Improving Amharic Question Answering with Fine-tuning in NLP
This Omdena Local Chapter Challenge runs for 6 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world.
You will work on solving a local problem, initiated by Addis Ababa, Ethiopia Chapter.
This project aims to enhance the performance of Amharic question answering by expanding the current AmQA dataset and fine-tuning a more robust multilingual model on it. This will help improve the ability of NLP models to extract relevant information from Amharic text, enabling the development of accurate and efficient chatbots and search engines for the language.
The ultimate goal of this project is to make advances towards the practical use of QA NLP models for Amharic, driving inclusivity and making the building of chatbots, and automated systems much more practical and widespread. The expected outcomes of this project are:
- Create an AmQA dataset with 8,000 question-answer pairs.
- A fine-tuned multilingual model that is better suited to handle the nuances of the Amharic language.
- Improved performance of the QA models on the AmQA dataset.
- Web app to demonstrate how the fine-tuned model works
Why join? The uniqueness of Omdena Local Chapter Challenges
Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.
A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.
And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.