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Named Entity Recognition Ner On Jnlpba

Metrics

F1

Results

Performance results of various models on this benchmark

Model Name
F1
Paper TitleRepository
Spark NLP81.29Biomedical Named Entity Recognition at Scale-
KeBioLM82.0Improving Biomedical Pretrained Language Models with Knowledge-
BioBERT77.59BioBERT: a pre-trained biomedical language representation model for biomedical text mining-
BioMobileBERT80.13On the Effectiveness of Compact Biomedical Transformers-
PubMedBERT uncased79.1Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing-
CompactBioBERT79.88On the Effectiveness of Compact Biomedical Transformers-
SciBERT (Base Vocab)75.77SciBERT: A Pretrained Language Model for Scientific Text-
DistilBioBERT79.97On the Effectiveness of Compact Biomedical Transformers-
BioLinkBERT (large)80.06LinkBERT: Pretraining Language Models with Document Links-
BioKMNER + BioBERT77.83Improving Biomedical Named Entity Recognition with Syntactic Information
BioFLAIR77.03BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks-
BINDER80.3Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning-
BioDistilBERT79.1On the Effectiveness of Compact Biomedical Transformers-
SciFive-Large77.55SciFive: a text-to-text transformer model for biomedical literature-
aimped77.72--
MINER77.03MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective-
BLSTM-CNN-Char (SparkNLP)81.29Biomedical Named Entity Recognition at Scale-
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Named Entity Recognition Ner On Jnlpba | SOTA | HyperAI