Instructions to use deprem-ml/name_anonymization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deprem-ml/name_anonymization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="deprem-ml/name_anonymization")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("deprem-ml/name_anonymization") model = AutoModelForTokenClassification.from_pretrained("deprem-ml/name_anonymization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b44fb9aba0bf35fa268e79859094e5fdb093f3efee1e18cd41915b0566f149f4
- Size of remote file:
- 440 MB
- SHA256:
- 063771ed088e2fc2c74a50b4d3fb6c0cf893308cd810a56e260d04b72ba3f4d1
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