Instructions to use privacy-tech-lab/RegionDistilledModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/RegionDistilledModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/RegionDistilledModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/RegionDistilledModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/RegionDistilledModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cd8371d40a277a3c3e62ff81eabdc10d49e8b220f6fd4a999cb3b53e7655cec
- Size of remote file:
- 57.4 MB
- SHA256:
- c86d408ba938ba4c17cc90f1f5042cfb980f6b4e883023acbc9126816fad10bc
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