Text Classification
Transformers
Safetensors
Korean
electra
korean
multi-label-classification
kcelectra
fine-tuned
Eval Results (legacy)
Instructions to use Kaaeun/Labeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kaaeun/Labeling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kaaeun/Labeling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kaaeun/Labeling") model = AutoModelForSequenceClassification.from_pretrained("Kaaeun/Labeling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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split: "8:1:1 (train 7119 / val 890 / test 890)"
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metrics:
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- type: subset-accuracy
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value: 0.516
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---
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split: "8:1:1 (train 7119 / val 890 / test 890)"
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metrics:
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- type: micro-f1
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value: 0.896
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- type: macro-f1
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value: 0.892
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- type: subset-accuracy
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value: 0.516
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---
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