Instructions to use Makhmud/Kun_uz_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Makhmud/Kun_uz_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Makhmud/Kun_uz_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Makhmud/Kun_uz_classification") model = AutoModelForSequenceClassification.from_pretrained("Makhmud/Kun_uz_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Makhmud/Kun_uz_classification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Makhmud/Kun_uz_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Makhmud/Kun_uz_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Makhmud/Kun_uz_classification/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 7f4ca048178c71b198573b1755c43fc7018055f562eb72a74aee3650333ee22d
- Size of remote file:
- 438 MB
- SHA256:
- 97fa94e3eca6862a6de4c276ea7be237818e77388cfe531361b15bd1bc665ac0
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