Instructions to use Sunbird/t5_small_language_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sunbird/t5_small_language_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sunbird/t5_small_language_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sunbird/t5_small_language_Classification") model = AutoModelForSequenceClassification.from_pretrained("Sunbird/t5_small_language_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fafb1ebcbcc9e845b90ff3c8c4de597dbc95921b227d1f0af8006904bb97123
- Size of remote file:
- 243 MB
- SHA256:
- 5ec42756be004665acae7e7623c81b2f134ad630cd14e03c21382f7ba086b7d4
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