Token Classification
Transformers
Safetensors
English
deberta-v2
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
chemistry
chem
Instructions to use OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-184M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-184M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-184M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-184M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-184M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-ChemicalDetect-SuperClinical-184M
d135908 verified - Xet hash:
- be694193f39b3af4346ca305348a64008ad0264226208b2e419e06bbf99089bb
- Size of remote file:
- 368 MB
- SHA256:
- f3c59399020b2f8c7b326bc4268f082a4c0d66041d0a5d392377ea0009f7f3fe
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