Token Classification
Transformers
Safetensors
English
deberta-v2
named-entity-recognition
biomedical-nlp
anatomical-entity-recognition
medical-terminology
anatomy
healthcare
Instructions to use OpenMed/OpenMed-NER-AnatomyDetect-SuperClinical-434M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-AnatomyDetect-SuperClinical-434M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-AnatomyDetect-SuperClinical-434M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-SuperClinical-434M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-SuperClinical-434M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-AnatomyDetect-SuperClinical-434M
5b662bb verified - Xet hash:
- 447387e4351b87b8a93d757e6b69191bd18890f6993671ebbe7e4368a8db1c09
- Size of remote file:
- 868 MB
- SHA256:
- e92feb5b6fb60f891a84870c342b7ee22419a6b4327d29110ff3fd5701bbbd10
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