Instructions to use eprasad/distilled-t5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eprasad/distilled-t5-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eprasad/distilled-t5-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eprasad/distilled-t5-small") model = AutoModelForSequenceClassification.from_pretrained("eprasad/distilled-t5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 066543a9499054ae4fc8965c88d6e4236a76d8289772b8495fc17268855891f3
- Size of remote file:
- 5.78 kB
- SHA256:
- 4e0060e0f2548c44112b8e2a1553dc97a8f70516b387164fb36cb293c0fdf3d1
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