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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kaaeun/Labeling") model = AutoModelForSequenceClassification.from_pretrained("Kaaeun/Labeling") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7fbb685e5aab051a4a16c2c0869cfb08e29eeb830377fc174104190f6c086f23
- Size of remote file:
- 436 MB
- SHA256:
- 6535874eea6a239c63ce25e41bde1167aa21046cd8b8456012aff6e34874482e
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