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Chinese Named Entity Recognition On Msra

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이 벤치마크에서 각 모델의 성능 결과

모델 이름
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Paper TitleRepository
ERNIE 2.0 Base93.8ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
NFLAT94.55NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition
BERT-MRC95.75A Unified MRC Framework for Named Entity Recognition
FLAT+BERT96.09FLAT: Chinese NER Using Flat-Lattice Transformer
DiffusionNER94.91DiffusionNER: Boundary Diffusion for Named Entity Recognition
LSTM + Lexicon augment93.5Simplify the Usage of Lexicon in Chinese NER
FLAT94.12FLAT: Chinese NER Using Flat-Lattice Transformer
Glyce + BERT95.54Glyce: Glyph-vectors for Chinese Character Representations
FGN95.64FGN: Fusion Glyph Network for Chinese Named Entity Recognition
ZEN (Init with Chinese BERT)95.25ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
ERNIE 2.0 Large95ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
ZEN (Random Init)93.24ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
Baseline + BS96.26Boundary Smoothing for Named Entity Recognition
BERT-CRF (Replicated in AdaSeq)96.69Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
TENER92.74TENER: Adapting Transformer Encoder for Named Entity Recognition-
Lattice93.18Chinese NER Using Lattice LSTM
W2NER96.10Unified Named Entity Recognition as Word-Word Relation Classification
PIQN93.48Parallel Instance Query Network for Named Entity Recognition
BERT-MRC+DSC96.72Dice Loss for Data-imbalanced NLP Tasks
CAN-NER Model92.97CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
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