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
File size: 586 Bytes
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language: ko
library_name: transformers
pipeline_tag: text-classification
tags:
- text-classification
- korean
- transformers
- pytorch
base_model: beomi/KcELECTRA-base
---
# Korean Text Classification Model
Base model: `beomi/KcELECTRA-base`
This model was fine-tuned with Hugging Face Transformers `Trainer`.
## Labels
??
## Inference Example
from transformers import pipeline
repo_id = "YOUR_USERNAME/YOUR_REPO_NAME"
classifier = pipeline(
"text-classification",
model=repo_id,
tokenizer=repo_id
)
classifier("여기에 분류할 문장을 입력하세요.")
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