README.md

---
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- feature-extraction
- sentence-transformers
- llama-cpp
- gguf-my-repo
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
base_model: intfloat/multilingual-e5-large
model-index:
- name: multilingual-e5-large
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 79.05970149253731
    - type: ap
      value: 43.486574390835635
    - type: f1
      value: 73.32700092140148
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (de)
      type: mteb/amazon_counterfactual
      config: de
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 71.22055674518201
    - type: ap
      value: 81.55756710830498
    - type: f1
      value: 69.28271787752661
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en-ext)
      type: mteb/amazon_counterfactual
      config: en-ext
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 80.41979010494754
    - type: ap
      value: 29.34879922376344
    - type: f1
      value: 67.62475449011278
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (ja)
      type: mteb/amazon_counterfactual
      config: ja
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 77.8372591006424
    - type: ap
      value: 26.557560591210738
    - type: f1
      value: 64.96619417368707
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 93.489875
    - type: ap
      value: 90.98758636917603
    - type: f1
      value: 93.48554819717332
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 47.564
    - type: f1
      value: 46.75122173518047
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (de)
      type: mteb/amazon_reviews_multi
      config: de
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 45.400000000000006
    - type: f1
      value: 44.17195682400632
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (es)
      type: mteb/amazon_reviews_multi
      config: es
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 43.068
    - type: f1
      value: 42.38155696855596
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (fr)
      type: mteb/amazon_reviews_multi
      config: fr
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 41.89
    - type: f1
      value: 40.84407321682663
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (ja)
      type: mteb/amazon_reviews_multi
      config: ja
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 40.120000000000005
    - type: f1
      value: 39.522976223819114
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (zh)
      type: mteb/amazon_reviews_multi
      config: zh
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 38.832
    - type: f1
      value: 38.0392533394713
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 30.725
    - type: map_at_10
      value: 46.055
    - type: map_at_100
      value: 46.900999999999996
    - type: map_at_1000
      value: 46.911
    - type: map_at_3
      value: 41.548
    - type: map_at_5
      value: 44.297
    - type: mrr_at_1
      value: 31.152
    - type: mrr_at_10
      value: 46.231
    - type: mrr_at_100
      value: 47.07
    - type: mrr_at_1000
      value: 47.08
    - type: mrr_at_3
      value: 41.738
    - type: mrr_at_5
      value: 44.468999999999994
    - type: ndcg_at_1
      value: 30.725
    - type: ndcg_at_10
      value: 54.379999999999995
    - type: ndcg_at_100
      value: 58.138
    - type: ndcg_at_1000
      value: 58.389
    - type: ndcg_at_3
      value: 45.156
    - type: ndcg_at_5
      value: 50.123
    - type: precision_at_1
      value: 30.725
    - type: precision_at_10
      value: 8.087
    - type: precision_at_100
      value: 0.9769999999999999
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 18.54
    - type: precision_at_5
      value: 13.542000000000002
    - type: recall_at_1
      value: 30.725
    - type: recall_at_10
      value: 80.868
    - type: recall_at_100
      value: 97.653
    - type: recall_at_1000
      value: 99.57300000000001
    - type: recall_at_3
      value: 55.619
    - type: recall_at_5
      value: 67.71000000000001
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 44.30960650674069
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 38.427074197498996
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 60.28270056031872
    - type: mrr
      value: 74.38332673789738
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 84.05942144105269
    - type: cos_sim_spearman
      value: 82.51212105850809
    - type: euclidean_pearson
      value: 81.95639829909122
    - type: euclidean_spearman
      value: 82.3717564144213
    - type: manhattan_pearson
      value: 81.79273425468256
    - type: manhattan_spearman
      value: 82.20066817871039
  - task:
      type: BitextMining
    dataset:
      name: MTEB BUCC (de-en)
      type: mteb/bucc-bitext-mining
      config: de-en
      split: test
      revision: d51519689f32196a32af33b075a01d0e7c51e252
    metrics:
    - type: accuracy
      value: 99.46764091858039
    - type: f1
      value: 99.37717466945023
    - type: precision
      value: 99.33194154488518
    - type: recall
      value: 99.46764091858039
  - task:
      type: BitextMining
    dataset:
      name: MTEB BUCC (fr-en)
      type: mteb/bucc-bitext-mining
      config: fr-en
      split: test
      revision: d51519689f32196a32af33b075a01d0e7c51e252
    metrics:
    - type: accuracy
      value: 98.29407880255337
    - type: f1
      value: 98.11248073959938
    - type: precision
      value: 98.02443319392472
    - type: recall
      value: 98.29407880255337
  - task:
      type: BitextMining
    dataset:
      name: MTEB BUCC (ru-en)
      type: mteb/bucc-bitext-mining
      config: ru-en
      split: test
      revision: d51519689f32196a32af33b075a01d0e7c51e252
    metrics:
    - type: accuracy
      value: 97.79009352268791
    - type: f1
      value: 97.5176076665512
    - type: precision
      value: 97.38136473848286
    - type: recall
      value: 97.79009352268791
  - task:
      type: BitextMining
    dataset:
      name: MTEB BUCC (zh-en)
      type: mteb/bucc-bitext-mining
      config: zh-en
      split: test
      revision: d51519689f32196a32af33b075a01d0e7c51e252
    metrics:
    - type: accuracy
      value: 99.26276987888363
    - type: f1
      value: 99.20133403545726
    - type: precision
      value: 99.17500438827453
    - type: recall
      value: 99.26276987888363
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 84.72727272727273
    - type: f1
      value: 84.67672206031433
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 35.34220182511161
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 33.4987096128766
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 25.558249999999997
    - type: map_at_10
      value: 34.44425000000001
    - type: map_at_100
      value: 35.59833333333333
    - type: map_at_1000
      value: 35.706916666666665
    - type: map_at_3
      value: 31.691749999999995
    - type: map_at_5
      value: 33.252916666666664
    - type: mrr_at_1
      value: 30.252666666666666
    - type: mrr_at_10
      value: 38.60675
    - type: mrr_at_100
      value: 39.42666666666666
    - type: mrr_at_1000
      value: 39.48408333333334
    - type: mrr_at_3
      value: 36.17441666666665
    - type: mrr_at_5
      value: 37.56275
    - type: ndcg_at_1
      value: 30.252666666666666
    - type: ndcg_at_10
      value: 39.683
    - type: ndcg_at_100
      value: 44.68541666666667
    - type: ndcg_at_1000
      value: 46.94316666666668
    - type: ndcg_at_3
      value: 34.961749999999995
    - type: ndcg_at_5
      value: 37.215666666666664
    - type: precision_at_1
      value: 30.252666666666666
    - type: precision_at_10
      value: 6.904166666666667
    - type: precision_at_100
      value: 1.0989999999999995
    - type: precision_at_1000
      value: 0.14733333333333334
    - type: precision_at_3
      value: 16.037666666666667
    - type: precision_at_5
      value: 11.413583333333333
    - type: recall_at_1
      value: 25.558249999999997
    - type: recall_at_10
      value: 51.13341666666666
    - type: recall_at_100
      value: 73.08366666666667
    - type: recall_at_1000
      value: 88.79483333333334
    - type: recall_at_3
      value: 37.989083333333326
    - type: recall_at_5
      value: 43.787833333333325
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 10.338
    - type: map_at_10
      value: 18.360000000000003
    - type: map_at_100
      value: 19.942
    - type: map_at_1000
      value: 20.134
    - type: map_at_3
      value: 15.174000000000001
    - type: map_at_5
      value: 16.830000000000002
    - type: mrr_at_1
      value: 23.257
    - type: mrr_at_10
      value: 33.768
    - type: mrr_at_100
      value: 34.707
    - type: mrr_at_1000
      value: 34.766000000000005
    - type: mrr_at_3
      value: 30.977
    - type: mrr_at_5
      value: 32.528
    - type: ndcg_at_1
      value: 23.257
    - type: ndcg_at_10
      value: 25.733
    - type: ndcg_at_100
      value: 32.288
    - type: ndcg_at_1000
      value: 35.992000000000004
    - type: ndcg_at_3
      value: 20.866
    - type: ndcg_at_5
      value: 22.612
    - type: precision_at_1
      value: 23.257
    - type: precision_at_10
      value: 8.124
    - type: precision_at_100
      value: 1.518
    - type: precision_at_1000
      value: 0.219
    - type: precision_at_3
      value: 15.679000000000002
    - type: precision_at_5
      value: 12.117
    - type: recall_at_1
      value: 10.338
    - type: recall_at_10
      value: 31.154
    - type: recall_at_100
      value: 54.161
    - type: recall_at_1000
      value: 75.21900000000001
    - type: recall_at_3
      value: 19.427
    - type: recall_at_5
      value: 24.214
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 8.498
    - type: map_at_10
      value: 19.103
    - type: map_at_100
      value: 27.375
    - type: map_at_1000
      value: 28.981
    - type: map_at_3
      value: 13.764999999999999
    - type: map_at_5
      value: 15.950000000000001
    - type: mrr_at_1
      value: 65.5
    - type: mrr_at_10
      value: 74.53800000000001
    - type: mrr_at_100
      value: 74.71799999999999
    - type: mrr_at_1000
      value: 74.725
    - type: mrr_at_3
      value: 72.792
    - type: mrr_at_5
      value: 73.554
    - type: ndcg_at_1
      value: 53.37499999999999
    - type: ndcg_at_10
      value: 41.286
    - type: ndcg_at_100
      value: 45.972
    - type: ndcg_at_1000
      value: 53.123
    - type: ndcg_at_3
      value: 46.172999999999995
    - type: ndcg_at_5
      value: 43.033
    - type: precision_at_1
      value: 65.5
    - type: precision_at_10
      value: 32.725
    - type: precision_at_100
      value: 10.683
    - type: precision_at_1000
      value: 1.978
    - type: precision_at_3
      value: 50
    - type: precision_at_5
      value: 41.349999999999994
    - type: recall_at_1
      value: 8.498
    - type: recall_at_10
      value: 25.070999999999998
    - type: recall_at_100
      value: 52.383
    - type: recall_at_1000
      value: 74.91499999999999
    - type: recall_at_3
      value: 15.207999999999998
    - type: recall_at_5
      value: 18.563
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 46.5
    - type: f1
      value: 41.93833713984145
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 67.914
    - type: map_at_10
      value: 78.10000000000001
    - type: map_at_100
      value: 78.333
    - type: map_at_1000
      value: 78.346
    - type: map_at_3
      value: 76.626
    - type: map_at_5
      value: 77.627
    - type: mrr_at_1
      value: 72.74199999999999
    - type: mrr_at_10
      value: 82.414
    - type: mrr_at_100
      value: 82.511
    - type: mrr_at_1000
      value: 82.513
    - type: mrr_at_3
      value: 81.231
    - type: mrr_at_5
      value: 82.065
    - type: ndcg_at_1
      value: 72.74199999999999
    - type: ndcg_at_10
      value: 82.806
    - type: ndcg_at_100
      value: 83.677
    - type: ndcg_at_1000
      value: 83.917
    - type: ndcg_at_3
      value: 80.305
    - type: ndcg_at_5
      value: 81.843
    - type: precision_at_1
      value: 72.74199999999999
    - type: precision_at_10
      value: 10.24
    - type: precision_at_100
      value: 1.089
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 31.268
    - type: precision_at_5
      value: 19.706000000000003
    - type: recall_at_1
      value: 67.914
    - type: recall_at_10
      value: 92.889
    - type: recall_at_100
      value: 96.42699999999999
    - type: recall_at_1000
      value: 97.92
    - type: recall_at_3
      value: 86.21
    - type: recall_at_5
      value: 90.036
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 22.166
    - type: map_at_10
      value: 35.57
    - type: map_at_100
      value: 37.405
    - type: map_at_1000
      value: 37.564
    - type: map_at_3
      value: 30.379
    - type: map_at_5
      value: 33.324
    - type: mrr_at_1
      value: 43.519000000000005
    - type: mrr_at_10
      value: 51.556000000000004
    - type: mrr_at_100
      value: 52.344
    - type: mrr_at_1000
      value: 52.373999999999995
    - type: mrr_at_3
      value: 48.868
    - type: mrr_at_5
      value: 50.319
    - type: ndcg_at_1
      value: 43.519000000000005
    - type: ndcg_at_10
      value: 43.803
    - type: ndcg_at_100
      value: 50.468999999999994
    - type: ndcg_at_1000
      value: 53.111
    - type: ndcg_at_3
      value: 38.893
    - type: ndcg_at_5
      value: 40.653
    - type: precision_at_1
      value: 43.519000000000005
    - type: precision_at_10
      value: 12.253
    - type: precision_at_100
      value: 1.931
    - type: precision_at_1000
      value: 0.242
    - type: precision_at_3
      value: 25.617
    - type: precision_at_5
      value: 19.383
    - type: recall_at_1
      value: 22.166
    - type: recall_at_10
      value: 51.6
    - type: recall_at_100
      value: 76.574
    - type: recall_at_1000
      value: 92.192
    - type: recall_at_3
      value: 34.477999999999994
    - type: recall_at_5
      value: 41.835
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 39.041
    - type: map_at_10
      value: 62.961999999999996
    - type: map_at_100
      value: 63.79899999999999
    - type: map_at_1000
      value: 63.854
    - type: map_at_3
      value: 59.399
    - type: map_at_5
      value: 61.669
    - type: mrr_at_1
      value: 78.082
    - type: mrr_at_10
      value: 84.321
    - type: mrr_at_100
      value: 84.49600000000001
    - type: mrr_at_1000
      value: 84.502
    - type: mrr_at_3
      value: 83.421
    - type: mrr_at_5
      value: 83.977
    - type: ndcg_at_1
      value: 78.082
    - type: ndcg_at_10
      value: 71.229
    - type: ndcg_at_100
      value: 74.10900000000001
    - type: ndcg_at_1000
      value: 75.169
    - type: ndcg_at_3
      value: 66.28699999999999
    - type: ndcg_at_5
      value: 69.084
    - type: precision_at_1
      value: 78.082
    - type: precision_at_10
      value: 14.993
    - type: precision_at_100
      value: 1.7239999999999998
    - type: precision_at_1000
      value: 0.186
    - type: precision_at_3
      value: 42.737
    - type: precision_at_5
      value: 27.843
    - type: recall_at_1
      value: 39.041
    - type: recall_at_10
      value: 74.96300000000001
    - type: recall_at_100
      value: 86.199
    - type: recall_at_1000
      value: 93.228
    - type: recall_at_3
      value: 64.105
    - type: recall_at_5
      value: 69.608
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 90.23160000000001
    - type: ap
      value: 85.5674856808308
    - type: f1
      value: 90.18033354786317
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: msmarco
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 24.091
    - type: map_at_10
      value: 36.753
    - type: map_at_100
      value: 37.913000000000004
    - type: map_at_1000
      value: 37.958999999999996
    - type: map_at_3
      value: 32.818999999999996
    - type: map_at_5
      value: 35.171
    - type: mrr_at_1
      value: 24.742
    - type: mrr_at_10
      value: 37.285000000000004
    - type: mrr_at_100
      value: 38.391999999999996
    - type: mrr_at_1000
      value: 38.431
    - type: mrr_at_3
      value: 33.440999999999995
    - type: mrr_at_5
      value: 35.75
    - type: ndcg_at_1
      value: 24.742
    - type: ndcg_at_10
      value: 43.698
    - type: ndcg_at_100
      value: 49.145
    - type: ndcg_at_1000
      value: 50.23800000000001
    - type: ndcg_at_3
      value: 35.769
    - type: ndcg_at_5
      value: 39.961999999999996
    - type: precision_at_1
      value: 24.742
    - type: precision_at_10
      value: 6.7989999999999995
    - type: precision_at_100
      value: 0.95
    - type: precision_at_1000
      value: 0.104
    - type: precision_at_3
      value: 15.096000000000002
    - type: precision_at_5
      value: 11.183
    - type: recall_at_1
      value: 24.091
    - type: recall_at_10
      value: 65.068
    - type: recall_at_100
      value: 89.899
    - type: recall_at_1000
      value: 98.16
    - type: recall_at_3
      value: 43.68
    - type: recall_at_5
      value: 53.754999999999995
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 93.66621067031465
    - type: f1
      value: 93.49622853272142
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (de)
      type: mteb/mtop_domain
      config: de
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 91.94702733164272
    - type: f1
      value: 91.17043441745282
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (es)
      type: mteb/mtop_domain
      config: es
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 92.20146764509674
    - type: f1
      value: 91.98359080555608
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (fr)
      type: mteb/mtop_domain
      config: fr
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 88.99780770435328
    - type: f1
      value: 89.19746342724068
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (hi)
      type: mteb/mtop_domain
      config: hi
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 89.78486912871998
    - type: f1
      value: 89.24578823628642
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (th)
      type: mteb/mtop_domain
      config: th
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 88.74502712477394
    - type: f1
      value: 89.00297573881542
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 77.9046967624259
    - type: f1
      value: 59.36787125785957
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (de)
      type: mteb/mtop_intent
      config: de
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 74.5280360664976
    - type: f1
      value: 57.17723440888718
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (es)
      type: mteb/mtop_intent
      config: es
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 75.44029352901934
    - type: f1
      value: 54.052855531072964
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (fr)
      type: mteb/mtop_intent
      config: fr
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 70.5606013153774
    - type: f1
      value: 52.62215934386531
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (hi)
      type: mteb/mtop_intent
      config: hi
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 73.11581211903908
    - type: f1
      value: 52.341291845645465
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (th)
      type: mteb/mtop_intent
      config: th
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 74.28933092224233
    - type: f1
      value: 57.07918745504911
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (af)
      type: mteb/amazon_massive_intent
      config: af
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 62.38063214525892
    - type: f1
      value: 59.46463723443009
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (am)
      type: mteb/amazon_massive_intent
      config: am
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 56.06926698049766
    - type: f1
      value: 52.49084283283562
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (ar)
      type: mteb/amazon_massive_intent
      config: ar
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 60.74983187626093
    - type: f1
      value: 56.960640620165904
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (az)
      type: mteb/amazon_massive_intent
      config: az
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 64.86550100874243
    - type: f1
      value: 62.47370548140688
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (bn)
      type: mteb/amazon_massive_intent
      config: bn
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 63.971082716879636
    - type: f1
      value: 61.03812421957381
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (cy)
      type: mteb/amazon_massive_intent
      config: cy
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 54.98318762609282
    - type: f1
      value: 51.51207916008392
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (da)
      type: mteb/amazon_massive_intent
      config: da
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 69.45527908540686
    - type: f1
      value: 66.16631905400318
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (de)
      type: mteb/amazon_massive_intent
      config: de
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 69.32750504371216
    - type: f1
      value: 66.16755288646591
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (el)
      type: mteb/amazon_massive_intent
      config: el
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 69.09213180901143
    - type: f1
      value: 66.95654394661507
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 73.75588433086752
    - type: f1
      value: 71.79973779656923
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (es)
      type: mteb/amazon_massive_intent
      config: es
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 70.49428379287154
    - type: f1
      value: 68.37494379215734
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (fa)
      type: mteb/amazon_massive_intent
      config: fa
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 69.90921318090115
    - type: f1
      value: 66.79517376481645
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (fi)
      type: mteb/amazon_massive_intent
      config: fi
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 70.12104909213181
    - type: f1
      value: 67.29448842879584
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (fr)
      type: mteb/amazon_massive_intent
      config: fr
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 69.34095494283793
    - type: f1
      value: 67.01134288992947
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (he)
      type: mteb/amazon_massive_intent
      config: he
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 67.61264290517822
    - type: f1
      value: 64.68730512660757
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (hi)
      type: mteb/amazon_massive_intent
      config: hi
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 67.79757901815738
    - type: f1
      value: 65.24938539425598
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (hu)
      type: mteb/amazon_massive_intent
      config: hu
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 69.68728984532616
    - type: f1
      value: 67.0487169762553
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (hy)
      type: mteb/amazon_massive_intent
      config: hy
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 62.07464694014795
    - type: f1
      value: 59.183532276789286
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (id)
      type: mteb/amazon_massive_intent
      config: id
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 70.04707464694015
    - type: f1
      value: 67.66829629003848
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (is)
      type: mteb/amazon_massive_intent
      config: is
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 62.42434431741762
    - type: f1
      value: 59.01617226544757
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (it)
      type: mteb/amazon_massive_intent
      config: it
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 70.53127101546738
    - type: f1
      value: 68.10033760906255
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (ja)
      type: mteb/amazon_massive_intent
      config: ja
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 72.50504371217215
    - type: f1
      value: 69.74931103158923
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (jv)
      type: mteb/amazon_massive_intent
      config: jv
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 57.91190316072628
    - type: f1
      value: 54.05551136648796
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (ka)
      type: mteb/amazon_massive_intent
      config: ka
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 51.78211163416275
    - type: f1
      value: 49.874888544058535
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (km)
      type: mteb/amazon_massive_intent
      config: km
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 47.017484868863484
    - type: f1
      value: 44.53364263352014
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (kn)
      type: mteb/amazon_massive_intent
      config: kn
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 62.16207128446537
    - type: f1
      value: 59.01185692320829
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (ko)
      type: mteb/amazon_massive_intent
      config: ko
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 69.42501681237391
    - type: f1
      value: 67.13169450166086
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (lv)
      type: mteb/amazon_massive_intent
      config: lv
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 67.0780094149294
    - type: f1
      value: 64.41720167850707
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (ml)
      type: mteb/amazon_massive_intent
      config: ml
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 65.57162071284466
    - type: f1
      value: 62.414138683804424
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (mn)
      type: mteb/amazon_massive_intent
      config: mn
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 61.71149966375252
    - type: f1
      value: 58.594805125087234
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (ms)
      type: mteb/amazon_massive_intent
      config: ms
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 66.03900470746471
    - type: f1
      value: 63.87937257883887
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (my)
      type: mteb/amazon_massive_intent
      config: my
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 60.8776059179556
    - type: f1
      value: 57.48587618059131
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (nb)
      type: mteb/amazon_massive_intent
      config: nb
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 69.87895090786819
    - type: f1
      value: 66.8141299430347
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (nl)
      type: mteb/amazon_massive_intent
      config: nl
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 70.45057162071285
    - type: f1
      value: 67.46444039673516
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (pl)
      type: mteb/amazon_massive_intent
      config: pl
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 71.546738399462
    - type: f1
      value: 68.63640876702655
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (pt)
      type: mteb/amazon_massive_intent
      config: pt
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 70.72965702757229
    - type: f1
      value: 68.54119560379115
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (ro)
      type: mteb/amazon_massive_intent
      config: ro
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 68.35574983187625
    - type: f1
      value: 65.88844917691927
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (ru)
      type: mteb/amazon_massive_intent
      config: ru
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 71.70477471418964
    - type: f1
      value: 69.19665697061978
  - task:
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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      revision: 7d571f92784cd94a019292a1f45445077d0ef634
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      type: Classification
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  - task:
      type: Classification
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      revision: 7d571f92784cd94a019292a1f45445077d0ef634
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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      revision: 7d571f92784cd94a019292a1f45445077d0ef634
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  - task:
      type: Classification
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  - task:
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  - task:
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  - task:
      type: Classification
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      type: Classification
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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    - type: f1
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  - task:
      type: Classification
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      type: mteb/amazon_massive_scenario
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      revision: 7d571f92784cd94a019292a1f45445077d0ef634
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  - task:
      type: Classification
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
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      type: mteb/amazon_massive_scenario
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      revision: 7d571f92784cd94a019292a1f45445077d0ef634
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  - task:
      type: Classification
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
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      value: 64.61463095996173
  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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      revision: 7d571f92784cd94a019292a1f45445077d0ef634
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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      revision: 7d571f92784cd94a019292a1f45445077d0ef634
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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      revision: 7d571f92784cd94a019292a1f45445077d0ef634
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  - task:
      type: Classification
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  - task:
      type: Classification
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
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  - task:
      type: Classification
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
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  - task:
      type: Classification
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
    dataset:
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
    dataset:
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      type: mteb/amazon_massive_scenario
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  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (tr)
      type: mteb/amazon_massive_scenario
      config: tr
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 73.9946200403497
    - type: f1
      value: 73.87348793725525
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (ur)
      type: mteb/amazon_massive_scenario
      config: ur
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 68.15400134498992
    - type: f1
      value: 67.09433241421094
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (vi)
      type: mteb/amazon_massive_scenario
      config: vi
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 73.11365164761264
    - type: f1
      value: 73.59502539433753
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (zh-CN)
      type: mteb/amazon_massive_scenario
      config: zh-CN
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 76.82582380632145
    - type: f1
      value: 76.89992945316313
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (zh-TW)
      type: mteb/amazon_massive_scenario
      config: zh-TW
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 71.81237390719569
    - type: f1
      value: 72.36499770986265
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 31.480506569594695
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 29.71252128004552
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 31.421396787056548
    - type: mrr
      value: 32.48155274872267
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 5.595
    - type: map_at_10
      value: 12.642000000000001
    - type: map_at_100
      value: 15.726
    - type: map_at_1000
      value: 17.061999999999998
    - type: map_at_3
      value: 9.125
    - type: map_at_5
      value: 10.866000000000001
    - type: mrr_at_1
      value: 43.344
    - type: mrr_at_10
      value: 52.227999999999994
    - type: mrr_at_100
      value: 52.898999999999994
    - type: mrr_at_1000
      value: 52.944
    - type: mrr_at_3
      value: 49.845
    - type: mrr_at_5
      value: 51.115
    - type: ndcg_at_1
      value: 41.949999999999996
    - type: ndcg_at_10
      value: 33.995
    - type: ndcg_at_100
      value: 30.869999999999997
    - type: ndcg_at_1000
      value: 39.487
    - type: ndcg_at_3
      value: 38.903999999999996
    - type: ndcg_at_5
      value: 37.236999999999995
    - type: precision_at_1
      value: 43.344
    - type: precision_at_10
      value: 25.480000000000004
    - type: precision_at_100
      value: 7.672
    - type: precision_at_1000
      value: 2.028
    - type: precision_at_3
      value: 36.636
    - type: precision_at_5
      value: 32.632
    - type: recall_at_1
      value: 5.595
    - type: recall_at_10
      value: 16.466
    - type: recall_at_100
      value: 31.226
    - type: recall_at_1000
      value: 62.778999999999996
    - type: recall_at_3
      value: 9.931
    - type: recall_at_5
      value: 12.884
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: nq
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 40.414
    - type: map_at_10
      value: 56.754000000000005
    - type: map_at_100
      value: 57.457
    - type: map_at_1000
      value: 57.477999999999994
    - type: map_at_3
      value: 52.873999999999995
    - type: map_at_5
      value: 55.175
    - type: mrr_at_1
      value: 45.278
    - type: mrr_at_10
      value: 59.192
    - type: mrr_at_100
      value: 59.650000000000006
    - type: mrr_at_1000
      value: 59.665
    - type: mrr_at_3
      value: 56.141
    - type: mrr_at_5
      value: 57.998000000000005
    - type: ndcg_at_1
      value: 45.278
    - type: ndcg_at_10
      value: 64.056
    - type: ndcg_at_100
      value: 66.89
    - type: ndcg_at_1000
      value: 67.364
    - type: ndcg_at_3
      value: 56.97
    - type: ndcg_at_5
      value: 60.719
    - type: precision_at_1
      value: 45.278
    - type: precision_at_10
      value: 9.994
    - type: precision_at_100
      value: 1.165
    - type: precision_at_1000
      value: 0.121
    - type: precision_at_3
      value: 25.512
    - type: precision_at_5
      value: 17.509
    - type: recall_at_1
      value: 40.414
    - type: recall_at_10
      value: 83.596
    - type: recall_at_100
      value: 95.72
    - type: recall_at_1000
      value: 99.24
    - type: recall_at_3
      value: 65.472
    - type: recall_at_5
      value: 74.039
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 70.352
    - type: map_at_10
      value: 84.369
    - type: map_at_100
      value: 85.02499999999999
    - type: map_at_1000
      value: 85.04
    - type: map_at_3
      value: 81.42399999999999
    - type: map_at_5
      value: 83.279
    - type: mrr_at_1
      value: 81.05
    - type: mrr_at_10
      value: 87.401
    - type: mrr_at_100
      value: 87.504
    - type: mrr_at_1000
      value: 87.505
    - type: mrr_at_3
      value: 86.443
    - type: mrr_at_5
      value: 87.10799999999999
    - type: ndcg_at_1
      value: 81.04
    - type: ndcg_at_10
      value: 88.181
    - type: ndcg_at_100
      value: 89.411
    - type: ndcg_at_1000
      value: 89.507
    - type: ndcg_at_3
      value: 85.28099999999999
    - type: ndcg_at_5
      value: 86.888
    - type: precision_at_1
      value: 81.04
    - type: precision_at_10
      value: 13.406
    - type: precision_at_100
      value: 1.5350000000000001
    - type: precision_at_1000
      value: 0.157
    - type: precision_at_3
      value: 37.31
    - type: precision_at_5
      value: 24.54
    - type: recall_at_1
      value: 70.352
    - type: recall_at_10
      value: 95.358
    - type: recall_at_100
      value: 99.541
    - type: recall_at_1000
      value: 99.984
    - type: recall_at_3
      value: 87.111
    - type: recall_at_5
      value: 91.643
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 46.54068723291946
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 63.216287629895994
  - 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: 10.071
    - type: map_at_100
      value: 11.892
    - type: map_at_1000
      value: 12.196
    - type: map_at_3
      value: 7.234
    - type: map_at_5
      value: 8.613999999999999
    - type: mrr_at_1
      value: 19.900000000000002
    - type: mrr_at_10
      value: 30.516
    - type: mrr_at_100
      value: 31.656000000000002
    - type: mrr_at_1000
      value: 31.723000000000003
    - type: mrr_at_3
      value: 27.400000000000002
    - type: mrr_at_5
      value: 29.270000000000003
    - type: ndcg_at_1
      value: 19.900000000000002
    - type: ndcg_at_10
      value: 17.474
    - type: ndcg_at_100
      value: 25.020999999999997
    - type: ndcg_at_1000
      value: 30.728
    - type: ndcg_at_3
      value: 16.588
    - type: ndcg_at_5
      value: 14.498
    - type: precision_at_1
      value: 19.900000000000002
    - type: precision_at_10
      value: 9.139999999999999
    - type: precision_at_100
      value: 2.011
    - type: precision_at_1000
      value: 0.33899999999999997
    - type: precision_at_3
      value: 15.667
    - type: precision_at_5
      value: 12.839999999999998
    - type: recall_at_1
      value: 4.023000000000001
    - type: recall_at_10
      value: 18.497
    - type: recall_at_100
      value: 40.8
    - type: recall_at_1000
      value: 68.812
    - type: recall_at_3
      value: 9.508
    - type: recall_at_5
      value: 12.983
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 83.967008785134
    - type: cos_sim_spearman
      value: 80.23142141101837
    - type: euclidean_pearson
      value: 81.20166064704539
    - type: euclidean_spearman
      value: 80.18961335654585
    - type: manhattan_pearson
      value: 81.13925443187625
    - type: manhattan_spearman
      value: 80.07948723044424
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 86.94262461316023
    - type: cos_sim_spearman
      value: 80.01596278563865
    - type: euclidean_pearson
      value: 83.80799622922581
    - type: euclidean_spearman
      value: 79.94984954947103
    - type: manhattan_pearson
      value: 83.68473841756281
    - type: manhattan_spearman
      value: 79.84990707951822
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 80.57346443146068
    - type: cos_sim_spearman
      value: 81.54689837570866
    - type: euclidean_pearson
      value: 81.10909881516007
    - type: euclidean_spearman
      value: 81.56746243261762
    - type: manhattan_pearson
      value: 80.87076036186582
    - type: manhattan_spearman
      value: 81.33074987964402
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 79.54733787179849
    - type: cos_sim_spearman
      value: 77.72202105610411
    - type: euclidean_pearson
      value: 78.9043595478849
    - type: euclidean_spearman
      value: 77.93422804309435
    - type: manhattan_pearson
      value: 78.58115121621368
    - type: manhattan_spearman
      value: 77.62508135122033
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 88.59880017237558
    - type: cos_sim_spearman
      value: 89.31088630824758
    - type: euclidean_pearson
      value: 88.47069261564656
    - type: euclidean_spearman
      value: 89.33581971465233
    - type: manhattan_pearson
      value: 88.40774264100956
    - type: manhattan_spearman
      value: 89.28657485627835
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 84.08055117917084
    - type: cos_sim_spearman
      value: 85.78491813080304
    - type: euclidean_pearson
      value: 84.99329155500392
    - type: euclidean_spearman
      value: 85.76728064677287
    - type: manhattan_pearson
      value: 84.87947428989587
    - type: manhattan_spearman
      value: 85.62429454917464
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (ko-ko)
      type: mteb/sts17-crosslingual-sts
      config: ko-ko
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 82.14190939287384
    - type: cos_sim_spearman
      value: 82.27331573306041
    - type: euclidean_pearson
      value: 81.891896953716
    - type: euclidean_spearman
      value: 82.37695542955998
    - type: manhattan_pearson
      value: 81.73123869460504
    - type: manhattan_spearman
      value: 82.19989168441421
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (ar-ar)
      type: mteb/sts17-crosslingual-sts
      config: ar-ar
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 76.84695301843362
    - type: cos_sim_spearman
      value: 77.87790986014461
    - type: euclidean_pearson
      value: 76.91981583106315
    - type: euclidean_spearman
      value: 77.88154772749589
    - type: manhattan_pearson
      value: 76.94953277451093
    - type: manhattan_spearman
      value: 77.80499230728604
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-ar)
      type: mteb/sts17-crosslingual-sts
      config: en-ar
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 75.44657840482016
    - type: cos_sim_spearman
      value: 75.05531095119674
    - type: euclidean_pearson
      value: 75.88161755829299
    - type: euclidean_spearman
      value: 74.73176238219332
    - type: manhattan_pearson
      value: 75.63984765635362
    - type: manhattan_spearman
      value: 74.86476440770737
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-de)
      type: mteb/sts17-crosslingual-sts
      config: en-de
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 85.64700140524133
    - type: cos_sim_spearman
      value: 86.16014210425672
    - type: euclidean_pearson
      value: 86.49086860843221
    - type: euclidean_spearman
      value: 86.09729326815614
    - type: manhattan_pearson
      value: 86.43406265125513
    - type: manhattan_spearman
      value: 86.17740150939994
  - 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: 87.91170098764921
    - type: cos_sim_spearman
      value: 88.12437004058931
    - type: euclidean_pearson
      value: 88.81828254494437
    - type: euclidean_spearman
      value: 88.14831794572122
    - type: manhattan_pearson
      value: 88.93442183448961
    - type: manhattan_spearman
      value: 88.15254630778304
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-tr)
      type: mteb/sts17-crosslingual-sts
      config: en-tr
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 72.91390577997292
    - type: cos_sim_spearman
      value: 71.22979457536074
    - type: euclidean_pearson
      value: 74.40314008106749
    - type: euclidean_spearman
      value: 72.54972136083246
    - type: manhattan_pearson
      value: 73.85687539530218
    - type: manhattan_spearman
      value: 72.09500771742637
  - 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: 80.9301067983089
    - type: cos_sim_spearman
      value: 80.74989828346473
    - type: euclidean_pearson
      value: 81.36781301814257
    - type: euclidean_spearman
      value: 80.9448819964426
    - type: manhattan_pearson
      value: 81.0351322685609
    - type: manhattan_spearman
      value: 80.70192121844177
  - 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: 87.13820465980005
    - type: cos_sim_spearman
      value: 86.73532498758757
    - type: euclidean_pearson
      value: 87.21329451846637
    - type: euclidean_spearman
      value: 86.57863198601002
    - type: manhattan_pearson
      value: 87.06973713818554
    - type: manhattan_spearman
      value: 86.47534918791499
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (fr-en)
      type: mteb/sts17-crosslingual-sts
      config: fr-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 85.48720108904415
    - type: cos_sim_spearman
      value: 85.62221757068387
    - type: euclidean_pearson
      value: 86.1010129512749
    - type: euclidean_spearman
      value: 85.86580966509942
    - type: manhattan_pearson
      value: 86.26800938808971
    - type: manhattan_spearman
      value: 85.88902721678429
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (it-en)
      type: mteb/sts17-crosslingual-sts
      config: it-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 83.98021347333516
    - type: cos_sim_spearman
      value: 84.53806553803501
    - type: euclidean_pearson
      value: 84.61483347248364
    - type: euclidean_spearman
      value: 85.14191408011702
    - type: manhattan_pearson
      value: 84.75297588825967
    - type: manhattan_spearman
      value: 85.33176753669242
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (nl-en)
      type: mteb/sts17-crosslingual-sts
      config: nl-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 84.51856644893233
    - type: cos_sim_spearman
      value: 85.27510748506413
    - type: euclidean_pearson
      value: 85.09886861540977
    - type: euclidean_spearman
      value: 85.62579245860887
    - type: manhattan_pearson
      value: 84.93017860464607
    - type: manhattan_spearman
      value: 85.5063988898453
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 62.581573200584195
    - type: cos_sim_spearman
      value: 63.05503590247928
    - type: euclidean_pearson
      value: 63.652564812602094
    - type: euclidean_spearman
      value: 62.64811520876156
    - type: manhattan_pearson
      value: 63.506842893061076
    - type: manhattan_spearman
      value: 62.51289573046917
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (de)
      type: mteb/sts22-crosslingual-sts
      config: de
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 48.2248801729127
    - type: cos_sim_spearman
      value: 56.5936604678561
    - type: euclidean_pearson
      value: 43.98149464089
    - type: euclidean_spearman
      value: 56.108561882423615
    - type: manhattan_pearson
      value: 43.86880305903564
    - type: manhattan_spearman
      value: 56.04671150510166
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (es)
      type: mteb/sts22-crosslingual-sts
      config: es
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 55.17564527009831
    - type: cos_sim_spearman
      value: 64.57978560979488
    - type: euclidean_pearson
      value: 58.8818330154583
    - type: euclidean_spearman
      value: 64.99214839071281
    - type: manhattan_pearson
      value: 58.72671436121381
    - type: manhattan_spearman
      value: 65.10713416616109
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (pl)
      type: mteb/sts22-crosslingual-sts
      config: pl
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 26.772131864023297
    - type: cos_sim_spearman
      value: 34.68200792408681
    - type: euclidean_pearson
      value: 16.68082419005441
    - type: euclidean_spearman
      value: 34.83099932652166
    - type: manhattan_pearson
      value: 16.52605949659529
    - type: manhattan_spearman
      value: 34.82075801399475
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (tr)
      type: mteb/sts22-crosslingual-sts
      config: tr
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 54.42415189043831
    - type: cos_sim_spearman
      value: 63.54594264576758
    - type: euclidean_pearson
      value: 57.36577498297745
    - type: euclidean_spearman
      value: 63.111466379158074
    - type: manhattan_pearson
      value: 57.584543715873885
    - type: manhattan_spearman
      value: 63.22361054139183
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (ar)
      type: mteb/sts22-crosslingual-sts
      config: ar
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 47.55216762405518
    - type: cos_sim_spearman
      value: 56.98670142896412
    - type: euclidean_pearson
      value: 50.15318757562699
    - type: euclidean_spearman
      value: 56.524941926541906
    - type: manhattan_pearson
      value: 49.955618528674904
    - type: manhattan_spearman
      value: 56.37102209240117
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (ru)
      type: mteb/sts22-crosslingual-sts
      config: ru
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 49.20540980338571
    - type: cos_sim_spearman
      value: 59.9009453504406
    - type: euclidean_pearson
      value: 49.557749853620535
    - type: euclidean_spearman
      value: 59.76631621172456
    - type: manhattan_pearson
      value: 49.62340591181147
    - type: manhattan_spearman
      value: 59.94224880322436
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (zh)
      type: mteb/sts22-crosslingual-sts
      config: zh
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 51.508169956576985
    - type: cos_sim_spearman
      value: 66.82461565306046
    - type: euclidean_pearson
      value: 56.2274426480083
    - type: euclidean_spearman
      value: 66.6775323848333
    - type: manhattan_pearson
      value: 55.98277796300661
    - type: manhattan_spearman
      value: 66.63669848497175
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (fr)
      type: mteb/sts22-crosslingual-sts
      config: fr
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 72.86478788045507
    - type: cos_sim_spearman
      value: 76.7946552053193
    - type: euclidean_pearson
      value: 75.01598530490269
    - type: euclidean_spearman
      value: 76.83618917858281
    - type: manhattan_pearson
      value: 74.68337628304332
    - type: manhattan_spearman
      value: 76.57480204017773
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (de-en)
      type: mteb/sts22-crosslingual-sts
      config: de-en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 55.922619099401984
    - type: cos_sim_spearman
      value: 56.599362477240774
    - type: euclidean_pearson
      value: 56.68307052369783
    - type: euclidean_spearman
      value: 54.28760436777401
    - type: manhattan_pearson
      value: 56.67763566500681
    - type: manhattan_spearman
      value: 53.94619541711359
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (es-en)
      type: mteb/sts22-crosslingual-sts
      config: es-en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 66.74357206710913
    - type: cos_sim_spearman
      value: 72.5208244925311
    - type: euclidean_pearson
      value: 67.49254562186032
    - type: euclidean_spearman
      value: 72.02469076238683
    - type: manhattan_pearson
      value: 67.45251772238085
    - type: manhattan_spearman
      value: 72.05538819984538
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (it)
      type: mteb/sts22-crosslingual-sts
      config: it
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 71.25734330033191
    - type: cos_sim_spearman
      value: 76.98349083946823
    - type: euclidean_pearson
      value: 73.71642838667736
    - type: euclidean_spearman
      value: 77.01715504651384
    - type: manhattan_pearson
      value: 73.61712711868105
    - type: manhattan_spearman
      value: 77.01392571153896
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (pl-en)
      type: mteb/sts22-crosslingual-sts
      config: pl-en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 63.18215462781212
    - type: cos_sim_spearman
      value: 65.54373266117607
    - type: euclidean_pearson
      value: 64.54126095439005
    - type: euclidean_spearman
      value: 65.30410369102711
    - type: manhattan_pearson
      value: 63.50332221148234
    - type: manhattan_spearman
      value: 64.3455878104313
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (zh-en)
      type: mteb/sts22-crosslingual-sts
      config: zh-en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 62.30509221440029
    - type: cos_sim_spearman
      value: 65.99582704642478
    - type: euclidean_pearson
      value: 63.43818859884195
    - type: euclidean_spearman
      value: 66.83172582815764
    - type: manhattan_pearson
      value: 63.055779168508764
    - type: manhattan_spearman
      value: 65.49585020501449
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (es-it)
      type: mteb/sts22-crosslingual-sts
      config: es-it
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 59.587830825340404
    - type: cos_sim_spearman
      value: 68.93467614588089
    - type: euclidean_pearson
      value: 62.3073527367404
    - type: euclidean_spearman
      value: 69.69758171553175
    - type: manhattan_pearson
      value: 61.9074580815789
    - type: manhattan_spearman
      value: 69.57696375597865
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (de-fr)
      type: mteb/sts22-crosslingual-sts
      config: de-fr
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 57.143220125577066
    - type: cos_sim_spearman
      value: 67.78857859159226
    - type: euclidean_pearson
      value: 55.58225107923733
    - type: euclidean_spearman
      value: 67.80662907184563
    - type: manhattan_pearson
      value: 56.24953502726514
    - type: manhattan_spearman
      value: 67.98262125431616
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (de-pl)
      type: mteb/sts22-crosslingual-sts
      config: de-pl
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 21.826928900322066
    - type: cos_sim_spearman
      value: 49.578506634400405
    - type: euclidean_pearson
      value: 27.939890138843214
    - type: euclidean_spearman
      value: 52.71950519136242
    - type: manhattan_pearson
      value: 26.39878683847546
    - type: manhattan_spearman
      value: 47.54609580342499
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (fr-pl)
      type: mteb/sts22-crosslingual-sts
      config: fr-pl
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 57.27603854632001
    - type: cos_sim_spearman
      value: 50.709255283710995
    - type: euclidean_pearson
      value: 59.5419024445929
    - type: euclidean_spearman
      value: 50.709255283710995
    - type: manhattan_pearson
      value: 59.03256832438492
    - type: manhattan_spearman
      value: 61.97797868009122
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 85.00757054859712
    - type: cos_sim_spearman
      value: 87.29283629622222
    - type: euclidean_pearson
      value: 86.54824171775536
    - type: euclidean_spearman
      value: 87.24364730491402
    - type: manhattan_pearson
      value: 86.5062156915074
    - type: manhattan_spearman
      value: 87.15052170378574
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 82.03549357197389
    - type: mrr
      value: 95.05437645143527
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 57.260999999999996
    - type: map_at_10
      value: 66.259
    - type: map_at_100
      value: 66.884
    - type: map_at_1000
      value: 66.912
    - type: map_at_3
      value: 63.685
    - type: map_at_5
      value: 65.35499999999999
    - type: mrr_at_1
      value: 60.333000000000006
    - type: mrr_at_10
      value: 67.5
    - type: mrr_at_100
      value: 68.013
    - type: mrr_at_1000
      value: 68.038
    - type: mrr_at_3
      value: 65.61099999999999
    - type: mrr_at_5
      value: 66.861
    - type: ndcg_at_1
      value: 60.333000000000006
    - type: ndcg_at_10
      value: 70.41
    - type: ndcg_at_100
      value: 73.10600000000001
    - type: ndcg_at_1000
      value: 73.846
    - type: ndcg_at_3
      value: 66.133
    - type: ndcg_at_5
      value: 68.499
    - type: precision_at_1
      value: 60.333000000000006
    - type: precision_at_10
      value: 9.232999999999999
    - type: precision_at_100
      value: 1.0630000000000002
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 25.667
    - type: precision_at_5
      value: 17.067
    - type: recall_at_1
      value: 57.260999999999996
    - type: recall_at_10
      value: 81.94399999999999
    - type: recall_at_100
      value: 93.867
    - type: recall_at_1000
      value: 99.667
    - type: recall_at_3
      value: 70.339
    - type: recall_at_5
      value: 76.25
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.74356435643564
    - type: cos_sim_ap
      value: 93.13411948212683
    - type: cos_sim_f1
      value: 86.80521991300147
    - type: cos_sim_precision
      value: 84.00374181478017
    - type: cos_sim_recall
      value: 89.8
    - type: dot_accuracy
      value: 99.67920792079208
    - type: dot_ap
      value: 89.27277565444479
    - type: dot_f1
      value: 83.9276990718124
    - type: dot_precision
      value: 82.04393505253104
    - type: dot_recall
      value: 85.9
    - type: euclidean_accuracy
      value: 99.74257425742574
    - type: euclidean_ap
      value: 93.17993008259062
    - type: euclidean_f1
      value: 86.69396110542476
    - type: euclidean_precision
      value: 88.78406708595388
    - type: euclidean_recall
      value: 84.7
    - type: manhattan_accuracy
      value: 99.74257425742574
    - type: manhattan_ap
      value: 93.14413755550099
    - type: manhattan_f1
      value: 86.82483594144371
    - type: manhattan_precision
      value: 87.66564729867483
    - type: manhattan_recall
      value: 86
    - type: max_accuracy
      value: 99.74356435643564
    - type: max_ap
      value: 93.17993008259062
    - type: max_f1
      value: 86.82483594144371
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 57.525863806168566
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 32.68850574423839
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 49.71580650644033
    - type: mrr
      value: 50.50971903913081
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 29.152190498799484
    - type: cos_sim_spearman
      value: 29.686180371952727
    - type: dot_pearson
      value: 27.248664793816342
    - type: dot_spearman
      value: 28.37748983721745
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.20400000000000001
    - type: map_at_10
      value: 1.6209999999999998
    - type: map_at_100
      value: 9.690999999999999
    - type: map_at_1000
      value: 23.733
    - type: map_at_3
      value: 0.575
    - type: map_at_5
      value: 0.885
    - type: mrr_at_1
      value: 78
    - type: mrr_at_10
      value: 86.56700000000001
    - type: mrr_at_100
      value: 86.56700000000001
    - type: mrr_at_1000
      value: 86.56700000000001
    - type: mrr_at_3
      value: 85.667
    - type: mrr_at_5
      value: 86.56700000000001
    - type: ndcg_at_1
      value: 76
    - type: ndcg_at_10
      value: 71.326
    - type: ndcg_at_100
      value: 54.208999999999996
    - type: ndcg_at_1000
      value: 49.252
    - type: ndcg_at_3
      value: 74.235
    - type: ndcg_at_5
      value: 73.833
    - type: precision_at_1
      value: 78
    - type: precision_at_10
      value: 74.8
    - type: precision_at_100
      value: 55.50000000000001
    - type: precision_at_1000
      value: 21.836
    - type: precision_at_3
      value: 78
    - type: precision_at_5
      value: 78
    - type: recall_at_1
      value: 0.20400000000000001
    - type: recall_at_10
      value: 1.894
    - type: recall_at_100
      value: 13.245999999999999
    - type: recall_at_1000
      value: 46.373
    - type: recall_at_3
      value: 0.613
    - type: recall_at_5
      value: 0.991
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (sqi-eng)
      type: mteb/tatoeba-bitext-mining
      config: sqi-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 95.89999999999999
    - type: f1
      value: 94.69999999999999
    - type: precision
      value: 94.11666666666667
    - type: recall
      value: 95.89999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (fry-eng)
      type: mteb/tatoeba-bitext-mining
      config: fry-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 68.20809248554913
    - type: f1
      value: 63.431048720066066
    - type: precision
      value: 61.69143958161298
    - type: recall
      value: 68.20809248554913
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (kur-eng)
      type: mteb/tatoeba-bitext-mining
      config: kur-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 71.21951219512195
    - type: f1
      value: 66.82926829268293
    - type: precision
      value: 65.1260162601626
    - type: recall
      value: 71.21951219512195
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (tur-eng)
      type: mteb/tatoeba-bitext-mining
      config: tur-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 97.2
    - type: f1
      value: 96.26666666666667
    - type: precision
      value: 95.8
    - type: recall
      value: 97.2
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (deu-eng)
      type: mteb/tatoeba-bitext-mining
      config: deu-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 99.3
    - type: f1
      value: 99.06666666666666
    - type: precision
      value: 98.95
    - type: recall
      value: 99.3
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (nld-eng)
      type: mteb/tatoeba-bitext-mining
      config: nld-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 97.39999999999999
    - type: f1
      value: 96.63333333333333
    - type: precision
      value: 96.26666666666668
    - type: recall
      value: 97.39999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ron-eng)
      type: mteb/tatoeba-bitext-mining
      config: ron-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 96
    - type: f1
      value: 94.86666666666666
    - type: precision
      value: 94.31666666666668
    - type: recall
      value: 96
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ang-eng)
      type: mteb/tatoeba-bitext-mining
      config: ang-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 47.01492537313433
    - type: f1
      value: 40.178867566927266
    - type: precision
      value: 38.179295828549556
    - type: recall
      value: 47.01492537313433
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ido-eng)
      type: mteb/tatoeba-bitext-mining
      config: ido-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 86.5
    - type: f1
      value: 83.62537480063796
    - type: precision
      value: 82.44555555555554
    - type: recall
      value: 86.5
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (jav-eng)
      type: mteb/tatoeba-bitext-mining
      config: jav-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 80.48780487804879
    - type: f1
      value: 75.45644599303138
    - type: precision
      value: 73.37398373983739
    - type: recall
      value: 80.48780487804879
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (isl-eng)
      type: mteb/tatoeba-bitext-mining
      config: isl-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 93.7
    - type: f1
      value: 91.95666666666666
    - type: precision
      value: 91.125
    - type: recall
      value: 93.7
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (slv-eng)
      type: mteb/tatoeba-bitext-mining
      config: slv-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 91.73754556500607
    - type: f1
      value: 89.65168084244632
    - type: precision
      value: 88.73025516403402
    - type: recall
      value: 91.73754556500607
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (cym-eng)
      type: mteb/tatoeba-bitext-mining
      config: cym-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 81.04347826086956
    - type: f1
      value: 76.2128364389234
    - type: precision
      value: 74.2
    - type: recall
      value: 81.04347826086956
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (kaz-eng)
      type: mteb/tatoeba-bitext-mining
      config: kaz-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 83.65217391304348
    - type: f1
      value: 79.4376811594203
    - type: precision
      value: 77.65797101449274
    - type: recall
      value: 83.65217391304348
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (est-eng)
      type: mteb/tatoeba-bitext-mining
      config: est-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 87.5
    - type: f1
      value: 85.02690476190476
    - type: precision
      value: 83.96261904761904
    - type: recall
      value: 87.5
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (heb-eng)
      type: mteb/tatoeba-bitext-mining
      config: heb-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 89.3
    - type: f1
      value: 86.52333333333333
    - type: precision
      value: 85.22833333333332
    - type: recall
      value: 89.3
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (gla-eng)
      type: mteb/tatoeba-bitext-mining
      config: gla-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 65.01809408926418
    - type: f1
      value: 59.00594446432805
    - type: precision
      value: 56.827215807915444
    - type: recall
      value: 65.01809408926418
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (mar-eng)
      type: mteb/tatoeba-bitext-mining
      config: mar-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 91.2
    - type: f1
      value: 88.58
    - type: precision
      value: 87.33333333333334
    - type: recall
      value: 91.2
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (lat-eng)
      type: mteb/tatoeba-bitext-mining
      config: lat-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 59.199999999999996
    - type: f1
      value: 53.299166276284915
    - type: precision
      value: 51.3383908045977
    - type: recall
      value: 59.199999999999996
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (bel-eng)
      type: mteb/tatoeba-bitext-mining
      config: bel-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 93.2
    - type: f1
      value: 91.2
    - type: precision
      value: 90.25
    - type: recall
      value: 93.2
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (pms-eng)
      type: mteb/tatoeba-bitext-mining
      config: pms-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 64.76190476190476
    - type: f1
      value: 59.867110667110666
    - type: precision
      value: 58.07390192653351
    - type: recall
      value: 64.76190476190476
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (gle-eng)
      type: mteb/tatoeba-bitext-mining
      config: gle-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 76.2
    - type: f1
      value: 71.48147546897547
    - type: precision
      value: 69.65409090909091
    - type: recall
      value: 76.2
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (pes-eng)
      type: mteb/tatoeba-bitext-mining
      config: pes-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 93.8
    - type: f1
      value: 92.14
    - type: precision
      value: 91.35833333333333
    - type: recall
      value: 93.8
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (nob-eng)
      type: mteb/tatoeba-bitext-mining
      config: nob-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 97.89999999999999
    - type: f1
      value: 97.2
    - type: precision
      value: 96.85000000000001
    - type: recall
      value: 97.89999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (bul-eng)
      type: mteb/tatoeba-bitext-mining
      config: bul-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 94.6
    - type: f1
      value: 92.93333333333334
    - type: precision
      value: 92.13333333333333
    - type: recall
      value: 94.6
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (cbk-eng)
      type: mteb/tatoeba-bitext-mining
      config: cbk-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 74.1
    - type: f1
      value: 69.14817460317461
    - type: precision
      value: 67.2515873015873
    - type: recall
      value: 74.1
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (hun-eng)
      type: mteb/tatoeba-bitext-mining
      config: hun-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 95.19999999999999
    - type: f1
      value: 94.01333333333335
    - type: precision
      value: 93.46666666666667
    - type: recall
      value: 95.19999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (uig-eng)
      type: mteb/tatoeba-bitext-mining
      config: uig-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 76.9
    - type: f1
      value: 72.07523809523809
    - type: precision
      value: 70.19777777777779
    - type: recall
      value: 76.9
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (rus-eng)
      type: mteb/tatoeba-bitext-mining
      config: rus-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 94.1
    - type: f1
      value: 92.31666666666666
    - type: precision
      value: 91.43333333333332
    - type: recall
      value: 94.1
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (spa-eng)
      type: mteb/tatoeba-bitext-mining
      config: spa-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 97.8
    - type: f1
      value: 97.1
    - type: precision
      value: 96.76666666666668
    - type: recall
      value: 97.8
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (hye-eng)
      type: mteb/tatoeba-bitext-mining
      config: hye-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 92.85714285714286
    - type: f1
      value: 90.92093441150045
    - type: precision
      value: 90.00449236298293
    - type: recall
      value: 92.85714285714286
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (tel-eng)
      type: mteb/tatoeba-bitext-mining
      config: tel-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 93.16239316239316
    - type: f1
      value: 91.33903133903132
    - type: precision
      value: 90.56267806267806
    - type: recall
      value: 93.16239316239316
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (afr-eng)
      type: mteb/tatoeba-bitext-mining
      config: afr-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 92.4
    - type: f1
      value: 90.25666666666666
    - type: precision
      value: 89.25833333333334
    - type: recall
      value: 92.4
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (mon-eng)
      type: mteb/tatoeba-bitext-mining
      config: mon-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 90.22727272727272
    - type: f1
      value: 87.53030303030303
    - type: precision
      value: 86.37121212121211
    - type: recall
      value: 90.22727272727272
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (arz-eng)
      type: mteb/tatoeba-bitext-mining
      config: arz-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 79.03563941299791
    - type: f1
      value: 74.7349505840072
    - type: precision
      value: 72.9035639412998
    - type: recall
      value: 79.03563941299791
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (hrv-eng)
      type: mteb/tatoeba-bitext-mining
      config: hrv-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 97
    - type: f1
      value: 96.15
    - type: precision
      value: 95.76666666666668
    - type: recall
      value: 97
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (nov-eng)
      type: mteb/tatoeba-bitext-mining
      config: nov-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 76.26459143968872
    - type: f1
      value: 71.55642023346303
    - type: precision
      value: 69.7544932369835
    - type: recall
      value: 76.26459143968872
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (gsw-eng)
      type: mteb/tatoeba-bitext-mining
      config: gsw-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 58.119658119658126
    - type: f1
      value: 51.65242165242165
    - type: precision
      value: 49.41768108434775
    - type: recall
      value: 58.119658119658126
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (nds-eng)
      type: mteb/tatoeba-bitext-mining
      config: nds-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 74.3
    - type: f1
      value: 69.52055555555555
    - type: precision
      value: 67.7574938949939
    - type: recall
      value: 74.3
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ukr-eng)
      type: mteb/tatoeba-bitext-mining
      config: ukr-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 94.8
    - type: f1
      value: 93.31666666666666
    - type: precision
      value: 92.60000000000001
    - type: recall
      value: 94.8
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (uzb-eng)
      type: mteb/tatoeba-bitext-mining
      config: uzb-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 76.63551401869158
    - type: f1
      value: 72.35202492211837
    - type: precision
      value: 70.60358255451713
    - type: recall
      value: 76.63551401869158
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (lit-eng)
      type: mteb/tatoeba-bitext-mining
      config: lit-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 90.4
    - type: f1
      value: 88.4811111111111
    - type: precision
      value: 87.7452380952381
    - type: recall
      value: 90.4
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ina-eng)
      type: mteb/tatoeba-bitext-mining
      config: ina-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 95
    - type: f1
      value: 93.60666666666667
    - type: precision
      value: 92.975
    - type: recall
      value: 95
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (lfn-eng)
      type: mteb/tatoeba-bitext-mining
      config: lfn-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 67.2
    - type: f1
      value: 63.01595782872099
    - type: precision
      value: 61.596587301587306
    - type: recall
      value: 67.2
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (zsm-eng)
      type: mteb/tatoeba-bitext-mining
      config: zsm-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 95.7
    - type: f1
      value: 94.52999999999999
    - type: precision
      value: 94
    - type: recall
      value: 95.7
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ita-eng)
      type: mteb/tatoeba-bitext-mining
      config: ita-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 94.6
    - type: f1
      value: 93.28999999999999
    - type: precision
      value: 92.675
    - type: recall
      value: 94.6
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (cmn-eng)
      type: mteb/tatoeba-bitext-mining
      config: cmn-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 96.39999999999999
    - type: f1
      value: 95.28333333333333
    - type: precision
      value: 94.75
    - type: recall
      value: 96.39999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (lvs-eng)
      type: mteb/tatoeba-bitext-mining
      config: lvs-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 91.9
    - type: f1
      value: 89.83
    - type: precision
      value: 88.92
    - type: recall
      value: 91.9
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (glg-eng)
      type: mteb/tatoeba-bitext-mining
      config: glg-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 94.69999999999999
    - type: f1
      value: 93.34222222222223
    - type: precision
      value: 92.75416666666668
    - type: recall
      value: 94.69999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ceb-eng)
      type: mteb/tatoeba-bitext-mining
      config: ceb-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 60.333333333333336
    - type: f1
      value: 55.31203703703703
    - type: precision
      value: 53.39971108326371
    - type: recall
      value: 60.333333333333336
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (bre-eng)
      type: mteb/tatoeba-bitext-mining
      config: bre-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 12.9
    - type: f1
      value: 11.099861903031458
    - type: precision
      value: 10.589187932631877
    - type: recall
      value: 12.9
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ben-eng)
      type: mteb/tatoeba-bitext-mining
      config: ben-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 86.7
    - type: f1
      value: 83.0152380952381
    - type: precision
      value: 81.37833333333333
    - type: recall
      value: 86.7
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (swg-eng)
      type: mteb/tatoeba-bitext-mining
      config: swg-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 63.39285714285714
    - type: f1
      value: 56.832482993197274
    - type: precision
      value: 54.56845238095237
    - type: recall
      value: 63.39285714285714
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (arq-eng)
      type: mteb/tatoeba-bitext-mining
      config: arq-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 48.73765093304062
    - type: f1
      value: 41.555736920720456
    - type: precision
      value: 39.06874531737319
    - type: recall
      value: 48.73765093304062
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (kab-eng)
      type: mteb/tatoeba-bitext-mining
      config: kab-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 41.099999999999994
    - type: f1
      value: 36.540165945165946
    - type: precision
      value: 35.05175685425686
    - type: recall
      value: 41.099999999999994
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (fra-eng)
      type: mteb/tatoeba-bitext-mining
      config: fra-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 94.89999999999999
    - type: f1
      value: 93.42333333333333
    - type: precision
      value: 92.75833333333333
    - type: recall
      value: 94.89999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (por-eng)
      type: mteb/tatoeba-bitext-mining
      config: por-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 94.89999999999999
    - type: f1
      value: 93.63333333333334
    - type: precision
      value: 93.01666666666665
    - type: recall
      value: 94.89999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (tat-eng)
      type: mteb/tatoeba-bitext-mining
      config: tat-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 77.9
    - type: f1
      value: 73.64833333333334
    - type: precision
      value: 71.90282106782105
    - type: recall
      value: 77.9
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (oci-eng)
      type: mteb/tatoeba-bitext-mining
      config: oci-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 59.4
    - type: f1
      value: 54.90521367521367
    - type: precision
      value: 53.432840025471606
    - type: recall
      value: 59.4
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (pol-eng)
      type: mteb/tatoeba-bitext-mining
      config: pol-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 97.39999999999999
    - type: f1
      value: 96.6
    - type: precision
      value: 96.2
    - type: recall
      value: 97.39999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (war-eng)
      type: mteb/tatoeba-bitext-mining
      config: war-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 67.2
    - type: f1
      value: 62.25926129426129
    - type: precision
      value: 60.408376623376626
    - type: recall
      value: 67.2
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (aze-eng)
      type: mteb/tatoeba-bitext-mining
      config: aze-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 90.2
    - type: f1
      value: 87.60666666666667
    - type: precision
      value: 86.45277777777778
    - type: recall
      value: 90.2
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (vie-eng)
      type: mteb/tatoeba-bitext-mining
      config: vie-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 97.7
    - type: f1
      value: 97
    - type: precision
      value: 96.65
    - type: recall
      value: 97.7
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (nno-eng)
      type: mteb/tatoeba-bitext-mining
      config: nno-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 93.2
    - type: f1
      value: 91.39746031746031
    - type: precision
      value: 90.6125
    - type: recall
      value: 93.2
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (cha-eng)
      type: mteb/tatoeba-bitext-mining
      config: cha-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 32.11678832116788
    - type: f1
      value: 27.210415386260234
    - type: precision
      value: 26.20408990846947
    - type: recall
      value: 32.11678832116788
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (mhr-eng)
      type: mteb/tatoeba-bitext-mining
      config: mhr-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 8.5
    - type: f1
      value: 6.787319277832475
    - type: precision
      value: 6.3452094433344435
    - type: recall
      value: 8.5
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (dan-eng)
      type: mteb/tatoeba-bitext-mining
      config: dan-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 96.1
    - type: f1
      value: 95.08
    - type: precision
      value: 94.61666666666667
    - type: recall
      value: 96.1
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ell-eng)
      type: mteb/tatoeba-bitext-mining
      config: ell-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 95.3
    - type: f1
      value: 93.88333333333333
    - type: precision
      value: 93.18333333333332
    - type: recall
      value: 95.3
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (amh-eng)
      type: mteb/tatoeba-bitext-mining
      config: amh-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 85.11904761904762
    - type: f1
      value: 80.69444444444444
    - type: precision
      value: 78.72023809523809
    - type: recall
      value: 85.11904761904762
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (pam-eng)
      type: mteb/tatoeba-bitext-mining
      config: pam-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 11.1
    - type: f1
      value: 9.276381801735853
    - type: precision
      value: 8.798174603174601
    - type: recall
      value: 11.1
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (hsb-eng)
      type: mteb/tatoeba-bitext-mining
      config: hsb-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 63.56107660455487
    - type: f1
      value: 58.70433569191332
    - type: precision
      value: 56.896926581464015
    - type: recall
      value: 63.56107660455487
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (srp-eng)
      type: mteb/tatoeba-bitext-mining
      config: srp-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 94.69999999999999
    - type: f1
      value: 93.10000000000001
    - type: precision
      value: 92.35
    - type: recall
      value: 94.69999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (epo-eng)
      type: mteb/tatoeba-bitext-mining
      config: epo-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 96.8
    - type: f1
      value: 96.01222222222222
    - type: precision
      value: 95.67083333333332
    - type: recall
      value: 96.8
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (kzj-eng)
      type: mteb/tatoeba-bitext-mining
      config: kzj-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 9.2
    - type: f1
      value: 7.911555250305249
    - type: precision
      value: 7.631246556216846
    - type: recall
      value: 9.2
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (awa-eng)
      type: mteb/tatoeba-bitext-mining
      config: awa-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 77.48917748917748
    - type: f1
      value: 72.27375798804371
    - type: precision
      value: 70.14430014430013
    - type: recall
      value: 77.48917748917748
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (fao-eng)
      type: mteb/tatoeba-bitext-mining
      config: fao-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 77.09923664122137
    - type: f1
      value: 72.61541257724463
    - type: precision
      value: 70.8998380754106
    - type: recall
      value: 77.09923664122137
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (mal-eng)
      type: mteb/tatoeba-bitext-mining
      config: mal-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 98.2532751091703
    - type: f1
      value: 97.69529354682193
    - type: precision
      value: 97.42843279961184
    - type: recall
      value: 98.2532751091703
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ile-eng)
      type: mteb/tatoeba-bitext-mining
      config: ile-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 82.8
    - type: f1
      value: 79.14672619047619
    - type: precision
      value: 77.59489247311828
    - type: recall
      value: 82.8
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (bos-eng)
      type: mteb/tatoeba-bitext-mining
      config: bos-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 94.35028248587571
    - type: f1
      value: 92.86252354048965
    - type: precision
      value: 92.2080979284369
    - type: recall
      value: 94.35028248587571
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (cor-eng)
      type: mteb/tatoeba-bitext-mining
      config: cor-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 8.5
    - type: f1
      value: 6.282429263935621
    - type: precision
      value: 5.783274240739785
    - type: recall
      value: 8.5
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (cat-eng)
      type: mteb/tatoeba-bitext-mining
      config: cat-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 92.7
    - type: f1
      value: 91.025
    - type: precision
      value: 90.30428571428571
    - type: recall
      value: 92.7
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (eus-eng)
      type: mteb/tatoeba-bitext-mining
      config: eus-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 81
    - type: f1
      value: 77.8232380952381
    - type: precision
      value: 76.60194444444444
    - type: recall
      value: 81
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (yue-eng)
      type: mteb/tatoeba-bitext-mining
      config: yue-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 91
    - type: f1
      value: 88.70857142857142
    - type: precision
      value: 87.7
    - type: recall
      value: 91
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (swe-eng)
      type: mteb/tatoeba-bitext-mining
      config: swe-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 96.39999999999999
    - type: f1
      value: 95.3
    - type: precision
      value: 94.76666666666667
    - type: recall
      value: 96.39999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (dtp-eng)
      type: mteb/tatoeba-bitext-mining
      config: dtp-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 8.1
    - type: f1
      value: 7.001008218834307
    - type: precision
      value: 6.708329562594269
    - type: recall
      value: 8.1
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (kat-eng)
      type: mteb/tatoeba-bitext-mining
      config: kat-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 87.1313672922252
    - type: f1
      value: 84.09070598748882
    - type: precision
      value: 82.79171454104429
    - type: recall
      value: 87.1313672922252
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (jpn-eng)
      type: mteb/tatoeba-bitext-mining
      config: jpn-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 96.39999999999999
    - type: f1
      value: 95.28333333333333
    - type: precision
      value: 94.73333333333332
    - type: recall
      value: 96.39999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (csb-eng)
      type: mteb/tatoeba-bitext-mining
      config: csb-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 42.29249011857708
    - type: f1
      value: 36.981018542283365
    - type: precision
      value: 35.415877813576024
    - type: recall
      value: 42.29249011857708
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (xho-eng)
      type: mteb/tatoeba-bitext-mining
      config: xho-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 83.80281690140845
    - type: f1
      value: 80.86854460093896
    - type: precision
      value: 79.60093896713614
    - type: recall
      value: 83.80281690140845
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (orv-eng)
      type: mteb/tatoeba-bitext-mining
      config: orv-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 45.26946107784431
    - type: f1
      value: 39.80235464678088
    - type: precision
      value: 38.14342660001342
    - type: recall
      value: 45.26946107784431
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ind-eng)
      type: mteb/tatoeba-bitext-mining
      config: ind-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 94.3
    - type: f1
      value: 92.9
    - type: precision
      value: 92.26666666666668
    - type: recall
      value: 94.3
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (tuk-eng)
      type: mteb/tatoeba-bitext-mining
      config: tuk-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 37.93103448275862
    - type: f1
      value: 33.15192743764172
    - type: precision
      value: 31.57456528146183
    - type: recall
      value: 37.93103448275862
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (max-eng)
      type: mteb/tatoeba-bitext-mining
      config: max-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 69.01408450704226
    - type: f1
      value: 63.41549295774648
    - type: precision
      value: 61.342778895595806
    - type: recall
      value: 69.01408450704226
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (swh-eng)
      type: mteb/tatoeba-bitext-mining
      config: swh-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 76.66666666666667
    - type: f1
      value: 71.60705960705961
    - type: precision
      value: 69.60683760683762
    - type: recall
      value: 76.66666666666667
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (hin-eng)
      type: mteb/tatoeba-bitext-mining
      config: hin-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 95.8
    - type: f1
      value: 94.48333333333333
    - type: precision
      value: 93.83333333333333
    - type: recall
      value: 95.8
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (dsb-eng)
      type: mteb/tatoeba-bitext-mining
      config: dsb-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 52.81837160751566
    - type: f1
      value: 48.435977731384824
    - type: precision
      value: 47.11291973845539
    - type: recall
      value: 52.81837160751566
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ber-eng)
      type: mteb/tatoeba-bitext-mining
      config: ber-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 44.9
    - type: f1
      value: 38.88962621607783
    - type: precision
      value: 36.95936507936508
    - type: recall
      value: 44.9
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (tam-eng)
      type: mteb/tatoeba-bitext-mining
      config: tam-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 90.55374592833876
    - type: f1
      value: 88.22553125484721
    - type: precision
      value: 87.26927252985884
    - type: recall
      value: 90.55374592833876
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (slk-eng)
      type: mteb/tatoeba-bitext-mining
      config: slk-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 94.6
    - type: f1
      value: 93.13333333333333
    - type: precision
      value: 92.45333333333333
    - type: recall
      value: 94.6
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (tgl-eng)
      type: mteb/tatoeba-bitext-mining
      config: tgl-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 93.7
    - type: f1
      value: 91.99666666666667
    - type: precision
      value: 91.26666666666668
    - type: recall
      value: 93.7
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ast-eng)
      type: mteb/tatoeba-bitext-mining
      config: ast-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 85.03937007874016
    - type: f1
      value: 81.75853018372703
    - type: precision
      value: 80.34120734908137
    - type: recall
      value: 85.03937007874016
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (mkd-eng)
      type: mteb/tatoeba-bitext-mining
      config: mkd-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 88.3
    - type: f1
      value: 85.5
    - type: precision
      value: 84.25833333333334
    - type: recall
      value: 88.3
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (khm-eng)
      type: mteb/tatoeba-bitext-mining
      config: khm-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 65.51246537396122
    - type: f1
      value: 60.02297410192148
    - type: precision
      value: 58.133467727289236
    - type: recall
      value: 65.51246537396122
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ces-eng)
      type: mteb/tatoeba-bitext-mining
      config: ces-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 96
    - type: f1
      value: 94.89
    - type: precision
      value: 94.39166666666667
    - type: recall
      value: 96
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (tzl-eng)
      type: mteb/tatoeba-bitext-mining
      config: tzl-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 57.692307692307686
    - type: f1
      value: 53.162393162393165
    - type: precision
      value: 51.70673076923077
    - type: recall
      value: 57.692307692307686
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (urd-eng)
      type: mteb/tatoeba-bitext-mining
      config: urd-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 91.60000000000001
    - type: f1
      value: 89.21190476190475
    - type: precision
      value: 88.08666666666667
    - type: recall
      value: 91.60000000000001
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (ara-eng)
      type: mteb/tatoeba-bitext-mining
      config: ara-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 88
    - type: f1
      value: 85.47
    - type: precision
      value: 84.43266233766234
    - type: recall
      value: 88
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (kor-eng)
      type: mteb/tatoeba-bitext-mining
      config: kor-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 92.7
    - type: f1
      value: 90.64999999999999
    - type: precision
      value: 89.68333333333332
    - type: recall
      value: 92.7
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (yid-eng)
      type: mteb/tatoeba-bitext-mining
      config: yid-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 80.30660377358491
    - type: f1
      value: 76.33044137466307
    - type: precision
      value: 74.78970125786164
    - type: recall
      value: 80.30660377358491
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (fin-eng)
      type: mteb/tatoeba-bitext-mining
      config: fin-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 96.39999999999999
    - type: f1
      value: 95.44
    - type: precision
      value: 94.99166666666666
    - type: recall
      value: 96.39999999999999
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (tha-eng)
      type: mteb/tatoeba-bitext-mining
      config: tha-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 96.53284671532847
    - type: f1
      value: 95.37712895377129
    - type: precision
      value: 94.7992700729927
    - type: recall
      value: 96.53284671532847
  - task:
      type: BitextMining
    dataset:
      name: MTEB Tatoeba (wuu-eng)
      type: mteb/tatoeba-bitext-mining
      config: wuu-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 89
    - type: f1
      value: 86.23190476190476
    - type: precision
      value: 85.035
    - type: recall
      value: 89
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: webis-touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 2.585
    - type: map_at_10
      value: 9.012
    - type: map_at_100
      value: 14.027000000000001
    - type: map_at_1000
      value: 15.565000000000001
    - type: map_at_3
      value: 5.032
    - type: map_at_5
      value: 6.657
    - type: mrr_at_1
      value: 28.571
    - type: mrr_at_10
      value: 45.377
    - type: mrr_at_100
      value: 46.119
    - type: mrr_at_1000
      value: 46.127
    - type: mrr_at_3
      value: 41.156
    - type: mrr_at_5
      value: 42.585
    - type: ndcg_at_1
      value: 27.551
    - type: ndcg_at_10
      value: 23.395
    - type: ndcg_at_100
      value: 33.342
    - type: ndcg_at_1000
      value: 45.523
    - type: ndcg_at_3
      value: 25.158
    - type: ndcg_at_5
      value: 23.427
    - type: precision_at_1
      value: 28.571
    - type: precision_at_10
      value: 21.429000000000002
    - type: precision_at_100
      value: 6.714
    - type: precision_at_1000
      value: 1.473
    - type: precision_at_3
      value: 27.211000000000002
    - type: precision_at_5
      value: 24.490000000000002
    - type: recall_at_1
      value: 2.585
    - type: recall_at_10
      value: 15.418999999999999
    - type: recall_at_100
      value: 42.485
    - type: recall_at_1000
      value: 79.536
    - type: recall_at_3
      value: 6.239999999999999
    - type: recall_at_5
      value: 8.996
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 71.3234
    - type: ap
      value: 14.361688653847423
    - type: f1
      value: 54.819068624319044
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 61.97792869269949
    - type: f1
      value: 62.28965628513728
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 38.90540145385218
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 86.53513739047506
    - type: cos_sim_ap
      value: 75.27741586677557
    - type: cos_sim_f1
      value: 69.18792902473774
    - type: cos_sim_precision
      value: 67.94708725515136
    - type: cos_sim_recall
      value: 70.47493403693932
    - type: dot_accuracy
      value: 84.7052512368123
    - type: dot_ap
      value: 69.36075482849378
    - type: dot_f1
      value: 64.44688376631296
    - type: dot_precision
      value: 59.92288500793831
    - type: dot_recall
      value: 69.70976253298153
    - type: euclidean_accuracy
      value: 86.60666388508076
    - type: euclidean_ap
      value: 75.47512772621097
    - type: euclidean_f1
      value: 69.413872536473
    - type: euclidean_precision
      value: 67.39562624254472
    - type: euclidean_recall
      value: 71.55672823218997
    - type: manhattan_accuracy
      value: 86.52917684925792
    - type: manhattan_ap
      value: 75.34000110496703
    - type: manhattan_f1
      value: 69.28489190226429
    - type: manhattan_precision
      value: 67.24608889992551
    - type: manhattan_recall
      value: 71.45118733509234
    - type: max_accuracy
      value: 86.60666388508076
    - type: max_ap
      value: 75.47512772621097
    - type: max_f1
      value: 69.413872536473
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 89.01695967710637
    - type: cos_sim_ap
      value: 85.8298270742901
    - type: cos_sim_f1
      value: 78.46988128389272
    - type: cos_sim_precision
      value: 74.86017897091722
    - type: cos_sim_recall
      value: 82.44533415460425
    - type: dot_accuracy
      value: 88.19420188613343
    - type: dot_ap
      value: 83.82679165901324
    - type: dot_f1
      value: 76.55833777304208
    - type: dot_precision
      value: 75.6884875846501
    - type: dot_recall
      value: 77.44841392054204
    - type: euclidean_accuracy
      value: 89.03054294252338
    - type: euclidean_ap
      value: 85.89089555185325
    - type: euclidean_f1
      value: 78.62997658079624
    - type: euclidean_precision
      value: 74.92329149232914
    - type: euclidean_recall
      value: 82.72251308900523
    - type: manhattan_accuracy
      value: 89.0266620095471
    - type: manhattan_ap
      value: 85.86458997929147
    - type: manhattan_f1
      value: 78.50685331000291
    - type: manhattan_precision
      value: 74.5499861534201
    - type: manhattan_recall
      value: 82.90729904527257
    - type: max_accuracy
      value: 89.03054294252338
    - type: max_ap
      value: 85.89089555185325
    - type: max_f1
      value: 78.62997658079624
---

# nnch/multilingual-e5-large-Q4_K_M-GGUF
This model was converted to GGUF format from [`intfloat/multilingual-e5-large`](https://huggingface.co/intfloat/multilingual-e5-large) 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/intfloat/multilingual-e5-large) 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 nnch/multilingual-e5-large-Q4_K_M-GGUF --hf-file multilingual-e5-large-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo nnch/multilingual-e5-large-Q4_K_M-GGUF --hf-file multilingual-e5-large-q4_k_m.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 nnch/multilingual-e5-large-Q4_K_M-GGUF --hf-file multilingual-e5-large-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo nnch/multilingual-e5-large-Q4_K_M-GGUF --hf-file multilingual-e5-large-q4_k_m.gguf -c 2048
```