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: 403 Bytes
f44f125 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"labels": [
"legitimate",
"spam"
],
"label2id": {
"legitimate": 0,
"spam": 1
},
"id2label": {
"0": "legitimate",
"1": "spam"
},
"task": "binary_spam",
"annotation_rules": "v1 (\u00a72b)",
"review_threshold": 0.55,
"max_length": 512,
"model_checkpoint": "distilbert/distilbert-base-uncased",
"model_revision": "12040accade4e8a0f71eabdb258fecc2e7e948be"
} |