Text Classification
Transformers
Safetensors
Korean
bert
klue
korean
minwon
complaint
public-administration
text-embeddings-inference
Instructions to use atti433/minde-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use atti433/minde-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="atti433/minde-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("atti433/minde-classifier") model = AutoModelForSequenceClassification.from_pretrained("atti433/minde-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 641d564869b4e26b6f6350d3887d2ebcd88c76f8bbe9a8c6c473bcd36dfecc69
- Size of remote file:
- 5.28 kB
- SHA256:
- 14da904259b836f2f026ca7dd689b1718871b3c687c70059119543f9aa095940
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.