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README.md

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----
-tags:
-  - sentence-transformers
-  - feature-extraction
-  - sentence-similarity
-  - mteb
-  - transformers
-  - transformers.js
-language:
-  - de
-  - en
-inference: false
-license: apache-2.0
-model-index:
-- name: jina-embeddings-v2-base-de
-  results:
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (en)
-      config: en
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 73.76119402985076
-    - type: ap
-      value: 35.99577188521176
-    - type: f1
-      value: 67.50397431543269
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (de)
-      config: de
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 68.9186295503212
-    - type: ap
-      value: 79.73307115840507
-    - type: f1
-      value: 66.66245744831339
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_polarity
-      name: MTEB AmazonPolarityClassification
-      config: default
-      split: test
-      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
-    metrics:
-    - type: accuracy
-      value: 77.52215
-    - type: ap
-      value: 71.85051037177416
-    - type: f1
-      value: 77.4171096157774
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (en)
-      config: en
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 38.498
-    - type: f1
-      value: 38.058193386555956
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (de)
-      config: de
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 37.717999999999996
-    - type: f1
-      value: 37.22674371574757
-  - task:
-      type: Retrieval
-    dataset:
-      type: arguana
-      name: MTEB ArguAna
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 25.319999999999997
-    - type: map_at_10
-      value: 40.351
-    - type: map_at_100
-      value: 41.435
-    - type: map_at_1000
-      value: 41.443000000000005
-    - type: map_at_3
-      value: 35.266
-    - type: map_at_5
-      value: 37.99
-    - type: mrr_at_1
-      value: 25.746999999999996
-    - type: mrr_at_10
-      value: 40.515
-    - type: mrr_at_100
-      value: 41.606
-    - type: mrr_at_1000
-      value: 41.614000000000004
-    - type: mrr_at_3
-      value: 35.42
-    - type: mrr_at_5
-      value: 38.112
-    - type: ndcg_at_1
-      value: 25.319999999999997
-    - type: ndcg_at_10
-      value: 49.332
-    - type: ndcg_at_100
-      value: 53.909
-    - type: ndcg_at_1000
-      value: 54.089
-    - type: ndcg_at_3
-      value: 38.705
-    - type: ndcg_at_5
-      value: 43.606
-    - type: precision_at_1
-      value: 25.319999999999997
-    - type: precision_at_10
-      value: 7.831
-    - type: precision_at_100
-      value: 0.9820000000000001
-    - type: precision_at_1000
-      value: 0.1
-    - type: precision_at_3
-      value: 16.24
-    - type: precision_at_5
-      value: 12.119
-    - type: recall_at_1
-      value: 25.319999999999997
-    - type: recall_at_10
-      value: 78.307
-    - type: recall_at_100
-      value: 98.222
-    - type: recall_at_1000
-      value: 99.57300000000001
-    - type: recall_at_3
-      value: 48.72
-    - type: recall_at_5
-      value: 60.597
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-p2p
-      name: MTEB ArxivClusteringP2P
-      config: default
-      split: test
-      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
-    metrics:
-    - type: v_measure
-      value: 41.43100588255654
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-s2s
-      name: MTEB ArxivClusteringS2S
-      config: default
-      split: test
-      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
-    metrics:
-    - type: v_measure
-      value: 32.08988904593667
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/askubuntudupquestions-reranking
-      name: MTEB AskUbuntuDupQuestions
-      config: default
-      split: test
-      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
-    metrics:
-    - type: map
-      value: 60.55514765595906
-    - type: mrr
-      value: 73.51393835465858
-  - task:
-      type: STS
-    dataset:
-      type: mteb/biosses-sts
-      name: MTEB BIOSSES
-      config: default
-      split: test
-      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
-    metrics:
-    - type: cos_sim_pearson
-      value: 79.6723823121172
-    - type: cos_sim_spearman
-      value: 76.90596922214986
-    - type: euclidean_pearson
-      value: 77.87910737957918
-    - type: euclidean_spearman
-      value: 76.66319260598262
-    - type: manhattan_pearson
-      value: 77.37039493457965
-    - type: manhattan_spearman
-      value: 76.09872191280964
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/bucc-bitext-mining
-      name: MTEB BUCC (de-en)
-      config: de-en
-      split: test
-      revision: d51519689f32196a32af33b075a01d0e7c51e252
-    metrics:
-    - type: accuracy
-      value: 98.97703549060543
-    - type: f1
-      value: 98.86569241475296
-    - type: precision
-      value: 98.81002087682673
-    - type: recall
-      value: 98.97703549060543
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/banking77
-      name: MTEB Banking77Classification
-      config: default
-      split: test
-      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
-    metrics:
-    - type: accuracy
-      value: 83.93506493506493
-    - type: f1
-      value: 83.91014949949302
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-p2p
-      name: MTEB BiorxivClusteringP2P
-      config: default
-      split: test
-      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
-    metrics:
-    - type: v_measure
-      value: 34.970675877585144
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-s2s
-      name: MTEB BiorxivClusteringS2S
-      config: default
-      split: test
-      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
-    metrics:
-    - type: v_measure
-      value: 28.779230269190954
-  - task:
-      type: Clustering
-    dataset:
-      type: slvnwhrl/blurbs-clustering-p2p
-      name: MTEB BlurbsClusteringP2P
-      config: default
-      split: test
-      revision: a2dd5b02a77de3466a3eaa98ae586b5610314496
-    metrics:
-    - type: v_measure
-      value: 35.490175601567216
-  - task:
-      type: Clustering
-    dataset:
-      type: slvnwhrl/blurbs-clustering-s2s
-      name: MTEB BlurbsClusteringS2S
-      config: default
-      split: test
-      revision: 9bfff9a7f8f6dc6ffc9da71c48dd48b68696471d
-    metrics:
-    - type: v_measure
-      value: 16.16638280560168
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackAndroidRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 30.830999999999996
-    - type: map_at_10
-      value: 41.355
-    - type: map_at_100
-      value: 42.791000000000004
-    - type: map_at_1000
-      value: 42.918
-    - type: map_at_3
-      value: 38.237
-    - type: map_at_5
-      value: 40.066
-    - type: mrr_at_1
-      value: 38.484
-    - type: mrr_at_10
-      value: 47.593
-    - type: mrr_at_100
-      value: 48.388
-    - type: mrr_at_1000
-      value: 48.439
-    - type: mrr_at_3
-      value: 45.279
-    - type: mrr_at_5
-      value: 46.724
-    - type: ndcg_at_1
-      value: 38.484
-    - type: ndcg_at_10
-      value: 47.27
-    - type: ndcg_at_100
-      value: 52.568000000000005
-    - type: ndcg_at_1000
-      value: 54.729000000000006
-    - type: ndcg_at_3
-      value: 43.061
-    - type: ndcg_at_5
-      value: 45.083
-    - type: precision_at_1
-      value: 38.484
-    - type: precision_at_10
-      value: 8.927
-    - type: precision_at_100
-      value: 1.425
-    - type: precision_at_1000
-      value: 0.19
-    - type: precision_at_3
-      value: 20.791999999999998
-    - type: precision_at_5
-      value: 14.85
-    - type: recall_at_1
-      value: 30.830999999999996
-    - type: recall_at_10
-      value: 57.87799999999999
-    - type: recall_at_100
-      value: 80.124
-    - type: recall_at_1000
-      value: 94.208
-    - type: recall_at_3
-      value: 45.083
-    - type: recall_at_5
-      value: 51.154999999999994
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackEnglishRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 25.782
-    - type: map_at_10
-      value: 34.492
-    - type: map_at_100
-      value: 35.521
-    - type: map_at_1000
-      value: 35.638
-    - type: map_at_3
-      value: 31.735999999999997
-    - type: map_at_5
-      value: 33.339
-    - type: mrr_at_1
-      value: 32.357
-    - type: mrr_at_10
-      value: 39.965
-    - type: mrr_at_100
-      value: 40.644000000000005
-    - type: mrr_at_1000
-      value: 40.695
-    - type: mrr_at_3
-      value: 37.739
-    - type: mrr_at_5
-      value: 39.061
-    - type: ndcg_at_1
-      value: 32.357
-    - type: ndcg_at_10
-      value: 39.644
-    - type: ndcg_at_100
-      value: 43.851
-    - type: ndcg_at_1000
-      value: 46.211999999999996
-    - type: ndcg_at_3
-      value: 35.675000000000004
-    - type: ndcg_at_5
-      value: 37.564
-    - type: precision_at_1
-      value: 32.357
-    - type: precision_at_10
-      value: 7.344
-    - type: precision_at_100
-      value: 1.201
-    - type: precision_at_1000
-      value: 0.168
-    - type: precision_at_3
-      value: 17.155
-    - type: precision_at_5
-      value: 12.166
-    - type: recall_at_1
-      value: 25.782
-    - type: recall_at_10
-      value: 49.132999999999996
-    - type: recall_at_100
-      value: 67.24
-    - type: recall_at_1000
-      value: 83.045
-    - type: recall_at_3
-      value: 37.021
-    - type: recall_at_5
-      value: 42.548
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGamingRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 35.778999999999996
-    - type: map_at_10
-      value: 47.038000000000004
-    - type: map_at_100
-      value: 48.064
-    - type: map_at_1000
-      value: 48.128
-    - type: map_at_3
-      value: 44.186
-    - type: map_at_5
-      value: 45.788000000000004
-    - type: mrr_at_1
-      value: 41.254000000000005
-    - type: mrr_at_10
-      value: 50.556999999999995
-    - type: mrr_at_100
-      value: 51.296
-    - type: mrr_at_1000
-      value: 51.331
-    - type: mrr_at_3
-      value: 48.318
-    - type: mrr_at_5
-      value: 49.619
-    - type: ndcg_at_1
-      value: 41.254000000000005
-    - type: ndcg_at_10
-      value: 52.454
-    - type: ndcg_at_100
-      value: 56.776
-    - type: ndcg_at_1000
-      value: 58.181000000000004
-    - type: ndcg_at_3
-      value: 47.713
-    - type: ndcg_at_5
-      value: 49.997
-    - type: precision_at_1
-      value: 41.254000000000005
-    - type: precision_at_10
-      value: 8.464
-    - type: precision_at_100
-      value: 1.157
-    - type: precision_at_1000
-      value: 0.133
-    - type: precision_at_3
-      value: 21.526
-    - type: precision_at_5
-      value: 14.696000000000002
-    - type: recall_at_1
-      value: 35.778999999999996
-    - type: recall_at_10
-      value: 64.85300000000001
-    - type: recall_at_100
-      value: 83.98400000000001
-    - type: recall_at_1000
-      value: 94.18299999999999
-    - type: recall_at_3
-      value: 51.929
-    - type: recall_at_5
-      value: 57.666
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGisRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 21.719
-    - type: map_at_10
-      value: 29.326999999999998
-    - type: map_at_100
-      value: 30.314000000000004
-    - type: map_at_1000
-      value: 30.397000000000002
-    - type: map_at_3
-      value: 27.101
-    - type: map_at_5
-      value: 28.141
-    - type: mrr_at_1
-      value: 23.503
-    - type: mrr_at_10
-      value: 31.225
-    - type: mrr_at_100
-      value: 32.096000000000004
-    - type: mrr_at_1000
-      value: 32.159
-    - type: mrr_at_3
-      value: 29.076999999999998
-    - type: mrr_at_5
-      value: 30.083
-    - type: ndcg_at_1
-      value: 23.503
-    - type: ndcg_at_10
-      value: 33.842
-    - type: ndcg_at_100
-      value: 39.038000000000004
-    - type: ndcg_at_1000
-      value: 41.214
-    - type: ndcg_at_3
-      value: 29.347
-    - type: ndcg_at_5
-      value: 31.121
-    - type: precision_at_1
-      value: 23.503
-    - type: precision_at_10
-      value: 5.266
-    - type: precision_at_100
-      value: 0.831
-    - type: precision_at_1000
-      value: 0.106
-    - type: precision_at_3
-      value: 12.504999999999999
-    - type: precision_at_5
-      value: 8.565000000000001
-    - type: recall_at_1
-      value: 21.719
-    - type: recall_at_10
-      value: 46.024
-    - type: recall_at_100
-      value: 70.78999999999999
-    - type: recall_at_1000
-      value: 87.022
-    - type: recall_at_3
-      value: 33.64
-    - type: recall_at_5
-      value: 37.992
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackMathematicaRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 15.601
-    - type: map_at_10
-      value: 22.054000000000002
-    - type: map_at_100
-      value: 23.177
-    - type: map_at_1000
-      value: 23.308
-    - type: map_at_3
-      value: 19.772000000000002
-    - type: map_at_5
-      value: 21.055
-    - type: mrr_at_1
-      value: 19.403000000000002
-    - type: mrr_at_10
-      value: 26.409
-    - type: mrr_at_100
-      value: 27.356
-    - type: mrr_at_1000
-      value: 27.441
-    - type: mrr_at_3
-      value: 24.108999999999998
-    - type: mrr_at_5
-      value: 25.427
-    - type: ndcg_at_1
-      value: 19.403000000000002
-    - type: ndcg_at_10
-      value: 26.474999999999998
-    - type: ndcg_at_100
-      value: 32.086
-    - type: ndcg_at_1000
-      value: 35.231
-    - type: ndcg_at_3
-      value: 22.289
-    - type: ndcg_at_5
-      value: 24.271
-    - type: precision_at_1
-      value: 19.403000000000002
-    - type: precision_at_10
-      value: 4.813
-    - type: precision_at_100
-      value: 0.8869999999999999
-    - type: precision_at_1000
-      value: 0.13
-    - type: precision_at_3
-      value: 10.531
-    - type: precision_at_5
-      value: 7.710999999999999
-    - type: recall_at_1
-      value: 15.601
-    - type: recall_at_10
-      value: 35.916
-    - type: recall_at_100
-      value: 60.8
-    - type: recall_at_1000
-      value: 83.245
-    - type: recall_at_3
-      value: 24.321
-    - type: recall_at_5
-      value: 29.372999999999998
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackPhysicsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 25.522
-    - type: map_at_10
-      value: 34.854
-    - type: map_at_100
-      value: 36.269
-    - type: map_at_1000
-      value: 36.387
-    - type: map_at_3
-      value: 32.187
-    - type: map_at_5
-      value: 33.692
-    - type: mrr_at_1
-      value: 31.375999999999998
-    - type: mrr_at_10
-      value: 40.471000000000004
-    - type: mrr_at_100
-      value: 41.481
-    - type: mrr_at_1000
-      value: 41.533
-    - type: mrr_at_3
-      value: 38.274
-    - type: mrr_at_5
-      value: 39.612
-    - type: ndcg_at_1
-      value: 31.375999999999998
-    - type: ndcg_at_10
-      value: 40.298
-    - type: ndcg_at_100
-      value: 46.255
-    - type: ndcg_at_1000
-      value: 48.522
-    - type: ndcg_at_3
-      value: 36.049
-    - type: ndcg_at_5
-      value: 38.095
-    - type: precision_at_1
-      value: 31.375999999999998
-    - type: precision_at_10
-      value: 7.305000000000001
-    - type: precision_at_100
-      value: 1.201
-    - type: precision_at_1000
-      value: 0.157
-    - type: precision_at_3
-      value: 17.132
-    - type: precision_at_5
-      value: 12.107999999999999
-    - type: recall_at_1
-      value: 25.522
-    - type: recall_at_10
-      value: 50.988
-    - type: recall_at_100
-      value: 76.005
-    - type: recall_at_1000
-      value: 91.11200000000001
-    - type: recall_at_3
-      value: 38.808
-    - type: recall_at_5
-      value: 44.279
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackProgrammersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 24.615000000000002
-    - type: map_at_10
-      value: 32.843
-    - type: map_at_100
-      value: 34.172999999999995
-    - type: map_at_1000
-      value: 34.286
-    - type: map_at_3
-      value: 30.125
-    - type: map_at_5
-      value: 31.495
-    - type: mrr_at_1
-      value: 30.023
-    - type: mrr_at_10
-      value: 38.106
-    - type: mrr_at_100
-      value: 39.01
-    - type: mrr_at_1000
-      value: 39.071
-    - type: mrr_at_3
-      value: 35.674
-    - type: mrr_at_5
-      value: 36.924
-    - type: ndcg_at_1
-      value: 30.023
-    - type: ndcg_at_10
-      value: 38.091
-    - type: ndcg_at_100
-      value: 43.771
-    - type: ndcg_at_1000
-      value: 46.315
-    - type: ndcg_at_3
-      value: 33.507
-    - type: ndcg_at_5
-      value: 35.304
-    - type: precision_at_1
-      value: 30.023
-    - type: precision_at_10
-      value: 6.837999999999999
-    - type: precision_at_100
-      value: 1.124
-    - type: precision_at_1000
-      value: 0.152
-    - type: precision_at_3
-      value: 15.562999999999999
-    - type: precision_at_5
-      value: 10.936
-    - type: recall_at_1
-      value: 24.615000000000002
-    - type: recall_at_10
-      value: 48.691
-    - type: recall_at_100
-      value: 72.884
-    - type: recall_at_1000
-      value: 90.387
-    - type: recall_at_3
-      value: 35.659
-    - type: recall_at_5
-      value: 40.602
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.223666666666666
-    - type: map_at_10
-      value: 31.338166666666673
-    - type: map_at_100
-      value: 32.47358333333333
-    - type: map_at_1000
-      value: 32.5955
-    - type: map_at_3
-      value: 28.84133333333333
-    - type: map_at_5
-      value: 30.20808333333333
-    - type: mrr_at_1
-      value: 27.62483333333333
-    - type: mrr_at_10
-      value: 35.385916666666674
-    - type: mrr_at_100
-      value: 36.23325
-    - type: mrr_at_1000
-      value: 36.29966666666667
-    - type: mrr_at_3
-      value: 33.16583333333333
-    - type: mrr_at_5
-      value: 34.41983333333334
-    - type: ndcg_at_1
-      value: 27.62483333333333
-    - type: ndcg_at_10
-      value: 36.222
-    - type: ndcg_at_100
-      value: 41.29491666666666
-    - type: ndcg_at_1000
-      value: 43.85508333333333
-    - type: ndcg_at_3
-      value: 31.95116666666667
-    - type: ndcg_at_5
-      value: 33.88541666666667
-    - type: precision_at_1
-      value: 27.62483333333333
-    - type: precision_at_10
-      value: 6.339916666666667
-    - type: precision_at_100
-      value: 1.0483333333333333
-    - type: precision_at_1000
-      value: 0.14608333333333334
-    - type: precision_at_3
-      value: 14.726500000000003
-    - type: precision_at_5
-      value: 10.395
-    - type: recall_at_1
-      value: 23.223666666666666
-    - type: recall_at_10
-      value: 46.778999999999996
-    - type: recall_at_100
-      value: 69.27141666666667
-    - type: recall_at_1000
-      value: 87.27383333333334
-    - type: recall_at_3
-      value: 34.678749999999994
-    - type: recall_at_5
-      value: 39.79900000000001
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackStatsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 21.677
-    - type: map_at_10
-      value: 27.828000000000003
-    - type: map_at_100
-      value: 28.538999999999998
-    - type: map_at_1000
-      value: 28.64
-    - type: map_at_3
-      value: 26.105
-    - type: map_at_5
-      value: 27.009
-    - type: mrr_at_1
-      value: 24.387
-    - type: mrr_at_10
-      value: 30.209999999999997
-    - type: mrr_at_100
-      value: 30.953000000000003
-    - type: mrr_at_1000
-      value: 31.029
-    - type: mrr_at_3
-      value: 28.707
-    - type: mrr_at_5
-      value: 29.610999999999997
-    - type: ndcg_at_1
-      value: 24.387
-    - type: ndcg_at_10
-      value: 31.378
-    - type: ndcg_at_100
-      value: 35.249
-    - type: ndcg_at_1000
-      value: 37.923
-    - type: ndcg_at_3
-      value: 28.213
-    - type: ndcg_at_5
-      value: 29.658
-    - type: precision_at_1
-      value: 24.387
-    - type: precision_at_10
-      value: 4.8309999999999995
-    - type: precision_at_100
-      value: 0.73
-    - type: precision_at_1000
-      value: 0.104
-    - type: precision_at_3
-      value: 12.168
-    - type: precision_at_5
-      value: 8.251999999999999
-    - type: recall_at_1
-      value: 21.677
-    - type: recall_at_10
-      value: 40.069
-    - type: recall_at_100
-      value: 58.077
-    - type: recall_at_1000
-      value: 77.97
-    - type: recall_at_3
-      value: 31.03
-    - type: recall_at_5
-      value: 34.838
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackTexRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 14.484
-    - type: map_at_10
-      value: 20.355
-    - type: map_at_100
-      value: 21.382
-    - type: map_at_1000
-      value: 21.511
-    - type: map_at_3
-      value: 18.448
-    - type: map_at_5
-      value: 19.451999999999998
-    - type: mrr_at_1
-      value: 17.584
-    - type: mrr_at_10
-      value: 23.825
-    - type: mrr_at_100
-      value: 24.704
-    - type: mrr_at_1000
-      value: 24.793000000000003
-    - type: mrr_at_3
-      value: 21.92
-    - type: mrr_at_5
-      value: 22.97
-    - type: ndcg_at_1
-      value: 17.584
-    - type: ndcg_at_10
-      value: 24.315
-    - type: ndcg_at_100
-      value: 29.354999999999997
-    - type: ndcg_at_1000
-      value: 32.641999999999996
-    - type: ndcg_at_3
-      value: 20.802
-    - type: ndcg_at_5
-      value: 22.335
-    - type: precision_at_1
-      value: 17.584
-    - type: precision_at_10
-      value: 4.443
-    - type: precision_at_100
-      value: 0.8160000000000001
-    - type: precision_at_1000
-      value: 0.128
-    - type: precision_at_3
-      value: 9.807
-    - type: precision_at_5
-      value: 7.0889999999999995
-    - type: recall_at_1
-      value: 14.484
-    - type: recall_at_10
-      value: 32.804
-    - type: recall_at_100
-      value: 55.679
-    - type: recall_at_1000
-      value: 79.63
-    - type: recall_at_3
-      value: 22.976
-    - type: recall_at_5
-      value: 26.939
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackUnixRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 22.983999999999998
-    - type: map_at_10
-      value: 30.812
-    - type: map_at_100
-      value: 31.938
-    - type: map_at_1000
-      value: 32.056000000000004
-    - type: map_at_3
-      value: 28.449999999999996
-    - type: map_at_5
-      value: 29.542
-    - type: mrr_at_1
-      value: 27.145999999999997
-    - type: mrr_at_10
-      value: 34.782999999999994
-    - type: mrr_at_100
-      value: 35.699
-    - type: mrr_at_1000
-      value: 35.768
-    - type: mrr_at_3
-      value: 32.572
-    - type: mrr_at_5
-      value: 33.607
-    - type: ndcg_at_1
-      value: 27.145999999999997
-    - type: ndcg_at_10
-      value: 35.722
-    - type: ndcg_at_100
-      value: 40.964
-    - type: ndcg_at_1000
-      value: 43.598
-    - type: ndcg_at_3
-      value: 31.379
-    - type: ndcg_at_5
-      value: 32.924
-    - type: precision_at_1
-      value: 27.145999999999997
-    - type: precision_at_10
-      value: 6.063000000000001
-    - type: precision_at_100
-      value: 0.9730000000000001
-    - type: precision_at_1000
-      value: 0.13
-    - type: precision_at_3
-      value: 14.366000000000001
-    - type: precision_at_5
-      value: 9.776
-    - type: recall_at_1
-      value: 22.983999999999998
-    - type: recall_at_10
-      value: 46.876
-    - type: recall_at_100
-      value: 69.646
-    - type: recall_at_1000
-      value: 88.305
-    - type: recall_at_3
-      value: 34.471000000000004
-    - type: recall_at_5
-      value: 38.76
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWebmastersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.017000000000003
-    - type: map_at_10
-      value: 31.049
-    - type: map_at_100
-      value: 32.582
-    - type: map_at_1000
-      value: 32.817
-    - type: map_at_3
-      value: 28.303
-    - type: map_at_5
-      value: 29.854000000000003
-    - type: mrr_at_1
-      value: 27.866000000000003
-    - type: mrr_at_10
-      value: 35.56
-    - type: mrr_at_100
-      value: 36.453
-    - type: mrr_at_1000
-      value: 36.519
-    - type: mrr_at_3
-      value: 32.938
-    - type: mrr_at_5
-      value: 34.391
-    - type: ndcg_at_1
-      value: 27.866000000000003
-    - type: ndcg_at_10
-      value: 36.506
-    - type: ndcg_at_100
-      value: 42.344
-    - type: ndcg_at_1000
-      value: 45.213
-    - type: ndcg_at_3
-      value: 31.805
-    - type: ndcg_at_5
-      value: 33.933
-    - type: precision_at_1
-      value: 27.866000000000003
-    - type: precision_at_10
-      value: 7.016
-    - type: precision_at_100
-      value: 1.468
-    - type: precision_at_1000
-      value: 0.23900000000000002
-    - type: precision_at_3
-      value: 14.822
-    - type: precision_at_5
-      value: 10.791
-    - type: recall_at_1
-      value: 23.017000000000003
-    - type: recall_at_10
-      value: 47.053
-    - type: recall_at_100
-      value: 73.177
-    - type: recall_at_1000
-      value: 91.47800000000001
-    - type: recall_at_3
-      value: 33.675
-    - type: recall_at_5
-      value: 39.36
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWordpressRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 16.673
-    - type: map_at_10
-      value: 24.051000000000002
-    - type: map_at_100
-      value: 24.933
-    - type: map_at_1000
-      value: 25.06
-    - type: map_at_3
-      value: 21.446
-    - type: map_at_5
-      value: 23.064
-    - type: mrr_at_1
-      value: 18.115000000000002
-    - type: mrr_at_10
-      value: 25.927
-    - type: mrr_at_100
-      value: 26.718999999999998
-    - type: mrr_at_1000
-      value: 26.817999999999998
-    - type: mrr_at_3
-      value: 23.383000000000003
-    - type: mrr_at_5
-      value: 25.008999999999997
-    - type: ndcg_at_1
-      value: 18.115000000000002
-    - type: ndcg_at_10
-      value: 28.669
-    - type: ndcg_at_100
-      value: 33.282000000000004
-    - type: ndcg_at_1000
-      value: 36.481
-    - type: ndcg_at_3
-      value: 23.574
-    - type: ndcg_at_5
-      value: 26.340000000000003
-    - type: precision_at_1
-      value: 18.115000000000002
-    - type: precision_at_10
-      value: 4.769
-    - type: precision_at_100
-      value: 0.767
-    - type: precision_at_1000
-      value: 0.116
-    - type: precision_at_3
-      value: 10.351
-    - type: precision_at_5
-      value: 7.8
-    - type: recall_at_1
-      value: 16.673
-    - type: recall_at_10
-      value: 41.063
-    - type: recall_at_100
-      value: 62.851
-    - type: recall_at_1000
-      value: 86.701
-    - type: recall_at_3
-      value: 27.532
-    - type: recall_at_5
-      value: 34.076
-  - task:
-      type: Retrieval
-    dataset:
-      type: climate-fever
-      name: MTEB ClimateFEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 8.752
-    - type: map_at_10
-      value: 15.120000000000001
-    - type: map_at_100
-      value: 16.678
-    - type: map_at_1000
-      value: 16.854
-    - type: map_at_3
-      value: 12.603
-    - type: map_at_5
-      value: 13.918
-    - type: mrr_at_1
-      value: 19.283
-    - type: mrr_at_10
-      value: 29.145
-    - type: mrr_at_100
-      value: 30.281000000000002
-    - type: mrr_at_1000
-      value: 30.339
-    - type: mrr_at_3
-      value: 26.069
-    - type: mrr_at_5
-      value: 27.864
-    - type: ndcg_at_1
-      value: 19.283
-    - type: ndcg_at_10
-      value: 21.804000000000002
-    - type: ndcg_at_100
-      value: 28.576
-    - type: ndcg_at_1000
-      value: 32.063
-    - type: ndcg_at_3
-      value: 17.511
-    - type: ndcg_at_5
-      value: 19.112000000000002
-    - type: precision_at_1
-      value: 19.283
-    - type: precision_at_10
-      value: 6.873
-    - type: precision_at_100
-      value: 1.405
-    - type: precision_at_1000
-      value: 0.20500000000000002
-    - type: precision_at_3
-      value: 13.16
-    - type: precision_at_5
-      value: 10.189
-    - type: recall_at_1
-      value: 8.752
-    - type: recall_at_10
-      value: 27.004
-    - type: recall_at_100
-      value: 50.648
-    - type: recall_at_1000
-      value: 70.458
-    - type: recall_at_3
-      value: 16.461000000000002
-    - type: recall_at_5
-      value: 20.973
-  - task:
-      type: Retrieval
-    dataset:
-      type: dbpedia-entity
-      name: MTEB DBPedia
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 6.81
-    - type: map_at_10
-      value: 14.056
-    - type: map_at_100
-      value: 18.961
-    - type: map_at_1000
-      value: 20.169
-    - type: map_at_3
-      value: 10.496
-    - type: map_at_5
-      value: 11.952
-    - type: mrr_at_1
-      value: 53.5
-    - type: mrr_at_10
-      value: 63.479
-    - type: mrr_at_100
-      value: 63.971999999999994
-    - type: mrr_at_1000
-      value: 63.993
-    - type: mrr_at_3
-      value: 61.541999999999994
-    - type: mrr_at_5
-      value: 62.778999999999996
-    - type: ndcg_at_1
-      value: 42.25
-    - type: ndcg_at_10
-      value: 31.471
-    - type: ndcg_at_100
-      value: 35.115
-    - type: ndcg_at_1000
-      value: 42.408
-    - type: ndcg_at_3
-      value: 35.458
-    - type: ndcg_at_5
-      value: 32.973
-    - type: precision_at_1
-      value: 53.5
-    - type: precision_at_10
-      value: 24.85
-    - type: precision_at_100
-      value: 7.79
-    - type: precision_at_1000
-      value: 1.599
-    - type: precision_at_3
-      value: 38.667
-    - type: precision_at_5
-      value: 31.55
-    - type: recall_at_1
-      value: 6.81
-    - type: recall_at_10
-      value: 19.344
-    - type: recall_at_100
-      value: 40.837
-    - type: recall_at_1000
-      value: 64.661
-    - type: recall_at_3
-      value: 11.942
-    - type: recall_at_5
-      value: 14.646
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/emotion
-      name: MTEB EmotionClassification
-      config: default
-      split: test
-      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
-    metrics:
-    - type: accuracy
-      value: 44.64499999999999
-    - type: f1
-      value: 39.39106911352714
-  - task:
-      type: Retrieval
-    dataset:
-      type: fever
-      name: MTEB FEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 48.196
-    - type: map_at_10
-      value: 61.404
-    - type: map_at_100
-      value: 61.846000000000004
-    - type: map_at_1000
-      value: 61.866
-    - type: map_at_3
-      value: 58.975
-    - type: map_at_5
-      value: 60.525
-    - type: mrr_at_1
-      value: 52.025
-    - type: mrr_at_10
-      value: 65.43299999999999
-    - type: mrr_at_100
-      value: 65.80799999999999
-    - type: mrr_at_1000
-      value: 65.818
-    - type: mrr_at_3
-      value: 63.146
-    - type: mrr_at_5
-      value: 64.64
-    - type: ndcg_at_1
-      value: 52.025
-    - type: ndcg_at_10
-      value: 67.889
-    - type: ndcg_at_100
-      value: 69.864
-    - type: ndcg_at_1000
-      value: 70.337
-    - type: ndcg_at_3
-      value: 63.315
-    - type: ndcg_at_5
-      value: 65.91799999999999
-    - type: precision_at_1
-      value: 52.025
-    - type: precision_at_10
-      value: 9.182
-    - type: precision_at_100
-      value: 1.027
-    - type: precision_at_1000
-      value: 0.108
-    - type: precision_at_3
-      value: 25.968000000000004
-    - type: precision_at_5
-      value: 17.006
-    - type: recall_at_1
-      value: 48.196
-    - type: recall_at_10
-      value: 83.885
-    - type: recall_at_100
-      value: 92.671
-    - type: recall_at_1000
-      value: 96.018
-    - type: recall_at_3
-      value: 71.59
-    - type: recall_at_5
-      value: 77.946
-  - task:
-      type: Retrieval
-    dataset:
-      type: fiqa
-      name: MTEB FiQA2018
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 15.193000000000001
-    - type: map_at_10
-      value: 25.168000000000003
-    - type: map_at_100
-      value: 27.017000000000003
-    - type: map_at_1000
-      value: 27.205000000000002
-    - type: map_at_3
-      value: 21.746
-    - type: map_at_5
-      value: 23.579
-    - type: mrr_at_1
-      value: 31.635999999999996
-    - type: mrr_at_10
-      value: 40.077
-    - type: mrr_at_100
-      value: 41.112
-    - type: mrr_at_1000
-      value: 41.160999999999994
-    - type: mrr_at_3
-      value: 37.937
-    - type: mrr_at_5
-      value: 39.18
-    - type: ndcg_at_1
-      value: 31.635999999999996
-    - type: ndcg_at_10
-      value: 32.298
-    - type: ndcg_at_100
-      value: 39.546
-    - type: ndcg_at_1000
-      value: 42.88
-    - type: ndcg_at_3
-      value: 29.221999999999998
-    - type: ndcg_at_5
-      value: 30.069000000000003
-    - type: precision_at_1
-      value: 31.635999999999996
-    - type: precision_at_10
-      value: 9.367
-    - type: precision_at_100
-      value: 1.645
-    - type: precision_at_1000
-      value: 0.22399999999999998
-    - type: precision_at_3
-      value: 20.01
-    - type: precision_at_5
-      value: 14.753
-    - type: recall_at_1
-      value: 15.193000000000001
-    - type: recall_at_10
-      value: 38.214999999999996
-    - type: recall_at_100
-      value: 65.95
-    - type: recall_at_1000
-      value: 85.85300000000001
-    - type: recall_at_3
-      value: 26.357000000000003
-    - type: recall_at_5
-      value: 31.319999999999997
-  - task:
-      type: Retrieval
-    dataset:
-      type: jinaai/ger_da_lir
-      name: MTEB GerDaLIR
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 10.363
-    - type: map_at_10
-      value: 16.222
-    - type: map_at_100
-      value: 17.28
-    - type: map_at_1000
-      value: 17.380000000000003
-    - type: map_at_3
-      value: 14.054
-    - type: map_at_5
-      value: 15.203
-    - type: mrr_at_1
-      value: 11.644
-    - type: mrr_at_10
-      value: 17.625
-    - type: mrr_at_100
-      value: 18.608
-    - type: mrr_at_1000
-      value: 18.695999999999998
-    - type: mrr_at_3
-      value: 15.481
-    - type: mrr_at_5
-      value: 16.659
-    - type: ndcg_at_1
-      value: 11.628
-    - type: ndcg_at_10
-      value: 20.028000000000002
-    - type: ndcg_at_100
-      value: 25.505
-    - type: ndcg_at_1000
-      value: 28.288000000000004
-    - type: ndcg_at_3
-      value: 15.603
-    - type: ndcg_at_5
-      value: 17.642
-    - type: precision_at_1
-      value: 11.628
-    - type: precision_at_10
-      value: 3.5589999999999997
-    - type: precision_at_100
-      value: 0.664
-    - type: precision_at_1000
-      value: 0.092
-    - type: precision_at_3
-      value: 7.109999999999999
-    - type: precision_at_5
-      value: 5.401
-    - type: recall_at_1
-      value: 10.363
-    - type: recall_at_10
-      value: 30.586000000000002
-    - type: recall_at_100
-      value: 56.43
-    - type: recall_at_1000
-      value: 78.142
-    - type: recall_at_3
-      value: 18.651
-    - type: recall_at_5
-      value: 23.493
-  - task:
-      type: Retrieval
-    dataset:
-      type: deepset/germandpr
-      name: MTEB GermanDPR
-      config: default
-      split: test
-      revision: 5129d02422a66be600ac89cd3e8531b4f97d347d
-    metrics:
-    - type: map_at_1
-      value: 60.78
-    - type: map_at_10
-      value: 73.91499999999999
-    - type: map_at_100
-      value: 74.089
-    - type: map_at_1000
-      value: 74.09400000000001
-    - type: map_at_3
-      value: 71.87
-    - type: map_at_5
-      value: 73.37700000000001
-    - type: mrr_at_1
-      value: 60.78
-    - type: mrr_at_10
-      value: 73.91499999999999
-    - type: mrr_at_100
-      value: 74.089
-    - type: mrr_at_1000
-      value: 74.09400000000001
-    - type: mrr_at_3
-      value: 71.87
-    - type: mrr_at_5
-      value: 73.37700000000001
-    - type: ndcg_at_1
-      value: 60.78
-    - type: ndcg_at_10
-      value: 79.35600000000001
-    - type: ndcg_at_100
-      value: 80.077
-    - type: ndcg_at_1000
-      value: 80.203
-    - type: ndcg_at_3
-      value: 75.393
-    - type: ndcg_at_5
-      value: 78.077
-    - type: precision_at_1
-      value: 60.78
-    - type: precision_at_10
-      value: 9.59
-    - type: precision_at_100
-      value: 0.9900000000000001
-    - type: precision_at_1000
-      value: 0.1
-    - type: precision_at_3
-      value: 28.52
-    - type: precision_at_5
-      value: 18.4
-    - type: recall_at_1
-      value: 60.78
-    - type: recall_at_10
-      value: 95.902
-    - type: recall_at_100
-      value: 99.024
-    - type: recall_at_1000
-      value: 100.0
-    - type: recall_at_3
-      value: 85.56099999999999
-    - type: recall_at_5
-      value: 92.0
-  - task:
-      type: STS
-    dataset:
-      type: jinaai/german-STSbenchmark
-      name: MTEB GermanSTSBenchmark
-      config: default
-      split: test
-      revision: 49d9b423b996fea62b483f9ee6dfb5ec233515ca
-    metrics:
-    - type: cos_sim_pearson
-      value: 88.49524420894356
-    - type: cos_sim_spearman
-      value: 88.32407839427714
-    - type: euclidean_pearson
-      value: 87.25098779877104
-    - type: euclidean_spearman
-      value: 88.22738098593608
-    - type: manhattan_pearson
-      value: 87.23872691839607
-    - type: manhattan_spearman
-      value: 88.2002968380165
-  - task:
-      type: Retrieval
-    dataset:
-      type: hotpotqa
-      name: MTEB HotpotQA
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 31.81
-    - type: map_at_10
-      value: 46.238
-    - type: map_at_100
-      value: 47.141
-    - type: map_at_1000
-      value: 47.213
-    - type: map_at_3
-      value: 43.248999999999995
-    - type: map_at_5
-      value: 45.078
-    - type: mrr_at_1
-      value: 63.619
-    - type: mrr_at_10
-      value: 71.279
-    - type: mrr_at_100
-      value: 71.648
-    - type: mrr_at_1000
-      value: 71.665
-    - type: mrr_at_3
-      value: 69.76599999999999
-    - type: mrr_at_5
-      value: 70.743
-    - type: ndcg_at_1
-      value: 63.619
-    - type: ndcg_at_10
-      value: 55.38999999999999
-    - type: ndcg_at_100
-      value: 58.80800000000001
-    - type: ndcg_at_1000
-      value: 60.331999999999994
-    - type: ndcg_at_3
-      value: 50.727
-    - type: ndcg_at_5
-      value: 53.284
-    - type: precision_at_1
-      value: 63.619
-    - type: precision_at_10
-      value: 11.668000000000001
-    - type: precision_at_100
-      value: 1.434
-    - type: precision_at_1000
-      value: 0.164
-    - type: precision_at_3
-      value: 32.001000000000005
-    - type: precision_at_5
-      value: 21.223
-    - type: recall_at_1
-      value: 31.81
-    - type: recall_at_10
-      value: 58.339
-    - type: recall_at_100
-      value: 71.708
-    - type: recall_at_1000
-      value: 81.85
-    - type: recall_at_3
-      value: 48.001
-    - type: recall_at_5
-      value: 53.059
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/imdb
-      name: MTEB ImdbClassification
-      config: default
-      split: test
-      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
-    metrics:
-    - type: accuracy
-      value: 68.60640000000001
-    - type: ap
-      value: 62.84296904042086
-    - type: f1
-      value: 68.50643633327537
-  - task:
-      type: Reranking
-    dataset:
-      type: jinaai/miracl
-      name: MTEB MIRACL
-      config: default
-      split: test
-      revision: 8741c3b61cd36ed9ca1b3d4203543a41793239e2
-    metrics:
-    - type: map
-      value: 64.29704335389768
-    - type: mrr
-      value: 72.11962197159565
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (en)
-      config: en
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 89.3844049247606
-    - type: f1
-      value: 89.2124328528015
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (de)
-      config: de
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 88.36855452240067
-    - type: f1
-      value: 87.35458822097442
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification (en)
-      config: en
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 66.48654810761514
-    - type: f1
-      value: 50.07229882504409
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification (de)
-      config: de
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 63.832065370526905
-    - type: f1
-      value: 46.283579383385806
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_intent
-      name: MTEB MassiveIntentClassification (de)
-      config: de
-      split: test
-      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
-    metrics:
-    - type: accuracy
-      value: 63.89038332212509
-    - type: f1
-      value: 61.86279849685129
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_intent
-      name: MTEB MassiveIntentClassification (en)
-      config: en
-      split: test
-      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
-    metrics:
-    - type: accuracy
-      value: 69.11230665770006
-    - type: f1
-      value: 67.44780095350535
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_scenario
-      name: MTEB MassiveScenarioClassification (de)
-      config: de
-      split: test
-      revision: 7d571f92784cd94a019292a1f45445077d0ef634
-    metrics:
-    - type: accuracy
-      value: 71.25084061869536
-    - type: f1
-      value: 71.43965023016408
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_scenario
-      name: MTEB MassiveScenarioClassification (en)
-      config: en
-      split: test
-      revision: 7d571f92784cd94a019292a1f45445077d0ef634
-    metrics:
-    - type: accuracy
-      value: 73.73907195696032
-    - type: f1
-      value: 73.69920814839061
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-p2p
-      name: MTEB MedrxivClusteringP2P
-      config: default
-      split: test
-      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
-    metrics:
-    - type: v_measure
-      value: 31.32577306498249
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-s2s
-      name: MTEB MedrxivClusteringS2S
-      config: default
-      split: test
-      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
-    metrics:
-    - type: v_measure
-      value: 28.759349326367783
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/mind_small
-      name: MTEB MindSmallReranking
-      config: default
-      split: test
-      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
-    metrics:
-    - type: map
-      value: 30.401342674703425
-    - type: mrr
-      value: 31.384379585660987
-  - task:
-      type: Retrieval
-    dataset:
-      type: nfcorpus
-      name: MTEB NFCorpus
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 4.855
-    - type: map_at_10
-      value: 10.01
-    - type: map_at_100
-      value: 12.461
-    - type: map_at_1000
-      value: 13.776
-    - type: map_at_3
-      value: 7.252
-    - type: map_at_5
-      value: 8.679
-    - type: mrr_at_1
-      value: 41.176
-    - type: mrr_at_10
-      value: 49.323
-    - type: mrr_at_100
-      value: 49.954
-    - type: mrr_at_1000
-      value: 49.997
-    - type: mrr_at_3
-      value: 46.904
-    - type: mrr_at_5
-      value: 48.375
-    - type: ndcg_at_1
-      value: 39.318999999999996
-    - type: ndcg_at_10
-      value: 28.607
-    - type: ndcg_at_100
-      value: 26.554
-    - type: ndcg_at_1000
-      value: 35.731
-    - type: ndcg_at_3
-      value: 32.897999999999996
-    - type: ndcg_at_5
-      value: 31.53
-    - type: precision_at_1
-      value: 41.176
-    - type: precision_at_10
-      value: 20.867
-    - type: precision_at_100
-      value: 6.796
-    - type: precision_at_1000
-      value: 1.983
-    - type: precision_at_3
-      value: 30.547
-    - type: precision_at_5
-      value: 27.245
-    - type: recall_at_1
-      value: 4.855
-    - type: recall_at_10
-      value: 14.08
-    - type: recall_at_100
-      value: 28.188000000000002
-    - type: recall_at_1000
-      value: 60.07900000000001
-    - type: recall_at_3
-      value: 7.947
-    - type: recall_at_5
-      value: 10.786
-  - task:
-      type: Retrieval
-    dataset:
-      type: nq
-      name: MTEB NQ
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 26.906999999999996
-    - type: map_at_10
-      value: 41.147
-    - type: map_at_100
-      value: 42.269
-    - type: map_at_1000
-      value: 42.308
-    - type: map_at_3
-      value: 36.638999999999996
-    - type: map_at_5
-      value: 39.285
-    - type: mrr_at_1
-      value: 30.359
-    - type: mrr_at_10
-      value: 43.607
-    - type: mrr_at_100
-      value: 44.454
-    - type: mrr_at_1000
-      value: 44.481
-    - type: mrr_at_3
-      value: 39.644
-    - type: mrr_at_5
-      value: 42.061
-    - type: ndcg_at_1
-      value: 30.330000000000002
-    - type: ndcg_at_10
-      value: 48.899
-    - type: ndcg_at_100
-      value: 53.612
-    - type: ndcg_at_1000
-      value: 54.51200000000001
-    - type: ndcg_at_3
-      value: 40.262
-    - type: ndcg_at_5
-      value: 44.787
-    - type: precision_at_1
-      value: 30.330000000000002
-    - type: precision_at_10
-      value: 8.323
-    - type: precision_at_100
-      value: 1.0959999999999999
-    - type: precision_at_1000
-      value: 0.11800000000000001
-    - type: precision_at_3
-      value: 18.395
-    - type: precision_at_5
-      value: 13.627
-    - type: recall_at_1
-      value: 26.906999999999996
-    - type: recall_at_10
-      value: 70.215
-    - type: recall_at_100
-      value: 90.61200000000001
-    - type: recall_at_1000
-      value: 97.294
-    - type: recall_at_3
-      value: 47.784
-    - type: recall_at_5
-      value: 58.251
-  - task:
-      type: PairClassification
-    dataset:
-      type: paws-x
-      name: MTEB PawsX
-      config: default
-      split: test
-      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
-    metrics:
-    - type: cos_sim_accuracy
-      value: 60.5
-    - type: cos_sim_ap
-      value: 57.606096528877494
-    - type: cos_sim_f1
-      value: 62.24240307369892
-    - type: cos_sim_precision
-      value: 45.27439024390244
-    - type: cos_sim_recall
-      value: 99.55307262569832
-    - type: dot_accuracy
-      value: 57.699999999999996
-    - type: dot_ap
-      value: 51.289351057160616
-    - type: dot_f1
-      value: 62.25953130465197
-    - type: dot_precision
-      value: 45.31568228105906
-    - type: dot_recall
-      value: 99.4413407821229
-    - type: euclidean_accuracy
-      value: 60.45
-    - type: euclidean_ap
-      value: 57.616461421424034
-    - type: euclidean_f1
-      value: 62.313697657913416
-    - type: euclidean_precision
-      value: 45.657826313052524
-    - type: euclidean_recall
-      value: 98.10055865921787
-    - type: manhattan_accuracy
-      value: 60.3
-    - type: manhattan_ap
-      value: 57.580565271667325
-    - type: manhattan_f1
-      value: 62.24240307369892
-    - type: manhattan_precision
-      value: 45.27439024390244
-    - type: manhattan_recall
-      value: 99.55307262569832
-    - type: max_accuracy
-      value: 60.5
-    - type: max_ap
-      value: 57.616461421424034
-    - type: max_f1
-      value: 62.313697657913416
-  - task:
-      type: Retrieval
-    dataset:
-      type: quora
-      name: MTEB QuoraRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 70.21300000000001
-    - type: map_at_10
-      value: 84.136
-    - type: map_at_100
-      value: 84.796
-    - type: map_at_1000
-      value: 84.812
-    - type: map_at_3
-      value: 81.182
-    - type: map_at_5
-      value: 83.027
-    - type: mrr_at_1
-      value: 80.91000000000001
-    - type: mrr_at_10
-      value: 87.155
-    - type: mrr_at_100
-      value: 87.27000000000001
-    - type: mrr_at_1000
-      value: 87.271
-    - type: mrr_at_3
-      value: 86.158
-    - type: mrr_at_5
-      value: 86.828
-    - type: ndcg_at_1
-      value: 80.88
-    - type: ndcg_at_10
-      value: 87.926
-    - type: ndcg_at_100
-      value: 89.223
-    - type: ndcg_at_1000
-      value: 89.321
-    - type: ndcg_at_3
-      value: 85.036
-    - type: ndcg_at_5
-      value: 86.614
-    - type: precision_at_1
-      value: 80.88
-    - type: precision_at_10
-      value: 13.350000000000001
-    - type: precision_at_100
-      value: 1.5310000000000001
-    - type: precision_at_1000
-      value: 0.157
-    - type: precision_at_3
-      value: 37.173
-    - type: precision_at_5
-      value: 24.476
-    - type: recall_at_1
-      value: 70.21300000000001
-    - type: recall_at_10
-      value: 95.12
-    - type: recall_at_100
-      value: 99.535
-    - type: recall_at_1000
-      value: 99.977
-    - type: recall_at_3
-      value: 86.833
-    - type: recall_at_5
-      value: 91.26100000000001
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering
-      name: MTEB RedditClustering
-      config: default
-      split: test
-      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
-    metrics:
-    - type: v_measure
-      value: 47.754688783184875
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering-p2p
-      name: MTEB RedditClusteringP2P
-      config: default
-      split: test
-      revision: 282350215ef01743dc01b456c7f5241fa8937f16
-    metrics:
-    - type: v_measure
-      value: 54.875736374329364
-  - task:
-      type: Retrieval
-    dataset:
-      type: scidocs
-      name: MTEB SCIDOCS
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 3.773
-    - type: map_at_10
-      value: 9.447
-    - type: map_at_100
-      value: 11.1
-    - type: map_at_1000
-      value: 11.37
-    - type: map_at_3
-      value: 6.787
-    - type: map_at_5
-      value: 8.077
-    - type: mrr_at_1
-      value: 18.5
-    - type: mrr_at_10
-      value: 28.227000000000004
-    - type: mrr_at_100
-      value: 29.445
-    - type: mrr_at_1000
-      value: 29.515
-    - type: mrr_at_3
-      value: 25.2
-    - type: mrr_at_5
-      value: 27.055
-    - type: ndcg_at_1
-      value: 18.5
-    - type: ndcg_at_10
-      value: 16.29
-    - type: ndcg_at_100
-      value: 23.250999999999998
-    - type: ndcg_at_1000
-      value: 28.445999999999998
-    - type: ndcg_at_3
-      value: 15.376000000000001
-    - type: ndcg_at_5
-      value: 13.528
-    - type: precision_at_1
-      value: 18.5
-    - type: precision_at_10
-      value: 8.51
-    - type: precision_at_100
-      value: 1.855
-    - type: precision_at_1000
-      value: 0.311
-    - type: precision_at_3
-      value: 14.533
-    - type: precision_at_5
-      value: 12.0
-    - type: recall_at_1
-      value: 3.773
-    - type: recall_at_10
-      value: 17.282
-    - type: recall_at_100
-      value: 37.645
-    - type: recall_at_1000
-      value: 63.138000000000005
-    - type: recall_at_3
-      value: 8.853
-    - type: recall_at_5
-      value: 12.168
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sickr-sts
-      name: MTEB SICK-R
-      config: default
-      split: test
-      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
-    metrics:
-    - type: cos_sim_pearson
-      value: 85.32789517976525
-    - type: cos_sim_spearman
-      value: 80.32750384145629
-    - type: euclidean_pearson
-      value: 81.5025131452508
-    - type: euclidean_spearman
-      value: 80.24797115147175
-    - type: manhattan_pearson
-      value: 81.51634463412002
-    - type: manhattan_spearman
-      value: 80.24614721495055
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts12-sts
-      name: MTEB STS12
-      config: default
-      split: test
-      revision: a0d554a64d88156834ff5ae9920b964011b16384
-    metrics:
-    - type: cos_sim_pearson
-      value: 88.47050448992432
-    - type: cos_sim_spearman
-      value: 80.58919997743621
-    - type: euclidean_pearson
-      value: 85.83258918113664
-    - type: euclidean_spearman
-      value: 80.97441389240902
-    - type: manhattan_pearson
-      value: 85.7798262013878
-    - type: manhattan_spearman
-      value: 80.97208703064196
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts13-sts
-      name: MTEB STS13
-      config: default
-      split: test
-      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
-    metrics:
-    - type: cos_sim_pearson
-      value: 85.95341439711532
-    - type: cos_sim_spearman
-      value: 86.59127484634989
-    - type: euclidean_pearson
-      value: 85.57850603454227
-    - type: euclidean_spearman
-      value: 86.47130477363419
-    - type: manhattan_pearson
-      value: 85.59387925447652
-    - type: manhattan_spearman
-      value: 86.50665427391583
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts14-sts
-      name: MTEB STS14
-      config: default
-      split: test
-      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
-    metrics:
-    - type: cos_sim_pearson
-      value: 85.39810909161844
-    - type: cos_sim_spearman
-      value: 82.98595295546008
-    - type: euclidean_pearson
-      value: 84.04681129969951
-    - type: euclidean_spearman
-      value: 82.98197460689866
-    - type: manhattan_pearson
-      value: 83.9918798171185
-    - type: manhattan_spearman
-      value: 82.91148131768082
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts15-sts
-      name: MTEB STS15
-      config: default
-      split: test
-      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
-    metrics:
-    - type: cos_sim_pearson
-      value: 88.02072712147692
-    - type: cos_sim_spearman
-      value: 88.78821332623012
-    - type: euclidean_pearson
-      value: 88.12132045572747
-    - type: euclidean_spearman
-      value: 88.74273451067364
-    - type: manhattan_pearson
-      value: 88.05431550059166
-    - type: manhattan_spearman
-      value: 88.67610233020723
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts16-sts
-      name: MTEB STS16
-      config: default
-      split: test
-      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
-    metrics:
-    - type: cos_sim_pearson
-      value: 82.96134704624787
-    - type: cos_sim_spearman
-      value: 84.44062976314666
-    - type: euclidean_pearson
-      value: 84.03642536310323
-    - type: euclidean_spearman
-      value: 84.4535014579785
-    - type: manhattan_pearson
-      value: 83.92874228901483
-    - type: manhattan_spearman
-      value: 84.33634314951631
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (en-de)
-      config: en-de
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 87.3154168064887
-    - type: cos_sim_spearman
-      value: 86.72393652571682
-    - type: euclidean_pearson
-      value: 86.04193246174164
-    - type: euclidean_spearman
-      value: 86.30482896608093
-    - type: manhattan_pearson
-      value: 85.95524084651859
-    - type: manhattan_spearman
-      value: 86.06031431994282
-  - 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: 89.91079682750804
-    - type: cos_sim_spearman
-      value: 89.30961836617064
-    - type: euclidean_pearson
-      value: 88.86249564158628
-    - type: euclidean_spearman
-      value: 89.04772899592396
-    - type: manhattan_pearson
-      value: 88.85579791315043
-    - type: manhattan_spearman
-      value: 88.94190462541333
-  - 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: 67.00558145551088
-    - type: cos_sim_spearman
-      value: 67.96601170393878
-    - type: euclidean_pearson
-      value: 67.87627043214336
-    - type: euclidean_spearman
-      value: 66.76402572303859
-    - type: manhattan_pearson
-      value: 67.88306560555452
-    - type: manhattan_spearman
-      value: 66.6273862035506
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (de)
-      config: de
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 50.83759332748726
-    - type: cos_sim_spearman
-      value: 59.066344562858006
-    - type: euclidean_pearson
-      value: 50.08955848154131
-    - type: euclidean_spearman
-      value: 58.36517305855221
-    - type: manhattan_pearson
-      value: 50.05257267223111
-    - type: manhattan_spearman
-      value: 58.37570252804986
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (de-en)
-      config: de-en
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 59.22749007956492
-    - type: cos_sim_spearman
-      value: 55.97282077657827
-    - type: euclidean_pearson
-      value: 62.10661533695752
-    - type: euclidean_spearman
-      value: 53.62780854854067
-    - type: manhattan_pearson
-      value: 62.37138085709719
-    - type: manhattan_spearman
-      value: 54.17556356828155
-  - task:
-      type: STS
-    dataset:
-      type: mteb/stsbenchmark-sts
-      name: MTEB STSBenchmark
-      config: default
-      split: test
-      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
-    metrics:
-    - type: cos_sim_pearson
-      value: 87.91145397065878
-    - type: cos_sim_spearman
-      value: 88.13960018389005
-    - type: euclidean_pearson
-      value: 87.67618876224006
-    - type: euclidean_spearman
-      value: 87.99119480810556
-    - type: manhattan_pearson
-      value: 87.67920297334753
-    - type: manhattan_spearman
-      value: 87.99113250064492
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/scidocs-reranking
-      name: MTEB SciDocsRR
-      config: default
-      split: test
-      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
-    metrics:
-    - type: map
-      value: 78.09133563707582
-    - type: mrr
-      value: 93.2415288052543
-  - task:
-      type: Retrieval
-    dataset:
-      type: scifact
-      name: MTEB SciFact
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 47.760999999999996
-    - type: map_at_10
-      value: 56.424
-    - type: map_at_100
-      value: 57.24399999999999
-    - type: map_at_1000
-      value: 57.278
-    - type: map_at_3
-      value: 53.68000000000001
-    - type: map_at_5
-      value: 55.442
-    - type: mrr_at_1
-      value: 50.666999999999994
-    - type: mrr_at_10
-      value: 58.012
-    - type: mrr_at_100
-      value: 58.736
-    - type: mrr_at_1000
-      value: 58.769000000000005
-    - type: mrr_at_3
-      value: 56.056
-    - type: mrr_at_5
-      value: 57.321999999999996
-    - type: ndcg_at_1
-      value: 50.666999999999994
-    - type: ndcg_at_10
-      value: 60.67700000000001
-    - type: ndcg_at_100
-      value: 64.513
-    - type: ndcg_at_1000
-      value: 65.62400000000001
-    - type: ndcg_at_3
-      value: 56.186
-    - type: ndcg_at_5
-      value: 58.692
-    - type: precision_at_1
-      value: 50.666999999999994
-    - type: precision_at_10
-      value: 8.200000000000001
-    - type: precision_at_100
-      value: 1.023
-    - type: precision_at_1000
-      value: 0.11199999999999999
-    - type: precision_at_3
-      value: 21.889
-    - type: precision_at_5
-      value: 14.866999999999999
-    - type: recall_at_1
-      value: 47.760999999999996
-    - type: recall_at_10
-      value: 72.006
-    - type: recall_at_100
-      value: 89.767
-    - type: recall_at_1000
-      value: 98.833
-    - type: recall_at_3
-      value: 60.211000000000006
-    - type: recall_at_5
-      value: 66.3
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/sprintduplicatequestions-pairclassification
-      name: MTEB SprintDuplicateQuestions
-      config: default
-      split: test
-      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
-    metrics:
-    - type: cos_sim_accuracy
-      value: 99.79009900990098
-    - type: cos_sim_ap
-      value: 94.86690691995835
-    - type: cos_sim_f1
-      value: 89.37875751503007
-    - type: cos_sim_precision
-      value: 89.5582329317269
-    - type: cos_sim_recall
-      value: 89.2
-    - type: dot_accuracy
-      value: 99.76336633663367
-    - type: dot_ap
-      value: 94.26453740761586
-    - type: dot_f1
-      value: 88.00783162016641
-    - type: dot_precision
-      value: 86.19367209971237
-    - type: dot_recall
-      value: 89.9
-    - type: euclidean_accuracy
-      value: 99.7940594059406
-    - type: euclidean_ap
-      value: 94.85459757524379
-    - type: euclidean_f1
-      value: 89.62779156327544
-    - type: euclidean_precision
-      value: 88.96551724137932
-    - type: euclidean_recall
-      value: 90.3
-    - type: manhattan_accuracy
-      value: 99.79009900990098
-    - type: manhattan_ap
-      value: 94.76971336654465
-    - type: manhattan_f1
-      value: 89.35323383084577
-    - type: manhattan_precision
-      value: 88.91089108910892
-    - type: manhattan_recall
-      value: 89.8
-    - type: max_accuracy
-      value: 99.7940594059406
-    - type: max_ap
-      value: 94.86690691995835
-    - type: max_f1
-      value: 89.62779156327544
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering
-      name: MTEB StackExchangeClustering
-      config: default
-      split: test
-      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
-    metrics:
-    - type: v_measure
-      value: 55.38197670064987
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering-p2p
-      name: MTEB StackExchangeClusteringP2P
-      config: default
-      split: test
-      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
-    metrics:
-    - type: v_measure
-      value: 33.08330158937971
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/stackoverflowdupquestions-reranking
-      name: MTEB StackOverflowDupQuestions
-      config: default
-      split: test
-      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
-    metrics:
-    - type: map
-      value: 49.50367079063226
-    - type: mrr
-      value: 50.30444943128768
-  - task:
-      type: Summarization
-    dataset:
-      type: mteb/summeval
-      name: MTEB SummEval
-      config: default
-      split: test
-      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
-    metrics:
-    - type: cos_sim_pearson
-      value: 30.37739520909561
-    - type: cos_sim_spearman
-      value: 31.548500943973913
-    - type: dot_pearson
-      value: 29.983610104303
-    - type: dot_spearman
-      value: 29.90185869098618
-  - task:
-      type: Retrieval
-    dataset:
-      type: trec-covid
-      name: MTEB TRECCOVID
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 0.198
-    - type: map_at_10
-      value: 1.5810000000000002
-    - type: map_at_100
-      value: 9.064
-    - type: map_at_1000
-      value: 22.161
-    - type: map_at_3
-      value: 0.536
-    - type: map_at_5
-      value: 0.8370000000000001
-    - type: mrr_at_1
-      value: 80.0
-    - type: mrr_at_10
-      value: 86.75
-    - type: mrr_at_100
-      value: 86.799
-    - type: mrr_at_1000
-      value: 86.799
-    - type: mrr_at_3
-      value: 85.0
-    - type: mrr_at_5
-      value: 86.5
-    - type: ndcg_at_1
-      value: 73.0
-    - type: ndcg_at_10
-      value: 65.122
-    - type: ndcg_at_100
-      value: 51.853
-    - type: ndcg_at_1000
-      value: 47.275
-    - type: ndcg_at_3
-      value: 66.274
-    - type: ndcg_at_5
-      value: 64.826
-    - type: precision_at_1
-      value: 80.0
-    - type: precision_at_10
-      value: 70.19999999999999
-    - type: precision_at_100
-      value: 53.480000000000004
-    - type: precision_at_1000
-      value: 20.946
-    - type: precision_at_3
-      value: 71.333
-    - type: precision_at_5
-      value: 70.0
-    - type: recall_at_1
-      value: 0.198
-    - type: recall_at_10
-      value: 1.884
-    - type: recall_at_100
-      value: 12.57
-    - type: recall_at_1000
-      value: 44.208999999999996
-    - type: recall_at_3
-      value: 0.5890000000000001
-    - type: recall_at_5
-      value: 0.95
-  - task:
-      type: Clustering
-    dataset:
-      type: slvnwhrl/tenkgnad-clustering-p2p
-      name: MTEB TenKGnadClusteringP2P
-      config: default
-      split: test
-      revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558
-    metrics:
-    - type: v_measure
-      value: 42.84199261133083
-  - task:
-      type: Clustering
-    dataset:
-      type: slvnwhrl/tenkgnad-clustering-s2s
-      name: MTEB TenKGnadClusteringS2S
-      config: default
-      split: test
-      revision: 6cddbe003f12b9b140aec477b583ac4191f01786
-    metrics:
-    - type: v_measure
-      value: 23.689557114798838
-  - task:
-      type: Retrieval
-    dataset:
-      type: webis-touche2020
-      name: MTEB Touche2020
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 1.941
-    - type: map_at_10
-      value: 8.222
-    - type: map_at_100
-      value: 14.277999999999999
-    - type: map_at_1000
-      value: 15.790000000000001
-    - type: map_at_3
-      value: 4.4670000000000005
-    - type: map_at_5
-      value: 5.762
-    - type: mrr_at_1
-      value: 24.490000000000002
-    - type: mrr_at_10
-      value: 38.784
-    - type: mrr_at_100
-      value: 39.724
-    - type: mrr_at_1000
-      value: 39.724
-    - type: mrr_at_3
-      value: 33.333
-    - type: mrr_at_5
-      value: 37.415
-    - type: ndcg_at_1
-      value: 22.448999999999998
-    - type: ndcg_at_10
-      value: 21.026
-    - type: ndcg_at_100
-      value: 33.721000000000004
-    - type: ndcg_at_1000
-      value: 45.045
-    - type: ndcg_at_3
-      value: 20.053
-    - type: ndcg_at_5
-      value: 20.09
-    - type: precision_at_1
-      value: 24.490000000000002
-    - type: precision_at_10
-      value: 19.796
-    - type: precision_at_100
-      value: 7.469
-    - type: precision_at_1000
-      value: 1.48
-    - type: precision_at_3
-      value: 21.769
-    - type: precision_at_5
-      value: 21.224
-    - type: recall_at_1
-      value: 1.941
-    - type: recall_at_10
-      value: 14.915999999999999
-    - type: recall_at_100
-      value: 46.155
-    - type: recall_at_1000
-      value: 80.664
-    - type: recall_at_3
-      value: 5.629
-    - type: recall_at_5
-      value: 8.437
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/toxic_conversations_50k
-      name: MTEB ToxicConversationsClassification
-      config: default
-      split: test
-      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
-    metrics:
-    - type: accuracy
-      value: 69.64800000000001
-    - type: ap
-      value: 12.914826731261094
-    - type: f1
-      value: 53.05213503422915
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/tweet_sentiment_extraction
-      name: MTEB TweetSentimentExtractionClassification
-      config: default
-      split: test
-      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
-    metrics:
-    - type: accuracy
-      value: 60.427277872099594
-    - type: f1
-      value: 60.78292007556828
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/twentynewsgroups-clustering
-      name: MTEB TwentyNewsgroupsClustering
-      config: default
-      split: test
-      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
-    metrics:
-    - type: v_measure
-      value: 40.48134168406559
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twittersemeval2015-pairclassification
-      name: MTEB TwitterSemEval2015
-      config: default
-      split: test
-      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
-    metrics:
-    - type: cos_sim_accuracy
-      value: 84.79465935506944
-    - type: cos_sim_ap
-      value: 70.24589055290592
-    - type: cos_sim_f1
-      value: 65.0994575045208
-    - type: cos_sim_precision
-      value: 63.76518218623482
-    - type: cos_sim_recall
-      value: 66.49076517150397
-    - type: dot_accuracy
-      value: 84.63968528342374
-    - type: dot_ap
-      value: 69.84683095084355
-    - type: dot_f1
-      value: 64.50606169727523
-    - type: dot_precision
-      value: 59.1719885487778
-    - type: dot_recall
-      value: 70.89709762532982
-    - type: euclidean_accuracy
-      value: 84.76485664898374
-    - type: euclidean_ap
-      value: 70.20556438685551
-    - type: euclidean_f1
-      value: 65.06796614516543
-    - type: euclidean_precision
-      value: 63.29840319361277
-    - type: euclidean_recall
-      value: 66.93931398416886
-    - type: manhattan_accuracy
-      value: 84.72313286046374
-    - type: manhattan_ap
-      value: 70.17151475534308
-    - type: manhattan_f1
-      value: 65.31379180759113
-    - type: manhattan_precision
-      value: 62.17505366086334
-    - type: manhattan_recall
-      value: 68.7862796833773
-    - type: max_accuracy
-      value: 84.79465935506944
-    - type: max_ap
-      value: 70.24589055290592
-    - type: max_f1
-      value: 65.31379180759113
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twitterurlcorpus-pairclassification
-      name: MTEB TwitterURLCorpus
-      config: default
-      split: test
-      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
-    metrics:
-    - type: cos_sim_accuracy
-      value: 88.95874568246207
-    - type: cos_sim_ap
-      value: 85.82517548264127
-    - type: cos_sim_f1
-      value: 78.22288041466125
-    - type: cos_sim_precision
-      value: 75.33875338753387
-    - type: cos_sim_recall
-      value: 81.33661841700031
-    - type: dot_accuracy
-      value: 88.836496293709
-    - type: dot_ap
-      value: 85.53430720252186
-    - type: dot_f1
-      value: 78.10616085869725
-    - type: dot_precision
-      value: 74.73269555430501
-    - type: dot_recall
-      value: 81.79858330766862
-    - type: euclidean_accuracy
-      value: 88.92769821865176
-    - type: euclidean_ap
-      value: 85.65904346964223
-    - type: euclidean_f1
-      value: 77.98774074208407
-    - type: euclidean_precision
-      value: 73.72282795035315
-    - type: euclidean_recall
-      value: 82.77640899291654
-    - type: manhattan_accuracy
-      value: 88.86366282454303
-    - type: manhattan_ap
-      value: 85.61599642231819
-    - type: manhattan_f1
-      value: 78.01480509061737
-    - type: manhattan_precision
-      value: 74.10460685833044
-    - type: manhattan_recall
-      value: 82.36064059131506
-    - type: max_accuracy
-      value: 88.95874568246207
-    - type: max_ap
-      value: 85.82517548264127
-    - type: max_f1
-      value: 78.22288041466125
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB WikiCLIR
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 3.9539999999999997
-    - type: map_at_10
-      value: 7.407
-    - type: map_at_100
-      value: 8.677999999999999
-    - type: map_at_1000
-      value: 9.077
-    - type: map_at_3
-      value: 5.987
-    - type: map_at_5
-      value: 6.6979999999999995
-    - type: mrr_at_1
-      value: 35.65
-    - type: mrr_at_10
-      value: 45.097
-    - type: mrr_at_100
-      value: 45.83
-    - type: mrr_at_1000
-      value: 45.871
-    - type: mrr_at_3
-      value: 42.63
-    - type: mrr_at_5
-      value: 44.104
-    - type: ndcg_at_1
-      value: 29.215000000000003
-    - type: ndcg_at_10
-      value: 22.694
-    - type: ndcg_at_100
-      value: 22.242
-    - type: ndcg_at_1000
-      value: 27.069
-    - type: ndcg_at_3
-      value: 27.641
-    - type: ndcg_at_5
-      value: 25.503999999999998
-    - type: precision_at_1
-      value: 35.65
-    - type: precision_at_10
-      value: 12.795000000000002
-    - type: precision_at_100
-      value: 3.354
-    - type: precision_at_1000
-      value: 0.743
-    - type: precision_at_3
-      value: 23.403
-    - type: precision_at_5
-      value: 18.474
-    - type: recall_at_1
-      value: 3.9539999999999997
-    - type: recall_at_10
-      value: 11.301
-    - type: recall_at_100
-      value: 22.919999999999998
-    - type: recall_at_1000
-      value: 40.146
-    - type: recall_at_3
-      value: 7.146
-    - type: recall_at_5
-      value: 8.844000000000001
-  - task:
-      type: Retrieval
-    dataset:
-      type: jinaai/xmarket_de
-      name: MTEB XMarket
-      config: default
-      split: test
-      revision: 2336818db4c06570fcdf263e1bcb9993b786f67a
-    metrics:
-    - type: map_at_1
-      value: 4.872
-    - type: map_at_10
-      value: 10.658
-    - type: map_at_100
-      value: 13.422999999999998
-    - type: map_at_1000
-      value: 14.245
-    - type: map_at_3
-      value: 7.857
-    - type: map_at_5
-      value: 9.142999999999999
-    - type: mrr_at_1
-      value: 16.744999999999997
-    - type: mrr_at_10
-      value: 24.416
-    - type: mrr_at_100
-      value: 25.432
-    - type: mrr_at_1000
-      value: 25.502999999999997
-    - type: mrr_at_3
-      value: 22.096
-    - type: mrr_at_5
-      value: 23.421
-    - type: ndcg_at_1
-      value: 16.695999999999998
-    - type: ndcg_at_10
-      value: 18.66
-    - type: ndcg_at_100
-      value: 24.314
-    - type: ndcg_at_1000
-      value: 29.846
-    - type: ndcg_at_3
-      value: 17.041999999999998
-    - type: ndcg_at_5
-      value: 17.585
-    - type: precision_at_1
-      value: 16.695999999999998
-    - type: precision_at_10
-      value: 10.374
-    - type: precision_at_100
-      value: 3.988
-    - type: precision_at_1000
-      value: 1.1860000000000002
-    - type: precision_at_3
-      value: 14.21
-    - type: precision_at_5
-      value: 12.623000000000001
-    - type: recall_at_1
-      value: 4.872
-    - type: recall_at_10
-      value: 18.624
-    - type: recall_at_100
-      value: 40.988
-    - type: recall_at_1000
-      value: 65.33
-    - type: recall_at_3
-      value: 10.162
-    - type: recall_at_5
-      value: 13.517999999999999
----
-<!-- TODO: add evaluation results here -->
-<br><br>
-
-<p align="center">
-<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" 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-de` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
-
-## Intended Usage & Model Info
-
-`jina-embeddings-v2-base-de` is a German/English bilingual text **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.
-We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed German-English input without bias. 
-Additionally, we provide the following embedding models:
-
-`jina-embeddings-v2-base-de` ist ein zweisprachiges **Text Embedding Modell** für Deutsch und Englisch,
-welches Texteingaben mit einer Länge von bis zu **8192 Token unterstützt**.
-Es basiert auf der adaptierten Bert-Modell-Architektur JinaBERT,
-welche mithilfe einer symmetrische Variante von [ALiBi](https://arxiv.org/abs/2108.12409) längere Eingabetexte erlaubt.
-Wir haben, das Model für hohe Performance in einsprachigen und cross-lingual Anwendungen entwickelt und speziell darauf trainiert,
-gemischte deutsch-englische Eingaben ohne einen Bias zu kodieren.
-Des Weiteren stellen wir folgende Embedding-Modelle bereit:
-
-- [`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.
-- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English Bilingual embeddings.
-- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English Bilingual embeddings **(you are here)**.
-- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon).
-- [`jina-embeddings-v2-base-code`](https://huggingface.co/jinaai/jina-embeddings-v2-base-code): 161 million parameters code embeddings.
-
-## Data & Parameters
-
-The data and training details are described in this [technical report](https://arxiv.org/abs/2402.17016).
-
-## 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-base-de')
-model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True, torch_dtype=torch.bfloat16)
-
-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
-import torch
-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-de', trust_remote_code=True, torch_dtype=torch.bfloat16)
-embeddings = model.encode(['How is the weather today?', 'Wie ist das Wetter heute?'])
-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-de", # 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?',
-    'Wie ist das Wetter heute?'
-])
-print(cos_sim(embeddings[0], embeddings[1]))
-```
-
-## Alternatives to Using Transformers 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).
-
-## Benchmark Results
-
-We evaluated our Bilingual model on all German and English evaluation tasks availble on the [MTEB benchmark](https://huggingface.co/blog/mteb). In addition, we evaluated the models agains a couple of other German, English, and multilingual models on additional German evaluation tasks:
-
-<img src="de_evaluation_results.png" width="780px">
-
-## 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">
-
-## 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:
-
-```
-@article{mohr2024multi,
-  title={Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings},
-  author={Mohr, Isabelle and Krimmel, Markus and Sturua, Saba and Akram, Mohammad Kalim and Koukounas, Andreas and G{\"u}nther, Michael and Mastrapas, Georgios and Ravishankar, Vinit and Mart{\'\i}nez, Joan Fontanals and Wang, Feng and others},
-  journal={arXiv preprint arXiv:2402.17016},
-  year={2024}
-}
-```