Duplicate from jinaai/jina-embeddings-v2-base-en

Co-authored-by: Bo Wang <[email protected]>

.gitattributes

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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

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

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+{
+  "_name_or_path": "jinaai/jina-bert-implementation",
+  "model_max_length": 8192,
+  "architectures": [
+    "JinaBertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "jinaai/jina-bert-implementation--configuration_bert.JinaBertConfig",
+    "AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM",
+    "AutoModel": "jinaai/jina-bert-implementation--modeling_bert.JinaBertModel",
+    "AutoModelForSequenceClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForSequenceClassification"
+  },
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "hidden_size": 768,
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+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
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+  "model_type": "bert",
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+  "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"
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.31.0",
+    "pytorch": "2.0.1"
+  }
+}

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+}

generation_config.json

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+}

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sentence_bert_config.json

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+}

special_tokens_map.json

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+{
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+  "mask_token": "[MASK]",
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+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
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+}