The benchmarking system comprises three main parts: a data repository, a submission and evaluation system, and a leaderboard that enhances NLP model performance assessment.
The model submission and evaluation system allows researchers to test new models by uploading predictions for test sets of raw sentences, facilitating improved comparative analysis.
The leaderboard provides a tabular view of all submissions, displaying performance metrics and rankings, incentivizing researchers by showcasing their contributions to the natural language preprocessing field.
Implemented as a web-based application using Django, the benchmarking system adopts an MVC design pattern, enabling user-friendly custom configurations and efficient data management.
#natural-language-processing #benchmarking-system #evaluation-methodology #web-based-application #data-repository
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