Instructions to use cp229/Bio_ClinicalBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cp229/Bio_ClinicalBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cp229/Bio_ClinicalBERT")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cp229/Bio_ClinicalBERT") model = AutoModel.from_pretrained("cp229/Bio_ClinicalBERT") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cp229/Bio_ClinicalBERT")
model = AutoModel.from_pretrained("cp229/Bio_ClinicalBERT")Quick Links
- Model Card for Model ID
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- Uses
- Bias, Risks, and Limitations
- How to Get Started with the Model
- Training Details
- Evaluation
- Model Examination [optional]
- Environmental Impact
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Paper for cp229/Bio_ClinicalBERT
Paper • 1910.09700 • Published • 53
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cp229/Bio_ClinicalBERT")