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SAEBase-Evaluation

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sparse-autoencoder
mechanistic-interpretability
evaluation
reproducibility
benchmark
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SAEBase-Evaluation / README.md
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metadata 
pretty_name: SAEBase Model and SAE Evaluation Records
tags:
  - sparse-autoencoder
  - mechanistic-interpretability
  - evaluation
  - reproducibility
  - benchmark
language:
  - en
configs:
  - config_name: verified_sites
    data_files:
      - split: train
        path: data/verified_sites.csv
  - config_name: metric_results
    data_files:
      - split: train
        path: data/metric_results.csv

SAEBase Evaluation Records

48 verified model/SAE layer sites · 18 model–SAE configurations · SAEBase v0.3.0

This dataset collects original-source, machine-readable verification records for the SAEBase software evaluation workflow. Every row links to its original per-site report in the SAEBase repository. The published source commit is d3fd1cb1.

Browse the data

  • verified_sites: 48 verified model/SAE layer entries, exact hooks, architecture, verification state and four recorded measurement fields.
  • metric_results: 192 numeric rows (four measured metrics per site): mean L0, reconstruction MSE, explained variance, and the logit L2 change for a selected feature-ablation condition.
  • source_manifest.json: source revision, record counts, CSV SHA256 and immutable GitHub evidence locations.

The two CSV files contain actual values extracted from the frozen original per-site JSON reports. They are not newly run experiments. The original reports preserve additional per-metric sample counts, experiment configurations, token ledgers and run identifiers.

Protocol

The recorded population evaluation uses 64 independent held-out prompt groups per site, kept separate from feature selection. Prompts are based on recurring controlled templates. These results test software compatibility, benchmark execution, and reliability reporting under this protocol, not general SAE interpretability or relative scientific quality.

Some metric units and scales differ by model, hook, baseline or checkpoint. Do not interpret cross-model comparisons as a benchmark ranking. A site's compatibility, benchmark, and reliability values represent completed acceptance of the recorded engineering protocol.

Provenance and original evidence

Individual measurements in this dataset can be cross-checked by opening the corresponding source_report_url. The source_manifest.json gives hashes for the two derived CSVs.

Usage 

from datasets import load_dataset

sites = load_dataset("OpenSAE/SAEBase-Evaluation", "verified_sites", split="train")
metrics = load_dataset("OpenSAE/SAEBase-Evaluation", "metric_results", split="train")
print(len(sites), len(metrics))  # 48, 192

See the SAEBase Research Lab for an interactive exploration of the same records.

Distribution scope

This dataset contains derived records and links, not original model or SAE weight binaries, private activations, restricted source datasets, or third-party checkpoints. The original archives remain at the GitHub evidence releases. The project software uses Apache-2.0; no separate license for third-party datasets or checkpoint contents is asserted by this card. Review redistribution terms before adding any non-original assets.