ISLAM-PO/arabic-to-code-8-langs-3m

arabic-to-code-8-langs-3m/
├── README.md
├── LICENSE
├── CITATION.cff
├── .gitattributes
├── 01_Python/          # 375K records
│   ├── 01_basics.jsonl      # 75,000
│   ├── 02_functions.jsonl   # 75,000
│   ├── 03_oop.jsonl         # 75,000
│   ├── 04_algorithms.jsonl  # 75,000
│   └── 05_projects.jsonl    # 75,000
├── 02_JavaScript/      # 375K - same 5 files
├── 03_Java/            # 375K
├── 04_Cpp/             # 375K
├── 05_HTML_CSS/        # 375K
├── 06_SQL/             # 375K
├── 07_PHP/             # 375K
└── 08_Go/              # 375K

graph TD
    ROOT[arabic-to-code-8-langs-3m/<br/>3M target records - 11.55GB]
    ROOT --> PY[01_Python<br/>375K]
    ROOT --> JS[02_JavaScript<br/>375K]
    ROOT --> JV[03_Java<br/>375K]
    ROOT --> CP[04_Cpp<br/>375K]
    ROOT --> WEB[05_HTML_CSS<br/>375K]
    ROOT --> SQ[06_SQL<br/>375K]
    ROOT --> PH[07_PHP<br/>375K]
    ROOT --> GO[08_Go<br/>375K]
    PY --> F1[basics/functions/oop/algorithms/projects]
    F1 --> REC[JSON record<br/>instruction - code - explanation]

pie title Records by language (375K each)
    "Python" : 375000
    "JavaScript" : 375000
    "Java" : 375000
    "C++" : 375000
    "HTML/CSS" : 375000
    "SQL" : 375000
    "PHP" : 375000
    "Go" : 375000

{
  "id": "python-basics-000001",
  "language": "python",
  "category": "basics",
  "instruction": "اكتب كود بايثون كامل وقابل للتشغيل يقوم بالمهمة التالية: حساب مضروب عدد...",
  "code": "# مثال 1 ...\ndef solve_omar(...):\n ... \nif __name__ == \"__main__\":\n    main()\n",
  "explanation": "هذا الحل بلغة بايثون يشرح ... التعقيد الزمني ... حالات اختبار ...",
  "difficulty": "متوسط"
}

from datasets import load_dataset
ds = load_dataset("ISLAM-PO/arabic-to-code-8-langs-3m", "full", split="train", streaming=True)
print(next(iter(ds)))
py = ds.filter(lambda x: x["language"] == "python")

from datasets import load_dataset
ds_py = load_dataset("json", data_files={"train": "hf://datasets/ISLAM-PO/arabic-to-code-8-langs-3m/01_Python/*.jsonl"}, split="train", streaming=True)

def to_prompt(row):
    return {
        "prompt": f"المستخدم: {row['instruction']}\nالمساعد:\n",
        "completion": f"{row['code']}\n\n# الشرح:\n{row['explanation']}",
    }
# Use with TRL SFTTrainer or axolotl / unsloth chat template

# pip install transformers datasets trl peft
from datasets import load_dataset
from trl import SFTTrainer
ds = load_dataset("json", data_files={"train": "hf://datasets/ISLAM-PO/arabic-to-code-8-langs-3m/01_Python/01_basics.jsonl"}, split="train")
# map with to_prompt(), then SFTTrainer(..., dataset_text_field="prompt")

python generate_code_dataset.py --demo                              # 4,000 records, ~11 MB
python generate_code_dataset.py --full --per-language 375000 --total-gb 10

@dataset{arabic_to_code_2026,
  title   = {Arabic-to-Code Dataset: 8 Languages, 3M target Records},
  author  = {ISLAM-PO and Contributors},
  year    = {2026},
  publisher = {Hugging Face},
  version = {1.0.0},
  url     = {https://huggingface.co/datasets/ISLAM-PO/arabic-to-code-8-langs-3m},
  note    = {3,000,000 records, 40 JSONL files, 11.55 GB, CC-BY-4.0}
}

pip install huggingface_hub datasets
huggingface-cli login
cd arabic-to-code-8-langs-3m
git init; git lfs install; git lfs track "*.jsonl"
huggingface-cli repo create arabic-to-code-8-langs-3m --type dataset --yes
git remote add origin https://huggingface.co/datasets/ISLAM-PO/arabic-to-code-8-langs-3m
git add README.md LICENSE CITATION.cff .gitattributes
git commit -m "docs: code dataset card"; git push origin main
git add 01_Python 02_JavaScript; git commit -m "data: batch 1"; git push origin main
# repeat for remaining languages