language stringclasses 8
values | tiobe_rank int64 1 47 | tiobe_pct float64 0.19 21.3 | condition stringclasses 1
value | pass_at_1 float64 88.3 100 | pass_at_1_std float64 0 5.77 | compile_rate float64 98.3 100 | num_problems int64 20 20 | num_runs int64 3 3 | per_problem listlengths 20 20 |
|---|---|---|---|---|---|---|---|---|---|
Python | 1 | 21.25 | zero-shot | 100 | 0 | 100 | 20 | 3 | [
{
"pass_rate": 1,
"problem_id": "M01"
},
{
"pass_rate": 1,
"problem_id": "M02"
},
{
"pass_rate": 1,
"problem_id": "M03"
},
{
"pass_rate": 1,
"problem_id": "M04"
},
{
"pass_rate": 1,
"problem_id": "M05"
},
{
"pass_rate": 1,
"problem_id": "M06"
},
... |
C++ | 3 | 8.18 | zero-shot | 95 | 0 | 98.333333 | 20 | 3 | [
{
"pass_rate": 1,
"problem_id": "M01"
},
{
"pass_rate": 1,
"problem_id": "M02"
},
{
"pass_rate": 1,
"problem_id": "M03"
},
{
"pass_rate": 1,
"problem_id": "M04"
},
{
"pass_rate": 1,
"problem_id": "M05"
},
{
"pass_rate": 1,
"problem_id": "M06"
},
... |
Java | 4 | 7.99 | zero-shot | 100 | 0 | 100 | 20 | 3 | [
{
"pass_rate": 1,
"problem_id": "M01"
},
{
"pass_rate": 1,
"problem_id": "M02"
},
{
"pass_rate": 1,
"problem_id": "M03"
},
{
"pass_rate": 1,
"problem_id": "M04"
},
{
"pass_rate": 1,
"problem_id": "M05"
},
{
"pass_rate": 1,
"problem_id": "M06"
},
... |
Perl | 11 | 1.75 | zero-shot | 96.666667 | 5.773503 | 100 | 20 | 3 | [
{
"pass_rate": 0.6666666666666666,
"problem_id": "M01"
},
{
"pass_rate": 1,
"problem_id": "M02"
},
{
"pass_rate": 1,
"problem_id": "M03"
},
{
"pass_rate": 1,
"problem_id": "M04"
},
{
"pass_rate": 1,
"problem_id": "M05"
},
{
"pass_rate": 0.6666666666666... |
Rust | 14 | 1.31 | zero-shot | 100 | 0 | 100 | 20 | 3 | [
{
"pass_rate": 1,
"problem_id": "M01"
},
{
"pass_rate": 1,
"problem_id": "M02"
},
{
"pass_rate": 1,
"problem_id": "M03"
},
{
"pass_rate": 1,
"problem_id": "M04"
},
{
"pass_rate": 1,
"problem_id": "M05"
},
{
"pass_rate": 1,
"problem_id": "M06"
},
... |
Go | 16 | 1.29 | zero-shot | 95 | 0 | 100 | 20 | 3 | [
{
"pass_rate": 1,
"problem_id": "M01"
},
{
"pass_rate": 1,
"problem_id": "M02"
},
{
"pass_rate": 1,
"problem_id": "M03"
},
{
"pass_rate": 1,
"problem_id": "M04"
},
{
"pass_rate": 1,
"problem_id": "M05"
},
{
"pass_rate": 1,
"problem_id": "M06"
},
... |
Haskell | 42 | 0.26 | zero-shot | 100 | 0 | 100 | 20 | 3 | [
{
"pass_rate": 1,
"problem_id": "M01"
},
{
"pass_rate": 1,
"problem_id": "M02"
},
{
"pass_rate": 1,
"problem_id": "M03"
},
{
"pass_rate": 1,
"problem_id": "M04"
},
{
"pass_rate": 1,
"problem_id": "M05"
},
{
"pass_rate": 1,
"problem_id": "M06"
},
... |
OCaml | 47 | 0.19 | zero-shot | 88.333333 | 2.886751 | 100 | 20 | 3 | [
{
"pass_rate": 1,
"problem_id": "M01"
},
{
"pass_rate": 1,
"problem_id": "M02"
},
{
"pass_rate": 1,
"problem_id": "M03"
},
{
"pass_rate": 1,
"problem_id": "M04"
},
{
"pass_rate": 1,
"problem_id": "M05"
},
{
"pass_rate": 1,
"problem_id": "M06"
},
... |
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
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