Text Classification
Transformers
Safetensors
Korean
bert
klue
korean
urgency
minwon
complaint
text-embeddings-inference
Instructions to use atti433/minde-urgency with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use atti433/minde-urgency with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="atti433/minde-urgency")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("atti433/minde-urgency") model = AutoModelForSequenceClassification.from_pretrained("atti433/minde-urgency") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b01c4c47e8a5f36f380fdb8e618b82670c0371434cf8d314db9599df6cac581
- Size of remote file:
- 5.28 kB
- SHA256:
- 131452a5573d80452d21eff9648b2ce42ebb4a780e4b202679efbc9b3299e4fd
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