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:
- c7a65a16ff9f7b315cd4cfed929a16f9adceba4523211b315f39cbd50f13c699
- Size of remote file:
- 735 MB
- SHA256:
- 69ced97e52faad35a73a78ff4c7f9f7697eaefe3cfbc2872b599868bed9f1e3d
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