Instructions to use privacy-tech-lab/ZipDistilledModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/ZipDistilledModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/ZipDistilledModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/ZipDistilledModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/ZipDistilledModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f06d4ca6504a6a6180734c1610bcdf4ac9eec92fc1ef016149079af07e62d26d
- Size of remote file:
- 3.12 kB
- SHA256:
- 10b6a8a08d6c083101486304f740a87686646a38a11b82bcb18d740140b78d9e
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