muse-bench/MUSE-Books
MUSE-Books MUSE is a comprehensive machine unlearning evaluation benchmark that assesses six key properties for unlearned models: (1) no verbatim memorization, (2) no knowledge memorization, (3) no privacy leakage, (4) utility preservation on data not intended for removal, (5) scalability with respect to the size of removal requests, and (6) sustainability over sequential unlearning requests. MUSE focuses on two types of textual data that commonly require unlearning: news… See the full description on the dataset page: https://huggingface.co/datasets/muse-bench/MUSE-Books.
| Author / organization | muse-bench |
|---|---|
| License (as declared) | cc-by-4.0 |
| Downloads | 2.6K (as reported by Hugging Face) |
| Likes | 3 |
| Created | 13 June 2024 |
| Last updated at source | 10 July 2024 |
Popularity, last 3 days
2,583 now (-100 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Recent changes
- 17 Oct 2026 Description: MUSE-Books MUSE is a comprehensive machine unlearning evaluation benchmark that assesses six key properties for unlearned models: (1) no verbatim memorization, (2) no knowledge memorization, (3) no privacy leakage, (4) utility preservation → MUSE-Books MUSE is a comprehensive machine unlearning evaluation benchmark that assesses six key properties for unlearned models: (1) no verbatim memorization, (2) no knowledge memorization, (3) no privacy leakage, (4) utility preservation
Changes detected by Silytics between refreshes of the source.
More from muse-bench
- muse-bench/MUSE-NewsDataset
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.