Instructions to use NimaKL/Anonymize with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NimaKL/Anonymize with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="NimaKL/Anonymize")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("NimaKL/Anonymize") model = AutoModelForTokenClassification.from_pretrained("NimaKL/Anonymize") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90b274d1f8327143e437d2fb91a2faf68ae891ba12345c2e33ff598518207535
- Size of remote file:
- 1.47 GB
- SHA256:
- 0e780e521a8303c71c0c60a8577266f178e566ecd78763a6b122a5eb57f96292
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