Instructions to use UMCU/PII_RobBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMCU/PII_RobBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="UMCU/PII_RobBERT", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("UMCU/PII_RobBERT", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("UMCU/PII_RobBERT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47d7836f5a54433d247dd7447abb8993027cd679dcb111eaf3ea6f043aecb8ba
- Size of remote file:
- 714 MB
- SHA256:
- 00e1aa055085f866f4a14ed8383ecfad1190f9de798d2519e7e6fae398acbd4c
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