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:
- fa811a7191642959fc0bcf128723d0e24812198bd84a1470cc7ebe858cb5f28b
- Size of remote file:
- 3.13 kB
- SHA256:
- bb8af8eecf0b9f58ba6bfd368fb7bfab7706fe5a862b49ebfd4897d389d112f3
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