Text Classification
Transformers
Safetensors
distilbert
email
spam-detection
text-embeddings-inference
Instructions to use akumar0205/phishing_spam_jev_like_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akumar0205/phishing_spam_jev_like_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="akumar0205/phishing_spam_jev_like_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("akumar0205/phishing_spam_jev_like_classifier") model = AutoModelForSequenceClassification.from_pretrained("akumar0205/phishing_spam_jev_like_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 420 Bytes
f44f125 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"note": "held-out test beds; SpamAssassin headline is BINARY; Enron provisional excluded from headline; email-corpus unlabeled",
"spamassassin_binary": null,
"enron_provisional": null,
"email_corpus_unlabeled": {
"n": 1429,
"predicted_mix": {
"spam": 1286,
"review": 13,
"legitimate": 130
},
"mean_conf": 0.9792895671618911,
"note": "no accuracy: unlabeled upstream"
}
} |