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JA
Yinka
Y
Yinka
Classicalによって開発
このモデルは中国語テキスト埋め込みベンチマーク(MTEB)で複数のタスク評価を行っており、テキスト類似度、分類、クラスタリング、検索などのタスクを含みます。
ダウンロード数 388
リリース時間 : 5/30/2024
モデル概要
これは中国語テキスト埋め込みベンチマーク(MTEB)で評価されたモデルで、意味類似度計算、テキスト分類、クラスタリング、情報検索などの様々な自然言語処理タスクをサポートします。
モデル特徴
マルチタスク評価
MTEB中国語ベンチマークの複数タスクで包括的な評価を行っており、STS、分類、クラスタリング、検索などを含みます。
中国語最適化
中国語テキスト処理に特化して最適化されており、複数の中国語データセットで良好な性能を発揮します。
多様な指標
ピアソン相関係数、スピアマン相関係数、正解率、F1スコアなど様々な評価指標を提供します。
モデル能力
テキスト類似度計算
テキスト分類
テキストクラスタリング
情報検索
意味マッチング
質問応答再ランキング
使用事例
電子商取引
商品レビュー分類
ECプラットフォームの商品レビューを感情分類
JDReviewデータセットで88.48%の正解率を達成
商品検索
ECプラットフォームの商品検索と推薦
EcomRetrievalデータセットでMAP@10が63.11を達成
医療健康
医療QA検索
医療分野の質問検索とマッチング
CMedQAv1とCMedQAv2データセットでMAPがそれぞれ89.26と90.05を達成
医学文献検索
医学関連文献の検索とランキング
MedicalRetrievalデータセットでNDCG@10が65.20を達成
汎用意味理解
意味類似度計算
2つのテキスト間の意味類似度を計算
LCQMCデータセットでコサイン類似度ピアソン相関係数が73.68を達成
テキスト分類
テキストの多クラス分類
IFlyTekデータセットで51.77%の正解率を達成
tags:
- mteb model-index:
- name: checkpoint-1431
results:
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson value: 56.306314279047875
- type: cos_sim_spearman value: 61.020227685004016
- type: euclidean_pearson value: 58.61821670933433
- type: euclidean_spearman value: 60.131457106640674
- type: manhattan_pearson value: 58.6189460369694
- type: manhattan_spearman value: 60.126350618526224
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 55.8612958476143
- type: cos_sim_spearman value: 59.01977664864512
- type: euclidean_pearson value: 62.028094897243655
- type: euclidean_spearman value: 58.6046814257705
- type: manhattan_pearson value: 62.02580042431887
- type: manhattan_spearman value: 58.60626890004892
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 49.496
- type: f1 value: 46.673963383873065
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 70.73971622592535
- type: cos_sim_spearman value: 72.76102992060764
- type: euclidean_pearson value: 71.04525865868672
- type: euclidean_spearman value: 72.4032852155075
- type: manhattan_pearson value: 71.03693009336658
- type: manhattan_spearman value: 72.39635701224252
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure value: 56.34751074520767
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure value: 48.4856662121073
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map value: 89.26384109024997
- type: mrr value: 91.27261904761905
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map value: 90.0464058154547
- type: mrr value: 92.06480158730159
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 27.236
- type: map_at_10 value: 40.778
- type: map_at_100 value: 42.692
- type: map_at_1000 value: 42.787
- type: map_at_3 value: 36.362
- type: map_at_5 value: 38.839
- type: mrr_at_1 value: 41.335
- type: mrr_at_10 value: 49.867
- type: mrr_at_100 value: 50.812999999999995
- type: mrr_at_1000 value: 50.848000000000006
- type: mrr_at_3 value: 47.354
- type: mrr_at_5 value: 48.718
- type: ndcg_at_1 value: 41.335
- type: ndcg_at_10 value: 47.642
- type: ndcg_at_100 value: 54.855
- type: ndcg_at_1000 value: 56.449000000000005
- type: ndcg_at_3 value: 42.203
- type: ndcg_at_5 value: 44.416
- type: precision_at_1 value: 41.335
- type: precision_at_10 value: 10.568
- type: precision_at_100 value: 1.6400000000000001
- type: precision_at_1000 value: 0.184
- type: precision_at_3 value: 23.998
- type: precision_at_5 value: 17.389
- type: recall_at_1 value: 27.236
- type: recall_at_10 value: 58.80800000000001
- type: recall_at_100 value: 88.411
- type: recall_at_1000 value: 99.032
- type: recall_at_3 value: 42.253
- type: recall_at_5 value: 49.118
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy value: 86.03728202044498
- type: cos_sim_ap value: 92.49469583272597
- type: cos_sim_f1 value: 86.74095974528088
- type: cos_sim_precision value: 84.43657294664601
- type: cos_sim_recall value: 89.17465513210195
- type: dot_accuracy value: 72.21888153938664
- type: dot_ap value: 80.59377163340332
- type: dot_f1 value: 74.96686040583258
- type: dot_precision value: 66.4737793851718
- type: dot_recall value: 85.94809445873275
- type: euclidean_accuracy value: 85.47203848466627
- type: euclidean_ap value: 91.89152584749868
- type: euclidean_f1 value: 86.38105975197294
- type: euclidean_precision value: 83.40953625081646
- type: euclidean_recall value: 89.5721299976619
- type: manhattan_accuracy value: 85.3758268190018
- type: manhattan_ap value: 91.88989707722311
- type: manhattan_f1 value: 86.39767519839052
- type: manhattan_precision value: 82.76231263383298
- type: manhattan_recall value: 90.36707972878185
- type: max_accuracy value: 86.03728202044498
- type: max_ap value: 92.49469583272597
- type: max_f1 value: 86.74095974528088
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 74.34100000000001
- type: map_at_10 value: 82.49499999999999
- type: map_at_100 value: 82.64200000000001
- type: map_at_1000 value: 82.643
- type: map_at_3 value: 81.142
- type: map_at_5 value: 81.95400000000001
- type: mrr_at_1 value: 74.71
- type: mrr_at_10 value: 82.553
- type: mrr_at_100 value: 82.699
- type: mrr_at_1000 value: 82.70100000000001
- type: mrr_at_3 value: 81.279
- type: mrr_at_5 value: 82.069
- type: ndcg_at_1 value: 74.605
- type: ndcg_at_10 value: 85.946
- type: ndcg_at_100 value: 86.607
- type: ndcg_at_1000 value: 86.669
- type: ndcg_at_3 value: 83.263
- type: ndcg_at_5 value: 84.71600000000001
- type: precision_at_1 value: 74.605
- type: precision_at_10 value: 9.758
- type: precision_at_100 value: 1.005
- type: precision_at_1000 value: 0.101
- type: precision_at_3 value: 29.996000000000002
- type: precision_at_5 value: 18.736
- type: recall_at_1 value: 74.34100000000001
- type: recall_at_10 value: 96.523
- type: recall_at_100 value: 99.473
- type: recall_at_1000 value: 100.0
- type: recall_at_3 value: 89.278
- type: recall_at_5 value: 92.83500000000001
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 26.950000000000003
- type: map_at_10 value: 82.408
- type: map_at_100 value: 85.057
- type: map_at_1000 value: 85.09100000000001
- type: map_at_3 value: 57.635999999999996
- type: map_at_5 value: 72.48
- type: mrr_at_1 value: 92.15
- type: mrr_at_10 value: 94.554
- type: mrr_at_100 value: 94.608
- type: mrr_at_1000 value: 94.61
- type: mrr_at_3 value: 94.292
- type: mrr_at_5 value: 94.459
- type: ndcg_at_1 value: 92.15
- type: ndcg_at_10 value: 89.108
- type: ndcg_at_100 value: 91.525
- type: ndcg_at_1000 value: 91.82900000000001
- type: ndcg_at_3 value: 88.44
- type: ndcg_at_5 value: 87.271
- type: precision_at_1 value: 92.15
- type: precision_at_10 value: 42.29
- type: precision_at_100 value: 4.812
- type: precision_at_1000 value: 0.48900000000000005
- type: precision_at_3 value: 79.14999999999999
- type: precision_at_5 value: 66.64
- type: recall_at_1 value: 26.950000000000003
- type: recall_at_10 value: 89.832
- type: recall_at_100 value: 97.921
- type: recall_at_1000 value: 99.471
- type: recall_at_3 value: 59.562000000000005
- type: recall_at_5 value: 76.533
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 53.5
- type: map_at_10 value: 63.105999999999995
- type: map_at_100 value: 63.63100000000001
- type: map_at_1000 value: 63.641999999999996
- type: map_at_3 value: 60.617
- type: map_at_5 value: 62.132
- type: mrr_at_1 value: 53.5
- type: mrr_at_10 value: 63.105999999999995
- type: mrr_at_100 value: 63.63100000000001
- type: mrr_at_1000 value: 63.641999999999996
- type: mrr_at_3 value: 60.617
- type: mrr_at_5 value: 62.132
- type: ndcg_at_1 value: 53.5
- type: ndcg_at_10 value: 67.92200000000001
- type: ndcg_at_100 value: 70.486
- type: ndcg_at_1000 value: 70.777
- type: ndcg_at_3 value: 62.853
- type: ndcg_at_5 value: 65.59899999999999
- type: precision_at_1 value: 53.5
- type: precision_at_10 value: 8.309999999999999
- type: precision_at_100 value: 0.951
- type: precision_at_1000 value: 0.097
- type: precision_at_3 value: 23.1
- type: precision_at_5 value: 15.2
- type: recall_at_1 value: 53.5
- type: recall_at_10 value: 83.1
- type: recall_at_100 value: 95.1
- type: recall_at_1000 value: 97.39999999999999
- type: recall_at_3 value: 69.3
- type: recall_at_5 value: 76.0
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy value: 51.773759138130046
- type: f1 value: 40.38600802756481
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy value: 88.48030018761726
- type: ap value: 59.2732541555627
- type: f1 value: 83.58836007358619
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 73.67511194245922
- type: cos_sim_spearman value: 79.43347759067298
- type: euclidean_pearson value: 79.04491504318766
- type: euclidean_spearman value: 79.14478545356785
- type: manhattan_pearson value: 79.03365022867428
- type: manhattan_spearman value: 79.13172717619908
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 67.184
- type: map_at_10 value: 76.24600000000001
- type: map_at_100 value: 76.563
- type: map_at_1000 value: 76.575
- type: map_at_3 value: 74.522
- type: map_at_5 value: 75.598
- type: mrr_at_1 value: 69.47
- type: mrr_at_10 value: 76.8
- type: mrr_at_100 value: 77.082
- type: mrr_at_1000 value: 77.093
- type: mrr_at_3 value: 75.29400000000001
- type: mrr_at_5 value: 76.24
- type: ndcg_at_1 value: 69.47
- type: ndcg_at_10 value: 79.81099999999999
- type: ndcg_at_100 value: 81.187
- type: ndcg_at_1000 value: 81.492
- type: ndcg_at_3 value: 76.536
- type: ndcg_at_5 value: 78.367
- type: precision_at_1 value: 69.47
- type: precision_at_10 value: 9.599
- type: precision_at_100 value: 1.026
- type: precision_at_1000 value: 0.105
- type: precision_at_3 value: 28.777
- type: precision_at_5 value: 18.232
- type: recall_at_1 value: 67.184
- type: recall_at_10 value: 90.211
- type: recall_at_100 value: 96.322
- type: recall_at_1000 value: 98.699
- type: recall_at_3 value: 81.556
- type: recall_at_5 value: 85.931
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (zh-CN)
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy value: 76.96032279757901
- type: f1 value: 73.48052314033545
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy value: 84.64357767316744
- type: f1 value: 83.58250539497922
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 56.00000000000001
- type: map_at_10 value: 62.066
- type: map_at_100 value: 62.553000000000004
- type: map_at_1000 value: 62.598
- type: map_at_3 value: 60.4
- type: map_at_5 value: 61.370000000000005
- type: mrr_at_1 value: 56.2
- type: mrr_at_10 value: 62.166
- type: mrr_at_100 value: 62.653000000000006
- type: mrr_at_1000 value: 62.699000000000005
- type: mrr_at_3 value: 60.5
- type: mrr_at_5 value: 61.47
- type: ndcg_at_1 value: 56.00000000000001
- type: ndcg_at_10 value: 65.199
- type: ndcg_at_100 value: 67.79899999999999
- type: ndcg_at_1000 value: 69.056
- type: ndcg_at_3 value: 61.814
- type: ndcg_at_5 value: 63.553000000000004
- type: precision_at_1 value: 56.00000000000001
- type: precision_at_10 value: 7.51
- type: precision_at_100 value: 0.878
- type: precision_at_1000 value: 0.098
- type: precision_at_3 value: 21.967
- type: precision_at_5 value: 14.02
- type: recall_at_1 value: 56.00000000000001
- type: recall_at_10 value: 75.1
- type: recall_at_100 value: 87.8
- type: recall_at_1000 value: 97.7
- type: recall_at_3 value: 65.9
- type: recall_at_5 value: 70.1
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map value: 32.74158258279793
- type: mrr value: 31.56071428571428
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy value: 78.96666666666667
- type: f1 value: 78.82528563818045
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy value: 83.54087709799674
- type: cos_sim_ap value: 87.26170197077586
- type: cos_sim_f1 value: 84.7609561752988
- type: cos_sim_precision value: 80.20735155513667
- type: cos_sim_recall value: 89.86272439281943
- type: dot_accuracy value: 72.22523010286952
- type: dot_ap value: 79.51975358187732
- type: dot_f1 value: 76.32183908045977
- type: dot_precision value: 67.58957654723126
- type: dot_recall value: 87.64519535374869
- type: euclidean_accuracy value: 82.0249052517596
- type: euclidean_ap value: 85.32829948726406
- type: euclidean_f1 value: 83.24924318869829
- type: euclidean_precision value: 79.71014492753623
- type: euclidean_recall value: 87.11721224920802
- type: manhattan_accuracy value: 82.13318895506227
- type: manhattan_ap value: 85.28856869288006
- type: manhattan_f1 value: 83.34946757018393
- type: manhattan_precision value: 76.94369973190348
- type: manhattan_recall value: 90.91869060190075
- type: max_accuracy value: 83.54087709799674
- type: max_ap value: 87.26170197077586
- type: max_f1 value: 84.7609561752988
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy value: 94.56
- type: ap value: 92.80848436710805
- type: f1 value: 94.54951966576111
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 39.0866558287863
- type: cos_sim_spearman value: 45.9211126233312
- type: euclidean_pearson value: 44.86568743222145
- type: euclidean_spearman value: 45.63882757207507
- type: manhattan_pearson value: 44.89480036909126
- type: manhattan_spearman value: 45.65929449046206
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 43.04701793979569
- type: cos_sim_spearman value: 44.87491033760315
- type: euclidean_pearson value: 36.2004061032567
- type: euclidean_spearman value: 41.44823909683865
- type: manhattan_pearson value: 36.136113427955095
- type: manhattan_spearman value: 41.39225495993949
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 61.65611315777857
- type: cos_sim_spearman value: 64.4067673105648
- type: euclidean_pearson value: 61.814977248797184
- type: euclidean_spearman value: 63.99473350700169
- type: manhattan_pearson value: 61.684304629588624
- type: manhattan_spearman value: 63.97831213239316
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 76.57324933064379
- type: cos_sim_spearman value: 79.23602286949782
- type: euclidean_pearson value: 80.28226284310948
- type: euclidean_spearman value: 80.32210477608423
- type: manhattan_pearson value: 80.27262188617811
- type: manhattan_spearman value: 80.31619185039723
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map value: 67.05266891356277
- type: mrr value: 77.1906333623497
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 28.212
- type: map_at_10 value: 78.932
- type: map_at_100 value: 82.51899999999999
- type: map_at_1000 value: 82.575
- type: map_at_3 value: 55.614
- type: map_at_5 value: 68.304
- type: mrr_at_1 value: 91.211
- type: mrr_at_10 value: 93.589
- type: mrr_at_100 value: 93.659
- type: mrr_at_1000 value: 93.662
- type: mrr_at_3 value: 93.218
- type: mrr_at_5 value: 93.453
- type: ndcg_at_1 value: 91.211
- type: ndcg_at_10 value: 86.24000000000001
- type: ndcg_at_100 value: 89.614
- type: ndcg_at_1000 value: 90.14
- type: ndcg_at_3 value: 87.589
- type: ndcg_at_5 value: 86.265
- type: precision_at_1 value: 91.211
- type: precision_at_10 value: 42.626
- type: precision_at_100 value: 5.043
- type: precision_at_1000 value: 0.517
- type: precision_at_3 value: 76.42
- type: precision_at_5 value: 64.045
- type: recall_at_1 value: 28.212
- type: recall_at_10 value: 85.223
- type: recall_at_100 value: 96.229
- type: recall_at_1000 value: 98.849
- type: recall_at_3 value: 57.30800000000001
- type: recall_at_5 value: 71.661
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy value: 54.385000000000005
- type: f1 value: 52.38762400903556
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure value: 74.55283855942916
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure value: 68.55115316700493
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 58.8
- type: map_at_10 value: 69.035
- type: map_at_100 value: 69.52000000000001
- type: map_at_1000 value: 69.529
- type: map_at_3 value: 67.417
- type: map_at_5 value: 68.407
- type: mrr_at_1 value: 58.8
- type: mrr_at_10 value: 69.035
- type: mrr_at_100 value: 69.52000000000001
- type: mrr_at_1000 value: 69.529
- type: mrr_at_3 value: 67.417
- type: mrr_at_5 value: 68.407
- type: ndcg_at_1 value: 58.8
- type: ndcg_at_10 value: 73.395
- type: ndcg_at_100 value: 75.62
- type: ndcg_at_1000 value: 75.90299999999999
- type: ndcg_at_3 value: 70.11800000000001
- type: ndcg_at_5 value: 71.87400000000001
- type: precision_at_1 value: 58.8
- type: precision_at_10 value: 8.68
- type: precision_at_100 value: 0.9690000000000001
- type: precision_at_1000 value: 0.099
- type: precision_at_3 value: 25.967000000000002
- type: precision_at_5 value: 16.42
- type: recall_at_1 value: 58.8
- type: recall_at_10 value: 86.8
- type: recall_at_100 value: 96.89999999999999
- type: recall_at_1000 value: 99.2
- type: recall_at_3 value: 77.9
- type: recall_at_5 value: 82.1
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy value: 89.42
- type: ap value: 75.35978503182068
- type: f1 value: 88.01006394348263
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
Yinka
Yinka embedding 模型是在开原模型stella-v3.5-mrl上续训的，采用了piccolo2提到的多任务混合损失(multi-task hybrid loss training)。同样本模型也支持了可变的向量维度。
使用方法
该模型的使用方法同stella-v3.5-mrl一样, 无需任何前缀。
from sentence_transformers import SentenceTransformer
from sklearn.preprocessing import normalize
model = SentenceTransformer("Classical/Yinka")
# 注意先不要normalize! 选取前n维后再normalize
vectors = model.encode(["text1", "text2"], normalize_embeddings=False)
print(vectors.shape) # shape is [2,1792]
n_dims = 768
cut_vecs = normalize(vectors[:, :n_dims])
结果
| Model Name | Model Size (GB) | Dimension | Sequence Length | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) | Average (35) |
|---|---|---|---|---|---|---|---|---|---|---|
| Yinka | 1.21 | 1792 | 512 | 74.30 | 61.99 | 89.87 | 69.77 | 74.40 | 63.30 | 70.79 |
| stella-v3.5-mrl | 1.21 | 1792 | 512 | 71.56 | 54.39 | 88.09 | 68.45 | 73.51 | 62.48 | 68.56 |
| piccolo-large-zh-v2 | 1.21 | 1792 | 512 | 74.59 | 62.17 | 90.24 | 70 | 74.36 | 63.5 | 70.95 |
训练细节
TODO
Licence
本模型采用MIT licence.
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