Instructions to use FartLabs/FART_Custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FartLabs/FART_Custom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FartLabs/FART_Custom")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FartLabs/FART_Custom") model = AutoModelForSequenceClassification.from_pretrained("FartLabs/FART_Custom") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08ba1f84446d0d576b80b1eb62cce4da4f636481405da6d4e72d064836385563
- Size of remote file:
- 351 MB
- SHA256:
- 7e6b74ac21249ac25bc5c3624a78227d38e10ada9b98db4b192caf79c83ac413
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