README.md

metadata

license: mit
language:
  - en
metrics:
  - accuracy
base_model:
  - distilbert/distilbert-base-uncased
pipeline_tag: text-classification
tags:
  - Bug Detection
  - Review Classification
  - NLP
  - Deep Learning
  - App Reviews
  - Game Reviews
  - BlaikHole

🚀 DistilBert Reviews Bug Classifier by BlaikHole

📌 Overview

This repository provides a fine-tuned model trained on our private Playstore reviews data using quick still efficient DistilBert architecture. It can be used for Reviews classification with 3 classes (with 4th class - No bug).

---

🎨 Model Outputs & Labels

The model identifies the following labels:

Label Name	Description
🟥 LABEL_0 > Graphics Issue	Screen touch controls issue, graphics flickering, rendering issues.
🟩 LABEL_1 > Network Issue	Login/signup, account issues, wi-fi/data or ping problems etc.
🟦 LABEL_2 > No Bug	No bug discussion found.
🟨 LABEL_3 > Performance Issue	Overheating mobile, lag, crash, stuck game and so on.

---

🚀 Quick Usage

You can easily load and use this model with transformers:

🔹 Named Entity Recognition (NER)

from transformers import pipeline

# Label Mapping
LABEL_MAP = {
    "LABEL_0": "Graphics issue", 
    "LABEL_1": "Network issue", 
    "LABEL_2": "No Bug", 
    "LABEL_3": "Performance issue"
}

# Load Text Classification Model
MODEL_NAME = "blaikhole/distilbert-review-bug-classifier"
classifier = pipeline("text-classification", model=MODEL_NAME, tokenizer=MODEL_NAME)

def classify_text(text):
    result = classifier(text)[0]
    label = LABEL_MAP.get(result["label"], "Unknown")
    return f"Predicted Label: {label} (Confidence: {result['score']:.2f})"

# Example Usage
if __name__ == "__main__":
    sample_text = "The game keeps lagging and frame rates drop frequently."
    print(classify_text(sample_text))

---

📦 Installation

To use this model, install the required dependencies:

pip install transformers torch

---

📜 License

MIT