datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
UnlearnCanvas
Dataset Card for UnlearnCanvas
This dataset card introduces "UnlearnCanvas", a high-resolution stylized image dataset for benchmarking generative modeling tasks, in particular for machine unlearning in diffusion models. Developed to address the societal concerns arising from diffusion models, such as harmful content generation, copyright disputes, and the perpetuation of stereotypes and biases, UnlearnCanvas aims at facilitating the evaluation and improvement of machine unlearning… See the full description on the dataset page: https://huggingface.co/datasets/OPTML-Group/UnlearnCanvas.data-unlearning-benchDataset for the evaluation of data-unlearning techniques using KLOM (KL-divergence of Margins).
How KLOM works:
KLOM works by:
training N models (original models)
Training N fully-retrained models (oracles) on forget set F
unlearning forget set F from the original models
Comparing the outputs of the unlearned models from the retrained models on different points
(specifically, computing the KL divergence between the distribution of margins of oracle models and distribution of… See the full description on the dataset page: https://huggingface.co/datasets/machine-unlearning-bench/data-unlearning-bench.Unlearning-Simplex
Towards Multi-reference Unlearning
jspace-unlearning
J-Access (J-space occupancy) × Machine Unlearning — 代码、lens、结果归档
归档日期:2026-08-22。对应论文草稿 paper/main_aaai.tex,数字权威来源
paper/MAINLINE_EXPERIMENTS.md(每个数字标注了来源 json)。
一句话:用 Jacobian lens(anthropics/jacobian-lens)在中间层读出"模型是否仍在
准备说出被遗忘的答案"(J-Access / JOcc),在 TOFU forget10 + Llama-3.2-1B-Instruct
的 398 个 OpenUnlearning 公开 checkpoint 上做三项研究:
Study 1 审计(残留普遍存在)、Study 2 预测(攻前占用 → 攻后复活,模型级成立/样本级失败)、
Study 3 优化压力(直接压制占用 = Goodhart,复活反而升高)。
1. 仓库内容
目录
内容
大小
src/
全部… See the full description on the dataset page: https://huggingface.co/datasets/Ziruibest/jspace-unlearning.evalhubble-8b-unlearning-resultslibero_unlearned_orange_juiceThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "panda",
"total_episodes": 1693,
"total_frames": 273465,
"total_tasks": 40,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 10.0,
"splits": {
"train": "0:1693"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": null… See the full description on the dataset page: https://huggingface.co/datasets/leonardo-russo/libero_unlearned_orange_juice.sae-unlearning-outputgenerations-llama-3_1-8b-rmu-baselinegenerations-simnpo_gemma-3-12b-pt_20260416_171305-corpus_sweep_post_evaligm-retrievals
igm-retrievals
Per-song infini-gram retrieval results from scripts/dataset_search/igm_batch.py. Each song's documents live in its own config (<slug>__<song_id[:8]>); _meta is the table-of-contents row-per-song; _matches aggregates every word-ngram match across all songs.
generations-21-DEBUG-qwen3-8b-simnpo-gentle-igm-10b-target-100-localtrain-checkpoint-1agnews-unlearning-mia
AGNEWS - Machine Unlearning + MIA Evaluation Dataset (Length-Filtered)
This dataset is prepared for evaluating machine unlearning methods on fine-tuned LLMs using Membership Inference Attacks (MIAs).
Dataset Splits
Training Sets (for Unlearning)
retain_set (9,000 samples): Data to retain during unlearning
forget_set (1,000 samples): Data to unlearn
Evaluation Sets (for MIA) - Length-Filtered
AGNews Length Variants
32 tokens (~32±10… See the full description on the dataset page: https://huggingface.co/datasets/h0ssn/agnews-unlearning-mia.generations-18-DEBUG-llama-3_1-8b-simnpo-gentle-bm25-10b-target-100-localtrain-checkpoint-1generations-17-DEBUG-qwen3-8b-simnpo-gentle-baseline-target-100-localtrain-checkpoint-1formatted_songsgenerations-llama-3_1-8b-simnpo-gentle-bm25-6tgenerations-olmo-3-32b-pre_valgenerations-10-llama-3_1-8b-simnpo-gentle-bm25-6t-target-100-checkpoint-187generations-qwen3-8b-rmu-baselinegenerations-qwen3-8b-simnpo-gentle-bm25-6tgenerations-olmo-3-7b-pre_valgenerations-04-gemma-3-12b-simnpo-baseline-target-100-checkpoint-2838generations-qwen3-8b-simnpo-gentle-igm-10bgenerations-nemotron-nano-9b-v2-simnpo-gentle-baselinegenerations-checkpoint-134-debug-checkpoint-134-llamagenerations-llama-3_1-8b-simnpo-gentle-baselineunlearn_dataset
📖 unlearn_dataset
The unlearn_dataset serves as a benchmark for evaluating unlearning methodologies in pre-trained large language models across diverse domains, including arXiv, GitHub.
🔍 Loading the datasets
To load the dataset:
from datasets import load_dataset
dataset = load_dataset("llmunlearn/unlearn_dataset", name="arxiv", split="forget")
Available configuration names and corresponding splits:
arxiv: forget, approximate, retain
github: forget, approximate… See the full description on the dataset page: https://huggingface.co/datasets/llmunlearn/unlearn_dataset.early_unlearning_mixed_tampering_datasetgenerations-qwen3-coder-next-pre_val