| --- |
| 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. |
|
|
