モデル概要

このモデルはQwen2アーキテクチャに基づく7Bパラメータ規模の大規模言語モデルで、命令微調整により最適化されており、特にテキスト生成と文類似度計算など、さまざまな自然言語処理タスクに適しています。

モデル特徴

マルチタスク処理能力

テキスト生成、分類、クラスタリング、検索など、さまざまな自然言語処理タスクをサポート

高性能

複数の標準データセットで優れたパフォーマンス指標を示しています

命令微調整最適化

専用の命令微調整を経ており、実際のアプリケーションシナリオにより適しています

MLXライブラリサポート

MLXライブラリと互換性があり、展開と使用が容易です

モデル能力

テキスト生成

文類似度計算

テキスト分類

テキストクラスタリング

情報検索

意味的テキスト類似度分析

質問応答システム

再ランキング

使用事例

電子商取引

商品レビュー分類

Amazon商品レビューの感情極性分類

MTEB Amazon極性分類タスクで97.5%の精度を達成

反事実的レビュー検出

Amazonプラットフォーム上の反事実的レビューを識別

MTEB Amazon反事実分類タスクで91.3%の精度を達成

金融

銀行カスタマーサービス質問分類

銀行のカスタマーサービス質問を自動分類

MTEB Banking77分類タスクで87.6%の精度を達成

学術研究

論文クラスタリング

arXivとbiorxivの論文を主題別にクラスタリング

MTEB論文クラスタリングタスクでV-measureが51.7-56.5%を達成

base_model: Alibaba-NLP/gte-Qwen2-7B-instruct library_name: mlx license: apache-2.0 pipeline_tag: text-generation tags:

mteb
sentence-transformers
transformers
Qwen2
sentence-similarity
mlx model-index:
name: gte-qwen2-7B-instruct results:
- task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics:
  - type: accuracy value: 91.31343283582089
  - type: ap value: 67.64251402604096
  - type: f1 value: 87.53372530755692
- task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics:
  - type: accuracy value: 97.497825
  - type: ap value: 96.30329547047529
  - type: f1 value: 97.49769793778039
- task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
  - type: accuracy value: 62.564
  - type: f1 value: 60.975777935041066
- task: type: Retrieval dataset: name: MTEB ArguAna type: mteb/arguana config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics:
  - type: map_at_1 value: 36.486000000000004
  - type: map_at_10 value: 54.842
  - type: map_at_100 value: 55.206999999999994
  - type: map_at_1000 value: 55.206999999999994
  - type: map_at_3 value: 49.893
  - type: map_at_5 value: 53.105000000000004
  - type: mrr_at_1 value: 37.34
  - type: mrr_at_10 value: 55.143
  - type: mrr_at_100 value: 55.509
  - type: mrr_at_1000 value: 55.509
  - type: mrr_at_3 value: 50.212999999999994
  - type: mrr_at_5 value: 53.432
  - type: ndcg_at_1 value: 36.486000000000004
  - type: ndcg_at_10 value: 64.273
  - type: ndcg_at_100 value: 65.66199999999999
  - type: ndcg_at_1000 value: 65.66199999999999
  - type: ndcg_at_3 value: 54.352999999999994
  - type: ndcg_at_5 value: 60.131
  - type: precision_at_1 value: 36.486000000000004
  - type: precision_at_10 value: 9.395000000000001
  - type: precision_at_100 value: 0.996
  - type: precision_at_1000 value: 0.1
  - type: precision_at_3 value: 22.428
  - type: precision_at_5 value: 16.259
  - type: recall_at_1 value: 36.486000000000004
  - type: recall_at_10 value: 93.95400000000001
  - type: recall_at_100 value: 99.644
  - type: recall_at_1000 value: 99.644
  - type: recall_at_3 value: 67.283
  - type: recall_at_5 value: 81.294
- task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics:
  - type: v_measure value: 56.461169803700564
- task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics:
  - type: v_measure value: 51.73600434466286
- task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics:
  - type: map value: 67.57827065898053
  - type: mrr value: 79.08136569493911
- task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics:
  - type: cos_sim_pearson value: 83.53324575999243
  - type: cos_sim_spearman value: 81.37173362822374
  - type: euclidean_pearson value: 82.19243335103444
  - type: euclidean_spearman value: 81.33679307304334
  - type: manhattan_pearson value: 82.38752665975699
  - type: manhattan_spearman value: 81.31510583189689
- task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics:
  - type: accuracy value: 87.56818181818181
  - type: f1 value: 87.25826722019875
- task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics:
  - type: v_measure value: 50.09239610327673
- task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics:
  - type: v_measure value: 46.64733054606282
- task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics:
  - type: map_at_1 value: 33.997
  - type: map_at_10 value: 48.176
  - type: map_at_100 value: 49.82
  - type: map_at_1000 value: 49.924
  - type: map_at_3 value: 43.626
  - type: map_at_5 value: 46.275
  - type: mrr_at_1 value: 42.059999999999995
  - type: mrr_at_10 value: 53.726
  - type: mrr_at_100 value: 54.398
  - type: mrr_at_1000 value: 54.416
  - type: mrr_at_3 value: 50.714999999999996
  - type: mrr_at_5 value: 52.639
  - type: ndcg_at_1 value: 42.059999999999995
  - type: ndcg_at_10 value: 55.574999999999996
  - type: ndcg_at_100 value: 60.744
  - type: ndcg_at_1000 value: 61.85699999999999
  - type: ndcg_at_3 value: 49.363
  - type: ndcg_at_5 value: 52.44
  - type: precision_at_1 value: 42.059999999999995
  - type: precision_at_10 value: 11.101999999999999
  - type: precision_at_100 value: 1.73
  - type: precision_at_1000 value: 0.218
  - type: precision_at_3 value: 24.464
  - type: precision_at_5 value: 18.026
  - type: recall_at_1 value: 33.997
  - type: recall_at_10 value: 70.35900000000001
  - type: recall_at_100 value: 91.642
  - type: recall_at_1000 value: 97.977
  - type: recall_at_3 value: 52.76
  - type: recall_at_5 value: 61.148
- task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: BeIR/cqadupstack config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics:
  - type: map_at_1 value: 35.884
  - type: map_at_10 value: 48.14
  - type: map_at_100 value: 49.5
  - type: map_at_1000 value: 49.63
  - type: map_at_3 value: 44.646
  - type: map_at_5 value: 46.617999999999995
  - type: mrr_at_1 value: 44.458999999999996
  - type: mrr_at_10 value: 53.751000000000005
  - type: mrr_at_100 value: 54.37800000000001
  - type: mrr_at_1000 value: 54.415
  - type: mrr_at_3 value: 51.815
  - type: mrr_at_5 value: 52.882
  - type: ndcg_at_1 value: 44.458999999999996
  - type: ndcg_at_10 value: 54.157
  - type: ndcg_at_100 value: 58.362
  - type: ndcg_at_1000 value: 60.178
  - type: ndcg_at_3 value: 49.661
  - type: ndcg_at_5 value: 51.74999999999999
  - type: precision_at_1 value: 44.458999999999996
  - type: precision_at_10 value: 10.248
  - type: precision_at_100 value: 1.5890000000000002
  - type: precision_at_1000 value: 0.207
  - type: precision_at_3 value: 23.928
  - type: precision_at_5 value: 16.878999999999998
  - type: recall_at_1 value: 35.884
  - type: recall_at_10 value: 64.798
  - type: recall_at_100 value: 82.345
  - type: recall_at_1000 value: 93.267
  - type: recall_at_3 value: 51.847
  - type: recall_at_5 value: 57.601
- task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: BeIR/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics:
  - type: map_at_1 value: 39.383
  - type: map_at_10 value: 53.714
  - type: map_at_100 value: 54.838
  - type: map_at_1000 value: 54.87800000000001
  - type: map_at_3 value: 50.114999999999995
  - type: map_at_5 value: 52.153000000000006
  - type: mrr_at_1 value: 45.016
  - type: mrr_at_10 value: 56.732000000000006
  - type: mrr_at_100 value: 57.411
  - type: mrr_at_1000 value: 57.431
  - type: mrr_at_3 value: 54.044000000000004
  - type: mrr_at_5 value: 55.639
  - type: ndcg_at_1 value: 45.016
  - type: ndcg_at_10 value: 60.228
  - type: ndcg_at_100 value: 64.277
  - type: ndcg_at_1000 value: 65.07
  - type: ndcg_at_3 value: 54.124
  - type: ndcg_at_5 value: 57.147000000000006
  - type: precision_at_1 value: 45.016
  - type: precision_at_10 value: 9.937
  - type: precision_at_100 value: 1.288
  - type: precision_at_1000 value: 0.13899999999999998
  - type: precision_at_3 value: 24.471999999999998
  - type: precision_at_5 value: 16.991
  - type: recall_at_1 value: 39.383
  - type: recall_at_10 value: 76.175
  - type: recall_at_100 value: 93.02
  - type: recall_at_1000 value: 98.60900000000001
  - type: recall_at_3 value: 60.265
  - type: recall_at_5 value: 67.46600000000001
- task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: BeIR/cqadupstack config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics:
  - type: map_at_1 value: 27.426000000000002
  - type: map_at_10 value: 37.397000000000006
  - type: map_at_100 value: 38.61
  - type: map_at_1000 value: 38.678000000000004
  - type: map_at_3 value: 34.150999999999996
  - type: map_at_5 value: 36.137
  - type: mrr_at_1 value: 29.944
  - type: mrr_at_10 value: 39.654
  - type: mrr_at_100 value: 40.638000000000005
  - type: mrr_at_1000 value: 40.691
  - type: mrr_at_3 value: 36.817
  - type: mrr_at_5 value: 38.524
  - type: ndcg_at_1 value: 29.944
  - type: ndcg_at_10 value: 43.094
  - type: ndcg_at_100 value: 48.789
  - type: ndcg_at_1000 value: 50.339999999999996
  - type: ndcg_at_3 value: 36.984
  - type: ndcg_at_5 value: 40.248
  - type: precision_at_1 value: 29.944
  - type: precision_at_10 value: 6.78
  - type: precision_at_100 value: 1.024
  - type: precision_at_1000 value: 0.11800000000000001
  - type: precision_at_3 value: 15.895000000000001
  - type: precision_at_5 value: 11.39
  - type: recall_at_1 value: 27.426000000000002
  - type: recall_at_10 value: 58.464000000000006
  - type: recall_at_100 value: 84.193
  - type: recall_at_1000 value: 95.52000000000001
  - type: recall_at_3 value: 42.172
  - type: recall_at_5 value: 50.101
- task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: BeIR/cqadupstack config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics:
  - type: map_at_1 value: 19.721
  - type: map_at_10 value: 31.604
  - type: map_at_100 value: 32.972
  - type: map_at_1000 value: 33.077
  - type: map_at_3 value: 27.218999999999998
  - type: map_at_5 value: 29.53
  - type: mrr_at_1 value: 25.0
  - type: mrr_at_10 value: 35.843
  - type: mrr_at_100 value: 36.785000000000004
  - type: mrr_at_1000 value: 36.842000000000006
  - type: mrr_at_3 value: 32.193
  - type: mrr_at_5 value: 34.264
  - type: ndcg_at_1 value: 25.0
  - type: ndcg_at_10 value: 38.606
  - type: ndcg_at_100 value: 44.272
  - type: ndcg_at_1000 value: 46.527
  - type: ndcg_at_3 value: 30.985000000000003
  - type: ndcg_at_5 value: 34.43
  - type: precision_at_1 value: 25.0
  - type: precision_at_10 value: 7.811
  - type: precision_at_100 value: 1.203
  - type: precision_at_1000 value: 0.15
  - type: precision_at_3 value: 15.423
  - type: precision_at_5 value: 11.791
  - type: recall_at_1 value: 19.721
  - type: recall_at_10 value: 55.625
  - type: recall_at_100 value: 79.34400000000001
  - type: recall_at_1000 value: 95.208
  - type: recall_at_3 value: 35.19
  - type: recall_at_5 value: 43.626
- task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: BeIR/cqadupstack config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics:
  - type: map_at_1 value: 33.784
  - type: map_at_10 value: 47.522
  - type: map_at_100 value: 48.949999999999996
  - type: map_at_1000 value: 49.038
  - type: map_at_3 value: 43.284
  - type: map_at_5 value: 45.629
  - type: mrr_at_1 value: 41.482
  - type: mrr_at_10 value: 52.830999999999996
  - type: mrr_at_100 value: 53.559999999999995
  - type: mrr_at_1000 value: 53.588
  - type: mrr_at_3 value: 50.016000000000005
  - type: mrr_at_5 value: 51.614000000000004
  - type: ndcg_at_1 value: 41.482
  - type: ndcg_at_10 value: 54.569
  - type: ndcg_at_100 value: 59.675999999999995
  - type: ndcg_at_1000 value: 60.989000000000004
  - type: ndcg_at_3 value: 48.187000000000005
  - type: ndcg_at_5 value: 51.183
  - type: precision_at_1 value: 41.482
  - type: precision_at_10 value: 10.221
  - type: precision_at_100 value: 1.486
  - type: precision_at_1000 value: 0.17500000000000002
  - type: precision_at_3 value: 23.548
  - type: precision_at_5 value: 16.805
  - type: recall_at_1 value: 33.784
  - type: recall_at_10 value: 69.798
  - type: recall_at_100 value: 90.098
  - type: recall_at_1000 value: 98.176
  - type: recall_at_3 value: 52.127
  - type: recall_at_5 value: 59.861
- task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: BeIR/cqadupstack config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics:
  - type: map_at_1 value: 28.038999999999998
  - type: map_at_10 value: 41.904
  - type: map_at_100 value: 43.36
  - type: map_at_1000 value: 43.453
  - type: map_at_3 value: 37.785999999999994
  - type: map_at_5 value: 40.105000000000004
  - type: mrr_at_1 value: 35.046
  - type: mrr_at_10 value: 46.926
  - type: mrr_at_100 value: 47.815000000000005
  - type: mrr_at_1000 value: 47.849000000000004
  - type: mrr_at_3 value: 44.273
  - type: mrr_at_5 value: 45.774
  - type: ndcg_at_1 value: 35.046
  - type: ndcg_at_10 value: 48.937000000000005
  - type: ndcg_at_100 value: 54.544000000000004
  - type: ndcg_at_1000 value: 56.069
  - type: ndcg_at_3 value: 42.858000000000004
  - type: ndcg_at_5 value: 45.644
  - type: precision_at_1 value: 35.046
  - type: precision_at_10 value: 9.452
  - type: precision_at_100 value: 1.429
  - type: precision_at_1000 value: 0.173
  - type: precision_at_3 value: 21.346999999999998
  - type: precision_at_5 value: 15.342
  - type: recall_at_1 value: 28.038999999999998
  - type: recall_at_10 value: 64.59700000000001
  - type: recall_at_100 value: 87.735
  - type: recall_at_1000 value: 97.41300000000001
  - type: recall_at_3 value: 47.368
  - type: recall_at_5 value: 54.93900000000001
- task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: BeIR/cqadupstack config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics:
  - type: map_at_1 value: 28.17291666666667
  - type: map_at_10 value: 40.025749999999995
  - type: map_at_100 value: 41.39208333333333
  - type: map_at_1000 value: 41.499249999999996
  - type: map_at_3 value: 36.347
  - type: map_at_5 value: 38.41391666666667
  - type: mrr_at_1 value: 33.65925
  - type: mrr_at_10 value: 44.085499999999996
  - type: mrr_at_100 value: 44.94116666666667
  - type: mrr_at_1000 value: 44.9855
  - type: mrr_at_3 value: 41.2815
  - type: mrr_at_5 value: 42.91491666666666
  - type: ndcg_at_1 value: 33.65925
  - type: ndcg_at_10 value: 46.430833333333325
  - type: ndcg_at_100 value: 51.761
  - type: ndcg_at_1000 value: 53.50899999999999
  - type: ndcg_at_3 value: 40.45133333333333
  - type: ndcg_at_5 value: 43.31483333333334
  - type: precision_at_1 value: 33.65925
  - type: precision_at_10 value: 8.4995
  - type: precision_at_100 value: 1.3210000000000004
  - type: precision_at_1000 value: 0.16591666666666666
  - type: precision_at_3 value: 19.165083333333335
  - type: precision_at_5 value: 13.81816666666667
  - type: recall_at_1 value: 28.17291666666667
  - type: recall_at_10 value: 61.12624999999999
  - type: recall_at_100 value: 83.97266666666667
  - type: recall_at_1000 value: 95.66550000000001
  - type: recall_at_3 value: 44.661249999999995
  - type: recall_at_5 value: 51.983333333333334
  - type: map_at_1 value: 17.936
  - type: map_at_10 value: 27.399
  - type: map_at_100 value: 28.632
  - type: map_at_1000 value: 28.738000000000003
  - type: map_at_3 value: 24.456
  - type: map_at_5 value: 26.06
  - type: mrr_at_1 value: 19.224
  - type: mrr_at_10 value: 28.998
  - type: mrr_at_100 value: 30.11
  - type: mrr_at_1000 value: 30.177
  - type: mrr_at_3 value: 26.247999999999998
  - type: mrr_at_5 value: 27.708
  - type: ndcg_at_1 value: 19.224
  - type: ndcg_at_10 value: 32.911
  - type: ndcg_at_100 value: 38.873999999999995
  - type: ndcg_at_1000 value: 41.277
  - type: ndcg_at_3 value: 27.142
  - type: ndcg_at_5 value: 29.755
  - type: precision_at_1 value: 19.224
  - type: precision_at_10 value: 5.6930000000000005
  - type: precision_at_100 value: 0.9259999999999999
  - type: precision_at_1000 value: 0.126
  - type: precision_at_3 value: 12.138
  - type: precision_at_5 value: 8.909
  - type: recall_at_1 value: 17.936
  - type: recall_at_10 value: 48.096
  - type: recall_at_100 value: 75.389
  - type: recall_at_1000 value: 92.803
  - type: recall_at_3 value: 32.812999999999995
  - type: recall_at_5 value: 38.851
- task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: BeIR/cqadupstack config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics:
  - type: map_at_1 value: 24.681
  - type: map_at_10 value: 34.892
  - type: map_at_100 value: 35.996
  - type: map_at_1000 value: 36.083
  - type: map_at_3 value: 31.491999999999997
  - type: map_at_5 value: 33.632
  - type: mrr_at_1 value: 28.528
  - type: mrr_at_10 value: 37.694
  - type: mrr_at_100 value: 38.613
  - type: mrr_at_1000 value: 38.668
  - type: mrr_at_3 value: 34.714
  - type: mrr_at_5 value: 36.616
  - type: ndcg_at_1 value: 28.528
  - type: ndcg_at_10 value: 40.703
  - type: ndcg_at_100 value: 45.993
  - type: ndcg_at_1000 value: 47.847
  - type: ndcg_at_3 value: 34.622
  - type: ndcg_at_5 value: 38.035999999999994
  - type: precision_at_1 value: 28.528
  - type: precision_at_10 value: 6.902
  - type: precision_at_100 value: 1.0370000000000001
  - type: precision_at_1000 value: 0.126
  - type: precision_at_3 value: 15.798000000000002
  - type: precision_at_5 value: 11.655999999999999
  - type: recall_at_1 value: 24.681
  - type: recall_at_10 value: 55.81
  - type: recall_at_100 value: 79.785
  - type: recall_at_1000 value: 92.959
  - type: recall_at_3 value: 39.074
  - type: recall_at_5 value: 47.568
- task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: BeIR/cqadupstack config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics:
  - type: map_at_1 value: 18.627
  - type: map_at_10 value: 27.872000000000003
  - type: map_at_100 value: 29.237999999999996
  - type: map_at_1000 value: 29.363
  - type: map_at_3 value: 24.751
  - type: map_at_5 value: 26.521
  - type: mrr_at_1 value: 23.021
  - type: mrr_at_10 value: 31.924000000000003
  - type: mrr_at_100 value: 32.922000000000004
  - type: mrr_at_1000 value: 32.988
  - type: mrr_at_3 value: 29.192
  - type: mrr_at_5 value: 30.798
  - type: ndcg_at_1 value: 23.021
  - type: ndcg_at_10 value: 33.535
  - type: ndcg_at_100 value: 39.732
  - type: ndcg_at_1000 value: 42.201
  - type: ndcg_at_3 value: 28.153
  - type: ndcg_at_5 value: 30.746000000000002
  - type: precision_at_1 value: 23.021
  - type: precision_at_10 value: 6.459
  - type: precision_at_100 value: 1.1320000000000001
  - type: precision_at_1000 value: 0.153
  - type: precision_at_3 value: 13.719000000000001
  - type: precision_at_5 value: 10.193000000000001
  - type: recall_at_1 value: 18.627
  - type: recall_at_10 value: 46.463
  - type: recall_at_100 value: 74.226
  - type: recall_at_1000 value: 91.28500000000001
  - type: recall_at_3 value: 31.357000000000003
  - type: recall_at_5 value: 38.067
- task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: BeIR/cqadupstack config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics:
  - type: map_at_1 value: 31.457
  - type: map_at_10 value: 42.888
  - type: map_at_100 value: 44.24
  - type: map_at_1000 value: 44.327
  - type: map_at_3 value: 39.588
  - type: map_at_5 value: 41.423
  - type: mrr_at_1 value: 37.126999999999995
  - type: mrr_at_10 value: 47.083000000000006
  - type: mrr_at_100 value: 47.997
  - type: mrr_at_1000 value: 48.044
  - type: mrr_at_3 value: 44.574000000000005
  - type: mrr_at_5 value: 46.202
  - type: ndcg_at_1 value: 37.126999999999995
  - type: ndcg_at_10 value: 48.833
  - type: ndcg_at_100 value: 54.327000000000005
  - type: ndcg_at_1000 value: 56.011
  - type: ndcg_at_3 value: 43.541999999999994
  - type: ndcg_at_5 value: 46.127
  - type: precision_at_1 value: 37.126999999999995
  - type: precision_at_10 value: 8.376999999999999
  - type: precision_at_100 value: 1.2309999999999999
  - type: precision_at_1000 value: 0.146
  - type: precision_at_3 value: 20.211000000000002
  - type: precision_at_5 value: 14.16
  - type: recall_at_1 value: 31.457
  - type: recall_at_10 value: 62.369
  - type: recall_at_100 value: 85.444
  - type: recall_at_1000 value: 96.65599999999999
  - type: recall_at_3 value: 47.961
  - type: recall_at_5 value: 54.676
- task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: BeIR/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics:
  - type: map_at_1 value: 27.139999999999997
  - type: map_at_10 value: 38.801
  - type: map_at_100 value: 40.549
  - type: map_at_1000 value: 40.802
  - type: map_at_3 value: 35.05
  - type: map_at_5 value: 36.884
  - type: mrr_at_1 value: 33.004
  - type: mrr_at_10 value: 43.864
  - type: mrr_at_100 value: 44.667
  - type: mrr_at_1000 value: 44.717
  - type: mrr_at_3 value: 40.777
  - type: mrr_at_5 value: 42.319
  - type: ndcg_at_1 value: 33.004
  - type: ndcg_at_10 value: 46.022
  - type: ndcg_at_100 value: 51.542
  - type: ndcg_at_1000 value: 53.742000000000004
  - type: ndcg_at_3 value: 39.795
  - type: ndcg_at_5 value: 42.272
  - type: precision_at_1 value: 33.004
  - type: precision_at_10 value: 9.012
  - type: precision_at_100 value: 1.7770000000000001
  - type: precision_at_1000 value: 0.26
  - type: precision_at_3 value: 19.038
  - type: precision_at_5 value: 13.675999999999998
  - type: recall_at_1 value: 27.139999999999997
  - type: recall_at_10 value: 60.961
  - type: recall_at_100 value: 84.451
  - type: recall_at_1000 value: 98.113
  - type: recall_at_3 value: 43.001
  - type: recall_at_5 value: 49.896
- task: type: Retrieval dataset: name: MTEB ClimateFEVER type: mteb/climate-fever config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics:
  - type: map_at_1 value: 22.076999999999998
  - type: map_at_10 value: 35.44
  - type: map_at_100 value: 37.651
  - type: map_at_1000 value: 37.824999999999996
  - type: map_at_3 value: 30.764999999999997
  - type: map_at_5 value: 33.26
  - type: mrr_at_1 value: 50.163000000000004
  - type: mrr_at_10 value: 61.207
  - type: mrr_at_100 value: 61.675000000000004
  - type: mrr_at_1000 value: 61.692
  - type: mrr_at_3 value: 58.60999999999999
  - type: mrr_at_5 value: 60.307
  - type: ndcg_at_1 value: 50.163000000000004
  - type: ndcg_at_10 value: 45.882
  - type: ndcg_at_100 value: 53.239999999999995
  - type: ndcg_at_1000 value: 55.852000000000004
  - type: ndcg_at_3 value: 40.514
  - type: ndcg_at_5 value: 42.038
  - type: precision_at_1 value: 50.163000000000004
  - type: precision_at_10 value: 13.466000000000001
  - type: precision_at_100 value: 2.164
  - type: precision_at_1000 value: 0.266
  - type: precision_at_3 value: 29.707
  - type: precision_at_5 value: 21.694
  - type: recall_at_1 value: 22.076999999999998
  - type: recall_at_10 value: 50.193
  - type: recall_at_100 value: 74.993
  - type: recall_at_1000 value: 89.131
  - type: recall_at_3 value: 35.472
  - type: recall_at_5 value: 41.814
- task: type: Retrieval dataset: name: MTEB DBPedia type: mteb/dbpedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics:
  - type: map_at_1 value: 9.953
  - type: map_at_10 value: 24.515
  - type: map_at_100 value: 36.173
  - type: map_at_1000 value: 38.351
  - type: map_at_3 value: 16.592000000000002
  - type: map_at_5 value: 20.036
  - type: mrr_at_1 value: 74.25
  - type: mrr_at_10 value: 81.813
  - type: mrr_at_100 value: 82.006
  - type: mrr_at_1000 value: 82.011
  - type: mrr_at_3 value: 80.875
  - type: mrr_at_5 value: 81.362
  - type: ndcg_at_1 value: 62.5
  - type: ndcg_at_10 value: 52.42
  - type: ndcg_at_100 value: 56.808
  - type: ndcg_at_1000 value: 63.532999999999994
  - type: ndcg_at_3 value: 56.654
  - type: ndcg_at_5 value: 54.18300000000001
  - type: precision_at_1 value: 74.25
  - type: precision_at_10 value: 42.699999999999996
  - type: precision_at_100 value: 13.675
  - type: precision_at_1000 value: 2.664
  - type: precision_at_3 value: 60.5
  - type: precision_at_5 value: 52.800000000000004
  - type: recall_at_1 value: 9.953
  - type: recall_at_10 value: 30.253999999999998
  - type: recall_at_100 value: 62.516000000000005
  - type: recall_at_1000 value: 84.163
  - type: recall_at_3 value: 18.13
  - type: recall_at_5 value: 22.771
- task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics:
  - type: accuracy value: 79.455
  - type: f1 value: 74.16798697647569
- task: type: Retrieval dataset: name: MTEB FEVER type: mteb/fever config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics:
  - type: map_at_1 value: 87.531
  - type: map_at_10 value: 93.16799999999999
  - type: map_at_100 value: 93.341
  - type: map_at_1000 value: 93.349
  - type: map_at_3 value: 92.444
  - type: map_at_5 value: 92.865
  - type: mrr_at_1 value: 94.014
  - type: mrr_at_10 value: 96.761
  - type: mrr_at_100 value: 96.762
  - type: mrr_at_1000 value: 96.762
  - type: mrr_at_3 value: 96.672
  - type: mrr_at_5 value: 96.736
  - type: ndcg_at_1 value: 94.014
  - type: ndcg_at_10 value: 95.112
  - type: ndcg_at_100 value: 95.578
  - type: ndcg_at_1000 value: 95.68900000000001
  - type: ndcg_at_3 value: 94.392
  - type: ndcg_at_5 value: 94.72500000000001
  - type: precision_at_1 value: 94.014
  - type: precision_at_10 value: 11.065
  - type: precision_at_100 value: 1.157
  - type: precision_at_1000 value: 0.11800000000000001
  - type: precision_at_3 value: 35.259
  - type: precision_at_5 value: 21.599
  - type: recall_at_1 value: 87.531
  - type: recall_at_10 value: 97.356
  - type: recall_at_100 value: 98.965
  - type: recall_at_1000 value: 99.607
  - type: recall_at_3 value: 95.312
  - type: recall_at_5 value: 96.295
- task: type: Retrieval dataset: name: MTEB FiQA2018 type: mteb/fiqa config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics:
  - type: map_at_1 value: 32.055
  - type: map_at_10 value: 53.114
  - type: map_at_100 value: 55.235
  - type: map_at_1000 value: 55.345
  - type: map_at_3 value: 45.854
  - type: map_at_5 value: 50.025
  - type: mrr_at_1 value: 60.34
  - type: mrr_at_10 value: 68.804
  - type: mrr_at_100 value: 69.309
  - type: mrr_at_1000 value: 69.32199999999999
  - type: mrr_at_3 value: 66.40899999999999
  - type: mrr_at_5 value: 67.976
  - type: ndcg_at_1 value: 60.34
  - type: ndcg_at_10 value: 62.031000000000006
  - type: ndcg_at_100 value: 68.00500000000001
  - type: ndcg_at_1000 value: 69.286
  - type: ndcg_at_3 value: 56.355999999999995
  - type: ndcg_at_5 value: 58.687
  - type: precision_at_1 value: 60.34
  - type: precision_at_10 value: 17.176
  - type: precision_at_100 value: 2.36
  - type: precision_at_1000 value: 0.259
  - type: precision_at_3 value: 37.14
  - type: precision_at_5 value: 27.809
  - type: recall_at_1 value: 32.055
  - type: recall_at_10 value: 70.91
  - type: recall_at_100 value: 91.83
  - type: recall_at_1000 value: 98.871
  - type: recall_at_3 value: 51.202999999999996
  - type: recall_at_5 value: 60.563
- task: type: Retrieval dataset: name: MTEB HotpotQA type: mteb/hotpotqa config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics:
  - type: map_at_1 value: 43.68
  - type: map_at_10 value: 64.389
  - type: map_at_100 value: 65.24
  - type: map_at_1000 value: 65.303
  - type: map_at_3 value: 61.309000000000005
  - type: map_at_5 value: 63.275999999999996
  - type: mrr_at_1 value: 87.36
  - type: mrr_at_10 value: 91.12
  - type: mrr_at_100 value: 91.227
  - type: mrr_at_1000 value: 91.229
  - type: mrr_at_3 value: 90.57600000000001
  - type: mrr_at_5 value: 90.912
  - type: ndcg_at_1 value: 87.36
  - type: ndcg_at_10 value: 73.076
  - type: ndcg_at_100 value: 75.895
  - type: ndcg_at_1000 value: 77.049
  - type: ndcg_at_3 value: 68.929
  - type: ndcg_at_5 value: 71.28
  - type: precision_at_1 value: 87.36
  - type: precision_at_10 value: 14.741000000000001
  - type: precision_at_100 value: 1.694
  - type: precision_at_1000 value: 0.185
  - type: precision_at_3 value: 43.043
  - type: precision_at_5 value: 27.681
  - type: recall_at_1 value: 43.68
  - type: recall_at_10 value: 73.707
  - type: recall_at_100 value: 84.7
  - type: recall_at_1000 value: 92.309
  - type: recall_at_3 value: 64.564
  - type: recall_at_5 value: 69.203
- task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics:
  - type: accuracy value: 96.75399999999999
  - type: ap value: 95.29389839242187
  - type: f1 value: 96.75348377433475
- task: type: Retrieval dataset: name: MTEB MSMARCO type: mteb/msmarco config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics:
  - type: map_at_1 value: 25.176
  - type: map_at_10 value: 38.598
  - type: map_at_100 value: 39.707
  - type: map_at_1000 value: 39.744
  - type: map_at_3 value: 34.566
  - type: map_at_5 value: 36.863
  - type: mrr_at_1 value: 25.874000000000002
  - type: mrr_at_10 value: 39.214
  - type: mrr_at_100 value: 40.251
  - type: mrr_at_1000 value: 40.281
  - type: mrr_at_3 value: 35.291
  - type: mrr_at_5 value: 37.545
  - type: ndcg_at_1 value: 25.874000000000002
  - type: ndcg_at_10 value: 45.98
  - type: ndcg_at_100 value: 51.197
  - type: ndcg_at_1000 value: 52.073
  - type: ndcg_at_3 value: 37.785999999999994
  - type: ndcg_at_5 value: 41.870000000000005
  - type: precision_at_1 value: 25.874000000000002
  - type: precision_at_10 value: 7.181
  - type: precision_at_100 value: 0.979
  - type: precision_at_1000 value: 0.106
  - type: precision_at_3 value: 16.051000000000002
  - type: precision_at_5 value: 11.713
  - type: recall_at_1 value: 25.176
  - type: recall_at_10 value: 68.67699999999999
  - type: recall_at_100 value: 92.55
  - type: recall_at_1000 value: 99.164
  - type: recall_at_3 value: 46.372
  - type: recall_at_5 value: 56.16
- task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
  - type: accuracy value: 99.03784769721841
  - type: f1 value: 98.97791641821495
- task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
  - type: accuracy value: 91.88326493388054
  - type: f1 value: 73.74809928034335
- task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
  - type: accuracy value: 85.41358439811701
  - type: f1 value: 83.503679460639
- task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 89.77135171486215
  - type: f1 value: 88.89843747468366
- task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics:
  - type: v_measure value: 46.22695362087359
- task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics:
  - type: v_measure value: 44.132372165849425
- task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics:
  - type: map value: 33.35680810650402
  - type: mrr value: 34.72625715637218
- task: type: Retrieval dataset: name: MTEB NFCorpus type: mteb/nfcorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics:
  - type: map_at_1 value: 7.165000000000001
  - type: map_at_10 value: 15.424
  - type: map_at_100 value: 20.28
  - type: map_at_1000 value: 22.065
  - type: map_at_3 value: 11.236
  - type: map_at_5 value: 13.025999999999998
  - type: mrr_at_1 value: 51.702999999999996
  - type: mrr_at_10 value: 59.965
  - type: mrr_at_100 value: 60.667
  - type: mrr_at_1000 value: 60.702999999999996
  - type: mrr_at_3 value: 58.772000000000006
  - type: mrr_at_5 value: 59.267
  - type: ndcg_at_1 value: 49.536
  - type: ndcg_at_10 value: 40.6
  - type: ndcg_at_100 value: 37.848
  - type: ndcg_at_1000 value: 46.657
  - type: ndcg_at_3 value: 46.117999999999995
  - type: ndcg_at_5 value: 43.619
  - type: precision_at_1 value: 51.393
  - type: precision_at_10 value: 30.31
  - type: precision_at_100 value: 9.972
  - type: precision_at_1000 value: 2.329
  - type: precision_at_3 value: 43.137
  - type: precision_at_5 value: 37.585
  - type: recall_at_1 value: 7.165000000000001
  - type: recall_at_10 value: 19.689999999999998
  - type: recall_at_100 value: 39.237
  - type: recall_at_1000 value: 71.417
  - type: recall_at_3 value: 12.247
  - type: recall_at_5 value: 14.902999999999999
- task: type: Retrieval dataset: name: MTEB NQ type: mteb/nq config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics:
  - type: map_at_1 value: 42.653999999999996
  - type: map_at_10 value: 59.611999999999995
  - type: map_at_100 value: 60.32300000000001
  - type: map_at_1000 value: 60.336
  - type: map_at_3 value: 55.584999999999994
  - type: map_at_5 value: 58.19
  - type: mrr_at_1 value: 47.683
  - type: mrr_at_10 value: 62.06700000000001
  - type: mrr_at_100 value: 62.537
  - type: mrr_at_1000 value: 62.544999999999995
  - type: mrr_at_3 value: 59.178
  - type: mrr_at_5 value: 61.034
  - type: ndcg_at_1 value: 47.654
  - type: ndcg_at_10 value: 67.001
  - type: ndcg_at_100 value: 69.73899999999999
  - type: ndcg_at_1000 value: 69.986
  - type: ndcg_at_3 value: 59.95700000000001
  - type: ndcg_at_5 value: 64.025
  - type: precision_at_1 value: 47.654
  - type: precision_at_10 value: 10.367999999999999
  - type: precision_at_100 value: 1.192
  - type: precision_at_1000 value: 0.121
  - type: precision_at_3 value: 26.651000000000003
  - type: precision_at_5 value: 18.459
  - type: recall_at_1 value: 42.653999999999996
  - type: recall_at_10 value: 86.619
  - type: recall_at_100 value: 98.04899999999999
  - type: recall_at_1000 value: 99.812
  - type: recall_at_3 value: 68.987
  - type: recall_at_5 value: 78.158
- task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: mteb/quora config: default split: test revision: None metrics:
  - type: map_at_1 value: 72.538
  - type: map_at_10 value: 86.702
  - type: map_at_100 value: 87.31
  - type: map_at_1000 value: 87.323
  - type: map_at_3 value: 83.87
  - type: map_at_5 value: 85.682
  - type: mrr_at_1 value: 83.31
  - type: mrr_at_10 value: 89.225
  - type: mrr_at_100 value: 89.30399999999999
  - type: mrr_at_1000 value: 89.30399999999999
  - type: mrr_at_3 value: 88.44300000000001
  - type: mrr_at_5 value: 89.005
  - type: ndcg_at_1 value: 83.32000000000001
  - type: ndcg_at_10 value: 90.095
  - type: ndcg_at_100 value: 91.12
  - type: ndcg_at_1000 value: 91.179
  - type: ndcg_at_3 value: 87.606
  - type: ndcg_at_5 value: 89.031
  - type: precision_at_1 value: 83.32000000000001
  - type: precision_at_10 value: 13.641
  - type: precision_at_100 value: 1.541
  - type: precision_at_1000 value: 0.157
  - type: precision_at_3 value: 38.377
  - type: precision_at_5 value: 25.162000000000003
  - type: recall_at_1 value: 72.538
  - type: recall_at_10 value: 96.47200000000001
  - type: recall_at_100 value: 99.785
  - type: recall_at_1000 value: 99.99900000000001
  - type: recall_at_3 value: 89.278
  - type: recall_at_5 value: 93.367
- task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics:
  - type: v_measure value: 73.55219145406065
- task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics:
  - type: v_measure value: 74.13437105242755
- task: type: Retrieval dataset: name: MTEB SCIDOCS type: mteb/scidocs config: default split: test revision: None metrics:
  - type: map_at_1 value: 6.873
  - type: map_at_10 value: 17.944
  - type: map_at_100 value: 21.171
  - type: map_at_1000 value: 21.528
  - type: map_at_3 value: 12.415
  - type: map_at_5 value: 15.187999999999999
  - type: mrr_at_1 value: 33.800000000000004
  - type: mrr_at_10 value: 46.455
  - type: mrr_at_100 value: 47.378
  - type: mrr_at_1000 value: 47.394999999999996
  - type: mrr_at_3 value: 42.367
  - type: mrr_at_5 value: 44.972
  - type: ndcg_at_1 value: 33.800000000000004
  - type: ndcg_at_10 value: 28.907
  - type: ndcg_at_100 value: 39.695
  - type: ndcg_at_1000 value: 44.582
  - type: ndcg_at_3 value: 26.949
  - type: ndcg_at_5 value: 23.988
  - type: precision_at_1 value: 33.800000000000004
  - type: precision_at_10 value: 15.079999999999998
  - type: precision_at_100 value: 3.056
  - type: precision_at_1000 value: 0.42100000000000004
  - type: precision_at_3 value: 25.167
  - type: precision_at_5 value: 21.26
  - type: recall_at_1 value: 6.873
  - type: recall_at_10 value: 30.568
  - type: recall_at_100 value: 62.062
  - type: recall_at_1000 value: 85.37700000000001
  - type: recall_at_3 value: 15.312999999999999
  - type: recall_at_5 value: 21.575
- task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics:
  - type: cos_sim_pearson value: 82.37009118256057
  - type: cos_sim_spearman value: 79.27986395671529
  - type: euclidean_pearson value: 79.18037715442115
  - type: euclidean_spearman value: 79.28004791561621
  - type: manhattan_pearson value: 79.34062972800541
  - type: manhattan_spearman value: 79.43106695543402
- task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics:
  - type: cos_sim_pearson value: 87.48474767383833
  - type: cos_sim_spearman value: 79.54505388752513
  - type: euclidean_pearson value: 83.43282704179565
  - type: euclidean_spearman value: 79.54579919925405
  - type: manhattan_pearson value: 83.77564492427952
  - type: manhattan_spearman value: 79.84558396989286
- task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics:
  - type: cos_sim_pearson value: 88.803698035802
  - type: cos_sim_spearman value: 88.83451367754881
  - type: euclidean_pearson value: 88.28939285711628
  - type: euclidean_spearman value: 88.83528996073112
  - type: manhattan_pearson value: 88.28017412671795
  - type: manhattan_spearman value: 88.9228828016344
- task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics:
  - type: cos_sim_pearson value: 85.27469288153428
  - type: cos_sim_spearman value: 83.87477064876288
  - type: euclidean_pearson value: 84.2601737035379
  - type: euclidean_spearman value: 83.87431082479074
  - type: manhattan_pearson value: 84.3621547772745
  - type: manhattan_spearman value: 84.12094375000423
- task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics:
  - type: cos_sim_pearson value: 88.12749863201587
  - type: cos_sim_spearman value: 88.54287568368565
  - type: euclidean_pearson value: 87.90429700607999
  - type: euclidean_spearman value: 88.5437689576261
  - type: manhattan_pearson value: 88.19276653356833
  - type: manhattan_spearman value: 88.99995393814679
- task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics:
  - type: cos_sim_pearson value: 85.68398747560902
  - type: cos_sim_spearman value: 86.48815303460574
  - type: euclidean_pearson value: 85.52356631237954
  - type: euclidean_spearman value: 86.486391949551
  - type: manhattan_pearson value: 85.67267981761788
  - type: manhattan_spearman value: 86.7073696332485
- task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
  - type: cos_sim_pearson value: 88.9057107443124
  - type: cos_sim_spearman value: 88.7312168757697
  - type: euclidean_pearson value: 88.72810439714794
  - type: euclidean_spearman value: 88.71976185854771
  - type: manhattan_pearson value: 88.50433745949111
  - type: manhattan_spearman value: 88.51726175544195
- task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics:
  - type: cos_sim_pearson value: 67.59391795109886
  - type: cos_sim_spearman value: 66.87613008631367
  - type: euclidean_pearson value: 69.23198488262217
  - type: euclidean_spearman value: 66.85427723013692
  - type: manhattan_pearson value: 69.50730124841084
  - type: manhattan_spearman value: 67.10404669820792
- task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics:
  - type: cos_sim_pearson value: 87.0820605344619
  - type: cos_sim_spearman value: 86.8518089863434
  - type: euclidean_pearson value: 86.31087134689284
  - type: euclidean_spearman value: 86.8518520517941
  - type: manhattan_pearson value: 86.47203796160612
  - type: manhattan_spearman value: 87.1080149734421
- task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics:
  - type: map value: 89.09255369305481
  - type: mrr value: 97.10323445617563
- task: type: Retrieval dataset: name: MTEB SciFact type: mteb/scifact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics:
  - type: map_at_1 value: 61.260999999999996
  - type: map_at_10 value: 74.043
  - type: map_at_100 value: 74.37700000000001
  - type: map_at_1000 value: 74.384
  - type: map_at_3 value: 71.222
  - type: map_at_5 value: 72.875
  - type: mrr_at_1 value: 64.333
  - type: mrr_at_10 value: 74.984
  - type: mrr_at_100 value: 75.247
  - type: mrr_at_1000 value: 75.25500000000001
  - type: mrr_at_3 value: 73.167
  - type: mrr_at_5 value: 74.35000000000001
  - type: ndcg_at_1 value: 64.333
  - type: ndcg_at_10 value: 79.06
  - type: ndcg_at_100 value: 80.416
  - type: ndcg_at_1000 value: 80.55600000000001
  - type: ndcg_at_3 value: 74.753
  - type: ndcg_at_5 value: 76.97500000000001
  - type: precision_at_1 value: 64.333
  - type: precision_at_10 value: 10.567
  - type: precision_at_100 value: 1.1199999999999999
  - type: precision_at_1000 value: 0.11299999999999999
  - type: precision_at_3 value: 29.889
  - type: precision_at_5 value: 19.533
  - type: recall_at_1 value: 61.260999999999996
  - type: recall_at_10 value: 93.167
  - type: recall_at_100 value: 99.0
  - type: recall_at_1000 value: 100.0
  - type: recall_at_3 value: 81.667
  - type: recall_at_5 value: 87.394
- task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics:
  - type: cos_sim_accuracy value: 99.71980198019801
  - type: cos_sim_ap value: 92.81616007802704
  - type: cos_sim_f1 value: 85.17548454688318
  - type: cos_sim_precision value: 89.43894389438944
  - type: cos_sim_recall value: 81.3
  - type: dot_accuracy value: 99.71980198019801
  - type: dot_ap value: 92.81398760591358
  - type: dot_f1 value: 85.17548454688318
  - type: dot_precision value: 89.43894389438944
  - type: dot_recall value: 81.3
  - type: euclidean_accuracy value: 99.71980198019801
  - type: euclidean_ap value: 92.81560637245072
  - type: euclidean_f1 value: 85.17548454688318
  - type: euclidean_precision value: 89.43894389438944
  - type: euclidean_recall value: 81.3
  - type: manhattan_accuracy value: 99.73069306930694
  - type: manhattan_ap value: 93.14005487480794
  - type: manhattan_f1 value: 85.56263269639068
  - type: manhattan_precision value: 91.17647058823529
  - type: manhattan_recall value: 80.60000000000001
  - type: max_accuracy value: 99.73069306930694
  - type: max_ap value: 93.14005487480794
  - type: max_f1 value: 85.56263269639068
- task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics:
  - type: v_measure value: 79.86443362395185
- task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics:
  - type: v_measure value: 49.40897096662564
- task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics:
  - type: map value: 55.66040806627947
  - type: mrr value: 56.58670475766064
- task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics:
  - type: cos_sim_pearson value: 31.51015090598575
  - type: cos_sim_spearman value: 31.35016454939226
  - type: dot_pearson value: 31.5150068731
  - type: dot_spearman value: 31.34790869023487
- task: type: Retrieval dataset: name: MTEB TRECCOVID type: mteb/trec-covid config: default split: test revision: None metrics:
  - type: map_at_1 value: 0.254
  - type: map_at_10 value: 2.064
  - type: map_at_100 value: 12.909
  - type: map_at_1000 value: 31.761
  - type: map_at_3 value: 0.738
  - type: map_at_5 value: 1.155
  - type: mrr_at_1 value: 96.0
  - type: mrr_at_10 value: 98.0
  - type: mrr_at_100 value: 98.0
  - type: mrr_at_1000 value: 98.0
  - type: mrr_at_3 value: 98.0
  - type: mrr_at_5 value: 98.0
  - type: ndcg_at_1 value: 93.0
  - type: ndcg_at_10 value: 82.258
  - type: ndcg_at_100 value: 64.34
  - type: ndcg_at_1000 value: 57.912
  - type: ndcg_at_3 value: 90.827
  - type: ndcg_at_5 value: 86.79
  - type: precision_at_1 value: 96.0
  - type: precision_at_10 value: 84.8
  - type: precision_at_100 value: 66.0
  - type: precision_at_1000 value: 25.356
  - type: precision_at_3 value: 94.667
  - type: precision_at_5 value: 90.4
  - type: recall_at_1 value: 0.254
  - type: recall_at_10 value: 2.1950000000000003
  - type: recall_at_100 value: 16.088
  - type: recall_at_1000 value: 54.559000000000005
  - type: recall_at_3 value: 0.75
  - type: recall_at_5 value: 1.191
- task: type: Retrieval dataset: name: MTEB Touche2020 type: mteb/touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics:
  - type: map_at_1 value: 2.976
  - type: map_at_10 value: 11.389000000000001
  - type: map_at_100 value: 18.429000000000002
  - type: map_at_1000 value: 20.113
  - type: map_at_3 value: 6.483
  - type: map_at_5 value: 8.770999999999999
  - type: mrr_at_1 value: 40.816
  - type: mrr_at_10 value: 58.118
  - type: mrr_at_100 value: 58.489999999999995
  - type: mrr_at_1000 value: 58.489999999999995
  - type: mrr_at_3 value: 53.061
  - type: mrr_at_5 value: 57.041
  - type: ndcg_at_1 value: 40.816
  - type: ndcg_at_10 value: 30.567
  - type: ndcg_at_100 value: 42.44
  - type: ndcg_at_1000 value: 53.480000000000004
  - type: ndcg_at_3 value: 36.016
  - type: ndcg_at_5 value: 34.257
  - type: precision_at_1 value: 42.857
  - type: precision_at_10 value: 25.714
  - type: precision_at_100 value: 8.429
  - type: precision_at_1000 value: 1.5939999999999999
  - type: precision_at_3 value: 36.735
  - type: precision_at_5 value: 33.878
  - type: recall_at_1 value: 2.976
  - type: recall_at_10 value: 17.854999999999997
  - type: recall_at_100 value: 51.833
  - type: recall_at_1000 value: 86.223
  - type: recall_at_3 value: 7.887
  - type: recall_at_5 value: 12.026
- task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics:
  - type: accuracy value: 85.1174
  - type: ap value: 30.169441069345748
  - type: f1 value: 69.79254701873245
- task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics:
  - type: accuracy value: 72.58347481607245
  - type: f1 value: 72.74877295564937
- task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics:
  - type: v_measure value: 53.90586138221305
- task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics:
  - type: cos_sim_accuracy value: 87.35769207844072
  - type: cos_sim_ap value: 77.9645072410354
  - type: cos_sim_f1 value: 71.32352941176471
  - type: cos_sim_precision value: 66.5903890160183
  - type: cos_sim_recall value: 76.78100263852242
  - type: dot_accuracy value: 87.37557370209214
  - type: dot_ap value: 77.96250046429908
  - type: dot_f1 value: 71.28932757557064
  - type: dot_precision value: 66.95249130938586
  - type: dot_recall value: 76.22691292875989
  - type: euclidean_accuracy value: 87.35173153722357
  - type: euclidean_ap value: 77.96520460741593
  - type: euclidean_f1 value: 71.32470733210104
  - type: euclidean_precision value: 66.91329479768785
  - type: euclidean_recall value: 76.35883905013192
  - type: manhattan_accuracy value: 87.25636287774931
  - type: manhattan_ap value: 77.77752485611796
  - type: manhattan_f1 value: 71.18148599269183
  - type: manhattan_precision value: 66.10859728506787
  - type: manhattan_recall value: 77.0976253298153
  - type: max_accuracy value: 87.37557370209214
  - type: max_ap value: 77.96520460741593
  - type: max_f1 value: 71.32470733210104
- task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics:
  - type: cos_sim_accuracy value: 89.38176737687739
  - type: cos_sim_ap value: 86.58811861657401
  - type: cos_sim_f1 value: 79.09430644097604
  - type: cos_sim_precision value: 75.45085977911366
  - type: cos_sim_recall value: 83.10748383122882
  - type: dot_accuracy value: 89.38370784336554
  - type: dot_ap value: 86.58840606004333
  - type: dot_f1 value: 79.10179860068133
  - type: dot_precision value: 75.44546153308643
  - type: dot_recall value: 83.13058207576223
  - type: euclidean_accuracy value: 89.38564830985369
  - type: euclidean_ap value: 86.58820721061164
  - type: euclidean_f1 value: 79.09070942235888
  - type: euclidean_precision value: 75.38729937194697
  - type: euclidean_recall value: 83.17677856482906
  - type: manhattan_accuracy value: 89.40699344122326
  - type: manhattan_ap value: 86.60631843011362
  - type: manhattan_f1 value: 79.14949970570925
  - type: manhattan_precision value: 75.78191039729502
  - type: manhattan_recall value: 82.83030489682784
  - type: max_accuracy value: 89.40699344122326
  - type: max_ap value: 86.60631843011362
  - type: max_f1 value: 79.14949970570925
- task: type: STS dataset: name: MTEB AFQMC type: C-MTEB/AFQMC config: default split: validation revision: b44c3b011063adb25877c13823db83bb193913c4 metrics:
  - type: cos_sim_pearson value: 65.58442135663871
  - type: cos_sim_spearman value: 72.2538631361313
  - type: euclidean_pearson value: 70.97255486607429
  - type: euclidean_spearman value: 72.25374250228647
  - type: manhattan_pearson value: 70.83250199989911
  - type: manhattan_spearman value: 72.14819496536272
- task: type: STS dataset: name: MTEB ATEC type: C-MTEB/ATEC config: default split: test revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 metrics:
  - type: cos_sim_pearson value: 59.99478404929932
  - type: cos_sim_spearman value: 62.61836216999812
  - type: euclidean_pearson value: 66.86429811933593
  - type: euclidean_spearman value: 62.6183520374191
  - type: manhattan_pearson value: 66.8063778911633
  - type: manhattan_spearman value: 62.569607573241115
- task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
  - type: accuracy value: 53.98400000000001
  - type: f1 value: 51.21447361350723
- task: type: STS dataset: name: MTEB BQ type: C-MTEB/BQ config: default split: test revision: e3dda5e115e487b39ec7e618c0c6a29137052a55 metrics:
  - type: cos_sim_pearson value: 79.11941660686553
  - type: cos_sim_spearman value: 81.25029594540435
  - type: euclidean_pearson value: 82.06973504238826
  - type: euclidean_spearman value: 81.2501989488524
  - type: manhattan_pearson value: 82.10094630392753
  - type: manhattan_spearman value: 81.27987244392389
- task: type: Clustering dataset: name: MTEB CLSClusteringP2P type: C-MTEB/CLSClusteringP2P config: default split: test revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476 metrics:
  - type: v_measure value: 47.07270168705156
- task: type: Clustering dataset: name: MTEB CLSClusteringS2S type: C-MTEB/CLSClusteringS2S config: default split: test revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f metrics:
  - type: v_measure value: 45.98511703185043
- task: type: Reranking dataset: name: MTEB CMedQAv1 type: C-MTEB/CMedQAv1-reranking config: default split: test revision: 8d7f1e942507dac42dc58017c1a001c3717da7df metrics:
  - type: map value: 88.19895157194931
  - type: mrr value: 90.21424603174603
- task: type: Reranking dataset: name: MTEB CMedQAv2 type: C-MTEB/CMedQAv2-reranking config: default split: test revision: 23d186750531a14a0357ca22cd92d712fd512ea0 metrics:
  - type: map value: 88.03317320980119
  - type: mrr value: 89.9461507936508
- task: type: Retrieval dataset: name: MTEB CmedqaRetrieval type: C-MTEB/CmedqaRetrieval config: default split: dev revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 metrics:
  - type: map_at_1 value: 29.037000000000003
  - type: map_at_10 value: 42.001
  - type: map_at_100 value: 43.773
  - type: map_at_1000 value: 43.878
  - type: map_at_3 value: 37.637
  - type: map_at_5 value: 40.034
  - type: mrr_at_1 value: 43.136
  - type: mrr_at_10 value: 51.158
  - type: mrr_at_100 value: 52.083
  - type: mrr_at_1000 value: 52.12
  - type: mrr_at_3 value: 48.733
  - type: mrr_at_5 value: 50.025
  - type: ndcg_at_1 value: 43.136
  - type: ndcg_at_10 value: 48.685
  - type: ndcg_at_100 value: 55.513
  - type: ndcg_at_1000 value: 57.242000000000004
  - type: ndcg_at_3 value: 43.329
  - type: ndcg_at_5 value: 45.438
  - type: precision_at_1 value: 43.136
  - type: precision_at_10 value: 10.56
  - type: precision_at_100 value: 1.6129999999999998
  - type: precision_at_1000 value: 0.184
  - type: precision_at_3 value: 24.064
  - type: precision_at_5 value: 17.269000000000002
  - type: recall_at_1 value: 29.037000000000003
  - type: recall_at_10 value: 59.245000000000005
  - type: recall_at_100 value: 87.355
  - type: recall_at_1000 value: 98.74000000000001
  - type: recall_at_3 value: 42.99
  - type: recall_at_5 value: 49.681999999999995
- task: type: PairClassification dataset: name: MTEB Cmnli type: C-MTEB/CMNLI config: default split: validation revision: 41bc36f332156f7adc9e38f53777c959b2ae9766 metrics:
  - type: cos_sim_accuracy value: 82.68190018039687
  - type: cos_sim_ap value: 90.18017125327886
  - type: cos_sim_f1 value: 83.64080906868193
  - type: cos_sim_precision value: 79.7076890489303
  - type: cos_sim_recall value: 87.98223053542202
  - type: dot_accuracy value: 82.68190018039687
  - type: dot_ap value: 90.18782350103646
  - type: dot_f1 value: 83.64242087729039
  - type: dot_precision value: 79.65313028764805
  - type: dot_recall value: 88.05237315875614
  - type: euclidean_accuracy value: 82.68190018039687
  - type: euclidean_ap value: 90.1801957900632
  - type: euclidean_f1 value: 83.63636363636364
  - type: euclidean_precision value: 79.52772506852203
  - type: euclidean_recall value: 88.19265840542437
  - type: manhattan_accuracy value: 82.14070956103427
  - type: manhattan_ap value: 89.96178420101427
  - type: manhattan_f1 value: 83.21087838578791
  - type: manhattan_precision value: 78.35605121850475
  - type: manhattan_recall value: 88.70703764320785
  - type: max_accuracy value: 82.68190018039687
  - type: max_ap value: 90.18782350103646
  - type: max_f1 value: 83.64242087729039
- task: type: Retrieval dataset: name: MTEB CovidRetrieval type: C-MTEB/CovidRetrieval config: default split: dev revision: 1271c7809071a13532e05f25fb53511ffce77117 metrics:
  - type: map_at_1 value: 72.234
  - type: map_at_10 value: 80.10000000000001
  - type: map_at_100 value: 80.36
  - type: map_at_1000 value: 80.363
  - type: map_at_3 value: 78.315
  - type: map_at_5 value: 79.607
  - type: mrr_at_1 value: 72.392
  - type: mrr_at_10 value: 80.117
  - type: mrr_at_100 value: 80.36999999999999
  - type: mrr_at_1000 value: 80.373
  - type: mrr_at_3 value: 78.469
  - type: mrr_at_5 value: 79.633
  - type: ndcg_at_1 value: 72.392
  - type: ndcg_at_10 value: 83.651
  - type: ndcg_at_100 value: 84.749
  - type: ndcg_at_1000 value: 84.83000000000001
  - type: ndcg_at_3 value: 80.253
  - type: ndcg_at_5 value: 82.485
  - type: precision_at_1 value: 72.392
  - type: precision_at_10 value: 9.557
  - type: precision_at_100 value: 1.004
  - type: precision_at_1000 value: 0.101
  - type: precision_at_3 value: 28.732000000000003
  - type: precision_at_5 value: 18.377
  - type: recall_at_1 value: 72.234
  - type: recall_at_10 value: 94.573
  - type: recall_at_100 value: 99.368
  - type: recall_at_1000 value: 100.0
  - type: recall_at_3 value: 85.669
  - type: recall_at_5 value: 91.01700000000001
- task: type: Retrieval dataset: name: MTEB DuRetrieval type: C-MTEB/DuRetrieval config: default split: dev revision: a1a333e290fe30b10f3f56498e3a0d911a693ced metrics:
  - type: map_at_1 value: 26.173999999999996
  - type: map_at_10 value: 80.04
  - type: map_at_100 value: 82.94500000000001
  - type: map_at_1000 value: 82.98100000000001
  - type: map_at_3 value: 55.562999999999995
  - type: map_at_5 value: 69.89800000000001
  - type: mrr_at_1 value: 89.5
  - type: mrr_at_10 value: 92.996
  - type: mrr_at_100 value: 93.06400000000001
  - type: mrr_at_1000 value: 93.065
  - type: mrr_at_3 value: 92.658
  - type: mrr_at_5 value: 92.84599999999999
  - type: ndcg_at_1 value: 89.5
  - type: ndcg_at_10 value: 87.443
  - type: ndcg_at_100 value: 90.253
  - type: ndcg_at_1000 value: 90.549
  - type: ndcg_at_3 value: 85.874
  - type: ndcg_at_5 value: 84.842
  - type: precision_at_1 value: 89.5
  - type: precision_at_10 value: 41.805
  - type: precision_at_100 value: 4.827
  - type: precision_at_1000 value: 0.49
  - type: precision_at_3 value: 76.85
  - type: precision_at_5 value: 64.8
  - type: recall_at_1 value: 26.173999999999996
  - type: recall_at_10 value: 89.101
  - type: recall_at_100 value: 98.08099999999999
  - type: recall_at_1000 value: 99.529
  - type: recall_at_3 value: 57.902
  - type: recall_at_5 value: 74.602
- task: type: Retrieval dataset: name: MTEB EcomRetrieval type: C-MTEB/EcomRetrieval config: default split: dev revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 metrics:
  - type: map_at_1 value: 56.10000000000001
  - type: map_at_10 value: 66.15299999999999
  - type: map_at_100 value: 66.625
  - type: map_at_1000 value: 66.636
  - type: map_at_3 value: 63.632999999999996
  - type: map_at_5 value: 65.293
  - type: mrr_at_1 value: 56.10000000000001
  - type: mrr_at_10 value: 66.15299999999999
  - type: mrr_at_100 value: 66.625
  - type: mrr_at_1000 value: 66.636
  - type: mrr_at_3 value: 63.632999999999996
  - type: mrr_at_5 value: 65.293
  - type: ndcg_at_1 value: 56.10000000000001
  - type: ndcg_at_10 value: 71.146
  - type: ndcg_at_100 value: 73.27799999999999
  - type: ndcg_at_1000 value: 73.529
  - type: ndcg_at_3 value: 66.09
  - type: ndcg_at_5 value: 69.08999999999999
  - type: precision_at_1 value: 56.10000000000001
  - type: precision_at_10 value: 8.68
  - type: precision_at_100 value: 0.964
  - type: precision_at_1000 value: 0.098
  - type: precision_at_3 value: 24.4
  - type: precision_at_5 value: 16.1
  - type: recall_at_1 value: 56.10000000000001
  - type: recall_at_10 value: 86.8
  - type: recall_at_100 value: 96.39999999999999
  - type: recall_at_1000 value: 98.3
  - type: recall_at_3 value: 73.2
  - type: recall_at_5 value: 80.5
- task: type: Classification dataset: name: MTEB IFlyTek type: C-MTEB/IFlyTek-classification config: default split: validation revision: 421605374b29664c5fc098418fe20ada9bd55f8a metrics:
  - type: accuracy value: 54.52096960369373
  - type: f1 value: 40.930845295808695
- task: type: Classification dataset: name: MTEB JDReview type: C-MTEB/JDReview-classification config: default split: test revision: b7c64bd89eb87f8ded463478346f76731f07bf8b metrics:
  - type: accuracy value: 86.51031894934334
  - type: ap value: 55.9516014323483
  - type: f1 value: 81.54813679326381
- task: type: STS dataset: name: MTEB LCQMC type: C-MTEB/LCQMC config: default split: test revision: 17f9b096f80380fce5ed12a9be8be7784b337daf metrics:
  - type: cos_sim_pearson value: 69.67437838574276
  - type: cos_sim_spearman value: 73.81314174653045
  - type: euclidean_pearson value: 72.63430276680275
  - type: euclidean_spearman value: 73.81358736777001
  - type: manhattan_pearson value: 72.58743833842829
  - type: manhattan_spearman value: 73.7590419009179
- task: type: Reranking dataset: name: MTEB MMarcoReranking type: C-MTEB/Mmarco-reranking config: default split: dev revision: None metrics:
  - type: map value: 31.648613483640254
  - type: mrr value: 30.37420634920635
- task: type: Retrieval dataset: name: MTEB MMarcoRetrieval type: C-MTEB/MMarcoRetrieval config: default split: dev revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 metrics:
  - type: map_at_1 value: 73.28099999999999
  - type: map_at_10 value: 81.977
  - type: map_at_100 value: 82.222
  - type: map_at_1000 value: 82.22699999999999
  - type: map_at_3 value: 80.441
  - type: map_at_5 value: 81.46600000000001
  - type: mrr_at_1 value: 75.673
  - type: mrr_at_10 value: 82.41000000000001
  - type: mrr_at_100 value: 82.616
  - type: mrr_at_1000 value: 82.621
  - type: mrr_at_3 value: 81.094
  - type: mrr_at_5 value: 81.962
  - type: ndcg_at_1 value: 75.673
  - type: ndcg_at_10 value: 85.15599999999999
  - type: ndcg_at_100 value: 86.151
  - type: ndcg_at_1000 value: 86.26899999999999
  - type: ndcg_at_3 value: 82.304
  - type: ndcg_at_5 value: 84.009
  - type: precision_at_1 value: 75.673
  - type: precision_at_10 value: 10.042
  - type: precision_at_100 value: 1.052
  - type: precision_at_1000 value: 0.106
  - type: precision_at_3 value: 30.673000000000002
  - type: precision_at_5 value: 19.326999999999998
  - type: recall_at_1 value: 73.28099999999999
  - type: recall_at_10 value: 94.446
  - type: recall_at_100 value: 98.737
  - type: recall_at_1000 value: 99.649
  - type: recall_at_3 value: 86.984
  - type: recall_at_5 value: 91.024
- task: type: Classification dataset: name: MTEB MassiveIntentClassification (zh-CN) type: mteb/amazon_massive_intent config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
  - type: accuracy value: 81.08607935440484
  - type: f1 value: 78.24879986066307
- task: type: Classification dataset: name: MTEB MassiveScenarioClassification (zh-CN) type: mteb/amazon_massive_scenario config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 86.05917955615332
  - type: f1 value: 85.05279279434997
- task: type: Retrieval dataset: name: MTEB MedicalRetrieval type: C-MTEB/MedicalRetrieval config: default split: dev revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 metrics:
  - type: map_at_1 value: 56.2
  - type: map_at_10 value: 62.57899999999999
  - type: map_at_100 value: 63.154999999999994
  - type: map_at_1000 value: 63.193
  - type: map_at_3 value: 61.217
  - type: map_at_5 value: 62.012
  - type: mrr_at_1 value: 56.3
  - type: mrr_at_10 value: 62.629000000000005
  - type: mrr_at_100 value: 63.205999999999996
  - type: mrr_at_1000 value: 63.244
  - type: mrr_at_3 value: 61.267
  - type: mrr_at_5 value: 62.062
  - type: ndcg_at_1 value: 56.2
  - type: ndcg_at_10 value: 65.592
  - type: ndcg_at_100 value: 68.657
  - type: ndcg_at_1000 value: 69.671
  - type: ndcg_at_3 value: 62.808
  - type: ndcg_at_5 value: 64.24499999999999
  - type: precision_at_1 value: 56.2
  - type: precision_at_10 value: 7.5
  - type: precision_at_100 value: 0.899
  - type: precision_at_1000 value: 0.098
  - type: precision_at_3 value: 22.467000000000002
  - type: precision_at_5 value: 14.180000000000001
  - type: recall_at_1 value: 56.2
  - type: recall_at_10 value: 75.0
  - type: recall_at_100 value: 89.9
  - type: recall_at_1000 value: 97.89999999999999
  - type: recall_at_3 value: 67.4
  - type: recall_at_5 value: 70.89999999999999
- task: type: Classification dataset: name: MTEB MultilingualSentiment type: C-MTEB/MultilingualSentiment-classification config: default split: validation revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a metrics:
  - type: accuracy value: 76.87666666666667
  - type: f1 value: 76.7317686219665
- task: type: PairClassification dataset: name: MTEB Ocnli type: C-MTEB/OCNLI config: default split: validation revision: 66e76a618a34d6d565d5538088562851e6daa7ec metrics:
  - type: cos_sim_accuracy value: 79.64266377910124
  - type: cos_sim_ap value: 84.78274442344829
  - type: cos_sim_f1 value: 81.16947472745292
  - type: cos_sim_precision value: 76.47058823529412
  - type: cos_sim_recall value: 86.48363252375924
  - type: dot_accuracy value: 79.64266377910124
  - type: dot_ap value: 84.7851404063692
  - type: dot_f1 value: 81.16947472745292
  - type: dot_precision value: 76.47058823529412
  - type: dot_recall value: 86.48363252375924
  - type: euclidean_accuracy value: 79.64266377910124
  - type: euclidean_ap value: 84.78068373762378
  - type: euclidean_f1 value: 81.14794656110837
  - type: euclidean_precision value: 76.35009310986965
  - type: euclidean_recall value: 86.58922914466737
  - type: manhattan_accuracy value: 79.48023822414727
  - type: manhattan_ap value: 84.72928897427576
  - type: manhattan_f1 value: 81.32084770823064
  - type: manhattan_precision value: 76.24768946395564
  - type: manhattan_recall value: 87.11721224920802
  - type: max_accuracy value: 79.64266377910124
  - type: max_ap value: 84.7851404063692
  - type: max_f1 value: 81.32084770823064
- task: type: Classification dataset: name: MTEB OnlineShopping type: C-MTEB/OnlineShopping-classification config: default split: test revision: e610f2ebd179a8fda30ae534c3878750a96db120 metrics:
  - type: accuracy value: 94.3
  - type: ap value: 92.8664032274438
  - type: f1 value: 94.29311102997727
- task: type: STS dataset: name: MTEB PAWSX type: C-MTEB/PAWSX config: default split: test revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1 metrics:
  - type: cos_sim_pearson value: 48.51392279882909
  - type: cos_sim_spearman value: 54.06338895994974
  - type: euclidean_pearson value: 52.58480559573412
  - type: euclidean_spearman value: 54.06417276612201
  - type: manhattan_pearson value: 52.69525121721343
  - type: manhattan_spearman value: 54.048147455389675
- task: type: STS dataset: name: MTEB QBQTC type: C-MTEB/QBQTC config: default split: test revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7 metrics:
  - type: cos_sim_pearson value: 29.728387290757325
  - type: cos_sim_spearman value: 31.366121633635284
  - type: euclidean_pearson value: 29.14588368552961
  - type: euclidean_spearman value: 31.36764411112844
  - type: manhattan_pearson value: 29.63517350523121
  - type: manhattan_spearman value: 31.94157020583762
- task: type: STS dataset: name: MTEB STS22 (zh) type: mteb/sts22-crosslingual-sts config: zh split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics:
  - type: cos_sim_pearson value: 63.64868296271406
  - type: cos_sim_spearman value: 66.12800618164744
  - type: euclidean_pearson value: 63.21405767340238
  - type: euclidean_spearman value: 66.12786567790748
  - type: manhattan_pearson value: 64.04300276525848
  - type: manhattan_spearman value: 66.5066857145652
- task: type: STS dataset: name: MTEB STSB type: C-MTEB/STSB config: default split: test revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0 metrics:
  - type: cos_sim_pearson value: 81.2302623912794
  - type: cos_sim_spearman value: 81.16833673266562
  - type: euclidean_pearson value: 79.47647843876024
  - type: euclidean_spearman value: 81.16944349524972
  - type: manhattan_pearson value: 79.84947238492208
  - type: manhattan_spearman value: 81.64626599410026
- task: type: Reranking dataset: name: MTEB T2Reranking type: C-MTEB/T2Reranking config: default split: dev revision: 76631901a18387f85eaa53e5450019b87ad58ef9 metrics:
  - type: map value: 67.80129586475687
  - type: mrr value: 77.77402311635554
- task: type: Retrieval dataset: name: MTEB T2Retrieval type: C-MTEB/T2Retrieval config: default split: dev revision: 8731a845f1bf500a4f111cf1070785c793d10e64 metrics:
  - type: map_at_1 value: 28.666999999999998
  - type: map_at_10 value: 81.063
  - type: map_at_100 value: 84.504
  - type: map_at_1000 value: 84.552
  - type: map_at_3 value: 56.897
  - type: map_at_5 value: 70.073
  - type: mrr_at_1 value: 92.087
  - type: mrr_at_10 value: 94.132
  - type: mrr_at_100 value: 94.19800000000001
  - type: mrr_at_1000 value: 94.19999999999999
  - type: mrr_at_3 value: 93.78999999999999
  - type: mrr_at_5 value: 94.002
  - type: ndcg_at_1 value: 92.087
  - type: ndcg_at_10 value: 87.734
  - type: ndcg_at_100 value: 90.736
  - type: ndcg_at_1000 value: 91.184
  - type: ndcg_at_3 value: 88.78
  - type: ndcg_at_5 value: 87.676
  - type: precision_at_1 value: 92.087
  - type: precision_at_10 value: 43.46
  - type: precision_at_100 value: 5.07
  - type: precision_at_1000 value: 0.518
  - type: precision_at_3 value: 77.49000000000001
  - type: precision_at_5 value: 65.194
  - type: recall_at_1 value: 28.666999999999998
  - type: recall_at_10 value: 86.632
  - type: recall_at_100 value: 96.646
  - type: recall_at_1000 value: 98.917
  - type: recall_at_3 value: 58.333999999999996
  - type: recall_at_5 value: 72.974
- task: type: Classification dataset: name: MTEB TNews type: C-MTEB/TNews-classification config: default split: validation revision: 317f262bf1e6126357bbe89e875451e4b0938fe4 metrics:
  - type: accuracy value: 52.971999999999994
  - type: f1 value: 50.2898280984929
- task: type: Clustering dataset: name: MTEB ThuNewsClusteringP2P type: C-MTEB/ThuNewsClusteringP2P config: default split: test revision: 5798586b105c0434e4f0fe5e767abe619442cf93 metrics:
  - type: v_measure value: 86.0797948663824
- task: type: Clustering dataset: name: MTEB ThuNewsClusteringS2S type: C-MTEB/ThuNewsClusteringS2S config: default split: test revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d metrics:
  - type: v_measure value: 85.10759092255017
- task: type: Retrieval dataset: name: MTEB VideoRetrieval type: C-MTEB/VideoRetrieval config: default split: dev revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 metrics:
  - type: map_at_1 value: 65.60000000000001
  - type: map_at_10 value: 74.773
  - type: map_at_100 value: 75.128
  - type: map_at_1000 value: 75.136
  - type: map_at_3 value: 73.05
  - type: map_at_5 value: 74.13499999999999
  - type: mrr_at_1 value: 65.60000000000001
  - type: mrr_at_10 value: 74.773
  - type: mrr_at_100 value: 75.128
  - type: mrr_at_1000 value: 75.136
  - type: mrr_at_3 value: 73.05
  - type: mrr_at_5 value: 74.13499999999999
  - type: ndcg_at_1 value: 65.60000000000001
  - type: ndcg_at_10 value: 78.84299999999999
  - type: ndcg_at_100 value: 80.40899999999999
  - type: ndcg_at_1000 value: 80.57
  - type: ndcg_at_3 value: 75.40599999999999
  - type: ndcg_at_5 value: 77.351
  - type: precision_at_1 value: 65.60000000000001
  - type: precision_at_10 value: 9.139999999999999
  - type: precision_at_100 value: 0.984
  - type: precision_at_1000 value: 0.1
  - type: precision_at_3 value: 27.400000000000002
  - type: precision_at_5 value: 17.380000000000003
  - type: recall_at_1 value: 65.60000000000001
  - type: recall_at_10 value: 91.4
  - type: recall_at_100 value: 98.4
  - type: recall_at_1000 value: 99.6
  - type: recall_at_3 value: 82.19999999999999
  - type: recall_at_5 value: 86.9
- task: type: Classification dataset: name: MTEB Waimai type: C-MTEB/waimai-classification config: default split: test revision: 339287def212450dcaa9df8c22bf93e9980c7023 metrics:
  - type: accuracy value: 89.47
  - type: ap value: 75.59561751845389
  - type: f1 value: 87.95207751382563
- task: type: Clustering dataset: name: MTEB AlloProfClusteringP2P type: lyon-nlp/alloprof config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics:
  - type: v_measure value: 76.05592323841036
  - type: v_measure value: 64.51718058866508
- task: type: Reranking dataset: name: MTEB AlloprofReranking type: lyon-nlp/mteb-fr-reranking-alloprof-s2p config: default split: test revision: 666fdacebe0291776e86f29345663dfaf80a0db9 metrics:
  - type: map value: 73.08278490943373
  - type: mrr value: 74.66561454570449
- task: type: Retrieval dataset: name: MTEB AlloprofRetrieval type: lyon-nlp/alloprof config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics:
  - type: map_at_1 value: 38.912
  - type: map_at_10 value: 52.437999999999995
  - type: map_at_100 value: 53.38
  - type: map_at_1000 value: 53.427
  - type: map_at_3 value: 48.879
  - type: map_at_5 value: 50.934000000000005
  - type: mrr_at_1 value: 44.085
  - type: mrr_at_10 value: 55.337
  - type: mrr_at_100 value: 56.016999999999996
  - type: mrr_at_1000 value: 56.043
  - type: mrr_at_3 value: 52.55499999999999
  - type: mrr_at_5 value: 54.20399999999999
  - type: ndcg_at_1 value: 44.085
  - type: ndcg_at_10 value: 58.876
  - type: ndcg_at_100 value: 62.714000000000006
  - type: ndcg_at_1000 value: 63.721000000000004
  - type: ndcg_at_3 value: 52.444
  - type: ndcg_at_5 value: 55.692
  - type: precision_at_1 value: 44.085
  - type: precision_at_10 value: 9.21
  - type: precision_at_100 value: 1.164
  - type: precision_at_1000 value: 0.128
  - type: precision_at_3 value: 23.043
  - type: precision_at_5 value: 15.898000000000001
  - type: recall_at_1 value: 38.912
  - type: recall_at_10 value: 75.577
  - type: recall_at_100 value: 92.038
  - type: recall_at_1000 value: 99.325
  - type: recall_at_3 value: 58.592
  - type: recall_at_5 value: 66.235
- task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
  - type: accuracy value: 55.532000000000004
  - type: f1 value: 52.5783943471605
- task: type: Retrieval dataset: name: MTEB BSARDRetrieval type: maastrichtlawtech/bsard config: default split: test revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59 metrics:
  - type: map_at_1 value: 8.108
  - type: map_at_10 value: 14.710999999999999
  - type: map_at_100 value: 15.891
  - type: map_at_1000 value: 15.983
  - type: map_at_3 value: 12.237
  - type: map_at_5 value: 13.679
  - type: mrr_at_1 value: 8.108
  - type: mrr_at_10 value: 14.710999999999999
  - type: mrr_at_100 value: 15.891
  - type: mrr_at_1000 value: 15.983
  - type: mrr_at_3 value: 12.237
  - type: mrr_at_5 value: 13.679
  - type: ndcg_at_1 value: 8.108
  - type: ndcg_at_10 value: 18.796
  - type: ndcg_at_100 value: 25.098
  - type: ndcg_at_1000 value: 27.951999999999998
  - type: ndcg_at_3 value: 13.712
  - type: ndcg_at_5 value: 16.309
  - type: precision_at_1 value: 8.108
  - type: precision_at_10 value: 3.198
  - type: precision_at_100 value: 0.626
  - type: precision_at_1000 value: 0.086
  - type: precision_at_3 value: 6.006
  - type: precision_at_5 value: 4.865
  - type: recall_at_1 value: 8.108
  - type: recall_at_10 value: 31.982
  - type: recall_at_100 value: 62.613
  - type: recall_at_1000 value: 86.036
  - type: recall_at_3 value: 18.018
  - type: recall_at_5 value: 24.324
- task: type: Clustering dataset: name: MTEB HALClusteringS2S type: lyon-nlp/clustering-hal-s2s config: default split: test revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915 metrics:
  - type: v_measure value: 30.833269778867116
- task: type: Clustering dataset: name: MTEB MLSUMClusteringP2P type: mlsum config: default split: test revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 metrics:
  - type: v_measure value: 50.0281928004713
  - type: v_measure value: 43.699961510636534
- task: type: Classification dataset: name: MTEB MTOPDomainClassification (fr) type: mteb/mtop_domain config: fr split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
  - type: accuracy value: 96.68963357344191
  - type: f1 value: 96.45175170820961
- task: type: Classification dataset: name: MTEB MTOPIntentClassification (fr) type: mteb/mtop_intent config: fr split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
  - type: accuracy value: 87.46946445349202
  - type: f1 value: 65.79860440988624
- task: type: Classification dataset: name: MTEB MasakhaNEWSClassification (fra) type: masakhane/masakhanews config: fra split: test revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 metrics:
  - type: accuracy value: 82.60663507109005
  - type: f1 value: 77.20462646604777
- task: type: Clustering dataset: name: MTEB MasakhaNEWSClusteringP2P (fra) type: masakhane/masakhanews config: fra split: test revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 metrics:
  - type: v_measure value: 60.19311264967803
  - type: v_measure value: 63.6235764409785
- task: type: Classification dataset: name: MTEB MassiveIntentClassification (fr) type: mteb/amazon_massive_intent config: fr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
  - type: accuracy value: 81.65097511768661
  - type: f1 value: 78.77796091490924
- task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fr) type: mteb/amazon_massive_scenario config: fr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 86.64425016812373
  - type: f1 value: 85.4912728670017
- task: type: Retrieval dataset: name: MTEB MintakaRetrieval (fr) type: jinaai/mintakaqa config: fr split: test revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e metrics:
  - type: map_at_1 value: 35.913000000000004
  - type: map_at_10 value: 48.147
  - type: map_at_100 value: 48.91
  - type: map_at_1000 value: 48.949
  - type: map_at_3 value: 45.269999999999996
  - type: map_at_5 value: 47.115
  - type: mrr_at_1 value: 35.913000000000004
  - type: mrr_at_10 value: 48.147
  - type: mrr_at_100 value: 48.91
  - type: mrr_at_1000 value: 48.949
  - type: mrr_at_3 value: 45.269999999999996
  - type: mrr_at_5 value: 47.115
  - type: ndcg_at_1 value: 35.913000000000004
  - type: ndcg_at_10 value: 54.03
  - type: ndcg_at_100 value: 57.839
  - type: ndcg_at_1000 value: 58.925000000000004
  - type: ndcg_at_3 value: 48.217999999999996
  - type: ndcg_at_5 value: 51.56699999999999
  - type: precision_at_1 value: 35.913000000000004
  - type: precision_at_10 value: 7.244000000000001
  - type: precision_at_100 value: 0.9039999999999999
  - type: precision_at_1000 value: 0.099
  - type: precision_at_3 value: 18.905
  - type: precision_at_5 value: 12.981000000000002
  - type: recall_at_1 value: 35.913000000000004
  - type: recall_at_10 value: 72.441
  - type: recall_at_100 value: 90.41799999999999
  - type: recall_at_1000 value: 99.099
  - type: recall_at_3 value: 56.716
  - type: recall_at_5 value: 64.90599999999999
- task: type: PairClassification dataset: name: MTEB OpusparcusPC (fr) type: GEM/opusparcus config: fr split: test revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a 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: name: MTEB PawsX (fr) type: paws-x config: fr split: test revision: 8a04d940a42cd40658986fdd8e3da561533a3646 metrics:
  - type: cos_sim_accuracy value: 75.25
  - type: cos_sim_ap value: 80.86376001270014
  - type: cos_sim_f1 value: 73.65945437441204
  - type: cos_sim_precision value: 64.02289452166802
  - type: cos_sim_recall value: 86.71096345514951
  - type: dot_accuracy value: 75.25
  - type: dot_ap value: 80.93686107633002
  - type: dot_f1 value: 73.65945437441204
  - type: dot_precision value: 64.02289452166802
  - type: dot_recall value: 86.71096345514951
  - type: euclidean_accuracy value: 75.25
  - type: euclidean_ap value: 80.86379136218862
  - type: euclidean_f1 value: 73.65945437441204
  - type: euclidean_precision value: 64.02289452166802
  - type: euclidean_recall value: 86.71096345514951
  - type: manhattan_accuracy value: 75.3
  - type: manhattan_ap value: 80.87826606097734
  - type: manhattan_f1 value: 73.68421052631581
  - type: manhattan_precision value: 64.0
  - type: manhattan_recall value: 86.82170542635659
  - type: max_accuracy value: 75.3
  - type: max_ap value: 80.93686107633002
  - type: max_f1 value: 73.68421052631581
- task: type: STS dataset: name: MTEB SICKFr type: Lajavaness/SICK-fr config: default split: test revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a metrics:
  - type: cos_sim_pearson value: 81.42349425981143
  - type: cos_sim_spearman value: 78.90454327031226
  - type: euclidean_pearson value: 78.39086497435166
  - type: euclidean_spearman value: 78.9046133980509
  - type: manhattan_pearson value: 78.63743094286502
  - type: manhattan_spearman value: 79.12136348449269
- task: type: STS dataset: name: MTEB STS22 (fr) type: mteb/sts22-crosslingual-sts config: fr split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics:
  - type: cos_sim_pearson value: 81.452697919749
  - type: cos_sim_spearman value: 82.58116836039301
  - type: euclidean_pearson value: 81.04038478932786
  - type: euclidean_spearman value: 82.58116836039301
  - type: manhattan_pearson value: 81.37075396187771
  - type: manhattan_spearman value: 82.73678231355368
- task: type: STS dataset: name: MTEB STSBenchmarkMultilingualSTS (fr) type: stsb_multi_mt config: fr split: test revision: 93d57ef91790589e3ce9c365164337a8a78b7632 metrics:
  - type: cos_sim_pearson value: 85.7419764013806
  - type: cos_sim_spearman value: 85.46085808849622
  - type: euclidean_pearson value: 83.70449639870063
  - type: euclidean_spearman value: 85.46159013076233
  - type: manhattan_pearson value: 83.95259510313929
  - type: manhattan_spearman value: 85.8029724659458
- task: type: Summarization dataset: name: MTEB SummEvalFr type: lyon-nlp/summarization-summeval-fr-p2p config: default split: test revision: b385812de6a9577b6f4d0f88c6a6e35395a94054 metrics:
  - type: cos_sim_pearson value: 32.61063271753325
  - type: cos_sim_spearman value: 31.454589417353603
  - type: dot_pearson value: 32.6106288643431
  - type: dot_spearman value: 31.454589417353603
- task: type: Reranking dataset: name: MTEB SyntecReranking type: lyon-nlp/mteb-fr-reranking-syntec-s2p config: default split: test revision: b205c5084a0934ce8af14338bf03feb19499c84d metrics:
  - type: map value: 84.31666666666666
  - type: mrr value: 84.31666666666666
- task: type: Retrieval dataset: name: MTEB SyntecRetrieval type: lyon-nlp/mteb-fr-retrieval-syntec-s2p config: default split: test revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff metrics:
  - type: map_at_1 value: 63.0
  - type: map_at_10 value: 73.471
  - type: map_at_100 value: 73.87
  - type: map_at_1000 value: 73.87
  - type: map_at_3 value: 70.5
  - type: map_at_5 value: 73.05
  - type: mrr_at_1 value: 63.0
  - type: mrr_at_10 value: 73.471
  - type: mrr_at_100 value: 73.87
  - type: mrr_at_1000 value: 73.87
  - type: mrr_at_3 value: 70.5
  - type: mrr_at_5 value: 73.05
  - type: ndcg_at_1 value: 63.0
  - type: ndcg_at_10 value: 78.255
  - type: ndcg_at_100 value: 79.88
  - type: ndcg_at_1000 value: 79.88
  - type: ndcg_at_3 value: 72.702
  - type: ndcg_at_5 value: 77.264
  - type: precision_at_1 value: 63.0
  - type: precision_at_10 value: 9.3
  - type: precision_at_100 value: 1.0
  - type: precision_at_1000 value: 0.1
  - type: precision_at_3 value: 26.333000000000002
  - type: precision_at_5 value: 18.0
  - type: recall_at_1 value: 63.0
  - type: recall_at_10 value: 93.0
  - type: recall_at_100 value: 100.0
  - type: recall_at_1000 value: 100.0
  - type: recall_at_3 value: 79.0
  - type: recall_at_5 value: 90.0
- task: type: Retrieval dataset: name: MTEB XPQARetrieval (fr) type: jinaai/xpqa config: fr split: test revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f metrics:
  - type: map_at_1 value: 40.338
  - type: map_at_10 value: 61.927
  - type: map_at_100 value: 63.361999999999995
  - type: map_at_1000 value: 63.405
  - type: map_at_3 value: 55.479
  - type: map_at_5 value: 59.732
  - type: mrr_at_1 value: 63.551
  - type: mrr_at_10 value: 71.006
  - type: mrr_at_100 value: 71.501
  - type: mrr_at_1000 value: 71.509
  - type: mrr_at_3 value: 69.07
  - type: mrr_at_5 value: 70.165
  - type: ndcg_at_1 value: 63.551
  - type: ndcg_at_10 value: 68.297
  - type: ndcg_at_100 value: 73.13199999999999
  - type: ndcg_at_1000 value: 73.751
  - type: ndcg_at_3 value: 62.999
  - type: ndcg_at_5 value: 64.89
  - type: precision_at_1 value: 63.551
  - type: precision_at_10 value: 15.661
  - type: precision_at_100 value: 1.9789999999999999
  - type: precision_at_1000 value: 0.207
  - type: precision_at_3 value: 38.273
  - type: precision_at_5 value: 27.61
  - type: recall_at_1 value: 40.338
  - type: recall_at_10 value: 77.267
  - type: recall_at_100 value: 95.892
  - type: recall_at_1000 value: 99.75500000000001
  - type: recall_at_3 value: 60.36
  - type: recall_at_5 value: 68.825
- task: type: Clustering dataset: name: MTEB 8TagsClustering type: PL-MTEB/8tags-clustering config: default split: test revision: None metrics:
  - type: v_measure value: 51.36126303874126
- task: type: Classification dataset: name: MTEB AllegroReviews type: PL-MTEB/allegro-reviews config: default split: test revision: None metrics:
  - type: accuracy value: 67.13717693836979
  - type: f1 value: 57.27609848003782
- task: type: Retrieval dataset: name: MTEB ArguAna-PL type: clarin-knext/arguana-pl config: default split: test revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 metrics:
  - type: map_at_1 value: 35.276999999999994
  - type: map_at_10 value: 51.086
  - type: map_at_100 value: 51.788000000000004
  - type: map_at_1000 value: 51.791
  - type: map_at_3 value: 46.147
  - type: map_at_5 value: 49.078
  - type: mrr_at_1 value: 35.917
  - type: mrr_at_10 value: 51.315999999999995
  - type: mrr_at_100 value: 52.018
  - type: mrr_at_1000 value: 52.022
  - type: mrr_at_3 value: 46.349000000000004
  - type: mrr_at_5 value: 49.297000000000004
  - type: ndcg_at_1 value: 35.276999999999994
  - type: ndcg_at_10 value: 59.870999999999995
  - type: ndcg_at_100 value: 62.590999999999994
  - type: ndcg_at_1000 value: 62.661
  - type: ndcg_at_3 value: 49.745
  - type: ndcg_at_5 value: 55.067
  - type: precision_at_1 value: 35.276999999999994
  - type: precision_at_10 value: 8.791
  - type: precision_at_100 value: 0.991
  - type: precision_at_1000 value: 0.1
  - type: precision_at_3 value: 20.057
  - type: precision_at_5 value: 14.637
  - type: recall_at_1 value: 35.276999999999994
  - type: recall_at_10 value: 87.909
  - type: recall_at_100 value: 99.14699999999999
  - type: recall_at_1000 value: 99.644
  - type: recall_at_3 value: 60.171
  - type: recall_at_5 value: 73.18599999999999
- task: type: Classification dataset: name: MTEB CBD type: PL-MTEB/cbd config: default split: test revision: None metrics:
  - type: accuracy value: 78.03000000000002
  - type: ap value: 29.12548553897622
  - type: f1 value: 66.54857118886073
- task: type: PairClassification dataset: name: MTEB CDSC-E type: PL-MTEB/cdsce-pairclassification config: default split: test revision: None metrics:
  - type: cos_sim_accuracy value: 89.0
  - type: cos_sim_ap value: 76.75437826834582
  - type: cos_sim_f1 value: 66.4850136239782
  - type: cos_sim_precision value: 68.92655367231639
  - type: cos_sim_recall value: 64.21052631578948
  - type: dot_accuracy value: 89.0
  - type: dot_ap value: 76.75437826834582
  - type: dot_f1 value: 66.4850136239782
  - type: dot_precision value: 68.92655367231639
  - type: dot_recall value: 64.21052631578948
  - type: euclidean_accuracy value: 89.0
  - type: euclidean_ap value: 76.75437826834582
  - type: euclidean_f1 value: 66.4850136239782
  - type: euclidean_precision value: 68.92655367231639
  - type: euclidean_recall value: 64.21052631578948
  - type: manhattan_accuracy value: 89.0
  - type: manhattan_ap value: 76.66074220647083
  - type: manhattan_f1 value: 66.47058823529412
  - type: manhattan_precision value: 75.33333333333333
  - type: manhattan_recall value: 59.473684210526315
  - type: max_accuracy value: 89.0
  - type: max_ap value: 76.75437826834582
  - type: max_f1 value: 66.4850136239782
- task: type: STS dataset: name: MTEB CDSC-R type: PL-MTEB/cdscr-sts config: default split: test revision: None metrics:
  - type: cos_sim_pearson value: 93.12903172428328
  - type: cos_sim_spearman value: 92.66381487060741
  - type: euclidean_pearson value: 90.37278396708922
  - type: euclidean_spearman value: 92.66381487060741
  - type: manhattan_pearson value: 90.32503296540962
  - type: manhattan_spearman value: 92.6902938354313
- task: type: Retrieval dataset: name: MTEB DBPedia-PL type: clarin-knext/dbpedia-pl config: default split: test revision: 76afe41d9af165cc40999fcaa92312b8b012064a metrics:
  - type: map_at_1 value: 8.83
  - type: map_at_10 value: 18.326
  - type: map_at_100 value: 26.496
  - type: map_at_1000 value: 28.455000000000002
  - type: map_at_3 value: 12.933
  - type: map_at_5 value: 15.168000000000001
  - type: mrr_at_1 value: 66.0
  - type: mrr_at_10 value: 72.76700000000001
  - type: mrr_at_100 value: 73.203
  - type: mrr_at_1000 value: 73.219
  - type: mrr_at_3 value: 71.458
  - type: mrr_at_5 value: 72.246
  - type: ndcg_at_1 value: 55.375
  - type: ndcg_at_10 value: 41.3
  - type: ndcg_at_100 value: 45.891
  - type: ndcg_at_1000 value: 52.905
  - type: ndcg_at_3 value: 46.472
  - type: ndcg_at_5 value: 43.734
  - type: precision_at_1 value: 66.0
  - type: precision_at_10 value: 33.074999999999996
  - type: precision_at_100 value: 11.094999999999999
  - type: precision_at_1000 value: 2.374
  - type: precision_at_3 value: 48.583
  - type: precision_at_5 value: 42.0
  - type: recall_at_1 value: 8.83
  - type: recall_at_10 value: 22.587
  - type: recall_at_100 value: 50.61600000000001
  - type: recall_at_1000 value: 73.559
  - type: recall_at_3 value: 13.688
  - type: recall_at_5 value: 16.855
- task: type: Retrieval dataset: name: MTEB FiQA-PL type: clarin-knext/fiqa-pl config: default split: test revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e metrics:
  - type: map_at_1 value: 20.587
  - type: map_at_10 value: 33.095
  - type: map_at_100 value: 35.24
  - type: map_at_1000 value: 35.429
  - type: map_at_3 value: 28.626
  - type: map_at_5 value: 31.136999999999997
  - type: mrr_at_1 value: 40.586
  - type: mrr_at_10 value: 49.033
  - type: mrr_at_100 value: 49.952999999999996
  - type: mrr_at_1000 value: 49.992
  - type: mrr_at_3 value: 46.553
  - type: mrr_at_5 value: 48.035
  - type: ndcg_at_1 value: 40.586
  - type: ndcg_at_10 value: 41.046
  - type: ndcg_at_100 value: 48.586
  - type: ndcg_at_1000 value: 51.634
  - type: ndcg_at_3 value: 36.773
  - type: ndcg_at_5 value: 38.389
  - type: precision_at_1 value: 40.586
  - type: precision_at_10 value: 11.466
  - type: precision_at_100 value: 1.909
  - type: precision_at_1000 value: 0.245
  - type: precision_at_3 value: 24.434
  - type: precision_at_5 value: 18.426000000000002
  - type: recall_at_1 value: 20.587
  - type: recall_at_10 value: 47.986000000000004
  - type: recall_at_100 value: 75.761
  - type: recall_at_1000 value: 94.065
  - type: recall_at_3 value: 33.339
  - type: recall_at_5 value: 39.765
- task: type: Retrieval dataset: name: MTEB HotpotQA-PL type: clarin-knext/hotpotqa-pl config: default split: test revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907 metrics:
  - type: map_at_1 value: 40.878
  - type: map_at_10 value: 58.775999999999996
  - type: map_at_100 value: 59.632
  - type: map_at_1000 value: 59.707
  - type: map_at_3 value: 56.074
  - type: map_at_5 value: 57.629
  - type: mrr_at_1 value: 81.756
  - type: mrr_at_10 value: 86.117
  - type: mrr_at_100 value: 86.299
  - type: mrr_at_1000 value: 86.30600000000001
  - type: mrr_at_3 value: 85.345
  - type: mrr_at_5 value: 85.832
  - type: ndcg_at_1 value: 81.756
  - type: ndcg_at_10 value: 67.608
  - type: ndcg_at_100 value: 70.575
  - type: ndcg_at_1000 value: 71.99600000000001
  - type: ndcg_at_3 value: 63.723
  - type: ndcg_at_5 value: 65.70700000000001
  - type: precision_at_1 value: 81.756
  - type: precision_at_10 value: 13.619
  - type: precision_at_100 value: 1.5939999999999999
  - type: precision_at_1000 value: 0.178
  - type: precision_at_3 value: 39.604
  - type: precision_at_5 value: 25.332
  - type: recall_at_1 value: 40.878
  - type: recall_at_10 value: 68.096
  - type: recall_at_100 value: 79.696
  - type: recall_at_1000 value: 89.082
  - type: recall_at_3 value: 59.406000000000006
  - type: recall_at_5 value: 63.329
- task: type: Retrieval dataset: name: MTEB MSMARCO-PL type: clarin-knext/msmarco-pl config: default split: test revision: 8634c07806d5cce3a6138e260e59b81760a0a640 metrics:
  - type: map_at_1 value: 2.1839999999999997
  - type: map_at_10 value: 11.346
  - type: map_at_100 value: 30.325000000000003
  - type: map_at_1000 value: 37.806
  - type: map_at_3 value: 4.842
  - type: map_at_5 value: 6.891
  - type: mrr_at_1 value: 86.047
  - type: mrr_at_10 value: 89.14699999999999
  - type: mrr_at_100 value: 89.46600000000001
  - type: mrr_at_1000 value: 89.46600000000001
  - type: mrr_at_3 value: 89.14699999999999
  - type: mrr_at_5 value: 89.14699999999999
  - type: ndcg_at_1 value: 67.829
  - type: ndcg_at_10 value: 62.222
  - type: ndcg_at_100 value: 55.337
  - type: ndcg_at_1000 value: 64.076
  - type: ndcg_at_3 value: 68.12700000000001
  - type: ndcg_at_5 value: 64.987
  - type: precision_at_1 value: 86.047
  - type: precision_at_10 value: 69.535
  - type: precision_at_100 value: 32.93
  - type: precision_at_1000 value: 6.6049999999999995
  - type: precision_at_3 value: 79.845
  - type: precision_at_5 value: 75.349
  - type: recall_at_1 value: 2.1839999999999997
  - type: recall_at_10 value: 12.866
  - type: recall_at_100 value: 43.505
  - type: recall_at_1000 value: 72.366
  - type: recall_at_3 value: 4.947
  - type: recall_at_5 value: 7.192
- task: type: Classification dataset: name: MTEB MassiveIntentClassification (pl) type: mteb/amazon_massive_intent config: pl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
  - type: accuracy value: 80.75319435104238
  - type: f1 value: 77.58961444860606
- task: type: Classification dataset: name: MTEB MassiveScenarioClassification (pl) type: mteb/amazon_massive_scenario config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 85.54472091459313
  - type: f1 value: 84.29498563572106
- task: type: Retrieval dataset: name: MTEB NFCorpus-PL type: clarin-knext/nfcorpus-pl config: default split: test revision: 9a6f9567fda928260afed2de480d79c98bf0bec0 metrics:
  - type: map_at_1 value: 4.367
  - type: map_at_10 value: 10.38
  - type: map_at_100 value: 13.516
  - type: map_at_1000 value: 14.982000000000001
  - type: map_at_3 value: 7.367
  - type: map_at_5 value: 8.59
  - type: mrr_at_1 value: 41.486000000000004
  - type: mrr_at_10 value: 48.886
  - type: mrr_at_100 value: 49.657000000000004
  - type: mrr_at_1000 value: 49.713
  - type: mrr_at_3 value: 46.904
  - type: mrr_at_5 value: 48.065000000000005
  - type: ndcg_at_1 value: 40.402
  - type: ndcg_at_10 value: 30.885
  - type: ndcg_at_100 value: 28.393
  - type: ndcg_at_1000 value: 37.428
  - type: ndcg_at_3 value: 35.394999999999996
  - type: ndcg_at_5 value: 33.391999999999996
  - type: precision_at_1 value: 41.486000000000004
  - type: precision_at_10 value: 23.437
  - type: precision_at_100 value: 7.638
  - type: precision_at_1000 value: 2.0389999999999997
  - type: precision_at_3 value: 32.817
  - type: precision_at_5 value: 28.915999999999997
  - type: recall_at_1 value: 4.367
  - type: recall_at_10 value: 14.655000000000001
  - type: recall_at_100 value: 29.665999999999997
  - type: recall_at_1000 value: 62.073
  - type: recall_at_3 value: 8.51
  - type: recall_at_5 value: 10.689
- task: type: Retrieval dataset: name: MTEB NQ-PL type: clarin-knext/nq-pl config: default split: test revision: f171245712cf85dd4700b06bef18001578d0ca8d metrics:
  - type: map_at_1 value: 28.616000000000003
  - type: map_at_10 value: 41.626000000000005
  - type: map_at_100 value: 42.689
  - type: map_at_1000 value: 42.733
  - type: map_at_3 value: 37.729
  - type: map_at_5 value: 39.879999999999995
  - type: mrr_at_1 value: 32.068000000000005
  - type: mrr_at_10 value: 44.029
  - type: mrr_at_100 value: 44.87
  - type: mrr_at_1000 value: 44.901
  - type: mrr_at_3 value: 40.687
  - type: mrr_at_5 value: 42.625
  - type: ndcg_at_1 value: 32.068000000000005
  - type: ndcg_at_10 value: 48.449999999999996
  - type: ndcg_at_100 value: 53.13
  - type: ndcg_at_1000 value: 54.186
  - type: ndcg_at_3 value: 40.983999999999995
  - type: ndcg_at_5 value: 44.628
  - type: precision_at_1 value: 32.068000000000005
  - type: precision_at_10 value: 7.9750000000000005
  - type: precision_at_100 value: 1.061
  - type: precision_at_1000 value: 0.116
  - type: precision_at_3 value: 18.404999999999998
  - type: precision_at_5 value: 13.111
  - type: recall_at_1 value: 28.616000000000003
  - type: recall_at_10 value: 66.956
  - type: recall_at_100 value: 87.657
  - type: recall_at_1000 value: 95.548
  - type: recall_at_3 value: 47.453
  - type: recall_at_5 value: 55.87800000000001
- task: type: Classification dataset: name: MTEB PAC type: laugustyniak/abusive-clauses-pl config: default split: test revision: None metrics:
  - type: accuracy value: 69.04141326382856
  - type: ap value: 77.47589122111044
  - type: f1 value: 66.6332277374775
- task: type: PairClassification dataset: name: MTEB PPC type: PL-MTEB/ppc-pairclassification config: default split: test revision: None metrics:
  - type: cos_sim_accuracy value: 86.4
  - type: cos_sim_ap value: 94.1044939667201
  - type: cos_sim_f1 value: 88.78048780487805
  - type: cos_sim_precision value: 87.22044728434504
  - type: cos_sim_recall value: 90.39735099337747
  - type: dot_accuracy value: 86.4
  - type: dot_ap value: 94.1044939667201
  - type: dot_f1 value: 88.78048780487805
  - type: dot_precision value: 87.22044728434504
  - type: dot_recall value: 90.39735099337747
  - type: euclidean_accuracy value: 86.4
  - type: euclidean_ap value: 94.1044939667201
  - type: euclidean_f1 value: 88.78048780487805
  - type: euclidean_precision value: 87.22044728434504
  - type: euclidean_recall value: 90.39735099337747
  - type: manhattan_accuracy value: 86.4
  - type: manhattan_ap value: 94.11438365697387
  - type: manhattan_f1 value: 88.77968877968877
  - type: manhattan_precision value: 87.84440842787681
  - type: manhattan_recall value: 89.73509933774835
  - type: max_accuracy value: 86.4
  - type: max_ap value: 94.11438365697387
  - type: max_f1 value: 88.78048780487805
- task: type: PairClassification dataset: name: MTEB PSC type: PL-MTEB/psc-pairclassification config: default split: test revision: None metrics:
  - type: cos_sim_accuracy value: 97.86641929499072
  - type: cos_sim_ap value: 99.36904211868182
  - type: cos_sim_f1 value: 96.56203288490283
  - type: cos_sim_precision value: 94.72140762463343
  - type: cos_sim_recall value: 98.47560975609755
  - type: dot_accuracy value: 97.86641929499072
  - type: dot_ap value: 99.36904211868183
  - type: dot_f1 value: 96.56203288490283
  - type: dot_precision value: 94.72140762463343
  - type: dot_recall value: 98.47560975609755
  - type: euclidean_accuracy value: 97.86641929499072
  - type: euclidean_ap value: 99.36904211868183
  - type: euclidean_f1 value: 96.56203288490283
  - type: euclidean_precision value: 94.72140762463343
  - type: euclidean_recall value: 98.47560975609755
  - type: manhattan_accuracy value: 98.14471243042672
  - type: manhattan_ap value: 99.43359540492416
  - type: manhattan_f1 value: 96.98795180722892
  - type: manhattan_precision value: 95.83333333333334
  - type: manhattan_recall value: 98.17073170731707
  - type: max_accuracy value: 98.14471243042672
  - type: max_ap value: 99.43359540492416
  - type: max_f1 value: 96.98795180722892
- task: type: Classification dataset: name: MTEB PolEmo2.0-IN type: PL-MTEB/polemo2_in config: default split: test revision: None metrics:
  - type: accuracy value: 89.39058171745152
  - type: f1 value: 86.8552093529568
- task: type: Classification dataset: name: MTEB PolEmo2.0-OUT type: PL-MTEB/polemo2_out config: default split: test revision: None metrics:
  - type: accuracy value: 74.97975708502024
  - type: f1 value: 58.73081628832407
- task: type: Retrieval dataset: name: MTEB Quora-PL type: clarin-knext/quora-pl config: default split: test revision: 0be27e93455051e531182b85e85e425aba12e9d4 metrics:
  - type: map_at_1 value: 64.917
  - type: map_at_10 value: 78.74600000000001
  - type: map_at_100 value: 79.501
  - type: map_at_1000 value: 79.524
  - type: map_at_3 value: 75.549
  - type: map_at_5 value: 77.495
  - type: mrr_at_1 value: 74.9
  - type: mrr_at_10 value: 82.112
  - type: mrr_at_100 value: 82.314
  - type: mrr_at_1000 value: 82.317
  - type: mrr_at_3 value: 80.745
  - type: mrr_at_5 value: 81.607
  - type: ndcg_at_1 value: 74.83999999999999
  - type: ndcg_at_10 value: 83.214
  - type: ndcg_at_100 value: 84.997
  - type: ndcg_at_1000 value: 85.207
  - type: ndcg_at_3 value: 79.547
  - type: ndcg_at_5 value: 81.46600000000001
  - type: precision_at_1 value: 74.83999999999999
  - type: precision_at_10 value: 12.822
  - type: precision_at_100 value: 1.506
  - type: precision_at_1000 value: 0.156
  - type: precision_at_3 value: 34.903
  - type: precision_at_5 value: 23.16
  - type: recall_at_1 value: 64.917
  - type: recall_at_10 value: 92.27199999999999
  - type: recall_at_100 value: 98.715
  - type: recall_at_1000 value: 99.854
  - type: recall_at_3 value: 82.04599999999999
  - type: recall_at_5 value: 87.2
- task: type: Retrieval dataset: name: MTEB SCIDOCS-PL type: clarin-knext/scidocs-pl config: default split: test revision: 45452b03f05560207ef19149545f168e596c9337 metrics:
  - type: map_at_1 value: 3.51
  - type: map_at_10 value: 9.046999999999999
  - type: map_at_100 value: 10.823
  - type: map_at_1000 value: 11.144
  - type: map_at_3 value: 6.257
  - type: map_at_5 value: 7.648000000000001
  - type: mrr_at_1 value: 17.299999999999997
  - type: mrr_at_10 value: 27.419
  - type: mrr_at_100 value: 28.618
  - type: mrr_at_1000 value: 28.685
  - type: mrr_at_3 value: 23.817
  - type: mrr_at_5 value: 25.927
  - type: ndcg_at_1 value: 17.299999999999997
  - type: ndcg_at_10 value: 16.084
  - type: ndcg_at_100 value: 23.729
  - type: ndcg_at_1000 value: 29.476999999999997
  - type: ndcg_at_3 value: 14.327000000000002
  - type: ndcg_at_5 value: 13.017999999999999
  - type: precision_at_1 value: 17.299999999999997
  - type: precision_at_10 value: 8.63
  - type: precision_at_100 value: 1.981
  - type: precision_at_1000 value: 0.336
  - type: precision_at_3 value: 13.4
  - type: precision_at_5 value: 11.700000000000001
  - type: recall_at_1 value: 3.51
  - type: recall_at_10 value: 17.518
  - type: recall_at_100 value: 40.275
  - type: recall_at_1000 value: 68.203
  - type: recall_at_3 value: 8.155
  - type: recall_at_5 value: 11.875
- task: type: PairClassification dataset: name: MTEB SICK-E-PL type: PL-MTEB/sicke-pl-pairclassification config: default split: test revision: None metrics:
  - type: cos_sim_accuracy value: 86.30248675091724
  - type: cos_sim_ap value: 83.6756734006714
  - type: cos_sim_f1 value: 74.97367497367497
  - type: cos_sim_precision value: 73.91003460207612
  - type: cos_sim_recall value: 76.06837606837607
  - type: dot_accuracy value: 86.30248675091724
  - type: dot_ap value: 83.6756734006714
  - type: dot_f1 value: 74.97367497367497
  - type: dot_precision value: 73.91003460207612
  - type: dot_recall value: 76.06837606837607
  - type: euclidean_accuracy value: 86.30248675091724
  - type: euclidean_ap value: 83.67566984333091
  - type: euclidean_f1 value: 74.97367497367497
  - type: euclidean_precision value: 73.91003460207612
  - type: euclidean_recall value: 76.06837606837607
  - type: manhattan_accuracy value: 86.28210354667753
  - type: manhattan_ap value: 83.64216119130171
  - type: manhattan_f1 value: 74.92152075340078
  - type: manhattan_precision value: 73.4107997265892
  - type: manhattan_recall value: 76.49572649572649
  - type: max_accuracy value: 86.30248675091724
  - type: max_ap value: 83.6756734006714
  - type: max_f1 value: 74.97367497367497
- task: type: STS dataset: name: MTEB SICK-R-PL type: PL-MTEB/sickr-pl-sts config: default split: test revision: None metrics:
  - type: cos_sim_pearson value: 82.23295940859121
  - type: cos_sim_spearman value: 78.89329160768719
  - type: euclidean_pearson value: 79.56019107076818
  - type: euclidean_spearman value: 78.89330209904084
  - type: manhattan_pearson value: 79.76098513973719
  - type: manhattan_spearman value: 79.05490162570123
- task: type: STS dataset: name: MTEB STS22 (pl) type: mteb/sts22-crosslingual-sts config: pl split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics:
  - type: cos_sim_pearson value: 37.732606308062486
  - type: cos_sim_spearman value: 41.01645667030284
  - type: euclidean_pearson value: 26.61722556367085
  - type: euclidean_spearman value: 41.01645667030284
  - type: manhattan_pearson value: 26.60917378970807
  - type: manhattan_spearman value: 41.51335727617614
- task: type: Retrieval dataset: name: MTEB SciFact-PL type: clarin-knext/scifact-pl config: default split: test revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e metrics:
  - type: map_at_1 value: 54.31700000000001
  - type: map_at_10 value: 65.564
  - type: map_at_100 value: 66.062
  - type: map_at_1000 value: 66.08699999999999
  - type: map_at_3 value: 62.592999999999996
  - type: map_at_5 value: 63.888
  - type: mrr_at_1 value: 56.99999999999999
  - type: mrr_at_10 value: 66.412
  - type: mrr_at_100 value: 66.85900000000001
  - type: mrr_at_1000 value: 66.88
  - type: mrr_at_3 value: 64.22200000000001
  - type: mrr_at_5 value: 65.206
  - type: ndcg_at_1 value: 56.99999999999999
  - type: ndcg_at_10 value: 70.577
  - type: ndcg_at_100 value: 72.879
  - type: ndcg_at_1000 value: 73.45
  - type: ndcg_at_3 value: 65.5
  - type: ndcg_at_5 value: 67.278
  - type: precision_at_1 value: 56.99999999999999
  - type: precision_at_10 value: 9.667
  - type: precision_at_100 value: 1.083
  - type: precision_at_1000 value: 0.11299999999999999
  - type: precision_at_3 value: 26.0
  - type: precision_at_5 value: 16.933
  - type: recall_at_1 value: 54.31700000000001
  - type: recall_at_10 value: 85.056
  - type: recall_at_100 value: 95.667
  - type: recall_at_1000 value: 100.0
  - type: recall_at_3 value: 71.0
  - type: recall_at_5 value: 75.672
- task: type: Retrieval dataset: name: MTEB TRECCOVID-PL type: clarin-knext/trec-covid-pl config: default split: test revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd metrics:
  - type: map_at_1 value: 0.245
  - type: map_at_10 value: 2.051
  - type: map_at_100 value: 12.009
  - type: map_at_1000 value: 27.448
  - type: map_at_3 value: 0.721
  - type: map_at_5 value: 1.13
  - type: mrr_at_1 value: 88.0
  - type: mrr_at_10 value: 93.0
  - type: mrr_at_100 value: 93.0
  - type: mrr_at_1000 value: 93.0
  - type: mrr_at_3 value: 93.0
  - type: mrr_at_5 value: 93.0
  - type: ndcg_at_1 value: 85.0
  - type: ndcg_at_10 value: 80.303
  - type: ndcg_at_100 value: 61.23499999999999
  - type: ndcg_at_1000 value: 52.978
  - type: ndcg_at_3 value: 84.419
  - type: ndcg_at_5 value: 82.976
  - type: precision_at_1 value: 88.0
  - type: precision_at_10 value: 83.39999999999999
  - type: precision_at_100 value: 61.96
  - type: precision_at_1000 value: 22.648
  - type: precision_at_3 value: 89.333
  - type: precision_at_5 value: 87.2
  - type: recall_at_1 value: 0.245
  - type: recall_at_10 value: 2.193
  - type: recall_at_100 value: 14.938
  - type: recall_at_1000 value: 48.563
  - type: recall_at_3 value: 0.738
  - type: recall_at_5 value: 1.173

mlx-community/gte-Qwen2-7B-instruct-4bit-DWQ

This model mlx-community/gte-Qwen2-7B-instruct-4bit-DWQ was converted to MLX format from Alibaba-NLP/gte-Qwen2-7B-instruct using mlx-lm version 0.24.0.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/gte-Qwen2-7B-instruct-4bit-DWQ")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)