README.md

---
license: mit
language:
- fa
metrics:
- f1
- accuracy
base_model:
- HooshvareLab/bert-fa-base-uncased
pipeline_tag: text-classification
---

# Fine-tuned BERT for Persian Comment Discrepancy Classification

This project fine-tunes a BERT model to classify Persian comments into two categories: complaints about Product discrepancy (`True`) and not (`False`). The model is trained on the [Basalam Comments](https://www.kaggle.com/datasets/alirezaazizkhani/labeled-persian-comments) dataset.

## 🛠 Training Details
- **Base Model**: `HooshvareLab/bert-fa-base-uncased`
- **Fine-Tuning Dataset**: Basalam comments
- **[NoteBook](https://www.kaggle.com/code/alirezaazizkhani/finetune-bert-for-discrepancy)**
- **Evaluation Metrics**:
  - **Accuracy**: 95.89%
  - **F1 Score**: 95.62%


## 📥 How to Use
You can load and use the fine-tuned model as follows:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

def classify_comment(text):
    model_name = "alireza-2003/bert-fa-discrepancy-detection"
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits).item()
    
    return "Discrepancy Complaint" if prediction == 1 else "Not a Complaint"

comment = "دو تا سفارش داده بودم  یدونه ابی و یدونه قرمز ولی هردوتاش قرمز بود"
print(classify_comment(comment))
```

---
📝 **Author**: [Alireza]  
📅 **Last Updated**: [2/16/2025]  
🔗 **Dataset**: [Kaggle Dataset](https://www.kaggle.com/datasets/alirezaazizkhani/labeled-persian-comments)