I’ve been experimenting with AI on my own and have now reached the limits of what quick tutorials can teach me. The focus is Machine Learning, and the exact task is building a robust Named Entity Recognition (NER) pipeline. What I need: • Clean, well-commented code (Python preferred) that trains and evaluates an NER model. • Guidance on choosing or preparing an appropriate dataset, plus a brief explanation of the preprocessing steps. • A reproducible training script—command-line runnable—and instructions for fine-tuning or updating the model later. • Evaluation results with standard NER metrics (precision, recall, F1) on a held-out test set. • A short README that explains environment setup and how to run everything from data prep through inference. You’re free to suggest the best library—spaCy, Hugging Face Transformers, or another proven toolkit—so long as the end result is easy for me to maintain and extend. Feel free to recommend transfer-learning approaches if that speeds up convergence. I’ll be hands-on reviewing the code, so clarity and modularity matter as much as raw performance. Looking forward to collaborating and finally getting my NER model into shape.