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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("atti433/minde-urgency") model = AutoModelForSequenceClassification.from_pretrained("atti433/minde-urgency", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from atti433/minde-urgency: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/atti433/minde-urgency/resolve/main/model.safetensors
- Command line
-
hf download hf://atti433/minde-urgency/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/atti433/minde-urgency/resolve/main/model.safetensors
443 MB
- Xet hash:
- d6d6449420a8e5e90ef471621782be44c677ddc60995df5236c948e9d22855a1
- Size of remote file:
- 443 MB
- SHA256:
- 14b8aed99c7b5849c9f472881214c0d0847125ff6f9f65de58a01e7c5ec4d23e
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