Text Classification
Transformers
PyTorch
TensorFlow
Safetensors
English
bert
medical
clinical
assertion
negation
Instructions to use bvanaken/clinical-assertion-negation-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bvanaken/clinical-assertion-negation-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bvanaken/clinical-assertion-negation-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert") model = AutoModelForSequenceClassification.from_pretrained("bvanaken/clinical-assertion-negation-bert", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73aed8c53a0e3e9a57f7d93845b4d8c580c66eace845e9ffbe186701a45accb6
- Size of remote file:
- 434 MB
- SHA256:
- 0537f74a4b3242c99c01deff65749a5b518f9b056f84d12eab36088a277f8958
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