add exemple in README

README.md

@@ -27,3 +27,64 @@ inputs = tokenizer(text, return_tensors="pt")
 outputs = model(**inputs)
 
 print(outputs.logits)
+```
+
+### Exemple for list
+
+```python
+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")
+```