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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de6dd9be37fb31ef8da967a5c9e2d1ed5006d27422ffdfe9bd3f000d14444e19
- Size of remote file:
- 16.3 MB
- SHA256:
- 54e5c72a5ea09da48b2f316760b8bc5a445683ab9a5bc6b68db5d8db624ecceb
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