---
license: cdla-permissive-2.0
task_categories:
- tabular-regression
language:
- en
pretty_name: BenchPress Score Matrix
configs:
- config_name: scores_all
  data_files:
  - split: train
    path: data/scores_all.parquet
- config_name: scores_paper
  data_files:
  - split: train
    path: data/scores_paper.parquet
- config_name: models
  data_files:
  - split: train
    path: data/models.parquet
- config_name: benchmarks
  data_files:
  - split: train
    path: data/benchmarks.parquet
---

# BenchPress Score Matrix

This dataset contains the public model-by-benchmark score matrix used by
BenchPress. The release is a tabular artifact: model metadata, benchmark
metadata, one row per observed score, and the paper-canonical dense subset used
in the BenchPress experiments.

The source repository is
[`anadim/BenchPress`](https://github.com/anadim/BenchPress). This export was
generated from commit `5be3b4eddf0188721ff25f00713b589b2cbed8e0`.

## Files

| File | Contents |
|---|---|
| `data/scores_all.csv` / `.parquet` | All numeric score rows in the audit pool, with source and audit metadata. |
| `data/scores_paper.csv` / `.parquet` | Long-form rows for the paper-canonical matrix. |
| `data/models.csv` / `.parquet` | Model metadata and canonical evaluation settings. |
| `data/benchmarks.csv` / `.parquet` | Benchmark metadata and canonical benchmark settings. |
| `data/score_matrix_paper_wide.csv` | Wide model × benchmark matrix for the paper-canonical subset. |
| `metadata.json` | Export counts, source commit, and matrix construction metadata. |

## Quick start

```python
from datasets import load_dataset

scores = load_dataset("yzeng58/benchpress-score-matrix", "scores_paper")["train"].to_pandas()
models = load_dataset("yzeng58/benchpress-score-matrix", "models")["train"].to_pandas()
benchmarks = load_dataset("yzeng58/benchpress-score-matrix", "benchmarks")["train"].to_pandas()
```

For a complete audit-pool table:

```python
scores_all = load_dataset("yzeng58/benchpress-score-matrix", "scores_all")["train"].to_pandas()
```

## Schema

`scores_all` and `scores_paper` include:

- `model_id`, `model_name`, `provider`
- `benchmark_id`, `benchmark_name`, `category`, `metric`
- `score`
- `reference_url`, `source_type`, `audit_status`, `matches_canonical`
- `reported_setting_json`, `notes`

`models` and `benchmarks` include an `in_paper_matrix` flag that identifies
rows retained by the paper-canonical threshold filter.

## Matrix construction

The paper-canonical matrix applies the BenchPress construction pipeline:
audit-status filtering, canonical representative selection, and the iterative
threshold filter. Current export counts:

- audit pool: 189 models, 316 benchmarks, 4903 numeric score rows
- paper matrix: 84 models × 133 benchmarks, 2604 observed cells (23.3% fill)

## Caveats

Scores come from heterogeneous public sources: model cards, official blogs,
technical reports, benchmark leaderboards, and third-party aggregators. Each row
keeps the source URL, source type, audit status, and canonical-setting match
flag so downstream users can choose their own filtering policy.