Instructions to use TomUdale/sec_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TomUdale/sec_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="TomUdale/sec_example")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("TomUdale/sec_example") model = AutoModelForTokenClassification.from_pretrained("TomUdale/sec_example") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2769dfc60c8a981bd07a49b2beab9f59842f8901df7f02444e2b21984da67c14
- Size of remote file:
- 266 MB
- SHA256:
- c7a1f24f1fcd0ebdda451c740be93b00bb128e4fbee41f65297ac4cea4b370b8
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