Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
anatomical-entity-recognition
medical-terminology
anatomy
healthcare
Instructions to use OpenMed/OpenMed-NER-AnatomyDetect-ElectraMed-33M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-AnatomyDetect-ElectraMed-33M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-AnatomyDetect-ElectraMed-33M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-ElectraMed-33M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-ElectraMed-33M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57421b9abff91eb5a76244d497b3b68902afe43a618e300237ef6407e6e629f9
- Size of remote file:
- 66.4 MB
- SHA256:
- 8f893dcfb0bb84f279cd4784c9a0a28c9b0dfa8c59007aa6b00d911adef44cf0
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