I have a collection of digital documents that need to be automatically sorted into predefined categories. The job is centered on text data only—no images, no numbers—and the key task is accurate text classification. Here’s what I need from you: • Prepare and preprocess the documents so they’re ready for modeling (tokenization, stop-word removal, etc.). • Build a classification pipeline—Python with scikit-learn, spaCy, or any comparable NLP library is fine as long as it runs reproducibly on my end. • Train, validate, and fine-tune the model to achieve reliable accuracy (I will share the target label set once we start). • Deliver well-commented source code, a short README explaining environment setup and run steps, and a brief report highlighting accuracy metrics and any improvement suggestions. I’ll supply the raw documents in batches. You’re free to choose classical algorithms or modern transformer methods, provided the final solution can be executed on a standard workstation without special hardware. Let me know your estimated turnaround time and a quick outline of your approach so we can get started right away.