The growth in machine learning research has not been accompanied by a corresponding improvement in the reproducibility of the results. This thesis presents a novel, fully-automated end-to-end system that evaluates the reproducibility of machine learning studies based on the content of the associated GitHub project’s Readme fle. This evaluation relies on a readme template derived from an analysis of popular repositories. The template suggests a structure that promotes reproducibility. Our system generates a reproducibility score for each Readme fle assessed, and it employs two distinct models, one based on section classifcation and the other on hierarchical transformers. The experimental outcomes indicate that the system based on section similarity outperforms the hierarchical transformer model. Furthermore, it has a superior edge concerning explainability, as it allows for a direct correlation of the scores with the respective sections of the Readme fles. The proposed framework provides an important tool for improving the quality of code sharing and ultimately helps to increase reproducibility in machine learning research.