As part of a confidential AI research project, Python parsers and Bash automation scripts were developed to help extract and structure data. A focused contribution on parsing and automation, upstream of the work on the models.
Artificial intelligence models are only as good as the data that feeds them. The research lab had large but heterogeneous corpora: different formats, inconsistent encodings, duplicates and corrupted data. Feeding this raw data into the models would have produced mediocre results.
The parsers and scripts developed contributed to several stages of data preparation: extraction from the sources, cleaning of anomalies and duplicates, and formatting into the structures expected downstream.
Automating these repetitive tasks through Bash scripts made it possible to make previously manual steps more reliable and reproducible. Reproducibility matters in research: the same experiment must be able to be re-run on data prepared in an identical way.
The automated extraction tools turn raw data into formats usable by artificial intelligence models.
The automation scripts reduce repetitive manual work in data preparation.
Automation promotes the reproducibility of the processing, which matters for scientific rigor.
The scripts fit into the existing processing pipeline to limit manual handling.
Data preparation for models
Repetitive workflows
Data validation
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