      value: 99.6108910891089
+    - type: euclidean_ap
+      value: 82.5195111329438
+    - type: euclidean_f1
+      value: 78.2847718526663
+    - type: euclidean_precision
+      value: 86.93528693528694
+    - type: euclidean_recall
+      value: 71.2
+    - type: manhattan_accuracy
+      value: 99.5970297029703
+    - type: manhattan_ap
+      value: 81.96876777875492
+    - type: manhattan_f1
+      value: 77.33773377337734
+    - type: manhattan_precision
+      value: 85.94132029339853
+    - type: manhattan_recall
+      value: 70.3
+    - type: max_accuracy
+      value: 99.8019801980198
+    - type: max_ap
+      value: 94.9287554635848
+    - type: max_f1
+      value: 89.83739837398375
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 46.34997003954114
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 31.462336020554893
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 47.1757817459526
+    - type: mrr
+      value: 47.941057104660054
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.56106249068471
+    - type: cos_sim_spearman
+      value: 31.24613190558528
+    - type: dot_pearson
+      value: 20.486610035794257
+    - type: dot_spearman
+      value: 23.115667545894546
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.182
+    - type: map_at_10
+      value: 1.155
+    - type: map_at_100
+      value: 5.118
+    - type: map_at_1000
+      value: 11.827
+    - type: map_at_3
+      value: 0.482
+    - type: map_at_5
+      value: 0.712
+    - type: mrr_at_1
+      value: 70.0
+    - type: mrr_at_10
+      value: 79.483
+    - type: mrr_at_100
+      value: 79.637
+    - type: mrr_at_1000
+      value: 79.637
+    - type: mrr_at_3
+      value: 77.667
+    - type: mrr_at_5
+      value: 78.567
+    - type: ndcg_at_1
+      value: 63.0
+    - type: ndcg_at_10
+      value: 52.303
+    - type: ndcg_at_100
+      value: 37.361
+    - type: ndcg_at_1000
+      value: 32.84
+    - type: ndcg_at_3
+      value: 58.274
+    - type: ndcg_at_5
+      value: 55.601
+    - type: precision_at_1
+      value: 70.0
+    - type: precision_at_10
+      value: 55.60000000000001
+    - type: precision_at_100
+      value: 37.96
+    - type: precision_at_1000
+      value: 14.738000000000001
+    - type: precision_at_3
+      value: 62.666999999999994
+    - type: precision_at_5
+      value: 60.0
+    - type: recall_at_1
+      value: 0.182
+    - type: recall_at_10
+      value: 1.4120000000000001
+    - type: recall_at_100
+      value: 8.533
+    - type: recall_at_1000
+      value: 30.572
+    - type: recall_at_3
+      value: 0.5309999999999999
+    - type: recall_at_5
+      value: 0.814
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 1.385
+    - type: map_at_10
+      value: 7.185999999999999
+    - type: map_at_100
+      value: 11.642
+    - type: map_at_1000
+      value: 12.953000000000001
+    - type: map_at_3
+      value: 3.496
+    - type: map_at_5
+      value: 4.82
+    - type: mrr_at_1
+      value: 16.326999999999998
+    - type: mrr_at_10
+      value: 29.461
+    - type: mrr_at_100
+      value: 31.436999999999998
+    - type: mrr_at_1000
+      value: 31.436999999999998
+    - type: mrr_at_3
+      value: 24.490000000000002
+    - type: mrr_at_5
+      value: 27.857
+    - type: ndcg_at_1
+      value: 14.285999999999998
+    - type: ndcg_at_10
+      value: 16.672
+    - type: ndcg_at_100
+      value: 28.691
+    - type: ndcg_at_1000
+      value: 39.817
+    - type: ndcg_at_3
+      value: 15.277
+    - type: ndcg_at_5
+      value: 15.823
+    - type: precision_at_1
+      value: 16.326999999999998
+    - type: precision_at_10
+      value: 15.509999999999998
+    - type: precision_at_100
+      value: 6.49
+    - type: precision_at_1000
+      value: 1.4080000000000001
+    - type: precision_at_3
+      value: 16.326999999999998
+    - type: precision_at_5
+      value: 16.735
+    - type: recall_at_1
+      value: 1.385
+    - type: recall_at_10
+      value: 12.586
+    - type: recall_at_100
+      value: 40.765
+    - type: recall_at_1000
+      value: 75.198
+    - type: recall_at_3
+      value: 4.326
+    - type: recall_at_5
+      value: 7.074999999999999
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 59.4402
+    - type: ap
+      value: 10.16922814263879
+    - type: f1
+      value: 45.374485104940476
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 54.25863044708545
+    - type: f1
+      value: 54.20154252609619
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 34.3883169293051
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 81.76670441676104
+    - type: cos_sim_ap
+      value: 59.29878710961347
+    - type: cos_sim_f1
+      value: 57.33284971587474
+    - type: cos_sim_precision
+      value: 52.9122963624191
+    - type: cos_sim_recall
+      value: 62.559366754617415
+    - type: dot_accuracy
+      value: 77.52279907015557
+    - type: dot_ap
+      value: 34.17588904643467
+    - type: dot_f1
+      value: 41.063567529494634
+    - type: dot_precision
+      value: 30.813953488372093
+    - type: dot_recall
+      value: 61.53034300791557
+    - type: euclidean_accuracy
+      value: 80.61631996185254
+    - type: euclidean_ap
+      value: 54.00362361479352
+    - type: euclidean_f1
+      value: 53.99111751290361
+    - type: euclidean_precision
+      value: 49.52653600528518
+    - type: euclidean_recall
+      value: 59.340369393139845
+    - type: manhattan_accuracy
+      value: 80.65208320915539
+    - type: manhattan_ap
+      value: 54.18329507159467
+    - type: manhattan_f1
+      value: 53.85550960836779
+    - type: manhattan_precision
+      value: 49.954873646209386
+    - type: manhattan_recall
+      value: 58.41688654353562
+    - type: max_accuracy
+      value: 81.76670441676104
+    - type: max_ap
+      value: 59.29878710961347
+    - type: max_f1
+      value: 57.33284971587474
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.99433383785463
+    - type: cos_sim_ap
+      value: 83.43513915159009
+    - type: cos_sim_f1
+      value: 76.3906784964842
+    - type: cos_sim_precision
+      value: 73.19223985890653
+    - type: cos_sim_recall
+      value: 79.88142901139513
+    - type: dot_accuracy
+      value: 81.96142352621571
+    - type: dot_ap
+      value: 67.78764755689359
+    - type: dot_f1
+      value: 64.42823356983445
+    - type: dot_precision
+      value: 56.77801913931779
+    - type: dot_recall
+      value: 74.46104096088698
+    - type: euclidean_accuracy
+      value: 81.9478402607987
+    - type: euclidean_ap
+      value: 67.13958457373279
+    - type: euclidean_f1
+      value: 60.45118343195266
+    - type: euclidean_precision
+      value: 58.1625391403359
+    - type: euclidean_recall
+      value: 62.92731752386819
+    - type: manhattan_accuracy
+      value: 82.01769705437188
+    - type: manhattan_ap
+      value: 67.24709477497046
+    - type: manhattan_f1
+      value: 60.4103846436714
+    - type: manhattan_precision
+      value: 57.82063916654935
+    - type: manhattan_recall
+      value: 63.24299353249153
+    - type: max_accuracy
+      value: 87.99433383785463
+    - type: max_ap
+      value: 83.43513915159009
+    - type: max_f1
+      value: 76.3906784964842
+---
+# # 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-s-en-v1](https://huggingface.co/jinaai/jina-embedding-s-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-s-en-v1"
+model_name_orig="jinaai/jina-embedding-s-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-s-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 compact size of just 35 million parameters,
+the model enables lightning-fast inference while still delivering impressive performance.
+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 **(you are here)**.
+- [`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.
+- `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-s-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-s-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 +1,61 @@
+{
+    "_name_or_path": "tmp/",
+    "architectures": [
+        "T5EncoderModel"
+    ],
+    "auto_map": {
+        "AutoModel": "modeling_t5.T5EncoderModel"
+    },
+    "d_ff": 2048,
+    "d_kv": 64,
+    "d_model": 512,
+    "decoder_start_token_id": 0,
+    "dropout_rate": 0.1,
+    "eos_token_id": 1,
+    "initializer_factor": 1.0,
+    "is_encoder_decoder": true,
+    "layer_norm_epsilon": null,
+    "model_type": "t5",
+    "n_positions": 512,
+    "num_heads": 8,
+    "num_layers": 6,
+    "output_past": true,
+    "pad_token_id": 0,
+    "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": {
+            "early_stopping": true,
+            "max_length": 300,
+            "num_beams": 4,
+            "prefix": "translate English to Romanian: "
+        }
+    },
+    "vocab_size": 32128,
+    "add_source_bos": false,
+    "add_source_eos": false,
+    "bos_token": "<pad>",
+    "decoder_start_token": "<pad>",
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sentence_bert_config.json

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