Text Classification
Transformers
Safetensors
Korean
electra
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use jbh6357/ynat-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jbh6357/ynat-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jbh6357/ynat-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jbh6357/ynat-model") model = AutoModelForSequenceClassification.from_pretrained("jbh6357/ynat-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 135d55ae6068df33cfc0c99d145246b21ee0442dc64025b25714e2fdc93decfb
- Size of remote file:
- 5.3 kB
- SHA256:
- 10a527840473a11daa83cafc76ab91c570bc5958853d2bd2be04fcba0630fc37
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