Text Classification
Transformers
Safetensors
Korean
electra
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use sbaru/ynat-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sbaru/ynat-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sbaru/ynat-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sbaru/ynat-model") model = AutoModelForSequenceClassification.from_pretrained("sbaru/ynat-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6edac6993fe66cc229ede7b87e8c58ad1d7855a5655c50abf3784a68a97b76f7
- Size of remote file:
- 5.3 kB
- SHA256:
- 216c784e411c7b79843aa73f2484b3145ea142e607b867feaa440fc8ccbb4a9e
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