Token Classification
Transformers
Safetensors
English
roberta
named-entity-recognition
biomedical-nlp
anatomical-entity-recognition
medical-terminology
anatomy
healthcare
Instructions to use OpenMed/OpenMed-NER-AnatomyDetect-SuperMedical-125M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-AnatomyDetect-SuperMedical-125M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-AnatomyDetect-SuperMedical-125M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-SuperMedical-125M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-SuperMedical-125M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-AnatomyDetect-SuperMedical-125M
cf0b5ee verified - Xet hash:
- 7e2c95ec6b4818f498001708a98897b9b6bbbe780cc2ae9e0543809e59f83581
- Size of remote file:
- 248 MB
- SHA256:
- 749ceac1ff123c631f82df2a9cc39abcacd34783eb7ebb573cd606bfb6652b3c
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