Instructions to use SetFit/distilbert-base-uncased__enron_spam__all-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SetFit/distilbert-base-uncased__enron_spam__all-train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SetFit/distilbert-base-uncased__enron_spam__all-train")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/distilbert-base-uncased__enron_spam__all-train") model = AutoModelForSequenceClassification.from_pretrained("SetFit/distilbert-base-uncased__enron_spam__all-train") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26b3961c46d5dde1d6ba5a906a9ab15c9b09178589b25dbbe7a7a11c8be0c448
- Size of remote file:
- 3.12 kB
- SHA256:
- 0601911d9ea87fd3ec40aafd44101697c2cbd36b10943e163c137a6f5bc3a925
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.