Instructions to use debajyotidatta/deberta_edu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use debajyotidatta/deberta_edu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="debajyotidatta/deberta_edu")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("debajyotidatta/deberta_edu") model = AutoModelForSequenceClassification.from_pretrained("debajyotidatta/deberta_edu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 646549aa450c7f215168da3aeb21ae6a1e0d83d1c0b07b96589dede843f3e36c
- Size of remote file:
- 268 MB
- SHA256:
- 8a8d72a2fc16b3787a0cadb4c90e5a9f57fdc01341f260a91643181ccabc9f5c
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