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 label_map.json from akumar0205/phishing_spam_jev_like_classifier: direct link, hf CLI and curl.
- Browser
- Download file 403 Bytes
-
https://huggingface.co/akumar0205/phishing_spam_jev_like_classifier/resolve/main/label_map.json
- Command line
-
hf download hf://akumar0205/phishing_spam_jev_like_classifier/label_map.json
-
curl -L -o label_map.json https://huggingface.co/akumar0205/phishing_spam_jev_like_classifier/resolve/main/label_map.json
403 Bytes
| { | |
| "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" | |
| } |