Named Entity Recognition Ner On Bc5Cdr

평가 결과

이 벤치마크에서 각 모델의 성능 결과

		Paper Title	Repository
BINDER	91.9	Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning
ConNER	91.3	Enhancing Label Consistency on Document-level Named Entity Recognition
CL-L2	90.99	Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
aimped	90.95	-	-
BertForTokenClassification (Spark NLP)	90.89	Accurate clinical and biomedical Named entity recognition at scale	-
BioLinkBERT (large)	90.22	LinkBERT: Pretraining Language Models with Document Links
ELECTRAMed	90.03	ELECTRAMed: a new pre-trained language representation model for biomedical NLP
BLSTM-CNN-Char (SparkNLP)	89.73	Biomedical Named Entity Recognition at Scale
Spark NLP	89.73	Biomedical Named Entity Recognition at Scale
UniNER-7B	89.34	UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition
SciBERT (SciVocab)	88.94	SciBERT: A Pretrained Language Model for Scientific Text
GoLLIE	88.4	GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
SciBERT (Base Vocab)	88.11	SciBERT: A Pretrained Language Model for Scientific Text
RDANER	87.38	A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition
CollaboNet	87.12	CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
BERT-CRF	86	Focusing on Potential Named Entities During Active Label Acquisition

0 of 16 row(s) selected.