Token Classification
Transformers
Safetensors
English
eurobert
named-entity-recognition
biomedical-nlp
disease-entity-recognition
medical-diagnosis
ncbi
pathology
disease
custom_code
Instructions to use OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9877ad90b8c072fdcca2b3fe3d6f4bfb243ee14dced2f4bda7fafdb5935b6bf7
- Size of remote file:
- 17.2 MB
- SHA256:
- a5ced525276e4f0b096912a287a1962dbcc14e8addd12b1c89f03a52ef0cbb14
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