Instructions to use dima806/email-spam-detection-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/email-spam-detection-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dima806/email-spam-detection-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dima806/email-spam-detection-distilbert") model = AutoModelForSequenceClassification.from_pretrained("dima806/email-spam-detection-distilbert") - Notebooks
- Google Colab
- Kaggle
See https://www.kaggle.com/code/dima806/email-spam-classification-distilbert for details.
Classification report:
precision recall f1-score support
No spam 0.9933 0.9898 0.9915 7908
Spam 0.9908 0.9940 0.9924 8782
accuracy 0.9920 16690
macro avg 0.9920 0.9919 0.9919 16690
weighted avg 0.9920 0.9920 0.9920 16690
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Model tree for dima806/email-spam-detection-distilbert
Base model
distilbert/distilbert-base-cased