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• PashtoCorp is a 1.25-billion-word corpus for Pashto, which is 40x larger than the OSCAR Pashto subset and 83x larger than the previously largest dedicated Pashto corpus.result 95%
• Continued MLM pretraining of XLM-R-base on PashtoCorp reduces held-out perplexity by 25.1% (8.08->6.06).result 95%
• On WikiANN Pashto NER, the pretrained model improves entity F1 by 10% relative (19.0%->21.0%) and reduces training variance nearly 7x.result 95%
• A leave-one-out source ablation shows that Wikipedia (0.7% of documents) is the most critical source for NER: removing it alone reduces entity F1 by 47%.result 95%
• The corpus is assembled from 39 sources spanning seven HuggingFace datasets and 32 purpose-built web scrapers, processed through a reproducible pipeline with Arabic-script tokenization, SHA-256 deduplication, and quality filtering.method 90%
• On Belebele Pashto reading comprehension, Gemma-3n achieves 64.6% accuracy, the first published LLM baseline for Pashto on this benchmark.result 90%
• Pashto is a language spoken by 60 million people that remains severely underrepresented in NLP.market 85%
• The largest gain in NER performance appears at 50 training sentences (+27%), with PashtoCorp covering 97.9% of WikiANN entity vocabulary.result 90%

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