Instructions to use JunHwi/kmhas_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunHwi/kmhas_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JunHwi/kmhas_binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JunHwi/kmhas_binary") model = AutoModelForSequenceClassification.from_pretrained("JunHwi/kmhas_binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e3a1923d2dcdfea88467a5d008c2ca1dbe09e473ab74b18bd1d2fc3e5e3c467
- Size of remote file:
- 3.31 kB
- SHA256:
- 0a1d846a75c21826ff344e9b62dfb775be406202e0f4524d00c83a527aaa63d8
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