Build Text Classification ML Model

I have a concept that relies on automatically classifying blocks of text, and I’m ready to move from idea to a production-ready machine learning model. The data is already in hand—clean, labeled text that spans several categories—and I want a purpose-built classification solution trained on this set.

Here’s what I need from you:

• Prepare and explore the text data, handling any necessary preprocessing or feature engineering (tokenisation, vectorisation, etc.).
• Design, train, and tune a machine learning architecture suited to text classification. I’m open to traditional approaches (e.g., scikit-learn, XGBoost) or neural models (TensorFlow / PyTorch) as long as performance and interpretability remain strong.
• Evaluate the model with clear metrics (accuracy, precision, recall, F1) and provide a concise report that explains strengths, weaknesses, and recommended next steps.
• Deliver well-commented code, a reproducible training pipeline, saved model files, and simple instructions for re-training or inference so I can integrate the classifier into my wider application.

We will agree on acceptance criteria together—primarily centred on the evaluation metrics above—and break the work into milestones from data prep through to a deployable artefact. If you have experience turning raw text into high-performing classifiers and can communicate findings clearly, I’d love to collaborate and get this model into production.