Named Entity Recognition Ner On Jnlpba
Metrics
F1
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | F1 |
|---|---|
| biomedical-named-entity-recognition-at-scale | 81.29 |
| improving-biomedical-pretrained-language | 82.0 |
| biobert-a-pre-trained-biomedical-language | 77.59 |
| on-the-effectiveness-of-compact-biomedical | 80.13 |
| domain-specific-language-model-pretraining | 79.1 |
| on-the-effectiveness-of-compact-biomedical | 79.88 |
| scibert-pretrained-contextualized-embeddings | 75.77 |
| on-the-effectiveness-of-compact-biomedical | 79.97 |
| linkbert-pretraining-language-models-with | 80.06 |
| improving-biomedical-named-entity-recognition | 77.83 |
| bioflair-pretrained-pooled-contextualized | 77.03 |
| optimizing-bi-encoder-for-named-entity | 80.3 |
| on-the-effectiveness-of-compact-biomedical | 79.1 |
| scifive-a-text-to-text-transformer-model-for | 77.55 |
| Model 15 | 77.72 |
| miner-improving-out-of-vocabulary-named-1 | 77.03 |
| biomedical-named-entity-recognition-at-scale | 81.29 |