Instructions to use alishudi/distil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alishudi/distil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alishudi/distil")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alishudi/distil") model = AutoModelForSequenceClassification.from_pretrained("alishudi/distil") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66936dcefc61c1a368056a6cac1556264b0a47bba75702ca4522ec0753f383a7
- Size of remote file:
- 268 MB
- SHA256:
- 7e4caf2a6823b0da38b61eae66c4b6ddd35d46a99c0b6957c3c936a834a28781
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