Instructions to use sheduele/testDeBERTaMULTI3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sheduele/testDeBERTaMULTI3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sheduele/testDeBERTaMULTI3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sheduele/testDeBERTaMULTI3") model = AutoModelForSequenceClassification.from_pretrained("sheduele/testDeBERTaMULTI3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9189d5d14fd67cc7a54bc9771146c0900059eab107045169ceb8ead7192f6de
- Size of remote file:
- 16.3 MB
- SHA256:
- 98a35c39489d89eb0d70781631b2c387687cb0ba71b62527811538220eb3395a
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