mteb/NQ_test_top_250_only_w_correct-v2
NQHardNegatives An MTEB dataset Massive Text Embedding Benchmark NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval. The hard negative version has been created by pooling the 250 top documents per query from BM25, e5-multilingual-large and e5-mistral-instruct. Task category t2t Domains None Reference https://ai.google.com/research/NaturalQuestions/ How to evaluate on this task You can evaluate an embedding model on… See the full description on the dataset page: https://huggingface.co/datasets/mteb/NQ_test_top_250_only_w_correct-v2.
| Author / organization | mteb |
|---|---|
| Downloads | 603 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 28 September 2024 |
| Last updated at source | 4 May 2025 |
Popularity, last 3 days
603 now (-18 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Recent changes
- 17 Oct 2026 Description: NQHardNegatives An MTEB dataset Massive Text Embedding Benchmark NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval. The hard negative version has been created by pooling the 250 top documents per query from BM → NQHardNegatives An MTEB dataset Massive Text Embedding Benchmark NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval. The hard negative version has been created by pooling the 250 top documents per query from BM
Changes detected by Silytics between refreshes of the source.
More from mteb
- mteb/resultsDataset
- mteb/sts12-stsDataset
- mteb/nfcorpusDataset
- mteb/banking77Dataset
- mteb/amazon_massive_intentDataset
- mteb/scifactDataset
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 Information may be incomplete or out of date.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.