Instructions to use ran/c9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ran/c9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ran/c9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ran/c9") model = AutoModelForSequenceClassification.from_pretrained("ran/c9") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9088a3f19946a27062846ec27d28b86144790ba05360f5ee1f1059260067e279
- Size of remote file:
- 438 MB
- SHA256:
- 9ac940779a61e7a884d66ab9ddeda2211abd5e812221ce5863de3c46acec7b43
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