Instructions to use JunHyung1206/kote_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunHyung1206/kote_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JunHyung1206/kote_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JunHyung1206/kote_classification") model = AutoModelForSequenceClassification.from_pretrained("JunHyung1206/kote_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from JunHyung1206/kote_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.35 GB
-
https://huggingface.co/JunHyung1206/kote_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://JunHyung1206/kote_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JunHyung1206/kote_classification/resolve/main/pytorch_model.bin
1.35 GB
- Xet hash:
- f5ba456dcbbbf591248da19a3417303dc6929de80d379b2a34a65a3714363942
- Size of remote file:
- 1.35 GB
- SHA256:
- c89dfdb801586e569eb28d8814f6f60a13f3caa67747e6bd9d94b869b86bfc8b
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