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-Medium-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-Medium-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Pharma-Medium-209M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc5af76b7133042b5b8bfa14b3a7eccae3f9dc95edd4ba58f44f1baeec04a7e8
- Size of remote file:
- 781 MB
- SHA256:
- 2861ef0b2cd795706ad01629968d67513474f3a02f41c6f2c7eb58e6dbb9ff7e
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