Instructions to use KhangSimple/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KhangSimple/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KhangSimple/output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KhangSimple/output") model = AutoModelForSequenceClassification.from_pretrained("KhangSimple/output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 249e4af9616a181a6008c2c554a5c0b45c111c9d3c458ef9866921cf4919eca8
- Size of remote file:
- 90.9 MB
- SHA256:
- f2c0a4a1bb0fbc0604871ca64fd545c67545dd7f2ca72633ac55d10f306bb54a
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