We present a modular training approach for deep text classification in Guarani, where networks are split into sectors trained independently and later combined. This sector-wise backpropagation improves stability, reduces training time, and adapts to standard architectures like CNNs, LSTMs, and Transformers. Evaluated on three Guarani datasets—emotion, humor, and offensive language—our method outperforms traditional Bayesian-optimized training in both accuracy and efficiency.