Instructions to use macavaney/deepct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macavaney/deepct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="macavaney/deepct")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("macavaney/deepct") model = AutoModelForTokenClassification.from_pretrained("macavaney/deepct") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- adb6739ab29b14dd4b8620500616e2e041df33598f4f13c651f66468e47c2f5a
- Size of remote file:
- 436 MB
- SHA256:
- fc39bf83058009e909d4d9a6cbea242f8cc9c7188ac66143bfd0ff8957017d13
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