Using Machine Learning to Classify News Articles
Project by Polygence alum Aarav
Project's result
Research Paper, GitHub Repository
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Summary
This research paper delves into various approaches and techniques for classifying news articles into different categories using machine learning and which ones resulted in the best outcome. We tested various models for this classification task. We used 4 models in total, including two that we created ourselves with the library SciKit Learn, and the other two, we found on HuggingFace, which we fine-tuned for our specific task. The best-performing model for this task was one of the deep learning models (Mini LM) from HuggingFace, which had an accuracy of 75.42% out of the 10,000 articles it was tested on. This model was also one of the most diminutive models that we used for classifying news articles.
Efthimios
Polygence mentor
MS Master of Science
Subjects
Computer Science, Quantitative
Expertise
Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, Data Science
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Aarav
Student
Hello, my name is Aarav and my Polygence project is on using deep-learning models to classify news articles into different categories. I have also written a paper that delves into the performance of my models as well as models from other organizations. In addition, I also made a GitHub repository with my code.
Graduation Year
2028
Project review
“I had a fabulous experience with my project and I learned many new things about machine learning. It went smoothly and we got to do everything on time.”
About my mentor
“Tim has a lot of knowledge about computer science, especially machine learning and AI. He answers questions very well and is an excellent teacher. He replies very fast on the messaging platform and is easy to communicate with. Overall, if you are interested in AI/ML, he is an excellent mentor.”
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