oddadmix/gsm8k-reasoning-ar

GSM8K Reasoning — Arabic (مترجم آليًا)

142,969 grade-school math reasoning items translated from English into Arabic with ByteDance-Seed/Seed-X-PPO-7B, a 7B translation model.

Source: Ajhesh7/gsm8k-reasoning-SFT-datas (600,000 rows).

بالعربية: مجموعة بيانات للاستدلال الرياضي بالعربية، مترجمة آليًا من الإنجليزية. كل مثال يحتوي على سؤال، وخطوات التفكير، والإجابة النهائية.

Format

text keeps the source's tag layout, with Arabic content:

<question>يجمع فريا 168 صندوقًا وجمعت هانا 19 صندوقًا...</question> <thinking>دعونا نفكر خطوة بخطوة...</thinking> <answer>187</answer>

The parts are also available as separate columns — question, thinking, answer (Arabic; answer is the untouched numeral) — with question_en / thinking_en carrying the English source so every row is auditable, and source_index pointing back into the source dataset.

How it was built

1. Sampling. 150,000 of the 600,000 source rows. The corpus is generated from only 2,814 underlying question patterns (numbers and names masked), so the sample is stratified by pattern with a floor of 13 rows per pattern — every pattern is represented rather than over-weighting the common ones.
2. Translation. Question and reasoning translated separately, each as its own sentence, with Translate the following English sentence into Arabic:\n{text} <ar> and greedy decoding. Numbers and names were left in place rather than masked, so Arabic gender agreement follows the actual name (اشترت for Aisha) and number agreement follows the actual quantity. The final answer numeral is never sent to the translator.
3. Validation. A row is kept only if, for both segments, the numbers in the Arabic exactly match the English (order-insensitive), the output is non-empty Arabic script, has no degenerate repetition loop, and has no significant Latin-script residue. 95.31% of translated rows passed.

Rejection breakdown: {'number_dropped': 5555, 'invented_number': 1466, 'latin_residue': 10}

Limitations

This is machine translation, not human-verified Arabic. It inherits the source's synthetic, templated phrasing — 150,000 rows expand from 2,814 patterns, so linguistic diversity is far lower than the row count suggests.

Gender agreement. The English source pairs names with pronouns arbitrarily ("This week Emil did chores and earned $76. She bought a bottle…"), which English mostly hides but Arabic does not: a row can read قام جورج … and then اشترت … for the same person. The translator rendered the source faithfully; the disagreement is upstream, and it is visible throughout. The arithmetic itself is copied from the source and was not re-verified; in the source, the reasoning's final number agrees with the answer field ~96.6% of the time, so a small fraction of items are internally inconsistent. Suitable for SFT on reasoning format and basic Arabic math phrasing; not a benchmark.

Trained model

This dataset is part of the training mix for oddadmix/Nawah-Math-Reasoning — a 51.8M-parameter Arabic model that reasons step by step inside <think>…</think> before answering. The full training code, including the pipeline that built this dataset, ships in code/ in that repo, and there is a live demo Space.