kai-02/MAPLE
MAPLE: Multi-Aspect Full-Paper Scientific Retrieval Benchmark MAPLE is an expert-validated benchmark for multi-aspect full-paper retrieval. It contains 2,095 fine-grained queries derived from 210 recent machine learning papers, together with a retrieval corpus of 73,973 candidate papers. Each target paper is paired with multiple queries grounded in textual or multimodal evidence and covering different aspects of the paper, including motivation, method, and experimental findings.… See the full description on the dataset page: https://huggingface.co/datasets/kai-02/MAPLE.
| Author / organization | kai-02 |
|---|---|
| Downloads | 1.1K (as reported by Hugging Face) |
| Likes | 2 |
| Created | 10 June 2026 |
| Last updated at source | 10 August 2026 |
Popularity, last 2 days
1,136 now (-13 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.