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:
- 97b6a1edece0cdb8e71c40a6e276f26ec5901f749d7b92cc8c192bbdfd45165c
- Size of remote file:
- 57.4 MB
- SHA256:
- 0561da1aa02a0a383ec3d03177ffdaea00757a3314f7ca0947824af28b0f18b7
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