I have an existing AI pipeline that handles text data, and the data transformation stage has started throwing errors that halt the entire workflow. The cleaning and feature-extraction steps are stable, so the problem is isolated to the transformation logic—tokenisation, normalisation, or any custom functions that sit between them. I’ll give you the current Python scripts, sample input files, and the error logs so you can reproduce the issue locally. Your task is to trace the root cause, correct the code, and return a fully working transformation module that plugs straight back into the rest of the pipeline. A brief rundown of what you changed and a quick verification run (unit test or notebook) that shows the fix in action will close the loop. If you’re comfortable debugging text-handling routines that rely on libraries such as pandas, spaCy, NLTK or similar—and you can turn this around quickly—let’s get this sorted today.