Text Classification
Transformers
Safetensors
Korean
electra
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use Jongha611/ynat_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jongha611/ynat_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jongha611/ynat_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jongha611/ynat_model") model = AutoModelForSequenceClassification.from_pretrained("Jongha611/ynat_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 529def4fd48fbc7c1c497500c87ca95e98a962b289390cb220fc6d6b78d82c54
- Size of remote file:
- 452 MB
- SHA256:
- 8c6a395c353d539621f198850b268d120a418a5ec33851d91b566a0081886353
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