Instructions to use UMCU/RobBERT_NegationDetection_32xTokenWindow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMCU/RobBERT_NegationDetection_32xTokenWindow with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="UMCU/RobBERT_NegationDetection_32xTokenWindow")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("UMCU/RobBERT_NegationDetection_32xTokenWindow") model = AutoModelForTokenClassification.from_pretrained("UMCU/RobBERT_NegationDetection_32xTokenWindow") - Notebooks
- Google Colab
- Kaggle
File size: 239 Bytes
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