Text Classification
Transformers
Safetensors
Korean
electra
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use junbeom2/ynat-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junbeom2/ynat-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="junbeom2/ynat-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("junbeom2/ynat-model") model = AutoModelForSequenceClassification.from_pretrained("junbeom2/ynat-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1857a961fdb64807d6dc36a7634e683d3c4340576dcb115b7e26827c6dece540
- Size of remote file:
- 5.84 kB
- SHA256:
- 4e7ffad7a7e4bd7d5cd43fc08679519b9cdc835040830505098e89469dc87f75
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