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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- distilbert/distilbert-base-uncased |
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pipeline_tag: text-classification |
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tags: |
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- Bug Detection |
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- Review Classification |
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- NLP |
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- Deep Learning |
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- App Reviews |
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- Game Reviews |
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- BlaikHole |
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--- |
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# 🚀 DistilBert Reviews Bug Classifier by BlaikHole |
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<p align="center"> |
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<a href="https://huggingface.co/blaikhole/distilbert-review-bug-classifier" target="_blank" rel="noopener noreferrer"> |
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<img src="https://img.shields.io/badge/HuggingFace-Model-yellow?logo=huggingface" height="35"> |
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</a> |
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<a href="https://huggingface.co/spaces/blaikhole/review-bug-classifier" target="_blank" rel="noopener noreferrer"> |
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<img src="https://img.shields.io/badge/Demo-Space-blue?style=flat-square" height="35"> |
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</a> |
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</p> |
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## 📌 Overview |
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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)**. |
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## 🎨 Model Outputs & Labels |
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The model identifies the following labels: |
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| Label Name | Description | |
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|------------|-------------| |
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| 🟥 **LABEL_0 > Graphics Issue** | Screen touch controls issue, graphics flickering, rendering issues. | |
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| 🟩 **LABEL_1 > Network Issue** | Login/signup, account issues, wi-fi/data or ping problems etc. | |
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| 🟦 **LABEL_2 > No Bug** | No bug discussion found. | |
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| 🟨 **LABEL_3 > Performance Issue** | Overheating mobile, lag, crash, stuck game and so on. | |
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## 🚀 Quick Usage |
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You can easily load and use this model with `transformers`: |
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### 🔹 Named Entity Recognition (NER) |
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```python |
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from transformers import pipeline |
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# Label Mapping |
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LABEL_MAP = { |
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"LABEL_0": "Graphics issue", |
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"LABEL_1": "Network issue", |
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"LABEL_2": "No Bug", |
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"LABEL_3": "Performance issue" |
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} |
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# Load Text Classification Model |
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MODEL_NAME = "blaikhole/distilbert-review-bug-classifier" |
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classifier = pipeline("text-classification", model=MODEL_NAME, tokenizer=MODEL_NAME) |
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def classify_text(text): |
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result = classifier(text)[0] |
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label = LABEL_MAP.get(result["label"], "Unknown") |
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return f"Predicted Label: {label} (Confidence: {result['score']:.2f})" |
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# Example Usage |
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if __name__ == "__main__": |
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sample_text = "The game keeps lagging and frame rates drop frequently." |
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print(classify_text(sample_text)) |
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``` |
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--- |
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## 📦 Installation |
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To use this model, install the required dependencies: |
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```bash |
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pip install transformers torch |
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``` |
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## 📜 License |
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MIT |
