Instructions to use Rifky/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rifky/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rifky/output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rifky/output") model = AutoModelForSequenceClassification.from_pretrained("Rifky/output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16fd391c8bebf6d01b3c3e3e3090010cec9849f057bc9efdb32fc0a835317acd
- Size of remote file:
- 4.16 kB
- SHA256:
- d509c6a13c68d1f8bd0bd6337c19a23a7c4d0abaaec9b20aa22242f7cbbadf64
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