Spam Mail Classifier

🚀 垃圾郵件分類器

本模型基於microsoft/Multilingual-MiniLM-L12-H384進行微調，用於將電子郵件主題分類為垃圾郵件（SPAM）或非垃圾郵件（NOSPAM）。

🚀 快速開始

本模型基於microsoft/Multilingual-MiniLM-L12-H384進行微調，用於將電子郵件主題分類為垃圾郵件（SPAM）或非垃圾郵件（NOSPAM）。

💻 使用示例

基礎用法

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "Goodmotion/spam-mail-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(
    model_name
)

text = "Félicitations ! Vous avez gagné un iPhone."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
print(outputs.logits)

高級用法

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "Goodmotion/spam-mail-classifier"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

texts = [
'Join us for a webinar on AI innovations',
'Urgent: Verify your account immediately.',
'Meeting rescheduled to 3 PM',
'Happy Birthday!',
'Limited time offer: Act now!',
'Join us for a webinar on AI innovations',
'Claim your free prize now!',
'You have unclaimed rewards waiting!',
'Weekly newsletter from Tech World',
'Update on the project status',
'Lunch tomorrow at 12:30?',
'Get rich quick with this amazing opportunity!',
'Invoice for your recent purchase',
'Don\'t forget: Gym session at 6 AM',
'Join us for a webinar on AI innovations',
'bonjour comment allez vous ?',
'Documents suite à notre rendez-vous',
'Valentin Dupond mentioned you in a comment',
'Bolt x Supabase = 🤯',
'Modification site web de la société',
'Image de mise en avant sur les articles',
'Bring new visitors to your site',
'Le Cloud Éthique sans bullshit',
'Remix Newsletter #25: React Router v7',
'Votre essai auprès de X va bientôt prendre fin',
'Introducing a Google Docs integration, styles and more in Claude.ai',
'Carte de crédit sur le point d’expirer sur Cloudflare'
]
inputs = tokenizer(texts, padding=True, truncation=True, max_length=128, return_tensors="pt")
outputs = model(**inputs)

# Convertir les logits en probabilités avec softmax
logits = outputs.logits
probabilities = torch.softmax(logits, dim=1)

# Décoder les classes pour chaque texte
labels = ["NOSPAM", "SPAM"]  # Mapping des indices à des labels
results = [
    {"text": text, "label": labels[torch.argmax(prob).item()], "confidence": prob.max().item()}
    for text, prob in zip(texts, probabilities)
]

# Afficher les résultats
for result in results:
    print(f"Texte : {result['text']}")
    print(f"Résultat : {result['label']} (Confiance : {result['confidence']:.2%})\n")

📚 詳細文檔

模型詳情

📄 許可證

本項目採用Apache-2.0許可證。