Text Classification
Transformers
Safetensors
Korean
electra
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use ezpzvv/ynat_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ezpzvv/ynat_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ezpzvv/ynat_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ezpzvv/ynat_model") model = AutoModelForSequenceClassification.from_pretrained("ezpzvv/ynat_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edf19ced74b153f911523ada4394521ab9025137778694f303855d598860aff6
- Size of remote file:
- 5.41 kB
- SHA256:
- 773b0f45f219eee9264c4ef6f65f0714f49fee704eee3c06f2fd59b8d58dd645
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