Token Classification
Transformers
Safetensors
English
modernbert
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
chemistry
chem
Instructions to use OpenMed/OpenMed-NER-ChemicalDetect-ModernMed-395M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ChemicalDetect-ModernMed-395M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ChemicalDetect-ModernMed-395M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-ModernMed-395M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-ModernMed-395M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-ChemicalDetect-ModernMed-395M
ac9f2e8 verified - Xet hash:
- 76afe66b24afabe267c429824f55e18813277b34987a6a8397f20ac64f29b427
- Size of remote file:
- 792 MB
- SHA256:
- 12d6eb618943a55899ef9ce4931db76878f904a81bcbbb959b4939351f5ce82d
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