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Named Entity Recognition Ner On Scierc
Metrics
F1
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| SciDeBERTa v2 | 72.4 | SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction Tasks | - |
| SpERT | 70.33 | Span-based Joint Entity and Relation Extraction with Transformer Pre-training | |
| RDANER | 68.96 | A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition | |
| Ours: cross-sentence | 68.2 | A Frustratingly Easy Approach for Entity and Relation Extraction | |
| SciBERT (SciVocab) | 67.57 | SciBERT: A Pretrained Language Model for Scientific Text | |
| SciBERT (Base Vocab) | 65.24 | SciBERT: A Pretrained Language Model for Scientific Text | |
| SCIIE | 64.20 | Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction |
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