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:
- 50cdd4f92a2c3cedb6992e8b2b50ee5b95fa4d31d5466ae7aa4e8121f89b01f3
- Size of remote file:
- 268 MB
- SHA256:
- bf17b29f2de911811c4cb417642544b895667e5ebc050f07e3356870edbedf41
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.