Instructions to use privacy-tech-lab/MultitaskDistilledModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/MultitaskDistilledModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/MultitaskDistilledModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/MultitaskDistilledModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/MultitaskDistilledModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 676145c1ad16c5c217ace2d0a985dade61770ab1ed5cb275974dd037739833cf
- Size of remote file:
- 57.4 MB
- SHA256:
- 0cec34582fd44ed350451ab3a070ea895efac7f792b955b3b979201d5cf5a93b
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