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
chemistry
chemical
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Chemical-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-Chemical-Medium-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Chemical-Medium-209M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-Chemical-Medium-209M
5885efd verified - Xet hash:
- 1eeae0b9916f03b9230d7707358c236f6b15fc04f3c1c73bbbce830f6f4bdb13
- Size of remote file:
- 781 MB
- SHA256:
- 569d343bcd5759a9c4628662455a3b95e0993035fd9ba263034ac6f727eb9cde
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