Instructions to use sohaibdevv/Medical-NER-2026-Success with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sohaibdevv/Medical-NER-2026-Success with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sohaibdevv/Medical-NER-2026-Success")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sohaibdevv/Medical-NER-2026-Success") model = AutoModelForTokenClassification.from_pretrained("sohaibdevv/Medical-NER-2026-Success") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59b9f136190e96cef927b3abff9ab81d1a43ce035b91dcf48f72a92a28b5e549
- Size of remote file:
- 5.2 kB
- SHA256:
- d3eaea294f5d98a5d4f36b966441cd04768fe329acc7d865b6f89ccb2d6b5a38
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