| | --- |
| | pretty_name: Genesis AI Code 50K (Expert) |
| | tags: |
| | - within-us-ai |
| | - genesis |
| | - code |
| | - agentic |
| | - reasoning |
| | - tool-calling |
| | - evaluation |
| | - security |
| | - governance |
| | - moe |
| | task_categories: |
| | - text-generation |
| | - question-answering |
| | language: |
| | - en |
| | license: cc0-1.0 |
| | size_categories: |
| | - 10K<n<100K |
| | --- |
| | |
| | # Genesis AI Code 50K (Expert) |
| |
|
| | **Developed by: Within Us AI** |
| |
|
| | Expert dataset with diff supervision, failure→reflection→correction, MoE labels, and governance flags. |
| |
|
| | ## Splits |
| | - train: 49,000 |
| | - validation: 1,000 |
| |
|
| | ## Highlights |
| | - Tests-as-truth supervision patterns |
| | - Diff-first patching |
| | - Agentic loops (plan→edit→test→reflect) with bounded budgets |
| | - Tool-call trace supervision (where present) |
| | - Governance/audit & policy-gate awareness |
| |
|
| | ## Storage format |
| | Parquet unavailable (No module named 'pyarrow'); JSONL shards in `/data`. |
| |
|
| | ## Quick start |
| | ```python |
| | from datasets import load_dataset |
| | ds = load_dataset("<YOUR_ORG_OR_USER>/Genesis AI Code 50K (Expert)", split="train") |
| | print(ds[0]) |
| | ``` |
| |
|
