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JA
SFR Embedding Mistral
S
SFR Embedding Mistral
Salesforceによって開発
Salesforce Researchによって開発されたテキスト埋め込みモデルで、E5-mistral-7b-instructとMistral-7B-v0.1を基に訓練されており、主にテキスト検索タスクに使用されます。
ダウンロード数 34.75k
リリース時間 : 1/24/2024
モデル概要
このモデルはMistralアーキテクチャに基づくテキスト埋め込みモデルで、テキスト検索タスクの性能向上に焦点を当てています。転移学習技術によって強化されており、様々な情報検索シナリオに適用可能です。
モデル特徴
効率的な検索能力
テキスト検索タスクに最適化されており、クエリと関連ドキュメントを効率的にマッチングできます
長文処理
最大4096トークンのテキスト入力をサポートし、長文ドキュメントの処理に適しています
転移学習強化
E5-mistral-7b-instructとMistral-7B-v0.1を基に訓練されており、より優れた性能を発揮します
モデル能力
テキスト埋め込み生成
意味的類似度計算
情報検索
ドキュメントマッチング
使用事例
情報検索
ウェブ検索
ユーザークエリとウェブコンテンツをマッチングし、最も関連性の高い結果を返します
例ではクエリと関連段落の高いマッチングスコアが表示されます
ナレッジベースQA
ナレッジベースから質問に最も関連する回答段落を検索します
コンテンツ推薦
関連記事推薦
ユーザーが読んだコンテンツに基づいて、意味的に類似した他の記事を推薦します
tags:
- mteb
- sentence-transformers
- transformers model-index:
- name: SFR-Embedding-Mistral
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy value: 77.92537313432834
- type: ap value: 40.86767661556651
- type: f1 value: 71.65758897929837
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy value: 95.967
- type: ap value: 94.46300829592593
- type: f1 value: 95.96507173189292
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 54.352000000000004
- type: f1 value: 53.636682615380174
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 43.314
- type: ndcg_at_2 value: 54.757
- type: ndcg_at_3 value: 58.84700000000001
- type: ndcg_at_5 value: 63.634
- type: ndcg_at_7 value: 65.741
- type: ndcg_at_10 value: 67.171
- type: ndcg_at_20 value: 68.585
- type: ndcg_at_30 value: 68.81
- type: ndcg_at_50 value: 68.932
- type: ndcg_at_70 value: 68.992
- type: ndcg_at_100 value: 69.014
- type: ndcg_at_200 value: 69.014
- type: ndcg_at_300 value: 69.014
- type: ndcg_at_500 value: 69.014
- type: ndcg_at_700 value: 69.014
- type: ndcg_at_1000 value: 69.014
- type: map_at_1 value: 43.314
- type: map_at_2 value: 52.383
- type: map_at_3 value: 55.108999999999995
- type: map_at_5 value: 57.772999999999996
- type: map_at_7 value: 58.718
- type: map_at_10 value: 59.256
- type: map_at_20 value: 59.668
- type: map_at_30 value: 59.709999999999994
- type: map_at_50 value: 59.727
- type: map_at_70 value: 59.733999999999995
- type: map_at_100 value: 59.73500000000001
- type: map_at_200 value: 59.73500000000001
- type: map_at_300 value: 59.73500000000001
- type: map_at_500 value: 59.73500000000001
- type: map_at_700 value: 59.73500000000001
- type: map_at_1000 value: 59.73500000000001
- type: recall_at_1 value: 43.314
- type: recall_at_2 value: 61.451
- type: recall_at_3 value: 69.63000000000001
- type: recall_at_5 value: 81.223
- type: recall_at_7 value: 87.33999999999999
- type: recall_at_10 value: 92.034
- type: recall_at_20 value: 97.44
- type: recall_at_30 value: 98.506
- type: recall_at_50 value: 99.14699999999999
- type: recall_at_70 value: 99.502
- type: recall_at_100 value: 99.644
- type: recall_at_200 value: 99.644
- type: recall_at_300 value: 99.644
- type: recall_at_500 value: 99.644
- type: recall_at_700 value: 99.644
- type: recall_at_1000 value: 99.644
- type: precision_at_1 value: 43.314
- type: precision_at_2 value: 30.725
- type: precision_at_3 value: 23.21
- type: precision_at_5 value: 16.245
- type: precision_at_7 value: 12.477
- type: precision_at_10 value: 9.203
- type: precision_at_20 value: 4.872
- type: precision_at_30 value: 3.2840000000000003
- type: precision_at_50 value: 1.983
- type: precision_at_70 value: 1.421
- type: precision_at_100 value: 0.996
- type: precision_at_200 value: 0.498
- type: precision_at_300 value: 0.332
- type: precision_at_500 value: 0.199
- type: precision_at_700 value: 0.14200000000000002
- type: precision_at_1000 value: 0.1
- type: mrr_at_1 value: 44.666
- type: mrr_at_2 value: 52.418
- type: mrr_at_3 value: 55.595000000000006
- type: mrr_at_5 value: 58.205
- type: mrr_at_7 value: 59.202999999999996
- type: mrr_at_10 value: 59.727
- type: mrr_at_20 value: 60.133
- type: mrr_at_30 value: 60.178
- type: mrr_at_50 value: 60.192
- type: mrr_at_70 value: 60.19799999999999
- type: mrr_at_100 value: 60.199999999999996
- type: mrr_at_200 value: 60.199999999999996
- type: mrr_at_300 value: 60.199999999999996
- type: mrr_at_500 value: 60.199999999999996
- type: mrr_at_700 value: 60.199999999999996
- type: mrr_at_1000 value: 60.199999999999996
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure value: 52.07508593014336
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure value: 47.381339333240675
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map value: 67.58376647859171
- type: mrr value: 80.56885635140483
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson value: 88.40107280274783
- type: cos_sim_spearman value: 86.07003345325681
- type: euclidean_pearson value: 87.1726034325395
- type: euclidean_spearman value: 86.07003345325681
- type: manhattan_pearson value: 87.25660625029772
- type: manhattan_spearman value: 86.3808839096893
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy value: 88.81168831168831
- type: f1 value: 88.76514496560141
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure value: 43.9382520874344
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure value: 41.14351847240913
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 34.51166666666667
- type: ndcg_at_2 value: 38.51591666666667
- type: ndcg_at_3 value: 40.95083333333333
- type: ndcg_at_5 value: 43.580666666666666
- type: ndcg_at_7 value: 45.0625
- type: ndcg_at_10 value: 46.49083333333333
- type: ndcg_at_20 value: 48.731333333333325
- type: ndcg_at_30 value: 49.78666666666667
- type: ndcg_at_50 value: 50.84049999999999
- type: ndcg_at_70 value: 51.393750000000004
- type: ndcg_at_100 value: 51.883333333333326
- type: ndcg_at_200 value: 52.65225
- type: ndcg_at_300 value: 52.98241666666669
- type: ndcg_at_500 value: 53.28541666666668
- type: ndcg_at_700 value: 53.49241666666668
- type: ndcg_at_1000 value: 53.63758333333334
- type: map_at_1 value: 29.10075
- type: map_at_2 value: 34.636500000000005
- type: map_at_3 value: 36.92033333333333
- type: map_at_5 value: 38.81641666666666
- type: map_at_7 value: 39.635416666666664
- type: map_at_10 value: 40.294583333333335
- type: map_at_20 value: 41.07574999999999
- type: map_at_30 value: 41.333
- type: map_at_50 value: 41.529333333333334
- type: map_at_70 value: 41.606833333333334
- type: map_at_100 value: 41.66224999999999
- type: map_at_200 value: 41.72691666666666
- type: map_at_300 value: 41.746583333333334
- type: map_at_500 value: 41.75983333333333
- type: map_at_700 value: 41.76558333333333
- type: map_at_1000 value: 41.769000000000005
- type: recall_at_1 value: 29.10075
- type: recall_at_2 value: 39.07658333333333
- type: recall_at_3 value: 44.93591666666667
- type: recall_at_5 value: 51.66883333333333
- type: recall_at_7 value: 55.881000000000014
- type: recall_at_10 value: 60.34691666666667
- type: recall_at_20 value: 68.44016666666667
- type: recall_at_30 value: 72.90766666666667
- type: recall_at_50 value: 77.843
- type: recall_at_70 value: 80.70366666666668
- type: recall_at_100 value: 83.42866666666667
- type: recall_at_200 value: 88.06816666666668
- type: recall_at_300 value: 90.249
- type: recall_at_500 value: 92.37616666666668
- type: recall_at_700 value: 93.978
- type: recall_at_1000 value: 95.12791666666666
- type: precision_at_1 value: 34.51166666666667
- type: precision_at_2 value: 24.326333333333327
- type: precision_at_3 value: 19.099249999999998
- type: precision_at_5 value: 13.672666666666666
- type: precision_at_7 value: 10.772
- type: precision_at_10 value: 8.302166666666668
- type: precision_at_20 value: 4.8960833333333325
- type: precision_at_30 value: 3.551083333333333
- type: precision_at_50 value: 2.3386666666666662
- type: precision_at_70 value: 1.7605833333333334
- type: precision_at_100 value: 1.2965
- type: precision_at_200 value: 0.7106666666666668
- type: precision_at_300 value: 0.4955
- type: precision_at_500 value: 0.3106666666666667
- type: precision_at_700 value: 0.22791666666666668
- type: precision_at_1000 value: 0.1635833333333333
- type: mrr_at_1 value: 34.51166666666667
- type: mrr_at_2 value: 39.954249999999995
- type: mrr_at_3 value: 41.93741666666668
- type: mrr_at_5 value: 43.487166666666674
- type: mrr_at_7 value: 44.14983333333333
- type: mrr_at_10 value: 44.62766666666666
- type: mrr_at_20 value: 45.15291666666668
- type: mrr_at_30 value: 45.317
- type: mrr_at_50 value: 45.42875
- type: mrr_at_70 value: 45.46966666666667
- type: mrr_at_100 value: 45.49716666666667
- type: mrr_at_200 value: 45.525166666666664
- type: mrr_at_300 value: 45.53233333333335
- type: mrr_at_500 value: 45.5365
- type: mrr_at_700 value: 45.538583333333335
- type: mrr_at_1000 value: 45.539583333333326
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 35.179
- type: ndcg_at_2 value: 31.243
- type: ndcg_at_3 value: 30.562
- type: ndcg_at_5 value: 32.409
- type: ndcg_at_7 value: 34.525
- type: ndcg_at_10 value: 36.415
- type: ndcg_at_20 value: 39.443
- type: ndcg_at_30 value: 40.796
- type: ndcg_at_50 value: 42.16
- type: ndcg_at_70 value: 42.971
- type: ndcg_at_100 value: 43.691
- type: ndcg_at_200 value: 45.004
- type: ndcg_at_300 value: 45.527
- type: ndcg_at_500 value: 46.072
- type: ndcg_at_700 value: 46.387
- type: ndcg_at_1000 value: 46.663
- type: map_at_1 value: 15.692
- type: map_at_2 value: 20.116
- type: map_at_3 value: 22.6
- type: map_at_5 value: 24.701
- type: map_at_7 value: 25.934
- type: map_at_10 value: 26.843
- type: map_at_20 value: 27.975
- type: map_at_30 value: 28.372000000000003
- type: map_at_50 value: 28.671000000000003
- type: map_at_70 value: 28.803
- type: map_at_100 value: 28.895
- type: map_at_200 value: 29.011
- type: map_at_300 value: 29.042
- type: map_at_500 value: 29.065
- type: map_at_700 value: 29.075
- type: map_at_1000 value: 29.081000000000003
- type: recall_at_1 value: 15.692
- type: recall_at_2 value: 22.602
- type: recall_at_3 value: 27.814
- type: recall_at_5 value: 33.756
- type: recall_at_7 value: 38.073
- type: recall_at_10 value: 42.553000000000004
- type: recall_at_20 value: 51.121
- type: recall_at_30 value: 55.523999999999994
- type: recall_at_50 value: 60.586
- type: recall_at_70 value: 63.94
- type: recall_at_100 value: 67.134
- type: recall_at_200 value: 73.543
- type: recall_at_300 value: 76.372
- type: recall_at_500 value: 79.60199999999999
- type: recall_at_700 value: 81.536
- type: recall_at_1000 value: 83.37400000000001
- type: precision_at_1 value: 35.179
- type: precision_at_2 value: 27.199
- type: precision_at_3 value: 22.953000000000003
- type: precision_at_5 value: 17.224999999999998
- type: precision_at_7 value: 14.238999999999999
- type: precision_at_10 value: 11.303
- type: precision_at_20 value: 6.954000000000001
- type: precision_at_30 value: 5.116
- type: precision_at_50 value: 3.395
- type: precision_at_70 value: 2.579
- type: precision_at_100 value: 1.9109999999999998
- type: precision_at_200 value: 1.065
- type: precision_at_300 value: 0.743
- type: precision_at_500 value: 0.46699999999999997
- type: precision_at_700 value: 0.344
- type: precision_at_1000 value: 0.247
- type: mrr_at_1 value: 35.179
- type: mrr_at_2 value: 41.792
- type: mrr_at_3 value: 44.484
- type: mrr_at_5 value: 46.39
- type: mrr_at_7 value: 47.125
- type: mrr_at_10 value: 47.711999999999996
- type: mrr_at_20 value: 48.214
- type: mrr_at_30 value: 48.325
- type: mrr_at_50 value: 48.392
- type: mrr_at_70 value: 48.418
- type: mrr_at_100 value: 48.44
- type: mrr_at_200 value: 48.46
- type: mrr_at_300 value: 48.461999999999996
- type: mrr_at_500 value: 48.466
- type: mrr_at_700 value: 48.466
- type: mrr_at_1000 value: 48.467
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 62.375
- type: ndcg_at_2 value: 56.286
- type: ndcg_at_3 value: 53.665
- type: ndcg_at_5 value: 51.139
- type: ndcg_at_7 value: 49.873
- type: ndcg_at_10 value: 49.056
- type: ndcg_at_20 value: 48.783
- type: ndcg_at_30 value: 49.166
- type: ndcg_at_50 value: 51.141999999999996
- type: ndcg_at_70 value: 52.774
- type: ndcg_at_100 value: 54.403
- type: ndcg_at_200 value: 57.419
- type: ndcg_at_300 value: 58.778
- type: ndcg_at_500 value: 60.228
- type: ndcg_at_700 value: 61.07599999999999
- type: ndcg_at_1000 value: 61.846000000000004
- type: map_at_1 value: 10.359
- type: map_at_2 value: 14.446
- type: map_at_3 value: 16.689
- type: map_at_5 value: 20.096
- type: map_at_7 value: 22.247
- type: map_at_10 value: 24.468999999999998
- type: map_at_20 value: 28.938000000000002
- type: map_at_30 value: 31.134
- type: map_at_50 value: 33.403
- type: map_at_70 value: 34.486
- type: map_at_100 value: 35.337
- type: map_at_200 value: 36.364999999999995
- type: map_at_300 value: 36.735
- type: map_at_500 value: 37.057
- type: map_at_700 value: 37.225
- type: map_at_1000 value: 37.379
- type: recall_at_1 value: 10.359
- type: recall_at_2 value: 14.945
- type: recall_at_3 value: 17.694
- type: recall_at_5 value: 22.677
- type: recall_at_7 value: 26.131
- type: recall_at_10 value: 30.053
- type: recall_at_20 value: 39.518
- type: recall_at_30 value: 44.925
- type: recall_at_50 value: 52.154
- type: recall_at_70 value: 56.729
- type: recall_at_100 value: 61.18900000000001
- type: recall_at_200 value: 70.407
- type: recall_at_300 value: 74.412
- type: recall_at_500 value: 78.891
- type: recall_at_700 value: 81.74
- type: recall_at_1000 value: 84.253
- type: precision_at_1 value: 75
- type: precision_at_2 value: 64.125
- type: precision_at_3 value: 57.833
- type: precision_at_5 value: 50.24999999999999
- type: precision_at_7 value: 44.75
- type: precision_at_10 value: 39.75
- type: precision_at_20 value: 30.412
- type: precision_at_30 value: 25.141999999999996
- type: precision_at_50 value: 19.2
- type: precision_at_70 value: 15.729000000000001
- type: precision_at_100 value: 12.552
- type: precision_at_200 value: 7.866
- type: precision_at_300 value: 5.9270000000000005
- type: precision_at_500 value: 4.1129999999999995
- type: precision_at_700 value: 3.2460000000000004
- type: precision_at_1000 value: 2.5260000000000002
- type: mrr_at_1 value: 75
- type: mrr_at_2 value: 78.625
- type: mrr_at_3 value: 79.708
- type: mrr_at_5 value: 80.446
- type: mrr_at_7 value: 80.862
- type: mrr_at_10 value: 81.161
- type: mrr_at_20 value: 81.3
- type: mrr_at_30 value: 81.348
- type: mrr_at_50 value: 81.361
- type: mrr_at_70 value: 81.361
- type: mrr_at_100 value: 81.361
- type: mrr_at_200 value: 81.367
- type: mrr_at_300 value: 81.367
- type: mrr_at_500 value: 81.368
- type: mrr_at_700 value: 81.368
- type: mrr_at_1000 value: 81.368
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy value: 50.239999999999995
- type: f1 value: 46.42361822342044
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 83.723
- type: ndcg_at_2 value: 86.777
- type: ndcg_at_3 value: 87.997
- type: ndcg_at_5 value: 88.864
- type: ndcg_at_7 value: 89.143
- type: ndcg_at_10 value: 89.349
- type: ndcg_at_20 value: 89.709
- type: ndcg_at_30 value: 89.82900000000001
- type: ndcg_at_50 value: 89.923
- type: ndcg_at_70 value: 89.982
- type: ndcg_at_100 value: 90.026
- type: ndcg_at_200 value: 90.10000000000001
- type: ndcg_at_300 value: 90.12599999999999
- type: ndcg_at_500 value: 90.17399999999999
- type: ndcg_at_700 value: 90.19
- type: ndcg_at_1000 value: 90.208
- type: map_at_1 value: 77.64999999999999
- type: map_at_2 value: 83.769
- type: map_at_3 value: 85.041
- type: map_at_5 value: 85.736
- type: map_at_7 value: 85.924
- type: map_at_10 value: 86.032
- type: map_at_20 value: 86.177
- type: map_at_30 value: 86.213
- type: map_at_50 value: 86.233
- type: map_at_70 value: 86.24300000000001
- type: map_at_100 value: 86.249
- type: map_at_200 value: 86.256
- type: map_at_300 value: 86.258
- type: map_at_500 value: 86.26
- type: map_at_700 value: 86.26
- type: map_at_1000 value: 86.261
- type: recall_at_1 value: 77.64999999999999
- type: recall_at_2 value: 88.53999999999999
- type: recall_at_3 value: 91.696
- type: recall_at_5 value: 93.916
- type: recall_at_7 value: 94.731
- type: recall_at_10 value: 95.318
- type: recall_at_20 value: 96.507
- type: recall_at_30 value: 96.956
- type: recall_at_50 value: 97.34899999999999
- type: recall_at_70 value: 97.61
- type: recall_at_100 value: 97.83
- type: recall_at_200 value: 98.223
- type: recall_at_300 value: 98.374
- type: recall_at_500 value: 98.67899999999999
- type: recall_at_700 value: 98.787
- type: recall_at_1000 value: 98.919
- type: precision_at_1 value: 83.723
- type: precision_at_2 value: 48.425000000000004
- type: precision_at_3 value: 33.638
- type: precision_at_5 value: 20.843
- type: precision_at_7 value: 15.079
- type: precision_at_10 value: 10.674999999999999
- type: precision_at_20 value: 5.457999999999999
- type: precision_at_30 value: 3.6740000000000004
- type: precision_at_50 value: 2.2239999999999998
- type: precision_at_70 value: 1.599
- type: precision_at_100 value: 1.125
- type: precision_at_200 value: 0.5680000000000001
- type: precision_at_300 value: 0.38
- type: precision_at_500 value: 0.22999999999999998
- type: precision_at_700 value: 0.165
- type: precision_at_1000 value: 0.116
- type: mrr_at_1 value: 83.723
- type: mrr_at_2 value: 88.794
- type: mrr_at_3 value: 89.679
- type: mrr_at_5 value: 90.049
- type: mrr_at_7 value: 90.129
- type: mrr_at_10 value: 90.167
- type: mrr_at_20 value: 90.208
- type: mrr_at_30 value: 90.214
- type: mrr_at_50 value: 90.217
- type: mrr_at_70 value: 90.218
- type: mrr_at_100 value: 90.21900000000001
- type: mrr_at_200 value: 90.21900000000001
- type: mrr_at_300 value: 90.21900000000001
- type: mrr_at_500 value: 90.21900000000001
- type: mrr_at_700 value: 90.21900000000001
- type: mrr_at_1000 value: 90.21900000000001
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 59.721999999999994
- type: ndcg_at_2 value: 56.85
- type: ndcg_at_3 value: 56.462999999999994
- type: ndcg_at_5 value: 57.75599999999999
- type: ndcg_at_7 value: 59.109
- type: ndcg_at_10 value: 60.402
- type: ndcg_at_20 value: 63.071999999999996
- type: ndcg_at_30 value: 64.302
- type: ndcg_at_50 value: 65.619
- type: ndcg_at_70 value: 66.161
- type: ndcg_at_100 value: 66.645
- type: ndcg_at_200 value: 67.353
- type: ndcg_at_300 value: 67.646
- type: ndcg_at_500 value: 67.852
- type: ndcg_at_700 value: 67.974
- type: ndcg_at_1000 value: 68.084
- type: map_at_1 value: 31.56
- type: map_at_2 value: 42.093
- type: map_at_3 value: 46.177
- type: map_at_5 value: 49.78
- type: map_at_7 value: 51.410999999999994
- type: map_at_10 value: 52.524
- type: map_at_20 value: 53.815000000000005
- type: map_at_30 value: 54.201
- type: map_at_50 value: 54.531
- type: map_at_70 value: 54.625
- type: map_at_100 value: 54.686
- type: map_at_200 value: 54.757999999999996
- type: map_at_300 value: 54.776
- type: map_at_500 value: 54.786
- type: map_at_700 value: 54.790000000000006
- type: map_at_1000 value: 54.793000000000006
- type: recall_at_1 value: 31.56
- type: recall_at_2 value: 44.858
- type: recall_at_3 value: 51.11
- type: recall_at_5 value: 58.394
- type: recall_at_7 value: 63.001
- type: recall_at_10 value: 66.81200000000001
- type: recall_at_20 value: 74.901
- type: recall_at_30 value: 79.218
- type: recall_at_50 value: 84.49
- type: recall_at_70 value: 87.003
- type: recall_at_100 value: 89.345
- type: recall_at_200 value: 93.173
- type: recall_at_300 value: 94.906
- type: recall_at_500 value: 96.223
- type: recall_at_700 value: 97.043
- type: recall_at_1000 value: 97.785
- type: precision_at_1 value: 59.721999999999994
- type: precision_at_2 value: 46.682
- type: precision_at_3 value: 37.602999999999994
- type: precision_at_5 value: 27.500000000000004
- type: precision_at_7 value: 21.847
- type: precision_at_10 value: 16.667
- type: precision_at_20 value: 9.545
- type: precision_at_30 value: 6.795
- type: precision_at_50 value: 4.38
- type: precision_at_70 value: 3.221
- type: precision_at_100 value: 2.319
- type: precision_at_200 value: 1.2149999999999999
- type: precision_at_300 value: 0.827
- type: precision_at_500 value: 0.504
- type: precision_at_700 value: 0.364
- type: precision_at_1000 value: 0.257
- type: mrr_at_1 value: 59.721999999999994
- type: mrr_at_2 value: 64.506
- type: mrr_at_3 value: 65.792
- type: mrr_at_5 value: 66.965
- type: mrr_at_7 value: 67.34700000000001
- type: mrr_at_10 value: 67.57
- type: mrr_at_20 value: 67.896
- type: mrr_at_30 value: 68.008
- type: mrr_at_50 value: 68.083
- type: mrr_at_70 value: 68.105
- type: mrr_at_100 value: 68.116
- type: mrr_at_200 value: 68.12700000000001
- type: mrr_at_300 value: 68.13
- type: mrr_at_500 value: 68.132
- type: mrr_at_700 value: 68.133
- type: mrr_at_1000 value: 68.133
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 81.796
- type: ndcg_at_2 value: 67.999
- type: ndcg_at_3 value: 72.15599999999999
- type: ndcg_at_5 value: 74.99900000000001
- type: ndcg_at_7 value: 76.179
- type: ndcg_at_10 value: 77.022
- type: ndcg_at_20 value: 78.173
- type: ndcg_at_30 value: 78.648
- type: ndcg_at_50 value: 79.104
- type: ndcg_at_70 value: 79.335
- type: ndcg_at_100 value: 79.56
- type: ndcg_at_200 value: 79.911
- type: ndcg_at_300 value: 80.045
- type: ndcg_at_500 value: 80.19500000000001
- type: ndcg_at_700 value: 80.281
- type: ndcg_at_1000 value: 80.35
- type: map_at_1 value: 40.898
- type: map_at_2 value: 62.016000000000005
- type: map_at_3 value: 66.121
- type: map_at_5 value: 68.471
- type: map_at_7 value: 69.261
- type: map_at_10 value: 69.738
- type: map_at_20 value: 70.208
- type: map_at_30 value: 70.343
- type: map_at_50 value: 70.43700000000001
- type: map_at_70 value: 70.47099999999999
- type: map_at_100 value: 70.498
- type: map_at_200 value: 70.526
- type: map_at_300 value: 70.533
- type: map_at_500 value: 70.538
- type: map_at_700 value: 70.541
- type: map_at_1000 value: 70.542
- type: recall_at_1 value: 40.898
- type: recall_at_2 value: 63.964
- type: recall_at_3 value: 70.743
- type: recall_at_5 value: 76.36699999999999
- type: recall_at_7 value: 79.142
- type: recall_at_10 value: 81.404
- type: recall_at_20 value: 85.111
- type: recall_at_30 value: 86.92800000000001
- type: recall_at_50 value: 88.899
- type: recall_at_70 value: 90.01400000000001
- type: recall_at_100 value: 91.19500000000001
- type: recall_at_200 value: 93.234
- type: recall_at_300 value: 94.105
- type: recall_at_500 value: 95.159
- type: recall_at_700 value: 95.8
- type: recall_at_1000 value: 96.34700000000001
- type: precision_at_1 value: 81.796
- type: precision_at_2 value: 63.964
- type: precision_at_3 value: 47.162
- type: precision_at_5 value: 30.547
- type: precision_at_7 value: 22.612
- type: precision_at_10 value: 16.281000000000002
- type: precision_at_20 value: 8.511000000000001
- type: precision_at_30 value: 5.795
- type: precision_at_50 value: 3.556
- type: precision_at_70 value: 2.572
- type: precision_at_100 value: 1.8239999999999998
- type: precision_at_200 value: 0.932
- type: precision_at_300 value: 0.627
- type: precision_at_500 value: 0.381
- type: precision_at_700 value: 0.27399999999999997
- type: precision_at_1000 value: 0.193
- type: mrr_at_1 value: 81.796
- type: mrr_at_2 value: 85.69200000000001
- type: mrr_at_3 value: 86.52
- type: mrr_at_5 value: 86.973
- type: mrr_at_7 value: 87.13300000000001
- type: mrr_at_10 value: 87.208
- type: mrr_at_20 value: 87.303
- type: mrr_at_30 value: 87.32799999999999
- type: mrr_at_50 value: 87.347
- type: mrr_at_70 value: 87.35199999999999
- type: mrr_at_100 value: 87.355
- type: mrr_at_200 value: 87.357
- type: mrr_at_300 value: 87.357
- type: mrr_at_500 value: 87.358
- type: mrr_at_700 value: 87.358
- type: mrr_at_1000 value: 87.358
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy value: 94.79200000000002
- type: ap value: 92.54484356773553
- type: f1 value: 94.78965313682525
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: ndcg_at_1 value: 24.398
- type: ndcg_at_2 value: 31.336000000000002
- type: ndcg_at_3 value: 35.266999999999996
- type: ndcg_at_5 value: 39.356
- type: ndcg_at_7 value: 41.562
- type: ndcg_at_10 value: 43.408
- type: ndcg_at_20 value: 46.107
- type: ndcg_at_30 value: 47.164
- type: ndcg_at_50 value: 48.126000000000005
- type: ndcg_at_70 value: 48.626999999999995
- type: ndcg_at_100 value: 49.043
- type: ndcg_at_200 value: 49.575
- type: ndcg_at_300 value: 49.794
- type: ndcg_at_500 value: 49.942
- type: ndcg_at_700 value: 50.014
- type: ndcg_at_1000 value: 50.077000000000005
- type: map_at_1 value: 23.723
- type: map_at_2 value: 29.593000000000004
- type: map_at_3 value: 32.273
- type: map_at_5 value: 34.587
- type: map_at_7 value: 35.589999999999996
- type: map_at_10 value: 36.296
- type: map_at_20 value: 37.059999999999995
- type: map_at_30 value: 37.265
- type: map_at_50 value: 37.402
- type: map_at_70 value: 37.454
- type: map_at_100 value: 37.486999999999995
- type: map_at_200 value: 37.516
- type: map_at_300 value: 37.524
- type: map_at_500 value: 37.528
- type: map_at_700 value: 37.529
- type: map_at_1000 value: 37.53
- type: recall_at_1 value: 23.723
- type: recall_at_2 value: 35.355
- type: recall_at_3 value: 43.22
- type: recall_at_5 value: 53.025
- type: recall_at_7 value: 59.327
- type: recall_at_10 value: 65.302
- type: recall_at_20 value: 75.765
- type: recall_at_30 value: 80.632
- type: recall_at_50 value: 85.63499999999999
- type: recall_at_70 value: 88.554
- type: recall_at_100 value: 91.16300000000001
- type: recall_at_200 value: 94.85
- type: recall_at_300 value: 96.532
- type: recall_at_500 value: 97.751
- type: recall_at_700 value: 98.383
- type: recall_at_1000 value: 98.97
- type: precision_at_1 value: 24.398
- type: precision_at_2 value: 18.274
- type: precision_at_3 value: 14.951999999999998
- type: precision_at_5 value: 11.052
- type: precision_at_7 value: 8.84
- type: precision_at_10 value: 6.8309999999999995
- type: precision_at_20 value: 3.978
- type: precision_at_30 value: 2.827
- type: precision_at_50 value: 1.807
- type: precision_at_70 value: 1.336
- type: precision_at_100 value: 0.964
- type: precision_at_200 value: 0.502
- type: precision_at_300 value: 0.34099999999999997
- type: precision_at_500 value: 0.208
- type: precision_at_700 value: 0.15
- type: precision_at_1000 value: 0.105
- type: mrr_at_1 value: 24.398
- type: mrr_at_2 value: 30.351
- type: mrr_at_3 value: 33.001000000000005
- type: mrr_at_5 value: 35.228
- type: mrr_at_7 value: 36.223
- type: mrr_at_10 value: 36.903999999999996
- type: mrr_at_20 value: 37.631
- type: mrr_at_30 value: 37.830000000000005
- type: mrr_at_50 value: 37.955
- type: mrr_at_70 value: 38.003
- type: mrr_at_100 value: 38.033
- type: mrr_at_200 value: 38.059
- type: mrr_at_300 value: 38.066
- type: mrr_at_500 value: 38.068999999999996
- type: mrr_at_700 value: 38.07
- type: mrr_at_1000 value: 38.07
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy value: 96.35658914728683
- type: f1 value: 96.15039630903114
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy value: 86.29730962152303
- type: f1 value: 71.12166316567485
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy value: 79.98991257565568
- type: f1 value: 77.41680115095276
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy value: 82.1990585070612
- type: f1 value: 82.23719179179362
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure value: 40.03019554933584
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure value: 38.999760551497815
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map value: 32.72383151953079
- type: mrr value: 33.93989699030721
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 51.858000000000004
- type: ndcg_at_2 value: 49.675999999999995
- type: ndcg_at_3 value: 47.519
- type: ndcg_at_5 value: 45.198
- type: ndcg_at_7 value: 43.504
- type: ndcg_at_10 value: 41.88
- type: ndcg_at_20 value: 39.122
- type: ndcg_at_30 value: 37.95
- type: ndcg_at_50 value: 37.602999999999994
- type: ndcg_at_70 value: 37.836
- type: ndcg_at_100 value: 38.493
- type: ndcg_at_200 value: 40.187
- type: ndcg_at_300 value: 41.524
- type: ndcg_at_500 value: 43.657000000000004
- type: ndcg_at_700 value: 45.234
- type: ndcg_at_1000 value: 47.047
- type: map_at_1 value: 6.392
- type: map_at_2 value: 10.113
- type: map_at_3 value: 11.543000000000001
- type: map_at_5 value: 13.729
- type: map_at_7 value: 14.985000000000001
- type: map_at_10 value: 16.217000000000002
- type: map_at_20 value: 18.106
- type: map_at_30 value: 18.878
- type: map_at_50 value: 19.822
- type: map_at_70 value: 20.352999999999998
- type: map_at_100 value: 20.827
- type: map_at_200 value: 21.512
- type: map_at_300 value: 21.826
- type: map_at_500 value: 22.155
- type: map_at_700 value: 22.349
- type: map_at_1000 value: 22.531000000000002
- type: recall_at_1 value: 6.392
- type: recall_at_2 value: 11.215
- type: recall_at_3 value: 13.231000000000002
- type: recall_at_5 value: 16.66
- type: recall_at_7 value: 18.802
- type: recall_at_10 value: 21.185000000000002
- type: recall_at_20 value: 25.35
- type: recall_at_30 value: 27.91
- type: recall_at_50 value: 32.845
- type: recall_at_70 value: 35.789
- type: recall_at_100 value: 39.247
- type: recall_at_200 value: 46.655
- type: recall_at_300 value: 51.43299999999999
- type: recall_at_500 value: 59.472
- type: recall_at_700 value: 64.742
- type: recall_at_1000 value: 70.97099999999999
- type: precision_at_1 value: 53.559999999999995
- type: precision_at_2 value: 48.762
- type: precision_at_3 value: 44.169000000000004
- type: precision_at_5 value: 39.071
- type: precision_at_7 value: 35.161
- type: precision_at_10 value: 31.238
- type: precision_at_20 value: 23.064999999999998
- type: precision_at_30 value: 18.844
- type: precision_at_50 value: 14.601
- type: precision_at_70 value: 12.088000000000001
- type: precision_at_100 value: 9.844999999999999
- type: precision_at_200 value: 6.358
- type: precision_at_300 value: 4.915
- type: precision_at_500 value: 3.531
- type: precision_at_700 value: 2.8649999999999998
- type: precision_at_1000 value: 2.289
- type: mrr_at_1 value: 54.17999999999999
- type: mrr_at_2 value: 59.288
- type: mrr_at_3 value: 60.836
- type: mrr_at_5 value: 62.275999999999996
- type: mrr_at_7 value: 62.688
- type: mrr_at_10 value: 62.865
- type: mrr_at_20 value: 63.11
- type: mrr_at_30 value: 63.193999999999996
- type: mrr_at_50 value: 63.258
- type: mrr_at_70 value: 63.278
- type: mrr_at_100 value: 63.297000000000004
- type: mrr_at_200 value: 63.315999999999995
- type: mrr_at_300 value: 63.318
- type: mrr_at_500 value: 63.32299999999999
- type: mrr_at_700 value: 63.324000000000005
- type: mrr_at_1000 value: 63.324999999999996
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 50.897999999999996
- type: ndcg_at_2 value: 59.126
- type: ndcg_at_3 value: 63.093999999999994
- type: ndcg_at_5 value: 67.197
- type: ndcg_at_7 value: 68.719
- type: ndcg_at_10 value: 69.915
- type: ndcg_at_20 value: 71.229
- type: ndcg_at_30 value: 71.667
- type: ndcg_at_50 value: 71.98
- type: ndcg_at_70 value: 72.127
- type: ndcg_at_100 value: 72.217
- type: ndcg_at_200 value: 72.319
- type: ndcg_at_300 value: 72.347
- type: ndcg_at_500 value: 72.37
- type: ndcg_at_700 value: 72.379
- type: ndcg_at_1000 value: 72.381
- type: map_at_1 value: 45.297
- type: map_at_2 value: 55.596000000000004
- type: map_at_3 value: 58.724
- type: map_at_5 value: 61.387
- type: map_at_7 value: 62.173
- type: map_at_10 value: 62.69
- type: map_at_20 value: 63.125
- type: map_at_30 value: 63.223
- type: map_at_50 value: 63.27700000000001
- type: map_at_70 value: 63.295
- type: map_at_100 value: 63.303
- type: map_at_200 value: 63.31
- type: map_at_300 value: 63.31099999999999
- type: map_at_500 value: 63.312000000000005
- type: map_at_700 value: 63.312000000000005
- type: map_at_1000 value: 63.312000000000005
- type: recall_at_1 value: 45.297
- type: recall_at_2 value: 63.866
- type: recall_at_3 value: 71.898
- type: recall_at_5 value: 81.16600000000001
- type: recall_at_7 value: 85.301
- type: recall_at_10 value: 88.94800000000001
- type: recall_at_20 value: 93.719
- type: recall_at_30 value: 95.628
- type: recall_at_50 value: 97.14699999999999
- type: recall_at_70 value: 97.955
- type: recall_at_100 value: 98.48599999999999
- type: recall_at_200 value: 99.157
- type: recall_at_300 value: 99.355
- type: recall_at_500 value: 99.53699999999999
- type: recall_at_700 value: 99.62299999999999
- type: recall_at_1000 value: 99.638
- type: precision_at_1 value: 50.897999999999996
- type: precision_at_2 value: 36.703
- type: precision_at_3 value: 27.926000000000002
- type: precision_at_5 value: 19.276
- type: precision_at_7 value: 14.533999999999999
- type: precision_at_10 value: 10.678
- type: precision_at_20 value: 5.663
- type: precision_at_30 value: 3.8600000000000003
- type: precision_at_50 value: 2.358
- type: precision_at_70 value: 1.7000000000000002
- type: precision_at_100 value: 1.198
- type: precision_at_200 value: 0.603
- type: precision_at_300 value: 0.40299999999999997
- type: precision_at_500 value: 0.242
- type: precision_at_700 value: 0.173
- type: precision_at_1000 value: 0.121
- type: mrr_at_1 value: 50.897999999999996
- type: mrr_at_2 value: 59.994
- type: mrr_at_3 value: 62.553000000000004
- type: mrr_at_5 value: 64.307
- type: mrr_at_7 value: 64.864
- type: mrr_at_10 value: 65.22200000000001
- type: mrr_at_20 value: 65.499
- type: mrr_at_30 value: 65.561
- type: mrr_at_50 value: 65.592
- type: mrr_at_70 value: 65.602
- type: mrr_at_100 value: 65.607
- type: mrr_at_200 value: 65.61099999999999
- type: mrr_at_300 value: 65.61200000000001
- type: mrr_at_500 value: 65.61200000000001
- type: mrr_at_700 value: 65.61200000000001
- type: mrr_at_1000 value: 65.61200000000001
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 82.96
- type: ndcg_at_2 value: 85.614
- type: ndcg_at_3 value: 87.19
- type: ndcg_at_5 value: 88.654
- type: ndcg_at_7 value: 89.287
- type: ndcg_at_10 value: 89.785
- type: ndcg_at_20 value: 90.384
- type: ndcg_at_30 value: 90.589
- type: ndcg_at_50 value: 90.738
- type: ndcg_at_70 value: 90.789
- type: ndcg_at_100 value: 90.824
- type: ndcg_at_200 value: 90.869
- type: ndcg_at_300 value: 90.881
- type: ndcg_at_500 value: 90.886
- type: ndcg_at_700 value: 90.889
- type: ndcg_at_1000 value: 90.889
- type: map_at_1 value: 72.152
- type: map_at_2 value: 80.818
- type: map_at_3 value: 83.462
- type: map_at_5 value: 85.286
- type: map_at_7 value: 85.921
- type: map_at_10 value: 86.334
- type: map_at_20 value: 86.737
- type: map_at_30 value: 86.847
- type: map_at_50 value: 86.911
- type: map_at_70 value: 86.932
- type: map_at_100 value: 86.943
- type: map_at_200 value: 86.953
- type: map_at_300 value: 86.955
- type: map_at_500 value: 86.956
- type: map_at_700 value: 86.956
- type: map_at_1000 value: 86.956
- type: recall_at_1 value: 72.152
- type: recall_at_2 value: 84.129
- type: recall_at_3 value: 88.87
- type: recall_at_5 value: 93.067
- type: recall_at_7 value: 94.882
- type: recall_at_10 value: 96.353
- type: recall_at_20 value: 98.26700000000001
- type: recall_at_30 value: 98.92999999999999
- type: recall_at_50 value: 99.441
- type: recall_at_70 value: 99.619
- type: recall_at_100 value: 99.748
- type: recall_at_200 value: 99.911
- type: recall_at_300 value: 99.956
- type: recall_at_500 value: 99.98
- type: recall_at_700 value: 99.991
- type: recall_at_1000 value: 99.996
- type: precision_at_1 value: 82.96
- type: precision_at_2 value: 52.175000000000004
- type: precision_at_3 value: 38.223
- type: precision_at_5 value: 25.056
- type: precision_at_7 value: 18.717
- type: precision_at_10 value: 13.614999999999998
- type: precision_at_20 value: 7.208
- type: precision_at_30 value: 4.928
- type: precision_at_50 value: 3.024
- type: precision_at_70 value: 2.183
- type: precision_at_100 value: 1.54
- type: precision_at_200 value: 0.779
- type: precision_at_300 value: 0.521
- type: precision_at_500 value: 0.313
- type: precision_at_700 value: 0.22399999999999998
- type: precision_at_1000 value: 0.157
- type: mrr_at_1 value: 82.96
- type: mrr_at_2 value: 87.005
- type: mrr_at_3 value: 88.07199999999999
- type: mrr_at_5 value: 88.634
- type: mrr_at_7 value: 88.793
- type: mrr_at_10 value: 88.87899999999999
- type: mrr_at_20 value: 88.94999999999999
- type: mrr_at_30 value: 88.96
- type: mrr_at_50 value: 88.965
- type: mrr_at_70 value: 88.966
- type: mrr_at_100 value: 88.967
- type: mrr_at_200 value: 88.967
- type: mrr_at_300 value: 88.967
- type: mrr_at_500 value: 88.967
- type: mrr_at_700 value: 88.967
- type: mrr_at_1000 value: 88.967
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure value: 59.90388554491155
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure value: 67.64232539036783
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 22.6
- type: ndcg_at_2 value: 20.355999999999998
- type: ndcg_at_3 value: 18.536
- type: ndcg_at_5 value: 16.523
- type: ndcg_at_7 value: 17.979
- type: ndcg_at_10 value: 19.908
- type: ndcg_at_20 value: 22.887
- type: ndcg_at_30 value: 24.43
- type: ndcg_at_50 value: 25.959
- type: ndcg_at_70 value: 26.989
- type: ndcg_at_100 value: 27.977
- type: ndcg_at_200 value: 29.831000000000003
- type: ndcg_at_300 value: 30.787
- type: ndcg_at_500 value: 31.974999999999998
- type: ndcg_at_700 value: 32.554
- type: ndcg_at_1000 value: 33.277
- type: map_at_1 value: 4.593
- type: map_at_2 value: 6.923
- type: map_at_3 value: 8.3
- type: map_at_5 value: 10.072000000000001
- type: map_at_7 value: 10.782
- type: map_at_10 value: 11.72
- type: map_at_20 value: 12.838
- type: map_at_30 value: 13.257
- type: map_at_50 value: 13.569
- type: map_at_70 value: 13.733
- type: map_at_100 value: 13.858999999999998
- type: map_at_200 value: 14.018
- type: map_at_300 value: 14.072999999999999
- type: map_at_500 value: 14.126
- type: map_at_700 value: 14.145
- type: map_at_1000 value: 14.161999999999999
- type: recall_at_1 value: 4.593
- type: recall_at_2 value: 7.997999999999999
- type: recall_at_3 value: 10.563
- type: recall_at_5 value: 14.907
- type: recall_at_7 value: 17.4
- type: recall_at_10 value: 21.18
- type: recall_at_20 value: 28.144999999999996
- type: recall_at_30 value: 32.462
- type: recall_at_50 value: 37.267
- type: recall_at_70 value: 40.875
- type: recall_at_100 value: 44.641999999999996
- type: recall_at_200 value: 52.573
- type: recall_at_300 value: 57.089999999999996
- type: recall_at_500 value: 63.14300000000001
- type: recall_at_700 value: 66.313
- type: recall_at_1000 value: 70.458
- type: precision_at_1 value: 22.6
- type: precision_at_2 value: 19.7
- type: precision_at_3 value: 17.333000000000002
- type: precision_at_5 value: 14.680000000000001
- type: precision_at_7 value: 12.243
- type: precision_at_10 value: 10.440000000000001
- type: precision_at_20 value: 6.944999999999999
- type: precision_at_30 value: 5.333
- type: precision_at_50 value: 3.678
- type: precision_at_70 value: 2.881
- type: precision_at_100 value: 2.2030000000000003
- type: precision_at_200 value: 1.295
- type: precision_at_300 value: 0.9369999999999999
- type: precision_at_500 value: 0.622
- type: precision_at_700 value: 0.466
- type: precision_at_1000 value: 0.347
- type: mrr_at_1 value: 22.6
- type: mrr_at_2 value: 27.900000000000002
- type: mrr_at_3 value: 30.067
- type: mrr_at_5 value: 32.207
- type: mrr_at_7 value: 33.004
- type: mrr_at_10 value: 33.596
- type: mrr_at_20 value: 34.268
- type: mrr_at_30 value: 34.492
- type: mrr_at_50 value: 34.628
- type: mrr_at_70 value: 34.681
- type: mrr_at_100 value: 34.717
- type: mrr_at_200 value: 34.757
- type: mrr_at_300 value: 34.768
- type: mrr_at_500 value: 34.772
- type: mrr_at_700 value: 34.774
- type: mrr_at_1000 value: 34.775
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson value: 86.90122745229677
- type: cos_sim_spearman value: 82.92294737327579
- type: euclidean_pearson value: 84.08979655773187
- type: euclidean_spearman value: 82.92294657285412
- type: manhattan_pearson value: 84.09347480531832
- type: manhattan_spearman value: 82.91564613948087
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson value: 87.01218713698583
- type: cos_sim_spearman value: 79.46865215168464
- type: euclidean_pearson value: 83.22621889891909
- type: euclidean_spearman value: 79.46853821709514
- type: manhattan_pearson value: 83.69962580788805
- type: manhattan_spearman value: 79.9561593356932
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson value: 88.98438696342964
- type: cos_sim_spearman value: 89.15419511870839
- type: euclidean_pearson value: 88.49646141802894
- type: euclidean_spearman value: 89.15419503946019
- type: manhattan_pearson value: 88.6420585616327
- type: manhattan_spearman value: 89.42648950757743
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson value: 87.30772547759544
- type: cos_sim_spearman value: 84.93199878424691
- type: euclidean_pearson value: 86.16266630395455
- type: euclidean_spearman value: 84.93198798543634
- type: manhattan_pearson value: 86.14285723189803
- type: manhattan_spearman value: 85.0361672522687
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson value: 90.21342071197127
- type: cos_sim_spearman value: 90.7407512744838
- type: euclidean_pearson value: 90.1517933113061
- type: euclidean_spearman value: 90.74075125431919
- type: manhattan_pearson value: 90.17963034676193
- type: manhattan_spearman value: 90.88999275865135
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson value: 86.82518054100498
- type: cos_sim_spearman value: 87.81570533154735
- type: euclidean_pearson value: 86.91684561573618
- type: euclidean_spearman value: 87.81570533154735
- type: manhattan_pearson value: 86.98311935744032
- type: manhattan_spearman value: 87.9594667151966
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson value: 92.09578436612053
- type: cos_sim_spearman value: 92.01519349090438
- type: euclidean_pearson value: 92.07113635890894
- type: euclidean_spearman value: 92.01519349090438
- type: manhattan_pearson value: 91.89343820765625
- type: manhattan_spearman value: 91.7443476810177
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson value: 69.29997751464549
- type: cos_sim_spearman value: 68.36425436812782
- type: euclidean_pearson value: 69.81381677661783
- type: euclidean_spearman value: 68.36425436812782
- type: manhattan_pearson value: 69.92823397008026
- type: manhattan_spearman value: 68.35770640039254
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson value: 88.39126315452359
- type: cos_sim_spearman value: 88.99708463265337
- type: euclidean_pearson value: 88.60793820038607
- type: euclidean_spearman value: 88.99708463265337
- type: manhattan_pearson value: 88.69860633571047
- type: manhattan_spearman value: 89.20094593888012
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map value: 86.58028062818582
- type: mrr value: 96.53586790841693
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 66.333
- type: ndcg_at_2 value: 70.655
- type: ndcg_at_3 value: 72.801
- type: ndcg_at_5 value: 75.793
- type: ndcg_at_7 value: 76.946
- type: ndcg_at_10 value: 77.66199999999999
- type: ndcg_at_20 value: 78.786
- type: ndcg_at_30 value: 79.066
- type: ndcg_at_50 value: 79.255
- type: ndcg_at_70 value: 79.423
- type: ndcg_at_100 value: 79.476
- type: ndcg_at_200 value: 79.65299999999999
- type: ndcg_at_300 value: 79.696
- type: ndcg_at_500 value: 79.73599999999999
- type: ndcg_at_700 value: 79.77199999999999
- type: ndcg_at_1000 value: 79.77199999999999
- type: map_at_1 value: 63.383
- type: map_at_2 value: 68.144
- type: map_at_3 value: 70.19800000000001
- type: map_at_5 value: 72.38
- type: map_at_7 value: 72.955
- type: map_at_10 value: 73.312
- type: map_at_20 value: 73.678
- type: map_at_30 value: 73.72800000000001
- type: map_at_50 value: 73.75500000000001
- type: map_at_70 value: 73.771
- type: map_at_100 value: 73.776
- type: map_at_200 value: 73.783
- type: map_at_300 value: 73.784
- type: map_at_500 value: 73.785
- type: map_at_700 value: 73.786
- type: map_at_1000 value: 73.786
- type: recall_at_1 value: 63.383
- type: recall_at_2 value: 72.283
- type: recall_at_3 value: 77.183
- type: recall_at_5 value: 84.56099999999999
- type: recall_at_7 value: 87.67200000000001
- type: recall_at_10 value: 89.822
- type: recall_at_20 value: 94
- type: recall_at_30 value: 95.333
- type: recall_at_50 value: 96.333
- type: recall_at_70 value: 97.333
- type: recall_at_100 value: 97.667
- type: recall_at_200 value: 99
- type: recall_at_300 value: 99.333
- type: recall_at_500 value: 99.667
- type: recall_at_700 value: 100
- type: recall_at_1000 value: 100
- type: precision_at_1 value: 66.333
- type: precision_at_2 value: 38.667
- type: precision_at_3 value: 28.111000000000004
- type: precision_at_5 value: 18.933
- type: precision_at_7 value: 14.094999999999999
- type: precision_at_10 value: 10.167
- type: precision_at_20 value: 5.35
- type: precision_at_30 value: 3.611
- type: precision_at_50 value: 2.1870000000000003
- type: precision_at_70 value: 1.576
- type: precision_at_100 value: 1.107
- type: precision_at_200 value: 0.5599999999999999
- type: precision_at_300 value: 0.374
- type: precision_at_500 value: 0.22499999999999998
- type: precision_at_700 value: 0.161
- type: precision_at_1000 value: 0.11299999999999999
- type: mrr_at_1 value: 66.333
- type: mrr_at_2 value: 70.833
- type: mrr_at_3 value: 72.167
- type: mrr_at_5 value: 73.6
- type: mrr_at_7 value: 74.084
- type: mrr_at_10 value: 74.283
- type: mrr_at_20 value: 74.54499999999999
- type: mrr_at_30 value: 74.59599999999999
- type: mrr_at_50 value: 74.622
- type: mrr_at_70 value: 74.639
- type: mrr_at_100 value: 74.643
- type: mrr_at_200 value: 74.65
- type: mrr_at_300 value: 74.652
- type: mrr_at_500 value: 74.653
- type: mrr_at_700 value: 74.653
- type: mrr_at_1000 value: 74.653
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy value: 99.84554455445544
- type: cos_sim_ap value: 96.31178339136798
- type: cos_sim_f1 value: 92.1921921921922
- type: cos_sim_precision value: 92.28456913827655
- type: cos_sim_recall value: 92.10000000000001
- type: dot_accuracy value: 99.84554455445544
- type: dot_ap value: 96.31178339136797
- type: dot_f1 value: 92.1921921921922
- type: dot_precision value: 92.28456913827655
- type: dot_recall value: 92.10000000000001
- type: euclidean_accuracy value: 99.84554455445544
- type: euclidean_ap value: 96.31178339136798
- type: euclidean_f1 value: 92.1921921921922
- type: euclidean_precision value: 92.28456913827655
- type: euclidean_recall value: 92.10000000000001
- type: manhattan_accuracy value: 99.84752475247525
- type: manhattan_ap value: 96.4591954606088
- type: manhattan_f1 value: 92.25352112676056
- type: manhattan_precision value: 92.81376518218623
- type: manhattan_recall value: 91.7
- type: max_accuracy value: 99.84752475247525
- type: max_ap value: 96.4591954606088
- type: max_f1 value: 92.25352112676056
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure value: 74.24659759283294
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure value: 46.77690051260451
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map value: 55.68436757803185
- type: mrr value: 56.82157711569475
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson value: 31.652482405629843
- type: cos_sim_spearman value: 31.16341822347735
- type: dot_pearson value: 31.652479892699837
- type: dot_spearman value: 31.16341822347735
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 92
- type: ndcg_at_2 value: 90.839
- type: ndcg_at_3 value: 90.642
- type: ndcg_at_5 value: 90.348
- type: ndcg_at_7 value: 89.015
- type: ndcg_at_10 value: 87.599
- type: ndcg_at_20 value: 84.434
- type: ndcg_at_30 value: 81.655
- type: ndcg_at_50 value: 77.278
- type: ndcg_at_70 value: 73.957
- type: ndcg_at_100 value: 69.56
- type: ndcg_at_200 value: 60.724000000000004
- type: ndcg_at_300 value: 57.245000000000005
- type: ndcg_at_500 value: 56.316
- type: ndcg_at_700 value: 58.399
- type: ndcg_at_1000 value: 62.21600000000001
- type: map_at_1 value: 0.247
- type: map_at_2 value: 0.488
- type: map_at_3 value: 0.7230000000000001
- type: map_at_5 value: 1.204
- type: map_at_7 value: 1.6500000000000001
- type: map_at_10 value: 2.292
- type: map_at_20 value: 4.274
- type: map_at_30 value: 6.027
- type: map_at_50 value: 9.083
- type: map_at_70 value: 11.751000000000001
- type: map_at_100 value: 14.912
- type: map_at_200 value: 22.213
- type: map_at_300 value: 26.667999999999996
- type: map_at_500 value: 31.556
- type: map_at_700 value: 34.221000000000004
- type: map_at_1000 value: 36.443999999999996
- type: recall_at_1 value: 0.247
- type: recall_at_2 value: 0.49899999999999994
- type: recall_at_3 value: 0.742
- type: recall_at_5 value: 1.247
- type: recall_at_7 value: 1.722
- type: recall_at_10 value: 2.405
- type: recall_at_20 value: 4.583
- type: recall_at_30 value: 6.587999999999999
- type: recall_at_50 value: 10.188
- type: recall_at_70 value: 13.496
- type: recall_at_100 value: 17.578
- type: recall_at_200 value: 28.158
- type: recall_at_300 value: 35.532000000000004
- type: recall_at_500 value: 45.31
- type: recall_at_700 value: 51.822
- type: recall_at_1000 value: 58.53
- type: precision_at_1 value: 96
- type: precision_at_2 value: 96
- type: precision_at_3 value: 95.333
- type: precision_at_5 value: 94.8
- type: precision_at_7 value: 93.429
- type: precision_at_10 value: 91.4
- type: precision_at_20 value: 87.7
- type: precision_at_30 value: 84.867
- type: precision_at_50 value: 80.24
- type: precision_at_70 value: 76.371
- type: precision_at_100 value: 71.08
- type: precision_at_200 value: 59.4
- type: precision_at_300 value: 51.459999999999994
- type: precision_at_500 value: 40.644000000000005
- type: precision_at_700 value: 33.889
- type: precision_at_1000 value: 27.250000000000004
- type: mrr_at_1 value: 96
- type: mrr_at_2 value: 98
- type: mrr_at_3 value: 98
- type: mrr_at_5 value: 98
- type: mrr_at_7 value: 98
- type: mrr_at_10 value: 98
- type: mrr_at_20 value: 98
- type: mrr_at_30 value: 98
- type: mrr_at_50 value: 98
- type: mrr_at_70 value: 98
- type: mrr_at_100 value: 98
- type: mrr_at_200 value: 98
- type: mrr_at_300 value: 98
- type: mrr_at_500 value: 98
- type: mrr_at_700 value: 98
- type: mrr_at_1000 value: 98
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1 value: 43.878
- type: ndcg_at_2 value: 37.956
- type: ndcg_at_3 value: 35.053
- type: ndcg_at_5 value: 32.59
- type: ndcg_at_7 value: 30.226
- type: ndcg_at_10 value: 29.005
- type: ndcg_at_20 value: 30.11
- type: ndcg_at_30 value: 32.019999999999996
- type: ndcg_at_50 value: 34.354
- type: ndcg_at_70 value: 36.665
- type: ndcg_at_100 value: 38.888
- type: ndcg_at_200 value: 43.435
- type: ndcg_at_300 value: 45.795
- type: ndcg_at_500 value: 48.699999999999996
- type: ndcg_at_700 value: 50.242
- type: ndcg_at_1000 value: 51.529
- type: map_at_1 value: 3.521
- type: map_at_2 value: 5.309
- type: map_at_3 value: 6.576
- type: map_at_5 value: 8.97
- type: map_at_7 value: 10.194
- type: map_at_10 value: 11.949
- type: map_at_20 value: 14.686
- type: map_at_30 value: 15.8
- type: map_at_50 value: 16.59
- type: map_at_70 value: 17.2
- type: map_at_100 value: 17.765
- type: map_at_200 value: 18.636
- type: map_at_300 value: 18.972
- type: map_at_500 value: 19.301
- type: map_at_700 value: 19.445
- type: map_at_1000 value: 19.546
- type: recall_at_1 value: 3.521
- type: recall_at_2 value: 5.848
- type: recall_at_3 value: 7.657
- type: recall_at_5 value: 11.368
- type: recall_at_7 value: 13.748
- type: recall_at_10 value: 18.061
- type: recall_at_20 value: 26.844
- type: recall_at_30 value: 31.186000000000003
- type: recall_at_50 value: 35.951
- type: recall_at_70 value: 40.961999999999996
- type: recall_at_100 value: 46.743
- type: recall_at_200 value: 58.483
- type: recall_at_300 value: 65.973
- type: recall_at_500 value: 75.233
- type: recall_at_700 value: 80.472
- type: recall_at_1000 value: 85.02
- type: precision_at_1 value: 46.939
- type: precision_at_2 value: 38.775999999999996
- type: precision_at_3 value: 34.694
- type: precision_at_5 value: 31.429000000000002
- type: precision_at_7 value: 27.697
- type: precision_at_10 value: 24.490000000000002
- type: precision_at_20 value: 18.776
- type: precision_at_30 value: 15.034
- type: precision_at_50 value: 10.857
- type: precision_at_70 value: 9.096
- type: precision_at_100 value: 7.51
- type: precision_at_200 value: 4.929
- type: precision_at_300 value: 3.7760000000000002
- type: precision_at_500 value: 2.6780000000000004
- type: precision_at_700 value: 2.085
- type: precision_at_1000 value: 1.5709999999999997
- type: mrr_at_1 value: 46.939
- type: mrr_at_2 value: 55.102
- type: mrr_at_3 value: 57.823
- type: mrr_at_5 value: 60.68
- type: mrr_at_7 value: 60.972
- type: mrr_at_10 value: 61.199000000000005
- type: mrr_at_20 value: 61.831
- type: mrr_at_30 value: 61.831
- type: mrr_at_50 value: 61.873
- type: mrr_at_70 value: 61.873
- type: mrr_at_100 value: 61.873
- type: mrr_at_200 value: 61.873
- type: mrr_at_300 value: 61.873
- type: mrr_at_500 value: 61.873
- type: mrr_at_700 value: 61.873
- type: mrr_at_1000 value: 61.873
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy value: 69.3294
- type: ap value: 14.561333393364736
- type: f1 value: 53.992309820496466
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy value: 63.63893604980192
- type: f1 value: 63.92959380489434
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure value: 56.270879258659775
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy value: 88.71073493473207
- type: cos_sim_ap value: 81.52392540284202
- type: cos_sim_f1 value: 74.71162377994676
- type: cos_sim_precision value: 71.89558428885094
- type: cos_sim_recall value: 77.75725593667546
- type: dot_accuracy value: 88.71073493473207
- type: dot_ap value: 81.52394754041109
- type: dot_f1 value: 74.71162377994676
- type: dot_precision value: 71.89558428885094
- type: dot_recall value: 77.75725593667546
- type: euclidean_accuracy value: 88.71073493473207
- type: euclidean_ap value: 81.52392035435321
- type: euclidean_f1 value: 74.71162377994676
- type: euclidean_precision value: 71.89558428885094
- type: euclidean_recall value: 77.75725593667546
- type: manhattan_accuracy value: 88.47231328604637
- type: manhattan_ap value: 81.22907439267321
- type: manhattan_f1 value: 74.3351571446749
- type: manhattan_precision value: 71.78667977390022
- type: manhattan_recall value: 77.0712401055409
- type: max_accuracy value: 88.71073493473207
- type: max_ap value: 81.52394754041109
- type: max_f1 value: 74.71162377994676
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy value: 89.85136026700819
- type: cos_sim_ap value: 87.7768002924216
- type: cos_sim_f1 value: 80.358908624794
- type: cos_sim_precision value: 76.62918209122023
- type: cos_sim_recall value: 84.47028025870034
- type: dot_accuracy value: 89.85136026700819
- type: dot_ap value: 87.77680027889778
- type: dot_f1 value: 80.358908624794
- type: dot_precision value: 76.62918209122023
- type: dot_recall value: 84.47028025870034
- type: euclidean_accuracy value: 89.85136026700819
- type: euclidean_ap value: 87.77680174697751
- type: euclidean_f1 value: 80.358908624794
- type: euclidean_precision value: 76.62918209122023
- type: euclidean_recall value: 84.47028025870034
- type: manhattan_accuracy value: 89.86300306593705
- type: manhattan_ap value: 87.78613271895861
- type: manhattan_f1 value: 80.31831016905645
- type: manhattan_precision value: 76.68230516070304
- type: manhattan_recall value: 84.3162919618109
- type: max_accuracy value: 89.86300306593705
- type: max_ap value: 87.78613271895861
- type: max_f1 value: 80.358908624794 language:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- en license: cc-by-nc-4.0
Salesforce/SFR-Embedding-Mistral
SFR-Embedding by Salesforce Research.
The model is trained on top of E5-mistral-7b-instruct and Mistral-7B-v0.1.
This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses. Please refer to specific papers for more details:
More technical details will be updated later.
Ethical Considerations
This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
How to run
Transformers
The models can be used as follows:
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'How to bake a chocolate cake'),
get_detailed_instruct(task, 'Symptoms of the flu')
]
# No need to add instruction for retrieval documents
passages = [
"To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!",
"The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness."
]
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Salesforce/SFR-Embedding-Mistral')
model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Mistral')
# get the embeddings
max_length = 4096
input_texts = queries + passages
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors="pt")
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[86.7153549194336, 36.64569091796875], [35.00493621826172, 82.0738525390625]]
Sentence Transformers
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer("Salesforce/SFR-Embedding-Mistral")
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'How to bake a chocolate cake'),
get_detailed_instruct(task, 'Symptoms of the flu')
]
# No need to add instruction for retrieval documents
passages = [
"To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!",
"The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness."
]
embeddings = model.encode(queries + passages)
scores = util.cos_sim(embeddings[:2], embeddings[2:]) * 100
print(scores.tolist())
# [[86.71537780761719, 36.645721435546875], [35.00497055053711, 82.07388305664062]]
MTEB Benchmark Evaluation
Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark.
SFR-Embedding Team (∗indicates lead contributors).
- Rui Meng*
- Ye Liu*
- Shafiq Rayhan Joty
- Caiming Xiong
- Yingbo Zhou
- Semih Yavuz
Citation
@misc{SFRAIResearch2024,
title={SFR-Embedding-Mistral:Enhance Text Retrieval with Transfer Learning},
author={Rui Meng, Ye Liu, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, Semih Yavuz},
howpublished={Salesforce AI Research Blog},
year={2024},
url={https://www.salesforce.com/blog/sfr-embedding/}
}
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