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
Download testbed_eval.json from akumar0205/phishing_spam_jev_like_classifier: direct link, hf CLI and curl.
- Browser
- Download file 420 Bytes
-
https://huggingface.co/akumar0205/phishing_spam_jev_like_classifier/resolve/main/testbed_eval.json
- Command line
-
hf download hf://akumar0205/phishing_spam_jev_like_classifier/testbed_eval.json
-
curl -L -o testbed_eval.json https://huggingface.co/akumar0205/phishing_spam_jev_like_classifier/resolve/main/testbed_eval.json
420 Bytes
| { | |
| "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" | |
| } | |
| } |