Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chemical
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Pharma-Multi-209M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Pharma-Multi-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Pharma-Multi-209M") - Notebooks
- Google Colab
- Kaggle
| { | |
| "eval_loss": 45.94039535522461, | |
| "seqeval_accuracy": 0.9776350144290229, | |
| "seqeval_f1": 0.8931070678796361, | |
| "seqeval_precision": 0.8442202745162891, | |
| "seqeval_recall": 0.9480037140204272 | |
| } |