Named Entity Recognition On Bc5Cdr Chemical
Métriques
F1
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | F1 | Paper Title | Repository |
|---|---|---|---|
| BioKMNER + BioBERT | 94.22 | Improving Biomedical Named Entity Recognition with Syntactic Information | |
| HGN | 94.59 | Hero-Gang Neural Model For Named Entity Recognition | |
| CompactBioBERT | 94.31 | On the Effectiveness of Compact Biomedical Transformers | |
| SciFive-Large | 94.76 | SciFive: a text-to-text transformer model for biomedical literature | |
| KeBioLM | 93.3 | Improving Biomedical Pretrained Language Models with Knowledge | |
| BioDistilBERT | 94.48 | On the Effectiveness of Compact Biomedical Transformers | |
| Spark NLP | 94.88 | Biomedical Named Entity Recognition at Scale | - |
| Att-BiLSTM-CRF | 92.57 | An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition | |
| BioMobileBERT | 94.23 | On the Effectiveness of Compact Biomedical Transformers | |
| DistilBioBERT | 94.53 | On the Effectiveness of Compact Biomedical Transformers | |
| BioLinkBERT (large) | 94.04 | LinkBERT: Pretraining Language Models with Document Links | |
| BioMegatron | 92.9 | BioMegatron: Larger Biomedical Domain Language Model | |
| NCBI_BERT(base) (P) | 93.5 | - | - |
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