%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
JA
Jina Embeddings GGUF
Jina Embeddings V2 Base は効率的な英文文埋め込みモデルで、文の類似度と特徴抽出タスクに特化しています。
ダウンロード数 139
リリース時間 : 1/8/2025
モデル概要
このモデルはJina AIの技術に基づいており、高品質な文埋め込みを生成するために使用され、テキスト類似度計算、情報検索、クラスタリング分析などのタスクに適しています。
モデル特徴
効率的な文埋め込み
大規模なテキスト処理に適した高品質な文埋め込みを迅速に生成できます。
マルチタスクサポート
分類、クラスタリング、検索など、さまざまな自然言語処理タスクをサポートします。
高性能
特にテキスト類似度タスクにおいて、複数のベンチマークテストで優れたパフォーマンスを発揮します。
モデル能力
文埋め込み生成
テキスト類似度計算
情報検索
テキスト分類
テキストクラスタリング
使用事例
情報検索
ドキュメント検索
文埋め込みを使用して関連ドキュメントを迅速に検索します。
MTEB ArguAnaデータセットで優れたパフォーマンスを発揮
テキスト分類
感情分析
テキストの感情極性を分類します。
MTEB AmazonPolarityClassificationで88.54%の精度を達成
テキストクラスタリング
学術論文クラスタリング
学術論文をテーマ別にクラスタリングします。
MTEB ArxivClusteringP2PでV-measure45.39を達成
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- llama-cpp
- gguf-my-repo datasets:
- allenai/c4 language: en inference: false license: apache-2.0 base_model: jinaai/jina-embeddings-v2-base-en model-index:
- name: jina-embedding-b-en-v2
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy value: 74.73134328358209
- type: ap value: 37.765427081831035
- type: f1 value: 68.79367444339518
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy value: 88.544275
- type: ap value: 84.61328675662887
- type: f1 value: 88.51879035862375
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 45.263999999999996
- type: f1 value: 43.778759656699435
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 21.693
- type: map_at_10 value: 35.487
- type: map_at_100 value: 36.862
- type: map_at_1000 value: 36.872
- type: map_at_3 value: 30.049999999999997
- type: map_at_5 value: 32.966
- type: mrr_at_1 value: 21.977
- type: mrr_at_10 value: 35.565999999999995
- type: mrr_at_100 value: 36.948
- type: mrr_at_1000 value: 36.958
- type: mrr_at_3 value: 30.121
- type: mrr_at_5 value: 33.051
- type: ndcg_at_1 value: 21.693
- type: ndcg_at_10 value: 44.181
- type: ndcg_at_100 value: 49.982
- type: ndcg_at_1000 value: 50.233000000000004
- type: ndcg_at_3 value: 32.830999999999996
- type: ndcg_at_5 value: 38.080000000000005
- type: precision_at_1 value: 21.693
- type: precision_at_10 value: 7.248
- type: precision_at_100 value: 0.9769999999999999
- type: precision_at_1000 value: 0.1
- type: precision_at_3 value: 13.632
- type: precision_at_5 value: 10.725
- type: recall_at_1 value: 21.693
- type: recall_at_10 value: 72.475
- type: recall_at_100 value: 97.653
- type: recall_at_1000 value: 99.57300000000001
- type: recall_at_3 value: 40.896
- type: recall_at_5 value: 53.627
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure value: 45.39242428696777
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure value: 36.675626784714
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map value: 62.247725694904034
- type: mrr value: 74.91359978894604
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson value: 82.68003802970496
- type: cos_sim_spearman value: 81.23438110096286
- type: euclidean_pearson value: 81.87462986142582
- type: euclidean_spearman value: 81.23438110096286
- type: manhattan_pearson value: 81.61162566600755
- type: manhattan_spearman value: 81.11329400456184
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy value: 84.01298701298701
- type: f1 value: 83.31690714969382
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure value: 37.050108150972086
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure value: 30.15731442819715
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 31.391999999999996
- type: map_at_10 value: 42.597
- type: map_at_100 value: 44.07
- type: map_at_1000 value: 44.198
- type: map_at_3 value: 38.957
- type: map_at_5 value: 40.961
- type: mrr_at_1 value: 37.196
- type: mrr_at_10 value: 48.152
- type: mrr_at_100 value: 48.928
- type: mrr_at_1000 value: 48.964999999999996
- type: mrr_at_3 value: 45.446
- type: mrr_at_5 value: 47.205999999999996
- type: ndcg_at_1 value: 37.196
- type: ndcg_at_10 value: 49.089
- type: ndcg_at_100 value: 54.471000000000004
- type: ndcg_at_1000 value: 56.385
- type: ndcg_at_3 value: 43.699
- type: ndcg_at_5 value: 46.22
- type: precision_at_1 value: 37.196
- type: precision_at_10 value: 9.313
- type: precision_at_100 value: 1.478
- type: precision_at_1000 value: 0.198
- type: precision_at_3 value: 20.839
- type: precision_at_5 value: 14.936
- type: recall_at_1 value: 31.391999999999996
- type: recall_at_10 value: 61.876
- type: recall_at_100 value: 84.214
- type: recall_at_1000 value: 95.985
- type: recall_at_3 value: 46.6
- type: recall_at_5 value: 53.588
- type: map_at_1 value: 29.083
- type: map_at_10 value: 38.812999999999995
- type: map_at_100 value: 40.053
- type: map_at_1000 value: 40.188
- type: map_at_3 value: 36.111
- type: map_at_5 value: 37.519000000000005
- type: mrr_at_1 value: 36.497
- type: mrr_at_10 value: 44.85
- type: mrr_at_100 value: 45.546
- type: mrr_at_1000 value: 45.593
- type: mrr_at_3 value: 42.686
- type: mrr_at_5 value: 43.909
- type: ndcg_at_1 value: 36.497
- type: ndcg_at_10 value: 44.443
- type: ndcg_at_100 value: 48.979
- type: ndcg_at_1000 value: 51.154999999999994
- type: ndcg_at_3 value: 40.660000000000004
- type: ndcg_at_5 value: 42.193000000000005
- type: precision_at_1 value: 36.497
- type: precision_at_10 value: 8.433
- type: precision_at_100 value: 1.369
- type: precision_at_1000 value: 0.185
- type: precision_at_3 value: 19.894000000000002
- type: precision_at_5 value: 13.873
- type: recall_at_1 value: 29.083
- type: recall_at_10 value: 54.313
- type: recall_at_100 value: 73.792
- type: recall_at_1000 value: 87.629
- type: recall_at_3 value: 42.257
- type: recall_at_5 value: 47.066
- type: map_at_1 value: 38.556000000000004
- type: map_at_10 value: 50.698
- type: map_at_100 value: 51.705
- type: map_at_1000 value: 51.768
- type: map_at_3 value: 47.848
- type: map_at_5 value: 49.358000000000004
- type: mrr_at_1 value: 43.95
- type: mrr_at_10 value: 54.191
- type: mrr_at_100 value: 54.852999999999994
- type: mrr_at_1000 value: 54.885
- type: mrr_at_3 value: 51.954
- type: mrr_at_5 value: 53.13
- type: ndcg_at_1 value: 43.95
- type: ndcg_at_10 value: 56.516
- type: ndcg_at_100 value: 60.477000000000004
- type: ndcg_at_1000 value: 61.746
- type: ndcg_at_3 value: 51.601
- type: ndcg_at_5 value: 53.795
- type: precision_at_1 value: 43.95
- type: precision_at_10 value: 9.009
- type: precision_at_100 value: 1.189
- type: precision_at_1000 value: 0.135
- type: precision_at_3 value: 22.989
- type: precision_at_5 value: 15.473
- type: recall_at_1 value: 38.556000000000004
- type: recall_at_10 value: 70.159
- type: recall_at_100 value: 87.132
- type: recall_at_1000 value: 96.16
- type: recall_at_3 value: 56.906
- type: recall_at_5 value: 62.332
- type: map_at_1 value: 24.238
- type: map_at_10 value: 32.5
- type: map_at_100 value: 33.637
- type: map_at_1000 value: 33.719
- type: map_at_3 value: 30.026999999999997
- type: map_at_5 value: 31.555
- type: mrr_at_1 value: 26.328000000000003
- type: mrr_at_10 value: 34.44
- type: mrr_at_100 value: 35.455999999999996
- type: mrr_at_1000 value: 35.521
- type: mrr_at_3 value: 32.034
- type: mrr_at_5 value: 33.565
- type: ndcg_at_1 value: 26.328000000000003
- type: ndcg_at_10 value: 37.202
- type: ndcg_at_100 value: 42.728
- type: ndcg_at_1000 value: 44.792
- type: ndcg_at_3 value: 32.368
- type: ndcg_at_5 value: 35.008
- type: precision_at_1 value: 26.328000000000003
- type: precision_at_10 value: 5.7059999999999995
- type: precision_at_100 value: 0.8880000000000001
- type: precision_at_1000 value: 0.11100000000000002
- type: precision_at_3 value: 13.672
- type: precision_at_5 value: 9.74
- type: recall_at_1 value: 24.238
- type: recall_at_10 value: 49.829
- type: recall_at_100 value: 75.21
- type: recall_at_1000 value: 90.521
- type: recall_at_3 value: 36.867
- type: recall_at_5 value: 43.241
- type: map_at_1 value: 15.378
- type: map_at_10 value: 22.817999999999998
- type: map_at_100 value: 23.977999999999998
- type: map_at_1000 value: 24.108
- type: map_at_3 value: 20.719
- type: map_at_5 value: 21.889
- type: mrr_at_1 value: 19.03
- type: mrr_at_10 value: 27.022000000000002
- type: mrr_at_100 value: 28.011999999999997
- type: mrr_at_1000 value: 28.096
- type: mrr_at_3 value: 24.855
- type: mrr_at_5 value: 26.029999999999998
- type: ndcg_at_1 value: 19.03
- type: ndcg_at_10 value: 27.526
- type: ndcg_at_100 value: 33.040000000000006
- type: ndcg_at_1000 value: 36.187000000000005
- type: ndcg_at_3 value: 23.497
- type: ndcg_at_5 value: 25.334
- type: precision_at_1 value: 19.03
- type: precision_at_10 value: 4.963
- type: precision_at_100 value: 0.893
- type: precision_at_1000 value: 0.13
- type: precision_at_3 value: 11.360000000000001
- type: precision_at_5 value: 8.134
- type: recall_at_1 value: 15.378
- type: recall_at_10 value: 38.061
- type: recall_at_100 value: 61.754
- type: recall_at_1000 value: 84.259
- type: recall_at_3 value: 26.788
- type: recall_at_5 value: 31.326999999999998
- type: map_at_1 value: 27.511999999999997
- type: map_at_10 value: 37.429
- type: map_at_100 value: 38.818000000000005
- type: map_at_1000 value: 38.924
- type: map_at_3 value: 34.625
- type: map_at_5 value: 36.064
- type: mrr_at_1 value: 33.300999999999995
- type: mrr_at_10 value: 43.036
- type: mrr_at_100 value: 43.894
- type: mrr_at_1000 value: 43.936
- type: mrr_at_3 value: 40.825
- type: mrr_at_5 value: 42.028
- type: ndcg_at_1 value: 33.300999999999995
- type: ndcg_at_10 value: 43.229
- type: ndcg_at_100 value: 48.992000000000004
- type: ndcg_at_1000 value: 51.02100000000001
- type: ndcg_at_3 value: 38.794000000000004
- type: ndcg_at_5 value: 40.65
- type: precision_at_1 value: 33.300999999999995
- type: precision_at_10 value: 7.777000000000001
- type: precision_at_100 value: 1.269
- type: precision_at_1000 value: 0.163
- type: precision_at_3 value: 18.351
- type: precision_at_5 value: 12.762
- type: recall_at_1 value: 27.511999999999997
- type: recall_at_10 value: 54.788000000000004
- type: recall_at_100 value: 79.105
- type: recall_at_1000 value: 92.49199999999999
- type: recall_at_3 value: 41.924
- type: recall_at_5 value: 47.026
- type: map_at_1 value: 24.117
- type: map_at_10 value: 33.32
- type: map_at_100 value: 34.677
- type: map_at_1000 value: 34.78
- type: map_at_3 value: 30.233999999999998
- type: map_at_5 value: 31.668000000000003
- type: mrr_at_1 value: 29.566
- type: mrr_at_10 value: 38.244
- type: mrr_at_100 value: 39.245000000000005
- type: mrr_at_1000 value: 39.296
- type: mrr_at_3 value: 35.864000000000004
- type: mrr_at_5 value: 36.919999999999995
- type: ndcg_at_1 value: 29.566
- type: ndcg_at_10 value: 39.127
- type: ndcg_at_100 value: 44.989000000000004
- type: ndcg_at_1000 value: 47.189
- type: ndcg_at_3 value: 34.039
- type: ndcg_at_5 value: 35.744
- type: precision_at_1 value: 29.566
- type: precision_at_10 value: 7.385999999999999
- type: precision_at_100 value: 1.204
- type: precision_at_1000 value: 0.158
- type: precision_at_3 value: 16.286
- type: precision_at_5 value: 11.484
- type: recall_at_1 value: 24.117
- type: recall_at_10 value: 51.559999999999995
- type: recall_at_100 value: 77.104
- type: recall_at_1000 value: 91.79899999999999
- type: recall_at_3 value: 36.82
- type: recall_at_5 value: 41.453
- type: map_at_1 value: 25.17625
- type: map_at_10 value: 34.063916666666664
- type: map_at_100 value: 35.255500000000005
- type: map_at_1000 value: 35.37275
- type: map_at_3 value: 31.351666666666667
- type: map_at_5 value: 32.80608333333333
- type: mrr_at_1 value: 29.59783333333333
- type: mrr_at_10 value: 38.0925
- type: mrr_at_100 value: 38.957249999999995
- type: mrr_at_1000 value: 39.01608333333333
- type: mrr_at_3 value: 35.77625
- type: mrr_at_5 value: 37.04991666666667
- type: ndcg_at_1 value: 29.59783333333333
- type: ndcg_at_10 value: 39.343666666666664
- type: ndcg_at_100 value: 44.488249999999994
- type: ndcg_at_1000 value: 46.83358333333334
- type: ndcg_at_3 value: 34.69708333333333
- type: ndcg_at_5 value: 36.75075
- type: precision_at_1 value: 29.59783333333333
- type: precision_at_10 value: 6.884083333333332
- type: precision_at_100 value: 1.114
- type: precision_at_1000 value: 0.15108333333333332
- type: precision_at_3 value: 15.965250000000003
- type: precision_at_5 value: 11.246500000000001
- type: recall_at_1 value: 25.17625
- type: recall_at_10 value: 51.015999999999984
- type: recall_at_100 value: 73.60174999999998
- type: recall_at_1000 value: 89.849
- type: recall_at_3 value: 37.88399999999999
- type: recall_at_5 value: 43.24541666666666
- type: map_at_1 value: 24.537
- type: map_at_10 value: 31.081999999999997
- type: map_at_100 value: 32.042
- type: map_at_1000 value: 32.141
- type: map_at_3 value: 29.137
- type: map_at_5 value: 30.079
- type: mrr_at_1 value: 27.454
- type: mrr_at_10 value: 33.694
- type: mrr_at_100 value: 34.579
- type: mrr_at_1000 value: 34.649
- type: mrr_at_3 value: 32.004
- type: mrr_at_5 value: 32.794000000000004
- type: ndcg_at_1 value: 27.454
- type: ndcg_at_10 value: 34.915
- type: ndcg_at_100 value: 39.641
- type: ndcg_at_1000 value: 42.105
- type: ndcg_at_3 value: 31.276
- type: ndcg_at_5 value: 32.65
- type: precision_at_1 value: 27.454
- type: precision_at_10 value: 5.337
- type: precision_at_100 value: 0.8250000000000001
- type: precision_at_1000 value: 0.11199999999999999
- type: precision_at_3 value: 13.241
- type: precision_at_5 value: 8.895999999999999
- type: recall_at_1 value: 24.537
- type: recall_at_10 value: 44.324999999999996
- type: recall_at_100 value: 65.949
- type: recall_at_1000 value: 84.017
- type: recall_at_3 value: 33.857
- type: recall_at_5 value: 37.316
- type: map_at_1 value: 17.122
- type: map_at_10 value: 24.32
- type: map_at_100 value: 25.338
- type: map_at_1000 value: 25.462
- type: map_at_3 value: 22.064
- type: map_at_5 value: 23.322000000000003
- type: mrr_at_1 value: 20.647
- type: mrr_at_10 value: 27.858
- type: mrr_at_100 value: 28.743999999999996
- type: mrr_at_1000 value: 28.819
- type: mrr_at_3 value: 25.769
- type: mrr_at_5 value: 26.964
- type: ndcg_at_1 value: 20.647
- type: ndcg_at_10 value: 28.849999999999998
- type: ndcg_at_100 value: 33.849000000000004
- type: ndcg_at_1000 value: 36.802
- type: ndcg_at_3 value: 24.799
- type: ndcg_at_5 value: 26.682
- type: precision_at_1 value: 20.647
- type: precision_at_10 value: 5.2170000000000005
- type: precision_at_100 value: 0.906
- type: precision_at_1000 value: 0.134
- type: precision_at_3 value: 11.769
- type: precision_at_5 value: 8.486
- type: recall_at_1 value: 17.122
- type: recall_at_10 value: 38.999
- type: recall_at_100 value: 61.467000000000006
- type: recall_at_1000 value: 82.716
- type: recall_at_3 value: 27.601
- type: recall_at_5 value: 32.471
- type: map_at_1 value: 24.396
- type: map_at_10 value: 33.415
- type: map_at_100 value: 34.521
- type: map_at_1000 value: 34.631
- type: map_at_3 value: 30.703999999999997
- type: map_at_5 value: 32.166
- type: mrr_at_1 value: 28.825
- type: mrr_at_10 value: 37.397000000000006
- type: mrr_at_100 value: 38.286
- type: mrr_at_1000 value: 38.346000000000004
- type: mrr_at_3 value: 35.028
- type: mrr_at_5 value: 36.32
- type: ndcg_at_1 value: 28.825
- type: ndcg_at_10 value: 38.656
- type: ndcg_at_100 value: 43.856
- type: ndcg_at_1000 value: 46.31
- type: ndcg_at_3 value: 33.793
- type: ndcg_at_5 value: 35.909
- type: precision_at_1 value: 28.825
- type: precision_at_10 value: 6.567
- type: precision_at_100 value: 1.0330000000000001
- type: precision_at_1000 value: 0.135
- type: precision_at_3 value: 15.516
- type: precision_at_5 value: 10.914
- type: recall_at_1 value: 24.396
- type: recall_at_10 value: 50.747
- type: recall_at_100 value: 73.477
- type: recall_at_1000 value: 90.801
- type: recall_at_3 value: 37.1
- type: recall_at_5 value: 42.589
- type: map_at_1 value: 25.072
- type: map_at_10 value: 34.307
- type: map_at_100 value: 35.725
- type: map_at_1000 value: 35.943999999999996
- type: map_at_3 value: 30.906
- type: map_at_5 value: 32.818000000000005
- type: mrr_at_1 value: 29.644
- type: mrr_at_10 value: 38.673
- type: mrr_at_100 value: 39.459
- type: mrr_at_1000 value: 39.527
- type: mrr_at_3 value: 35.771
- type: mrr_at_5 value: 37.332
- type: ndcg_at_1 value: 29.644
- type: ndcg_at_10 value: 40.548
- type: ndcg_at_100 value: 45.678999999999995
- type: ndcg_at_1000 value: 48.488
- type: ndcg_at_3 value: 34.887
- type: ndcg_at_5 value: 37.543
- type: precision_at_1 value: 29.644
- type: precision_at_10 value: 7.688000000000001
- type: precision_at_100 value: 1.482
- type: precision_at_1000 value: 0.23600000000000002
- type: precision_at_3 value: 16.206
- type: precision_at_5 value: 12.016
- type: recall_at_1 value: 25.072
- type: recall_at_10 value: 53.478
- type: recall_at_100 value: 76.07300000000001
- type: recall_at_1000 value: 93.884
- type: recall_at_3 value: 37.583
- type: recall_at_5 value: 44.464
- type: map_at_1 value: 20.712
- type: map_at_10 value: 27.467999999999996
- type: map_at_100 value: 28.502
- type: map_at_1000 value: 28.610000000000003
- type: map_at_3 value: 24.887999999999998
- type: map_at_5 value: 26.273999999999997
- type: mrr_at_1 value: 22.736
- type: mrr_at_10 value: 29.553
- type: mrr_at_100 value: 30.485
- type: mrr_at_1000 value: 30.56
- type: mrr_at_3 value: 27.078999999999997
- type: mrr_at_5 value: 28.401
- type: ndcg_at_1 value: 22.736
- type: ndcg_at_10 value: 32.023
- type: ndcg_at_100 value: 37.158
- type: ndcg_at_1000 value: 39.823
- type: ndcg_at_3 value: 26.951999999999998
- type: ndcg_at_5 value: 29.281000000000002
- type: precision_at_1 value: 22.736
- type: precision_at_10 value: 5.213
- type: precision_at_100 value: 0.832
- type: precision_at_1000 value: 0.116
- type: precision_at_3 value: 11.459999999999999
- type: precision_at_5 value: 8.244
- type: recall_at_1 value: 20.712
- type: recall_at_10 value: 44.057
- type: recall_at_100 value: 67.944
- type: recall_at_1000 value: 87.925
- type: recall_at_3 value: 30.305
- type: recall_at_5 value: 36.071999999999996
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 10.181999999999999
- type: map_at_10 value: 16.66
- type: map_at_100 value: 18.273
- type: map_at_1000 value: 18.45
- type: map_at_3 value: 14.141
- type: map_at_5 value: 15.455
- type: mrr_at_1 value: 22.15
- type: mrr_at_10 value: 32.062000000000005
- type: mrr_at_100 value: 33.116
- type: mrr_at_1000 value: 33.168
- type: mrr_at_3 value: 28.827
- type: mrr_at_5 value: 30.892999999999997
- type: ndcg_at_1 value: 22.15
- type: ndcg_at_10 value: 23.532
- type: ndcg_at_100 value: 30.358
- type: ndcg_at_1000 value: 33.783
- type: ndcg_at_3 value: 19.222
- type: ndcg_at_5 value: 20.919999999999998
- type: precision_at_1 value: 22.15
- type: precision_at_10 value: 7.185999999999999
- type: precision_at_100 value: 1.433
- type: precision_at_1000 value: 0.207
- type: precision_at_3 value: 13.941
- type: precision_at_5 value: 10.906
- type: recall_at_1 value: 10.181999999999999
- type: recall_at_10 value: 28.104000000000003
- type: recall_at_100 value: 51.998999999999995
- type: recall_at_1000 value: 71.311
- type: recall_at_3 value: 17.698
- type: recall_at_5 value: 22.262999999999998
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 6.669
- type: map_at_10 value: 15.552
- type: map_at_100 value: 21.865000000000002
- type: map_at_1000 value: 23.268
- type: map_at_3 value: 11.309
- type: map_at_5 value: 13.084000000000001
- type: mrr_at_1 value: 55.50000000000001
- type: mrr_at_10 value: 66.46600000000001
- type: mrr_at_100 value: 66.944
- type: mrr_at_1000 value: 66.956
- type: mrr_at_3 value: 64.542
- type: mrr_at_5 value: 65.717
- type: ndcg_at_1 value: 44.75
- type: ndcg_at_10 value: 35.049
- type: ndcg_at_100 value: 39.073
- type: ndcg_at_1000 value: 46.208
- type: ndcg_at_3 value: 39.525
- type: ndcg_at_5 value: 37.156
- type: precision_at_1 value: 55.50000000000001
- type: precision_at_10 value: 27.800000000000004
- type: precision_at_100 value: 9.013
- type: precision_at_1000 value: 1.8800000000000001
- type: precision_at_3 value: 42.667
- type: precision_at_5 value: 36.0
- type: recall_at_1 value: 6.669
- type: recall_at_10 value: 21.811
- type: recall_at_100 value: 45.112
- type: recall_at_1000 value: 67.806
- type: recall_at_3 value: 13.373
- type: recall_at_5 value: 16.615
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy value: 48.769999999999996
- type: f1 value: 42.91448356376592
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 54.013
- type: map_at_10 value: 66.239
- type: map_at_100 value: 66.62599999999999
- type: map_at_1000 value: 66.644
- type: map_at_3 value: 63.965
- type: map_at_5 value: 65.45400000000001
- type: mrr_at_1 value: 58.221000000000004
- type: mrr_at_10 value: 70.43700000000001
- type: mrr_at_100 value: 70.744
- type: mrr_at_1000 value: 70.75099999999999
- type: mrr_at_3 value: 68.284
- type: mrr_at_5 value: 69.721
- type: ndcg_at_1 value: 58.221000000000004
- type: ndcg_at_10 value: 72.327
- type: ndcg_at_100 value: 73.953
- type: ndcg_at_1000 value: 74.312
- type: ndcg_at_3 value: 68.062
- type: ndcg_at_5 value: 70.56400000000001
- type: precision_at_1 value: 58.221000000000004
- type: precision_at_10 value: 9.521
- type: precision_at_100 value: 1.045
- type: precision_at_1000 value: 0.109
- type: precision_at_3 value: 27.348
- type: precision_at_5 value: 17.794999999999998
- type: recall_at_1 value: 54.013
- type: recall_at_10 value: 86.957
- type: recall_at_100 value: 93.911
- type: recall_at_1000 value: 96.38
- type: recall_at_3 value: 75.555
- type: recall_at_5 value: 81.671
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 21.254
- type: map_at_10 value: 33.723
- type: map_at_100 value: 35.574
- type: map_at_1000 value: 35.730000000000004
- type: map_at_3 value: 29.473
- type: map_at_5 value: 31.543
- type: mrr_at_1 value: 41.358
- type: mrr_at_10 value: 49.498
- type: mrr_at_100 value: 50.275999999999996
- type: mrr_at_1000 value: 50.308
- type: mrr_at_3 value: 47.016000000000005
- type: mrr_at_5 value: 48.336
- type: ndcg_at_1 value: 41.358
- type: ndcg_at_10 value: 41.579
- type: ndcg_at_100 value: 48.455
- type: ndcg_at_1000 value: 51.165000000000006
- type: ndcg_at_3 value: 37.681
- type: ndcg_at_5 value: 38.49
- type: precision_at_1 value: 41.358
- type: precision_at_10 value: 11.543000000000001
- type: precision_at_100 value: 1.87
- type: precision_at_1000 value: 0.23600000000000002
- type: precision_at_3 value: 24.743000000000002
- type: precision_at_5 value: 17.994
- type: recall_at_1 value: 21.254
- type: recall_at_10 value: 48.698
- type: recall_at_100 value: 74.588
- type: recall_at_1000 value: 91.00200000000001
- type: recall_at_3 value: 33.939
- type: recall_at_5 value: 39.367000000000004
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 35.922
- type: map_at_10 value: 52.32599999999999
- type: map_at_100 value: 53.18000000000001
- type: map_at_1000 value: 53.245
- type: map_at_3 value: 49.294
- type: map_at_5 value: 51.202999999999996
- type: mrr_at_1 value: 71.843
- type: mrr_at_10 value: 78.24600000000001
- type: mrr_at_100 value: 78.515
- type: mrr_at_1000 value: 78.527
- type: mrr_at_3 value: 77.17500000000001
- type: mrr_at_5 value: 77.852
- type: ndcg_at_1 value: 71.843
- type: ndcg_at_10 value: 61.379
- type: ndcg_at_100 value: 64.535
- type: ndcg_at_1000 value: 65.888
- type: ndcg_at_3 value: 56.958
- type: ndcg_at_5 value: 59.434
- type: precision_at_1 value: 71.843
- type: precision_at_10 value: 12.686
- type: precision_at_100 value: 1.517
- type: precision_at_1000 value: 0.16999999999999998
- type: precision_at_3 value: 35.778
- type: precision_at_5 value: 23.422
- type: recall_at_1 value: 35.922
- type: recall_at_10 value: 63.43
- type: recall_at_100 value: 75.868
- type: recall_at_1000 value: 84.88900000000001
- type: recall_at_3 value: 53.666000000000004
- type: recall_at_5 value: 58.555
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy value: 79.4408
- type: ap value: 73.52820871620366
- type: f1 value: 79.36240238685001
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 21.826999999999998
- type: map_at_10 value: 34.04
- type: map_at_100 value: 35.226
- type: map_at_1000 value: 35.275
- type: map_at_3 value: 30.165999999999997
- type: map_at_5 value: 32.318000000000005
- type: mrr_at_1 value: 22.464000000000002
- type: mrr_at_10 value: 34.631
- type: mrr_at_100 value: 35.752
- type: mrr_at_1000 value: 35.795
- type: mrr_at_3 value: 30.798
- type: mrr_at_5 value: 32.946999999999996
- type: ndcg_at_1 value: 22.464000000000002
- type: ndcg_at_10 value: 40.919
- type: ndcg_at_100 value: 46.632
- type: ndcg_at_1000 value: 47.833
- type: ndcg_at_3 value: 32.992
- type: ndcg_at_5 value: 36.834
- type: precision_at_1 value: 22.464000000000002
- type: precision_at_10 value: 6.494
- type: precision_at_100 value: 0.9369999999999999
- type: precision_at_1000 value: 0.104
- type: precision_at_3 value: 14.021
- type: precision_at_5 value: 10.347000000000001
- type: recall_at_1 value: 21.826999999999998
- type: recall_at_10 value: 62.132
- type: recall_at_100 value: 88.55199999999999
- type: recall_at_1000 value: 97.707
- type: recall_at_3 value: 40.541
- type: recall_at_5 value: 49.739
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy value: 95.68399452804377
- type: f1 value: 95.25490609832268
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy value: 83.15321477428182
- type: f1 value: 60.35476439087966
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy value: 71.92669804976462
- type: f1 value: 69.22815107207565
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy value: 74.4855413584398
- type: f1 value: 72.92107516103387
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure value: 32.412679360205544
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure value: 28.09211869875204
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map value: 30.540919056982545
- type: mrr value: 31.529904607063536
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 5.745
- type: map_at_10 value: 12.013
- type: map_at_100 value: 15.040000000000001
- type: map_at_1000 value: 16.427
- type: map_at_3 value: 8.841000000000001
- type: map_at_5 value: 10.289
- type: mrr_at_1 value: 45.201
- type: mrr_at_10 value: 53.483999999999995
- type: mrr_at_100 value: 54.20700000000001
- type: mrr_at_1000 value: 54.252
- type: mrr_at_3 value: 51.29
- type: mrr_at_5 value: 52.73
- type: ndcg_at_1 value: 43.808
- type: ndcg_at_10 value: 32.445
- type: ndcg_at_100 value: 30.031000000000002
- type: ndcg_at_1000 value: 39.007
- type: ndcg_at_3 value: 37.204
- type: ndcg_at_5 value: 35.07
- type: precision_at_1 value: 45.201
- type: precision_at_10 value: 23.684
- type: precision_at_100 value: 7.600999999999999
- type: precision_at_1000 value: 2.043
- type: precision_at_3 value: 33.953
- type: precision_at_5 value: 29.412
- type: recall_at_1 value: 5.745
- type: recall_at_10 value: 16.168
- type: recall_at_100 value: 30.875999999999998
- type: recall_at_1000 value: 62.686
- type: recall_at_3 value: 9.75
- type: recall_at_5 value: 12.413
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 37.828
- type: map_at_10 value: 53.239000000000004
- type: map_at_100 value: 54.035999999999994
- type: map_at_1000 value: 54.067
- type: map_at_3 value: 49.289
- type: map_at_5 value: 51.784
- type: mrr_at_1 value: 42.497
- type: mrr_at_10 value: 55.916999999999994
- type: mrr_at_100 value: 56.495
- type: mrr_at_1000 value: 56.516999999999996
- type: mrr_at_3 value: 52.800000000000004
- type: mrr_at_5 value: 54.722
- type: ndcg_at_1 value: 42.468
- type: ndcg_at_10 value: 60.437
- type: ndcg_at_100 value: 63.731
- type: ndcg_at_1000 value: 64.41799999999999
- type: ndcg_at_3 value: 53.230999999999995
- type: ndcg_at_5 value: 57.26
- type: precision_at_1 value: 42.468
- type: precision_at_10 value: 9.47
- type: precision_at_100 value: 1.1360000000000001
- type: precision_at_1000 value: 0.12
- type: precision_at_3 value: 23.724999999999998
- type: precision_at_5 value: 16.593
- type: recall_at_1 value: 37.828
- type: recall_at_10 value: 79.538
- type: recall_at_100 value: 93.646
- type: recall_at_1000 value: 98.72999999999999
- type: recall_at_3 value: 61.134
- type: recall_at_5 value: 70.377
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 70.548
- type: map_at_10 value: 84.466
- type: map_at_100 value: 85.10600000000001
- type: map_at_1000 value: 85.123
- type: map_at_3 value: 81.57600000000001
- type: map_at_5 value: 83.399
- type: mrr_at_1 value: 81.24
- type: mrr_at_10 value: 87.457
- type: mrr_at_100 value: 87.574
- type: mrr_at_1000 value: 87.575
- type: mrr_at_3 value: 86.507
- type: mrr_at_5 value: 87.205
- type: ndcg_at_1 value: 81.25
- type: ndcg_at_10 value: 88.203
- type: ndcg_at_100 value: 89.457
- type: ndcg_at_1000 value: 89.563
- type: ndcg_at_3 value: 85.465
- type: ndcg_at_5 value: 87.007
- type: precision_at_1 value: 81.25
- type: precision_at_10 value: 13.373
- type: precision_at_100 value: 1.5270000000000001
- type: precision_at_1000 value: 0.157
- type: precision_at_3 value: 37.417
- type: precision_at_5 value: 24.556
- type: recall_at_1 value: 70.548
- type: recall_at_10 value: 95.208
- type: recall_at_100 value: 99.514
- type: recall_at_1000 value: 99.988
- type: recall_at_3 value: 87.214
- type: recall_at_5 value: 91.696
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure value: 53.04822095496839
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure value: 60.30778476474675
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 4.692
- type: map_at_10 value: 11.766
- type: map_at_100 value: 13.904
- type: map_at_1000 value: 14.216999999999999
- type: map_at_3 value: 8.245
- type: map_at_5 value: 9.92
- type: mrr_at_1 value: 23.0
- type: mrr_at_10 value: 33.78
- type: mrr_at_100 value: 34.922
- type: mrr_at_1000 value: 34.973
- type: mrr_at_3 value: 30.2
- type: mrr_at_5 value: 32.565
- type: ndcg_at_1 value: 23.0
- type: ndcg_at_10 value: 19.863
- type: ndcg_at_100 value: 28.141
- type: ndcg_at_1000 value: 33.549
- type: ndcg_at_3 value: 18.434
- type: ndcg_at_5 value: 16.384
- type: precision_at_1 value: 23.0
- type: precision_at_10 value: 10.39
- type: precision_at_100 value: 2.235
- type: precision_at_1000 value: 0.35300000000000004
- type: precision_at_3 value: 17.133000000000003
- type: precision_at_5 value: 14.44
- type: recall_at_1 value: 4.692
- type: recall_at_10 value: 21.025
- type: recall_at_100 value: 45.324999999999996
- type: recall_at_1000 value: 71.675
- type: recall_at_3 value: 10.440000000000001
- type: recall_at_5 value: 14.64
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson value: 84.96178184892842
- type: cos_sim_spearman value: 79.6487740813199
- type: euclidean_pearson value: 82.06661161625023
- type: euclidean_spearman value: 79.64876769031183
- type: manhattan_pearson value: 82.07061164575131
- type: manhattan_spearman value: 79.65197039464537
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson value: 84.15305604100027
- type: cos_sim_spearman value: 74.27447427941591
- type: euclidean_pearson value: 80.52737337565307
- type: euclidean_spearman value: 74.27416077132192
- type: manhattan_pearson value: 80.53728571140387
- type: manhattan_spearman value: 74.28853605753457
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson value: 83.44386080639279
- type: cos_sim_spearman value: 84.17947648159536
- type: euclidean_pearson value: 83.34145388129387
- type: euclidean_spearman value: 84.17947648159536
- type: manhattan_pearson value: 83.30699061927966
- type: manhattan_spearman value: 84.18125737380451
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson value: 81.57392220985612
- type: cos_sim_spearman value: 78.80745014464101
- type: euclidean_pearson value: 80.01660371487199
- type: euclidean_spearman value: 78.80741240102256
- type: manhattan_pearson value: 79.96810779507953
- type: manhattan_spearman value: 78.75600400119448
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson value: 86.85421063026625
- type: cos_sim_spearman value: 87.55320285299192
- type: euclidean_pearson value: 86.69750143323517
- type: euclidean_spearman value: 87.55320284326378
- type: manhattan_pearson value: 86.63379169960379
- type: manhattan_spearman value: 87.4815029877984
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson value: 84.31314130411842
- type: cos_sim_spearman value: 85.3489588181433
- type: euclidean_pearson value: 84.13240933463535
- type: euclidean_spearman value: 85.34902871403281
- type: manhattan_pearson value: 84.01183086503559
- type: manhattan_spearman value: 85.19316703166102
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson value: 89.09979781689536
- type: cos_sim_spearman value: 88.87813323759015
- type: euclidean_pearson value: 88.65413031123792
- type: euclidean_spearman value: 88.87813323759015
- type: manhattan_pearson value: 88.61818758256024
- type: manhattan_spearman value: 88.81044100494604
- 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: 62.30693258111531
- type: cos_sim_spearman value: 62.195516523251946
- type: euclidean_pearson value: 62.951283701049476
- type: euclidean_spearman value: 62.195516523251946
- type: manhattan_pearson value: 63.068322281439535
- type: manhattan_spearman value: 62.10621171028406
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson value: 84.27092833763909
- type: cos_sim_spearman value: 84.84429717949759
- type: euclidean_pearson value: 84.8516966060792
- type: euclidean_spearman value: 84.84429717949759
- type: manhattan_pearson value: 84.82203139242881
- type: manhattan_spearman value: 84.8358503952945
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map value: 83.10290863981409
- type: mrr value: 95.31168450286097
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 52.161
- type: map_at_10 value: 62.138000000000005
- type: map_at_100 value: 62.769
- type: map_at_1000 value: 62.812
- type: map_at_3 value: 59.111000000000004
- type: map_at_5 value: 60.995999999999995
- type: mrr_at_1 value: 55.333
- type: mrr_at_10 value: 63.504000000000005
- type: mrr_at_100 value: 64.036
- type: mrr_at_1000 value: 64.08
- type: mrr_at_3 value: 61.278
- type: mrr_at_5 value: 62.778
- type: ndcg_at_1 value: 55.333
- type: ndcg_at_10 value: 66.678
- type: ndcg_at_100 value: 69.415
- type: ndcg_at_1000 value: 70.453
- type: ndcg_at_3 value: 61.755
- type: ndcg_at_5 value: 64.546
- type: precision_at_1 value: 55.333
- type: precision_at_10 value: 9.033
- type: precision_at_100 value: 1.043
- type: precision_at_1000 value: 0.11199999999999999
- type: precision_at_3 value: 24.221999999999998
- type: precision_at_5 value: 16.333000000000002
- type: recall_at_1 value: 52.161
- type: recall_at_10 value: 79.156
- type: recall_at_100 value: 91.333
- type: recall_at_1000 value: 99.333
- type: recall_at_3 value: 66.43299999999999
- type: recall_at_5 value: 73.272
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy value: 99.81287128712871
- type: cos_sim_ap value: 95.30034785910676
- type: cos_sim_f1 value: 90.28629856850716
- type: cos_sim_precision value: 92.36401673640168
- type: cos_sim_recall value: 88.3
- type: dot_accuracy value: 99.81287128712871
- type: dot_ap value: 95.30034785910676
- type: dot_f1 value: 90.28629856850716
- type: dot_precision value: 92.36401673640168
- type: dot_recall value: 88.3
- type: euclidean_accuracy value: 99.81287128712871
- type: euclidean_ap value: 95.30034785910676
- type: euclidean_f1 value: 90.28629856850716
- type: euclidean_precision value: 92.36401673640168
- type: euclidean_recall value: 88.3
- type: manhattan_accuracy value: 99.80990099009901
- type: manhattan_ap value: 95.26880751950654
- type: manhattan_f1 value: 90.22177419354838
- type: manhattan_precision value: 90.95528455284553
- type: manhattan_recall value: 89.5
- type: max_accuracy value: 99.81287128712871
- type: max_ap value: 95.30034785910676
- type: max_f1 value: 90.28629856850716
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure value: 58.518662504351184
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure value: 34.96168178378587
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map value: 52.04862593471896
- type: mrr value: 52.97238402936932
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson value: 30.092545236479946
- type: cos_sim_spearman value: 31.599851000175498
- type: dot_pearson value: 30.092542723901676
- type: dot_spearman value: 31.599851000175498
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 0.189
- type: map_at_10 value: 1.662
- type: map_at_100 value: 9.384
- type: map_at_1000 value: 22.669
- type: map_at_3 value: 0.5559999999999999
- type: map_at_5 value: 0.9039999999999999
- type: mrr_at_1 value: 68.0
- type: mrr_at_10 value: 81.01899999999999
- type: mrr_at_100 value: 81.01899999999999
- type: mrr_at_1000 value: 81.01899999999999
- type: mrr_at_3 value: 79.333
- type: mrr_at_5 value: 80.733
- type: ndcg_at_1 value: 63.0
- type: ndcg_at_10 value: 65.913
- type: ndcg_at_100 value: 51.895
- type: ndcg_at_1000 value: 46.967
- type: ndcg_at_3 value: 65.49199999999999
- type: ndcg_at_5 value: 66.69699999999999
- type: precision_at_1 value: 68.0
- type: precision_at_10 value: 71.6
- type: precision_at_100 value: 53.66
- type: precision_at_1000 value: 21.124000000000002
- type: precision_at_3 value: 72.667
- type: precision_at_5 value: 74.0
- type: recall_at_1 value: 0.189
- type: recall_at_10 value: 1.913
- type: recall_at_100 value: 12.601999999999999
- type: recall_at_1000 value: 44.296
- type: recall_at_3 value: 0.605
- type: recall_at_5 value: 1.018
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 2.701
- type: map_at_10 value: 10.445
- type: map_at_100 value: 17.324
- type: map_at_1000 value: 19.161
- type: map_at_3 value: 5.497
- type: map_at_5 value: 7.278
- type: mrr_at_1 value: 30.612000000000002
- type: mrr_at_10 value: 45.534
- type: mrr_at_100 value: 45.792
- type: mrr_at_1000 value: 45.806999999999995
- type: mrr_at_3 value: 37.755
- type: mrr_at_5 value: 43.469
- type: ndcg_at_1 value: 26.531
- type: ndcg_at_10 value: 26.235000000000003
- type: ndcg_at_100 value: 39.17
- type: ndcg_at_1000 value: 51.038
- type: ndcg_at_3 value: 23.625
- type: ndcg_at_5 value: 24.338
- type: precision_at_1 value: 30.612000000000002
- type: precision_at_10 value: 24.285999999999998
- type: precision_at_100 value: 8.224
- type: precision_at_1000 value: 1.6179999999999999
- type: precision_at_3 value: 24.490000000000002
- type: precision_at_5 value: 24.898
- type: recall_at_1 value: 2.701
- type: recall_at_10 value: 17.997
- type: recall_at_100 value: 51.766999999999996
- type: recall_at_1000 value: 87.863
- type: recall_at_3 value: 6.295000000000001
- type: recall_at_5 value: 9.993
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy value: 73.3474
- type: ap value: 15.393431414459924
- type: f1 value: 56.466681887882416
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy value: 62.062818336163
- type: f1 value: 62.11230840463252
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure value: 42.464892820845115
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy value: 86.15962329379508
- type: cos_sim_ap value: 74.73674057919256
- type: cos_sim_f1 value: 68.81245642574947
- type: cos_sim_precision value: 61.48255813953488
- type: cos_sim_recall value: 78.12664907651715
- type: dot_accuracy value: 86.15962329379508
- type: dot_ap value: 74.7367634988281
- type: dot_f1 value: 68.81245642574947
- type: dot_precision value: 61.48255813953488
- type: dot_recall value: 78.12664907651715
- type: euclidean_accuracy value: 86.15962329379508
- type: euclidean_ap value: 74.7367761466634
- type: euclidean_f1 value: 68.81245642574947
- type: euclidean_precision value: 61.48255813953488
- type: euclidean_recall value: 78.12664907651715
- type: manhattan_accuracy value: 86.21326816474935
- type: manhattan_ap value: 74.64416473733951
- type: manhattan_f1 value: 68.80924855491331
- type: manhattan_precision value: 61.23456790123457
- type: manhattan_recall value: 78.52242744063325
- type: max_accuracy value: 86.21326816474935
- type: max_ap value: 74.7367761466634
- type: max_f1 value: 68.81245642574947
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy value: 88.97620988085536
- type: cos_sim_ap value: 86.08680845745758
- type: cos_sim_f1 value: 78.02793637114438
- type: cos_sim_precision value: 73.11082699683736
- type: cos_sim_recall value: 83.65414228518632
- type: dot_accuracy value: 88.97620988085536
- type: dot_ap value: 86.08681149437946
- type: dot_f1 value: 78.02793637114438
- type: dot_precision value: 73.11082699683736
- type: dot_recall value: 83.65414228518632
- type: euclidean_accuracy value: 88.97620988085536
- type: euclidean_ap value: 86.08681215460771
- type: euclidean_f1 value: 78.02793637114438
- type: euclidean_precision value: 73.11082699683736
- type: euclidean_recall value: 83.65414228518632
- type: manhattan_accuracy value: 88.88888888888889
- type: manhattan_ap value: 86.02916327562438
- type: manhattan_f1 value: 78.02063045516843
- type: manhattan_precision value: 73.38851947346994
- type: manhattan_recall value: 83.2768709578072
- type: max_accuracy value: 88.97620988085536
- type: max_ap value: 86.08681215460771
- type: max_f1 value: 78.02793637114438
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
narainp/jina-embeddings-v2-base-en-Q8_0-GGUF
This model was converted to GGUF format from jinaai/jina-embeddings-v2-base-en 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 narainp/jina-embeddings-v2-base-en-Q8_0-GGUF --hf-file jina-embeddings-v2-base-en-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo narainp/jina-embeddings-v2-base-en-Q8_0-GGUF --hf-file jina-embeddings-v2-base-en-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 narainp/jina-embeddings-v2-base-en-Q8_0-GGUF --hf-file jina-embeddings-v2-base-en-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo narainp/jina-embeddings-v2-base-en-Q8_0-GGUF --hf-file jina-embeddings-v2-base-en-q8_0.gguf -c 2048
Jina Embeddings V3
Jina Embeddings V3 は100以上の言語をサポートする多言語文埋め込みモデルで、文の類似度と特徴抽出タスクに特化しています。
テキスト埋め込み
Transformers 複数言語対応
Transformers 複数言語対応J
jinaai
3.7M
911
Ms Marco MiniLM L6 V2
Apache-2.0
MS Marcoパッセージランキングタスクで訓練されたクロスエンコーダモデル、情報検索におけるクエリ-パッセージ関連性スコアリング用
テキスト埋め込み 英語
M
cross-encoder
2.5M
86
Opensearch Neural Sparse Encoding Doc V2 Distill
Apache-2.0
蒸留技術に基づくスパース検索モデルで、OpenSearch向けに最適化されており、推論不要のドキュメントエンコーディングをサポートし、検索関連性と効率性においてV1版を上回ります
テキスト埋め込み Transformers 英語
Transformers 英語O
opensearch-project
1.8M
7
Sapbert From PubMedBERT Fulltext
Apache-2.0
PubMedBERTに基づく生物医学エンティティ表現モデルで、自己アライメント事前学習により意味関係の捕捉を最適化します。
テキスト埋め込み 英語
S
cambridgeltl
1.7M
49
Gte Large
MIT
GTE-Largeは強力なセンテンストランスフォーマーモデルで、文の類似度とテキスト埋め込みタスクに特化しており、複数のベンチマークテストで優れた性能を発揮します。
テキスト埋め込み 英語
G
thenlper
1.5M
278
Gte Base En V1.5
Apache-2.0
GTE-base-en-v1.5 は英語の文章変換モデルで、文章類似度タスクに特化しており、複数のテキスト埋め込みベンチマークで優れた性能を発揮します。
テキスト埋め込み Transformers 複数言語対応
Transformers 複数言語対応G
Alibaba-NLP
1.5M
63
Gte Multilingual Base
Apache-2.0
GTE Multilingual Base は50以上の言語をサポートする多言語文埋め込みモデルで、文類似度計算などのタスクに適しています。
テキスト埋め込み Transformers 複数言語対応
Transformers 複数言語対応G
Alibaba-NLP
1.2M
246
Polybert
polyBERTは、完全に機械駆動の超高速ポリマー情報学を実現するための化学言語モデルです。PSMILES文字列を600次元の密なフィンガープリントにマッピングし、ポリマー化学構造を数値形式で表現します。
テキスト埋め込み Transformers
TransformersP
kuelumbus
1.0M
5
Bert Base Turkish Cased Mean Nli Stsb Tr
Apache-2.0
トルコ語BERTベースの文埋め込みモデルで、意味的類似性タスクに最適化
テキスト埋め込み Transformers その他
Transformers その他B
emrecan
1.0M
40
GIST Small Embedding V0
MIT
BAAI/bge-small-en-v1.5モデルを微調整したテキスト埋め込みモデルで、MEDIデータセットとMTEB分類タスクデータセットで訓練され、検索タスクのクエリエンコーディング能力を最適化しました。
テキスト埋め込み
Safetensors 英語
Safetensors 英語G
avsolatorio
945.68k
29
おすすめAIモデル
Llama 3 Typhoon V1.5x 8b Instruct
タイ語専用に設計された80億パラメータの命令モデルで、GPT-3.5-turboに匹敵する性能を持ち、アプリケーションシナリオ、検索拡張生成、制限付き生成、推論タスクを最適化
大規模言語モデル Transformers 複数言語対応
Transformers 複数言語対応L
scb10x
3,269
16
Cadet Tiny
Openrail
Cadet-TinyはSODAデータセットでトレーニングされた超小型対話モデルで、エッジデバイス推論向けに設計されており、体積はCosmo-3Bモデルの約2％です。
対話システム Transformers 英語
Transformers 英語C
ToddGoldfarb
2,691
6
Roberta Base Chinese Extractive Qa
RoBERTaアーキテクチャに基づく中国語抽出型QAモデルで、与えられたテキストから回答を抽出するタスクに適しています。
質問応答システム 中国語
R
uer
2,694
98