Instructions to use UMCU/MedRoBERTa.nl_NegationDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMCU/MedRoBERTa.nl_NegationDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="UMCU/MedRoBERTa.nl_NegationDetection")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("UMCU/MedRoBERTa.nl_NegationDetection") model = AutoModelForTokenClassification.from_pretrained("UMCU/MedRoBERTa.nl_NegationDetection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dabde3d6108c5a51c2a76195a7a7272b158ead92e833d73b54cd0092ee80a132
- Size of remote file:
- 502 MB
- SHA256:
- 4539223cc5e42cf66bc80ee38cd84e3cf0c502cf64d404991416d9626125669f
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