Token Classification
Transformers
Safetensors
English
modernbert
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chem
Instructions to use OpenMed/OpenMed-NER-PharmaDetect-ModernClinical-395M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PharmaDetect-ModernClinical-395M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-ModernClinical-395M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-ModernClinical-395M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-ModernClinical-395M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-PharmaDetect-ModernClinical-395M
6a2290a verified - Xet hash:
- 78cf23e2745d500e6c429a896943e141ecfd6391989a8d83149b6e7ae21fba64
- Size of remote file:
- 792 MB
- SHA256:
- c22305d1b25c09c21be74b6bd70193756844a546179e4c923f3b523153e9c77b
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