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
- Xet hash:
- 6940e9b54eb608c8408bca332ca22b5ae991e4064e20c7ec0389dfef57b1d8ef
- Size of remote file:
- 465 MB
- SHA256:
- f5980dce3a5037fbd651419aaef77ba15418c13083252bfe53903b92df375cb6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.