Token Classification
Transformers
Safetensors
English
eurobert
named-entity-recognition
biomedical-nlp
anatomical-entity-recognition
medical-terminology
anatomy
healthcare
custom_code
Instructions to use OpenMed/OpenMed-NER-AnatomyDetect-EuroMed-212M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-AnatomyDetect-EuroMed-212M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-AnatomyDetect-EuroMed-212M", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-EuroMed-212M", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-EuroMed-212M", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2817a9579dd64c469c5db1518f794b2e0982b822ca783ac86c3e6f4802639136
- Size of remote file:
- 424 MB
- SHA256:
- 4e2246e7c414952f8299f4e6aef8b57a365679d2b6a84d7af6e479379083286b
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