sentence-transformers gguf feature-extraction sentence-similarity mteb llama-cpp gguf-my-repo es en base_model:jinaai/jina-embeddings-v2-base-es base_model:quantized:jinaai/jina-embeddings-v2-base-es license:apache-2.0 model-index autotrain_compatible region:us
---
base_model: jinaai/jina-embeddings-v2-base-es
language:
- es
- en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- llama-cpp
- gguf-my-repo
inference: false
model-index:
- name: jina-embeddings-v2-base-es
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 74.25373134328358
    - type: ap
      value: 37.05201236793268
    - type: f1
      value: 68.16770391201077
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 78.30885
    - type: ap
      value: 73.01622441156408
    - type: f1
      value: 78.20769284466313
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 38.324
    - type: f1
      value: 37.89543008761673
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (es)
      type: mteb/amazon_reviews_multi
      config: es
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 38.678000000000004
    - type: f1
      value: 38.122639506976
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 23.968999999999998
    - type: map_at_10
      value: 40.691
    - type: map_at_100
      value: 41.713
    - type: map_at_1000
      value: 41.719
    - type: map_at_3
      value: 35.42
    - type: map_at_5
      value: 38.442
    - type: mrr_at_1
      value: 24.395
    - type: mrr_at_10
      value: 40.853
    - type: mrr_at_100
      value: 41.869
    - type: mrr_at_1000
      value: 41.874
    - type: mrr_at_3
      value: 35.68
    - type: mrr_at_5
      value: 38.572
    - type: ndcg_at_1
      value: 23.968999999999998
    - type: ndcg_at_10
      value: 50.129999999999995
    - type: ndcg_at_100
      value: 54.364000000000004
    - type: ndcg_at_1000
      value: 54.494
    - type: ndcg_at_3
      value: 39.231
    - type: ndcg_at_5
      value: 44.694
    - type: precision_at_1
      value: 23.968999999999998
    - type: precision_at_10
      value: 8.036999999999999
    - type: precision_at_100
      value: 0.9860000000000001
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 16.761
    - type: precision_at_5
      value: 12.717
    - type: recall_at_1
      value: 23.968999999999998
    - type: recall_at_10
      value: 80.36999999999999
    - type: recall_at_100
      value: 98.578
    - type: recall_at_1000
      value: 99.57300000000001
    - type: recall_at_3
      value: 50.28399999999999
    - type: recall_at_5
      value: 63.585
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 41.54886683150053
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 32.186028697637234
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 61.19432643698725
    - type: mrr
      value: 75.28646176845622
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 86.3828259381228
    - type: cos_sim_spearman
      value: 83.04647058342209
    - type: euclidean_pearson
      value: 84.02895346096244
    - type: euclidean_spearman
      value: 82.34524978635342
    - type: manhattan_pearson
      value: 84.35030723233426
    - type: manhattan_spearman
      value: 83.17177464337936
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 85.25649350649351
    - type: f1
      value: 85.22320474023192
  - task:
      type: Clustering
    dataset:
      name: MTEB BigPatentClustering
      type: jinaai/big-patent-clustering
      config: default
      split: test
      revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
    metrics:
    - type: v_measure
      value: 20.42929408254094
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 35.165318177498136
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 28.89030154229562
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 30.119
    - type: map_at_10
      value: 42.092
    - type: map_at_100
      value: 43.506
    - type: map_at_1000
      value: 43.631
    - type: map_at_3
      value: 38.373000000000005
    - type: map_at_5
      value: 40.501
    - type: mrr_at_1
      value: 38.196999999999996
    - type: mrr_at_10
      value: 48.237
    - type: mrr_at_100
      value: 48.914
    - type: mrr_at_1000
      value: 48.959
    - type: mrr_at_3
      value: 45.279
    - type: mrr_at_5
      value: 47.11
    - type: ndcg_at_1
      value: 38.196999999999996
    - type: ndcg_at_10
      value: 48.849
    - type: ndcg_at_100
      value: 53.713
    - type: ndcg_at_1000
      value: 55.678000000000004
    - type: ndcg_at_3
      value: 43.546
    - type: ndcg_at_5
      value: 46.009
    - type: precision_at_1
      value: 38.196999999999996
    - type: precision_at_10
      value: 9.642000000000001
    - type: precision_at_100
      value: 1.5190000000000001
    - type: precision_at_1000
      value: 0.199
    - type: precision_at_3
      value: 21.65
    - type: precision_at_5
      value: 15.708
    - type: recall_at_1
      value: 30.119
    - type: recall_at_10
      value: 61.788
    - type: recall_at_100
      value: 82.14399999999999
    - type: recall_at_1000
      value: 95.003
    - type: recall_at_3
      value: 45.772
    - type: recall_at_5
      value: 53.04600000000001
    - type: map_at_1
      value: 28.979
    - type: map_at_10
      value: 37.785000000000004
    - type: map_at_100
      value: 38.945
    - type: map_at_1000
      value: 39.071
    - type: map_at_3
      value: 35.083999999999996
    - type: map_at_5
      value: 36.571999999999996
    - type: mrr_at_1
      value: 36.242000000000004
    - type: mrr_at_10
      value: 43.552
    - type: mrr_at_100
      value: 44.228
    - type: mrr_at_1000
      value: 44.275999999999996
    - type: mrr_at_3
      value: 41.359
    - type: mrr_at_5
      value: 42.598
    - type: ndcg_at_1
      value: 36.242000000000004
    - type: ndcg_at_10
      value: 42.94
    - type: ndcg_at_100
      value: 47.343
    - type: ndcg_at_1000
      value: 49.538
    - type: ndcg_at_3
      value: 39.086999999999996
    - type: ndcg_at_5
      value: 40.781
    - type: precision_at_1
      value: 36.242000000000004
    - type: precision_at_10
      value: 7.954999999999999
    - type: precision_at_100
      value: 1.303
    - type: precision_at_1000
      value: 0.178
    - type: precision_at_3
      value: 18.556
    - type: precision_at_5
      value: 13.145999999999999
    - type: recall_at_1
      value: 28.979
    - type: recall_at_10
      value: 51.835
    - type: recall_at_100
      value: 70.47
    - type: recall_at_1000
      value: 84.68299999999999
    - type: recall_at_3
      value: 40.410000000000004
    - type: recall_at_5
      value: 45.189
    - type: map_at_1
      value: 37.878
    - type: map_at_10
      value: 49.903
    - type: map_at_100
      value: 50.797000000000004
    - type: map_at_1000
      value: 50.858000000000004
    - type: map_at_3
      value: 46.526
    - type: map_at_5
      value: 48.615
    - type: mrr_at_1
      value: 43.135
    - type: mrr_at_10
      value: 53.067
    - type: mrr_at_100
      value: 53.668000000000006
    - type: mrr_at_1000
      value: 53.698
    - type: mrr_at_3
      value: 50.449
    - type: mrr_at_5
      value: 52.117000000000004
    - type: ndcg_at_1
      value: 43.135
    - type: ndcg_at_10
      value: 55.641
    - type: ndcg_at_100
      value: 59.427
    - type: ndcg_at_1000
      value: 60.655
    - type: ndcg_at_3
      value: 49.969
    - type: ndcg_at_5
      value: 53.075
    - type: precision_at_1
      value: 43.135
    - type: precision_at_10
      value: 8.997
    - type: precision_at_100
      value: 1.1809999999999998
    - type: precision_at_1000
      value: 0.133
    - type: precision_at_3
      value: 22.215
    - type: precision_at_5
      value: 15.586
    - type: recall_at_1
      value: 37.878
    - type: recall_at_10
      value: 69.405
    - type: recall_at_100
      value: 86.262
    - type: recall_at_1000
      value: 95.012
    - type: recall_at_3
      value: 54.458
    - type: recall_at_5
      value: 61.965
    - type: map_at_1
      value: 24.853
    - type: map_at_10
      value: 32.402
    - type: map_at_100
      value: 33.417
    - type: map_at_1000
      value: 33.498
    - type: map_at_3
      value: 30.024
    - type: map_at_5
      value: 31.407
    - type: mrr_at_1
      value: 26.667
    - type: mrr_at_10
      value: 34.399
    - type: mrr_at_100
      value: 35.284
    - type: mrr_at_1000
      value: 35.345
    - type: mrr_at_3
      value: 32.109
    - type: mrr_at_5
      value: 33.375
    - type: ndcg_at_1
      value: 26.667
    - type: ndcg_at_10
      value: 36.854
    - type: ndcg_at_100
      value: 42.196
    - type: ndcg_at_1000
      value: 44.303
    - type: ndcg_at_3
      value: 32.186
    - type: ndcg_at_5
      value: 34.512
    - type: precision_at_1
      value: 26.667
    - type: precision_at_10
      value: 5.559
    - type: precision_at_100
      value: 0.88
    - type: precision_at_1000
      value: 0.109
    - type: precision_at_3
      value: 13.333
    - type: precision_at_5
      value: 9.379
    - type: recall_at_1
      value: 24.853
    - type: recall_at_10
      value: 48.636
    - type: recall_at_100
      value: 73.926
    - type: recall_at_1000
      value: 89.94
    - type: recall_at_3
      value: 36.266
    - type: recall_at_5
      value: 41.723
    - type: map_at_1
      value: 14.963999999999999
    - type: map_at_10
      value: 22.591
    - type: map_at_100
      value: 23.735999999999997
    - type: map_at_1000
      value: 23.868000000000002
    - type: map_at_3
      value: 20.093
    - type: map_at_5
      value: 21.499
    - type: mrr_at_1
      value: 18.407999999999998
    - type: mrr_at_10
      value: 26.863
    - type: mrr_at_100
      value: 27.87
    - type: mrr_at_1000
      value: 27.947
    - type: mrr_at_3
      value: 24.254
    - type: mrr_at_5
      value: 25.784000000000002
    - type: ndcg_at_1
      value: 18.407999999999998
    - type: ndcg_at_10
      value: 27.549
    - type: ndcg_at_100
      value: 33.188
    - type: ndcg_at_1000
      value: 36.312
    - type: ndcg_at_3
      value: 22.862
    - type: ndcg_at_5
      value: 25.130999999999997
    - type: precision_at_1
      value: 18.407999999999998
    - type: precision_at_10
      value: 5.087
    - type: precision_at_100
      value: 0.923
    - type: precision_at_1000
      value: 0.133
    - type: precision_at_3
      value: 10.987
    - type: precision_at_5
      value: 8.209
    - type: recall_at_1
      value: 14.963999999999999
    - type: recall_at_10
      value: 38.673
    - type: recall_at_100
      value: 63.224999999999994
    - type: recall_at_1000
      value: 85.443
    - type: recall_at_3
      value: 25.840000000000003
    - type: recall_at_5
      value: 31.503999999999998
    - type: map_at_1
      value: 27.861000000000004
    - type: map_at_10
      value: 37.562
    - type: map_at_100
      value: 38.906
    - type: map_at_1000
      value: 39.021
    - type: map_at_3
      value: 34.743
    - type: map_at_5
      value: 36.168
    - type: mrr_at_1
      value: 34.455999999999996
    - type: mrr_at_10
      value: 43.428
    - type: mrr_at_100
      value: 44.228
    - type: mrr_at_1000
      value: 44.278
    - type: mrr_at_3
      value: 41.001
    - type: mrr_at_5
      value: 42.315000000000005
    - type: ndcg_at_1
      value: 34.455999999999996
    - type: ndcg_at_10
      value: 43.477
    - type: ndcg_at_100
      value: 48.953
    - type: ndcg_at_1000
      value: 51.19200000000001
    - type: ndcg_at_3
      value: 38.799
    - type: ndcg_at_5
      value: 40.743
    - type: precision_at_1
      value: 34.455999999999996
    - type: precision_at_10
      value: 7.902000000000001
    - type: precision_at_100
      value: 1.244
    - type: precision_at_1000
      value: 0.161
    - type: precision_at_3
      value: 18.511
    - type: precision_at_5
      value: 12.859000000000002
    - type: recall_at_1
      value: 27.861000000000004
    - type: recall_at_10
      value: 55.36
    - type: recall_at_100
      value: 78.384
    - type: recall_at_1000
      value: 93.447
    - type: recall_at_3
      value: 41.926
    - type: recall_at_5
      value: 47.257
    - type: map_at_1
      value: 26.375
    - type: map_at_10
      value: 35.571000000000005
    - type: map_at_100
      value: 36.785000000000004
    - type: map_at_1000
      value: 36.905
    - type: map_at_3
      value: 32.49
    - type: map_at_5
      value: 34.123999999999995
    - type: mrr_at_1
      value: 32.647999999999996
    - type: mrr_at_10
      value: 40.598
    - type: mrr_at_100
      value: 41.484
    - type: mrr_at_1000
      value: 41.546
    - type: mrr_at_3
      value: 37.9
    - type: mrr_at_5
      value: 39.401
    - type: ndcg_at_1
      value: 32.647999999999996
    - type: ndcg_at_10
      value: 41.026
    - type: ndcg_at_100
      value: 46.365
    - type: ndcg_at_1000
      value: 48.876
    - type: ndcg_at_3
      value: 35.843
    - type: ndcg_at_5
      value: 38.118
    - type: precision_at_1
      value: 32.647999999999996
    - type: precision_at_10
      value: 7.443
    - type: precision_at_100
      value: 1.18
    - type: precision_at_1000
      value: 0.158
    - type: precision_at_3
      value: 16.819
    - type: precision_at_5
      value: 11.985999999999999
    - type: recall_at_1
      value: 26.375
    - type: recall_at_10
      value: 52.471000000000004
    - type: recall_at_100
      value: 75.354
    - type: recall_at_1000
      value: 92.35
    - type: recall_at_3
      value: 37.893
    - type: recall_at_5
      value: 43.935
    - type: map_at_1
      value: 25.012666666666668
    - type: map_at_10
      value: 33.685833333333335
    - type: map_at_100
      value: 34.849250000000005
    - type: map_at_1000
      value: 34.970083333333335
    - type: map_at_3
      value: 31.065083333333334
    - type: map_at_5
      value: 32.494416666666666
    - type: mrr_at_1
      value: 29.772666666666662
    - type: mrr_at_10
      value: 37.824666666666666
    - type: mrr_at_100
      value: 38.66741666666666
    - type: mrr_at_1000
      value: 38.72916666666666
    - type: mrr_at_3
      value: 35.54575
    - type: mrr_at_5
      value: 36.81524999999999
    - type: ndcg_at_1
      value: 29.772666666666662
    - type: ndcg_at_10
      value: 38.78241666666666
    - type: ndcg_at_100
      value: 43.84591666666667
    - type: ndcg_at_1000
      value: 46.275416666666665
    - type: ndcg_at_3
      value: 34.33416666666667
    - type: ndcg_at_5
      value: 36.345166666666664
    - type: precision_at_1
      value: 29.772666666666662
    - type: precision_at_10
      value: 6.794916666666667
    - type: precision_at_100
      value: 1.106416666666667
    - type: precision_at_1000
      value: 0.15033333333333335
    - type: precision_at_3
      value: 15.815083333333336
    - type: precision_at_5
      value: 11.184166666666664
    - type: recall_at_1
      value: 25.012666666666668
    - type: recall_at_10
      value: 49.748500000000014
    - type: recall_at_100
      value: 72.11341666666667
    - type: recall_at_1000
      value: 89.141
    - type: recall_at_3
      value: 37.242999999999995
    - type: recall_at_5
      value: 42.49033333333333
    - type: map_at_1
      value: 23.177
    - type: map_at_10
      value: 29.310000000000002
    - type: map_at_100
      value: 30.188
    - type: map_at_1000
      value: 30.29
    - type: map_at_3
      value: 27.356
    - type: map_at_5
      value: 28.410999999999998
    - type: mrr_at_1
      value: 26.074
    - type: mrr_at_10
      value: 32.002
    - type: mrr_at_100
      value: 32.838
    - type: mrr_at_1000
      value: 32.909
    - type: mrr_at_3
      value: 30.317
    - type: mrr_at_5
      value: 31.222
    - type: ndcg_at_1
      value: 26.074
    - type: ndcg_at_10
      value: 32.975
    - type: ndcg_at_100
      value: 37.621
    - type: ndcg_at_1000
      value: 40.253
    - type: ndcg_at_3
      value: 29.452
    - type: ndcg_at_5
      value: 31.020999999999997
    - type: precision_at_1
      value: 26.074
    - type: precision_at_10
      value: 5.077
    - type: precision_at_100
      value: 0.8049999999999999
    - type: precision_at_1000
      value: 0.11100000000000002
    - type: precision_at_3
      value: 12.526000000000002
    - type: precision_at_5
      value: 8.588999999999999
    - type: recall_at_1
      value: 23.177
    - type: recall_at_10
      value: 41.613
    - type: recall_at_100
      value: 63.287000000000006
    - type: recall_at_1000
      value: 83.013
    - type: recall_at_3
      value: 31.783
    - type: recall_at_5
      value: 35.769
    - type: map_at_1
      value: 15.856
    - type: map_at_10
      value: 22.651
    - type: map_at_100
      value: 23.649
    - type: map_at_1000
      value: 23.783
    - type: map_at_3
      value: 20.591
    - type: map_at_5
      value: 21.684
    - type: mrr_at_1
      value: 19.408
    - type: mrr_at_10
      value: 26.51
    - type: mrr_at_100
      value: 27.356
    - type: mrr_at_1000
      value: 27.439999999999998
    - type: mrr_at_3
      value: 24.547
    - type: mrr_at_5
      value: 25.562
    - type: ndcg_at_1
      value: 19.408
    - type: ndcg_at_10
      value: 27.072000000000003
    - type: ndcg_at_100
      value: 31.980999999999998
    - type: ndcg_at_1000
      value: 35.167
    - type: ndcg_at_3
      value: 23.338
    - type: ndcg_at_5
      value: 24.94
    - type: precision_at_1
      value: 19.408
    - type: precision_at_10
      value: 4.9590000000000005
    - type: precision_at_100
      value: 0.8710000000000001
    - type: precision_at_1000
      value: 0.132
    - type: precision_at_3
      value: 11.138
    - type: precision_at_5
      value: 7.949000000000001
    - type: recall_at_1
      value: 15.856
    - type: recall_at_10
      value: 36.578
    - type: recall_at_100
      value: 58.89
    - type: recall_at_1000
      value: 81.743
    - type: recall_at_3
      value: 25.94
    - type: recall_at_5
      value: 30.153999999999996
    - type: map_at_1
      value: 25.892
    - type: map_at_10
      value: 33.899
    - type: map_at_100
      value: 34.955000000000005
    - type: map_at_1000
      value: 35.066
    - type: map_at_3
      value: 31.41
    - type: map_at_5
      value: 32.669
    - type: mrr_at_1
      value: 30.224
    - type: mrr_at_10
      value: 37.936
    - type: mrr_at_100
      value: 38.777
    - type: mrr_at_1000
      value: 38.85
    - type: mrr_at_3
      value: 35.821
    - type: mrr_at_5
      value: 36.894
    - type: ndcg_at_1
      value: 30.224
    - type: ndcg_at_10
      value: 38.766
    - type: ndcg_at_100
      value: 43.806
    - type: ndcg_at_1000
      value: 46.373999999999995
    - type: ndcg_at_3
      value: 34.325
    - type: ndcg_at_5
      value: 36.096000000000004
    - type: precision_at_1
      value: 30.224
    - type: precision_at_10
      value: 6.446000000000001
    - type: precision_at_100
      value: 1.0
    - type: precision_at_1000
      value: 0.133
    - type: precision_at_3
      value: 15.392
    - type: precision_at_5
      value: 10.671999999999999
    - type: recall_at_1
      value: 25.892
    - type: recall_at_10
      value: 49.573
    - type: recall_at_100
      value: 71.885
    - type: recall_at_1000
      value: 89.912
    - type: recall_at_3
      value: 37.226
    - type: recall_at_5
      value: 41.74
    - type: map_at_1
      value: 23.915
    - type: map_at_10
      value: 33.613
    - type: map_at_100
      value: 35.333999999999996
    - type: map_at_1000
      value: 35.563
    - type: map_at_3
      value: 31.203999999999997
    - type: map_at_5
      value: 32.479
    - type: mrr_at_1
      value: 29.447000000000003
    - type: mrr_at_10
      value: 38.440000000000005
    - type: mrr_at_100
      value: 39.459
    - type: mrr_at_1000
      value: 39.513999999999996
    - type: mrr_at_3
      value: 36.495
    - type: mrr_at_5
      value: 37.592
    - type: ndcg_at_1
      value: 29.447000000000003
    - type: ndcg_at_10
      value: 39.341
    - type: ndcg_at_100
      value: 45.382
    - type: ndcg_at_1000
      value: 47.921
    - type: ndcg_at_3
      value: 35.671
    - type: ndcg_at_5
      value: 37.299
    - type: precision_at_1
      value: 29.447000000000003
    - type: precision_at_10
      value: 7.648000000000001
    - type: precision_at_100
      value: 1.567
    - type: precision_at_1000
      value: 0.241
    - type: precision_at_3
      value: 17.194000000000003
    - type: precision_at_5
      value: 12.253
    - type: recall_at_1
      value: 23.915
    - type: recall_at_10
      value: 49.491
    - type: recall_at_100
      value: 76.483
    - type: recall_at_1000
      value: 92.674
    - type: recall_at_3
      value: 38.878
    - type: recall_at_5
      value: 43.492
    - type: map_at_1
      value: 20.283
    - type: map_at_10
      value: 26.851000000000003
    - type: map_at_100
      value: 27.973
    - type: map_at_1000
      value: 28.087
    - type: map_at_3
      value: 24.887
    - type: map_at_5
      value: 25.804
    - type: mrr_at_1
      value: 22.366
    - type: mrr_at_10
      value: 28.864
    - type: mrr_at_100
      value: 29.903000000000002
    - type: mrr_at_1000
      value: 29.988
    - type: mrr_at_3
      value: 27.017999999999997
    - type: mrr_at_5
      value: 27.813
    - type: ndcg_at_1
      value: 22.366
    - type: ndcg_at_10
      value: 30.898999999999997
    - type: ndcg_at_100
      value: 36.176
    - type: ndcg_at_1000
      value: 39.036
    - type: ndcg_at_3
      value: 26.932000000000002
    - type: ndcg_at_5
      value: 28.416999999999998
    - type: precision_at_1
      value: 22.366
    - type: precision_at_10
      value: 4.824
    - type: precision_at_100
      value: 0.804
    - type: precision_at_1000
      value: 0.116
    - type: precision_at_3
      value: 11.459999999999999
    - type: precision_at_5
      value: 7.8740000000000006
    - type: recall_at_1
      value: 20.283
    - type: recall_at_10
      value: 41.559000000000005
    - type: recall_at_100
      value: 65.051
    - type: recall_at_1000
      value: 86.47200000000001
    - type: recall_at_3
      value: 30.524
    - type: recall_at_5
      value: 34.11
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 11.326
    - type: map_at_10
      value: 19.357
    - type: map_at_100
      value: 21.014
    - type: map_at_1000
      value: 21.188000000000002
    - type: map_at_3
      value: 16.305
    - type: map_at_5
      value: 17.886
    - type: mrr_at_1
      value: 24.820999999999998
    - type: mrr_at_10
      value: 36.150999999999996
    - type: mrr_at_100
      value: 37.080999999999996
    - type: mrr_at_1000
      value: 37.123
    - type: mrr_at_3
      value: 32.952999999999996
    - type: mrr_at_5
      value: 34.917
    - type: ndcg_at_1
      value: 24.820999999999998
    - type: ndcg_at_10
      value: 27.131
    - type: ndcg_at_100
      value: 33.841
    - type: ndcg_at_1000
      value: 37.159
    - type: ndcg_at_3
      value: 22.311
    - type: ndcg_at_5
      value: 24.026
    - type: precision_at_1
      value: 24.820999999999998
    - type: precision_at_10
      value: 8.450000000000001
    - type: precision_at_100
      value: 1.557
    - type: precision_at_1000
      value: 0.218
    - type: precision_at_3
      value: 16.612
    - type: precision_at_5
      value: 12.808
    - type: recall_at_1
      value: 11.326
    - type: recall_at_10
      value: 32.548
    - type: recall_at_100
      value: 55.803000000000004
    - type: recall_at_1000
      value: 74.636
    - type: recall_at_3
      value: 20.549
    - type: recall_at_5
      value: 25.514
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 7.481
    - type: map_at_10
      value: 15.043999999999999
    - type: map_at_100
      value: 20.194000000000003
    - type: map_at_1000
      value: 21.423000000000002
    - type: map_at_3
      value: 11.238
    - type: map_at_5
      value: 12.828999999999999
    - type: mrr_at_1
      value: 54.50000000000001
    - type: mrr_at_10
      value: 64.713
    - type: mrr_at_100
      value: 65.216
    - type: mrr_at_1000
      value: 65.23
    - type: mrr_at_3
      value: 62.74999999999999
    - type: mrr_at_5
      value: 63.87500000000001
    - type: ndcg_at_1
      value: 43.375
    - type: ndcg_at_10
      value: 32.631
    - type: ndcg_at_100
      value: 36.338
    - type: ndcg_at_1000
      value: 43.541000000000004
    - type: ndcg_at_3
      value: 36.746
    - type: ndcg_at_5
      value: 34.419
    - type: precision_at_1
      value: 54.50000000000001
    - type: precision_at_10
      value: 24.825
    - type: precision_at_100
      value: 7.698
    - type: precision_at_1000
      value: 1.657
    - type: precision_at_3
      value: 38.917
    - type: precision_at_5
      value: 32.35
    - type: recall_at_1
      value: 7.481
    - type: recall_at_10
      value: 20.341
    - type: recall_at_100
      value: 41.778
    - type: recall_at_1000
      value: 64.82
    - type: recall_at_3
      value: 12.748000000000001
    - type: recall_at_5
      value: 15.507000000000001
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 46.580000000000005
    - type: f1
      value: 41.5149462395095
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 61.683
    - type: map_at_10
      value: 73.071
    - type: map_at_100
      value: 73.327
    - type: map_at_1000
      value: 73.341
    - type: map_at_3
      value: 71.446
    - type: map_at_5
      value: 72.557
    - type: mrr_at_1
      value: 66.44200000000001
    - type: mrr_at_10
      value: 77.725
    - type: mrr_at_100
      value: 77.89399999999999
    - type: mrr_at_1000
      value: 77.898
    - type: mrr_at_3
      value: 76.283
    - type: mrr_at_5
      value: 77.29700000000001
    - type: ndcg_at_1
      value: 66.44200000000001
    - type: ndcg_at_10
      value: 78.43
    - type: ndcg_at_100
      value: 79.462
    - type: ndcg_at_1000
      value: 79.754
    - type: ndcg_at_3
      value: 75.53800000000001
    - type: ndcg_at_5
      value: 77.332
    - type: precision_at_1
      value: 66.44200000000001
    - type: precision_at_10
      value: 9.878
    - type: precision_at_100
      value: 1.051
    - type: precision_at_1000
      value: 0.109
    - type: precision_at_3
      value: 29.878
    - type: precision_at_5
      value: 18.953
    - type: recall_at_1
      value: 61.683
    - type: recall_at_10
      value: 90.259
    - type: recall_at_100
      value: 94.633
    - type: recall_at_1000
      value: 96.60499999999999
    - type: recall_at_3
      value: 82.502
    - type: recall_at_5
      value: 86.978
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 17.724
    - type: map_at_10
      value: 29.487999999999996
    - type: map_at_100
      value: 31.243
    - type: map_at_1000
      value: 31.419999999999998
    - type: map_at_3
      value: 25.612000000000002
    - type: map_at_5
      value: 27.859
    - type: mrr_at_1
      value: 35.802
    - type: mrr_at_10
      value: 44.684000000000005
    - type: mrr_at_100
      value: 45.578
    - type: mrr_at_1000
      value: 45.621
    - type: mrr_at_3
      value: 42.361
    - type: mrr_at_5
      value: 43.85
    - type: ndcg_at_1
      value: 35.802
    - type: ndcg_at_10
      value: 37.009
    - type: ndcg_at_100
      value: 43.903
    - type: ndcg_at_1000
      value: 47.019
    - type: ndcg_at_3
      value: 33.634
    - type: ndcg_at_5
      value: 34.965
    - type: precision_at_1
      value: 35.802
    - type: precision_at_10
      value: 10.386
    - type: precision_at_100
      value: 1.7309999999999999
    - type: precision_at_1000
      value: 0.231
    - type: precision_at_3
      value: 22.84
    - type: precision_at_5
      value: 17.037
    - type: recall_at_1
      value: 17.724
    - type: recall_at_10
      value: 43.708000000000006
    - type: recall_at_100
      value: 69.902
    - type: recall_at_1000
      value: 88.51
    - type: recall_at_3
      value: 30.740000000000002
    - type: recall_at_5
      value: 36.742000000000004
  - task:
      type: Clustering
    dataset:
      name: MTEB FloresClusteringS2S
      type: jinaai/flores_clustering
      config: default
      split: test
      revision: 480b580487f53a46f881354a8348335d4edbb2de
    metrics:
    - type: v_measure
      value: 39.79120149869612
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 34.801
    - type: map_at_10
      value: 50.42100000000001
    - type: map_at_100
      value: 51.254
    - type: map_at_1000
      value: 51.327999999999996
    - type: map_at_3
      value: 47.56
    - type: map_at_5
      value: 49.379
    - type: mrr_at_1
      value: 69.602
    - type: mrr_at_10
      value: 76.385
    - type: mrr_at_100
      value: 76.668
    - type: mrr_at_1000
      value: 76.683
    - type: mrr_at_3
      value: 75.102
    - type: mrr_at_5
      value: 75.949
    - type: ndcg_at_1
      value: 69.602
    - type: ndcg_at_10
      value: 59.476
    - type: ndcg_at_100
      value: 62.527
    - type: ndcg_at_1000
      value: 64.043
    - type: ndcg_at_3
      value: 55.155
    - type: ndcg_at_5
      value: 57.623000000000005
    - type: precision_at_1
      value: 69.602
    - type: precision_at_10
      value: 12.292
    - type: precision_at_100
      value: 1.467
    - type: precision_at_1000
      value: 0.167
    - type: precision_at_3
      value: 34.634
    - type: precision_at_5
      value: 22.728
    - type: recall_at_1
      value: 34.801
    - type: recall_at_10
      value: 61.458
    - type: recall_at_100
      value: 73.363
    - type: recall_at_1000
      value: 83.43
    - type: recall_at_3
      value: 51.951
    - type: recall_at_5
      value: 56.82000000000001
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 67.46079999999999
    - type: ap
      value: 61.81278199159353
    - type: f1
      value: 67.26505019954826
  - task:
      type: Reranking
    dataset:
      name: MTEB MIRACL
      type: jinaai/miracl
      config: default
      split: test
      revision: d28a029f35c4ff7f616df47b0edf54e6882395e6
    metrics:
    - type: map
      value: 73.90464144118539
    - type: mrr
      value: 82.44674693216022
  - task:
      type: Retrieval
    dataset:
      name: MTEB MIRACLRetrieval
      type: jinaai/miracl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 21.299
    - type: map_at_10
      value: 70.547
    - type: map_at_100
      value: 72.394
    - type: map_at_1000
      value: 72.39999999999999
    - type: map_at_3
      value: 41.317
    - type: map_at_5
      value: 53.756
    - type: mrr_at_1
      value: 72.84
    - type: mrr_at_10
      value: 82.466
    - type: mrr_at_100
      value: 82.52199999999999
    - type: mrr_at_1000
      value: 82.52199999999999
    - type: mrr_at_3
      value: 80.607
    - type: mrr_at_5
      value: 82.065
    - type: ndcg_at_1
      value: 72.994
    - type: ndcg_at_10
      value: 80.89
    - type: ndcg_at_100
      value: 83.30199999999999
    - type: ndcg_at_1000
      value: 83.337
    - type: ndcg_at_3
      value: 70.357
    - type: ndcg_at_5
      value: 72.529
    - type: precision_at_1
      value: 72.994
    - type: precision_at_10
      value: 43.056
    - type: precision_at_100
      value: 4.603
    - type: precision_at_1000
      value: 0.461
    - type: precision_at_3
      value: 61.626000000000005
    - type: precision_at_5
      value: 55.525000000000006
    - type: recall_at_1
      value: 21.299
    - type: recall_at_10
      value: 93.903
    - type: recall_at_100
      value: 99.86699999999999
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_3
      value: 46.653
    - type: recall_at_5
      value: 65.72200000000001
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 90.37163702690378
    - type: f1
      value: 90.18615216514222
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (es)
      type: mteb/mtop_domain
      config: es
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 89.88992661774515
    - type: f1
      value: 89.3738963046966
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 71.97218422252622
    - type: f1
      value: 54.03096570916335
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (es)
      type: mteb/mtop_intent
      config: es
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 68.75917278185457
    - type: f1
      value: 49.144083814705844
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 70.75991930060525
    - type: f1
      value: 69.37993796176502
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (es)
      type: mteb/amazon_massive_intent
      config: es
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 66.93006052454606
    - type: f1
      value: 66.04029135274683
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 73.81977135171486
    - type: f1
      value: 74.10477122507747
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (es)
      type: mteb/amazon_massive_scenario
      config: es
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 71.23402824478816
    - type: f1
      value: 71.75572665880296
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 32.189750849969215
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 28.78357393555938
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 30.605612998328358
    - type: mrr
      value: 31.595529205695833
  - task:
      type: Retrieval
    dataset:
      name: MTEB MintakaESRetrieval
      type: jinaai/mintakaqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 16.213
    - type: map_at_10
      value: 24.079
    - type: map_at_100
      value: 25.039
    - type: map_at_1000
      value: 25.142999999999997
    - type: map_at_3
      value: 21.823
    - type: map_at_5
      value: 23.069
    - type: mrr_at_1
      value: 16.213
    - type: mrr_at_10
      value: 24.079
    - type: mrr_at_100
      value: 25.039
    - type: mrr_at_1000
      value: 25.142999999999997
    - type: mrr_at_3
      value: 21.823
    - type: mrr_at_5
      value: 23.069
    - type: ndcg_at_1
      value: 16.213
    - type: ndcg_at_10
      value: 28.315
    - type: ndcg_at_100
      value: 33.475
    - type: ndcg_at_1000
      value: 36.838
    - type: ndcg_at_3
      value: 23.627000000000002
    - type: ndcg_at_5
      value: 25.879
    - type: precision_at_1
      value: 16.213
    - type: precision_at_10
      value: 4.183
    - type: precision_at_100
      value: 0.6709999999999999
    - type: precision_at_1000
      value: 0.095
    - type: precision_at_3
      value: 9.612
    - type: precision_at_5
      value: 6.865
    - type: recall_at_1
      value: 16.213
    - type: recall_at_10
      value: 41.832
    - type: recall_at_100
      value: 67.12
    - type: recall_at_1000
      value: 94.843
    - type: recall_at_3
      value: 28.837000000000003
    - type: recall_at_5
      value: 34.323
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.692
    - type: map_at_10
      value: 10.783
    - type: map_at_100
      value: 13.447999999999999
    - type: map_at_1000
      value: 14.756
    - type: map_at_3
      value: 7.646
    - type: map_at_5
      value: 9.311
    - type: mrr_at_1
      value: 42.415000000000006
    - type: mrr_at_10
      value: 50.471
    - type: mrr_at_100
      value: 51.251999999999995
    - type: mrr_at_1000
      value: 51.292
    - type: mrr_at_3
      value: 48.4
    - type: mrr_at_5
      value: 49.809
    - type: ndcg_at_1
      value: 40.867
    - type: ndcg_at_10
      value: 30.303
    - type: ndcg_at_100
      value: 27.915
    - type: ndcg_at_1000
      value: 36.734
    - type: ndcg_at_3
      value: 35.74
    - type: ndcg_at_5
      value: 33.938
    - type: precision_at_1
      value: 42.415000000000006
    - type: precision_at_10
      value: 22.105
    - type: precision_at_100
      value: 7.173
    - type: precision_at_1000
      value: 2.007
    - type: precision_at_3
      value: 33.437
    - type: precision_at_5
      value: 29.349999999999998
    - type: recall_at_1
      value: 4.692
    - type: recall_at_10
      value: 14.798
    - type: recall_at_100
      value: 28.948
    - type: recall_at_1000
      value: 59.939
    - type: recall_at_3
      value: 8.562
    - type: recall_at_5
      value: 11.818
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: nq
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 27.572999999999997
    - type: map_at_10
      value: 42.754
    - type: map_at_100
      value: 43.8
    - type: map_at_1000
      value: 43.838
    - type: map_at_3
      value: 38.157000000000004
    - type: map_at_5
      value: 40.9
    - type: mrr_at_1
      value: 31.373
    - type: mrr_at_10
      value: 45.321
    - type: mrr_at_100
      value: 46.109
    - type: mrr_at_1000
      value: 46.135
    - type: mrr_at_3
      value: 41.483
    - type: mrr_at_5
      value: 43.76
    - type: ndcg_at_1
      value: 31.373
    - type: ndcg_at_10
      value: 50.7
    - type: ndcg_at_100
      value: 55.103
    - type: ndcg_at_1000
      value: 55.955999999999996
    - type: ndcg_at_3
      value: 42.069
    - type: ndcg_at_5
      value: 46.595
    - type: precision_at_1
      value: 31.373
    - type: precision_at_10
      value: 8.601
    - type: precision_at_100
      value: 1.11
    - type: precision_at_1000
      value: 0.11900000000000001
    - type: precision_at_3
      value: 19.399
    - type: precision_at_5
      value: 14.224
    - type: recall_at_1
      value: 27.572999999999997
    - type: recall_at_10
      value: 72.465
    - type: recall_at_100
      value: 91.474
    - type: recall_at_1000
      value: 97.78099999999999
    - type: recall_at_3
      value: 50.087
    - type: recall_at_5
      value: 60.516000000000005
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 70.525
    - type: map_at_10
      value: 84.417
    - type: map_at_100
      value: 85.07000000000001
    - type: map_at_1000
      value: 85.085
    - type: map_at_3
      value: 81.45
    - type: map_at_5
      value: 83.317
    - type: mrr_at_1
      value: 81.17999999999999
    - type: mrr_at_10
      value: 87.34100000000001
    - type: mrr_at_100
      value: 87.461
    - type: mrr_at_1000
      value: 87.46199999999999
    - type: mrr_at_3
      value: 86.372
    - type: mrr_at_5
      value: 87.046
    - type: ndcg_at_1
      value: 81.17999999999999
    - type: ndcg_at_10
      value: 88.144
    - type: ndcg_at_100
      value: 89.424
    - type: ndcg_at_1000
      value: 89.517
    - type: ndcg_at_3
      value: 85.282
    - type: ndcg_at_5
      value: 86.874
    - type: precision_at_1
      value: 81.17999999999999
    - type: precision_at_10
      value: 13.385
    - type: precision_at_100
      value: 1.533
    - type: precision_at_1000
      value: 0.157
    - type: precision_at_3
      value: 37.29
    - type: precision_at_5
      value: 24.546
    - type: recall_at_1
      value: 70.525
    - type: recall_at_10
      value: 95.22500000000001
    - type: recall_at_100
      value: 99.572
    - type: recall_at_1000
      value: 99.98899999999999
    - type: recall_at_3
      value: 87.035
    - type: recall_at_5
      value: 91.526
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 48.284384328108736
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 56.02508021518392
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.023000000000001
    - type: map_at_10
      value: 10.046
    - type: map_at_100
      value: 11.802999999999999
    - type: map_at_1000
      value: 12.074
    - type: map_at_3
      value: 7.071
    - type: map_at_5
      value: 8.556
    - type: mrr_at_1
      value: 19.8
    - type: mrr_at_10
      value: 30.105999999999998
    - type: mrr_at_100
      value: 31.16
    - type: mrr_at_1000
      value: 31.224
    - type: mrr_at_3
      value: 26.633000000000003
    - type: mrr_at_5
      value: 28.768
    - type: ndcg_at_1
      value: 19.8
    - type: ndcg_at_10
      value: 17.358
    - type: ndcg_at_100
      value: 24.566
    - type: ndcg_at_1000
      value: 29.653000000000002
    - type: ndcg_at_3
      value: 16.052
    - type: ndcg_at_5
      value: 14.325
    - type: precision_at_1
      value: 19.8
    - type: precision_at_10
      value: 9.07
    - type: precision_at_100
      value: 1.955
    - type: precision_at_1000
      value: 0.318
    - type: precision_at_3
      value: 14.933
    - type: precision_at_5
      value: 12.68
    - type: recall_at_1
      value: 4.023000000000001
    - type: recall_at_10
      value: 18.398
    - type: recall_at_100
      value: 39.683
    - type: recall_at_1000
      value: 64.625
    - type: recall_at_3
      value: 9.113
    - type: recall_at_5
      value: 12.873000000000001
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 87.90508618312852
    - type: cos_sim_spearman
      value: 83.01323463129205
    - type: euclidean_pearson
      value: 84.35845059002891
    - type: euclidean_spearman
      value: 82.85508559018527
    - type: manhattan_pearson
      value: 84.3682368950498
    - type: manhattan_spearman
      value: 82.8619728517302
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 89.28294535873366
    - type: cos_sim_spearman
      value: 81.61879268131732
    - type: euclidean_pearson
      value: 85.99053604863724
    - type: euclidean_spearman
      value: 80.95176684739084
    - type: manhattan_pearson
      value: 85.98054086663903
    - type: manhattan_spearman
      value: 80.9911070430335
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 86.15898098455258
    - type: cos_sim_spearman
      value: 86.8247985072307
    - type: euclidean_pearson
      value: 86.25342429918649
    - type: euclidean_spearman
      value: 87.13468603023252
    - type: manhattan_pearson
      value: 86.2006134067688
    - type: manhattan_spearman
      value: 87.06135811996896
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 85.57403998481877
    - type: cos_sim_spearman
      value: 83.55947075172618
    - type: euclidean_pearson
      value: 84.97097562965358
    - type: euclidean_spearman
      value: 83.6287075601467
    - type: manhattan_pearson
      value: 84.87092197104133
    - type: manhattan_spearman
      value: 83.53783891641335
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 88.14632780204231
    - type: cos_sim_spearman
      value: 88.74903634923868
    - type: euclidean_pearson
      value: 88.03922995855112
    - type: euclidean_spearman
      value: 88.72852190525855
    - type: manhattan_pearson
      value: 87.9694791024271
    - type: manhattan_spearman
      value: 88.66461452107418
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 84.75989818558652
    - type: cos_sim_spearman
      value: 86.03107893122942
    - type: euclidean_pearson
      value: 85.21908960133018
    - type: euclidean_spearman
      value: 85.93012720153482
    - type: manhattan_pearson
      value: 85.1969170195502
    - type: manhattan_spearman
      value: 85.8975254197784
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      config: en-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 89.16803898789955
    - type: cos_sim_spearman
      value: 88.56139047950525
    - type: euclidean_pearson
      value: 88.09685325747859
    - type: euclidean_spearman
      value: 88.0457609458947
    - type: manhattan_pearson
      value: 88.07054413001431
    - type: manhattan_spearman
      value: 88.10784098889314
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (es-en)
      type: mteb/sts17-crosslingual-sts
      config: es-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 86.7160384474547
    - type: cos_sim_spearman
      value: 86.4899235500562
    - type: euclidean_pearson
      value: 85.90854477703468
    - type: euclidean_spearman
      value: 86.16085009124498
    - type: manhattan_pearson
      value: 85.9249735317884
    - type: manhattan_spearman
      value: 86.25038421339116
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (es-es)
      type: mteb/sts17-crosslingual-sts
      config: es-es
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 89.37914622360788
    - type: cos_sim_spearman
      value: 88.24619159322809
    - type: euclidean_pearson
      value: 89.00538382632769
    - type: euclidean_spearman
      value: 88.44675863524736
    - type: manhattan_pearson
      value: 88.97372120683606
    - type: manhattan_spearman
      value: 88.33509324222129
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
    metrics:
    - type: cos_sim_pearson
      value: 66.22181360203069
    - type: cos_sim_spearman
      value: 65.6218291833768
    - type: euclidean_pearson
      value: 67.14543788822508
    - type: euclidean_spearman
      value: 65.21269939987857
    - type: manhattan_pearson
      value: 67.03304607195636
    - type: manhattan_spearman
      value: 65.18885316423805
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (es)
      type: mteb/sts22-crosslingual-sts
      config: es
      split: test
      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
    metrics:
    - type: cos_sim_pearson
      value: 65.71694059677084
    - type: cos_sim_spearman
      value: 67.96591844540954
    - type: euclidean_pearson
      value: 65.6964079162296
    - type: euclidean_spearman
      value: 67.53027948900173
    - type: manhattan_pearson
      value: 65.93545097673741
    - type: manhattan_spearman
      value: 67.7261811805062
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (es-en)
      type: mteb/sts22-crosslingual-sts
      config: es-en
      split: test
      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
    metrics:
    - type: cos_sim_pearson
      value: 75.43544796375058
    - type: cos_sim_spearman
      value: 78.80462701160789
    - type: euclidean_pearson
      value: 76.19135575163138
    - type: euclidean_spearman
      value: 78.4974732597096
    - type: manhattan_pearson
      value: 76.3254742699264
    - type: manhattan_spearman
      value: 78.51884307690416
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 87.46805293607684
    - type: cos_sim_spearman
      value: 87.83792784689113
    - type: euclidean_pearson
      value: 87.3872143683234
    - type: euclidean_spearman
      value: 87.61611384542778
    - type: manhattan_pearson
      value: 87.38542672601992
    - type: manhattan_spearman
      value: 87.61423971087297
  - task:
      type: STS
    dataset:
      name: MTEB STSES
      type: PlanTL-GOB-ES/sts-es
      config: default
      split: test
      revision: 0912bb6c9393c76d62a7c5ee81c4c817ff47c9f4
    metrics:
    - type: cos_sim_pearson
      value: 82.55286866116202
    - type: cos_sim_spearman
      value: 80.22150503320272
    - type: euclidean_pearson
      value: 83.27223445187087
    - type: euclidean_spearman
      value: 80.59078590992925
    - type: manhattan_pearson
      value: 83.23095887013197
    - type: manhattan_spearman
      value: 80.87994285189795
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 79.29717302265792
    - type: mrr
      value: 94.02156304117088
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 49.9
    - type: map_at_10
      value: 58.626
    - type: map_at_100
      value: 59.519999999999996
    - type: map_at_1000
      value: 59.55200000000001
    - type: map_at_3
      value: 56.232000000000006
    - type: map_at_5
      value: 57.833
    - type: mrr_at_1
      value: 52.333
    - type: mrr_at_10
      value: 60.039
    - type: mrr_at_100
      value: 60.732
    - type: mrr_at_1000
      value: 60.75899999999999
    - type: mrr_at_3
      value: 58.278
    - type: mrr_at_5
      value: 59.428000000000004
    - type: ndcg_at_1
      value: 52.333
    - type: ndcg_at_10
      value: 62.67
    - type: ndcg_at_100
      value: 66.465
    - type: ndcg_at_1000
      value: 67.425
    - type: ndcg_at_3
      value: 58.711999999999996
    - type: ndcg_at_5
      value: 60.958999999999996
    - type: precision_at_1
      value: 52.333
    - type: precision_at_10
      value: 8.333
    - type: precision_at_100
      value: 1.027
    - type: precision_at_1000
      value: 0.11100000000000002
    - type: precision_at_3
      value: 22.778000000000002
    - type: precision_at_5
      value: 15.267
    - type: recall_at_1
      value: 49.9
    - type: recall_at_10
      value: 73.394
    - type: recall_at_100
      value: 90.43299999999999
    - type: recall_at_1000
      value: 98.167
    - type: recall_at_3
      value: 63.032999999999994
    - type: recall_at_5
      value: 68.444
  - task:
      type: Clustering
    dataset:
      name: MTEB SpanishNewsClusteringP2P
      type: jinaai/spanish_news_clustering
      config: default
      split: test
      revision: b5edc3d3d7c12c7b9f883e9da50f6732f3624142
    metrics:
    - type: v_measure
      value: 48.30543557796266
  - task:
      type: Retrieval
    dataset:
      name: MTEB SpanishPassageRetrievalS2P
      type: jinaai/spanish_passage_retrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 14.443
    - type: map_at_10
      value: 28.736
    - type: map_at_100
      value: 34.514
    - type: map_at_1000
      value: 35.004000000000005
    - type: map_at_3
      value: 20.308
    - type: map_at_5
      value: 25.404
    - type: mrr_at_1
      value: 50.29900000000001
    - type: mrr_at_10
      value: 63.757
    - type: mrr_at_100
      value: 64.238
    - type: mrr_at_1000
      value: 64.24600000000001
    - type: mrr_at_3
      value: 59.480999999999995
    - type: mrr_at_5
      value: 62.924
    - type: ndcg_at_1
      value: 50.29900000000001
    - type: ndcg_at_10
      value: 42.126999999999995
    - type: ndcg_at_100
      value: 57.208000000000006
    - type: ndcg_at_1000
      value: 60.646
    - type: ndcg_at_3
      value: 38.722
    - type: ndcg_at_5
      value: 40.007999999999996
    - type: precision_at_1
      value: 50.29900000000001
    - type: precision_at_10
      value: 19.82
    - type: precision_at_100
      value: 4.82
    - type: precision_at_1000
      value: 0.5910000000000001
    - type: precision_at_3
      value: 31.537
    - type: precision_at_5
      value: 28.262999999999998
    - type: recall_at_1
      value: 14.443
    - type: recall_at_10
      value: 43.885999999999996
    - type: recall_at_100
      value: 85.231
    - type: recall_at_1000
      value: 99.07000000000001
    - type: recall_at_3
      value: 22.486
    - type: recall_at_5
      value: 33.035
    - type: map_at_1
      value: 15.578
    - type: map_at_10
      value: 52.214000000000006
    - type: map_at_100
      value: 64.791
    - type: map_at_1000
      value: 64.791
    - type: map_at_3
      value: 33.396
    - type: map_at_5
      value: 41.728
    - type: mrr_at_1
      value: 73.653
    - type: mrr_at_10
      value: 85.116
    - type: mrr_at_100
      value: 85.205
    - type: mrr_at_1000
      value: 85.205
    - type: mrr_at_3
      value: 84.631
    - type: mrr_at_5
      value: 85.05
    - type: ndcg_at_1
      value: 76.64699999999999
    - type: ndcg_at_10
      value: 70.38600000000001
    - type: ndcg_at_100
      value: 82.27600000000001
    - type: ndcg_at_1000
      value: 82.27600000000001
    - type: ndcg_at_3
      value: 70.422
    - type: ndcg_at_5
      value: 69.545
    - type: precision_at_1
      value: 76.64699999999999
    - type: precision_at_10
      value: 43.653
    - type: precision_at_100
      value: 7.718999999999999
    - type: precision_at_1000
      value: 0.772
    - type: precision_at_3
      value: 64.671
    - type: precision_at_5
      value: 56.766000000000005
    - type: recall_at_1
      value: 15.578
    - type: recall_at_10
      value: 67.459
    - type: recall_at_100
      value: 100.0
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_3
      value: 36.922
    - type: recall_at_5
      value: 49.424
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.81683168316832
    - type: cos_sim_ap
      value: 95.61502659412484
    - type: cos_sim_f1
      value: 90.6813627254509
    - type: cos_sim_precision
      value: 90.86345381526104
    - type: cos_sim_recall
      value: 90.5
    - type: dot_accuracy
      value: 99.8039603960396
    - type: dot_ap
      value: 95.36783483182609
    - type: dot_f1
      value: 89.90825688073394
    - type: dot_precision
      value: 91.68399168399168
    - type: dot_recall
      value: 88.2
    - type: euclidean_accuracy
      value: 99.81188118811882
    - type: euclidean_ap
      value: 95.51583052324564
    - type: euclidean_f1
      value: 90.46214355948868
    - type: euclidean_precision
      value: 88.97485493230174
    - type: euclidean_recall
      value: 92.0
    - type: manhattan_accuracy
      value: 99.8079207920792
    - type: manhattan_ap
      value: 95.44030644653718
    - type: manhattan_f1
      value: 90.37698412698413
    - type: manhattan_precision
      value: 89.66535433070865
    - type: manhattan_recall
      value: 91.10000000000001
    - type: max_accuracy
      value: 99.81683168316832
    - type: max_ap
      value: 95.61502659412484
    - type: max_f1
      value: 90.6813627254509
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 55.39046705023096
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 33.57429225651293
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 50.17622570658746
    - type: mrr
      value: 50.99844293778118
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 29.97416289382191
    - type: cos_sim_spearman
      value: 29.871890597161432
    - type: dot_pearson
      value: 28.768845892613644
    - type: dot_spearman
      value: 28.872458999448686
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.22599999999999998
    - type: map_at_10
      value: 1.646
    - type: map_at_100
      value: 9.491
    - type: map_at_1000
      value: 23.75
    - type: map_at_3
      value: 0.588
    - type: map_at_5
      value: 0.9129999999999999
    - type: mrr_at_1
      value: 84.0
    - type: mrr_at_10
      value: 89.889
    - type: mrr_at_100
      value: 89.889
    - type: mrr_at_1000
      value: 89.889
    - type: mrr_at_3
      value: 89.667
    - type: mrr_at_5
      value: 89.667
    - type: ndcg_at_1
      value: 75.0
    - type: ndcg_at_10
      value: 67.368
    - type: ndcg_at_100
      value: 52.834
    - type: ndcg_at_1000
      value: 49.144
    - type: ndcg_at_3
      value: 72.866
    - type: ndcg_at_5
      value: 70.16
    - type: precision_at_1
      value: 84.0
    - type: precision_at_10
      value: 71.8
    - type: precision_at_100
      value: 54.04
    - type: precision_at_1000
      value: 21.709999999999997
    - type: precision_at_3
      value: 77.333
    - type: precision_at_5
      value: 74.0
    - type: recall_at_1
      value: 0.22599999999999998
    - type: recall_at_10
      value: 1.9029999999999998
    - type: recall_at_100
      value: 13.012
    - type: recall_at_1000
      value: 46.105000000000004
    - type: recall_at_3
      value: 0.63
    - type: recall_at_5
      value: 1.0030000000000001
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: webis-touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 1.5
    - type: map_at_10
      value: 8.193999999999999
    - type: map_at_100
      value: 14.01
    - type: map_at_1000
      value: 15.570999999999998
    - type: map_at_3
      value: 4.361000000000001
    - type: map_at_5
      value: 5.9270000000000005
    - type: mrr_at_1
      value: 16.326999999999998
    - type: mrr_at_10
      value: 33.326
    - type: mrr_at_100
      value: 34.592
    - type: mrr_at_1000
      value: 34.592
    - type: mrr_at_3
      value: 29.252
    - type: mrr_at_5
      value: 30.680000000000003
    - type: ndcg_at_1
      value: 15.306000000000001
    - type: ndcg_at_10
      value: 19.819
    - type: ndcg_at_100
      value: 33.428000000000004
    - type: ndcg_at_1000
      value: 45.024
    - type: ndcg_at_3
      value: 19.667
    - type: ndcg_at_5
      value: 19.625
    - type: precision_at_1
      value: 16.326999999999998
    - type: precision_at_10
      value: 18.367
    - type: precision_at_100
      value: 7.367
    - type: precision_at_1000
      value: 1.496
    - type: precision_at_3
      value: 23.128999999999998
    - type: precision_at_5
      value: 21.633
    - type: recall_at_1
      value: 1.5
    - type: recall_at_10
      value: 14.362
    - type: recall_at_100
      value: 45.842
    - type: recall_at_1000
      value: 80.42
    - type: recall_at_3
      value: 5.99
    - type: recall_at_5
      value: 8.701
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 70.04740000000001
    - type: ap
      value: 13.58661943759992
    - type: f1
      value: 53.727487131754195
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 61.06395019807584
    - type: f1
      value: 61.36753664680866
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 40.19881263066229
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 85.19401561661799
    - type: cos_sim_ap
      value: 71.62462506173092
    - type: cos_sim_f1
      value: 66.0641327225455
    - type: cos_sim_precision
      value: 62.234662934453
    - type: cos_sim_recall
      value: 70.3957783641161
    - type: dot_accuracy
      value: 84.69333015437802
    - type: dot_ap
      value: 69.83805526490895
    - type: dot_f1
      value: 64.85446235265817
    - type: dot_precision
      value: 59.59328028293546
    - type: dot_recall
      value: 71.13456464379946
    - type: euclidean_accuracy
      value: 85.38475293556655
    - type: euclidean_ap
      value: 72.05594596250286
    - type: euclidean_f1
      value: 66.53543307086615
    - type: euclidean_precision
      value: 62.332872291378514
    - type: euclidean_recall
      value: 71.34564643799473
    - type: manhattan_accuracy
      value: 85.3907134767837
    - type: manhattan_ap
      value: 72.04585410650152
    - type: manhattan_f1
      value: 66.57132642116554
    - type: manhattan_precision
      value: 60.704194740273856
    - type: manhattan_recall
      value: 73.6939313984169
    - type: max_accuracy
      value: 85.3907134767837
    - type: max_ap
      value: 72.05594596250286
    - type: max_f1
      value: 66.57132642116554
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 89.30414871735165
    - type: cos_sim_ap
      value: 86.4398673359918
    - type: cos_sim_f1
      value: 78.9243598692186
    - type: cos_sim_precision
      value: 75.47249350101876
    - type: cos_sim_recall
      value: 82.7071142593163
    - type: dot_accuracy
      value: 89.26145845461248
    - type: dot_ap
      value: 86.32172118414802
    - type: dot_f1
      value: 78.8277467755645
    - type: dot_precision
      value: 75.79418662497335
    - type: dot_recall
      value: 82.11425931629196
    - type: euclidean_accuracy
      value: 89.24205378973105
    - type: euclidean_ap
      value: 86.23988673522649
    - type: euclidean_f1
      value: 78.67984857951413
    - type: euclidean_precision
      value: 75.2689684269742
    - type: euclidean_recall
      value: 82.41453649522637
    - type: manhattan_accuracy
      value: 89.18189932859859
    - type: manhattan_ap
      value: 86.21003833972824
    - type: manhattan_f1
      value: 78.70972564850115
    - type: manhattan_precision
      value: 76.485544094145
    - type: manhattan_recall
      value: 81.0671388974438
    - type: max_accuracy
      value: 89.30414871735165
    - type: max_ap
      value: 86.4398673359918
    - type: max_f1
      value: 78.9243598692186
  - task:
      type: Clustering
    dataset:
      name: MTEB WikiCitiesClustering
      type: jinaai/cities_wiki_clustering
      config: default
      split: test
      revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
    metrics:
    - type: v_measure
      value: 73.254610626148
  - task:
      type: Retrieval
    dataset:
      name: MTEB XMarketES
      type: jinaai/xmarket_ml
      config: default
      split: test
      revision: 705db869e8107dfe6e34b832af90446e77d813e3
    metrics:
    - type: map_at_1
      value: 5.506
    - type: map_at_10
      value: 11.546
    - type: map_at_100
      value: 14.299999999999999
    - type: map_at_1000
      value: 15.146999999999998
    - type: map_at_3
      value: 8.748000000000001
    - type: map_at_5
      value: 10.036000000000001
    - type: mrr_at_1
      value: 17.902
    - type: mrr_at_10
      value: 25.698999999999998
    - type: mrr_at_100
      value: 26.634
    - type: mrr_at_1000
      value: 26.704
    - type: mrr_at_3
      value: 23.244999999999997
    - type: mrr_at_5
      value: 24.555
    - type: ndcg_at_1
      value: 17.902
    - type: ndcg_at_10
      value: 19.714000000000002
    - type: ndcg_at_100
      value: 25.363000000000003
    - type: ndcg_at_1000
      value: 30.903999999999996
    - type: ndcg_at_3
      value: 17.884
    - type: ndcg_at_5
      value: 18.462
    - type: precision_at_1
      value: 17.902
    - type: precision_at_10
      value: 10.467
    - type: precision_at_100
      value: 3.9699999999999998
    - type: precision_at_1000
      value: 1.1320000000000001
    - type: precision_at_3
      value: 14.387
    - type: precision_at_5
      value: 12.727
    - type: recall_at_1
      value: 5.506
    - type: recall_at_10
      value: 19.997999999999998
    - type: recall_at_100
      value: 42.947
    - type: recall_at_1000
      value: 67.333
    - type: recall_at_3
      value: 11.158
    - type: recall_at_5
      value: 14.577000000000002
  - task:
      type: Retrieval
    dataset:
      name: MTEB XPQAESRetrieval
      type: jinaai/xpqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 32.53
    - type: map_at_10
      value: 58.68600000000001
    - type: map_at_100
      value: 60.45399999999999
    - type: map_at_1000
      value: 60.51499999999999
    - type: map_at_3
      value: 50.356
    - type: map_at_5
      value: 55.98
    - type: mrr_at_1
      value: 61.791
    - type: mrr_at_10
      value: 68.952
    - type: mrr_at_100
      value: 69.524
    - type: mrr_at_1000
      value: 69.538
    - type: mrr_at_3
      value: 67.087
    - type: mrr_at_5
      value: 68.052
    - type: ndcg_at_1
      value: 61.791
    - type: ndcg_at_10
      value: 65.359
    - type: ndcg_at_100
      value: 70.95700000000001
    - type: ndcg_at_1000
      value: 71.881
    - type: ndcg_at_3
      value: 59.999
    - type: ndcg_at_5
      value: 61.316
    - type: precision_at_1
      value: 61.791
    - type: precision_at_10
      value: 18.184
    - type: precision_at_100
      value: 2.317
    - type: precision_at_1000
      value: 0.245
    - type: precision_at_3
      value: 42.203
    - type: precision_at_5
      value: 31.374999999999996
    - type: recall_at_1
      value: 32.53
    - type: recall_at_10
      value: 73.098
    - type: recall_at_100
      value: 94.029
    - type: recall_at_1000
      value: 99.842
    - type: recall_at_3
      value: 54.525
    - type: recall_at_5
      value: 63.796
---

# AndreasX/jina-embeddings-v2-base-es-Q2_K-GGUF
This model was converted to GGUF format from [`jinaai/jina-embeddings-v2-base-es`](https://huggingface.co/jinaai/jina-embeddings-v2-base-es) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/jinaai/jina-embeddings-v2-base-es) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo AndreasX/jina-embeddings-v2-base-es-Q2_K-GGUF --hf-file jina-embeddings-v2-base-es-q2_k.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo AndreasX/jina-embeddings-v2-base-es-Q2_K-GGUF --hf-file jina-embeddings-v2-base-es-q2_k.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo AndreasX/jina-embeddings-v2-base-es-Q2_K-GGUF --hf-file jina-embeddings-v2-base-es-q2_k.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo AndreasX/jina-embeddings-v2-base-es-Q2_K-GGUF --hf-file jina-embeddings-v2-base-es-q2_k.gguf -c 2048
```
