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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 965341f680db7c2e011316b961c5dde631fadd0e93ce84d2d00c399b82a8d274
- Size of remote file:
- 433 MB
- SHA256:
- a5eb2077bb4192ba2ef24496c24b6c15fd2c7cc6d332fdb07170f4d602658221
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