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ontocord/auto_redteam_triplets

published by @ontocord

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
264
Last 30 days
9
Likes
1
Size
41.7 MB
Created on HF
2024-03-01
Age
2 years ago

Description

Auto Redteam Triplets (ART): a synthetic dataset to perform reinforcement learning redteaming

Dataset Summary

The ART dataset contains triplets of prompts and responses related to concerns under the proposed EU AI Act and Biden-Harris AI Executive Order concerns, in addition to common redteaming concerns.

Column 'text' is the positive response answer pair, and text2 and text3 are both rejections. These columns can be used to perform reinforcement learning, or… See the full description on the dataset page: https://huggingface.co/datasets/ontocord/auto_redteam_triplets.

Tags

size_categories:1K<n<10Kformat:jsonmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:us

README current version from Hugging Face


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Auto Redteam Triplets (ART): a synthetic dataset to perform reinforcement learning redteaming

Dataset Summary

The ART dataset contains triplets of prompts and responses related to concerns under the proposed EU AI Act and Biden-Harris AI Executive Order concerns, in addition to common redteaming concerns.

Column 'text' is the positive response answer pair, and text2 and text3 are both rejections. These columns can be used to perform reinforcement learning, or simply text can be used to do supervised finetuning.

More details to come.

YOU MUST BE 18 YEARS OR OLDER TO DOWNLOAD OR USE THIS DATASET

This dataset contains sensitive and triggering content.

Limitations

This is a work-in-progress dataset which means there can be issues related to style, quality, length of response, or accuracy. So please be aware and review the data before using.

We are actively improving the dataset and recognize that both the EU AI Act and the Biden-Harris AI Executive Order are still being fully fleshed out - with local legislation in the first case and regulatory guidence development in the second case. Thus, we base our work on the general concerns provided in the text of the respective Act and Order. And we will improve the datasets as the law evolves. Please note that use of this dataset is not given as legal advice nor a gurantee of compliance with law.

Citation

To cite the ART dataset, please use:

@misc{ontocord2024art,
      title={Auto Redteam Triplets (ART): a synthetic dataset to perform reinforcement learning redteaming for the EU AI Act and Biden-Harris AI Executive Order concerns}, 
      author={Ontocord.AI},
      year={2024},
}

README history 8 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.

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  8. 2024-03-01initial commit16d47a127 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. 2024-03-02[bot] Conversion to Parquetopen1 💬#1
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Repository

Download the dataset

Gated dataset. You need to accept the author's terms on Hugging Face before downloading. Visit the repository, click Agree and access repository, then log in locally with huggingface-cli login and use one of the snippets below.

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("ontocord/auto_redteam_triplets")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="ontocord/auto_redteam_triplets", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/ontocord/auto_redteam_triplets
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