Text Classification
Transformers
Safetensors
Korean
electra
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use hwirang/ynat-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hwirang/ynat-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hwirang/ynat-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hwirang/ynat-model") model = AutoModelForSequenceClassification.from_pretrained("hwirang/ynat-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1eb86577a162c0c38bbf1c33fe94546802562b765397e301b76003b0f61d599e
- Size of remote file:
- 5.2 kB
- SHA256:
- fa2dc7a9636bf4f011f579bd6e5aa6aa3636d2b376e49b1a001ce39c3a5cc021
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