Instructions to use Posos/ClinicalNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Posos/ClinicalNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Posos/ClinicalNER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Posos/ClinicalNER") model = AutoModelForTokenClassification.from_pretrained("Posos/ClinicalNER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bc49a4b25c31cb2448ad04361f0b82a184f5f552c781aa9b3e854e3499ccc94
- Size of remote file:
- 3.45 kB
- SHA256:
- 5400e4c60c0ddd294c0d91827182ecfb96608d25ae82387085ee7e61756052a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.