Instructions to use jinmang2/textcnn-ko-dialect-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinmang2/textcnn-ko-dialect-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jinmang2/textcnn-ko-dialect-classifier", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("jinmang2/textcnn-ko-dialect-classifier", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49b1c3096dd647f5e9e42d9ef289eeb5f954301712e52a7291e035992e30791e
- Size of remote file:
- 39.1 MB
- SHA256:
- b271e8d9541314c9d2343eb49c07ebe39dc1818926bc99383ea70285ad1edac4
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