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title: Albert Latest 96
emoji: π¨
colorFrom: blue
colorTo: pink
sdk: docker
pinned: false
short_description: AI model to classify accessibility-related bugs.
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# Accessibility Bug Prediction Using ALBERT π
This project leverages the **ALBERT (A Lite BERT)** model to classify software bug reports into two categories:
1. Accessibility-related bugs.
2. Non-accessibility bugs.
It also includes a **custom Jira plugin** to integrate the AI model into the bug-tracking workflow, making it easier for development teams to identify and prioritize accessibility issues.
## Key Features β¨
- **State-of-the-Art NLP**: Utilizes the ALBERT transformer model, fine-tuned for high accuracy on bug report classification tasks.
- **Custom Dataset**: The model was trained from scratch on a dataset collected by the research team.
- **Jira Plugin Integration**: Seamlessly integrates the classification system into Jira to enhance accessibility compliance workflows.
- **Research Collaboration**: Developed under the guidance of **Professor Wajdi Aljedaani**, a UX and Human-Centered AI researcher.
## How It Works π
1. **Input**: Provide a textual description of a bug report.
2. **Prediction**: The ALBERT model analyzes the text and classifies the bug as either accessibility-related or not.
3. **Output**: Use the results directly or integrate them into Jira for workflow optimization.
## Applications π οΈ
- **Software Development**: Identify accessibility bugs to ensure compliance with standards like WCAG.
- **Quality Assurance**: Optimize testing and prioritization for accessibility-related issues.
- **Research in UX and AI**: Leverage insights for designing inclusive and accessible systems.
## Deployment π
The model is hosted on **Hugging Face Spaces**, providing an interactive and user-friendly web interface.
[Try the Model on Hugging Face](https://huggingface.co/spaces/shivamjadhav/albert_latest_96)
## About the Research π€
This project was developed as part of a research initiative at **UNT** under **Professor Wajdi Aljedaani**'s guidance. It emphasizes the intersection of AI, UX, and accessibility to drive impactful solutions for software development.
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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