Instructions to use csariyildiz/enron_spam_bert_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use csariyildiz/enron_spam_bert_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="csariyildiz/enron_spam_bert_base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("csariyildiz/enron_spam_bert_base") model = AutoModelForSequenceClassification.from_pretrained("csariyildiz/enron_spam_bert_base") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Finetuned BERT For Spam Detection On Enron Dataset
- Labels: Ham 0, Spam 1
- Accuracy: 0.9964012595591543
- Dataset contains a total of 17.171 spam and 16.545 non-spam ("ham") e-mail messages (33.716 e-mails total).
- Downloads last month
- 1