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:
- 6f961d4307f5e5e470b40a9f32d803df2326f4bdeea6dd0bb8015b6b114d6a3d
- Size of remote file:
- 465 MB
- SHA256:
- 521d60cf30697ef44915adda5b91f7ac8d1ed0443874fe6dba816dbd6e8541eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.