DrQA

Achieves Exact Match and F1 scores nearly identical to the original paper and official implementations, as validated in the results table with EM 69.64 and F1 78.76.

Strips away extraneous features like document retrieval, offering a lightweight implementation that is easy to read, modify, and experiment with for SQuAD tasks, as emphasized in the philosophy.

Uses spaCy for lemma, POS, and named entity tags, which is faster than Stanford CoreNLP while maintaining similar accuracy, as noted in the detailed comparisons section.

Optimized for single-GPU environments, reducing configuration complexity and making it accessible for research on standard hardware, unlike bulkier official versions.