---
language: he
license: apache-2.0
datasets:
- custom
tags:
- text-classification
- intent-classification
- hebrew
- nlp
- bert
- customer-service
widget:
- text: "שכחתי את הסיסמה שלי"
  example_title: "Password Reset"
- text: "רוצה לבטל את המנוי"
  example_title: "Cancel Subscription"
- text: "כמה עולה החבילה"
  example_title: "General Question"
- text: "האתר לא עובד"
  example_title: "Technical Support"
---

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

## Usage

```python
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

```python
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

| Hebrew Text | Predicted Intent | English Translation |
|------------|------------------|-------------------|
| שכחתי את הסיסמה שלי | שכחת סיסמה | I forgot my password |
| רוצה לבטל את המנוי | ביטול מנוי | Want to cancel subscription |
| כמה עולה החבילה | שאלה כללית | How much does the package cost |
| האתר לא עובד | תמיכה טכנית | The website doesn't work |

## 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

```bibtex
@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.