MARKDOWN ORDERING ON JUPYTER NOTEBOOK USING PAIRWISE METHOD ON AI4CODE DATASET
The development of Machine Learning research has resulted in its use in various fields. Microsoft Fx and GitHub Copilot, which utilize Machine Learning to assist programmers, have initiated much research on using machine learning in the software engineering pipeline. Google launched a competition...
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| 主要作者: | |
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| 格式: | Final Project |
| 語言: | Indonesia |
| 在線閱讀: | https://digilib.itb.ac.id/gdl/view/78314 |
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| 機構: | Institut Teknologi Bandung |
| 語言: | Indonesia |
| 總結: | The development of Machine Learning research has resulted in its use in various
fields. Microsoft Fx and GitHub Copilot, which utilize Machine Learning to assist
programmers, have initiated much research on using machine learning in the
software engineering pipeline. Google launched a competition to build a model to
order Jupyter Notebook with jumbled Markdown Cells to find the relationship
between markdown (comments) and programming languages. The task to order
Jupyter Notebook with jumbled Markdown Cells will be called Markdown
Ordering in this final project paper. This final project aims to build a Markdown
Ordering model using the pairwise method to order Markdown Cells on the
Jumbled Jupyter Notebook.
This final project uses the AI4CODE dataset, which contains a collection of Jupyter
Notebook Kaggle containing cell markdowns in English and code cells in Python.
This final project focuses on experiments to build machine learning models using
the pairwise method, referred to in research (Manku & Paul, 2022), and determines
each model's performance. The performance of each model built will be compared
to find the best performance model.
Based on the experimental results, using the Pairwise-BERT-Softmax model can
increase the performance of the baseline model by 10%. This model is the best
model built in this final project. |
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