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JA
Mmlw E5 Base
M
Mmlw E5 Base
sdadasによって開発
mmlw-e5-baseは、文類似度タスクに特化した文変換器ベースの特徴抽出モデルで、ポーランド語をサポートしています。
ダウンロード数 37
リリース時間 : 11/17/2023
モデル概要
このモデルは主に文類似度計算と特徴抽出に使用され、テキストクラスタリング、分類、検索などの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
マルチタスクサポート
モデルはクラスタリング、分類、検索、文類似度計算など、複数のタスクをサポートします。
ポーランド語最適化
ポーランド語向けに最適化されており、複数のポーランド語データセットで優れた性能を発揮します。
高性能特徴抽出
文レベルの特徴表現を効率的に抽出でき、下流タスクに適しています。
モデル能力
文類似度計算
テキスト特徴抽出
テキストクラスタリング
テキスト分類
情報検索
使用事例
テキスト分析
製品レビュー分類
ポーランド語の製品レビューを感情分類
MTEB AllegroReviewsデータセットで36.38%の精度を達成
法律条項分類
法律条項における不正使用内容の識別
MTEB PACデータセットで64.50%の精度を達成
情報検索
質問応答システム
ポーランド語の質問応答システムを構築
MTEB NQ-PLデータセットで24.67%のmap@1を達成
ドキュメント検索
ポーランド語ドキュメント検索システム
MTEB DBPedia-PLデータセットで8.27%のmap@1を達成
pipeline_tag: sentence-similarity tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb model-index:
- name: mmlw-e5-base
results:
- task:
type: Clustering
dataset:
type: PL-MTEB/8tags-clustering
name: MTEB 8TagsClustering
config: default
split: test
revision: None
metrics:
- type: v_measure value: 30.249113010261492
- task:
type: Classification
dataset:
type: PL-MTEB/allegro-reviews
name: MTEB AllegroReviews
config: default
split: test
revision: None
metrics:
- type: accuracy value: 36.3817097415507
- type: f1 value: 32.77742158736663
- task:
type: Retrieval
dataset:
type: arguana-pl
name: MTEB ArguAna-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 32.646
- type: map_at_10 value: 49.488
- type: map_at_100 value: 50.190999999999995
- type: map_at_1000 value: 50.194
- type: map_at_3 value: 44.749
- type: map_at_5 value: 47.571999999999996
- type: mrr_at_1 value: 34.211000000000006
- type: mrr_at_10 value: 50.112
- type: mrr_at_100 value: 50.836000000000006
- type: mrr_at_1000 value: 50.839
- type: mrr_at_3 value: 45.614
- type: mrr_at_5 value: 48.242000000000004
- type: ndcg_at_1 value: 32.646
- type: ndcg_at_10 value: 58.396
- type: ndcg_at_100 value: 61.285000000000004
- type: ndcg_at_1000 value: 61.358999999999995
- type: ndcg_at_3 value: 48.759
- type: ndcg_at_5 value: 53.807
- type: precision_at_1 value: 32.646
- type: precision_at_10 value: 8.663
- type: precision_at_100 value: 0.9900000000000001
- type: precision_at_1000 value: 0.1
- type: precision_at_3 value: 20.128
- type: precision_at_5 value: 14.509
- type: recall_at_1 value: 32.646
- type: recall_at_10 value: 86.629
- type: recall_at_100 value: 99.004
- type: recall_at_1000 value: 99.57300000000001
- type: recall_at_3 value: 60.38400000000001
- type: recall_at_5 value: 72.54599999999999
- task:
type: Classification
dataset:
type: PL-MTEB/cbd
name: MTEB CBD
config: default
split: test
revision: None
metrics:
- type: accuracy value: 65.53999999999999
- type: ap value: 19.75395945379771
- type: f1 value: 55.00481388401326
- task:
type: PairClassification
dataset:
type: PL-MTEB/cdsce-pairclassification
name: MTEB CDSC-E
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy value: 89.5
- type: cos_sim_ap value: 77.26879308078568
- type: cos_sim_f1 value: 65.13157894736842
- type: cos_sim_precision value: 86.8421052631579
- type: cos_sim_recall value: 52.10526315789473
- type: dot_accuracy value: 88.0
- type: dot_ap value: 69.17235659054914
- type: dot_f1 value: 65.71428571428571
- type: dot_precision value: 71.875
- type: dot_recall value: 60.526315789473685
- type: euclidean_accuracy value: 89.5
- type: euclidean_ap value: 77.1905400565015
- type: euclidean_f1 value: 64.91803278688525
- type: euclidean_precision value: 86.08695652173914
- type: euclidean_recall value: 52.10526315789473
- type: manhattan_accuracy value: 89.5
- type: manhattan_ap value: 77.19531778873724
- type: manhattan_f1 value: 64.72491909385113
- type: manhattan_precision value: 84.03361344537815
- type: manhattan_recall value: 52.63157894736842
- type: max_accuracy value: 89.5
- type: max_ap value: 77.26879308078568
- type: max_f1 value: 65.71428571428571
- task:
type: STS
dataset:
type: PL-MTEB/cdscr-sts
name: MTEB CDSC-R
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 93.18498922236566
- type: cos_sim_spearman value: 93.26224500108704
- type: euclidean_pearson value: 92.25462061070286
- type: euclidean_spearman value: 93.18623989769242
- type: manhattan_pearson value: 92.16291103586255
- type: manhattan_spearman value: 93.14403078934417
- task:
type: Retrieval
dataset:
type: dbpedia-pl
name: MTEB DBPedia-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 8.268
- type: map_at_10 value: 17.391000000000002
- type: map_at_100 value: 24.266
- type: map_at_1000 value: 25.844
- type: map_at_3 value: 12.636
- type: map_at_5 value: 14.701
- type: mrr_at_1 value: 62.74999999999999
- type: mrr_at_10 value: 70.25200000000001
- type: mrr_at_100 value: 70.601
- type: mrr_at_1000 value: 70.613
- type: mrr_at_3 value: 68.083
- type: mrr_at_5 value: 69.37100000000001
- type: ndcg_at_1 value: 51.87500000000001
- type: ndcg_at_10 value: 37.185
- type: ndcg_at_100 value: 41.949
- type: ndcg_at_1000 value: 49.523
- type: ndcg_at_3 value: 41.556
- type: ndcg_at_5 value: 39.278
- type: precision_at_1 value: 63.24999999999999
- type: precision_at_10 value: 29.225
- type: precision_at_100 value: 9.745
- type: precision_at_1000 value: 2.046
- type: precision_at_3 value: 43.833
- type: precision_at_5 value: 37.9
- type: recall_at_1 value: 8.268
- type: recall_at_10 value: 22.542
- type: recall_at_100 value: 48.154
- type: recall_at_1000 value: 72.62100000000001
- type: recall_at_3 value: 13.818
- type: recall_at_5 value: 17.137
- task:
type: Retrieval
dataset:
type: fiqa-pl
name: MTEB FiQA-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 16.489
- type: map_at_10 value: 26.916
- type: map_at_100 value: 28.582
- type: map_at_1000 value: 28.774
- type: map_at_3 value: 23.048
- type: map_at_5 value: 24.977
- type: mrr_at_1 value: 33.642
- type: mrr_at_10 value: 41.987
- type: mrr_at_100 value: 42.882
- type: mrr_at_1000 value: 42.93
- type: mrr_at_3 value: 39.48
- type: mrr_at_5 value: 40.923
- type: ndcg_at_1 value: 33.488
- type: ndcg_at_10 value: 34.528
- type: ndcg_at_100 value: 41.085
- type: ndcg_at_1000 value: 44.474000000000004
- type: ndcg_at_3 value: 30.469
- type: ndcg_at_5 value: 31.618000000000002
- type: precision_at_1 value: 33.488
- type: precision_at_10 value: 9.783999999999999
- type: precision_at_100 value: 1.6389999999999998
- type: precision_at_1000 value: 0.22699999999999998
- type: precision_at_3 value: 20.525
- type: precision_at_5 value: 15.093
- type: recall_at_1 value: 16.489
- type: recall_at_10 value: 42.370000000000005
- type: recall_at_100 value: 67.183
- type: recall_at_1000 value: 87.211
- type: recall_at_3 value: 27.689999999999998
- type: recall_at_5 value: 33.408
- task:
type: Retrieval
dataset:
type: hotpotqa-pl
name: MTEB HotpotQA-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 37.373
- type: map_at_10 value: 57.509
- type: map_at_100 value: 58.451
- type: map_at_1000 value: 58.524
- type: map_at_3 value: 54.064
- type: map_at_5 value: 56.257999999999996
- type: mrr_at_1 value: 74.895
- type: mrr_at_10 value: 81.233
- type: mrr_at_100 value: 81.461
- type: mrr_at_1000 value: 81.47
- type: mrr_at_3 value: 80.124
- type: mrr_at_5 value: 80.862
- type: ndcg_at_1 value: 74.747
- type: ndcg_at_10 value: 66.249
- type: ndcg_at_100 value: 69.513
- type: ndcg_at_1000 value: 70.896
- type: ndcg_at_3 value: 61.312
- type: ndcg_at_5 value: 64.132
- type: precision_at_1 value: 74.747
- type: precision_at_10 value: 13.873
- type: precision_at_100 value: 1.641
- type: precision_at_1000 value: 0.182
- type: precision_at_3 value: 38.987
- type: precision_at_5 value: 25.621
- type: recall_at_1 value: 37.373
- type: recall_at_10 value: 69.365
- type: recall_at_100 value: 82.039
- type: recall_at_1000 value: 91.148
- type: recall_at_3 value: 58.48100000000001
- type: recall_at_5 value: 64.051
- task:
type: Retrieval
dataset:
type: msmarco-pl
name: MTEB MSMARCO-PL
config: default
split: validation
revision: None
metrics:
- type: map_at_1 value: 16.753999999999998
- type: map_at_10 value: 26.764
- type: map_at_100 value: 27.929
- type: map_at_1000 value: 27.994999999999997
- type: map_at_3 value: 23.527
- type: map_at_5 value: 25.343
- type: mrr_at_1 value: 17.192
- type: mrr_at_10 value: 27.141
- type: mrr_at_100 value: 28.269
- type: mrr_at_1000 value: 28.327999999999996
- type: mrr_at_3 value: 23.906
- type: mrr_at_5 value: 25.759999999999998
- type: ndcg_at_1 value: 17.177999999999997
- type: ndcg_at_10 value: 32.539
- type: ndcg_at_100 value: 38.383
- type: ndcg_at_1000 value: 40.132
- type: ndcg_at_3 value: 25.884
- type: ndcg_at_5 value: 29.15
- type: precision_at_1 value: 17.177999999999997
- type: precision_at_10 value: 5.268
- type: precision_at_100 value: 0.823
- type: precision_at_1000 value: 0.097
- type: precision_at_3 value: 11.122
- type: precision_at_5 value: 8.338
- type: recall_at_1 value: 16.753999999999998
- type: recall_at_10 value: 50.388
- type: recall_at_100 value: 77.86999999999999
- type: recall_at_1000 value: 91.55
- type: recall_at_3 value: 32.186
- type: recall_at_5 value: 40.048
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (pl)
config: pl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy value: 70.9280430396772
- type: f1 value: 68.7099581466286
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (pl)
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy value: 74.76126429051783
- type: f1 value: 74.72274307018111
- task:
type: Retrieval
dataset:
type: nfcorpus-pl
name: MTEB NFCorpus-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 5.348
- type: map_at_10 value: 12.277000000000001
- type: map_at_100 value: 15.804000000000002
- type: map_at_1000 value: 17.277
- type: map_at_3 value: 8.783000000000001
- type: map_at_5 value: 10.314
- type: mrr_at_1 value: 43.963
- type: mrr_at_10 value: 52.459999999999994
- type: mrr_at_100 value: 53.233
- type: mrr_at_1000 value: 53.26499999999999
- type: mrr_at_3 value: 50.464
- type: mrr_at_5 value: 51.548
- type: ndcg_at_1 value: 40.711999999999996
- type: ndcg_at_10 value: 33.709
- type: ndcg_at_100 value: 31.398
- type: ndcg_at_1000 value: 40.042
- type: ndcg_at_3 value: 37.85
- type: ndcg_at_5 value: 36.260999999999996
- type: precision_at_1 value: 43.344
- type: precision_at_10 value: 25.851000000000003
- type: precision_at_100 value: 8.279
- type: precision_at_1000 value: 2.085
- type: precision_at_3 value: 36.326
- type: precision_at_5 value: 32.074000000000005
- type: recall_at_1 value: 5.348
- type: recall_at_10 value: 16.441
- type: recall_at_100 value: 32.975
- type: recall_at_1000 value: 64.357
- type: recall_at_3 value: 9.841999999999999
- type: recall_at_5 value: 12.463000000000001
- task:
type: Retrieval
dataset:
type: nq-pl
name: MTEB NQ-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 24.674
- type: map_at_10 value: 37.672
- type: map_at_100 value: 38.767
- type: map_at_1000 value: 38.82
- type: map_at_3 value: 33.823
- type: map_at_5 value: 36.063
- type: mrr_at_1 value: 27.839000000000002
- type: mrr_at_10 value: 40.129
- type: mrr_at_100 value: 41.008
- type: mrr_at_1000 value: 41.048
- type: mrr_at_3 value: 36.718
- type: mrr_at_5 value: 38.841
- type: ndcg_at_1 value: 27.839000000000002
- type: ndcg_at_10 value: 44.604
- type: ndcg_at_100 value: 49.51
- type: ndcg_at_1000 value: 50.841
- type: ndcg_at_3 value: 37.223
- type: ndcg_at_5 value: 41.073
- type: precision_at_1 value: 27.839000000000002
- type: precision_at_10 value: 7.5
- type: precision_at_100 value: 1.03
- type: precision_at_1000 value: 0.116
- type: precision_at_3 value: 17.005
- type: precision_at_5 value: 12.399000000000001
- type: recall_at_1 value: 24.674
- type: recall_at_10 value: 63.32299999999999
- type: recall_at_100 value: 85.088
- type: recall_at_1000 value: 95.143
- type: recall_at_3 value: 44.157999999999994
- type: recall_at_5 value: 53.054
- task:
type: Classification
dataset:
type: laugustyniak/abusive-clauses-pl
name: MTEB PAC
config: default
split: test
revision: None
metrics:
- type: accuracy value: 64.5033304373009
- type: ap value: 75.81507275237081
- type: f1 value: 62.24617820785985
- task:
type: PairClassification
dataset:
type: PL-MTEB/ppc-pairclassification
name: MTEB PPC
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy value: 85.39999999999999
- type: cos_sim_ap value: 91.75881977787009
- type: cos_sim_f1 value: 87.79264214046823
- type: cos_sim_precision value: 88.68243243243244
- type: cos_sim_recall value: 86.9205298013245
- type: dot_accuracy value: 71.0
- type: dot_ap value: 82.97829049033108
- type: dot_f1 value: 78.77055039313797
- type: dot_precision value: 69.30817610062893
- type: dot_recall value: 91.22516556291392
- type: euclidean_accuracy value: 85.2
- type: euclidean_ap value: 91.85245521151309
- type: euclidean_f1 value: 87.64607679465777
- type: euclidean_precision value: 88.38383838383838
- type: euclidean_recall value: 86.9205298013245
- type: manhattan_accuracy value: 85.39999999999999
- type: manhattan_ap value: 91.85497100160649
- type: manhattan_f1 value: 87.77219430485762
- type: manhattan_precision value: 88.8135593220339
- type: manhattan_recall value: 86.75496688741721
- type: max_accuracy value: 85.39999999999999
- type: max_ap value: 91.85497100160649
- type: max_f1 value: 87.79264214046823
- task:
type: PairClassification
dataset:
type: PL-MTEB/psc-pairclassification
name: MTEB PSC
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy value: 97.58812615955473
- type: cos_sim_ap value: 99.14945370088302
- type: cos_sim_f1 value: 96.06060606060606
- type: cos_sim_precision value: 95.48192771084338
- type: cos_sim_recall value: 96.64634146341463
- type: dot_accuracy value: 95.17625231910947
- type: dot_ap value: 97.05592933601112
- type: dot_f1 value: 92.14501510574019
- type: dot_precision value: 91.31736526946108
- type: dot_recall value: 92.98780487804879
- type: euclidean_accuracy value: 97.6808905380334
- type: euclidean_ap value: 99.18538119402824
- type: euclidean_f1 value: 96.20637329286798
- type: euclidean_precision value: 95.77039274924472
- type: euclidean_recall value: 96.64634146341463
- type: manhattan_accuracy value: 97.58812615955473
- type: manhattan_ap value: 99.17870990853292
- type: manhattan_f1 value: 96.02446483180427
- type: manhattan_precision value: 96.31901840490798
- type: manhattan_recall value: 95.73170731707317
- type: max_accuracy value: 97.6808905380334
- type: max_ap value: 99.18538119402824
- type: max_f1 value: 96.20637329286798
- task:
type: Classification
dataset:
type: PL-MTEB/polemo2_in
name: MTEB PolEmo2.0-IN
config: default
split: test
revision: None
metrics:
- type: accuracy value: 68.69806094182825
- type: f1 value: 68.0619984307764
- task:
type: Classification
dataset:
type: PL-MTEB/polemo2_out
name: MTEB PolEmo2.0-OUT
config: default
split: test
revision: None
metrics:
- type: accuracy value: 35.80971659919028
- type: f1 value: 31.13081621324864
- task:
type: Retrieval
dataset:
type: quora-pl
name: MTEB Quora-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 66.149
- type: map_at_10 value: 80.133
- type: map_at_100 value: 80.845
- type: map_at_1000 value: 80.866
- type: map_at_3 value: 76.983
- type: map_at_5 value: 78.938
- type: mrr_at_1 value: 76.09
- type: mrr_at_10 value: 83.25099999999999
- type: mrr_at_100 value: 83.422
- type: mrr_at_1000 value: 83.42500000000001
- type: mrr_at_3 value: 82.02199999999999
- type: mrr_at_5 value: 82.831
- type: ndcg_at_1 value: 76.14999999999999
- type: ndcg_at_10 value: 84.438
- type: ndcg_at_100 value: 86.048
- type: ndcg_at_1000 value: 86.226
- type: ndcg_at_3 value: 80.97999999999999
- type: ndcg_at_5 value: 82.856
- type: precision_at_1 value: 76.14999999999999
- type: precision_at_10 value: 12.985
- type: precision_at_100 value: 1.513
- type: precision_at_1000 value: 0.156
- type: precision_at_3 value: 35.563
- type: precision_at_5 value: 23.586
- type: recall_at_1 value: 66.149
- type: recall_at_10 value: 93.195
- type: recall_at_100 value: 98.924
- type: recall_at_1000 value: 99.885
- type: recall_at_3 value: 83.439
- type: recall_at_5 value: 88.575
- task:
type: Retrieval
dataset:
type: scidocs-pl
name: MTEB SCIDOCS-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 3.688
- type: map_at_10 value: 10.23
- type: map_at_100 value: 12.077
- type: map_at_1000 value: 12.382
- type: map_at_3 value: 7.149
- type: map_at_5 value: 8.689
- type: mrr_at_1 value: 18.2
- type: mrr_at_10 value: 28.816999999999997
- type: mrr_at_100 value: 29.982
- type: mrr_at_1000 value: 30.058
- type: mrr_at_3 value: 25.983
- type: mrr_at_5 value: 27.418
- type: ndcg_at_1 value: 18.2
- type: ndcg_at_10 value: 17.352999999999998
- type: ndcg_at_100 value: 24.859
- type: ndcg_at_1000 value: 30.535
- type: ndcg_at_3 value: 16.17
- type: ndcg_at_5 value: 14.235000000000001
- type: precision_at_1 value: 18.2
- type: precision_at_10 value: 9.19
- type: precision_at_100 value: 2.01
- type: precision_at_1000 value: 0.338
- type: precision_at_3 value: 15.5
- type: precision_at_5 value: 12.78
- type: recall_at_1 value: 3.688
- type: recall_at_10 value: 18.632
- type: recall_at_100 value: 40.822
- type: recall_at_1000 value: 68.552
- type: recall_at_3 value: 9.423
- type: recall_at_5 value: 12.943
- task:
type: PairClassification
dataset:
type: PL-MTEB/sicke-pl-pairclassification
name: MTEB SICK-E-PL
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy value: 83.12270688952303
- type: cos_sim_ap value: 76.4528312253856
- type: cos_sim_f1 value: 68.69627507163324
- type: cos_sim_precision value: 69.0922190201729
- type: cos_sim_recall value: 68.30484330484332
- type: dot_accuracy value: 79.20913167549939
- type: dot_ap value: 65.03147071986633
- type: dot_f1 value: 62.812160694896846
- type: dot_precision value: 50.74561403508772
- type: dot_recall value: 82.4074074074074
- type: euclidean_accuracy value: 83.16347329800244
- type: euclidean_ap value: 76.49405838298205
- type: euclidean_f1 value: 68.66738120757414
- type: euclidean_precision value: 68.88888888888889
- type: euclidean_recall value: 68.44729344729345
- type: manhattan_accuracy value: 83.16347329800244
- type: manhattan_ap value: 76.5080551733795
- type: manhattan_f1 value: 68.73883529832084
- type: manhattan_precision value: 68.9605734767025
- type: manhattan_recall value: 68.51851851851852
- type: max_accuracy value: 83.16347329800244
- type: max_ap value: 76.5080551733795
- type: max_f1 value: 68.73883529832084
- task:
type: STS
dataset:
type: PL-MTEB/sickr-pl-sts
name: MTEB SICK-R-PL
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 82.60225159739653
- type: cos_sim_spearman value: 76.76667220288542
- type: euclidean_pearson value: 80.16302518898615
- type: euclidean_spearman value: 76.76131897866455
- type: manhattan_pearson value: 80.11881021613914
- type: manhattan_spearman value: 76.74246419368048
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson value: 38.2744776092718
- type: cos_sim_spearman value: 40.35664941442517
- type: euclidean_pearson value: 29.148502128336585
- type: euclidean_spearman value: 40.45531563224982
- type: manhattan_pearson value: 29.124177399433098
- type: manhattan_spearman value: 40.2801387844354
- task:
type: Retrieval
dataset:
type: scifact-pl
name: MTEB SciFact-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 52.994
- type: map_at_10 value: 63.612
- type: map_at_100 value: 64.294
- type: map_at_1000 value: 64.325
- type: map_at_3 value: 61.341
- type: map_at_5 value: 62.366
- type: mrr_at_1 value: 56.667
- type: mrr_at_10 value: 65.333
- type: mrr_at_100 value: 65.89399999999999
- type: mrr_at_1000 value: 65.91900000000001
- type: mrr_at_3 value: 63.666999999999994
- type: mrr_at_5 value: 64.36699999999999
- type: ndcg_at_1 value: 56.333
- type: ndcg_at_10 value: 68.292
- type: ndcg_at_100 value: 71.136
- type: ndcg_at_1000 value: 71.90100000000001
- type: ndcg_at_3 value: 64.387
- type: ndcg_at_5 value: 65.546
- type: precision_at_1 value: 56.333
- type: precision_at_10 value: 9.133
- type: precision_at_100 value: 1.0630000000000002
- type: precision_at_1000 value: 0.11299999999999999
- type: precision_at_3 value: 25.556
- type: precision_at_5 value: 16.267
- type: recall_at_1 value: 52.994
- type: recall_at_10 value: 81.178
- type: recall_at_100 value: 93.767
- type: recall_at_1000 value: 99.667
- type: recall_at_3 value: 69.906
- type: recall_at_5 value: 73.18299999999999
- task:
type: Retrieval
dataset:
type: trec-covid-pl
name: MTEB TRECCOVID-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 0.231
- type: map_at_10 value: 1.822
- type: map_at_100 value: 10.134
- type: map_at_1000 value: 24.859
- type: map_at_3 value: 0.615
- type: map_at_5 value: 0.9939999999999999
- type: mrr_at_1 value: 84.0
- type: mrr_at_10 value: 90.4
- type: mrr_at_100 value: 90.4
- type: mrr_at_1000 value: 90.4
- type: mrr_at_3 value: 89.0
- type: mrr_at_5 value: 90.4
- type: ndcg_at_1 value: 81.0
- type: ndcg_at_10 value: 73.333
- type: ndcg_at_100 value: 55.35099999999999
- type: ndcg_at_1000 value: 49.875
- type: ndcg_at_3 value: 76.866
- type: ndcg_at_5 value: 75.472
- type: precision_at_1 value: 86.0
- type: precision_at_10 value: 78.2
- type: precision_at_100 value: 57.18
- type: precision_at_1000 value: 22.332
- type: precision_at_3 value: 82.0
- type: precision_at_5 value: 81.2
- type: recall_at_1 value: 0.231
- type: recall_at_10 value: 2.056
- type: recall_at_100 value: 13.468
- type: recall_at_1000 value: 47.038999999999994
- type: recall_at_3 value: 0.6479999999999999
- type: recall_at_5 value: 1.088 language: pl license: apache-2.0 widget:
- task:
type: Clustering
dataset:
type: PL-MTEB/8tags-clustering
name: MTEB 8TagsClustering
config: default
split: test
revision: None
metrics:
- source_sentence: "query: Jak dożyć 100 lat?"
sentences:
- "passage: Trzeba zdrowo się odżywiać i uprawiać sport."
- "passage: Trzeba pić alkohol, imprezować i jeździć szybkimi autami."
- "passage: Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu."
MMLW-e5-base
MMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish. This is a distilled model that can be used to generate embeddings applicable to many tasks such as semantic similarity, clustering, information retrieval. The model can also serve as a base for further fine-tuning. It transforms texts to 768 dimensional vectors. The model was initialized with multilingual E5 checkpoint, and then trained with multilingual knowledge distillation method on a diverse corpus of 60 million Polish-English text pairs. We utilised English FlagEmbeddings (BGE) as teacher models for distillation.
Usage (Sentence-Transformers)
⚠️ Our embedding models require the use of specific prefixes and suffixes when encoding texts. For this model, queries should be prefixed with "query: " and passages with "passage: " ⚠️
You can use the model like this with sentence-transformers:
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
query_prefix = "query: "
answer_prefix = "passage: "
queries = [query_prefix + "Jak dożyć 100 lat?"]
answers = [
answer_prefix + "Trzeba zdrowo się odżywiać i uprawiać sport.",
answer_prefix + "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.",
answer_prefix + "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu."
]
model = SentenceTransformer("sdadas/mmlw-e5-base")
queries_emb = model.encode(queries, convert_to_tensor=True, show_progress_bar=False)
answers_emb = model.encode(answers, convert_to_tensor=True, show_progress_bar=False)
best_answer = cos_sim(queries_emb, answers_emb).argmax().item()
print(answers[best_answer])
# Trzeba zdrowo się odżywiać i uprawiać sport.
Evaluation Results
- The model achieves an Average Score of 59.71 on the Polish Massive Text Embedding Benchmark (MTEB). See MTEB Leaderboard for detailed results.
- The model achieves NDCG@10 of 53.56 on the Polish Information Retrieval Benchmark. See PIRB Leaderboard for detailed results.
Acknowledgements
This model was trained with the A100 GPU cluster support delivered by the Gdansk University of Technology within the TASK center initiative.
Citation
@article{dadas2024pirb,
title={{PIRB}: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods},
author={Sławomir Dadas and Michał Perełkiewicz and Rafał Poświata},
year={2024},
eprint={2402.13350},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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