Token Classification
Transformers
Safetensors
English
xlm-roberta
named-entity-recognition
biomedical-nlp
disease-entity-recognition
medical-diagnosis
ncbi
pathology
disease
Instructions to use OpenMed/OpenMed-NER-PathologyDetect-BigMed-560M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PathologyDetect-BigMed-560M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PathologyDetect-BigMed-560M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-BigMed-560M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-BigMed-560M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebf1c62bc51992f351dec54c82b903b6eb6779f9bdb928dfcaa1809420fb81b7
- Size of remote file:
- 1.12 GB
- SHA256:
- c1ade65a5c2804bb9fa1f3484b449821261924f145b48f039f25ed091dadad30
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