Update README.md

README.md

@@ -14,16 +14,39 @@ pipeline_tag: text-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.
 
-## Key Metrics
+## 🛠 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%
 
-- **Accuracy**: 95.89%
-- **F1 Score**: 95.62%
 
+## 📥 How to Use
+You can load and use the fine-tuned model as follows:
 
-## Code
-The code for fine-tuning the model is available on [Kaggle](https://www.kaggle.com/code/alirezaazizkhani/finetune-bert-for-discrepancy/).
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
 
-## Installation
-To run the code, install the necessary dependencies:
-```bash
-pip install transformers datasets scikit-learn matplotlib seaborn
+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)