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cs-552-2026-qwenlifegivesyoulemons/safety_sft_data

published by @cs-552-2026-qwenlifegivesyoulemons

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
104
Last 30 days
27
Likes
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Size
1.7 MB
Created on HF
2026-06-09
Age
4 months ago

Description

Safety SFT data (multi-source MCQ)

Nine public safety datasets (BeaverTails, WildJailbreak, Nemotron-Content-Safety/aegis_v2, ToxiGen, Civil Comments, BBQ, BEAD, ETHICS, TruthfulQA) reformatted into a uniform boxed-answer MCQ schema with uniformly-shuffled options. Produced by data/prepare_sft.py (seed=42). Used to SFT the CS-552 safety_model. Human labels only; harmful text is a classification input.

Tags

task_categories:text-classificationtask_categories:multiple-choicelanguage:enlicense:othersize_categories:1K<n<10Kformat:jsonmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:us

README current version from Hugging Face

license: other
task_categories: [text-classification, multiple-choice]
language: [en]

Safety SFT data (multi-source MCQ)

Nine public safety datasets (BeaverTails, WildJailbreak, Nemotron-Content-Safety/aegis_v2, ToxiGen, Civil Comments, BBQ, BEAD, ETHICS, TruthfulQA) reformatted into a uniform boxed-answer MCQ schema with uniformly-shuffled options. Produced by data/prepare_sft.py (seed=42). Used to SFT the CS-552 safety_model. Human labels only; harmful text is a classification input.

README history 4 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. 2026-06-09Upload sft_eval.jsonl with huggingface_hub98b019a501 B
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  2. 2026-06-09Upload sft_train.jsonl with huggingface_hube91d362501 B
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  3. 2026-06-09Upload README.md with huggingface_hubfce546e501 B
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  4. 2026-06-09initial commit5efa7540 B
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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
other

Repository

FILE .gitattributes FILE README.md FILE sft_eval.jsonl FILE sft_train.jsonl

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

Download the dataset

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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("cs-552-2026-qwenlifegivesyoulemons/safety_sft_data")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="cs-552-2026-qwenlifegivesyoulemons/safety_sft_data", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/cs-552-2026-qwenlifegivesyoulemons/safety_sft_data
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