File size: 1,814 Bytes
112219f 6c8aa9d 2ae1880 6c8aa9d 245334a 6c8aa9d 245334a 6c8aa9d 245334a 6c8aa9d 245334a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
---
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) |