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1_Pooling/config.json

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

README.md

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+---
+tags:
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+  - mteb
+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://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Jina AI logo: Jina AI is your Portal to Multimodal AI" 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).
+
+## Data & Parameters
+
+We will publish a report with technical details about the training of the bilingual models soon.
+The training of the English model is described in this [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-base-de')
+model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True)
+
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+embeddings = F.normalize(embeddings, p=2, dim=1)
+```
+
+</p>
+</details>
+
+You can use Jina Embedding models directly from transformers package.
+
+First, you need to make sure that you are logged into huggingface. You can either use the huggingface-cli tool (after installing the `transformers` package) and pass your [hugginface access token](https://huggingface.co/docs/hub/security-tokens):
+```bash
+huggingface-cli login
+```
+Alternatively, you can provide the access token as an environment variable in the shell:
+```bash
+export HF_TOKEN="<your token here>"
+```
+or in Python:
+```python
+import os
+
+os.environ['HF_TOKEN'] = "<your token here>"
+```
+
+Then, you can use load and use the model via the `AutoModel` class:
+```python
+!pip install transformers
+from transformers import AutoModel
+from numpy.linalg import norm
+
+cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
+model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True) # trust_remote_code is needed to use the encode method
+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">
+
+## Trouble Shooting
+
+**Loading of Model Code failed**
+
+If you forgot to pass the `trust_remote_code=True` flag when calling `AutoModel.from_pretrained` or initializing the model via the `SentenceTransformer` class, you will receive an error that the model weights could not be initialized.
+This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model:
+
+```bash
+Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-en were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ...
+```
+
+
+**User is not logged into Huggingface**
+
+The model is only availabe under [gated access](https://huggingface.co/docs/hub/models-gated).
+This means you need to be logged into huggingface load load it.
+If you receive the following error, you need to provide an access token, either by using the huggingface-cli or providing the token via an environment variable as described above:
+```bash
+OSError: jinaai/jina-embeddings-v2-base-en is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
+If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`.
+```
+
+## Contact
+
+Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
+
+## Citation
+
+If you find Jina Embeddings useful in your research, please cite the following paper:
+
+```
+@misc{günther2023jina,
+      title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents}, 
+      author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao},
+      year={2023},
+      eprint={2310.19923},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

config.json

@@ -0,0 +1,35 @@
+{
+  "_name_or_path": "jinaai/jina-bert-implementation",
+  "model_max_length": 8192,
+  "architectures": [
+    "JinaBertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "jinaai/jina-bert-implementation--configuration_bert.JinaBertConfig",
+    "AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM",
+    "AutoModel": "jinaai/jina-bert-implementation--modeling_bert.JinaBertModel",
+    "AutoModelForSequenceClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForSequenceClassification"
+  },
+  "classifier_dropout": null,
+  "emb_pooler": "mean",
+  "feed_forward_type": "geglu",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 8192,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "alibi",
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 61056
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.31.0",
+    "pytorch": "2.0.1"
+  }
+}

modules.json

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

onnx/model.onnx

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pytorch_model.bin

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+size 321664570

sentence_bert_config.json

@@ -0,0 +1,5 @@
+{
+  "max_seq_length": 8192,
+  "do_lower_case": false,
+  "model_args": {"trust_remote_code": true}
+}

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}