Instructions to use blanchefort/rubert-base-cased-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blanchefort/rubert-base-cased-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="blanchefort/rubert-base-cased-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("blanchefort/rubert-base-cased-sentiment") model = AutoModelForSequenceClassification.from_pretrained("blanchefort/rubert-base-cased-sentiment") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 862718ab74fc73912e69d7a327166385ba695d4fa6a6caf5a9c07ab36a56f999
- Size of remote file:
- 711 MB
- SHA256:
- 5eeceb6d296548d2813033b637594612edbdf83b28dcc0a937645efac1f604a2
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