---
license: mit
base_model:
- HooshvareLab/bert-fa-base-uncased
tags:
- persian
- comments
- delivery
- bert
- bert-persian
language:
- fa
metrics:
- accuracy
- f1
pipeline_tag: text-classification
---
# Persian Sentiment Analysis for Delivery Complaints

## 📌 Introduction
This model is fine-tuned **HooshvareLab/bert-fa-base-uncased** model to classify Persian comments related to delivery complaints. The model predicts whether a comment is about a negative delivery experience or not.

## 📊 Dataset
The model was fine-tuned using the ** Basalam comments ** dataset from Kaggle:
[📂 Basalam Comments Dataset](https://www.kaggle.com/datasets/alirezaazizkhani/labeled-persian-comments)

This dataset contains Persian comments labeled as:
- **1 (Bad Delivery Complaint)**: The comment expresses dissatisfaction with delivery service.
- **0 (Not a Delivery Complaint)**: The comment is unrelated to delivery issues.

## 🛠 Training Details
- **Base Model**: `HooshvareLab/bert-fa-base-uncased`
- **Fine-Tuning Dataset**: Basalam comments
- **[NoteBook](https://www.kaggle.com/code/alirezaazizkhani/fine-tune-parsbert-for-bad-delivery)**
- **Evaluation Metrics**:
  - **Accuracy**: `91%`
  - **F1-score**: `87%`

## 📥 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_bad_delivery"
    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 "Bad Delivery Complaint" if prediction == 1 else "Not a Complaint"

comment = "بسته من خیلی دیر رسید و اصلا راضی نبودم!"
print(classify_comment(comment))
```

## 📌 Next Steps
- Further fine-tuning with more diverse Persian datasets.
- Improve model performance by handling edge cases better.
- Deploy as an API for real-time classification.

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