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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 17e64d6e1d24ae53228e55d1e36ba3e7c922650ffd5a828f08b9fce2a6e19a52
- Size of remote file:
- 57.4 MB
- SHA256:
- c42aa107a4943706244ce39db77644780849adea649fad505ef6dcbb7daa573a
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