Instructions to use xshubhamx/google-t5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/google-t5-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/google-t5-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/google-t5-small") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/google-t5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 787bd55075190eca78c6f1dec2179347bcbbfea231391fee533434367e579215
- Size of remote file:
- 4.92 kB
- SHA256:
- 88c068c031256c746e161c5fd0f98b093658c0931eb211f6a169e78859d6fac0
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