Instructions to use emilyalsentzer/Bio_ClinicalBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emilyalsentzer/Bio_ClinicalBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="emilyalsentzer/Bio_ClinicalBERT")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
#7
by mockingjayca - opened
Model converted by the transformers' pt_to_tf CLI. All converted model outputs and hidden layers were validated against its PyTorch counterpart.
Maximum crossload output difference=3.174e-06; Maximum crossload hidden layer difference=1.144e-05;
Maximum conversion output difference=3.174e-06; Maximum conversion hidden layer difference=1.144e-05;