Instructions to use lowem1/cms-ext-Bio_ClinicalBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lowem1/cms-ext-Bio_ClinicalBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lowem1/cms-ext-Bio_ClinicalBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lowem1/cms-ext-Bio_ClinicalBERT") model = AutoModelForMaskedLM.from_pretrained("lowem1/cms-ext-Bio_ClinicalBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c211a2f68746d34563ca3958dd6763fda9a24faab0fb90c774c3807d0d8ace9
- Size of remote file:
- 15.9 MB
- SHA256:
- c60a36279696cba8f5bdcc1b4a016130278b5139b1f74e7c8d0479a6da9e2f7e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.