Stop struggling with synthetic hallucinations. Access 1.6 Million human-verified Arabic-Chinese segments optimized for RLHF, Game Localization, and Multilingual AI.
Why Choose SinoArabic Data?
Every feature was designed to solve real production problems that synthetic datasets simply cannot address.
100% human-curated data. No machine-generated filler. Every segment has been manually reviewed and validated.
Seamlessly handles %s, %d, {0}, and HTML/XML tags. Your UI will never break again during localization.
Captures the deep conversational nuances of the MENA region that web-scraped data misses entirely.
LQA-verified by native speakers. Ready for immediate integration into your LLM training pipelines.
Optimized for gaming environments with character skills, combat mechanics, and UI dialogue.
Each segment includes metadata for MSA, Gulf, Levantine, and Egyptian dialects.
Technical Overview
Comprehensive specifications for engineers and data scientists evaluating our dataset.
| Metric | Value |
|---|---|
| Arabic Words | 1,600,000+ |
| Chinese Words | 717,000+ |
| Verified Segments | 120,000+ |
| Curation Time | 9 Years |
| LQA Status | 100% Human-Reviewed |
| Variable Preservation | %s, %d, {0}, HTML/XML |
| Dialect Coverage | MSA, Gulf, Levantine, Egyptian |
| Domains Covered | Gaming, Social, E-commerce, Voice Chat |
Designed for Engineers
Structured for Reinforcement Learning from Human Feedback. Seamless integration with modern LLM fine-tuning frameworks.
Unlike synthetic datasets, our data preserves all dynamic parameters. No broken tags, no corrupted placeholders.
Every Arabic-Chinese pair is aligned for meaning, not just tokens. Perfect for multilingual model evaluation.
Optimized for gaming, social platforms, payment systems, and voice chat environments.
Human-verified ground truth eliminates synthetic noise that plagues machine-generated datasets.
Includes Pinyin for Chinese, enabling applications in speech AI and language learning.
Access production-ready linguistic infrastructure built for the next generation of LLMs.