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JA
Gte Qwen2 1.5B Instruct
Qwen2-1.5Bを基に構築された汎用テキスト埋め込みモデルで、多言語と長文処理をサポート
ダウンロード数 242.12k
リリース時間 : 6/29/2024
モデル概要
このモデルは汎用テキスト埋め込み(gte)シリーズの最新メンバーで、高品質なテキスト埋め込み表現の生成に特化しており、情報検索、意味的類似度計算など様々な自然言語処理タスクに適しています。
モデル特徴
双方向注意メカニズム
コンテキスト理解能力を強化し、埋め込み品質を向上
効率的な命令微調整
クエリ側のみに命令微調整を適用し、効率を向上
多言語サポート
クロスドメイン・マルチシナリオの多言語テキストコーパスで訓練
長文処理
最大32kトークンの入力長をサポート
モデル能力
テキスト埋め込み生成
意味的類似度計算
情報検索
多言語テキスト処理
使用事例
情報検索
ウェブ検索クエリ
ユーザークエリに基づいて関連文書段落を検索
MTEB評価で67.16点を獲得
意味的類似度
文書類似度計算
異なる文書間の意味的類似度を計算
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity license: apache-2.0 model-index:
- name: gte-qwen2-7B-instruct
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy value: 83.98507462686567
- type: ap value: 50.93015252587014
- type: f1 value: 78.50416599051215 task: type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy value: 96.61065
- type: ap value: 94.89174052954196
- type: f1 value: 96.60942596940565 task: type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy value: 55.614000000000004
- type: f1 value: 54.90553480294904 task: type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
- type: map_at_1 value: 45.164
- type: map_at_10 value: 61.519
- type: map_at_100 value: 61.769
- type: map_at_1000 value: 61.769
- type: map_at_3 value: 57.443999999999996
- type: map_at_5 value: 60.058
- type: mrr_at_1 value: 46.088
- type: mrr_at_10 value: 61.861
- type: mrr_at_100 value: 62.117999999999995
- type: mrr_at_1000 value: 62.117999999999995
- type: mrr_at_3 value: 57.729
- type: mrr_at_5 value: 60.392
- type: ndcg_at_1 value: 45.164
- type: ndcg_at_10 value: 69.72
- type: ndcg_at_100 value: 70.719
- type: ndcg_at_1000 value: 70.719
- type: ndcg_at_3 value: 61.517999999999994
- type: ndcg_at_5 value: 66.247
- type: precision_at_1 value: 45.164
- type: precision_at_10 value: 9.545
- type: precision_at_100 value: 0.996
- type: precision_at_1000 value: 0.1
- type: precision_at_3 value: 24.443
- type: precision_at_5 value: 16.97
- type: recall_at_1 value: 45.164
- type: recall_at_10 value: 95.448
- type: recall_at_100 value: 99.644
- type: recall_at_1000 value: 99.644
- type: recall_at_3 value: 73.329
- type: recall_at_5 value: 84.851 task: type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure value: 50.511868162026175 task: type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure value: 45.007803189284004 task: type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map value: 64.55292107723382
- type: mrr value: 77.66158818097877 task: type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cos_sim_pearson value: 85.65459047085452
- type: cos_sim_spearman value: 82.10729255710761
- type: euclidean_pearson value: 82.78079159312476
- type: euclidean_spearman value: 80.50002701880933
- type: manhattan_pearson value: 82.41372641383016
- type: manhattan_spearman value: 80.57412509272639 task: type: STS
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy value: 87.30844155844156
- type: f1 value: 87.25307322443255 task: type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure value: 43.20754608934859 task: type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure value: 38.818037697335505 task: type: Clustering
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 35.423
- type: map_at_10 value: 47.198
- type: map_at_100 value: 48.899
- type: map_at_1000 value: 49.004
- type: map_at_3 value: 43.114999999999995
- type: map_at_5 value: 45.491
- type: mrr_at_1 value: 42.918
- type: mrr_at_10 value: 53.299
- type: mrr_at_100 value: 54.032000000000004
- type: mrr_at_1000 value: 54.055
- type: mrr_at_3 value: 50.453
- type: mrr_at_5 value: 52.205999999999996
- type: ndcg_at_1 value: 42.918
- type: ndcg_at_10 value: 53.98
- type: ndcg_at_100 value: 59.57
- type: ndcg_at_1000 value: 60.879000000000005
- type: ndcg_at_3 value: 48.224000000000004
- type: ndcg_at_5 value: 50.998
- type: precision_at_1 value: 42.918
- type: precision_at_10 value: 10.299999999999999
- type: precision_at_100 value: 1.687
- type: precision_at_1000 value: 0.211
- type: precision_at_3 value: 22.842000000000002
- type: precision_at_5 value: 16.681
- type: recall_at_1 value: 35.423
- type: recall_at_10 value: 66.824
- type: recall_at_100 value: 89.564
- type: recall_at_1000 value: 97.501
- type: recall_at_3 value: 50.365
- type: recall_at_5 value: 57.921 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackEnglishRetrieval
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 33.205
- type: map_at_10 value: 44.859
- type: map_at_100 value: 46.135
- type: map_at_1000 value: 46.259
- type: map_at_3 value: 41.839
- type: map_at_5 value: 43.662
- type: mrr_at_1 value: 41.146
- type: mrr_at_10 value: 50.621
- type: mrr_at_100 value: 51.207
- type: mrr_at_1000 value: 51.246
- type: mrr_at_3 value: 48.535000000000004
- type: mrr_at_5 value: 49.818
- type: ndcg_at_1 value: 41.146
- type: ndcg_at_10 value: 50.683
- type: ndcg_at_100 value: 54.82
- type: ndcg_at_1000 value: 56.69
- type: ndcg_at_3 value: 46.611000000000004
- type: ndcg_at_5 value: 48.66
- type: precision_at_1 value: 41.146
- type: precision_at_10 value: 9.439
- type: precision_at_100 value: 1.465
- type: precision_at_1000 value: 0.194
- type: precision_at_3 value: 22.59
- type: precision_at_5 value: 15.86
- type: recall_at_1 value: 33.205
- type: recall_at_10 value: 61.028999999999996
- type: recall_at_100 value: 78.152
- type: recall_at_1000 value: 89.59700000000001
- type: recall_at_3 value: 49.05
- type: recall_at_5 value: 54.836 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGamingRetrieval
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 41.637
- type: map_at_10 value: 55.162
- type: map_at_100 value: 56.142
- type: map_at_1000 value: 56.188
- type: map_at_3 value: 51.564
- type: map_at_5 value: 53.696
- type: mrr_at_1 value: 47.524
- type: mrr_at_10 value: 58.243
- type: mrr_at_100 value: 58.879999999999995
- type: mrr_at_1000 value: 58.9
- type: mrr_at_3 value: 55.69499999999999
- type: mrr_at_5 value: 57.284
- type: ndcg_at_1 value: 47.524
- type: ndcg_at_10 value: 61.305
- type: ndcg_at_100 value: 65.077
- type: ndcg_at_1000 value: 65.941
- type: ndcg_at_3 value: 55.422000000000004
- type: ndcg_at_5 value: 58.516
- type: precision_at_1 value: 47.524
- type: precision_at_10 value: 9.918000000000001
- type: precision_at_100 value: 1.276
- type: precision_at_1000 value: 0.13899999999999998
- type: precision_at_3 value: 24.765
- type: precision_at_5 value: 17.204
- type: recall_at_1 value: 41.637
- type: recall_at_10 value: 76.185
- type: recall_at_100 value: 92.149
- type: recall_at_1000 value: 98.199
- type: recall_at_3 value: 60.856
- type: recall_at_5 value: 68.25099999999999 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGisRetrieval
revision: 5003b3064772da1887988e05400cf3806fe491f2
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 26.27
- type: map_at_10 value: 37.463
- type: map_at_100 value: 38.434000000000005
- type: map_at_1000 value: 38.509
- type: map_at_3 value: 34.226
- type: map_at_5 value: 36.161
- type: mrr_at_1 value: 28.588
- type: mrr_at_10 value: 39.383
- type: mrr_at_100 value: 40.23
- type: mrr_at_1000 value: 40.281
- type: mrr_at_3 value: 36.422
- type: mrr_at_5 value: 38.252
- type: ndcg_at_1 value: 28.588
- type: ndcg_at_10 value: 43.511
- type: ndcg_at_100 value: 48.274
- type: ndcg_at_1000 value: 49.975
- type: ndcg_at_3 value: 37.319
- type: ndcg_at_5 value: 40.568
- type: precision_at_1 value: 28.588
- type: precision_at_10 value: 6.893000000000001
- type: precision_at_100 value: 0.9900000000000001
- type: precision_at_1000 value: 0.117
- type: precision_at_3 value: 16.347
- type: precision_at_5 value: 11.661000000000001
- type: recall_at_1 value: 26.27
- type: recall_at_10 value: 60.284000000000006
- type: recall_at_100 value: 81.902
- type: recall_at_1000 value: 94.43
- type: recall_at_3 value: 43.537
- type: recall_at_5 value: 51.475 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackMathematicaRetrieval
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 18.168
- type: map_at_10 value: 28.410000000000004
- type: map_at_100 value: 29.78
- type: map_at_1000 value: 29.892999999999997
- type: map_at_3 value: 25.238
- type: map_at_5 value: 26.96
- type: mrr_at_1 value: 23.507
- type: mrr_at_10 value: 33.382
- type: mrr_at_100 value: 34.404
- type: mrr_at_1000 value: 34.467999999999996
- type: mrr_at_3 value: 30.637999999999998
- type: mrr_at_5 value: 32.199
- type: ndcg_at_1 value: 23.507
- type: ndcg_at_10 value: 34.571000000000005
- type: ndcg_at_100 value: 40.663
- type: ndcg_at_1000 value: 43.236000000000004
- type: ndcg_at_3 value: 29.053
- type: ndcg_at_5 value: 31.563999999999997
- type: precision_at_1 value: 23.507
- type: precision_at_10 value: 6.654
- type: precision_at_100 value: 1.113
- type: precision_at_1000 value: 0.146
- type: precision_at_3 value: 14.427999999999999
- type: precision_at_5 value: 10.498000000000001
- type: recall_at_1 value: 18.168
- type: recall_at_10 value: 48.443000000000005
- type: recall_at_100 value: 74.47
- type: recall_at_1000 value: 92.494
- type: recall_at_3 value: 33.379999999999995
- type: recall_at_5 value: 39.76 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackPhysicsRetrieval
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 32.39
- type: map_at_10 value: 44.479
- type: map_at_100 value: 45.977000000000004
- type: map_at_1000 value: 46.087
- type: map_at_3 value: 40.976
- type: map_at_5 value: 43.038
- type: mrr_at_1 value: 40.135
- type: mrr_at_10 value: 50.160000000000004
- type: mrr_at_100 value: 51.052
- type: mrr_at_1000 value: 51.087
- type: mrr_at_3 value: 47.818
- type: mrr_at_5 value: 49.171
- type: ndcg_at_1 value: 40.135
- type: ndcg_at_10 value: 50.731
- type: ndcg_at_100 value: 56.452000000000005
- type: ndcg_at_1000 value: 58.123000000000005
- type: ndcg_at_3 value: 45.507
- type: ndcg_at_5 value: 48.11
- type: precision_at_1 value: 40.135
- type: precision_at_10 value: 9.192
- type: precision_at_100 value: 1.397
- type: precision_at_1000 value: 0.169
- type: precision_at_3 value: 21.816
- type: precision_at_5 value: 15.476
- type: recall_at_1 value: 32.39
- type: recall_at_10 value: 63.597
- type: recall_at_100 value: 86.737
- type: recall_at_1000 value: 97.039
- type: recall_at_3 value: 48.906
- type: recall_at_5 value: 55.659000000000006 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackProgrammersRetrieval
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 28.397
- type: map_at_10 value: 39.871
- type: map_at_100 value: 41.309000000000005
- type: map_at_1000 value: 41.409
- type: map_at_3 value: 36.047000000000004
- type: map_at_5 value: 38.104
- type: mrr_at_1 value: 34.703
- type: mrr_at_10 value: 44.773
- type: mrr_at_100 value: 45.64
- type: mrr_at_1000 value: 45.678999999999995
- type: mrr_at_3 value: 41.705
- type: mrr_at_5 value: 43.406
- type: ndcg_at_1 value: 34.703
- type: ndcg_at_10 value: 46.271
- type: ndcg_at_100 value: 52.037
- type: ndcg_at_1000 value: 53.81700000000001
- type: ndcg_at_3 value: 39.966
- type: ndcg_at_5 value: 42.801
- type: precision_at_1 value: 34.703
- type: precision_at_10 value: 8.744
- type: precision_at_100 value: 1.348
- type: precision_at_1000 value: 0.167
- type: precision_at_3 value: 19.102
- type: precision_at_5 value: 13.836
- type: recall_at_1 value: 28.397
- type: recall_at_10 value: 60.299
- type: recall_at_100 value: 84.595
- type: recall_at_1000 value: 96.155
- type: recall_at_3 value: 43.065
- type: recall_at_5 value: 50.371 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 28.044333333333338
- type: map_at_10 value: 38.78691666666666
- type: map_at_100 value: 40.113
- type: map_at_1000 value: 40.22125
- type: map_at_3 value: 35.52966666666667
- type: map_at_5 value: 37.372749999999996
- type: mrr_at_1 value: 33.159083333333335
- type: mrr_at_10 value: 42.913583333333335
- type: mrr_at_100 value: 43.7845
- type: mrr_at_1000 value: 43.830333333333336
- type: mrr_at_3 value: 40.29816666666667
- type: mrr_at_5 value: 41.81366666666667
- type: ndcg_at_1 value: 33.159083333333335
- type: ndcg_at_10 value: 44.75750000000001
- type: ndcg_at_100 value: 50.13658333333334
- type: ndcg_at_1000 value: 52.037
- type: ndcg_at_3 value: 39.34258333333334
- type: ndcg_at_5 value: 41.93708333333333
- type: precision_at_1 value: 33.159083333333335
- type: precision_at_10 value: 7.952416666666667
- type: precision_at_100 value: 1.2571666666666668
- type: precision_at_1000 value: 0.16099999999999998
- type: precision_at_3 value: 18.303833333333337
- type: precision_at_5 value: 13.057083333333333
- type: recall_at_1 value: 28.044333333333338
- type: recall_at_10 value: 58.237249999999996
- type: recall_at_100 value: 81.35391666666666
- type: recall_at_1000 value: 94.21283333333334
- type: recall_at_3 value: 43.32341666666667
- type: recall_at_5 value: 49.94908333333333 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackStatsRetrieval
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 27.838
- type: map_at_10 value: 36.04
- type: map_at_100 value: 37.113
- type: map_at_1000 value: 37.204
- type: map_at_3 value: 33.585
- type: map_at_5 value: 34.845
- type: mrr_at_1 value: 30.982
- type: mrr_at_10 value: 39.105000000000004
- type: mrr_at_100 value: 39.98
- type: mrr_at_1000 value: 40.042
- type: mrr_at_3 value: 36.912
- type: mrr_at_5 value: 38.062000000000005
- type: ndcg_at_1 value: 30.982
- type: ndcg_at_10 value: 40.982
- type: ndcg_at_100 value: 46.092
- type: ndcg_at_1000 value: 48.25
- type: ndcg_at_3 value: 36.41
- type: ndcg_at_5 value: 38.379999999999995
- type: precision_at_1 value: 30.982
- type: precision_at_10 value: 6.534
- type: precision_at_100 value: 0.9820000000000001
- type: precision_at_1000 value: 0.124
- type: precision_at_3 value: 15.745999999999999
- type: precision_at_5 value: 10.828
- type: recall_at_1 value: 27.838
- type: recall_at_10 value: 52.971000000000004
- type: recall_at_100 value: 76.357
- type: recall_at_1000 value: 91.973
- type: recall_at_3 value: 40.157
- type: recall_at_5 value: 45.147999999999996 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackTexRetrieval
revision: 46989137a86843e03a6195de44b09deda022eec7
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 19.059
- type: map_at_10 value: 27.454
- type: map_at_100 value: 28.736
- type: map_at_1000 value: 28.865000000000002
- type: map_at_3 value: 24.773999999999997
- type: map_at_5 value: 26.266000000000002
- type: mrr_at_1 value: 23.125
- type: mrr_at_10 value: 31.267
- type: mrr_at_100 value: 32.32
- type: mrr_at_1000 value: 32.394
- type: mrr_at_3 value: 28.894
- type: mrr_at_5 value: 30.281000000000002
- type: ndcg_at_1 value: 23.125
- type: ndcg_at_10 value: 32.588
- type: ndcg_at_100 value: 38.432
- type: ndcg_at_1000 value: 41.214
- type: ndcg_at_3 value: 27.938000000000002
- type: ndcg_at_5 value: 30.127
- type: precision_at_1 value: 23.125
- type: precision_at_10 value: 5.9639999999999995
- type: precision_at_100 value: 1.047
- type: precision_at_1000 value: 0.148
- type: precision_at_3 value: 13.294
- type: precision_at_5 value: 9.628
- type: recall_at_1 value: 19.059
- type: recall_at_10 value: 44.25
- type: recall_at_100 value: 69.948
- type: recall_at_1000 value: 89.35300000000001
- type: recall_at_3 value: 31.114000000000004
- type: recall_at_5 value: 36.846000000000004 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackUnixRetrieval
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 28.355999999999998
- type: map_at_10 value: 39.055
- type: map_at_100 value: 40.486
- type: map_at_1000 value: 40.571
- type: map_at_3 value: 35.69
- type: map_at_5 value: 37.605
- type: mrr_at_1 value: 33.302
- type: mrr_at_10 value: 42.986000000000004
- type: mrr_at_100 value: 43.957
- type: mrr_at_1000 value: 43.996
- type: mrr_at_3 value: 40.111999999999995
- type: mrr_at_5 value: 41.735
- type: ndcg_at_1 value: 33.302
- type: ndcg_at_10 value: 44.962999999999994
- type: ndcg_at_100 value: 50.917
- type: ndcg_at_1000 value: 52.622
- type: ndcg_at_3 value: 39.182
- type: ndcg_at_5 value: 41.939
- type: precision_at_1 value: 33.302
- type: precision_at_10 value: 7.779999999999999
- type: precision_at_100 value: 1.203
- type: precision_at_1000 value: 0.145
- type: precision_at_3 value: 18.035
- type: precision_at_5 value: 12.873000000000001
- type: recall_at_1 value: 28.355999999999998
- type: recall_at_10 value: 58.782000000000004
- type: recall_at_100 value: 84.02199999999999
- type: recall_at_1000 value: 95.511
- type: recall_at_3 value: 43.126999999999995
- type: recall_at_5 value: 50.14999999999999 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWebmastersRetrieval
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 27.391
- type: map_at_10 value: 37.523
- type: map_at_100 value: 39.312000000000005
- type: map_at_1000 value: 39.54
- type: map_at_3 value: 34.231
- type: map_at_5 value: 36.062
- type: mrr_at_1 value: 32.016
- type: mrr_at_10 value: 41.747
- type: mrr_at_100 value: 42.812
- type: mrr_at_1000 value: 42.844
- type: mrr_at_3 value: 39.129999999999995
- type: mrr_at_5 value: 40.524
- type: ndcg_at_1 value: 32.016
- type: ndcg_at_10 value: 43.826
- type: ndcg_at_100 value: 50.373999999999995
- type: ndcg_at_1000 value: 52.318
- type: ndcg_at_3 value: 38.479
- type: ndcg_at_5 value: 40.944
- type: precision_at_1 value: 32.016
- type: precision_at_10 value: 8.280999999999999
- type: precision_at_100 value: 1.6760000000000002
- type: precision_at_1000 value: 0.25
- type: precision_at_3 value: 18.05
- type: precision_at_5 value: 13.083
- type: recall_at_1 value: 27.391
- type: recall_at_10 value: 56.928999999999995
- type: recall_at_100 value: 85.169
- type: recall_at_1000 value: 96.665
- type: recall_at_3 value: 42.264
- type: recall_at_5 value: 48.556 task: type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWordpressRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1 value: 18.398
- type: map_at_10 value: 27.929
- type: map_at_100 value: 29.032999999999998
- type: map_at_1000 value: 29.126
- type: map_at_3 value: 25.070999999999998
- type: map_at_5 value: 26.583000000000002
- type: mrr_at_1 value: 19.963
- type: mrr_at_10 value: 29.997
- type: mrr_at_100 value: 30.9
- type: mrr_at_1000 value: 30.972
- type: mrr_at_3 value: 27.264
- type: mrr_at_5 value: 28.826
- type: ndcg_at_1 value: 19.963
- type: ndcg_at_10 value: 33.678999999999995
- type: ndcg_at_100 value: 38.931
- type: ndcg_at_1000 value: 41.379
- type: ndcg_at_3 value: 28.000000000000004
- type: ndcg_at_5 value: 30.637999999999998
- type: precision_at_1 value: 19.963
- type: precision_at_10 value: 5.7299999999999995
- type: precision_at_100 value: 0.902
- type: precision_at_1000 value: 0.122
- type: precision_at_3 value: 12.631
- type: precision_at_5 value: 9.057
- type: recall_at_1 value: 18.398
- type: recall_at_10 value: 49.254
- type: recall_at_100 value: 73.182
- type: recall_at_1000 value: 91.637
- type: recall_at_3 value: 34.06
- type: recall_at_5 value: 40.416000000000004 task: type: Retrieval
- dataset:
config: default
name: MTEB ClimateFEVER
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
split: test
type: mteb/climate-fever
metrics:
- type: map_at_1 value: 19.681
- type: map_at_10 value: 32.741
- type: map_at_100 value: 34.811
- type: map_at_1000 value: 35.003
- type: map_at_3 value: 27.697
- type: map_at_5 value: 30.372
- type: mrr_at_1 value: 44.951
- type: mrr_at_10 value: 56.34400000000001
- type: mrr_at_100 value: 56.961
- type: mrr_at_1000 value: 56.987
- type: mrr_at_3 value: 53.681
- type: mrr_at_5 value: 55.407
- type: ndcg_at_1 value: 44.951
- type: ndcg_at_10 value: 42.905
- type: ndcg_at_100 value: 49.95
- type: ndcg_at_1000 value: 52.917
- type: ndcg_at_3 value: 36.815
- type: ndcg_at_5 value: 38.817
- type: precision_at_1 value: 44.951
- type: precision_at_10 value: 12.989999999999998
- type: precision_at_100 value: 2.068
- type: precision_at_1000 value: 0.263
- type: precision_at_3 value: 27.275
- type: precision_at_5 value: 20.365
- type: recall_at_1 value: 19.681
- type: recall_at_10 value: 48.272999999999996
- type: recall_at_100 value: 71.87400000000001
- type: recall_at_1000 value: 87.929
- type: recall_at_3 value: 32.653999999999996
- type: recall_at_5 value: 39.364 task: type: Retrieval
- dataset:
config: default
name: MTEB DBPedia
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
split: test
type: mteb/dbpedia
metrics:
- type: map_at_1 value: 10.231
- type: map_at_10 value: 22.338
- type: map_at_100 value: 31.927
- type: map_at_1000 value: 33.87
- type: map_at_3 value: 15.559999999999999
- type: map_at_5 value: 18.239
- type: mrr_at_1 value: 75.0
- type: mrr_at_10 value: 81.303
- type: mrr_at_100 value: 81.523
- type: mrr_at_1000 value: 81.53
- type: mrr_at_3 value: 80.083
- type: mrr_at_5 value: 80.758
- type: ndcg_at_1 value: 64.625
- type: ndcg_at_10 value: 48.687000000000005
- type: ndcg_at_100 value: 52.791
- type: ndcg_at_1000 value: 60.041999999999994
- type: ndcg_at_3 value: 53.757999999999996
- type: ndcg_at_5 value: 50.76500000000001
- type: precision_at_1 value: 75.0
- type: precision_at_10 value: 38.3
- type: precision_at_100 value: 12.025
- type: precision_at_1000 value: 2.3970000000000002
- type: precision_at_3 value: 55.417
- type: precision_at_5 value: 47.5
- type: recall_at_1 value: 10.231
- type: recall_at_10 value: 27.697
- type: recall_at_100 value: 57.409
- type: recall_at_1000 value: 80.547
- type: recall_at_3 value: 16.668
- type: recall_at_5 value: 20.552 task: type: Retrieval
- dataset:
config: default
name: MTEB EmotionClassification
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics:
- type: accuracy value: 61.365
- type: f1 value: 56.7540827912991 task: type: Classification
- dataset:
config: default
name: MTEB FEVER
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
split: test
type: mteb/fever
metrics:
- type: map_at_1 value: 83.479
- type: map_at_10 value: 88.898
- type: map_at_100 value: 89.11
- type: map_at_1000 value: 89.12400000000001
- type: map_at_3 value: 88.103
- type: map_at_5 value: 88.629
- type: mrr_at_1 value: 89.934
- type: mrr_at_10 value: 93.91000000000001
- type: mrr_at_100 value: 93.937
- type: mrr_at_1000 value: 93.938
- type: mrr_at_3 value: 93.62700000000001
- type: mrr_at_5 value: 93.84599999999999
- type: ndcg_at_1 value: 89.934
- type: ndcg_at_10 value: 91.574
- type: ndcg_at_100 value: 92.238
- type: ndcg_at_1000 value: 92.45
- type: ndcg_at_3 value: 90.586
- type: ndcg_at_5 value: 91.16300000000001
- type: precision_at_1 value: 89.934
- type: precision_at_10 value: 10.555
- type: precision_at_100 value: 1.1159999999999999
- type: precision_at_1000 value: 0.11499999999999999
- type: precision_at_3 value: 33.588
- type: precision_at_5 value: 20.642
- type: recall_at_1 value: 83.479
- type: recall_at_10 value: 94.971
- type: recall_at_100 value: 97.397
- type: recall_at_1000 value: 98.666
- type: recall_at_3 value: 92.24799999999999
- type: recall_at_5 value: 93.797 task: type: Retrieval
- dataset:
config: default
name: MTEB FiQA2018
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
split: test
type: mteb/fiqa
metrics:
- type: map_at_1 value: 27.16
- type: map_at_10 value: 45.593
- type: map_at_100 value: 47.762
- type: map_at_1000 value: 47.899
- type: map_at_3 value: 39.237
- type: map_at_5 value: 42.970000000000006
- type: mrr_at_1 value: 52.623
- type: mrr_at_10 value: 62.637
- type: mrr_at_100 value: 63.169
- type: mrr_at_1000 value: 63.185
- type: mrr_at_3 value: 59.928000000000004
- type: mrr_at_5 value: 61.702999999999996
- type: ndcg_at_1 value: 52.623
- type: ndcg_at_10 value: 54.701
- type: ndcg_at_100 value: 61.263
- type: ndcg_at_1000 value: 63.134
- type: ndcg_at_3 value: 49.265
- type: ndcg_at_5 value: 51.665000000000006
- type: precision_at_1 value: 52.623
- type: precision_at_10 value: 15.185
- type: precision_at_100 value: 2.202
- type: precision_at_1000 value: 0.254
- type: precision_at_3 value: 32.767
- type: precision_at_5 value: 24.722
- type: recall_at_1 value: 27.16
- type: recall_at_10 value: 63.309000000000005
- type: recall_at_100 value: 86.722
- type: recall_at_1000 value: 97.505
- type: recall_at_3 value: 45.045
- type: recall_at_5 value: 54.02400000000001 task: type: Retrieval
- dataset:
config: default
name: MTEB HotpotQA
revision: ab518f4d6fcca38d87c25209f94beba119d02014
split: test
type: mteb/hotpotqa
metrics:
- type: map_at_1 value: 42.573
- type: map_at_10 value: 59.373
- type: map_at_100 value: 60.292
- type: map_at_1000 value: 60.358999999999995
- type: map_at_3 value: 56.159000000000006
- type: map_at_5 value: 58.123999999999995
- type: mrr_at_1 value: 85.14500000000001
- type: mrr_at_10 value: 89.25999999999999
- type: mrr_at_100 value: 89.373
- type: mrr_at_1000 value: 89.377
- type: mrr_at_3 value: 88.618
- type: mrr_at_5 value: 89.036
- type: ndcg_at_1 value: 85.14500000000001
- type: ndcg_at_10 value: 68.95
- type: ndcg_at_100 value: 71.95
- type: ndcg_at_1000 value: 73.232
- type: ndcg_at_3 value: 64.546
- type: ndcg_at_5 value: 66.945
- type: precision_at_1 value: 85.14500000000001
- type: precision_at_10 value: 13.865
- type: precision_at_100 value: 1.619
- type: precision_at_1000 value: 0.179
- type: precision_at_3 value: 39.703
- type: precision_at_5 value: 25.718000000000004
- type: recall_at_1 value: 42.573
- type: recall_at_10 value: 69.325
- type: recall_at_100 value: 80.932
- type: recall_at_1000 value: 89.446
- type: recall_at_3 value: 59.553999999999995
- type: recall_at_5 value: 64.294 task: type: Retrieval
- dataset:
config: default
name: MTEB ImdbClassification
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics:
- type: accuracy value: 95.8336
- type: ap value: 93.78862962194073
- type: f1 value: 95.83192650728371 task: type: Classification
- dataset:
config: default
name: MTEB MSMARCO
revision: c5a29a104738b98a9e76336939199e264163d4a0
split: dev
type: mteb/msmarco
metrics:
- type: map_at_1 value: 23.075000000000003
- type: map_at_10 value: 36.102000000000004
- type: map_at_100 value: 37.257
- type: map_at_1000 value: 37.3
- type: map_at_3 value: 32.144
- type: map_at_5 value: 34.359
- type: mrr_at_1 value: 23.711
- type: mrr_at_10 value: 36.671
- type: mrr_at_100 value: 37.763999999999996
- type: mrr_at_1000 value: 37.801
- type: mrr_at_3 value: 32.775
- type: mrr_at_5 value: 34.977000000000004
- type: ndcg_at_1 value: 23.711
- type: ndcg_at_10 value: 43.361
- type: ndcg_at_100 value: 48.839
- type: ndcg_at_1000 value: 49.88
- type: ndcg_at_3 value: 35.269
- type: ndcg_at_5 value: 39.224
- type: precision_at_1 value: 23.711
- type: precision_at_10 value: 6.866999999999999
- type: precision_at_100 value: 0.96
- type: precision_at_1000 value: 0.105
- type: precision_at_3 value: 15.096000000000002
- type: precision_at_5 value: 11.083
- type: recall_at_1 value: 23.075000000000003
- type: recall_at_10 value: 65.756
- type: recall_at_100 value: 90.88199999999999
- type: recall_at_1000 value: 98.739
- type: recall_at_3 value: 43.691
- type: recall_at_5 value: 53.15800000000001 task: type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy value: 97.69493844049248
- type: f1 value: 97.55048089616261 task: type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy value: 88.75968992248062
- type: f1 value: 72.26321223399123 task: type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy value: 82.40080699394754
- type: f1 value: 79.62590029057968 task: type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy value: 84.49562878278414
- type: f1 value: 84.0040193313333 task: type: Classification
- dataset:
config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
- type: v_measure value: 39.386760057101945 task: type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
- type: v_measure value: 37.89687154075537 task: type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
split: test
type: mteb/mind_small
metrics:
- type: map value: 33.94151656057482
- type: mrr value: 35.32684700746953 task: type: Reranking
- dataset:
config: default
name: MTEB NFCorpus
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
metrics:
- type: map_at_1 value: 6.239999999999999
- type: map_at_10 value: 14.862
- type: map_at_100 value: 18.955
- type: map_at_1000 value: 20.694000000000003
- type: map_at_3 value: 10.683
- type: map_at_5 value: 12.674
- type: mrr_at_1 value: 50.15500000000001
- type: mrr_at_10 value: 59.697
- type: mrr_at_100 value: 60.095
- type: mrr_at_1000 value: 60.129999999999995
- type: mrr_at_3 value: 58.35900000000001
- type: mrr_at_5 value: 58.839
- type: ndcg_at_1 value: 48.452
- type: ndcg_at_10 value: 39.341
- type: ndcg_at_100 value: 35.866
- type: ndcg_at_1000 value: 45.111000000000004
- type: ndcg_at_3 value: 44.527
- type: ndcg_at_5 value: 42.946
- type: precision_at_1 value: 50.15500000000001
- type: precision_at_10 value: 29.536
- type: precision_at_100 value: 9.142
- type: precision_at_1000 value: 2.2849999999999997
- type: precision_at_3 value: 41.899
- type: precision_at_5 value: 37.647000000000006
- type: recall_at_1 value: 6.239999999999999
- type: recall_at_10 value: 19.278000000000002
- type: recall_at_100 value: 36.074
- type: recall_at_1000 value: 70.017
- type: recall_at_3 value: 12.066
- type: recall_at_5 value: 15.254000000000001 task: type: Retrieval
- dataset:
config: default
name: MTEB NQ
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
metrics:
- type: map_at_1 value: 39.75
- type: map_at_10 value: 56.443
- type: map_at_100 value: 57.233999999999995
- type: map_at_1000 value: 57.249
- type: map_at_3 value: 52.032999999999994
- type: map_at_5 value: 54.937999999999995
- type: mrr_at_1 value: 44.728
- type: mrr_at_10 value: 58.939
- type: mrr_at_100 value: 59.489000000000004
- type: mrr_at_1000 value: 59.499
- type: mrr_at_3 value: 55.711999999999996
- type: mrr_at_5 value: 57.89
- type: ndcg_at_1 value: 44.728
- type: ndcg_at_10 value: 63.998999999999995
- type: ndcg_at_100 value: 67.077
- type: ndcg_at_1000 value: 67.40899999999999
- type: ndcg_at_3 value: 56.266000000000005
- type: ndcg_at_5 value: 60.88
- type: precision_at_1 value: 44.728
- type: precision_at_10 value: 10.09
- type: precision_at_100 value: 1.1809999999999998
- type: precision_at_1000 value: 0.121
- type: precision_at_3 value: 25.145
- type: precision_at_5 value: 17.822
- type: recall_at_1 value: 39.75
- type: recall_at_10 value: 84.234
- type: recall_at_100 value: 97.055
- type: recall_at_1000 value: 99.517
- type: recall_at_3 value: 64.851
- type: recall_at_5 value: 75.343 task: type: Retrieval
- dataset:
config: default
name: MTEB QuoraRetrieval
revision: None
split: test
type: mteb/quora
metrics:
- type: map_at_1 value: 72.085
- type: map_at_10 value: 86.107
- type: map_at_100 value: 86.727
- type: map_at_1000 value: 86.74
- type: map_at_3 value: 83.21
- type: map_at_5 value: 85.06
- type: mrr_at_1 value: 82.94
- type: mrr_at_10 value: 88.845
- type: mrr_at_100 value: 88.926
- type: mrr_at_1000 value: 88.927
- type: mrr_at_3 value: 87.993
- type: mrr_at_5 value: 88.62299999999999
- type: ndcg_at_1 value: 82.97
- type: ndcg_at_10 value: 89.645
- type: ndcg_at_100 value: 90.717
- type: ndcg_at_1000 value: 90.78
- type: ndcg_at_3 value: 86.99900000000001
- type: ndcg_at_5 value: 88.52600000000001
- type: precision_at_1 value: 82.97
- type: precision_at_10 value: 13.569
- type: precision_at_100 value: 1.539
- type: precision_at_1000 value: 0.157
- type: precision_at_3 value: 38.043
- type: precision_at_5 value: 24.992
- type: recall_at_1 value: 72.085
- type: recall_at_10 value: 96.262
- type: recall_at_100 value: 99.77000000000001
- type: recall_at_1000 value: 99.997
- type: recall_at_3 value: 88.652
- type: recall_at_5 value: 93.01899999999999 task: type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: v_measure value: 55.82153952668092 task: type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P
revision: 282350215ef01743dc01b456c7f5241fa8937f16
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: v_measure value: 62.094465801879295 task: type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS
revision: None
split: test
type: mteb/scidocs
metrics:
- type: map_at_1 value: 5.688
- type: map_at_10 value: 15.201999999999998
- type: map_at_100 value: 18.096
- type: map_at_1000 value: 18.481
- type: map_at_3 value: 10.734
- type: map_at_5 value: 12.94
- type: mrr_at_1 value: 28.000000000000004
- type: mrr_at_10 value: 41.101
- type: mrr_at_100 value: 42.202
- type: mrr_at_1000 value: 42.228
- type: mrr_at_3 value: 37.683
- type: mrr_at_5 value: 39.708
- type: ndcg_at_1 value: 28.000000000000004
- type: ndcg_at_10 value: 24.976000000000003
- type: ndcg_at_100 value: 35.129
- type: ndcg_at_1000 value: 40.77
- type: ndcg_at_3 value: 23.787
- type: ndcg_at_5 value: 20.816000000000003
- type: precision_at_1 value: 28.000000000000004
- type: precision_at_10 value: 13.04
- type: precision_at_100 value: 2.761
- type: precision_at_1000 value: 0.41000000000000003
- type: precision_at_3 value: 22.6
- type: precision_at_5 value: 18.52
- type: recall_at_1 value: 5.688
- type: recall_at_10 value: 26.43
- type: recall_at_100 value: 56.02
- type: recall_at_1000 value: 83.21
- type: recall_at_3 value: 13.752
- type: recall_at_5 value: 18.777 task: type: Retrieval
- dataset:
config: default
name: MTEB SICK-R
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
split: test
type: mteb/sickr-sts
metrics:
- type: cos_sim_pearson value: 85.15084859283178
- type: cos_sim_spearman value: 80.49030614009419
- type: euclidean_pearson value: 81.84574978672468
- type: euclidean_spearman value: 79.89787150656818
- type: manhattan_pearson value: 81.63076538567131
- type: manhattan_spearman value: 79.69867352121841 task: type: STS
- dataset:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cos_sim_pearson value: 84.64097921490992
- type: cos_sim_spearman value: 77.25370084896514
- type: euclidean_pearson value: 82.71210826468788
- type: euclidean_spearman value: 78.50445584994826
- type: manhattan_pearson value: 82.92580164330298
- type: manhattan_spearman value: 78.69686891301019 task: type: STS
- dataset:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cos_sim_pearson value: 87.24596417308994
- type: cos_sim_spearman value: 87.79454220555091
- type: euclidean_pearson value: 87.40242561671164
- type: euclidean_spearman value: 88.25955597373556
- type: manhattan_pearson value: 87.25160240485849
- type: manhattan_spearman value: 88.155794979818 task: type: STS
- dataset:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cos_sim_pearson value: 84.44914233422564
- type: cos_sim_spearman value: 82.91015471820322
- type: euclidean_pearson value: 84.7206656630327
- type: euclidean_spearman value: 83.86408872059216
- type: manhattan_pearson value: 84.72816725158454
- type: manhattan_spearman value: 84.01603388572788 task: type: STS
- dataset:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cos_sim_pearson value: 87.6168026237477
- type: cos_sim_spearman value: 88.45414278092397
- type: euclidean_pearson value: 88.57023240882022
- type: euclidean_spearman value: 89.04102190922094
- type: manhattan_pearson value: 88.66695535796354
- type: manhattan_spearman value: 89.19898476680969 task: type: STS
- dataset:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cos_sim_pearson value: 84.27925826089424
- type: cos_sim_spearman value: 85.45291099550461
- type: euclidean_pearson value: 83.63853036580834
- type: euclidean_spearman value: 84.33468035821484
- type: manhattan_pearson value: 83.72778773251596
- type: manhattan_spearman value: 84.51583132445376 task: type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson value: 89.67375185692552
- type: cos_sim_spearman value: 90.32542469203855
- type: euclidean_pearson value: 89.63513717951847
- type: euclidean_spearman value: 89.87760271003745
- type: manhattan_pearson value: 89.28381452982924
- type: manhattan_spearman value: 89.53568197785721 task: type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson value: 66.24644693819846
- type: cos_sim_spearman value: 66.09889420525377
- type: euclidean_pearson value: 63.72551583520747
- type: euclidean_spearman value: 63.01385470780679
- type: manhattan_pearson value: 64.09258157214097
- type: manhattan_spearman value: 63.080517752822594 task: type: STS
- dataset:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cos_sim_pearson value: 86.27321463839989
- type: cos_sim_spearman value: 86.37572865993327
- type: euclidean_pearson value: 86.36268020198149
- type: euclidean_spearman value: 86.31089339478922
- type: manhattan_pearson value: 86.4260445761947
- type: manhattan_spearman value: 86.45885895320457 task: type: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map value: 86.52456702387798
- type: mrr value: 96.34556529164372 task: type: Reranking
- dataset:
config: default
name: MTEB SciFact
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: map_at_1 value: 61.99400000000001
- type: map_at_10 value: 73.38799999999999
- type: map_at_100 value: 73.747
- type: map_at_1000 value: 73.75
- type: map_at_3 value: 70.04599999999999
- type: map_at_5 value: 72.095
- type: mrr_at_1 value: 65.0
- type: mrr_at_10 value: 74.42800000000001
- type: mrr_at_100 value: 74.722
- type: mrr_at_1000 value: 74.725
- type: mrr_at_3 value: 72.056
- type: mrr_at_5 value: 73.60600000000001
- type: ndcg_at_1 value: 65.0
- type: ndcg_at_10 value: 78.435
- type: ndcg_at_100 value: 79.922
- type: ndcg_at_1000 value: 80.00500000000001
- type: ndcg_at_3 value: 73.05199999999999
- type: ndcg_at_5 value: 75.98
- type: precision_at_1 value: 65.0
- type: precision_at_10 value: 10.5
- type: precision_at_100 value: 1.123
- type: precision_at_1000 value: 0.11299999999999999
- type: precision_at_3 value: 28.555999999999997
- type: precision_at_5 value: 19.0
- type: recall_at_1 value: 61.99400000000001
- type: recall_at_10 value: 92.72200000000001
- type: recall_at_100 value: 99.333
- type: recall_at_1000 value: 100.0
- type: recall_at_3 value: 78.739
- type: recall_at_5 value: 85.828 task: type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cos_sim_accuracy value: 99.79009900990098
- type: cos_sim_ap value: 95.3203137438653
- type: cos_sim_f1 value: 89.12386706948641
- type: cos_sim_precision value: 89.75659229208925
- type: cos_sim_recall value: 88.5
- type: dot_accuracy value: 99.67821782178218
- type: dot_ap value: 89.94069840000675
- type: dot_f1 value: 83.45902463549521
- type: dot_precision value: 83.9231547017189
- type: dot_recall value: 83.0
- type: euclidean_accuracy value: 99.78613861386138
- type: euclidean_ap value: 95.10648259135526
- type: euclidean_f1 value: 88.77338877338877
- type: euclidean_precision value: 92.42424242424242
- type: euclidean_recall value: 85.39999999999999
- type: manhattan_accuracy value: 99.7950495049505
- type: manhattan_ap value: 95.29987661320946
- type: manhattan_f1 value: 89.21313183949972
- type: manhattan_precision value: 93.14472252448314
- type: manhattan_recall value: 85.6
- type: max_accuracy value: 99.7950495049505
- type: max_ap value: 95.3203137438653
- type: max_f1 value: 89.21313183949972 task: type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure value: 67.65446577183913 task: type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure value: 46.30749237193961 task: type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map value: 54.91481849959949
- type: mrr value: 55.853506175197346 task: type: Reranking
- dataset:
config: default
name: MTEB SummEval
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cos_sim_pearson value: 30.08196549170419
- type: cos_sim_spearman value: 31.16661390597077
- type: dot_pearson value: 29.892258410943466
- type: dot_spearman value: 30.51328811965085 task: type: Summarization
- dataset:
config: default
name: MTEB TRECCOVID
revision: None
split: test
type: mteb/trec-covid
metrics:
- type: map_at_1 value: 0.23900000000000002
- type: map_at_10 value: 2.173
- type: map_at_100 value: 14.24
- type: map_at_1000 value: 35.309000000000005
- type: map_at_3 value: 0.7100000000000001
- type: map_at_5 value: 1.163
- type: mrr_at_1 value: 92.0
- type: mrr_at_10 value: 96.0
- type: mrr_at_100 value: 96.0
- type: mrr_at_1000 value: 96.0
- type: mrr_at_3 value: 96.0
- type: mrr_at_5 value: 96.0
- type: ndcg_at_1 value: 90.0
- type: ndcg_at_10 value: 85.382
- type: ndcg_at_100 value: 68.03
- type: ndcg_at_1000 value: 61.021
- type: ndcg_at_3 value: 89.765
- type: ndcg_at_5 value: 88.444
- type: precision_at_1 value: 92.0
- type: precision_at_10 value: 88.0
- type: precision_at_100 value: 70.02000000000001
- type: precision_at_1000 value: 26.984
- type: precision_at_3 value: 94.0
- type: precision_at_5 value: 92.80000000000001
- type: recall_at_1 value: 0.23900000000000002
- type: recall_at_10 value: 2.313
- type: recall_at_100 value: 17.049
- type: recall_at_1000 value: 57.489999999999995
- type: recall_at_3 value: 0.737
- type: recall_at_5 value: 1.221 task: type: Retrieval
- dataset:
config: default
name: MTEB Touche2020
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
metrics:
- type: map_at_1 value: 2.75
- type: map_at_10 value: 11.29
- type: map_at_100 value: 18.032999999999998
- type: map_at_1000 value: 19.746
- type: map_at_3 value: 6.555
- type: map_at_5 value: 8.706999999999999
- type: mrr_at_1 value: 34.694
- type: mrr_at_10 value: 50.55
- type: mrr_at_100 value: 51.659
- type: mrr_at_1000 value: 51.659
- type: mrr_at_3 value: 47.278999999999996
- type: mrr_at_5 value: 49.728
- type: ndcg_at_1 value: 32.653
- type: ndcg_at_10 value: 27.894000000000002
- type: ndcg_at_100 value: 39.769
- type: ndcg_at_1000 value: 51.495999999999995
- type: ndcg_at_3 value: 32.954
- type: ndcg_at_5 value: 31.502999999999997
- type: precision_at_1 value: 34.694
- type: precision_at_10 value: 23.265
- type: precision_at_100 value: 7.898
- type: precision_at_1000 value: 1.58
- type: precision_at_3 value: 34.694
- type: precision_at_5 value: 31.429000000000002
- type: recall_at_1 value: 2.75
- type: recall_at_10 value: 16.953
- type: recall_at_100 value: 48.68
- type: recall_at_1000 value: 85.18599999999999
- type: recall_at_3 value: 7.710999999999999
- type: recall_at_5 value: 11.484 task: type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy value: 82.66099999999999
- type: ap value: 25.555698090238337
- type: f1 value: 66.48402012461622 task: type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy value: 72.94567062818335
- type: f1 value: 73.28139189595674 task: type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure value: 49.581627240203474 task: type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cos_sim_accuracy value: 87.78089050485785
- type: cos_sim_ap value: 79.64487116574168
- type: cos_sim_f1 value: 72.46563021970964
- type: cos_sim_precision value: 70.62359128474831
- type: cos_sim_recall value: 74.40633245382587
- type: dot_accuracy value: 86.2609524944865
- type: dot_ap value: 75.513046857613
- type: dot_f1 value: 68.58213616489695
- type: dot_precision value: 65.12455516014235
- type: dot_recall value: 72.42744063324538
- type: euclidean_accuracy value: 87.6080348095607
- type: euclidean_ap value: 79.00204933649795
- type: euclidean_f1 value: 72.14495342605589
- type: euclidean_precision value: 69.85421299728193
- type: euclidean_recall value: 74.5910290237467
- type: manhattan_accuracy value: 87.59611372712642
- type: manhattan_ap value: 78.78523756706264
- type: manhattan_f1 value: 71.86499137718648
- type: manhattan_precision value: 67.39833641404806
- type: manhattan_recall value: 76.96569920844327
- type: max_accuracy value: 87.78089050485785
- type: max_ap value: 79.64487116574168
- type: max_f1 value: 72.46563021970964 task: type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cos_sim_accuracy value: 89.98719292117825
- type: cos_sim_ap value: 87.58146137353202
- type: cos_sim_f1 value: 80.28543232369239
- type: cos_sim_precision value: 79.1735289714029
- type: cos_sim_recall value: 81.42901139513397
- type: dot_accuracy value: 88.9199363526992
- type: dot_ap value: 84.98499998630417
- type: dot_f1 value: 78.21951400757969
- type: dot_precision value: 75.58523624874336
- type: dot_recall value: 81.04404065291038
- type: euclidean_accuracy value: 89.77374160748244
- type: euclidean_ap value: 87.35151562835209
- type: euclidean_f1 value: 79.92160922940393
- type: euclidean_precision value: 76.88531587933979
- type: euclidean_recall value: 83.20757622420696
- type: manhattan_accuracy value: 89.72717041176699
- type: manhattan_ap value: 87.34065592142515
- type: manhattan_f1 value: 79.85603419187943
- type: manhattan_precision value: 77.82243332115455
- type: manhattan_recall value: 81.99876809362489
- type: max_accuracy value: 89.98719292117825
- type: max_ap value: 87.58146137353202
- type: max_f1 value: 80.28543232369239 task: type: PairClassification
- dataset:
config: default
name: MTEB AFQMC
revision: b44c3b011063adb25877c13823db83bb193913c4
split: validation
type: C-MTEB/AFQMC
metrics:
- type: cos_sim_pearson value: 53.45954203592337
- type: cos_sim_spearman value: 58.42154680418638
- type: euclidean_pearson value: 56.41543791722753
- type: euclidean_spearman value: 58.39328016640146
- type: manhattan_pearson value: 56.318510356833876
- type: manhattan_spearman value: 58.28423447818184 task: type: STS
- dataset:
config: default
name: MTEB ATEC
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
split: test
type: C-MTEB/ATEC
metrics:
- type: cos_sim_pearson value: 50.78356460675945
- type: cos_sim_spearman value: 55.6530411663269
- type: euclidean_pearson value: 56.50763660417816
- type: euclidean_spearman value: 55.733823335669065
- type: manhattan_pearson value: 56.45323093512866
- type: manhattan_spearman value: 55.63248619032702 task: type: STS
- dataset:
config: zh
name: MTEB AmazonReviewsClassification (zh)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy value: 47.209999999999994
- type: f1 value: 46.08892432018655 task: type: Classification
- dataset:
config: default
name: MTEB BQ
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
split: test
type: C-MTEB/BQ
metrics:
- type: cos_sim_pearson value: 70.25573992001478
- type: cos_sim_spearman value: 73.85247134951433
- type: euclidean_pearson value: 72.60033082168442
- type: euclidean_spearman value: 73.72445893756499
- type: manhattan_pearson value: 72.59932284620231
- type: manhattan_spearman value: 73.68002490614583 task: type: STS
- dataset:
config: default
name: MTEB CLSClusteringP2P
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
split: test
type: C-MTEB/CLSClusteringP2P
metrics:
- type: v_measure value: 45.21317724305628 task: type: Clustering
- dataset:
config: default
name: MTEB CLSClusteringS2S
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
split: test
type: C-MTEB/CLSClusteringS2S
metrics:
- type: v_measure value: 42.49825170976724 task: type: Clustering
- dataset:
config: default
name: MTEB CMedQAv1
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
split: test
type: C-MTEB/CMedQAv1-reranking
metrics:
- type: map value: 88.15661686810597
- type: mrr value: 90.11222222222223 task: type: Reranking
- dataset:
config: default
name: MTEB CMedQAv2
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
split: test
type: C-MTEB/CMedQAv2-reranking
metrics:
- type: map value: 88.1204726064383
- type: mrr value: 90.20142857142858 task: type: Reranking
- dataset:
config: default
name: MTEB CmedqaRetrieval
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
split: dev
type: C-MTEB/CmedqaRetrieval
metrics:
- type: map_at_1 value: 27.224999999999998
- type: map_at_10 value: 40.169
- type: map_at_100 value: 42.0
- type: map_at_1000 value: 42.109
- type: map_at_3 value: 35.76
- type: map_at_5 value: 38.221
- type: mrr_at_1 value: 40.56
- type: mrr_at_10 value: 49.118
- type: mrr_at_100 value: 50.092999999999996
- type: mrr_at_1000 value: 50.133
- type: mrr_at_3 value: 46.507
- type: mrr_at_5 value: 47.973
- type: ndcg_at_1 value: 40.56
- type: ndcg_at_10 value: 46.972
- type: ndcg_at_100 value: 54.04
- type: ndcg_at_1000 value: 55.862
- type: ndcg_at_3 value: 41.36
- type: ndcg_at_5 value: 43.704
- type: precision_at_1 value: 40.56
- type: precision_at_10 value: 10.302999999999999
- type: precision_at_100 value: 1.606
- type: precision_at_1000 value: 0.184
- type: precision_at_3 value: 23.064
- type: precision_at_5 value: 16.764000000000003
- type: recall_at_1 value: 27.224999999999998
- type: recall_at_10 value: 58.05200000000001
- type: recall_at_100 value: 87.092
- type: recall_at_1000 value: 99.099
- type: recall_at_3 value: 41.373
- type: recall_at_5 value: 48.453 task: type: Retrieval
- dataset:
config: default
name: MTEB Cmnli
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
split: validation
type: C-MTEB/CMNLI
metrics:
- type: cos_sim_accuracy value: 77.40228502705953
- type: cos_sim_ap value: 86.22359172956327
- type: cos_sim_f1 value: 78.96328293736501
- type: cos_sim_precision value: 73.36945615091311
- type: cos_sim_recall value: 85.48047696983868
- type: dot_accuracy value: 75.53818400481059
- type: dot_ap value: 83.70164011305312
- type: dot_f1 value: 77.67298719348754
- type: dot_precision value: 67.49482401656314
- type: dot_recall value: 91.46598082768296
- type: euclidean_accuracy value: 77.94347564642213
- type: euclidean_ap value: 86.4652108728609
- type: euclidean_f1 value: 79.15555555555555
- type: euclidean_precision value: 75.41816641964853
- type: euclidean_recall value: 83.28267477203647
- type: manhattan_accuracy value: 77.45039085989175
- type: manhattan_ap value: 86.09986583900665
- type: manhattan_f1 value: 78.93669264438988
- type: manhattan_precision value: 72.63261296660117
- type: manhattan_recall value: 86.43909282207154
- type: max_accuracy value: 77.94347564642213
- type: max_ap value: 86.4652108728609
- type: max_f1 value: 79.15555555555555 task: type: PairClassification
- dataset:
config: default
name: MTEB CovidRetrieval
revision: 1271c7809071a13532e05f25fb53511ffce77117
split: dev
type: C-MTEB/CovidRetrieval
metrics:
- type: map_at_1 value: 69.336
- type: map_at_10 value: 77.16
- type: map_at_100 value: 77.47500000000001
- type: map_at_1000 value: 77.482
- type: map_at_3 value: 75.42999999999999
- type: map_at_5 value: 76.468
- type: mrr_at_1 value: 69.44200000000001
- type: mrr_at_10 value: 77.132
- type: mrr_at_100 value: 77.43299999999999
- type: mrr_at_1000 value: 77.44
- type: mrr_at_3 value: 75.395
- type: mrr_at_5 value: 76.459
- type: ndcg_at_1 value: 69.547
- type: ndcg_at_10 value: 80.794
- type: ndcg_at_100 value: 82.245
- type: ndcg_at_1000 value: 82.40899999999999
- type: ndcg_at_3 value: 77.303
- type: ndcg_at_5 value: 79.168
- type: precision_at_1 value: 69.547
- type: precision_at_10 value: 9.305
- type: precision_at_100 value: 0.9979999999999999
- type: precision_at_1000 value: 0.101
- type: precision_at_3 value: 27.749000000000002
- type: precision_at_5 value: 17.576
- type: recall_at_1 value: 69.336
- type: recall_at_10 value: 92.097
- type: recall_at_100 value: 98.736
- type: recall_at_1000 value: 100.0
- type: recall_at_3 value: 82.64
- type: recall_at_5 value: 87.144 task: type: Retrieval
- dataset:
config: default
name: MTEB DuRetrieval
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
split: dev
type: C-MTEB/DuRetrieval
metrics:
- type: map_at_1 value: 26.817999999999998
- type: map_at_10 value: 82.67
- type: map_at_100 value: 85.304
- type: map_at_1000 value: 85.334
- type: map_at_3 value: 57.336
- type: map_at_5 value: 72.474
- type: mrr_at_1 value: 91.45
- type: mrr_at_10 value: 94.272
- type: mrr_at_100 value: 94.318
- type: mrr_at_1000 value: 94.32000000000001
- type: mrr_at_3 value: 94.0
- type: mrr_at_5 value: 94.17699999999999
- type: ndcg_at_1 value: 91.45
- type: ndcg_at_10 value: 89.404
- type: ndcg_at_100 value: 91.724
- type: ndcg_at_1000 value: 91.973
- type: ndcg_at_3 value: 88.104
- type: ndcg_at_5 value: 87.25699999999999
- type: precision_at_1 value: 91.45
- type: precision_at_10 value: 42.585
- type: precision_at_100 value: 4.838
- type: precision_at_1000 value: 0.49
- type: precision_at_3 value: 78.8
- type: precision_at_5 value: 66.66
- type: recall_at_1 value: 26.817999999999998
- type: recall_at_10 value: 90.67
- type: recall_at_100 value: 98.36200000000001
- type: recall_at_1000 value: 99.583
- type: recall_at_3 value: 59.614999999999995
- type: recall_at_5 value: 77.05199999999999 task: type: Retrieval
- dataset:
config: default
name: MTEB EcomRetrieval
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
split: dev
type: C-MTEB/EcomRetrieval
metrics:
- type: map_at_1 value: 47.699999999999996
- type: map_at_10 value: 57.589999999999996
- type: map_at_100 value: 58.226
- type: map_at_1000 value: 58.251
- type: map_at_3 value: 55.233
- type: map_at_5 value: 56.633
- type: mrr_at_1 value: 47.699999999999996
- type: mrr_at_10 value: 57.589999999999996
- type: mrr_at_100 value: 58.226
- type: mrr_at_1000 value: 58.251
- type: mrr_at_3 value: 55.233
- type: mrr_at_5 value: 56.633
- type: ndcg_at_1 value: 47.699999999999996
- type: ndcg_at_10 value: 62.505
- type: ndcg_at_100 value: 65.517
- type: ndcg_at_1000 value: 66.19800000000001
- type: ndcg_at_3 value: 57.643
- type: ndcg_at_5 value: 60.181
- type: precision_at_1 value: 47.699999999999996
- type: precision_at_10 value: 7.8
- type: precision_at_100 value: 0.919
- type: precision_at_1000 value: 0.097
- type: precision_at_3 value: 21.532999999999998
- type: precision_at_5 value: 14.16
- type: recall_at_1 value: 47.699999999999996
- type: recall_at_10 value: 78.0
- type: recall_at_100 value: 91.9
- type: recall_at_1000 value: 97.3
- type: recall_at_3 value: 64.60000000000001
- type: recall_at_5 value: 70.8 task: type: Retrieval
- dataset:
config: default
name: MTEB IFlyTek
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
split: validation
type: C-MTEB/IFlyTek-classification
metrics:
- type: accuracy value: 44.84801846864178
- type: f1 value: 37.47347897956339 task: type: Classification
- dataset:
config: default
name: MTEB JDReview
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
split: test
type: C-MTEB/JDReview-classification
metrics:
- type: accuracy value: 85.81613508442777
- type: ap value: 52.68244615477374
- type: f1 value: 80.0445640948843 task: type: Classification
- dataset:
config: default
name: MTEB LCQMC
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
split: test
type: C-MTEB/LCQMC
metrics:
- type: cos_sim_pearson value: 69.57786502217138
- type: cos_sim_spearman value: 75.39106054489906
- type: euclidean_pearson value: 73.72082954602402
- type: euclidean_spearman value: 75.14421475913619
- type: manhattan_pearson value: 73.62463076633642
- type: manhattan_spearman value: 75.01301565104112 task: type: STS
- dataset:
config: default
name: MTEB MMarcoReranking
revision: None
split: dev
type: C-MTEB/Mmarco-reranking
metrics:
- type: map value: 29.143797057999134
- type: mrr value: 28.08174603174603 task: type: Reranking
- dataset:
config: default
name: MTEB MMarcoRetrieval
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
split: dev
type: C-MTEB/MMarcoRetrieval
metrics:
- type: map_at_1 value: 70.492
- type: map_at_10 value: 79.501
- type: map_at_100 value: 79.728
- type: map_at_1000 value: 79.735
- type: map_at_3 value: 77.77
- type: map_at_5 value: 78.851
- type: mrr_at_1 value: 72.822
- type: mrr_at_10 value: 80.001
- type: mrr_at_100 value: 80.19
- type: mrr_at_1000 value: 80.197
- type: mrr_at_3 value: 78.484
- type: mrr_at_5 value: 79.42099999999999
- type: ndcg_at_1 value: 72.822
- type: ndcg_at_10 value: 83.013
- type: ndcg_at_100 value: 84.013
- type: ndcg_at_1000 value: 84.20400000000001
- type: ndcg_at_3 value: 79.728
- type: ndcg_at_5 value: 81.542
- type: precision_at_1 value: 72.822
- type: precision_at_10 value: 9.917
- type: precision_at_100 value: 1.042
- type: precision_at_1000 value: 0.106
- type: precision_at_3 value: 29.847
- type: precision_at_5 value: 18.871
- type: recall_at_1 value: 70.492
- type: recall_at_10 value: 93.325
- type: recall_at_100 value: 97.822
- type: recall_at_1000 value: 99.319
- type: recall_at_3 value: 84.636
- type: recall_at_5 value: 88.93100000000001 task: type: Retrieval
- dataset:
config: zh-CN
name: MTEB MassiveIntentClassification (zh-CN)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy value: 76.88298587760592
- type: f1 value: 73.89001762017176 task: type: Classification
- dataset:
config: zh-CN
name: MTEB MassiveScenarioClassification (zh-CN)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy value: 80.76328177538669
- type: f1 value: 80.24718532423358 task: type: Classification
- dataset:
config: default
name: MTEB MedicalRetrieval
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
split: dev
type: C-MTEB/MedicalRetrieval
metrics:
- type: map_at_1 value: 49.6
- type: map_at_10 value: 55.620999999999995
- type: map_at_100 value: 56.204
- type: map_at_1000 value: 56.251
- type: map_at_3 value: 54.132999999999996
- type: map_at_5 value: 54.933
- type: mrr_at_1 value: 49.7
- type: mrr_at_10 value: 55.67100000000001
- type: mrr_at_100 value: 56.254000000000005
- type: mrr_at_1000 value: 56.301
- type: mrr_at_3 value: 54.18300000000001
- type: mrr_at_5 value: 54.983000000000004
- type: ndcg_at_1 value: 49.6
- type: ndcg_at_10 value: 58.645
- type: ndcg_at_100 value: 61.789
- type: ndcg_at_1000 value: 63.219
- type: ndcg_at_3 value: 55.567
- type: ndcg_at_5 value: 57.008
- type: precision_at_1 value: 49.6
- type: precision_at_10 value: 6.819999999999999
- type: precision_at_100 value: 0.836
- type: precision_at_1000 value: 0.095
- type: precision_at_3 value: 19.900000000000002
- type: precision_at_5 value: 12.64
- type: recall_at_1 value: 49.6
- type: recall_at_10 value: 68.2
- type: recall_at_100 value: 83.6
- type: recall_at_1000 value: 95.3
- type: recall_at_3 value: 59.699999999999996
- type: recall_at_5 value: 63.2 task: type: Retrieval
- dataset:
config: default
name: MTEB MultilingualSentiment
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
split: validation
type: C-MTEB/MultilingualSentiment-classification
metrics:
- type: accuracy value: 74.45666666666666
- type: f1 value: 74.32582402190089 task: type: Classification
- dataset:
config: default
name: MTEB Ocnli
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
split: validation
type: C-MTEB/OCNLI
metrics:
- type: cos_sim_accuracy value: 80.67135896047645
- type: cos_sim_ap value: 87.60421240712051
- type: cos_sim_f1 value: 82.1304131408661
- type: cos_sim_precision value: 77.68361581920904
- type: cos_sim_recall value: 87.11721224920802
- type: dot_accuracy value: 79.04710341093666
- type: dot_ap value: 85.6370059719336
- type: dot_f1 value: 80.763723150358
- type: dot_precision value: 73.69337979094077
- type: dot_recall value: 89.33474128827878
- type: euclidean_accuracy value: 81.05035192203573
- type: euclidean_ap value: 87.7880240053663
- type: euclidean_f1 value: 82.50244379276637
- type: euclidean_precision value: 76.7970882620564
- type: euclidean_recall value: 89.1235480464625
- type: manhattan_accuracy value: 80.61721710882512
- type: manhattan_ap value: 87.43568120591175
- type: manhattan_f1 value: 81.89526184538653
- type: manhattan_precision value: 77.5992438563327
- type: manhattan_recall value: 86.6948257655755
- type: max_accuracy value: 81.05035192203573
- type: max_ap value: 87.7880240053663
- type: max_f1 value: 82.50244379276637 task: type: PairClassification
- dataset:
config: default
name: MTEB OnlineShopping
revision: e610f2ebd179a8fda30ae534c3878750a96db120
split: test
type: C-MTEB/OnlineShopping-classification
metrics:
- type: accuracy value: 93.5
- type: ap value: 91.31357903446782
- type: f1 value: 93.48088994006616 task: type: Classification
- dataset:
config: default
name: MTEB PAWSX
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
split: test
type: C-MTEB/PAWSX
metrics:
- type: cos_sim_pearson value: 36.93293453538077
- type: cos_sim_spearman value: 42.45972506308574
- type: euclidean_pearson value: 42.34945133152159
- type: euclidean_spearman value: 42.331610303674644
- type: manhattan_pearson value: 42.31455070249498
- type: manhattan_spearman value: 42.19887982891834 task: type: STS
- dataset:
config: default
name: MTEB QBQTC
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
split: test
type: C-MTEB/QBQTC
metrics:
- type: cos_sim_pearson value: 33.683290790043785
- type: cos_sim_spearman value: 35.149171171202994
- type: euclidean_pearson value: 32.33806561267862
- type: euclidean_spearman value: 34.483576387347966
- type: manhattan_pearson value: 32.47629754599608
- type: manhattan_spearman value: 34.66434471867615 task: type: STS
- dataset:
config: zh
name: MTEB STS22 (zh)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson value: 66.46322760516104
- type: cos_sim_spearman value: 67.398478319726
- type: euclidean_pearson value: 64.7223480293625
- type: euclidean_spearman value: 66.83118568812951
- type: manhattan_pearson value: 64.88440039828305
- type: manhattan_spearman value: 66.80429458952257 task: type: STS
- dataset:
config: default
name: MTEB STSB
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
split: test
type: C-MTEB/STSB
metrics:
- type: cos_sim_pearson value: 79.08991383232105
- type: cos_sim_spearman value: 79.39715677296854
- type: euclidean_pearson value: 78.63201279320496
- type: euclidean_spearman value: 79.40262660785731
- type: manhattan_pearson value: 78.98138363146906
- type: manhattan_spearman value: 79.79968413014194 task: type: STS
- dataset:
config: default
name: MTEB T2Reranking
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
split: dev
type: C-MTEB/T2Reranking
metrics:
- type: map value: 67.43289278789972
- type: mrr value: 77.53012460908535 task: type: Reranking
- dataset:
config: default
name: MTEB T2Retrieval
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
split: dev
type: C-MTEB/T2Retrieval
metrics:
- type: map_at_1 value: 27.733999999999998
- type: map_at_10 value: 78.24799999999999
- type: map_at_100 value: 81.765
- type: map_at_1000 value: 81.824
- type: map_at_3 value: 54.92
- type: map_at_5 value: 67.61399999999999
- type: mrr_at_1 value: 90.527
- type: mrr_at_10 value: 92.843
- type: mrr_at_100 value: 92.927
- type: mrr_at_1000 value: 92.93
- type: mrr_at_3 value: 92.45100000000001
- type: mrr_at_5 value: 92.693
- type: ndcg_at_1 value: 90.527
- type: ndcg_at_10 value: 85.466
- type: ndcg_at_100 value: 88.846
- type: ndcg_at_1000 value: 89.415
- type: ndcg_at_3 value: 86.768
- type: ndcg_at_5 value: 85.46000000000001
- type: precision_at_1 value: 90.527
- type: precision_at_10 value: 42.488
- type: precision_at_100 value: 5.024
- type: precision_at_1000 value: 0.516
- type: precision_at_3 value: 75.907
- type: precision_at_5 value: 63.727000000000004
- type: recall_at_1 value: 27.733999999999998
- type: recall_at_10 value: 84.346
- type: recall_at_100 value: 95.536
- type: recall_at_1000 value: 98.42999999999999
- type: recall_at_3 value: 56.455
- type: recall_at_5 value: 70.755 task: type: Retrieval
- dataset:
config: default
name: MTEB TNews
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
split: validation
type: C-MTEB/TNews-classification
metrics:
- type: accuracy value: 49.952000000000005
- type: f1 value: 48.264617195258054 task: type: Classification
- dataset:
config: default
name: MTEB ThuNewsClusteringP2P
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
split: test
type: C-MTEB/ThuNewsClusteringP2P
metrics:
- type: v_measure value: 68.23769904483508 task: type: Clustering
- dataset:
config: default
name: MTEB ThuNewsClusteringS2S
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
split: test
type: C-MTEB/ThuNewsClusteringS2S
metrics:
- type: v_measure value: 62.50294403136556 task: type: Clustering
- dataset:
config: default
name: MTEB VideoRetrieval
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
split: dev
type: C-MTEB/VideoRetrieval
metrics:
- type: map_at_1 value: 54.0
- type: map_at_10 value: 63.668
- type: map_at_100 value: 64.217
- type: map_at_1000 value: 64.23100000000001
- type: map_at_3 value: 61.7
- type: map_at_5 value: 62.870000000000005
- type: mrr_at_1 value: 54.0
- type: mrr_at_10 value: 63.668
- type: mrr_at_100 value: 64.217
- type: mrr_at_1000 value: 64.23100000000001
- type: mrr_at_3 value: 61.7
- type: mrr_at_5 value: 62.870000000000005
- type: ndcg_at_1 value: 54.0
- type: ndcg_at_10 value: 68.11399999999999
- type: ndcg_at_100 value: 70.723
- type: ndcg_at_1000 value: 71.123
- type: ndcg_at_3 value: 64.074
- type: ndcg_at_5 value: 66.178
- type: precision_at_1 value: 54.0
- type: precision_at_10 value: 8.200000000000001
- type: precision_at_100 value: 0.941
- type: precision_at_1000 value: 0.097
- type: precision_at_3 value: 23.633000000000003
- type: precision_at_5 value: 15.2
- type: recall_at_1 value: 54.0
- type: recall_at_10 value: 82.0
- type: recall_at_100 value: 94.1
- type: recall_at_1000 value: 97.3
- type: recall_at_3 value: 70.89999999999999
- type: recall_at_5 value: 76.0 task: type: Retrieval
- dataset:
config: default
name: MTEB Waimai
revision: 339287def212450dcaa9df8c22bf93e9980c7023
split: test
type: C-MTEB/waimai-classification
metrics:
- type: accuracy value: 86.63000000000001
- type: ap value: 69.99457882599567
- type: f1 value: 85.07735617998541 task: type: Classification
- dataset:
config: default
name: MTEB 8TagsClustering
revision: None
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure value: 44.594104491193555 task: type: Clustering
- dataset:
config: default
name: MTEB AllegroReviews
revision: None
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy value: 63.97614314115309
- type: f1 value: 52.15634261679283 task: type: Classification
- dataset:
config: default
name: MTEB ArguAna-PL
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
split: test
type: clarin-knext/arguana-pl
metrics:
- type: map_at_1 value: 32.646
- type: map_at_10 value: 47.963
- type: map_at_100 value: 48.789
- type: map_at_1000 value: 48.797000000000004
- type: map_at_3 value: 43.196
- type: map_at_5 value: 46.016
- type: mrr_at_1 value: 33.073
- type: mrr_at_10 value: 48.126000000000005
- type: mrr_at_100 value: 48.946
- type: mrr_at_1000 value: 48.953
- type: mrr_at_3 value: 43.374
- type: mrr_at_5 value: 46.147
- type: ndcg_at_1 value: 32.646
- type: ndcg_at_10 value: 56.481
- type: ndcg_at_100 value: 59.922
- type: ndcg_at_1000 value: 60.07
- type: ndcg_at_3 value: 46.675
- type: ndcg_at_5 value: 51.76500000000001
- type: precision_at_1 value: 32.646
- type: precision_at_10 value: 8.371
- type: precision_at_100 value: 0.9860000000000001
- type: precision_at_1000 value: 0.1
- type: precision_at_3 value: 18.919
- type: precision_at_5 value: 13.825999999999999
- type: recall_at_1 value: 32.646
- type: recall_at_10 value: 83.71300000000001
- type: recall_at_100 value: 98.578
- type: recall_at_1000 value: 99.644
- type: recall_at_3 value: 56.757000000000005
- type: recall_at_5 value: 69.132 task: type: Retrieval
- dataset:
config: default
name: MTEB CBD
revision: None
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy value: 68.56
- type: ap value: 23.310493680488513
- type: f1 value: 58.85369533105693 task: type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: None
split: test
type: PL-MTEB/cdsce-pairclassification
metrics:
- type: cos_sim_accuracy value: 88.5
- type: cos_sim_ap value: 72.42140924378361
- type: cos_sim_f1 value: 66.0919540229885
- type: cos_sim_precision value: 72.78481012658227
- type: cos_sim_recall value: 60.526315789473685
- type: dot_accuracy value: 88.5
- type: dot_ap value: 72.42140924378361
- type: dot_f1 value: 66.0919540229885
- type: dot_precision value: 72.78481012658227
- type: dot_recall value: 60.526315789473685
- type: euclidean_accuracy value: 88.5
- type: euclidean_ap value: 72.42140924378361
- type: euclidean_f1 value: 66.0919540229885
- type: euclidean_precision value: 72.78481012658227
- type: euclidean_recall value: 60.526315789473685
- type: manhattan_accuracy value: 88.5
- type: manhattan_ap value: 72.49745515311696
- type: manhattan_f1 value: 66.0968660968661
- type: manhattan_precision value: 72.04968944099379
- type: manhattan_recall value: 61.05263157894737
- type: max_accuracy value: 88.5
- type: max_ap value: 72.49745515311696
- type: max_f1 value: 66.0968660968661 task: type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: None
split: test
type: PL-MTEB/cdscr-sts
metrics:
- type: cos_sim_pearson value: 90.32269765590145
- type: cos_sim_spearman value: 89.73666311491672
- type: euclidean_pearson value: 88.2933868516544
- type: euclidean_spearman value: 89.73666311491672
- type: manhattan_pearson value: 88.33474590219448
- type: manhattan_spearman value: 89.8548364866583 task: type: STS
- dataset:
config: default
name: MTEB DBPedia-PL
revision: 76afe41d9af165cc40999fcaa92312b8b012064a
split: test
type: clarin-knext/dbpedia-pl
metrics:
- type: map_at_1 value: 7.632999999999999
- type: map_at_10 value: 16.426
- type: map_at_100 value: 22.651
- type: map_at_1000 value: 24.372
- type: map_at_3 value: 11.706
- type: map_at_5 value: 13.529
- type: mrr_at_1 value: 60.75000000000001
- type: mrr_at_10 value: 68.613
- type: mrr_at_100 value: 69.001
- type: mrr_at_1000 value: 69.021
- type: mrr_at_3 value: 67.0
- type: mrr_at_5 value: 67.925
- type: ndcg_at_1 value: 49.875
- type: ndcg_at_10 value: 36.978
- type: ndcg_at_100 value: 40.031
- type: ndcg_at_1000 value: 47.566
- type: ndcg_at_3 value: 41.148
- type: ndcg_at_5 value: 38.702
- type: precision_at_1 value: 60.75000000000001
- type: precision_at_10 value: 29.7
- type: precision_at_100 value: 9.278
- type: precision_at_1000 value: 2.099
- type: precision_at_3 value: 44.0
- type: precision_at_5 value: 37.6
- type: recall_at_1 value: 7.632999999999999
- type: recall_at_10 value: 22.040000000000003
- type: recall_at_100 value: 44.024
- type: recall_at_1000 value: 67.848
- type: recall_at_3 value: 13.093
- type: recall_at_5 value: 15.973 task: type: Retrieval
- dataset:
config: default
name: MTEB FiQA-PL
revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
split: test
type: clarin-knext/fiqa-pl
metrics:
- type: map_at_1 value: 15.473
- type: map_at_10 value: 24.579
- type: map_at_100 value: 26.387
- type: map_at_1000 value: 26.57
- type: map_at_3 value: 21.278
- type: map_at_5 value: 23.179
- type: mrr_at_1 value: 30.709999999999997
- type: mrr_at_10 value: 38.994
- type: mrr_at_100 value: 39.993
- type: mrr_at_1000 value: 40.044999999999995
- type: mrr_at_3 value: 36.342999999999996
- type: mrr_at_5 value: 37.846999999999994
- type: ndcg_at_1 value: 30.709999999999997
- type: ndcg_at_10 value: 31.608999999999998
- type: ndcg_at_100 value: 38.807
- type: ndcg_at_1000 value: 42.208
- type: ndcg_at_3 value: 28.086
- type: ndcg_at_5 value: 29.323
- type: precision_at_1 value: 30.709999999999997
- type: precision_at_10 value: 8.688
- type: precision_at_100 value: 1.608
- type: precision_at_1000 value: 0.22100000000000003
- type: precision_at_3 value: 18.724
- type: precision_at_5 value: 13.950999999999999
- type: recall_at_1 value: 15.473
- type: recall_at_10 value: 38.361000000000004
- type: recall_at_100 value: 65.2
- type: recall_at_1000 value: 85.789
- type: recall_at_3 value: 25.401
- type: recall_at_5 value: 30.875999999999998 task: type: Retrieval
- dataset:
config: default
name: MTEB HotpotQA-PL
revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907
split: test
type: clarin-knext/hotpotqa-pl
metrics:
- type: map_at_1 value: 38.096000000000004
- type: map_at_10 value: 51.44499999999999
- type: map_at_100 value: 52.325
- type: map_at_1000 value: 52.397000000000006
- type: map_at_3 value: 48.626999999999995
- type: map_at_5 value: 50.342
- type: mrr_at_1 value: 76.19200000000001
- type: mrr_at_10 value: 81.191
- type: mrr_at_100 value: 81.431
- type: mrr_at_1000 value: 81.443
- type: mrr_at_3 value: 80.30199999999999
- type: mrr_at_5 value: 80.85900000000001
- type: ndcg_at_1 value: 76.19200000000001
- type: ndcg_at_10 value: 60.9
- type: ndcg_at_100 value: 64.14699999999999
- type: ndcg_at_1000 value: 65.647
- type: ndcg_at_3 value: 56.818000000000005
- type: ndcg_at_5 value: 59.019999999999996
- type: precision_at_1 value: 76.19200000000001
- type: precision_at_10 value: 12.203
- type: precision_at_100 value: 1.478
- type: precision_at_1000 value: 0.168
- type: precision_at_3 value: 34.616
- type: precision_at_5 value: 22.515
- type: recall_at_1 value: 38.096000000000004
- type: recall_at_10 value: 61.013
- type: recall_at_100 value: 73.90299999999999
- type: recall_at_1000 value: 83.91
- type: recall_at_3 value: 51.92400000000001
- type: recall_at_5 value: 56.286 task: type: Retrieval
- dataset:
config: default
name: MTEB MSMARCO-PL
revision: 8634c07806d5cce3a6138e260e59b81760a0a640
split: test
type: clarin-knext/msmarco-pl
metrics:
- type: map_at_1 value: 1.548
- type: map_at_10 value: 11.049000000000001
- type: map_at_100 value: 28.874
- type: map_at_1000 value: 34.931
- type: map_at_3 value: 4.162
- type: map_at_5 value: 6.396
- type: mrr_at_1 value: 90.69800000000001
- type: mrr_at_10 value: 92.093
- type: mrr_at_100 value: 92.345
- type: mrr_at_1000 value: 92.345
- type: mrr_at_3 value: 91.86
- type: mrr_at_5 value: 91.86
- type: ndcg_at_1 value: 74.031
- type: ndcg_at_10 value: 63.978
- type: ndcg_at_100 value: 53.101
- type: ndcg_at_1000 value: 60.675999999999995
- type: ndcg_at_3 value: 71.421
- type: ndcg_at_5 value: 68.098
- type: precision_at_1 value: 90.69800000000001
- type: precision_at_10 value: 71.86
- type: precision_at_100 value: 31.395
- type: precision_at_1000 value: 5.981
- type: precision_at_3 value: 84.49600000000001
- type: precision_at_5 value: 79.07
- type: recall_at_1 value: 1.548
- type: recall_at_10 value: 12.149000000000001
- type: recall_at_100 value: 40.794999999999995
- type: recall_at_1000 value: 67.974
- type: recall_at_3 value: 4.244
- type: recall_at_5 value: 6.608 task: type: Retrieval
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy value: 73.55413584398119
- type: f1 value: 69.65610882318181 task: type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy value: 76.37188971082716
- type: f1 value: 75.64847309941361 task: type: Classification
- dataset:
config: default
name: MTEB NFCorpus-PL
revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
split: test
type: clarin-knext/nfcorpus-pl
metrics:
- type: map_at_1 value: 4.919
- type: map_at_10 value: 10.834000000000001
- type: map_at_100 value: 13.38
- type: map_at_1000 value: 14.581
- type: map_at_3 value: 8.198
- type: map_at_5 value: 9.428
- type: mrr_at_1 value: 41.176
- type: mrr_at_10 value: 50.083
- type: mrr_at_100 value: 50.559
- type: mrr_at_1000 value: 50.604000000000006
- type: mrr_at_3 value: 47.936
- type: mrr_at_5 value: 49.407000000000004
- type: ndcg_at_1 value: 39.628
- type: ndcg_at_10 value: 30.098000000000003
- type: ndcg_at_100 value: 27.061
- type: ndcg_at_1000 value: 35.94
- type: ndcg_at_3 value: 35.135
- type: ndcg_at_5 value: 33.335
- type: precision_at_1 value: 41.176
- type: precision_at_10 value: 22.259999999999998
- type: precision_at_100 value: 6.712
- type: precision_at_1000 value: 1.9060000000000001
- type: precision_at_3 value: 33.23
- type: precision_at_5 value: 29.04
- type: recall_at_1 value: 4.919
- type: recall_at_10 value: 14.196
- type: recall_at_100 value: 26.948
- type: recall_at_1000 value: 59.211000000000006
- type: recall_at_3 value: 9.44
- type: recall_at_5 value: 11.569 task: type: Retrieval
- dataset:
config: default
name: MTEB NQ-PL
revision: f171245712cf85dd4700b06bef18001578d0ca8d
split: test
type: clarin-knext/nq-pl
metrics:
- type: map_at_1 value: 25.35
- type: map_at_10 value: 37.884
- type: map_at_100 value: 38.955
- type: map_at_1000 value: 39.007999999999996
- type: map_at_3 value: 34.239999999999995
- type: map_at_5 value: 36.398
- type: mrr_at_1 value: 28.737000000000002
- type: mrr_at_10 value: 39.973
- type: mrr_at_100 value: 40.844
- type: mrr_at_1000 value: 40.885
- type: mrr_at_3 value: 36.901
- type: mrr_at_5 value: 38.721
- type: ndcg_at_1 value: 28.708
- type: ndcg_at_10 value: 44.204
- type: ndcg_at_100 value: 48.978
- type: ndcg_at_1000 value: 50.33
- type: ndcg_at_3 value: 37.36
- type: ndcg_at_5 value: 40.912
- type: precision_at_1 value: 28.708
- type: precision_at_10 value: 7.367
- type: precision_at_100 value: 1.0030000000000001
- type: precision_at_1000 value: 0.11299999999999999
- type: precision_at_3 value: 17.034
- type: precision_at_5 value: 12.293999999999999
- type: recall_at_1 value: 25.35
- type: recall_at_10 value: 61.411
- type: recall_at_100 value: 82.599
- type: recall_at_1000 value: 92.903
- type: recall_at_3 value: 43.728
- type: recall_at_5 value: 51.854 task: type: Retrieval
- dataset:
config: default
name: MTEB PAC
revision: None
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy value: 69.04141326382856
- type: ap value: 77.49422763833996
- type: f1 value: 66.73472657783407 task: type: Classification
- dataset:
config: default
name: MTEB PPC
revision: None
split: test
type: PL-MTEB/ppc-pairclassification
metrics:
- type: cos_sim_accuracy value: 81.0
- type: cos_sim_ap value: 91.47194213011349
- type: cos_sim_f1 value: 84.73767885532592
- type: cos_sim_precision value: 81.49847094801224
- type: cos_sim_recall value: 88.24503311258279
- type: dot_accuracy value: 81.0
- type: dot_ap value: 91.47194213011349
- type: dot_f1 value: 84.73767885532592
- type: dot_precision value: 81.49847094801224
- type: dot_recall value: 88.24503311258279
- type: euclidean_accuracy value: 81.0
- type: euclidean_ap value: 91.47194213011349
- type: euclidean_f1 value: 84.73767885532592
- type: euclidean_precision value: 81.49847094801224
- type: euclidean_recall value: 88.24503311258279
- type: manhattan_accuracy value: 81.0
- type: manhattan_ap value: 91.46464475050571
- type: manhattan_f1 value: 84.48687350835321
- type: manhattan_precision value: 81.31699846860643
- type: manhattan_recall value: 87.91390728476821
- type: max_accuracy value: 81.0
- type: max_ap value: 91.47194213011349
- type: max_f1 value: 84.73767885532592 task: type: PairClassification
- dataset:
config: default
name: MTEB PSC
revision: None
split: test
type: PL-MTEB/psc-pairclassification
metrics:
- type: cos_sim_accuracy value: 97.6808905380334
- type: cos_sim_ap value: 99.27948611836348
- type: cos_sim_f1 value: 96.15975422427034
- type: cos_sim_precision value: 96.90402476780186
- type: cos_sim_recall value: 95.42682926829268
- type: dot_accuracy value: 97.6808905380334
- type: dot_ap value: 99.2794861183635
- type: dot_f1 value: 96.15975422427034
- type: dot_precision value: 96.90402476780186
- type: dot_recall value: 95.42682926829268
- type: euclidean_accuracy value: 97.6808905380334
- type: euclidean_ap value: 99.2794861183635
- type: euclidean_f1 value: 96.15975422427034
- type: euclidean_precision value: 96.90402476780186
- type: euclidean_recall value: 95.42682926829268
- type: manhattan_accuracy value: 97.6808905380334
- type: manhattan_ap value: 99.28715055268721
- type: manhattan_f1 value: 96.14791987673343
- type: manhattan_precision value: 97.19626168224299
- type: manhattan_recall value: 95.1219512195122
- type: max_accuracy value: 97.6808905380334
- type: max_ap value: 99.28715055268721
- type: max_f1 value: 96.15975422427034 task: type: PairClassification
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: None
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy value: 86.16343490304708
- type: f1 value: 83.3442579486744 task: type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: None
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy value: 68.40080971659918
- type: f1 value: 53.13720751142237 task: type: Classification
- dataset:
config: default
name: MTEB Quora-PL
revision: 0be27e93455051e531182b85e85e425aba12e9d4
split: test
type: clarin-knext/quora-pl
metrics:
- type: map_at_1 value: 63.322
- type: map_at_10 value: 76.847
- type: map_at_100 value: 77.616
- type: map_at_1000 value: 77.644
- type: map_at_3 value: 73.624
- type: map_at_5 value: 75.603
- type: mrr_at_1 value: 72.88
- type: mrr_at_10 value: 80.376
- type: mrr_at_100 value: 80.604
- type: mrr_at_1000 value: 80.61
- type: mrr_at_3 value: 78.92
- type: mrr_at_5 value: 79.869
- type: ndcg_at_1 value: 72.89999999999999
- type: ndcg_at_10 value: 81.43
- type: ndcg_at_100 value: 83.394
- type: ndcg_at_1000 value: 83.685
- type: ndcg_at_3 value: 77.62599999999999
- type: ndcg_at_5 value: 79.656
- type: precision_at_1 value: 72.89999999999999
- type: precision_at_10 value: 12.548
- type: precision_at_100 value: 1.4869999999999999
- type: precision_at_1000 value: 0.155
- type: precision_at_3 value: 34.027
- type: precision_at_5 value: 22.654
- type: recall_at_1 value: 63.322
- type: recall_at_10 value: 90.664
- type: recall_at_100 value: 97.974
- type: recall_at_1000 value: 99.636
- type: recall_at_3 value: 80.067
- type: recall_at_5 value: 85.526 task: type: Retrieval
- dataset:
config: default
name: MTEB SCIDOCS-PL
revision: 45452b03f05560207ef19149545f168e596c9337
split: test
type: clarin-knext/scidocs-pl
metrics:
- type: map_at_1 value: 3.95
- type: map_at_10 value: 9.658999999999999
- type: map_at_100 value: 11.384
- type: map_at_1000 value: 11.677
- type: map_at_3 value: 7.055
- type: map_at_5 value: 8.244
- type: mrr_at_1 value: 19.5
- type: mrr_at_10 value: 28.777
- type: mrr_at_100 value: 29.936
- type: mrr_at_1000 value: 30.009999999999998
- type: mrr_at_3 value: 25.55
- type: mrr_at_5 value: 27.284999999999997
- type: ndcg_at_1 value: 19.5
- type: ndcg_at_10 value: 16.589000000000002
- type: ndcg_at_100 value: 23.879
- type: ndcg_at_1000 value: 29.279
- type: ndcg_at_3 value: 15.719
- type: ndcg_at_5 value: 13.572000000000001
- type: precision_at_1 value: 19.5
- type: precision_at_10 value: 8.62
- type: precision_at_100 value: 1.924
- type: precision_at_1000 value: 0.322
- type: precision_at_3 value: 14.6
- type: precision_at_5 value: 11.78
- type: recall_at_1 value: 3.95
- type: recall_at_10 value: 17.477999999999998
- type: recall_at_100 value: 38.99
- type: recall_at_1000 value: 65.417
- type: recall_at_3 value: 8.883000000000001
- type: recall_at_5 value: 11.933 task: type: Retrieval
- dataset:
config: default
name: MTEB SICK-E-PL
revision: None
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics:
- type: cos_sim_accuracy value: 83.48960456583775
- type: cos_sim_ap value: 76.31522115825375
- type: cos_sim_f1 value: 70.35573122529645
- type: cos_sim_precision value: 70.9934735315446
- type: cos_sim_recall value: 69.72934472934473
- type: dot_accuracy value: 83.48960456583775
- type: dot_ap value: 76.31522115825373
- type: dot_f1 value: 70.35573122529645
- type: dot_precision value: 70.9934735315446
- type: dot_recall value: 69.72934472934473
- type: euclidean_accuracy value: 83.48960456583775
- type: euclidean_ap value: 76.31522115825373
- type: euclidean_f1 value: 70.35573122529645
- type: euclidean_precision value: 70.9934735315446
- type: euclidean_recall value: 69.72934472934473
- type: manhattan_accuracy value: 83.46922136159804
- type: manhattan_ap value: 76.18474601388084
- type: manhattan_f1 value: 70.34779490856937
- type: manhattan_precision value: 70.83032490974729
- type: manhattan_recall value: 69.87179487179486
- type: max_accuracy value: 83.48960456583775
- type: max_ap value: 76.31522115825375
- type: max_f1 value: 70.35573122529645 task: type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: None
split: test
type: PL-MTEB/sickr-pl-sts
metrics:
- type: cos_sim_pearson value: 77.95374883876302
- type: cos_sim_spearman value: 73.77630219171942
- type: euclidean_pearson value: 75.81927069594934
- type: euclidean_spearman value: 73.7763211303831
- type: manhattan_pearson value: 76.03126859057528
- type: manhattan_spearman value: 73.96528138013369 task: type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson value: 37.388282764841826
- type: cos_sim_spearman value: 40.83477184710897
- type: euclidean_pearson value: 26.754737044177805
- type: euclidean_spearman value: 40.83477184710897
- type: manhattan_pearson value: 26.760453110872458
- type: manhattan_spearman value: 41.034477441383856 task: type: STS
- dataset:
config: default
name: MTEB SciFact-PL
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
split: test
type: clarin-knext/scifact-pl
metrics:
- type: map_at_1 value: 49.15
- type: map_at_10 value: 61.690999999999995
- type: map_at_100 value: 62.348000000000006
- type: map_at_1000 value: 62.38
- type: map_at_3 value: 58.824
- type: map_at_5 value: 60.662000000000006
- type: mrr_at_1 value: 51.333
- type: mrr_at_10 value: 62.731
- type: mrr_at_100 value: 63.245
- type: mrr_at_1000 value: 63.275000000000006
- type: mrr_at_3 value: 60.667
- type: mrr_at_5 value: 61.93300000000001
- type: ndcg_at_1 value: 51.333
- type: ndcg_at_10 value: 67.168
- type: ndcg_at_100 value: 69.833
- type: ndcg_at_1000 value: 70.56700000000001
- type: ndcg_at_3 value: 62.40599999999999
- type: ndcg_at_5 value: 65.029
- type: precision_at_1 value: 51.333
- type: precision_at_10 value: 9.333
- type: precision_at_100 value: 1.0699999999999998
- type: precision_at_1000 value: 0.11299999999999999
- type: precision_at_3 value: 25.333
- type: precision_at_5 value: 17.067
- type: recall_at_1 value: 49.15
- type: recall_at_10 value: 82.533
- type: recall_at_100 value: 94.167
- type: recall_at_1000 value: 99.667
- type: recall_at_3 value: 69.917
- type: recall_at_5 value: 76.356 task: type: Retrieval
- dataset:
config: default
name: MTEB TRECCOVID-PL
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
split: test
type: clarin-knext/trec-covid-pl
metrics:
- type: map_at_1 value: 0.261
- type: map_at_10 value: 2.1260000000000003
- type: map_at_100 value: 12.171999999999999
- type: map_at_1000 value: 26.884999999999998
- type: map_at_3 value: 0.695
- type: map_at_5 value: 1.134
- type: mrr_at_1 value: 96.0
- type: mrr_at_10 value: 96.952
- type: mrr_at_100 value: 96.952
- type: mrr_at_1000 value: 96.952
- type: mrr_at_3 value: 96.667
- type: mrr_at_5 value: 96.667
- type: ndcg_at_1 value: 92.0
- type: ndcg_at_10 value: 81.193
- type: ndcg_at_100 value: 61.129
- type: ndcg_at_1000 value: 51.157
- type: ndcg_at_3 value: 85.693
- type: ndcg_at_5 value: 84.129
- type: precision_at_1 value: 96.0
- type: precision_at_10 value: 85.39999999999999
- type: precision_at_100 value: 62.03999999999999
- type: precision_at_1000 value: 22.224
- type: precision_at_3 value: 88.0
- type: precision_at_5 value: 88.0
- type: recall_at_1 value: 0.261
- type: recall_at_10 value: 2.262
- type: recall_at_100 value: 14.981
- type: recall_at_1000 value: 46.837
- type: recall_at_3 value: 0.703
- type: recall_at_5 value: 1.172 task: type: Retrieval
- dataset:
config: default
name: MTEB AlloProfClusteringP2P
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure value: 70.55290063940157 task: type: Clustering
- dataset:
config: default
name: MTEB AlloProfClusteringS2S
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure value: 55.41500719337263 task: type: Clustering
- dataset:
config: default
name: MTEB AlloprofReranking
revision: 666fdacebe0291776e86f29345663dfaf80a0db9
split: test
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
metrics:
- type: map value: 73.48697375332002
- type: mrr value: 75.01836585523822 task: type: Reranking
- dataset:
config: default
name: MTEB AlloprofRetrieval
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: map_at_1 value: 38.454
- type: map_at_10 value: 51.605000000000004
- type: map_at_100 value: 52.653000000000006
- type: map_at_1000 value: 52.697
- type: map_at_3 value: 48.304
- type: map_at_5 value: 50.073
- type: mrr_at_1 value: 43.307
- type: mrr_at_10 value: 54.400000000000006
- type: mrr_at_100 value: 55.147999999999996
- type: mrr_at_1000 value: 55.174
- type: mrr_at_3 value: 51.77
- type: mrr_at_5 value: 53.166999999999994
- type: ndcg_at_1 value: 43.307
- type: ndcg_at_10 value: 57.891000000000005
- type: ndcg_at_100 value: 62.161
- type: ndcg_at_1000 value: 63.083
- type: ndcg_at_3 value: 51.851
- type: ndcg_at_5 value: 54.605000000000004
- type: precision_at_1 value: 43.307
- type: precision_at_10 value: 9.033
- type: precision_at_100 value: 1.172
- type: precision_at_1000 value: 0.127
- type: precision_at_3 value: 22.798
- type: precision_at_5 value: 15.492
- type: recall_at_1 value: 38.454
- type: recall_at_10 value: 74.166
- type: recall_at_100 value: 92.43599999999999
- type: recall_at_1000 value: 99.071
- type: recall_at_3 value: 58.087
- type: recall_at_5 value: 64.568 task: type: Retrieval
- dataset:
config: fr
name: MTEB AmazonReviewsClassification (fr)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy value: 53.474
- type: f1 value: 50.38275392350236 task: type: Classification
- dataset:
config: default
name: MTEB BSARDRetrieval
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
split: test
type: maastrichtlawtech/bsard
metrics:
- type: map_at_1 value: 2.252
- type: map_at_10 value: 4.661
- type: map_at_100 value: 5.271
- type: map_at_1000 value: 5.3629999999999995
- type: map_at_3 value: 3.604
- type: map_at_5 value: 4.3020000000000005
- type: mrr_at_1 value: 2.252
- type: mrr_at_10 value: 4.661
- type: mrr_at_100 value: 5.271
- type: mrr_at_1000 value: 5.3629999999999995
- type: mrr_at_3 value: 3.604
- type: mrr_at_5 value: 4.3020000000000005
- type: ndcg_at_1 value: 2.252
- type: ndcg_at_10 value: 6.3020000000000005
- type: ndcg_at_100 value: 10.342
- type: ndcg_at_1000 value: 13.475999999999999
- type: ndcg_at_3 value: 4.0649999999999995
- type: ndcg_at_5 value: 5.344
- type: precision_at_1 value: 2.252
- type: precision_at_10 value: 1.171
- type: precision_at_100 value: 0.333
- type: precision_at_1000 value: 0.059000000000000004
- type: precision_at_3 value: 1.802
- type: precision_at_5 value: 1.712
- type: recall_at_1 value: 2.252
- type: recall_at_10 value: 11.712
- type: recall_at_100 value: 33.333
- type: recall_at_1000 value: 59.458999999999996
- type: recall_at_3 value: 5.405
- type: recall_at_5 value: 8.559 task: type: Retrieval
- dataset:
config: default
name: MTEB HALClusteringS2S
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
split: test
type: lyon-nlp/clustering-hal-s2s
metrics:
- type: v_measure value: 28.301882091023288 task: type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringP2P
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure value: 45.26992995191701 task: type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringS2S
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure value: 42.773174876871145 task: type: Clustering
- dataset:
config: fr
name: MTEB MTOPDomainClassification (fr)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy value: 93.47635452552458
- type: f1 value: 93.19922617577213 task: type: Classification
- dataset:
config: fr
name: MTEB MTOPIntentClassification (fr)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy value: 80.2317569683683
- type: f1 value: 56.18060418621901 task: type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClassification (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: accuracy value: 85.18957345971565
- type: f1 value: 80.829981537394 task: type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringP2P (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure value: 71.04138999801822 task: type: Clustering
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringS2S (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure value: 71.7056263158008 task: type: Clustering
- dataset:
config: fr
name: MTEB MassiveIntentClassification (fr)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy value: 76.65097511768661
- type: f1 value: 73.82441070598712 task: type: Classification
- dataset:
config: fr
name: MTEB MassiveScenarioClassification (fr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy value: 79.09885675857431
- type: f1 value: 78.28407777434224 task: type: Classification
- dataset:
config: fr
name: MTEB MintakaRetrieval (fr)
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
split: test
type: jinaai/mintakaqa
metrics:
- type: map_at_1 value: 25.307000000000002
- type: map_at_10 value: 36.723
- type: map_at_100 value: 37.713
- type: map_at_1000 value: 37.769000000000005
- type: map_at_3 value: 33.77
- type: map_at_5 value: 35.463
- type: mrr_at_1 value: 25.307000000000002
- type: mrr_at_10 value: 36.723
- type: mrr_at_100 value: 37.713
- type: mrr_at_1000 value: 37.769000000000005
- type: mrr_at_3 value: 33.77
- type: mrr_at_5 value: 35.463
- type: ndcg_at_1 value: 25.307000000000002
- type: ndcg_at_10 value: 42.559999999999995
- type: ndcg_at_100 value: 47.457
- type: ndcg_at_1000 value: 49.162
- type: ndcg_at_3 value: 36.461
- type: ndcg_at_5 value: 39.504
- type: precision_at_1 value: 25.307000000000002
- type: precision_at_10 value: 6.106
- type: precision_at_100 value: 0.8420000000000001
- type: precision_at_1000 value: 0.098
- type: precision_at_3 value: 14.741999999999999
- type: precision_at_5 value: 10.319
- type: recall_at_1 value: 25.307000000000002
- type: recall_at_10 value: 61.056999999999995
- type: recall_at_100 value: 84.152
- type: recall_at_1000 value: 98.03399999999999
- type: recall_at_3 value: 44.226
- type: recall_at_5 value: 51.597 task: type: Retrieval
- dataset:
config: fr
name: MTEB OpusparcusPC (fr)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test
type: GEM/opusparcus
metrics:
- type: cos_sim_accuracy value: 99.90069513406156
- type: cos_sim_ap value: 100.0
- type: cos_sim_f1 value: 99.95032290114257
- type: cos_sim_precision value: 100.0
- type: cos_sim_recall value: 99.90069513406156
- type: dot_accuracy value: 99.90069513406156
- type: dot_ap value: 100.0
- type: dot_f1 value: 99.95032290114257
- type: dot_precision value: 100.0
- type: dot_recall value: 99.90069513406156
- type: euclidean_accuracy value: 99.90069513406156
- type: euclidean_ap value: 100.0
- type: euclidean_f1 value: 99.95032290114257
- type: euclidean_precision value: 100.0
- type: euclidean_recall value: 99.90069513406156
- type: manhattan_accuracy value: 99.90069513406156
- type: manhattan_ap value: 100.0
- type: manhattan_f1 value: 99.95032290114257
- type: manhattan_precision value: 100.0
- type: manhattan_recall value: 99.90069513406156
- type: max_accuracy value: 99.90069513406156
- type: max_ap value: 100.0
- type: max_f1 value: 99.95032290114257 task: type: PairClassification
- dataset:
config: fr
name: MTEB PawsX (fr)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: paws-x
metrics:
- type: cos_sim_accuracy value: 70.8
- type: cos_sim_ap value: 73.7671529695957
- type: cos_sim_f1 value: 68.80964339527875
- type: cos_sim_precision value: 62.95955882352941
- type: cos_sim_recall value: 75.85825027685493
- type: dot_accuracy value: 70.8
- type: dot_ap value: 73.78345265366947
- type: dot_f1 value: 68.80964339527875
- type: dot_precision value: 62.95955882352941
- type: dot_recall value: 75.85825027685493
- type: euclidean_accuracy value: 70.8
- type: euclidean_ap value: 73.7671529695957
- type: euclidean_f1 value: 68.80964339527875
- type: euclidean_precision value: 62.95955882352941
- type: euclidean_recall value: 75.85825027685493
- type: manhattan_accuracy value: 70.75
- type: manhattan_ap value: 73.78996383615953
- type: manhattan_f1 value: 68.79432624113475
- type: manhattan_precision value: 63.39869281045751
- type: manhattan_recall value: 75.1937984496124
- type: max_accuracy value: 70.8
- type: max_ap value: 73.78996383615953
- type: max_f1 value: 68.80964339527875 task: type: PairClassification
- dataset:
config: default
name: MTEB SICKFr
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
split: test
type: Lajavaness/SICK-fr
metrics:
- type: cos_sim_pearson value: 84.03253762760392
- type: cos_sim_spearman value: 79.68280105762004
- type: euclidean_pearson value: 80.98265050044444
- type: euclidean_spearman value: 79.68233242682867
- type: manhattan_pearson value: 80.9678911810704
- type: manhattan_spearman value: 79.70264097683109 task: type: STS
- dataset:
config: fr
name: MTEB STS22 (fr)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson value: 80.56896987572884
- type: cos_sim_spearman value: 81.84352499523287
- type: euclidean_pearson value: 80.40831759421305
- type: euclidean_spearman value: 81.84352499523287
- type: manhattan_pearson value: 80.74333857561238
- type: manhattan_spearman value: 82.41503246733892 task: type: STS
- dataset:
config: fr
name: MTEB STSBenchmarkMultilingualSTS (fr)
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
split: test
type: stsb_multi_mt
metrics:
- type: cos_sim_pearson value: 82.71826762276979
- type: cos_sim_spearman value: 82.25433354916042
- type: euclidean_pearson value: 81.87115571724316
- type: euclidean_spearman value: 82.25322342890107
- type: manhattan_pearson value: 82.11174867527224
- type: manhattan_spearman value: 82.55905365203084 task: type: STS
- dataset:
config: default
name: MTEB SummEvalFr
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
split: test
type: lyon-nlp/summarization-summeval-fr-p2p
metrics:
- type: cos_sim_pearson value: 30.659441623392887
- type: cos_sim_spearman value: 30.501134097353315
- type: dot_pearson value: 30.659444768851056
- type: dot_spearman value: 30.501134097353315 task: type: Summarization
- dataset:
config: default
name: MTEB SyntecReranking
revision: b205c5084a0934ce8af14338bf03feb19499c84d
split: test
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
metrics:
- type: map value: 94.03333333333333
- type: mrr value: 94.03333333333333 task: type: Reranking
- dataset:
config: default
name: MTEB SyntecRetrieval
revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
split: test
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
metrics:
- type: map_at_1 value: 79.0
- type: map_at_10 value: 87.61
- type: map_at_100 value: 87.655
- type: map_at_1000 value: 87.655
- type: map_at_3 value: 87.167
- type: map_at_5 value: 87.36699999999999
- type: mrr_at_1 value: 79.0
- type: mrr_at_10 value: 87.61
- type: mrr_at_100 value: 87.655
- type: mrr_at_1000 value: 87.655
- type: mrr_at_3 value: 87.167
- type: mrr_at_5 value: 87.36699999999999
- type: ndcg_at_1 value: 79.0
- type: ndcg_at_10 value: 90.473
- type: ndcg_at_100 value: 90.694
- type: ndcg_at_1000 value: 90.694
- type: ndcg_at_3 value: 89.464
- type: ndcg_at_5 value: 89.851
- type: precision_at_1 value: 79.0
- type: precision_at_10 value: 9.9
- type: precision_at_100 value: 1.0
- type: precision_at_1000 value: 0.1
- type: precision_at_3 value: 32.0
- type: precision_at_5 value: 19.400000000000002
- type: recall_at_1 value: 79.0
- type: recall_at_10 value: 99.0
- type: recall_at_100 value: 100.0
- type: recall_at_1000 value: 100.0
- type: recall_at_3 value: 96.0
- type: recall_at_5 value: 97.0 task: type: Retrieval
- dataset:
config: fr
name: MTEB XPQARetrieval (fr)
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
split: test
type: jinaai/xpqa
metrics:
- type: map_at_1 value: 39.395
- type: map_at_10 value: 59.123999999999995
- type: map_at_100 value: 60.704
- type: map_at_1000 value: 60.760000000000005
- type: map_at_3 value: 53.187
- type: map_at_5 value: 56.863
- type: mrr_at_1 value: 62.083
- type: mrr_at_10 value: 68.87299999999999
- type: mrr_at_100 value: 69.46900000000001
- type: mrr_at_1000 value: 69.48299999999999
- type: mrr_at_3 value: 66.8
- type: mrr_at_5 value: 67.928
- type: ndcg_at_1 value: 62.083
- type: ndcg_at_10 value: 65.583
- type: ndcg_at_100 value: 70.918
- type: ndcg_at_1000 value: 71.72800000000001
- type: ndcg_at_3 value: 60.428000000000004
- type: ndcg_at_5 value: 61.853
- type: precision_at_1 value: 62.083
- type: precision_at_10 value: 15.033
- type: precision_at_100 value: 1.9529999999999998
- type: precision_at_1000 value: 0.207
- type: precision_at_3 value: 36.315
- type: precision_at_5 value: 25.955000000000002
- type: recall_at_1 value: 39.395
- type: recall_at_10 value: 74.332
- type: recall_at_100 value: 94.729
- type: recall_at_1000 value: 99.75500000000001
- type: recall_at_3 value: 57.679
- type: recall_at_5 value: 65.036 task: type: Retrieval
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
gte-Qwen2-1.5B-instruct
gte-Qwen2-1.5B-instruct is the latest model in the gte (General Text Embedding) model family. The model is built on Qwen2-1.5B LLM model and use the same training data and strategies as the gte-Qwen2-7B-instruct model.
The model incorporates several key advancements:
- Integration of bidirectional attention mechanisms, enriching its contextual understanding.
- Instruction tuning, applied solely on the query side for streamlined efficiency
- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.
Model Information
- Model Size: 1.5B
- Embedding Dimension: 1536
- Max Input Tokens: 32k
Requirements
transformers>=4.39.2
flash_attn>=2.5.6
Usage
Sentence Transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-1.5B-instruct", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192
queries = [
"how much protein should a female eat",
"summit define",
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
]
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
Observe the config_sentence_transformers.json to see all pre-built prompt names. Otherwise, you can use model.encode(queries, prompt="Instruct: ...\nQuery: " to use a custom prompt of your choice.
Transformers
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)
max_length = 8192
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
infinity_emb
Usage via infinity, MIT Licensed.
docker run \
--gpus "0" -p "7997":"7997" \
michaelf34/infinity:0.0.68-trt-onnx \
v2 --model-id Alibaba-NLP/gte-Qwen2-1.5B-instruct --revision "refs/pr/20" --dtype bfloat16 --batch-size 16 --device cuda --engine torch --port 7997 --no-bettertransformer
Evaluation
MTEB & C-MTEB
You can use the scripts/eval_mteb.py to reproduce the following result of gte-Qwen2-1.5B-instruct on MTEB(English)/C-MTEB(Chinese):
| Model Name | MTEB(56) | C-MTEB(35) | MTEB-fr(26) | MTEB-pl(26) |
|---|---|---|---|---|
| bge-base-en-1.5 | 64.23 | - | - | - |
| bge-large-en-1.5 | 63.55 | - | - | - |
| gte-large-en-v1.5 | 65.39 | - | - | - |
| gte-base-en-v1.5 | 64.11 | - | - | - |
| mxbai-embed-large-v1 | 64.68 | - | - | - |
| acge_text_embedding | - | 69.07 | - | - |
| stella-mrl-large-zh-v3.5-1792d | - | 68.55 | - | - |
| gte-large-zh | - | 66.72 | - | - |
| multilingual-e5-base | 59.45 | 56.21 | - | - |
| multilingual-e5-large | 61.50 | 58.81 | - | - |
| e5-mistral-7b-instruct | 66.63 | 60.81 | - | - |
| gte-Qwen1.5-7B-instruct | 67.34 | 69.52 | - | - |
| NV-Embed-v1 | 69.32 | - | - | - |
| gte-Qwen2-7B-instruct | 70.24 | 72.05 | 68.25 | 67.86 |
| gte-Qwen2-1.5B-instruct | 67.16 | 67.65 | 66.60 | 64.04 |
GTE Models
The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture).
| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) |
|---|---|---|---|---|
| GTE-large-zh | Chinese | 512 | 1024 | 1.25GB |
| GTE-base-zh | Chinese | 512 | 512 | 0.41GB |
| GTE-small-zh | Chinese | 512 | 512 | 0.12GB |
| GTE-large | English | 512 | 1024 | 1.25GB |
| GTE-base | English | 512 | 512 | 0.21GB |
| GTE-small | English | 512 | 384 | 0.10GB |
| GTE-large-en-v1.5 | English | 8192 | 1024 | 1.74GB |
| GTE-base-en-v1.5 | English | 8192 | 768 | 0.51GB |
| GTE-Qwen1.5-7B-instruct | Multilingual | 32000 | 4096 | 26.45GB |
| GTE-Qwen2-7B-instruct | Multilingual | 32000 | 3584 | 26.45GB |
| GTE-Qwen2-1.5B-instruct | Multilingual | 32000 | 1536 | 6.62GB |
Cloud API Services
In addition to the open-source GTE series models, GTE series models are also available as commercial API services on Alibaba Cloud.
- Embedding Models: Three versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service.
- ReRank Models: The gte-rerank model service is available.
Note that the models behind the commercial APIs are not entirely identical to the open-source models.
Community support
Fine-tuning
GTE models can be fine-tuned with a third party framework SWIFT.
pip install ms-swift -U
# check: https://swift.readthedocs.io/en/latest/BestPractices/Embedding.html
nproc_per_node=8
NPROC_PER_NODE=$nproc_per_node \
USE_HF=1 \
swift sft \
--model Alibaba-NLP/gte-Qwen2-1.5B-instruct \
--train_type lora \
--dataset 'sentence-transformers/stsb' \
--torch_dtype bfloat16 \
--num_train_epochs 10 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps $(expr 64 / $nproc_per_node) \
--eval_steps 100 \
--save_steps 100 \
--eval_strategy steps \
--use_chat_template false \
--save_total_limit 5 \
--logging_steps 5 \
--output_dir output \
--warmup_ratio 0.05 \
--learning_rate 5e-6 \
--deepspeed zero3 \
--dataloader_num_workers 4 \
--task_type embedding \
--loss_type cosine_similarity \
--dataloader_drop_last true
Citation
If you find our paper or models helpful, please consider cite:
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
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