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README.md

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+---
+tags:
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+  - mteb
+datasets:
+  - allenai/c4
+language: en
+inference: false
+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: 74.73134328358209
+    - type: ap
+      value: 37.765427081831035
+    - type: f1
+      value: 68.79367444339518
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 88.544275
+    - type: ap
+      value: 84.61328675662887
+    - type: f1
+      value: 88.51879035862375
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 45.263999999999996
+    - type: f1
+      value: 43.778759656699435
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.693
+    - type: map_at_10
+      value: 35.487
+    - type: map_at_100
+      value: 36.862
+    - type: map_at_1000
+      value: 36.872
+    - type: map_at_3
+      value: 30.049999999999997
+    - type: map_at_5
+      value: 32.966
+    - type: mrr_at_1
+      value: 21.977
+    - type: mrr_at_10
+      value: 35.565999999999995
+    - type: mrr_at_100
+      value: 36.948
+    - type: mrr_at_1000
+      value: 36.958
+    - type: mrr_at_3
+      value: 30.121
+    - type: mrr_at_5
+      value: 33.051
+    - type: ndcg_at_1
+      value: 21.693
+    - type: ndcg_at_10
+      value: 44.181
+    - type: ndcg_at_100
+      value: 49.982
+    - type: ndcg_at_1000
+      value: 50.233000000000004
+    - type: ndcg_at_3
+      value: 32.830999999999996
+    - type: ndcg_at_5
+      value: 38.080000000000005
+    - type: precision_at_1
+      value: 21.693
+    - type: precision_at_10
+      value: 7.248
+    - type: precision_at_100
+      value: 0.9769999999999999
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 13.632
+    - type: precision_at_5
+      value: 10.725
+    - type: recall_at_1
+      value: 21.693
+    - type: recall_at_10
+      value: 72.475
+    - type: recall_at_100
+      value: 97.653
+    - type: recall_at_1000
+      value: 99.57300000000001
+    - type: recall_at_3
+      value: 40.896
+    - type: recall_at_5
+      value: 53.627
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 45.39242428696777
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 36.675626784714
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 62.247725694904034
+    - type: mrr
+      value: 74.91359978894604
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.68003802970496
+    - type: cos_sim_spearman
+      value: 81.23438110096286
+    - type: euclidean_pearson
+      value: 81.87462986142582
+    - type: euclidean_spearman
+      value: 81.23438110096286
+    - type: manhattan_pearson
+      value: 81.61162566600755
+    - type: manhattan_spearman
+      value: 81.11329400456184
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 84.01298701298701
+    - type: f1
+      value: 83.31690714969382
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 37.050108150972086
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 30.15731442819715
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.391999999999996
+    - type: map_at_10
+      value: 42.597
+    - type: map_at_100
+      value: 44.07
+    - type: map_at_1000
+      value: 44.198
+    - type: map_at_3
+      value: 38.957
+    - type: map_at_5
+      value: 40.961
+    - type: mrr_at_1
+      value: 37.196
+    - type: mrr_at_10
+      value: 48.152
+    - type: mrr_at_100
+      value: 48.928
+    - type: mrr_at_1000
+      value: 48.964999999999996
+    - type: mrr_at_3
+      value: 45.446
+    - type: mrr_at_5
+      value: 47.205999999999996
+    - type: ndcg_at_1
+      value: 37.196
+    - type: ndcg_at_10
+      value: 49.089
+    - type: ndcg_at_100
+      value: 54.471000000000004
+    - type: ndcg_at_1000
+      value: 56.385
+    - type: ndcg_at_3
+      value: 43.699
+    - type: ndcg_at_5
+      value: 46.22
+    - type: precision_at_1
+      value: 37.196
+    - type: precision_at_10
+      value: 9.313
+    - type: precision_at_100
+      value: 1.478
+    - type: precision_at_1000
+      value: 0.198
+    - type: precision_at_3
+      value: 20.839
+    - type: precision_at_5
+      value: 14.936
+    - type: recall_at_1
+      value: 31.391999999999996
+    - type: recall_at_10
+      value: 61.876
+    - type: recall_at_100
+      value: 84.214
+    - type: recall_at_1000
+      value: 95.985
+    - type: recall_at_3
+      value: 46.6
+    - type: recall_at_5
+      value: 53.588
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.083
+    - type: map_at_10
+      value: 38.812999999999995
+    - type: map_at_100
+      value: 40.053
+    - type: map_at_1000
+      value: 40.188
+    - type: map_at_3
+      value: 36.111
+    - type: map_at_5
+      value: 37.519000000000005
+    - type: mrr_at_1
+      value: 36.497
+    - type: mrr_at_10
+      value: 44.85
+    - type: mrr_at_100
+      value: 45.546
+    - type: mrr_at_1000
+      value: 45.593
+    - type: mrr_at_3
+      value: 42.686
+    - type: mrr_at_5
+      value: 43.909
+    - type: ndcg_at_1
+      value: 36.497
+    - type: ndcg_at_10
+      value: 44.443
+    - type: ndcg_at_100
+      value: 48.979
+    - type: ndcg_at_1000
+      value: 51.154999999999994
+    - type: ndcg_at_3
+      value: 40.660000000000004
+    - type: ndcg_at_5
+      value: 42.193000000000005
+    - type: precision_at_1
+      value: 36.497
+    - type: precision_at_10
+      value: 8.433
+    - type: precision_at_100
+      value: 1.369
+    - type: precision_at_1000
+      value: 0.185
+    - type: precision_at_3
+      value: 19.894000000000002
+    - type: precision_at_5
+      value: 13.873
+    - type: recall_at_1
+      value: 29.083
+    - type: recall_at_10
+      value: 54.313
+    - type: recall_at_100
+      value: 73.792
+    - type: recall_at_1000
+      value: 87.629
+    - type: recall_at_3
+      value: 42.257
+    - type: recall_at_5
+      value: 47.066
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.556000000000004
+    - type: map_at_10
+      value: 50.698
+    - type: map_at_100
+      value: 51.705
+    - type: map_at_1000
+      value: 51.768
+    - type: map_at_3
+      value: 47.848
+    - type: map_at_5
+      value: 49.358000000000004
+    - type: mrr_at_1
+      value: 43.95
+    - type: mrr_at_10
+      value: 54.191
+    - type: mrr_at_100
+      value: 54.852999999999994
+    - type: mrr_at_1000
+      value: 54.885
+    - type: mrr_at_3
+      value: 51.954
+    - type: mrr_at_5
+      value: 53.13
+    - type: ndcg_at_1
+      value: 43.95
+    - type: ndcg_at_10
+      value: 56.516
+    - type: ndcg_at_100
+      value: 60.477000000000004
+    - type: ndcg_at_1000
+      value: 61.746
+    - type: ndcg_at_3
+      value: 51.601
+    - type: ndcg_at_5
+      value: 53.795
+    - type: precision_at_1
+      value: 43.95
+    - type: precision_at_10
+      value: 9.009
+    - type: precision_at_100
+      value: 1.189
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 22.989
+    - type: precision_at_5
+      value: 15.473
+    - type: recall_at_1
+      value: 38.556000000000004
+    - type: recall_at_10
+      value: 70.159
+    - type: recall_at_100
+      value: 87.132
+    - type: recall_at_1000
+      value: 96.16
+    - type: recall_at_3
+      value: 56.906
+    - type: recall_at_5
+      value: 62.332
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.238
+    - type: map_at_10
+      value: 32.5
+    - type: map_at_100
+      value: 33.637
+    - type: map_at_1000
+      value: 33.719
+    - type: map_at_3
+      value: 30.026999999999997
+    - type: map_at_5
+      value: 31.555
+    - type: mrr_at_1
+      value: 26.328000000000003
+    - type: mrr_at_10
+      value: 34.44
+    - type: mrr_at_100
+      value: 35.455999999999996
+    - type: mrr_at_1000
+      value: 35.521
+    - type: mrr_at_3
+      value: 32.034
+    - type: mrr_at_5
+      value: 33.565
+    - type: ndcg_at_1
+      value: 26.328000000000003
+    - type: ndcg_at_10
+      value: 37.202
+    - type: ndcg_at_100
+      value: 42.728
+    - type: ndcg_at_1000
+      value: 44.792
+    - type: ndcg_at_3
+      value: 32.368
+    - type: ndcg_at_5
+      value: 35.008
+    - type: precision_at_1
+      value: 26.328000000000003
+    - type: precision_at_10
+      value: 5.7059999999999995
+    - type: precision_at_100
+      value: 0.8880000000000001
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 13.672
+    - type: precision_at_5
+      value: 9.74
+    - type: recall_at_1
+      value: 24.238
+    - type: recall_at_10
+      value: 49.829
+    - type: recall_at_100
+      value: 75.21
+    - type: recall_at_1000
+      value: 90.521
+    - type: recall_at_3
+      value: 36.867
+    - type: recall_at_5
+      value: 43.241
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.378
+    - type: map_at_10
+      value: 22.817999999999998
+    - type: map_at_100
+      value: 23.977999999999998
+    - type: map_at_1000
+      value: 24.108
+    - type: map_at_3
+      value: 20.719
+    - type: map_at_5
+      value: 21.889
+    - type: mrr_at_1
+      value: 19.03
+    - type: mrr_at_10
+      value: 27.022000000000002
+    - type: mrr_at_100
+      value: 28.011999999999997
+    - type: mrr_at_1000
+      value: 28.096
+    - type: mrr_at_3
+      value: 24.855
+    - type: mrr_at_5
+      value: 26.029999999999998
+    - type: ndcg_at_1
+      value: 19.03
+    - type: ndcg_at_10
+      value: 27.526
+    - type: ndcg_at_100
+      value: 33.040000000000006
+    - type: ndcg_at_1000
+      value: 36.187000000000005
+    - type: ndcg_at_3
+      value: 23.497
+    - type: ndcg_at_5
+      value: 25.334
+    - type: precision_at_1
+      value: 19.03
+    - type: precision_at_10
+      value: 4.963
+    - type: precision_at_100
+      value: 0.893
+    - type: precision_at_1000
+      value: 0.13
+    - type: precision_at_3
+      value: 11.360000000000001
+    - type: precision_at_5
+      value: 8.134
+    - type: recall_at_1
+      value: 15.378
+    - type: recall_at_10
+      value: 38.061
+    - type: recall_at_100
+      value: 61.754
+    - type: recall_at_1000
+      value: 84.259
+    - type: recall_at_3
+      value: 26.788
+    - type: recall_at_5
+      value: 31.326999999999998
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.511999999999997
+    - type: map_at_10
+      value: 37.429
+    - type: map_at_100
+      value: 38.818000000000005
+    - type: map_at_1000
+      value: 38.924
+    - type: map_at_3
+      value: 34.625
+    - type: map_at_5
+      value: 36.064
+    - type: mrr_at_1
+      value: 33.300999999999995
+    - type: mrr_at_10
+      value: 43.036
+    - type: mrr_at_100
+      value: 43.894
+    - type: mrr_at_1000
+      value: 43.936
+    - type: mrr_at_3
+      value: 40.825
+    - type: mrr_at_5
+      value: 42.028
+    - type: ndcg_at_1
+      value: 33.300999999999995
+    - type: ndcg_at_10
+      value: 43.229
+    - type: ndcg_at_100
+      value: 48.992000000000004
+    - type: ndcg_at_1000
+      value: 51.02100000000001
+    - type: ndcg_at_3
+      value: 38.794000000000004
+    - type: ndcg_at_5
+      value: 40.65
+    - type: precision_at_1
+      value: 33.300999999999995
+    - type: precision_at_10
+      value: 7.777000000000001
+    - type: precision_at_100
+      value: 1.269
+    - type: precision_at_1000
+      value: 0.163
+    - type: precision_at_3
+      value: 18.351
+    - type: precision_at_5
+      value: 12.762
+    - type: recall_at_1
+      value: 27.511999999999997
+    - type: recall_at_10
+      value: 54.788000000000004
+    - type: recall_at_100
+      value: 79.105
+    - type: recall_at_1000
+      value: 92.49199999999999
+    - type: recall_at_3
+      value: 41.924
+    - type: recall_at_5
+      value: 47.026
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.117
+    - type: map_at_10
+      value: 33.32
+    - type: map_at_100
+      value: 34.677
+    - type: map_at_1000
+      value: 34.78
+    - type: map_at_3
+      value: 30.233999999999998
+    - type: map_at_5
+      value: 31.668000000000003
+    - type: mrr_at_1
+      value: 29.566
+    - type: mrr_at_10
+      value: 38.244
+    - type: mrr_at_100
+      value: 39.245000000000005
+    - type: mrr_at_1000
+      value: 39.296
+    - type: mrr_at_3
+      value: 35.864000000000004
+    - type: mrr_at_5
+      value: 36.919999999999995
+    - type: ndcg_at_1
+      value: 29.566
+    - type: ndcg_at_10
+      value: 39.127
+    - type: ndcg_at_100
+      value: 44.989000000000004
+    - type: ndcg_at_1000
+      value: 47.189
+    - type: ndcg_at_3
+      value: 34.039
+    - type: ndcg_at_5
+      value: 35.744
+    - type: precision_at_1
+      value: 29.566
+    - type: precision_at_10
+      value: 7.385999999999999
+    - type: precision_at_100
+      value: 1.204
+    - type: precision_at_1000
+      value: 0.158
+    - type: precision_at_3
+      value: 16.286
+    - type: precision_at_5
+      value: 11.484
+    - type: recall_at_1
+      value: 24.117
+    - type: recall_at_10
+      value: 51.559999999999995
+    - type: recall_at_100
+      value: 77.104
+    - type: recall_at_1000
+      value: 91.79899999999999
+    - type: recall_at_3
+      value: 36.82
+    - type: recall_at_5
+      value: 41.453
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.17625
+    - type: map_at_10
+      value: 34.063916666666664
+    - type: map_at_100
+      value: 35.255500000000005
+    - type: map_at_1000
+      value: 35.37275
+    - type: map_at_3
+      value: 31.351666666666667
+    - type: map_at_5
+      value: 32.80608333333333
+    - type: mrr_at_1
+      value: 29.59783333333333
+    - type: mrr_at_10
+      value: 38.0925
+    - type: mrr_at_100
+      value: 38.957249999999995
+    - type: mrr_at_1000
+      value: 39.01608333333333
+    - type: mrr_at_3
+      value: 35.77625
+    - type: mrr_at_5
+      value: 37.04991666666667
+    - type: ndcg_at_1
+      value: 29.59783333333333
+    - type: ndcg_at_10
+      value: 39.343666666666664
+    - type: ndcg_at_100
+      value: 44.488249999999994
+    - type: ndcg_at_1000
+      value: 46.83358333333334
+    - type: ndcg_at_3
+      value: 34.69708333333333
+    - type: ndcg_at_5
+      value: 36.75075
+    - type: precision_at_1
+      value: 29.59783333333333
+    - type: precision_at_10
+      value: 6.884083333333332
+    - type: precision_at_100
+      value: 1.114
+    - type: precision_at_1000
+      value: 0.15108333333333332
+    - type: precision_at_3
+      value: 15.965250000000003
+    - type: precision_at_5
+      value: 11.246500000000001
+    - type: recall_at_1
+      value: 25.17625
+    - type: recall_at_10
+      value: 51.015999999999984
+    - type: recall_at_100
+      value: 73.60174999999998
+    - type: recall_at_1000
+      value: 89.849
+    - type: recall_at_3
+      value: 37.88399999999999
+    - type: recall_at_5
+      value: 43.24541666666666
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.537
+    - type: map_at_10
+      value: 31.081999999999997
+    - type: map_at_100
+      value: 32.042
+    - type: map_at_1000
+      value: 32.141
+    - type: map_at_3
+      value: 29.137
+    - type: map_at_5
+      value: 30.079
+    - type: mrr_at_1
+      value: 27.454
+    - type: mrr_at_10
+      value: 33.694
+    - type: mrr_at_100
+      value: 34.579
+    - type: mrr_at_1000
+      value: 34.649
+    - type: mrr_at_3
+      value: 32.004
+    - type: mrr_at_5
+      value: 32.794000000000004
+    - type: ndcg_at_1
+      value: 27.454
+    - type: ndcg_at_10
+      value: 34.915
+    - type: ndcg_at_100
+      value: 39.641
+    - type: ndcg_at_1000
+      value: 42.105
+    - type: ndcg_at_3
+      value: 31.276
+    - type: ndcg_at_5
+      value: 32.65
+    - type: precision_at_1
+      value: 27.454
+    - type: precision_at_10
+      value: 5.337
+    - type: precision_at_100
+      value: 0.8250000000000001
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 13.241
+    - type: precision_at_5
+      value: 8.895999999999999
+    - type: recall_at_1
+      value: 24.537
+    - type: recall_at_10
+      value: 44.324999999999996
+    - type: recall_at_100
+      value: 65.949
+    - type: recall_at_1000
+      value: 84.017
+    - type: recall_at_3
+      value: 33.857
+    - type: recall_at_5
+      value: 37.316
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.122
+    - type: map_at_10
+      value: 24.32
+    - type: map_at_100
+      value: 25.338
+    - type: map_at_1000
+      value: 25.462
+    - type: map_at_3
+      value: 22.064
+    - type: map_at_5
+      value: 23.322000000000003
+    - type: mrr_at_1
+      value: 20.647
+    - type: mrr_at_10
+      value: 27.858
+    - type: mrr_at_100
+      value: 28.743999999999996
+    - type: mrr_at_1000
+      value: 28.819
+    - type: mrr_at_3
+      value: 25.769
+    - type: mrr_at_5
+      value: 26.964
+    - type: ndcg_at_1
+      value: 20.647
+    - type: ndcg_at_10
+      value: 28.849999999999998
+    - type: ndcg_at_100
+      value: 33.849000000000004
+    - type: ndcg_at_1000
+      value: 36.802
+    - type: ndcg_at_3
+      value: 24.799
+    - type: ndcg_at_5
+      value: 26.682
+    - type: precision_at_1
+      value: 20.647
+    - type: precision_at_10
+      value: 5.2170000000000005
+    - type: precision_at_100
+      value: 0.906
+    - type: precision_at_1000
+      value: 0.134
+    - type: precision_at_3
+      value: 11.769
+    - type: precision_at_5
+      value: 8.486
+    - type: recall_at_1
+      value: 17.122
+    - type: recall_at_10
+      value: 38.999
+    - type: recall_at_100
+      value: 61.467000000000006
+    - type: recall_at_1000
+      value: 82.716
+    - type: recall_at_3
+      value: 27.601
+    - type: recall_at_5
+      value: 32.471
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.396
+    - type: map_at_10
+      value: 33.415
+    - type: map_at_100
+      value: 34.521
+    - type: map_at_1000
+      value: 34.631
+    - type: map_at_3
+      value: 30.703999999999997
+    - type: map_at_5
+      value: 32.166
+    - type: mrr_at_1
+      value: 28.825
+    - type: mrr_at_10
+      value: 37.397000000000006
+    - type: mrr_at_100
+      value: 38.286
+    - type: mrr_at_1000
+      value: 38.346000000000004
+    - type: mrr_at_3
+      value: 35.028
+    - type: mrr_at_5
+      value: 36.32
+    - type: ndcg_at_1
+      value: 28.825
+    - type: ndcg_at_10
+      value: 38.656
+    - type: ndcg_at_100
+      value: 43.856
+    - type: ndcg_at_1000
+      value: 46.31
+    - type: ndcg_at_3
+      value: 33.793
+    - type: ndcg_at_5
+      value: 35.909
+    - type: precision_at_1
+      value: 28.825
+    - type: precision_at_10
+      value: 6.567
+    - type: precision_at_100
+      value: 1.0330000000000001
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 15.516
+    - type: precision_at_5
+      value: 10.914
+    - type: recall_at_1
+      value: 24.396
+    - type: recall_at_10
+      value: 50.747
+    - type: recall_at_100
+      value: 73.477
+    - type: recall_at_1000
+      value: 90.801
+    - type: recall_at_3
+      value: 37.1
+    - type: recall_at_5
+      value: 42.589
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.072
+    - type: map_at_10
+      value: 34.307
+    - type: map_at_100
+      value: 35.725
+    - type: map_at_1000
+      value: 35.943999999999996
+    - type: map_at_3
+      value: 30.906
+    - type: map_at_5
+      value: 32.818000000000005
+    - type: mrr_at_1
+      value: 29.644
+    - type: mrr_at_10
+      value: 38.673
+    - type: mrr_at_100
+      value: 39.459
+    - type: mrr_at_1000
+      value: 39.527
+    - type: mrr_at_3
+      value: 35.771
+    - type: mrr_at_5
+      value: 37.332
+    - type: ndcg_at_1
+      value: 29.644
+    - type: ndcg_at_10
+      value: 40.548
+    - type: ndcg_at_100
+      value: 45.678999999999995
+    - type: ndcg_at_1000
+      value: 48.488
+    - type: ndcg_at_3
+      value: 34.887
+    - type: ndcg_at_5
+      value: 37.543
+    - type: precision_at_1
+      value: 29.644
+    - type: precision_at_10
+      value: 7.688000000000001
+    - type: precision_at_100
+      value: 1.482
+    - type: precision_at_1000
+      value: 0.23600000000000002
+    - type: precision_at_3
+      value: 16.206
+    - type: precision_at_5
+      value: 12.016
+    - type: recall_at_1
+      value: 25.072
+    - type: recall_at_10
+      value: 53.478
+    - type: recall_at_100
+      value: 76.07300000000001
+    - type: recall_at_1000
+      value: 93.884
+    - type: recall_at_3
+      value: 37.583
+    - type: recall_at_5
+      value: 44.464
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.712
+    - type: map_at_10
+      value: 27.467999999999996
+    - type: map_at_100
+      value: 28.502
+    - type: map_at_1000
+      value: 28.610000000000003
+    - type: map_at_3
+      value: 24.887999999999998
+    - type: map_at_5
+      value: 26.273999999999997
+    - type: mrr_at_1
+      value: 22.736
+    - type: mrr_at_10
+      value: 29.553
+    - type: mrr_at_100
+      value: 30.485
+    - type: mrr_at_1000
+      value: 30.56
+    - type: mrr_at_3
+      value: 27.078999999999997
+    - type: mrr_at_5
+      value: 28.401
+    - type: ndcg_at_1
+      value: 22.736
+    - type: ndcg_at_10
+      value: 32.023
+    - type: ndcg_at_100
+      value: 37.158
+    - type: ndcg_at_1000
+      value: 39.823
+    - type: ndcg_at_3
+      value: 26.951999999999998
+    - type: ndcg_at_5
+      value: 29.281000000000002
+    - type: precision_at_1
+      value: 22.736
+    - type: precision_at_10
+      value: 5.213
+    - type: precision_at_100
+      value: 0.832
+    - type: precision_at_1000
+      value: 0.116
+    - type: precision_at_3
+      value: 11.459999999999999
+    - type: precision_at_5
+      value: 8.244
+    - type: recall_at_1
+      value: 20.712
+    - type: recall_at_10
+      value: 44.057
+    - type: recall_at_100
+      value: 67.944
+    - type: recall_at_1000
+      value: 87.925
+    - type: recall_at_3
+      value: 30.305
+    - type: recall_at_5
+      value: 36.071999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 10.181999999999999
+    - type: map_at_10
+      value: 16.66
+    - type: map_at_100
+      value: 18.273
+    - type: map_at_1000
+      value: 18.45
+    - type: map_at_3
+      value: 14.141
+    - type: map_at_5
+      value: 15.455
+    - type: mrr_at_1
+      value: 22.15
+    - type: mrr_at_10
+      value: 32.062000000000005
+    - type: mrr_at_100
+      value: 33.116
+    - type: mrr_at_1000
+      value: 33.168
+    - type: mrr_at_3
+      value: 28.827
+    - type: mrr_at_5
+      value: 30.892999999999997
+    - type: ndcg_at_1
+      value: 22.15
+    - type: ndcg_at_10
+      value: 23.532
+    - type: ndcg_at_100
+      value: 30.358
+    - type: ndcg_at_1000
+      value: 33.783
+    - type: ndcg_at_3
+      value: 19.222
+    - type: ndcg_at_5
+      value: 20.919999999999998
+    - type: precision_at_1
+      value: 22.15
+    - type: precision_at_10
+      value: 7.185999999999999
+    - type: precision_at_100
+      value: 1.433
+    - type: precision_at_1000
+      value: 0.207
+    - type: precision_at_3
+      value: 13.941
+    - type: precision_at_5
+      value: 10.906
+    - type: recall_at_1
+      value: 10.181999999999999
+    - type: recall_at_10
+      value: 28.104000000000003
+    - type: recall_at_100
+      value: 51.998999999999995
+    - type: recall_at_1000
+      value: 71.311
+    - type: recall_at_3
+      value: 17.698
+    - type: recall_at_5
+      value: 22.262999999999998
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.669
+    - type: map_at_10
+      value: 15.552
+    - type: map_at_100
+      value: 21.865000000000002
+    - type: map_at_1000
+      value: 23.268
+    - type: map_at_3
+      value: 11.309
+    - type: map_at_5
+      value: 13.084000000000001
+    - type: mrr_at_1
+      value: 55.50000000000001
+    - type: mrr_at_10
+      value: 66.46600000000001
+    - type: mrr_at_100
+      value: 66.944
+    - type: mrr_at_1000
+      value: 66.956
+    - type: mrr_at_3
+      value: 64.542
+    - type: mrr_at_5
+      value: 65.717
+    - type: ndcg_at_1
+      value: 44.75
+    - type: ndcg_at_10
+      value: 35.049
+    - type: ndcg_at_100
+      value: 39.073
+    - type: ndcg_at_1000
+      value: 46.208
+    - type: ndcg_at_3
+      value: 39.525
+    - type: ndcg_at_5
+      value: 37.156
+    - type: precision_at_1
+      value: 55.50000000000001
+    - type: precision_at_10
+      value: 27.800000000000004
+    - type: precision_at_100
+      value: 9.013
+    - type: precision_at_1000
+      value: 1.8800000000000001
+    - type: precision_at_3
+      value: 42.667
+    - type: precision_at_5
+      value: 36.0
+    - type: recall_at_1
+      value: 6.669
+    - type: recall_at_10
+      value: 21.811
+    - type: recall_at_100
+      value: 45.112
+    - type: recall_at_1000
+      value: 67.806
+    - type: recall_at_3
+      value: 13.373
+    - type: recall_at_5
+      value: 16.615
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 48.769999999999996
+    - type: f1
+      value: 42.91448356376592
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 54.013
+    - type: map_at_10
+      value: 66.239
+    - type: map_at_100
+      value: 66.62599999999999
+    - type: map_at_1000
+      value: 66.644
+    - type: map_at_3
+      value: 63.965
+    - type: map_at_5
+      value: 65.45400000000001
+    - type: mrr_at_1
+      value: 58.221000000000004
+    - type: mrr_at_10
+      value: 70.43700000000001
+    - type: mrr_at_100
+      value: 70.744
+    - type: mrr_at_1000
+      value: 70.75099999999999
+    - type: mrr_at_3
+      value: 68.284
+    - type: mrr_at_5
+      value: 69.721
+    - type: ndcg_at_1
+      value: 58.221000000000004
+    - type: ndcg_at_10
+      value: 72.327
+    - type: ndcg_at_100
+      value: 73.953
+    - type: ndcg_at_1000
+      value: 74.312
+    - type: ndcg_at_3
+      value: 68.062
+    - type: ndcg_at_5
+      value: 70.56400000000001
+    - type: precision_at_1
+      value: 58.221000000000004
+    - type: precision_at_10
+      value: 9.521
+    - type: precision_at_100
+      value: 1.045
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 27.348
+    - type: precision_at_5
+      value: 17.794999999999998
+    - type: recall_at_1
+      value: 54.013
+    - type: recall_at_10
+      value: 86.957
+    - type: recall_at_100
+      value: 93.911
+    - type: recall_at_1000
+      value: 96.38
+    - type: recall_at_3
+      value: 75.555
+    - type: recall_at_5
+      value: 81.671
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.254
+    - type: map_at_10
+      value: 33.723
+    - type: map_at_100
+      value: 35.574
+    - type: map_at_1000
+      value: 35.730000000000004
+    - type: map_at_3
+      value: 29.473
+    - type: map_at_5
+      value: 31.543
+    - type: mrr_at_1
+      value: 41.358
+    - type: mrr_at_10
+      value: 49.498
+    - type: mrr_at_100
+      value: 50.275999999999996
+    - type: mrr_at_1000
+      value: 50.308
+    - type: mrr_at_3
+      value: 47.016000000000005
+    - type: mrr_at_5
+      value: 48.336
+    - type: ndcg_at_1
+      value: 41.358
+    - type: ndcg_at_10
+      value: 41.579
+    - type: ndcg_at_100
+      value: 48.455
+    - type: ndcg_at_1000
+      value: 51.165000000000006
+    - type: ndcg_at_3
+      value: 37.681
+    - type: ndcg_at_5
+      value: 38.49
+    - type: precision_at_1
+      value: 41.358
+    - type: precision_at_10
+      value: 11.543000000000001
+    - type: precision_at_100
+      value: 1.87
+    - type: precision_at_1000
+      value: 0.23600000000000002
+    - type: precision_at_3
+      value: 24.743000000000002
+    - type: precision_at_5
+      value: 17.994
+    - type: recall_at_1
+      value: 21.254
+    - type: recall_at_10
+      value: 48.698
+    - type: recall_at_100
+      value: 74.588
+    - type: recall_at_1000
+      value: 91.00200000000001
+    - type: recall_at_3
+      value: 33.939
+    - type: recall_at_5
+      value: 39.367000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 35.922
+    - type: map_at_10
+      value: 52.32599999999999
+    - type: map_at_100
+      value: 53.18000000000001
+    - type: map_at_1000
+      value: 53.245
+    - type: map_at_3
+      value: 49.294
+    - type: map_at_5
+      value: 51.202999999999996
+    - type: mrr_at_1
+      value: 71.843
+    - type: mrr_at_10
+      value: 78.24600000000001
+    - type: mrr_at_100
+      value: 78.515
+    - type: mrr_at_1000
+      value: 78.527
+    - type: mrr_at_3
+      value: 77.17500000000001
+    - type: mrr_at_5
+      value: 77.852
+    - type: ndcg_at_1
+      value: 71.843
+    - type: ndcg_at_10
+      value: 61.379
+    - type: ndcg_at_100
+      value: 64.535
+    - type: ndcg_at_1000
+      value: 65.888
+    - type: ndcg_at_3
+      value: 56.958
+    - type: ndcg_at_5
+      value: 59.434
+    - type: precision_at_1
+      value: 71.843
+    - type: precision_at_10
+      value: 12.686
+    - type: precision_at_100
+      value: 1.517
+    - type: precision_at_1000
+      value: 0.16999999999999998
+    - type: precision_at_3
+      value: 35.778
+    - type: precision_at_5
+      value: 23.422
+    - type: recall_at_1
+      value: 35.922
+    - type: recall_at_10
+      value: 63.43
+    - type: recall_at_100
+      value: 75.868
+    - type: recall_at_1000
+      value: 84.88900000000001
+    - type: recall_at_3
+      value: 53.666000000000004
+    - type: recall_at_5
+      value: 58.555
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 79.4408
+    - type: ap
+      value: 73.52820871620366
+    - type: f1
+      value: 79.36240238685001
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.826999999999998
+    - type: map_at_10
+      value: 34.04
+    - type: map_at_100
+      value: 35.226
+    - type: map_at_1000
+      value: 35.275
+    - type: map_at_3
+      value: 30.165999999999997
+    - type: map_at_5
+      value: 32.318000000000005
+    - type: mrr_at_1
+      value: 22.464000000000002
+    - type: mrr_at_10
+      value: 34.631
+    - type: mrr_at_100
+      value: 35.752
+    - type: mrr_at_1000
+      value: 35.795
+    - type: mrr_at_3
+      value: 30.798
+    - type: mrr_at_5
+      value: 32.946999999999996
+    - type: ndcg_at_1
+      value: 22.464000000000002
+    - type: ndcg_at_10
+      value: 40.919
+    - type: ndcg_at_100
+      value: 46.632
+    - type: ndcg_at_1000
+      value: 47.833
+    - type: ndcg_at_3
+      value: 32.992
+    - type: ndcg_at_5
+      value: 36.834
+    - type: precision_at_1
+      value: 22.464000000000002
+    - type: precision_at_10
+      value: 6.494
+    - type: precision_at_100
+      value: 0.9369999999999999
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.021
+    - type: precision_at_5
+      value: 10.347000000000001
+    - type: recall_at_1
+      value: 21.826999999999998
+    - type: recall_at_10
+      value: 62.132
+    - type: recall_at_100
+      value: 88.55199999999999
+    - type: recall_at_1000
+      value: 97.707
+    - type: recall_at_3
+      value: 40.541
+    - type: recall_at_5
+      value: 49.739
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 95.68399452804377
+    - type: f1
+      value: 95.25490609832268
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 83.15321477428182
+    - type: f1
+      value: 60.35476439087966
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 71.92669804976462
+    - type: f1
+      value: 69.22815107207565
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 74.4855413584398
+    - type: f1
+      value: 72.92107516103387
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 32.412679360205544
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 28.09211869875204
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 30.540919056982545
+    - type: mrr
+      value: 31.529904607063536
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.745
+    - type: map_at_10
+      value: 12.013
+    - type: map_at_100
+      value: 15.040000000000001
+    - type: map_at_1000
+      value: 16.427
+    - type: map_at_3
+      value: 8.841000000000001
+    - type: map_at_5
+      value: 10.289
+    - type: mrr_at_1
+      value: 45.201
+    - type: mrr_at_10
+      value: 53.483999999999995
+    - type: mrr_at_100
+      value: 54.20700000000001
+    - type: mrr_at_1000
+      value: 54.252
+    - type: mrr_at_3
+      value: 51.29
+    - type: mrr_at_5
+      value: 52.73
+    - type: ndcg_at_1
+      value: 43.808
+    - type: ndcg_at_10
+      value: 32.445
+    - type: ndcg_at_100
+      value: 30.031000000000002
+    - type: ndcg_at_1000
+      value: 39.007
+    - type: ndcg_at_3
+      value: 37.204
+    - type: ndcg_at_5
+      value: 35.07
+    - type: precision_at_1
+      value: 45.201
+    - type: precision_at_10
+      value: 23.684
+    - type: precision_at_100
+      value: 7.600999999999999
+    - type: precision_at_1000
+      value: 2.043
+    - type: precision_at_3
+      value: 33.953
+    - type: precision_at_5
+      value: 29.412
+    - type: recall_at_1
+      value: 5.745
+    - type: recall_at_10
+      value: 16.168
+    - type: recall_at_100
+      value: 30.875999999999998
+    - type: recall_at_1000
+      value: 62.686
+    - type: recall_at_3
+      value: 9.75
+    - type: recall_at_5
+      value: 12.413
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 37.828
+    - type: map_at_10
+      value: 53.239000000000004
+    - type: map_at_100
+      value: 54.035999999999994
+    - type: map_at_1000
+      value: 54.067
+    - type: map_at_3
+      value: 49.289
+    - type: map_at_5
+      value: 51.784
+    - type: mrr_at_1
+      value: 42.497
+    - type: mrr_at_10
+      value: 55.916999999999994
+    - type: mrr_at_100
+      value: 56.495
+    - type: mrr_at_1000
+      value: 56.516999999999996
+    - type: mrr_at_3
+      value: 52.800000000000004
+    - type: mrr_at_5
+      value: 54.722
+    - type: ndcg_at_1
+      value: 42.468
+    - type: ndcg_at_10
+      value: 60.437
+    - type: ndcg_at_100
+      value: 63.731
+    - type: ndcg_at_1000
+      value: 64.41799999999999
+    - type: ndcg_at_3
+      value: 53.230999999999995
+    - type: ndcg_at_5
+      value: 57.26
+    - type: precision_at_1
+      value: 42.468
+    - type: precision_at_10 
+      value: 9.47
+    - type: precision_at_100
+      value: 1.1360000000000001
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 23.724999999999998
+    - type: precision_at_5
+      value: 16.593
+    - type: recall_at_1
+      value: 37.828
+    - type: recall_at_10
+      value: 79.538
+    - type: recall_at_100
+      value: 93.646
+    - type: recall_at_1000
+      value: 98.72999999999999
+    - type: recall_at_3
+      value: 61.134
+    - type: recall_at_5
+      value: 70.377
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.548
+    - type: map_at_10
+      value: 84.466
+    - type: map_at_100
+      value: 85.10600000000001
+    - type: map_at_1000
+      value: 85.123
+    - type: map_at_3
+      value: 81.57600000000001
+    - type: map_at_5
+      value: 83.399
+    - type: mrr_at_1
+      value: 81.24
+    - type: mrr_at_10
+      value: 87.457
+    - type: mrr_at_100
+      value: 87.574
+    - type: mrr_at_1000
+      value: 87.575
+    - type: mrr_at_3
+      value: 86.507
+    - type: mrr_at_5
+      value: 87.205
+    - type: ndcg_at_1
+      value: 81.25
+    - type: ndcg_at_10
+      value: 88.203
+    - type: ndcg_at_100
+      value: 89.457
+    - type: ndcg_at_1000
+      value: 89.563
+    - type: ndcg_at_3
+      value: 85.465
+    - type: ndcg_at_5
+      value: 87.007
+    - type: precision_at_1
+      value: 81.25
+    - type: precision_at_10
+      value: 13.373
+    - type: precision_at_100
+      value: 1.5270000000000001
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.417
+    - type: precision_at_5
+      value: 24.556
+    - type: recall_at_1
+      value: 70.548
+    - type: recall_at_10
+      value: 95.208
+    - type: recall_at_100
+      value: 99.514
+    - type: recall_at_1000
+      value: 99.988
+    - type: recall_at_3
+      value: 87.214
+    - type: recall_at_5
+      value: 91.696
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 53.04822095496839
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 60.30778476474675
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.692
+    - type: map_at_10
+      value: 11.766
+    - type: map_at_100
+      value: 13.904
+    - type: map_at_1000
+      value: 14.216999999999999
+    - type: map_at_3
+      value: 8.245
+    - type: map_at_5
+      value: 9.92
+    - type: mrr_at_1
+      value: 23.0
+    - type: mrr_at_10
+      value: 33.78
+    - type: mrr_at_100
+      value: 34.922
+    - type: mrr_at_1000
+      value: 34.973
+    - type: mrr_at_3
+      value: 30.2
+    - type: mrr_at_5
+      value: 32.565
+    - type: ndcg_at_1
+      value: 23.0
+    - type: ndcg_at_10
+      value: 19.863
+    - type: ndcg_at_100
+      value: 28.141
+    - type: ndcg_at_1000
+      value: 33.549
+    - type: ndcg_at_3
+      value: 18.434
+    - type: ndcg_at_5
+      value: 16.384
+    - type: precision_at_1
+      value: 23.0
+    - type: precision_at_10
+      value: 10.39
+    - type: precision_at_100
+      value: 2.235
+    - type: precision_at_1000
+      value: 0.35300000000000004
+    - type: precision_at_3
+      value: 17.133000000000003
+    - type: precision_at_5
+      value: 14.44
+    - type: recall_at_1
+      value: 4.692
+    - type: recall_at_10
+      value: 21.025
+    - type: recall_at_100
+      value: 45.324999999999996
+    - type: recall_at_1000
+      value: 71.675
+    - type: recall_at_3
+      value: 10.440000000000001
+    - type: recall_at_5
+      value: 14.64
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.96178184892842
+    - type: cos_sim_spearman
+      value: 79.6487740813199
+    - type: euclidean_pearson
+      value: 82.06661161625023
+    - type: euclidean_spearman
+      value: 79.64876769031183
+    - type: manhattan_pearson
+      value: 82.07061164575131
+    - type: manhattan_spearman
+      value: 79.65197039464537
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.15305604100027
+    - type: cos_sim_spearman
+      value: 74.27447427941591
+    - type: euclidean_pearson
+      value: 80.52737337565307
+    - type: euclidean_spearman
+      value: 74.27416077132192
+    - type: manhattan_pearson
+      value: 80.53728571140387
+    - type: manhattan_spearman
+      value: 74.28853605753457
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.44386080639279
+    - type: cos_sim_spearman
+      value: 84.17947648159536
+    - type: euclidean_pearson
+      value: 83.34145388129387
+    - type: euclidean_spearman
+      value: 84.17947648159536
+    - type: manhattan_pearson
+      value: 83.30699061927966
+    - type: manhattan_spearman
+      value: 84.18125737380451
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.57392220985612
+    - type: cos_sim_spearman
+      value: 78.80745014464101
+    - type: euclidean_pearson
+      value: 80.01660371487199
+    - type: euclidean_spearman
+      value: 78.80741240102256
+    - type: manhattan_pearson
+      value: 79.96810779507953
+    - type: manhattan_spearman
+      value: 78.75600400119448
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.85421063026625
+    - type: cos_sim_spearman
+      value: 87.55320285299192
+    - type: euclidean_pearson
+      value: 86.69750143323517
+    - type: euclidean_spearman
+      value: 87.55320284326378
+    - type: manhattan_pearson
+      value: 86.63379169960379
+    - type: manhattan_spearman
+      value: 87.4815029877984
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.31314130411842
+    - type: cos_sim_spearman
+      value: 85.3489588181433
+    - type: euclidean_pearson
+      value: 84.13240933463535
+    - type: euclidean_spearman
+      value: 85.34902871403281
+    - type: manhattan_pearson
+      value: 84.01183086503559
+    - type: manhattan_spearman
+      value: 85.19316703166102
+  - 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: 89.09979781689536
+    - type: cos_sim_spearman
+      value: 88.87813323759015
+    - type: euclidean_pearson
+      value: 88.65413031123792
+    - type: euclidean_spearman
+      value: 88.87813323759015
+    - type: manhattan_pearson
+      value: 88.61818758256024
+    - type: manhattan_spearman
+      value: 88.81044100494604
+  - 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: 62.30693258111531
+    - type: cos_sim_spearman
+      value: 62.195516523251946
+    - type: euclidean_pearson
+      value: 62.951283701049476
+    - type: euclidean_spearman
+      value: 62.195516523251946
+    - type: manhattan_pearson
+      value: 63.068322281439535
+    - type: manhattan_spearman
+      value: 62.10621171028406
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.27092833763909
+    - type: cos_sim_spearman
+      value: 84.84429717949759
+    - type: euclidean_pearson
+      value: 84.8516966060792
+    - type: euclidean_spearman
+      value: 84.84429717949759
+    - type: manhattan_pearson
+      value: 84.82203139242881
+    - type: manhattan_spearman
+      value: 84.8358503952945
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 83.10290863981409
+    - type: mrr
+      value: 95.31168450286097
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 52.161
+    - type: map_at_10
+      value: 62.138000000000005
+    - type: map_at_100
+      value: 62.769
+    - type: map_at_1000
+      value: 62.812
+    - type: map_at_3
+      value: 59.111000000000004
+    - type: map_at_5
+      value: 60.995999999999995
+    - type: mrr_at_1
+      value: 55.333
+    - type: mrr_at_10
+      value: 63.504000000000005
+    - type: mrr_at_100
+      value: 64.036
+    - type: mrr_at_1000
+      value: 64.08
+    - type: mrr_at_3
+      value: 61.278
+    - type: mrr_at_5
+      value: 62.778
+    - type: ndcg_at_1
+      value: 55.333
+    - type: ndcg_at_10
+      value: 66.678
+    - type: ndcg_at_100
+      value: 69.415
+    - type: ndcg_at_1000
+      value: 70.453
+    - type: ndcg_at_3
+      value: 61.755
+    - type: ndcg_at_5
+      value: 64.546
+    - type: precision_at_1
+      value: 55.333
+    - type: precision_at_10
+      value: 9.033
+    - type: precision_at_100
+      value: 1.043
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 24.221999999999998
+    - type: precision_at_5
+      value: 16.333000000000002
+    - type: recall_at_1
+      value: 52.161
+    - type: recall_at_10
+      value: 79.156
+    - type: recall_at_100
+      value: 91.333
+    - type: recall_at_1000
+      value: 99.333
+    - type: recall_at_3
+      value: 66.43299999999999
+    - type: recall_at_5
+      value: 73.272
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.81287128712871
+    - type: cos_sim_ap
+      value: 95.30034785910676
+    - type: cos_sim_f1
+      value: 90.28629856850716
+    - type: cos_sim_precision
+      value: 92.36401673640168
+    - type: cos_sim_recall
+      value: 88.3
+    - type: dot_accuracy
+      value: 99.81287128712871
+    - type: dot_ap
+      value: 95.30034785910676
+    - type: dot_f1
+      value: 90.28629856850716
+    - type: dot_precision
+      value: 92.36401673640168
+    - type: dot_recall
+      value: 88.3
+    - type: euclidean_accuracy
+      value: 99.81287128712871
+    - type: euclidean_ap
+      value: 95.30034785910676
+    - type: euclidean_f1
+      value: 90.28629856850716
+    - type: euclidean_precision
+      value: 92.36401673640168
+    - type: euclidean_recall
+      value: 88.3
+    - type: manhattan_accuracy
+      value: 99.80990099009901
+    - type: manhattan_ap
+      value: 95.26880751950654
+    - type: manhattan_f1
+      value: 90.22177419354838
+    - type: manhattan_precision
+      value: 90.95528455284553
+    - type: manhattan_recall
+      value: 89.5
+    - type: max_accuracy
+      value: 99.81287128712871
+    - type: max_ap
+      value: 95.30034785910676
+    - type: max_f1
+      value: 90.28629856850716
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 58.518662504351184
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 34.96168178378587
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 52.04862593471896
+    - type: mrr
+      value: 52.97238402936932
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.092545236479946
+    - type: cos_sim_spearman
+      value: 31.599851000175498
+    - type: dot_pearson
+      value: 30.092542723901676
+    - type: dot_spearman
+      value: 31.599851000175498
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.189
+    - type: map_at_10
+      value: 1.662
+    - type: map_at_100
+      value: 9.384
+    - type: map_at_1000
+      value: 22.669
+    - type: map_at_3
+      value: 0.5559999999999999
+    - type: map_at_5
+      value: 0.9039999999999999
+    - type: mrr_at_1
+      value: 68.0
+    - type: mrr_at_10
+      value: 81.01899999999999
+    - type: mrr_at_100
+      value: 81.01899999999999
+    - type: mrr_at_1000
+      value: 81.01899999999999
+    - type: mrr_at_3
+      value: 79.333
+    - type: mrr_at_5
+      value: 80.733
+    - type: ndcg_at_1
+      value: 63.0
+    - type: ndcg_at_10
+      value: 65.913
+    - type: ndcg_at_100
+      value: 51.895
+    - type: ndcg_at_1000
+      value: 46.967
+    - type: ndcg_at_3
+      value: 65.49199999999999
+    - type: ndcg_at_5
+      value: 66.69699999999999
+    - type: precision_at_1
+      value: 68.0
+    - type: precision_at_10
+      value: 71.6
+    - type: precision_at_100
+      value: 53.66
+    - type: precision_at_1000
+      value: 21.124000000000002
+    - type: precision_at_3
+      value: 72.667
+    - type: precision_at_5
+      value: 74.0
+    - type: recall_at_1
+      value: 0.189
+    - type: recall_at_10
+      value: 1.913
+    - type: recall_at_100
+      value: 12.601999999999999
+    - type: recall_at_1000
+      value: 44.296
+    - type: recall_at_3
+      value: 0.605
+    - type: recall_at_5
+      value: 1.018
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 2.701
+    - type: map_at_10
+      value: 10.445
+    - type: map_at_100
+      value: 17.324
+    - type: map_at_1000
+      value: 19.161
+    - type: map_at_3
+      value: 5.497
+    - type: map_at_5
+      value: 7.278
+    - type: mrr_at_1
+      value: 30.612000000000002
+    - type: mrr_at_10
+      value: 45.534
+    - type: mrr_at_100
+      value: 45.792
+    - type: mrr_at_1000
+      value: 45.806999999999995
+    - type: mrr_at_3
+      value: 37.755
+    - type: mrr_at_5
+      value: 43.469
+    - type: ndcg_at_1
+      value: 26.531
+    - type: ndcg_at_10
+      value: 26.235000000000003
+    - type: ndcg_at_100
+      value: 39.17
+    - type: ndcg_at_1000
+      value: 51.038
+    - type: ndcg_at_3
+      value: 23.625
+    - type: ndcg_at_5
+      value: 24.338
+    - type: precision_at_1
+      value: 30.612000000000002
+    - type: precision_at_10
+      value: 24.285999999999998
+    - type: precision_at_100
+      value: 8.224
+    - type: precision_at_1000
+      value: 1.6179999999999999
+    - type: precision_at_3
+      value: 24.490000000000002
+    - type: precision_at_5
+      value: 24.898
+    - type: recall_at_1
+      value: 2.701
+    - type: recall_at_10
+      value: 17.997
+    - type: recall_at_100
+      value: 51.766999999999996
+    - type: recall_at_1000
+      value: 87.863
+    - type: recall_at_3
+      value: 6.295000000000001
+    - type: recall_at_5
+      value: 9.993
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 73.3474
+    - type: ap
+      value: 15.393431414459924
+    - type: f1
+      value: 56.466681887882416
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 62.062818336163
+    - type: f1
+      value: 62.11230840463252
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 42.464892820845115
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.15962329379508
+    - type: cos_sim_ap
+      value: 74.73674057919256
+    - type: cos_sim_f1
+      value: 68.81245642574947
+    - type: cos_sim_precision
+      value: 61.48255813953488
+    - type: cos_sim_recall
+      value: 78.12664907651715
+    - type: dot_accuracy
+      value: 86.15962329379508
+    - type: dot_ap
+      value: 74.7367634988281
+    - type: dot_f1
+      value: 68.81245642574947
+    - type: dot_precision
+      value: 61.48255813953488
+    - type: dot_recall
+      value: 78.12664907651715
+    - type: euclidean_accuracy
+      value: 86.15962329379508
+    - type: euclidean_ap
+      value: 74.7367761466634
+    - type: euclidean_f1
+      value: 68.81245642574947
+    - type: euclidean_precision
+      value: 61.48255813953488
+    - type: euclidean_recall
+      value: 78.12664907651715
+    - type: manhattan_accuracy
+      value: 86.21326816474935
+    - type: manhattan_ap
+      value: 74.64416473733951
+    - type: manhattan_f1
+      value: 68.80924855491331
+    - type: manhattan_precision
+      value: 61.23456790123457
+    - type: manhattan_recall
+      value: 78.52242744063325
+    - type: max_accuracy
+      value: 86.21326816474935
+    - type: max_ap
+      value: 74.7367761466634
+    - type: max_f1
+      value: 68.81245642574947
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.97620988085536
+    - type: cos_sim_ap
+      value: 86.08680845745758
+    - type: cos_sim_f1
+      value: 78.02793637114438
+    - type: cos_sim_precision
+      value: 73.11082699683736
+    - type: cos_sim_recall
+      value: 83.65414228518632
+    - type: dot_accuracy
+      value: 88.97620988085536
+    - type: dot_ap
+      value: 86.08681149437946
+    - type: dot_f1
+      value: 78.02793637114438
+    - type: dot_precision
+      value: 73.11082699683736
+    - type: dot_recall
+      value: 83.65414228518632
+    - type: euclidean_accuracy
+      value: 88.97620988085536
+    - type: euclidean_ap
+      value: 86.08681215460771
+    - type: euclidean_f1
+      value: 78.02793637114438
+    - type: euclidean_precision
+      value: 73.11082699683736
+    - type: euclidean_recall
+      value: 83.65414228518632
+    - type: manhattan_accuracy
+      value: 88.88888888888889
+    - type: manhattan_ap
+      value: 86.02916327562438
+    - type: manhattan_f1
+      value: 78.02063045516843
+    - type: manhattan_precision
+      value: 73.38851947346994
+    - type: manhattan_recall
+      value: 83.2768709578072
+    - type: max_accuracy
+      value: 88.97620988085536
+    - type: max_ap
+      value: 86.08681215460771
+    - type: max_f1
+      value: 78.02793637114438
+---
+<!-- TODO: add evaluation results here -->
+<br><br>
+
+<p align="center">
+<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" 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>.</b>
+</p>
+
+## Quick Start
+
+The easiest way to starting using `jina-embeddings-v2-base-en` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
+
+## Intended Usage & Model Info
+
+`jina-embeddings-v2-base-en` is an English, monolingual **embedding model** supporting **8192 sequence length**.
+It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
+The backbone `jina-bert-v2-base-en` is pretrained on the C4 dataset.
+The model is further trained on Jina AI's collection of more than 400 millions 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 (or even longer) 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, etc.
+
+With a standard size of 137 million parameters, the model enables fast inference while delivering better performance than our small model. It is recommended to use a single GPU for inference.
+Additionally, we provide the following embedding models:
+
+- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
+- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters **(you are here)**.
+- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): Chinese-English Bilingual embeddings.
+- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): German-English Bilingual embeddings.
+- [`jina-embeddings-v2-base-es`](https://huggingface.co/jinaai/jina-embeddings-v2-base-es): Spanish-English Bilingual embeddings.
+
+## Data & Parameters
+
+Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923)
+
+## Usage
+
+**<details><summary>Please apply mean pooling when integrating the model.</summary>**
+<p>
+
+### Why mean pooling?
+
+`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
+It has been proved to be the most effective way to produce high-quality sentence embeddings.
+We offer an `encode` function to deal with this.
+
+However, if you would like to do it without using the default `encode` function:
+
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+sentences = ['How is the weather today?', 'What is the current weather like today?']
+
+tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-small-en')
+model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True)
+
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+embeddings = F.normalize(embeddings, p=2, dim=1)
+```
+
+</p>
+</details>
+
+You can use Jina Embedding models directly from transformers package.
+
+First, you need to make sure that you are logged into huggingface. You can either use the huggingface-cli tool (after installing the `transformers` package) and pass your [hugginface access token](https://huggingface.co/docs/hub/security-tokens):
+```bash
+huggingface-cli login
+```
+Alternatively, you can provide the access token as an environment variable in the shell:
+```bash
+export HF_TOKEN="<your token here>"
+```
+or in Python:
+```python
+import os
+
+os.environ['HF_TOKEN'] = "<your token here>"
+```
+
+Then, you can use load and use the model via the `AutoModel` class:
+
+```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-embeddings-v2-base-en', 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]))
+```
+
+If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function:
+
+```python
+embeddings = model.encode(
+    ['Very long ... document'],
+    max_length=2048
+)
+```
+
+Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well):
+
+```python
+!pip install -U sentence-transformers
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+
+model = SentenceTransformer(
+    "jinaai/jina-embeddings-v2-base-en", # switch to en/zh for English or Chinese
+    trust_remote_code=True
+)
+
+# control your input sequence length up to 8192
+model.max_seq_length = 1024
+
+embeddings = model.encode([
+    'How is the weather today?',
+    'What is the current weather like today?'
+])
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+## Alternatives to Using Transformers (or SentencTransformers) Package
+
+1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/). 
+2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).
+
+
+## Use Jina Embeddings for RAG
+
+According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83),
+
+> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.
+
+<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px">
+
+
+## Plans
+
+1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese.
+2. Multimodal embedding models enable Multimodal RAG applications.
+3. High-performt rerankers.
+
+## Trouble Shooting
+
+**Loading of Model Code failed**
+
+If you forgot to pass the `trust_remote_code=True` flag when calling `AutoModel.from_pretrained` or initializing the model via the `SentenceTransformer` class, you will receive an error that the model weights could not be initialized.
+This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model:
+
+```bash
+Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-en were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ...
+```
+
+
+**User is not logged into Huggingface**
+
+The model is only availabe under [gated access](https://huggingface.co/docs/hub/models-gated).
+This means you need to be logged into huggingface load load it.
+If you receive the following error, you need to provide an access token, either by using the huggingface-cli or providing the token via an environment variable as described above:
+```bash
+OSError: jinaai/jina-embeddings-v2-base-en is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
+If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`.
+```
+
+## 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:
+
+```
+@misc{günther2023jina,
+      title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long 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 and Maximilian Werk and Nan Wang and Han Xiao},
+      year={2023},
+      eprint={2310.19923},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "jinaai/jina-bert-implementation",
+  "model_max_length": 8192,
+  "architectures": [
+    "JinaBertModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_jina.JinaBertConfig"
+  },
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 8192,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "alibi",
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30528,
+  "feed_forward_type": "geglu",
+  "emb_pooler": "mean"
+}

configuration_jina.py

@@ -0,0 +1,132 @@
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class JinaBertConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`JinaBertModel`]. It is used to
+    instantiate a BERT model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the BERT
+    [bert-base-uncased](https://huggingface.co/bert-base-uncased) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 30522):
+            Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (`int`, *optional*, defaults to 512):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        type_vocab_size (`int`, *optional*, defaults to 2):
+            The vocabulary size of the `token_type_ids` passed when calling [`BertModel`] or [`TFBertModel`].
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
+            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
+            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
+            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
+            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
+            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
+        is_decoder (`bool`, *optional*, defaults to `False`):
+            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        classifier_dropout (`float`, *optional*):
+            The dropout ratio for the classification head.
+        feed_forward_type (`str`, *optional*, defaults to `"original"`):
+            The type of feed forward layer to use in the bert layers.
+            Can be one of GLU variants, e.g. `"reglu"`, `"geglu"`
+        emb_pooler (`str`, *optional*, defaults to `None`):
+            The function to use for pooling the last layer embeddings to get the sentence embeddings.
+            Should be one of `None`, `"mean"`.
+        attn_implementation (`str`, *optional*, defaults to `"torch"`):
+            The implementation of the self-attention layer. Can be one of:
+            - `None` for the original implementation,
+            - `torch` for the PyTorch SDPA implementation,
+
+    Examples:
+
+    ```python
+    >>> from transformers import JinaBertConfig, JinaBertModel
+
+    >>> # Initializing a JinaBert configuration
+    >>> configuration = JinaBertConfig()
+
+    >>> # Initializing a model (with random weights) from the configuration
+    >>> model = JinaBertModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+
+    >>> # Encode text inputs
+    >>> embeddings = model.encode(text_inputs)
+    ```"""
+    model_type = "bert"
+
+    def __init__(
+        self,
+        vocab_size=30522,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=2,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        pad_token_id=0,
+        position_embedding_type="absolute",
+        use_cache=True,
+        classifier_dropout=None,
+        feed_forward_type="original",
+        emb_pooler=None,
+        attn_implementation="torch",
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.use_cache = use_cache
+        self.classifier_dropout = classifier_dropout
+        self.feed_forward_type = feed_forward_type
+        self.emb_pooler = emb_pooler
+        self.attn_implementation = attn_implementation

generation_config.json

@@ -0,0 +1,5 @@
+{
+  "_from_model_config": true,
+  "pad_token_id": 0,
+  "transformers_version": "4.26.0"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b70f1386f05b9703ea4edf7f1550a8925399f9580e4cc754cc099efc1e736d8
+size 274757256

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6cd5a65131aa1db04c4146f474bdf68fac06417cba56789f4e6aaabd190e2818
+size 274773117

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 2147483648,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}