metadata
license: mit
tags:
- semantic-distance-coding
- scores
- medium
sdc-scores-medium-v1
Aggregated pass@1 scores — Medium tier, mainstream languages only. Esoteric baselines removed (paper transcriptions, not per-tier measurements).
Dataset Info
- Rows: 8
- Columns: 10
Columns
| Column | Type | Description |
|---|---|---|
| language | Value('string') | Programming language name |
| tiobe_rank | Value('int64') | TIOBE index rank (1=Python, 47=OCaml) |
| tiobe_pct | Value('float64') | TIOBE index percentage share |
| condition | Value('string') | zero-shot |
| pass_at_1 | Value('float64') | % of 20 problems solved, averaged over 3 runs |
| pass_at_1_std | Value('float64') | Standard deviation of pass@1 across 3 runs |
| compile_rate | Value('float64') | % that compiled successfully |
| num_problems | Value('int64') | Number of problems evaluated |
| num_runs | Value('int64') | Number of independent runs |
| per_problem | List({'pass_rate': Value('float64'), 'problem_id': Value('string')}) | List of per-problem pass rates across runs |
Generation Parameters
{
"script_name": "run_medium_zero_shot.py",
"model": "gpt-5-2",
"description": "Aggregated pass@1 scores \u2014 Medium tier, mainstream languages only. Esoteric baselines removed (paper transcriptions, not per-tier measurements).",
"tier": "medium",
"hyperparameters": {},
"input_datasets": []
}
Experiment Documentation
For complete experiment details, see https://github.com/Zayne-sprague/SC-Research-Notes/tree/main/experiments/semantic-distance-coding
Usage
from datasets import load_dataset
dataset = load_dataset("reasoning-degeneration-dev/sdc-scores-medium-v1", split="train")
print(f"Loaded {len(dataset)} rows")
This dataset is tracked in reasoning-degeneration-dev/PROJECT-MANIFEST