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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db7a649a944ffe5d653a4b0c89e81c8fb1b6663f34ffae69bb676649e47f010f
- Size of remote file:
- 436 MB
- SHA256:
- 36d2162ad03215a242d2a29a62154303a3cfc8c1cde03c5a5c44ccc317478d80
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