👉 Samples engineering is a crucial process in the development and training of machine learning models, particularly in natural language processing (NLP). It involves creating and curating high-quality, representative datasets that accurately reflect the real-world scenarios the model will encounter. This process includes collecting, annotating, and preprocessing text data to ensure it is suitable for training models. Engineers design sampling strategies to address issues like class imbalance, where certain categories have significantly fewer instances than others, by oversampling minority classes or undersampling majority classes. They also implement techniques like stratified sampling, data augmentation, and active learning to enhance dataset diversity and model performance. Effective samples engineering ensures that the model learns robust patterns, leading to better generalization and more reliable predictions in practical applications.
