Severian
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embed

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----
-tags:
-  - finetuner
-  - mteb
-  - sentence-transformers
-  - feature-extraction
-  - sentence-similarity
-  - alibi
-datasets:
-  - allenai/c4
-language: en
-license: apache-2.0
-model-index:
-- name: jina-embedding-b-en-v2
-  results:
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (en)
-      config: en
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 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://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
-</p>
-<p align="center">
-<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>, <a href="https://github.com/jina-ai/finetuner"><b>Finetuner</b></a> team.</b>
-</p>
-## Intended Usage & Model Info
-`jina-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:
-**V1 (Based on T5, 512 Seq)**
-- [`jina-embeddings-v1-small-en`](https://huggingface.co/jinaai/jina-embedding-s-en-v1): 35 million parameters.
-- [`jina-embeddings-v1-base-en`](https://huggingface.co/jinaai/jina-embedding-b-en-v1): 110 million parameters.
-- [`jina-embeddings-v1-large-en`](https://huggingface.co/jinaai/jina-embedding-l-en-v1): 330 million parameters.
-**V2 (Based on JinaBert, 8k Seq)**
-- [`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-large-en`](): 435 million parameters (releasing soon).
-## 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:
-```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
-)
-```
-## Fully-managed Embeddings Service
-Alternatively, you can use Jina AI's [Embedding platform](https://jina.ai/embeddings/) for fully-managed access to Jina Embeddings models.
-## 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
-The development of new bilingual models is currently underway. We will be targeting mainly the German and Spanish languages.
-The upcoming models will be called `jina-embeddings-v2-base-de/es`.
-## 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}
-}
-```