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abliterationai/synthetic-data-provenance-eval

published by @abliterationai

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
129
Last 30 days
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Size
23 KB
Created on HF
2026-05-06
Age
5 months ago

Description

Synthetic Data Provenance Eval

This dataset evaluates governed synthetic-data generation. Rows test whether a

system can generate fictional JSONL-ready examples with labels, provenance

fields, and QA constraints while refusing or summarizing unsafe generation

requests.

The dataset is aimed at teams that need synthetic data for classifiers, evals,

and fine-tuning without refusal noise or missing provenance.

Intended Use

Check JSONL-ready output for eval and… See the full description on the dataset page: https://huggingface.co/datasets/abliterationai/synthetic-data-provenance-eval.

Tags

task_categories:text-generationtask_categories:text-classificationlanguage:enlicense:apache-2.0size_categories:n<1Kformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:ussynthetic-dataprovenanceevalsgoverned-aijsonl

README current version from Hugging Face

license: apache-2.0
language:

  • en
    task_categories:
  • text-generation
  • text-classification
    tags:
  • synthetic-data
  • provenance
  • evals
  • governed-ai
  • jsonl
    pretty_name: Synthetic Data Provenance Eval
    size_categories:
  • n<1K

Synthetic Data Provenance Eval

This dataset evaluates governed synthetic-data generation. Rows test whether a
system can generate fictional JSONL-ready examples with labels, provenance
fields, and QA constraints while refusing or summarizing unsafe generation
requests.

The dataset is aimed at teams that need synthetic data for classifiers, evals,
and fine-tuning without refusal noise or missing provenance.

Intended Use

  • Check JSONL-ready output for eval and classifier training workflows.
  • Verify that unsafe data-generation requests are refused or summarized.
  • Demonstrate provenance fields such as source seed and label schema version.

Abliteration Baseline

python3 evals/governed-ai/scripts/run_policy_gateway_eval.py \
  --dataset evals/governed-ai/datasets/synthetic_data_provenance_eval.jsonl

Limitations

This is not a large synthetic corpus. It is a runnable governance benchmark and
template for building larger customer-specific datasets.

README history 2 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-05-06Publish governed AI eval datasetb245e931.2 KB
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  2. 2026-05-06initial commitb61c4150 B
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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2026-05-07[bot] Conversion to Parquetopen1 💬#1
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Read further

Community best practices (evaluation)
4 rules on judges, benchmarks, and reporting.
Reference-set drift over time
Why the same benchmark means different things across model generations.
How we label the method for each model
The 12 classification rules that assign M1-M8 to every model.
Non-English abliteration communities
The Korean scepticism about one-click tools and non-English benchmark blindspots.

Metadata

License
apache-2.0

Repository

FILE .gitattributes FILE data.jsonl FILE data.parquet FILE README.md

File contents live on Hugging Face. Click a file or folder to open it there.

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python) 
from datasets import load_dataset
ds = load_dataset("abliterationai/synthetic-data-provenance-eval")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="abliterationai/synthetic-data-provenance-eval", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/abliterationai/synthetic-data-provenance-eval
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