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JA
Multilingual E5 Large Pooled Q8 0 GGUF
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Multilingual E5 Large Pooled Q8 0 GGUF
falan42によって開発
多言語E5大型プーリングモデル、複数言語の文類似度計算と特徴抽出タスクをサポート。
ダウンロード数 56
リリース時間 : 5/13/2025
モデル概要
これは多言語の文埋め込みモデルで、E5アーキテクチャに基づき、複数言語のテキストを処理し高品質な文埋め込み表現を生成可能。
モデル特徴
多言語サポート
100以上の言語のテキスト処理をサポート、主要言語からマイナー言語まで対応
高性能文埋め込み
複数言語の文類似度タスクで優れた性能を発揮、高品質な文ベクトル表現を生成可能
MTEBベンチマーク検証
MTEB(Massive Text Embedding Benchmark)の複数タスクで広範に評価され、信頼性の高い性能
モデル能力
多言語テキスト埋め込み
文類似度計算
テキスト特徴抽出
クロスランゲージ情報検索
使用事例
情報検索
クロスランゲージ文書検索
統一された埋め込み空間を使用して異なる言語の類似文書を検索
MTEB BUCCクロスランゲージbitext miningタスクで97-99%の精度を達成
テキスト分類
多言語感情分析
複数言語のテキストに対して感情傾向を分類
MTEB EmotionClassificationタスクで46.5%の精度を達成
製品レビュー分類
Amazonの多言語レビューを分類
MTEB AmazonReviewsClassificationタスクで英語47.56%の精度を達成
質問応答システム
事実検索QA
知識ベースから質問に関連する文書を検索
MTEB HotpotQAタスクで84.32%のMRR@10を達成
base_model: Hiveurban/multilingual-e5-large-pooled 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 tags:
- mteb
- Sentence Transformers
- sentence-similarity
- feature-extraction
- sentence-transformers
- llama-cpp
- gguf-my-repo 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
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config: default
split: test
revision: None
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split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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split: dev
revision: None
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
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split: test
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split: test
revision: None
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config: default
split: test
revision: None
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- 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)
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- 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
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
falan42/multilingual-e5-large-pooled-Q8_0-GGUF
This model was converted to GGUF format from Hiveurban/multilingual-e5-large-pooled using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo falan42/multilingual-e5-large-pooled-Q8_0-GGUF --hf-file multilingual-e5-large-pooled-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo falan42/multilingual-e5-large-pooled-Q8_0-GGUF --hf-file multilingual-e5-large-pooled-q8_0.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps 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 falan42/multilingual-e5-large-pooled-Q8_0-GGUF --hf-file multilingual-e5-large-pooled-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo falan42/multilingual-e5-large-pooled-Q8_0-GGUF --hf-file multilingual-e5-large-pooled-q8_0.gguf -c 2048
Jina Embeddings V3
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