Instructions to use debajyotidatta/eduopt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use debajyotidatta/eduopt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="debajyotidatta/eduopt")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("debajyotidatta/eduopt") model = AutoModelForSequenceClassification.from_pretrained("debajyotidatta/eduopt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 21d3222e1122f7829a4f66609253b9873394cc901e2b392a9edcc30e6d4d0bca
- Size of remote file:
- 1.32 GB
- SHA256:
- f75c504e42289c8f0db8d65610552e1a0d68e33ff615d19c0f3f1e6d41e037bd
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