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---
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license: apache-2.0
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tags:
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- transformers
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- text-classification
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- spam-detection
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---
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# SPAM Mail Classifier
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This model is fine-tuned from `microsoft/Multilingual-MiniLM-L12-H384` to classify email subjects as SPAM or NOSPAM.
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## Model Details
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- **Base model**: `microsoft/Multilingual-MiniLM-L12-H384`
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- **Fine-tuned for**: Text classification
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- **Number of classes**: 2 (SPAM, NOSPAM)
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- **Languages**: Multilingual
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## Usage
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This model is fine-tuned from `microsoft/Multilingual-MiniLM-L12-H384` to classify email subjects as SPAM or NOSPAM.
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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model_name = "Goodmotion/spam-mail-classifier"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(
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model_name
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)
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text = "Félicitations ! Vous avez gagné un iPhone."
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model(**inputs)
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print(outputs.logits)
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```
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### Exemple for list
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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model_name = "Goodmotion/spam-mail-classifier"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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texts = [
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'Join us for a webinar on AI innovations',
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'Urgent: Verify your account immediately.',
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'Meeting rescheduled to 3 PM',
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'Happy Birthday!',
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'Limited time offer: Act now!',
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'Join us for a webinar on AI innovations',
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'Claim your free prize now!',
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'You have unclaimed rewards waiting!',
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'Weekly newsletter from Tech World',
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'Update on the project status',
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'Lunch tomorrow at 12:30?',
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'Get rich quick with this amazing opportunity!',
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'Invoice for your recent purchase',
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'Don\'t forget: Gym session at 6 AM',
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'Join us for a webinar on AI innovations',
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'bonjour comment allez vous ?',
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'Documents suite à notre rendez-vous',
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'Valentin Dupond mentioned you in a comment',
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'Bolt x Supabase = 🤯',
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'Modification site web de la société',
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'Image de mise en avant sur les articles',
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'Bring new visitors to your site',
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'Le Cloud Éthique sans bullshit',
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'Remix Newsletter #25: React Router v7',
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'Votre essai auprès de X va bientôt prendre fin',
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'Introducing a Google Docs integration, styles and more in Claude.ai',
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'Carte de crédit sur le point d’expirer sur Cloudflare'
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]
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inputs = tokenizer(texts, padding=True, truncation=True, max_length=128, return_tensors="pt")
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outputs = model(**inputs)
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# Convertir les logits en probabilités avec softmax
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logits = outputs.logits
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probabilities = torch.softmax(logits, dim=1)
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# Décoder les classes pour chaque texte
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labels = ["NOSPAM", "SPAM"] # Mapping des indices à des labels
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results = [
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{"text": text, "label": labels[torch.argmax(prob).item()], "confidence": prob.max().item()}
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for text, prob in zip(texts, probabilities)
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]
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# Afficher les résultats
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for result in results:
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print(f"Texte : {result['text']}")
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print(f"Résultat : {result['label']} (Confiance : {result['confidence']:.2%})\n")
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```
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