Instructions to use ulugbeksalaev/Uzbek-Text-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ulugbeksalaev/Uzbek-Text-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ulugbeksalaev/Uzbek-Text-Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ulugbeksalaev/Uzbek-Text-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trained_model100.pkl from ulugbeksalaev/Uzbek-Text-Classification: direct link, hf CLI and curl.
- Browser
- Download file 17.8 MB
-
https://huggingface.co/ulugbeksalaev/Uzbek-Text-Classification/resolve/main/trained_model100.pkl
- Command line
-
hf download hf://ulugbeksalaev/Uzbek-Text-Classification/trained_model100.pkl
-
curl -L -o trained_model100.pkl https://huggingface.co/ulugbeksalaev/Uzbek-Text-Classification/resolve/main/trained_model100.pkl
17.8 MB
- Xet hash:
- f47d083524ae4a237166dc199d43682c2bd8c2ed1385c1d25ba49eb8075ef2d4
- Size of remote file:
- 17.8 MB
- SHA256:
- cd65b9f465deb9db509b9e7bf7a9e08d84a8b408672e2601b703bc9ff7f21cba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.