humy65/hebrew-intent-classifier

Hebrew Intent Classification Model

Model Description

This model is a fine-tuned BERT model for Hebrew intent classification, specifically designed for customer service scenarios. It can classify Hebrew text into 4 different intent categories commonly found in customer support interactions.

Supported Intent Classes

1. 1.ביטול מנוי (Cancel Subscription) - Requests to cancel or terminate services
2. 2.שאלה כללית (General Question) - General inquiries about services, pricing, or account management
3. 3.שכחת סיסמה (Password Reset) - Issues related to forgotten passwords or login problems
4. 4.תמיכה טכנית (Technical Support) - Technical issues, bugs, or system problems

Usage

from transformers import pipeline

# Load the model
classifier = pipeline("text-classification", model="Huggingm1r@n/hebrew-intent-classifier")

# Make predictions
result = classifier("שכחתי את הסיסמה שלי")
print(result)
# [{'label': 'שכחת סיסמה', 'score': 0.95}]

# Test other examples
examples = [
    "רוצה לבטל את המנוי",
    "כמה עולה החבילה", 
    "האתר לא עובד"
]

for text in examples:
    result = classifier(text)
    print(f"'{text}' -> {result[0]['label']} ({result[0]['score']:.2%})")

Direct Usage with Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Huggingm1r@n/hebrew-intent-classifier")
model = AutoModelForSequenceClassification.from_pretrained("Huggingm1r@n/hebrew-intent-classifier")

def predict_intent(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probabilities = torch.softmax(logits, dim=-1)
    
    predicted_id = torch.argmax(logits, dim=-1).item()
    predicted_label = model.config.id2label[predicted_id]
    confidence = probabilities[0][predicted_id].item()
    
    return predicted_label, confidence

# Example
intent, confidence = predict_intent("שכחתי את הסיסמה")
print(f"Intent: {intent}, Confidence: {confidence:.2%}")

Training Details

• —Base Model: bert-base-multilingual-cased
• —Training Data: 135 Hebrew customer service examples (augmented from 12 original)
• —Data Augmentation: Manual variations, formal/informal styles, polite forms
• —Performance: >90% accuracy on validation set

Example Predictions

Use Cases

• —Customer Service Chatbots: Route Hebrew customer queries automatically
• —Support Ticket Classification: Categorize support requests by intent
• —Voice of Customer Analysis: Analyze Hebrew customer feedback
• —Automated Response Systems: Trigger appropriate responses based on intent

Limitations

• —Designed for customer service domain specifically
• —Limited to 4 predefined intent classes
• —May not work well with very informal Hebrew or slang
• —Requires Hebrew text input

Model Files

• —Uses safetensors format for secure model storage
• —Compatible with latest Transformers library
• —Includes comprehensive tokenizer configuration

Citation

@misc{hebrew-intent-classifier-2025,
  title={Hebrew Intent Classification Model for Customer Service},
  author={Huggingm1r@n},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/Huggingm1r@n/hebrew-intent-classifier}
}

License

This model is released under the Apache 2.0 License.