jinaai
/

jina-embeddings-v2-base-en

+---
+pipeline_tag: sentence-similarity
+tags:
+  - finetuner
+  - mteb
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+  - alibi
+datasets:
+  - allenai/c4
+language: en
+license: apache-2.0
+model-index:
+- name: jina-embedding-b-en-v2
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 73.4179104477612
+    - type: ap
+      value: 35.798378234524705
+    - type: f1
+      value: 67.27708504551819
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 88.977575
+    - type: ap
+      value: 85.00359027707599
+    - type: f1
+      value: 88.9585285941142
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 44.455999999999996
+    - type: f1
+      value: 42.80615676169829
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 18.919
+    - type: map_at_10
+      value: 33.272
+    - type: map_at_100
+      value: 34.669
+    - type: map_at_1000
+      value: 34.68
+    - type: map_at_3
+      value: 28.011000000000003
+    - type: map_at_5
+      value: 30.767
+    - type: mrr_at_1
+      value: 19.061
+    - type: mrr_at_10
+      value: 33.352
+    - type: mrr_at_100
+      value: 34.75
+    - type: mrr_at_1000
+      value: 34.760999999999996
+    - type: mrr_at_3
+      value: 28.07
+    - type: mrr_at_5
+      value: 30.848
+    - type: ndcg_at_1
+      value: 18.919
+    - type: ndcg_at_10
+      value: 42.138
+    - type: ndcg_at_100
+      value: 48.165
+    - type: ndcg_at_1000
+      value: 48.435
+    - type: ndcg_at_3
+      value: 31.041
+    - type: ndcg_at_5
+      value: 36.015
+    - type: precision_at_1
+      value: 18.919
+    - type: precision_at_10
+      value: 7.098
+    - type: precision_at_100
+      value: 0.9740000000000001
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 13.276
+    - type: precision_at_5
+      value: 10.384
+    - type: recall_at_1
+      value: 18.919
+    - type: recall_at_10
+      value: 70.982
+    - type: recall_at_100
+      value: 97.44
+    - type: recall_at_1000
+      value: 99.502
+    - type: recall_at_3
+      value: 39.829
+    - type: recall_at_5
+      value: 51.92
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 45.38238451470738
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 37.12265635737745
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 62.473921100678695
+    - type: mrr
+      value: 75.28195488721803
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.46030780641742
+    - type: cos_sim_spearman
+      value: 83.29647627997147
+    - type: euclidean_pearson
+      value: 83.63127685751004
+    - type: euclidean_spearman
+      value: 83.29647627997147
+    - type: manhattan_pearson
+      value: 83.29505322210208
+    - type: manhattan_spearman
+      value: 82.8398393691656
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 83.94480519480521
+    - type: f1
+      value: 83.26406143364741
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 37.15926312173139
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 31.20469085642121
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.462
+    - type: map_at_10
+      value: 39.834
+    - type: map_at_100
+      value: 41.329
+    - type: map_at_1000
+      value: 41.465
+    - type: map_at_3
+      value: 36.586999999999996
+    - type: map_at_5
+      value: 38.239000000000004
+    - type: mrr_at_1
+      value: 34.335
+    - type: mrr_at_10
+      value: 45.493
+    - type: mrr_at_100
+      value: 46.323
+    - type: mrr_at_1000
+      value: 46.37
+    - type: mrr_at_3
+      value: 42.870999999999995
+    - type: mrr_at_5
+      value: 44.502
+    - type: ndcg_at_1
+      value: 34.335
+    - type: ndcg_at_10
+      value: 46.434
+    - type: ndcg_at_100
+      value: 52.013
+    - type: ndcg_at_1000
+      value: 54.079
+    - type: ndcg_at_3
+      value: 41.408
+    - type: ndcg_at_5
+      value: 43.562
+    - type: precision_at_1
+      value: 34.335
+    - type: precision_at_10
+      value: 8.913
+    - type: precision_at_100
+      value: 1.439
+    - type: precision_at_1000
+      value: 0.197
+    - type: precision_at_3
+      value: 20.029
+    - type: precision_at_5
+      value: 14.335
+    - type: recall_at_1
+      value: 28.462
+    - type: recall_at_10
+      value: 59.574000000000005
+    - type: recall_at_100
+      value: 82.631
+    - type: recall_at_1000
+      value: 95.45700000000001
+    - type: recall_at_3
+      value: 45.381
+    - type: recall_at_5
+      value: 51.18000000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.245
+    - type: map_at_10
+      value: 37.156
+    - type: map_at_100
+      value: 38.464999999999996
+    - type: map_at_1000
+      value: 38.607
+    - type: map_at_3
+      value: 34.613
+    - type: map_at_5
+      value: 35.924
+    - type: mrr_at_1
+      value: 34.777
+    - type: mrr_at_10
+      value: 43.425000000000004
+    - type: mrr_at_100
+      value: 44.163000000000004
+    - type: mrr_at_1000
+      value: 44.211
+    - type: mrr_at_3
+      value: 41.391
+    - type: mrr_at_5
+      value: 42.461
+    - type: ndcg_at_1
+      value: 34.777
+    - type: ndcg_at_10
+      value: 42.807
+    - type: ndcg_at_100
+      value: 47.629
+    - type: ndcg_at_1000
+      value: 49.84
+    - type: ndcg_at_3
+      value: 39.28
+    - type: ndcg_at_5
+      value: 40.671
+    - type: precision_at_1
+      value: 34.777
+    - type: precision_at_10
+      value: 8.134
+    - type: precision_at_100
+      value: 1.3599999999999999
+    - type: precision_at_1000
+      value: 0.186
+    - type: precision_at_3
+      value: 19.320999999999998
+    - type: precision_at_5
+      value: 13.286999999999999
+    - type: recall_at_1
+      value: 27.245
+    - type: recall_at_10
+      value: 52.491
+    - type: recall_at_100
+      value: 73.065
+    - type: recall_at_1000
+      value: 86.931
+    - type: recall_at_3
+      value: 41.257
+    - type: recall_at_5
+      value: 45.811
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 37.088
+    - type: map_at_10
+      value: 49.003
+    - type: map_at_100
+      value: 50.017999999999994
+    - type: map_at_1000
+      value: 50.07899999999999
+    - type: map_at_3
+      value: 45.846
+    - type: map_at_5
+      value: 47.733
+    - type: mrr_at_1
+      value: 42.193999999999996
+    - type: mrr_at_10
+      value: 52.522999999999996
+    - type: mrr_at_100
+      value: 53.177
+    - type: mrr_at_1000
+      value: 53.205999999999996
+    - type: mrr_at_3
+      value: 49.916
+    - type: mrr_at_5
+      value: 51.50900000000001
+    - type: ndcg_at_1
+      value: 42.193999999999996
+    - type: ndcg_at_10
+      value: 54.99699999999999
+    - type: ndcg_at_100
+      value: 59.058
+    - type: ndcg_at_1000
+      value: 60.355000000000004
+    - type: ndcg_at_3
+      value: 49.515
+    - type: ndcg_at_5
+      value: 52.412000000000006
+    - type: precision_at_1
+      value: 42.193999999999996
+    - type: precision_at_10
+      value: 8.84
+    - type: precision_at_100
+      value: 1.1820000000000002
+    - type: precision_at_1000
+      value: 0.134
+    - type: precision_at_3
+      value: 21.944
+    - type: precision_at_5
+      value: 15.197
+    - type: recall_at_1
+      value: 37.088
+    - type: recall_at_10
+      value: 69.13
+    - type: recall_at_100
+      value: 86.612
+    - type: recall_at_1000
+      value: 95.946
+    - type: recall_at_3
+      value: 54.76
+    - type: recall_at_5
+      value: 61.76199999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.816
+    - type: map_at_10
+      value: 30.630000000000003
+    - type: map_at_100
+      value: 31.641000000000002
+    - type: map_at_1000
+      value: 31.730999999999998
+    - type: map_at_3
+      value: 28.153
+    - type: map_at_5
+      value: 29.433
+    - type: mrr_at_1
+      value: 23.842
+    - type: mrr_at_10
+      value: 32.432
+    - type: mrr_at_100
+      value: 33.354
+    - type: mrr_at_1000
+      value: 33.421
+    - type: mrr_at_3
+      value: 30.131999999999998
+    - type: mrr_at_5
+      value: 31.358000000000004
+    - type: ndcg_at_1
+      value: 23.842
+    - type: ndcg_at_10
+      value: 35.626000000000005
+    - type: ndcg_at_100
+      value: 40.855999999999995
+    - type: ndcg_at_1000
+      value: 43.111
+    - type: ndcg_at_3
+      value: 30.712
+    - type: ndcg_at_5
+      value: 32.912
+    - type: precision_at_1
+      value: 23.842
+    - type: precision_at_10
+      value: 5.627
+    - type: precision_at_100
+      value: 0.873
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 13.333
+    - type: precision_at_5
+      value: 9.266
+    - type: recall_at_1
+      value: 21.816
+    - type: recall_at_10
+      value: 49.370000000000005
+    - type: recall_at_100
+      value: 73.855
+    - type: recall_at_1000
+      value: 90.67399999999999
+    - type: recall_at_3
+      value: 35.85
+    - type: recall_at_5
+      value: 41.282000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 14.402000000000001
+    - type: map_at_10
+      value: 21.401999999999997
+    - type: map_at_100
+      value: 22.425
+    - type: map_at_1000
+      value: 22.561
+    - type: map_at_3
+      value: 19.238
+    - type: map_at_5
+      value: 20.213
+    - type: mrr_at_1
+      value: 17.91
+    - type: mrr_at_10
+      value: 25.629999999999995
+    - type: mrr_at_100
+      value: 26.529999999999998
+    - type: mrr_at_1000
+      value: 26.616
+    - type: mrr_at_3
+      value: 23.362
+    - type: mrr_at_5
+      value: 24.438
+    - type: ndcg_at_1
+      value: 17.91
+    - type: ndcg_at_10
+      value: 26.161
+    - type: ndcg_at_100
+      value: 31.474000000000004
+    - type: ndcg_at_1000
+      value: 34.802
+    - type: ndcg_at_3
+      value: 21.965
+    - type: ndcg_at_5
+      value: 23.511000000000003
+    - type: precision_at_1
+      value: 17.91
+    - type: precision_at_10
+      value: 4.8629999999999995
+    - type: precision_at_100
+      value: 0.869
+    - type: precision_at_1000
+      value: 0.129
+    - type: precision_at_3
+      value: 10.655000000000001
+    - type: precision_at_5
+      value: 7.5120000000000005
+    - type: recall_at_1
+      value: 14.402000000000001
+    - type: recall_at_10
+      value: 36.760999999999996
+    - type: recall_at_100
+      value: 60.549
+    - type: recall_at_1000
+      value: 84.414
+    - type: recall_at_3
+      value: 25.130000000000003
+    - type: recall_at_5
+      value: 29.079
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.176
+    - type: map_at_10
+      value: 35.789
+    - type: map_at_100
+      value: 37.092000000000006
+    - type: map_at_1000
+      value: 37.206
+    - type: map_at_3
+      value: 33.207
+    - type: map_at_5
+      value: 34.436
+    - type: mrr_at_1
+      value: 31.569000000000003
+    - type: mrr_at_10
+      value: 41.219
+    - type: mrr_at_100
+      value: 42.016999999999996
+    - type: mrr_at_1000
+      value: 42.065000000000005
+    - type: mrr_at_3
+      value: 39.012
+    - type: mrr_at_5
+      value: 40.22
+    - type: ndcg_at_1
+      value: 31.569000000000003
+    - type: ndcg_at_10
+      value: 41.515
+    - type: ndcg_at_100
+      value: 47.125
+    - type: ndcg_at_1000
+      value: 49.314
+    - type: ndcg_at_3
+      value: 37.201
+    - type: ndcg_at_5
+      value: 38.906
+    - type: precision_at_1
+      value: 31.569000000000003
+    - type: precision_at_10
+      value: 7.517
+    - type: precision_at_100
+      value: 1.225
+    - type: precision_at_1000
+      value: 0.161
+    - type: precision_at_3
+      value: 17.485
+    - type: precision_at_5
+      value: 12.089
+    - type: recall_at_1
+      value: 26.176
+    - type: recall_at_10
+      value: 53.076
+    - type: recall_at_100
+      value: 77.049
+    - type: recall_at_1000
+      value: 91.51
+    - type: recall_at_3
+      value: 40.82
+    - type: recall_at_5
+      value: 45.479
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.675
+    - type: map_at_10
+      value: 31.752999999999997
+    - type: map_at_100
+      value: 33.19
+    - type: map_at_1000
+      value: 33.303
+    - type: map_at_3
+      value: 28.89
+    - type: map_at_5
+      value: 30.451
+    - type: mrr_at_1
+      value: 27.854
+    - type: mrr_at_10
+      value: 36.736999999999995
+    - type: mrr_at_100
+      value: 37.783
+    - type: mrr_at_1000
+      value: 37.836
+    - type: mrr_at_3
+      value: 34.266000000000005
+    - type: mrr_at_5
+      value: 35.577999999999996
+    - type: ndcg_at_1
+      value: 27.854
+    - type: ndcg_at_10
+      value: 37.391999999999996
+    - type: ndcg_at_100
+      value: 43.682
+    - type: ndcg_at_1000
+      value: 46.005
+    - type: ndcg_at_3
+      value: 32.66
+    - type: ndcg_at_5
+      value: 34.73
+    - type: precision_at_1
+      value: 27.854
+    - type: precision_at_10
+      value: 6.963
+    - type: precision_at_100
+      value: 1.184
+    - type: precision_at_1000
+      value: 0.159
+    - type: precision_at_3
+      value: 15.715000000000002
+    - type: precision_at_5
+      value: 11.256
+    - type: recall_at_1
+      value: 22.675
+    - type: recall_at_10
+      value: 49.15
+    - type: recall_at_100
+      value: 76.542
+    - type: recall_at_1000
+      value: 92.19000000000001
+    - type: recall_at_3
+      value: 35.607
+    - type: recall_at_5
+      value: 41.288000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.214499999999997
+    - type: map_at_10
+      value: 31.979833333333335
+    - type: map_at_100
+      value: 33.20666666666666
+    - type: map_at_1000
+      value: 33.328583333333334
+    - type: map_at_3
+      value: 29.341416666666664
+    - type: map_at_5
+      value: 30.718083333333336
+    - type: mrr_at_1
+      value: 27.328583333333338
+    - type: mrr_at_10
+      value: 35.88433333333333
+    - type: mrr_at_100
+      value: 36.80075000000001
+    - type: mrr_at_1000
+      value: 36.86175
+    - type: mrr_at_3
+      value: 33.51625
+    - type: mrr_at_5
+      value: 34.821416666666664
+    - type: ndcg_at_1
+      value: 27.328583333333338
+    - type: ndcg_at_10
+      value: 37.24475
+    - type: ndcg_at_100
+      value: 42.63825
+    - type: ndcg_at_1000
+      value: 45.08266666666667
+    - type: ndcg_at_3
+      value: 32.61783333333334
+    - type: ndcg_at_5
+      value: 34.631249999999994
+    - type: precision_at_1
+      value: 27.328583333333338
+    - type: precision_at_10
+      value: 6.5873333333333335
+    - type: precision_at_100
+      value: 1.094916666666667
+    - type: precision_at_1000
+      value: 0.15091666666666664
+    - type: precision_at_3
+      value: 15.073499999999997
+    - type: precision_at_5
+      value: 10.651916666666667
+    - type: recall_at_1
+      value: 23.214499999999997
+    - type: recall_at_10
+      value: 49.010250000000006
+    - type: recall_at_100
+      value: 72.70374999999999
+    - type: recall_at_1000
+      value: 89.66041666666666
+    - type: recall_at_3
+      value: 36.06008333333334
+    - type: recall_at_5
+      value: 41.289166666666674
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.497
+    - type: map_at_10
+      value: 29.176000000000002
+    - type: map_at_100
+      value: 30.218
+    - type: map_at_1000
+      value: 30.317
+    - type: map_at_3
+      value: 27.072000000000003
+    - type: map_at_5
+      value: 28.162
+    - type: mrr_at_1
+      value: 25.919999999999998
+    - type: mrr_at_10
+      value: 31.513
+    - type: mrr_at_100
+      value: 32.434000000000005
+    - type: mrr_at_1000
+      value: 32.507000000000005
+    - type: mrr_at_3
+      value: 29.576
+    - type: mrr_at_5
+      value: 30.45
+    - type: ndcg_at_1
+      value: 25.919999999999998
+    - type: ndcg_at_10
+      value: 32.958999999999996
+    - type: ndcg_at_100
+      value: 37.937
+    - type: ndcg_at_1000
+      value: 40.455000000000005
+    - type: ndcg_at_3
+      value: 28.969
+    - type: ndcg_at_5
+      value: 30.552
+    - type: precision_at_1
+      value: 25.919999999999998
+    - type: precision_at_10
+      value: 5.106999999999999
+    - type: precision_at_100
+      value: 0.8170000000000001
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 12.117
+    - type: precision_at_5
+      value: 8.373999999999999
+    - type: recall_at_1
+      value: 23.497
+    - type: recall_at_10
+      value: 42.506
+    - type: recall_at_100
+      value: 65.048
+    - type: recall_at_1000
+      value: 83.545
+    - type: recall_at_3
+      value: 31.078
+    - type: recall_at_5
+      value: 35.018
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.267
+    - type: map_at_10
+      value: 22.292
+    - type: map_at_100
+      value: 23.412
+    - type: map_at_1000
+      value: 23.543
+    - type: map_at_3
+      value: 19.993
+    - type: map_at_5
+      value: 21.256
+    - type: mrr_at_1
+      value: 18.445
+    - type: mrr_at_10
+      value: 25.698999999999998
+    - type: mrr_at_100
+      value: 26.682
+    - type: mrr_at_1000
+      value: 26.764
+    - type: mrr_at_3
+      value: 23.446
+    - type: mrr_at_5
+      value: 24.757
+    - type: ndcg_at_1
+      value: 18.445
+    - type: ndcg_at_10
+      value: 26.833000000000002
+    - type: ndcg_at_100
+      value: 32.151999999999994
+    - type: ndcg_at_1000
+      value: 35.235
+    - type: ndcg_at_3
+      value: 22.597
+    - type: ndcg_at_5
+      value: 24.585
+    - type: precision_at_1
+      value: 18.445
+    - type: precision_at_10
+      value: 4.942
+    - type: precision_at_100
+      value: 0.894
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 10.735999999999999
+    - type: precision_at_5
+      value: 7.915
+    - type: recall_at_1
+      value: 15.267
+    - type: recall_at_10
+      value: 37.198
+    - type: recall_at_100
+      value: 60.748999999999995
+    - type: recall_at_1000
+      value: 82.72699999999999
+    - type: recall_at_3
+      value: 25.419000000000004
+    - type: recall_at_5
+      value: 30.416999999999998
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.839000000000002
+    - type: map_at_10
+      value: 31.287
+    - type: map_at_100
+      value: 32.474
+    - type: map_at_1000
+      value: 32.586
+    - type: map_at_3
+      value: 28.735
+    - type: map_at_5
+      value: 30.11
+    - type: mrr_at_1
+      value: 26.959
+    - type: mrr_at_10
+      value: 34.943000000000005
+    - type: mrr_at_100
+      value: 35.957
+    - type: mrr_at_1000
+      value: 36.022
+    - type: mrr_at_3
+      value: 32.572
+    - type: mrr_at_5
+      value: 33.952
+    - type: ndcg_at_1
+      value: 26.959
+    - type: ndcg_at_10
+      value: 36.252
+    - type: ndcg_at_100
+      value: 41.915
+    - type: ndcg_at_1000
+      value: 44.461
+    - type: ndcg_at_3
+      value: 31.532
+    - type: ndcg_at_5
+      value: 33.674
+    - type: precision_at_1
+      value: 26.959
+    - type: precision_at_10
+      value: 6.166
+    - type: precision_at_100
+      value: 1.01
+    - type: precision_at_1000
+      value: 0.134
+    - type: precision_at_3
+      value: 14.302999999999999
+    - type: precision_at_5
+      value: 10.131
+    - type: recall_at_1
+      value: 22.839000000000002
+    - type: recall_at_10
+      value: 47.796
+    - type: recall_at_100
+      value: 72.68
+    - type: recall_at_1000
+      value: 90.556
+    - type: recall_at_3
+      value: 34.955000000000005
+    - type: recall_at_5
+      value: 40.293
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.676000000000002
+    - type: map_at_10
+      value: 30.742000000000004
+    - type: map_at_100
+      value: 32.332
+    - type: map_at_1000
+      value: 32.548
+    - type: map_at_3
+      value: 27.560000000000002
+    - type: map_at_5
+      value: 29.331000000000003
+    - type: mrr_at_1
+      value: 25.099
+    - type: mrr_at_10
+      value: 34.538999999999994
+    - type: mrr_at_100
+      value: 35.629
+    - type: mrr_at_1000
+      value: 35.687000000000005
+    - type: mrr_at_3
+      value: 31.621
+    - type: mrr_at_5
+      value: 33.419
+    - type: ndcg_at_1
+      value: 25.099
+    - type: ndcg_at_10
+      value: 36.741
+    - type: ndcg_at_100
+      value: 42.964
+    - type: ndcg_at_1000
+      value: 45.754
+    - type: ndcg_at_3
+      value: 31.356
+    - type: ndcg_at_5
+      value: 33.934999999999995
+    - type: precision_at_1
+      value: 25.099
+    - type: precision_at_10
+      value: 7.115
+    - type: precision_at_100
+      value: 1.46
+    - type: precision_at_1000
+      value: 0.23800000000000002
+    - type: precision_at_3
+      value: 14.954
+    - type: precision_at_5
+      value: 11.067
+    - type: recall_at_1
+      value: 21.676000000000002
+    - type: recall_at_10
+      value: 49.546
+    - type: recall_at_100
+      value: 76.544
+    - type: recall_at_1000
+      value: 94.39999999999999
+    - type: recall_at_3
+      value: 34.67
+    - type: recall_at_5
+      value: 41.528999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.431
+    - type: map_at_10
+      value: 24.694
+    - type: map_at_100
+      value: 25.884
+    - type: map_at_1000
+      value: 25.996999999999996
+    - type: map_at_3
+      value: 22.203
+    - type: map_at_5
+      value: 23.329
+    - type: mrr_at_1
+      value: 19.039
+    - type: mrr_at_10
+      value: 26.459
+    - type: mrr_at_100
+      value: 27.560000000000002
+    - type: mrr_at_1000
+      value: 27.636
+    - type: mrr_at_3
+      value: 24.03
+    - type: mrr_at_5
+      value: 25.213
+    - type: ndcg_at_1
+      value: 19.039
+    - type: ndcg_at_10
+      value: 29.220000000000002
+    - type: ndcg_at_100
+      value: 34.854
+    - type: ndcg_at_1000
+      value: 37.580999999999996
+    - type: ndcg_at_3
+      value: 24.218999999999998
+    - type: ndcg_at_5
+      value: 26.125
+    - type: precision_at_1
+      value: 19.039
+    - type: precision_at_10
+      value: 4.861
+    - type: precision_at_100
+      value: 0.826
+    - type: precision_at_1000
+      value: 0.116
+    - type: precision_at_3
+      value: 10.290000000000001
+    - type: precision_at_5
+      value: 7.394
+    - type: recall_at_1
+      value: 17.431
+    - type: recall_at_10
+      value: 41.525
+    - type: recall_at_100
+      value: 67.121
+    - type: recall_at_1000
+      value: 87.575
+    - type: recall_at_3
+      value: 27.794
+    - type: recall_at_5
+      value: 32.332
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 10.767
+    - type: map_at_10
+      value: 17.456
+    - type: map_at_100
+      value: 19.097
+    - type: map_at_1000
+      value: 19.272
+    - type: map_at_3
+      value: 14.530000000000001
+    - type: map_at_5
+      value: 15.943999999999999
+    - type: mrr_at_1
+      value: 23.583000000000002
+    - type: mrr_at_10
+      value: 33.391
+    - type: mrr_at_100
+      value: 34.43
+    - type: mrr_at_1000
+      value: 34.479
+    - type: mrr_at_3
+      value: 30.239
+    - type: mrr_at_5
+      value: 31.923000000000002
+    - type: ndcg_at_1
+      value: 23.583000000000002
+    - type: ndcg_at_10
+      value: 24.84
+    - type: ndcg_at_100
+      value: 31.749
+    - type: ndcg_at_1000
+      value: 35.161
+    - type: ndcg_at_3
+      value: 19.906
+    - type: ndcg_at_5
+      value: 21.543
+    - type: precision_at_1
+      value: 23.583000000000002
+    - type: precision_at_10
+      value: 7.739
+    - type: precision_at_100
+      value: 1.5110000000000001
+    - type: precision_at_1000
+      value: 0.215
+    - type: precision_at_3
+      value: 14.506
+    - type: precision_at_5
+      value: 11.179
+    - type: recall_at_1
+      value: 10.767
+    - type: recall_at_10
+      value: 30.270000000000003
+    - type: recall_at_100
+      value: 54.467
+    - type: recall_at_1000
+      value: 73.71799999999999
+    - type: recall_at_3
+      value: 18.251
+    - type: recall_at_5
+      value: 22.831000000000003
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.493
+    - type: map_at_10
+      value: 15.290999999999999
+    - type: map_at_100
+      value: 21.523999999999997
+    - type: map_at_1000
+      value: 22.980999999999998
+    - type: map_at_3
+      value: 11.015
+    - type: map_at_5
+      value: 12.631
+    - type: mrr_at_1
+      value: 55.50000000000001
+    - type: mrr_at_10
+      value: 65.068
+    - type: mrr_at_100
+      value: 65.608
+    - type: mrr_at_1000
+      value: 65.622
+    - type: mrr_at_3
+      value: 62.625
+    - type: mrr_at_5
+      value: 64.2
+    - type: ndcg_at_1
+      value: 44.875
+    - type: ndcg_at_10
+      value: 35.046
+    - type: ndcg_at_100
+      value: 38.662
+    - type: ndcg_at_1000
+      value: 45.916000000000004
+    - type: ndcg_at_3
+      value: 38.888
+    - type: ndcg_at_5
+      value: 36.411
+    - type: precision_at_1
+      value: 55.50000000000001
+    - type: precision_at_10
+      value: 28.175
+    - type: precision_at_100
+      value: 8.938
+    - type: precision_at_1000
+      value: 1.894
+    - type: precision_at_3
+      value: 41.917
+    - type: precision_at_5
+      value: 34.949999999999996
+    - type: recall_at_1
+      value: 6.493
+    - type: recall_at_10
+      value: 20.992
+    - type: recall_at_100
+      value: 44.138
+    - type: recall_at_1000
+      value: 67.181
+    - type: recall_at_3
+      value: 12.546
+    - type: recall_at_5
+      value: 15.552
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 45.955
+    - type: f1
+      value: 40.97084067876041
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 43.765
+    - type: map_at_10
+      value: 56.566
+    - type: map_at_100
+      value: 57.154
+    - type: map_at_1000
+      value: 57.181000000000004
+    - type: map_at_3
+      value: 53.637
+    - type: map_at_5
+      value: 55.457
+    - type: mrr_at_1
+      value: 47.03
+    - type: mrr_at_10
+      value: 59.938
+    - type: mrr_at_100
+      value: 60.44500000000001
+    - type: mrr_at_1000
+      value: 60.458999999999996
+    - type: mrr_at_3
+      value: 57.141
+    - type: mrr_at_5
+      value: 58.862
+    - type: ndcg_at_1
+      value: 47.03
+    - type: ndcg_at_10
+      value: 63.227
+    - type: ndcg_at_100
+      value: 65.846
+    - type: ndcg_at_1000
+      value: 66.412
+    - type: ndcg_at_3
+      value: 57.546
+    - type: ndcg_at_5
+      value: 60.638000000000005
+    - type: precision_at_1
+      value: 47.03
+    - type: precision_at_10
+      value: 8.831
+    - type: precision_at_100
+      value: 1.027
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 23.642
+    - type: precision_at_5
+      value: 15.884
+    - type: recall_at_1
+      value: 43.765
+    - type: recall_at_10
+      value: 80.537
+    - type: recall_at_100
+      value: 92.06400000000001
+    - type: recall_at_1000
+      value: 96.054
+    - type: recall_at_3
+      value: 65.27199999999999
+    - type: recall_at_5
+      value: 72.71
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.684
+    - type: map_at_10
+      value: 33.393
+    - type: map_at_100
+      value: 35.370000000000005
+    - type: map_at_1000
+      value: 35.539
+    - type: map_at_3
+      value: 28.810000000000002
+    - type: map_at_5
+      value: 31.484
+    - type: mrr_at_1
+      value: 41.049
+    - type: mrr_at_10
+      value: 49.736999999999995
+    - type: mrr_at_100
+      value: 50.541000000000004
+    - type: mrr_at_1000
+      value: 50.575
+    - type: mrr_at_3
+      value: 47.094
+    - type: mrr_at_5
+      value: 48.768
+    - type: ndcg_at_1
+      value: 41.049
+    - type: ndcg_at_10
+      value: 41.338
+    - type: ndcg_at_100
+      value: 48.386
+    - type: ndcg_at_1000
+      value: 51.209
+    - type: ndcg_at_3
+      value: 37.208000000000006
+    - type: ndcg_at_5
+      value: 38.788
+    - type: precision_at_1
+      value: 41.049
+    - type: precision_at_10
+      value: 11.466
+    - type: precision_at_100
+      value: 1.8769999999999998
+    - type: precision_at_1000
+      value: 0.23800000000000002
+    - type: precision_at_3
+      value: 24.691
+    - type: precision_at_5
+      value: 18.519
+    - type: recall_at_1
+      value: 20.684
+    - type: recall_at_10
+      value: 48.431000000000004
+    - type: recall_at_100
+      value: 74.331
+    - type: recall_at_1000
+      value: 91.268
+    - type: recall_at_3
+      value: 33.267
+    - type: recall_at_5
+      value: 40.313
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.242
+    - type: map_at_10
+      value: 47.49
+    - type: map_at_100
+      value: 48.409
+    - type: map_at_1000
+      value: 48.489
+    - type: map_at_3
+      value: 44.519
+    - type: map_at_5
+      value: 46.298
+    - type: mrr_at_1
+      value: 64.483
+    - type: mrr_at_10
+      value: 71.364
+    - type: mrr_at_100
+      value: 71.734
+    - type: mrr_at_1000
+      value: 71.751
+    - type: mrr_at_3
+      value: 69.899
+    - type: mrr_at_5
+      value: 70.791
+    - type: ndcg_at_1
+      value: 64.483
+    - type: ndcg_at_10
+      value: 56.274
+    - type: ndcg_at_100
+      value: 59.855999999999995
+    - type: ndcg_at_1000
+      value: 61.538000000000004
+    - type: ndcg_at_3
+      value: 51.636
+    - type: ndcg_at_5
+      value: 54.089
+    - type: precision_at_1
+      value: 64.483
+    - type: precision_at_10
+      value: 11.858
+    - type: precision_at_100
+      value: 1.47
+    - type: precision_at_1000
+      value: 0.169
+    - type: precision_at_3
+      value: 32.635999999999996
+    - type: precision_at_5
+      value: 21.521
+    - type: recall_at_1
+      value: 32.242
+    - type: recall_at_10
+      value: 59.291000000000004
+    - type: recall_at_100
+      value: 73.518
+    - type: recall_at_1000
+      value: 84.747
+    - type: recall_at_3
+      value: 48.953
+    - type: recall_at_5
+      value: 53.801
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 80.9492
+    - type: ap
+      value: 75.30846930618502
+    - type: f1
+      value: 80.89150705991759
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.033
+    - type: map_at_10
+      value: 34.331
+    - type: map_at_100
+      value: 35.536
+    - type: map_at_1000
+      value: 35.583
+    - type: map_at_3
+      value: 30.562
+    - type: map_at_5
+      value: 32.667
+    - type: mrr_at_1
+      value: 22.708000000000002
+    - type: mrr_at_10
+      value: 34.967999999999996
+    - type: mrr_at_100
+      value: 36.105
+    - type: mrr_at_1000
+      value: 36.147
+    - type: mrr_at_3
+      value: 31.256
+    - type: mrr_at_5
+      value: 33.322
+    - type: ndcg_at_1
+      value: 22.708000000000002
+    - type: ndcg_at_10
+      value: 41.211999999999996
+    - type: ndcg_at_100
+      value: 46.952
+    - type: ndcg_at_1000
+      value: 48.131
+    - type: ndcg_at_3
+      value: 33.501
+    - type: ndcg_at_5
+      value: 37.248999999999995
+    - type: precision_at_1
+      value: 22.708000000000002
+    - type: precision_at_10
+      value: 6.519
+    - type: precision_at_100
+      value: 0.9390000000000001
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.302999999999999
+    - type: precision_at_5
+      value: 10.481
+    - type: recall_at_1
+      value: 22.033
+    - type: recall_at_10
+      value: 62.348000000000006
+    - type: recall_at_100
+      value: 88.771
+    - type: recall_at_1000
+      value: 97.782
+    - type: recall_at_3
+      value: 41.331
+    - type: recall_at_5
+      value: 50.32600000000001
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 92.69037847697219
+    - type: f1
+      value: 92.20814766144707
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 61.12859097127223
+    - type: f1
+      value: 44.859837744275346
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 67.59246805648958
+    - type: f1
+      value: 65.35653843975764
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 72.82447881640888
+    - type: f1
+      value: 71.74294810351809
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 32.623627054114884
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 28.715250618201516
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 31.268319417897434
+    - type: mrr
+      value: 32.363138927039806
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.702
+    - type: map_at_10
+      value: 11.838999999999999
+    - type: map_at_100
+      value: 14.879999999999999
+    - type: map_at_1000
+      value: 16.277
+    - type: map_at_3
+      value: 8.912
+    - type: map_at_5
+      value: 10.213999999999999
+    - type: mrr_at_1
+      value: 44.891999999999996
+    - type: mrr_at_10
+      value: 53.15800000000001
+    - type: mrr_at_100
+      value: 53.830999999999996
+    - type: mrr_at_1000
+      value: 53.882
+    - type: mrr_at_3
+      value: 51.135
+    - type: mrr_at_5
+      value: 52.234
+    - type: ndcg_at_1
+      value: 43.808
+    - type: ndcg_at_10
+      value: 32.179
+    - type: ndcg_at_100
+      value: 29.842000000000002
+    - type: ndcg_at_1000
+      value: 38.858
+    - type: ndcg_at_3
+      value: 38.015
+    - type: ndcg_at_5
+      value: 35.574
+    - type: precision_at_1
+      value: 44.891999999999996
+    - type: precision_at_10
+      value: 23.375
+    - type: precision_at_100
+      value: 7.545
+    - type: precision_at_1000
+      value: 2.052
+    - type: precision_at_3
+      value: 35.088
+    - type: precision_at_5
+      value: 30.154999999999998
+    - type: recall_at_1
+      value: 5.702
+    - type: recall_at_10
+      value: 15.421000000000001
+    - type: recall_at_100
+      value: 30.708999999999996
+    - type: recall_at_1000
+      value: 62.487
+    - type: recall_at_3
+      value: 9.966999999999999
+    - type: recall_at_5
+      value: 12.059000000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 39.117000000000004
+    - type: map_at_10
+      value: 54.041
+    - type: map_at_100
+      value: 54.845
+    - type: map_at_1000
+      value: 54.876999999999995
+    - type: map_at_3
+      value: 50.339999999999996
+    - type: map_at_5
+      value: 52.678999999999995
+    - type: mrr_at_1
+      value: 43.627
+    - type: mrr_at_10
+      value: 56.752
+    - type: mrr_at_100
+      value: 57.32899999999999
+    - type: mrr_at_1000
+      value: 57.35
+    - type: mrr_at_3
+      value: 53.818999999999996
+    - type: mrr_at_5
+      value: 55.684999999999995
+    - type: ndcg_at_1
+      value: 43.627
+    - type: ndcg_at_10
+      value: 60.934
+    - type: ndcg_at_100
+      value: 64.277
+    - type: ndcg_at_1000
+      value: 64.97
+    - type: ndcg_at_3
+      value: 54.164
+    - type: ndcg_at_5
+      value: 57.994
+    - type: precision_at_1
+      value: 43.627
+    - type: precision_at_10
+      value: 9.383
+    - type: precision_at_100
+      value: 1.131
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 23.919
+    - type: precision_at_5
+      value: 16.541
+    - type: recall_at_1
+      value: 39.117000000000004
+    - type: recall_at_10
+      value: 79.012
+    - type: recall_at_100
+      value: 93.395
+    - type: recall_at_1000
+      value: 98.494
+    - type: recall_at_3
+      value: 61.714999999999996
+    - type: recall_at_5
+      value: 70.55799999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.832
+    - type: map_at_10
+      value: 84.82300000000001
+    - type: map_at_100
+      value: 85.44500000000001
+    - type: map_at_1000
+      value: 85.461
+    - type: map_at_3
+      value: 81.917
+    - type: map_at_5
+      value: 83.734
+    - type: mrr_at_1
+      value: 81.61
+    - type: mrr_at_10
+      value: 87.75500000000001
+    - type: mrr_at_100
+      value: 87.85300000000001
+    - type: mrr_at_1000
+      value: 87.854
+    - type: mrr_at_3
+      value: 86.855
+    - type: mrr_at_5
+      value: 87.465
+    - type: ndcg_at_1
+      value: 81.58999999999999
+    - type: ndcg_at_10
+      value: 88.536
+    - type: ndcg_at_100
+      value: 89.714
+    - type: ndcg_at_1000
+      value: 89.80799999999999
+    - type: ndcg_at_3
+      value: 85.8
+    - type: ndcg_at_5
+      value: 87.286
+    - type: precision_at_1
+      value: 81.58999999999999
+    - type: precision_at_10
+      value: 13.438
+    - type: precision_at_100
+      value: 1.5310000000000001
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.563
+    - type: precision_at_5
+      value: 24.65
+    - type: recall_at_1
+      value: 70.832
+    - type: recall_at_10
+      value: 95.574
+    - type: recall_at_100
+      value: 99.575
+    - type: recall_at_1000
+      value: 99.99
+    - type: recall_at_3
+      value: 87.61
+    - type: recall_at_5
+      value: 91.9
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 54.4131741738767
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 59.816632341901865
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.857
+    - type: map_at_10
+      value: 11.937000000000001
+    - type: map_at_100
+      value: 14.143
+    - type: map_at_1000
+      value: 14.451
+    - type: map_at_3
+      value: 8.376999999999999
+    - type: map_at_5
+      value: 10.172
+    - type: mrr_at_1
+      value: 23.799999999999997
+    - type: mrr_at_10
+      value: 34.134
+    - type: mrr_at_100
+      value: 35.285
+    - type: mrr_at_1000
+      value: 35.33
+    - type: mrr_at_3
+      value: 30.833
+    - type: mrr_at_5
+      value: 32.828
+    - type: ndcg_at_1
+      value: 23.799999999999997
+    - type: ndcg_at_10
+      value: 20.0
+    - type: ndcg_at_100
+      value: 28.486
+    - type: ndcg_at_1000
+      value: 33.781
+    - type: ndcg_at_3
+      value: 18.726000000000003
+    - type: ndcg_at_5
+      value: 16.587
+    - type: precision_at_1
+      value: 23.799999999999997
+    - type: precision_at_10
+      value: 10.39
+    - type: precision_at_100
+      value: 2.263
+    - type: precision_at_1000
+      value: 0.35300000000000004
+    - type: precision_at_3
+      value: 17.333000000000002
+    - type: precision_at_5
+      value: 14.56
+    - type: recall_at_1
+      value: 4.857
+    - type: recall_at_10
+      value: 21.02
+    - type: recall_at_100
+      value: 45.932
+    - type: recall_at_1000
+      value: 71.693
+    - type: recall_at_3
+      value: 10.552
+    - type: recall_at_5
+      value: 14.760000000000002
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.00513539036214
+    - type: cos_sim_spearman
+      value: 79.19581558052613
+    - type: euclidean_pearson
+      value: 82.46689229301268
+    - type: euclidean_spearman
+      value: 79.19581263972574
+    - type: manhattan_pearson
+      value: 82.46839559537645
+    - type: manhattan_spearman
+      value: 79.19301791744469
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.44111721768361
+    - type: cos_sim_spearman
+      value: 73.14524004507561
+    - type: euclidean_pearson
+      value: 78.70346379990235
+    - type: euclidean_spearman
+      value: 73.14518679640568
+    - type: manhattan_pearson
+      value: 78.68478215009414
+    - type: manhattan_spearman
+      value: 73.10912398034866
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.17030364533524
+    - type: cos_sim_spearman
+      value: 82.88382996129783
+    - type: euclidean_pearson
+      value: 82.25266887145027
+    - type: euclidean_spearman
+      value: 82.88382996129783
+    - type: manhattan_pearson
+      value: 82.21831434263969
+    - type: manhattan_spearman
+      value: 82.83144970048046
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.73413303490618
+    - type: cos_sim_spearman
+      value: 76.95203008005365
+    - type: euclidean_pearson
+      value: 79.09169854088067
+    - type: euclidean_spearman
+      value: 76.95202489005659
+    - type: manhattan_pearson
+      value: 79.04289364751341
+    - type: manhattan_spearman
+      value: 76.89976809512328
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.84421416279349
+    - type: cos_sim_spearman
+      value: 87.67393507190887
+    - type: euclidean_pearson
+      value: 86.81662915280972
+    - type: euclidean_spearman
+      value: 87.67395576051472
+    - type: manhattan_pearson
+      value: 86.76502179645067
+    - type: manhattan_spearman
+      value: 87.60931601838358
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.47603001840406
+    - type: cos_sim_spearman
+      value: 84.57363689562743
+    - type: euclidean_pearson
+      value: 83.62746191773213
+    - type: euclidean_spearman
+      value: 84.57363689562743
+    - type: manhattan_pearson
+      value: 83.5049257196953
+    - type: manhattan_spearman
+      value: 84.43576972291818
+  - 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: 89.17222804445805
+    - type: cos_sim_spearman
+      value: 89.04642204765032
+    - type: euclidean_pearson
+      value: 88.93412366747594
+    - type: euclidean_spearman
+      value: 89.04642204765032
+    - type: manhattan_pearson
+      value: 88.88891722217033
+    - type: manhattan_spearman
+      value: 88.95405155642727
+  - 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: 63.4232873899918
+    - type: cos_sim_spearman
+      value: 62.53261852485254
+    - type: euclidean_pearson
+      value: 63.95808586267597
+    - type: euclidean_spearman
+      value: 62.53261852485254
+    - type: manhattan_pearson
+      value: 64.07446205165546
+    - type: manhattan_spearman
+      value: 62.86514483815617
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.324835033109
+    - type: cos_sim_spearman
+      value: 84.75551248417419
+    - type: euclidean_pearson
+      value: 84.98725144123726
+    - type: euclidean_spearman
+      value: 84.75551248417419
+    - type: manhattan_pearson
+      value: 84.9546533100131
+    - type: manhattan_spearman
+      value: 84.73671830914728
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 83.62940531539546
+    - type: mrr
+      value: 95.50283503714876
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 52.428
+    - type: map_at_10
+      value: 62.731
+    - type: map_at_100
+      value: 63.327
+    - type: map_at_1000
+      value: 63.356
+    - type: map_at_3
+      value: 60.17400000000001
+    - type: map_at_5
+      value: 61.461
+    - type: mrr_at_1
+      value: 55.333
+    - type: mrr_at_10
+      value: 63.788999999999994
+    - type: mrr_at_100
+      value: 64.27000000000001
+    - type: mrr_at_1000
+      value: 64.298
+    - type: mrr_at_3
+      value: 61.944
+    - type: mrr_at_5
+      value: 62.861
+    - type: ndcg_at_1
+      value: 55.333
+    - type: ndcg_at_10
+      value: 67.309
+    - type: ndcg_at_100
+      value: 70.033
+    - type: ndcg_at_1000
+      value: 70.842
+    - type: ndcg_at_3
+      value: 63.05500000000001
+    - type: ndcg_at_5
+      value: 64.8
+    - type: precision_at_1
+      value: 55.333
+    - type: precision_at_10
+      value: 9.1
+    - type: precision_at_100
+      value: 1.057
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 25.111
+    - type: precision_at_5
+      value: 16.333000000000002
+    - type: recall_at_1
+      value: 52.428
+    - type: recall_at_10
+      value: 80.156
+    - type: recall_at_100
+      value: 92.833
+    - type: recall_at_1000
+      value: 99.333
+    - type: recall_at_3
+      value: 68.73899999999999
+    - type: recall_at_5
+      value: 73.13300000000001
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.8069306930693
+    - type: cos_sim_ap
+      value: 94.89496931806809
+    - type: cos_sim_f1
+      value: 90.0763358778626
+    - type: cos_sim_precision
+      value: 91.70984455958549
+    - type: cos_sim_recall
+      value: 88.5
+    - type: dot_accuracy
+      value: 99.8069306930693
+    - type: dot_ap
+      value: 94.89495820622456
+    - type: dot_f1
+      value: 90.0763358778626
+    - type: dot_precision
+      value: 91.70984455958549
+    - type: dot_recall
+      value: 88.5
+    - type: euclidean_accuracy
+      value: 99.8069306930693
+    - type: euclidean_ap
+      value: 94.8949693180681
+    - type: euclidean_f1
+      value: 90.0763358778626
+    - type: euclidean_precision
+      value: 91.70984455958549
+    - type: euclidean_recall
+      value: 88.5
+    - type: manhattan_accuracy
+      value: 99.8009900990099
+    - type: manhattan_ap
+      value: 94.81699021810266
+    - type: manhattan_f1
+      value: 89.82278481012658
+    - type: manhattan_precision
+      value: 90.97435897435898
+    - type: manhattan_recall
+      value: 88.7
+    - type: max_accuracy
+      value: 99.8069306930693
+    - type: max_ap
+      value: 94.8949693180681
+    - type: max_f1
+      value: 90.0763358778626
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 58.95255708336027
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 34.26328409998647
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 52.324949351182134
+    - type: mrr
+      value: 53.08798329938036
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.286127875761963
+    - type: cos_sim_spearman
+      value: 30.85723241148158
+    - type: dot_pearson
+      value: 30.28613033184199
+    - type: dot_spearman
+      value: 30.85723241148158
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.199
+    - type: map_at_10
+      value: 1.633
+    - type: map_at_100
+      value: 8.813
+    - type: map_at_1000
+      value: 21.015
+    - type: map_at_3
+      value: 0.577
+    - type: map_at_5
+      value: 0.907
+    - type: mrr_at_1
+      value: 72.0
+    - type: mrr_at_10
+      value: 82.667
+    - type: mrr_at_100
+      value: 82.667
+    - type: mrr_at_1000
+      value: 82.667
+    - type: mrr_at_3
+      value: 80.667
+    - type: mrr_at_5
+      value: 82.667
+    - type: ndcg_at_1
+      value: 67.0
+    - type: ndcg_at_10
+      value: 65.377
+    - type: ndcg_at_100
+      value: 50.693
+    - type: ndcg_at_1000
+      value: 45.449
+    - type: ndcg_at_3
+      value: 67.78800000000001
+    - type: ndcg_at_5
+      value: 67.19000000000001
+    - type: precision_at_1
+      value: 72.0
+    - type: precision_at_10
+      value: 70.6
+    - type: precision_at_100
+      value: 52.0
+    - type: precision_at_1000
+      value: 20.316000000000003
+    - type: precision_at_3
+      value: 72.667
+    - type: precision_at_5
+      value: 72.39999999999999
+    - type: recall_at_1
+      value: 0.199
+    - type: recall_at_10
+      value: 1.8800000000000001
+    - type: recall_at_100
+      value: 12.195
+    - type: recall_at_1000
+      value: 42.612
+    - type: recall_at_3
+      value: 0.608
+    - type: recall_at_5
+      value: 1.004
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 2.34
+    - type: map_at_10
+      value: 7.983
+    - type: map_at_100
+      value: 14.488999999999999
+    - type: map_at_1000
+      value: 16.133
+    - type: map_at_3
+      value: 4.312
+    - type: map_at_5
+      value: 6.3420000000000005
+    - type: mrr_at_1
+      value: 26.531
+    - type: mrr_at_10
+      value: 41.558
+    - type: mrr_at_100
+      value: 42.211999999999996
+    - type: mrr_at_1000
+      value: 42.211999999999996
+    - type: mrr_at_3
+      value: 36.054
+    - type: mrr_at_5
+      value: 39.217999999999996
+    - type: ndcg_at_1
+      value: 23.469
+    - type: ndcg_at_10
+      value: 21.077
+    - type: ndcg_at_100
+      value: 35.497
+    - type: ndcg_at_1000
+      value: 47.282000000000004
+    - type: ndcg_at_3
+      value: 20.906
+    - type: ndcg_at_5
+      value: 21.78
+    - type: precision_at_1
+      value: 26.531
+    - type: precision_at_10
+      value: 18.570999999999998
+    - type: precision_at_100
+      value: 7.673000000000001
+    - type: precision_at_1000
+      value: 1.551
+    - type: precision_at_3
+      value: 21.769
+    - type: precision_at_5
+      value: 22.448999999999998
+    - type: recall_at_1
+      value: 2.34
+    - type: recall_at_10
+      value: 14.154
+    - type: recall_at_100
+      value: 48.355
+    - type: recall_at_1000
+      value: 84.872
+    - type: recall_at_3
+      value: 5.19
+    - type: recall_at_5
+      value: 9.211
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 71.9318
+    - type: ap
+      value: 14.755439516631267
+    - type: f1
+      value: 55.39101096477449
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 61.06395019807584
+    - type: f1
+      value: 61.18513886850968
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 43.68814723462553
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.8258329856351
+    - type: cos_sim_ap
+      value: 73.51953909054856
+    - type: cos_sim_f1
+      value: 68.17958783120707
+    - type: cos_sim_precision
+      value: 63.70930765703806
+    - type: cos_sim_recall
+      value: 73.3245382585752
+    - type: dot_accuracy
+      value: 85.8258329856351
+    - type: dot_ap
+      value: 73.51954936569123
+    - type: dot_f1
+      value: 68.17958783120707
+    - type: dot_precision
+      value: 63.70930765703806
+    - type: dot_recall
+      value: 73.3245382585752
+    - type: euclidean_accuracy
+      value: 85.8258329856351
+    - type: euclidean_ap
+      value: 73.51954390509214
+    - type: euclidean_f1
+      value: 68.17958783120707
+    - type: euclidean_precision
+      value: 63.70930765703806
+    - type: euclidean_recall
+      value: 73.3245382585752
+    - type: manhattan_accuracy
+      value: 85.8258329856351
+    - type: manhattan_ap
+      value: 73.44954175022839
+    - type: manhattan_f1
+      value: 68.08816482989938
+    - type: manhattan_precision
+      value: 62.351908731899954
+    - type: manhattan_recall
+      value: 74.9868073878628
+    - type: max_accuracy
+      value: 85.8258329856351
+    - type: max_ap
+      value: 73.51954936569123
+    - type: max_f1
+      value: 68.17958783120707
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.6094617145962
+    - type: cos_sim_ap
+      value: 85.4121913477208
+    - type: cos_sim_f1
+      value: 77.61548157484985
+    - type: cos_sim_precision
+      value: 74.84627484627485
+    - type: cos_sim_recall
+      value: 80.59747459193102
+    - type: dot_accuracy
+      value: 88.6094617145962
+    - type: dot_ap
+      value: 85.41219830675979
+    - type: dot_f1
+      value: 77.61548157484985
+    - type: dot_precision
+      value: 74.84627484627485
+    - type: dot_recall
+      value: 80.59747459193102
+    - type: euclidean_accuracy
+      value: 88.6094617145962
+    - type: euclidean_ap
+      value: 85.41219328124808
+    - type: euclidean_f1
+      value: 77.61548157484985
+    - type: euclidean_precision
+      value: 74.84627484627485
+    - type: euclidean_recall
+      value: 80.59747459193102
+    - type: manhattan_accuracy
+      value: 88.53960492102301
+    - type: manhattan_ap
+      value: 85.35022078482446
+    - type: manhattan_f1
+      value: 77.56588974387569
+    - type: manhattan_precision
+      value: 74.98742183569324
+    - type: manhattan_recall
+      value: 80.3279950723745
+    - type: max_accuracy
+      value: 88.6094617145962
+    - type: max_ap
+      value: 85.41219830675979
+    - type: max_f1
+      value: 77.61548157484985
+---
+<!-- TODO: add evaluation results here -->
+<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>
+## Intended Usage & Model Info
+`jina-embedding-b-en-v2` is an English, monolingual embedding model supporting 8k sequence length.
+It is based on a Bert architecture that supports the symmetric bidirectional variant of ALiBi to support longer sequence length.
+The backbone Jina Bert Small model is pretrained on the C4 dataset.
+The model is further trained on Jina AI's collection of more than 40 datasets of sentence pairs and hard negatives.
+These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
+The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length thanks to ALiBi.
+This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search,...
+This model has 33 million parameters, which enables lightning-fast and memory efficient inference on long documents, while still delivering impressive performance.
+Additionally, we provide the following embedding models, supporting 8k sequence length as well:
+- [`jina-embedding-s-en-v2`](https://huggingface.co/jinaai/jina-embedding-s-en-v2): 33 million parameters.
+- [`jina-embedding-b-en-v2`](https://huggingface.co/jinaai/jina-embedding-b-en-v2): 137 million parameters **(you are here)**.
+- [`jina-embedding-l-en-v2`](https://huggingface.co/jinaai/jina-embedding-l-en-v2): 435 million parameters.
+## Data & Parameters
+<!-- TODO: update the paper ID once it is published on arxiv -->
+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:
+<!-- TODO: add evaluation table here -->
+## Usage
+You can use Jina Embedding models directly from transformers package:
+```python
+!pip install transformers
+from transformers import AutoModel
+from numpy.linalg import norm
+cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
+model = AutoModel.from_pretrained('jinaai/jina-embedding-b-en-v2', trust_remote_code=True) # trust_remote_code is needed to use the encode method
+embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+For long sequences, it's recommended to perform inference using Flash Attention. Using Flash Attention allows you to increase the batch size and throughput for long sequence length.
+We include an experimental implementation for Flash Attention, shipped with the model.
+Install the following triton version:
+`pip install triton==2.0.0.dev20221202`.
+Now run the same code above, but make sure to set the parameter `with_flash` to `True` when you load the model. You also have to use either `fp16` or `bf16`:
+```python
+from transformers import AutoModel
+from numpy.linalg import norm
+import torch
+cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
+model = AutoModel.from_pretrained('jinaai/jina-embedding-b-en-v2', trust_remote_code=True, with_flash=True, torch_dtype=torch.float16).cuda() # trust_remote_code is needed to use the encode method
+embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+## Fine-tuning
+Please consider [Finetuner](https://github.com/jina-ai/finetuner).
+## Plans
+The development of new multilingual models is currently underway. We will be targeting mainly the German and Spanish languages. The upcoming models will be called `jina-embedding-s/b/l-de/es-v2`.
+## 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:
+<!-- TODO: update the paper ID once it is published on arxiv -->
+``` latex
+@misc{günther2023jina,
+      title={Beyond the 512-Token Barrier: Training General-Purpose Text
+Embeddings for Large Documents},
+      author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang},
+      year={2023},
+      eprint={2307.11224},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```