Text Classification
Transformers
Safetensors
English
edge-computing
service-orchestration
intent-classification
Instructions to use UTSCybeR/Edge-Computing-JEV-classifiers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UTSCybeR/Edge-Computing-JEV-classifiers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UTSCybeR/Edge-Computing-JEV-classifiers")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UTSCybeR/Edge-Computing-JEV-classifiers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download clf_all/tokenizer.json from UTSCybeR/Edge-Computing-JEV-classifiers: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/UTSCybeR/Edge-Computing-JEV-classifiers/resolve/main/clf_all/tokenizer.json
- Command line
-
hf download hf://UTSCybeR/Edge-Computing-JEV-classifiers/clf_all/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/UTSCybeR/Edge-Computing-JEV-classifiers/resolve/main/clf_all/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.