Instructions to use wldn/korean-text-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wldn/korean-text-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wldn/korean-text-classification-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wldn/korean-text-classification-model") model = AutoModelForSequenceClassification.from_pretrained("wldn/korean-text-classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "base_model": "beomi/KcELECTRA-base", | |
| "task": "text-classification", | |
| "text_column": "text", | |
| "label_column": "label", | |
| "max_length": 128, | |
| "labels": [ | |
| "안전", | |
| "의심" | |
| ], | |
| "label2id": { | |
| "안전": 0, | |
| "의심": 1 | |
| }, | |
| "id2label": { | |
| "0": "안전", | |
| "1": "의심" | |
| }, | |
| "epochs": 3, | |
| "train_batch_size": 16, | |
| "eval_batch_size": 32, | |
| "learning_rate": 2e-05, | |
| "use_class_weights": true, | |
| "created_at": "2026-06-05T12:10:33.597009" | |
| } |