Named Entity Recognition Ner On Bc5Cdr

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	F1	Paper Title	Repository
GoLLIE	88.4	GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
BINDER	91.9	Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning
aimped	90.95	-	-
BLSTM-CNN-Char (SparkNLP)	89.73	Biomedical Named Entity Recognition at Scale	-
SciBERT (SciVocab)	88.94	SciBERT: A Pretrained Language Model for Scientific Text
RDANER	87.38	A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition
Spark NLP	89.73	Biomedical Named Entity Recognition at Scale	-
SciBERT (Base Vocab)	88.11	SciBERT: A Pretrained Language Model for Scientific Text
CollaboNet	87.12	CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
CL-L2	90.99	Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
BertForTokenClassification (Spark NLP)	90.89	Accurate clinical and biomedical Named entity recognition at scale
ELECTRAMed	90.03	ELECTRAMed: a new pre-trained language representation model for biomedical NLP
BERT-CRF	86	Focusing on Potential Named Entities During Active Label Acquisition
ConNER	91.3	Enhancing Label Consistency on Document-level Named Entity Recognition
BioLinkBERT (large)	90.22	LinkBERT: Pretraining Language Models with Document Links
UniNER-7B	89.34	UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition

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