Datasets:

microsoft
/

benchpress-score-matrix

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. 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

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:

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.