datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
scidocs-rerankingaskubuntudupquestions-rerankingjuristcu-reranking
JurisTCUReranking
Genuine legal-domain reranking: for each of 150 queries, rerank the ~100 first-stage candidates (BM25 top-100 over the full 16k-document TCU jurisprudence corpus, unioned with human-judged docs) under graded relevance 0-3. Candidates include lexical hard negatives, so the task is distinct from first-stage retrieval. Complements QuatiReranking (web) with a legal-domain reranking probe.
Part of MTEB-BR — the native Brazilian-Portuguese MTEB sub-benchmark. Task… See the full description on the dataset page: https://huggingface.co/datasets/MTEB-BR/juristcu-reranking.quati-reranking
QuatiReranking
Genuine web-domain reranking: for each of 50 PT-BR queries, rerank the ~100 first-stage candidates (BM25 top-100 over the full 1M-passage Quati Brazilian web corpus, unioned with human-judged passages) under graded relevance 0-3. Candidates include lexical hard negatives, so the task is distinct from first-stage retrieval. Complements JurisTCUReranking (legal) with a web-domain probe.
Part of MTEB-BR — the native Brazilian-Portuguese MTEB sub-benchmark. Task type:… See the full description on the dataset page: https://huggingface.co/datasets/MTEB-BR/quati-reranking.reranking-datasets-light
🔥 Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation 🔥
ReRanking Datasets : A comprehensive collection of retrieval and reranking datasets with full passage contexts, including titles, text, and metadata for in-depth research.
A curated collection of ready-to-use datasets for retrieval and reranking… See the full description on the dataset page: https://huggingface.co/datasets/abdoelsayed/reranking-datasets-light.CMedQAv2-reranking
CMedQAv2-reranking
An MTEB dataset
Massive Text Embedding Benchmark
Chinese community medical question answering
Task category
t2t
Domains
Medical, Written
Reference
https://github.com/zhangsheng93/cMedQA2
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["CMedQAv2-reranking"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/CMedQAv2-reranking.CMedQAv1-reranking
CMedQAv1-reranking
An MTEB dataset
Massive Text Embedding Benchmark
Chinese community medical question answering
Task category
t2t
Domains
Medical, Written
Reference
https://github.com/zhangsheng93/cMedQA
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["CMedQAv1-reranking"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/CMedQAv1-reranking.stackoverflowdupquestions-rerankingwikipedia-2023-11-reranking-multilingualThis dataset is derived from Cohere's wikipedia-2023-11 dataset, which is in turn derived from wikimedia/wikipedia.
The dataset is licensed under the Creative Commons CC BY-SA 3.0 license.
msmarco-llm-reranking-pointwisemsmarco-llm-reranking-pairwisemteb-fr-reranking-alloprof-s2p
Description
This dataset was built upon Alloprof Q&A dataset, negative samples were created using BM25. Please refer to our paper for more details.
Citation
If you use this dataset in your work, please consider citing:
@misc{ciancone2024extending,
title={Extending the Massive Text Embedding Benchmark to French},
author={Mathieu Ciancone and Imene Kerboua and Marion Schaeffer and Wissam Siblini},
year={2024},
eprint={2405.20468}… See the full description on the dataset page: https://huggingface.co/datasets/lyon-nlp/mteb-fr-reranking-alloprof-s2p.CMedQAv2-reranking
Dataset Card for "CMedQAv2-reranking"
More Information needed
CMedQAv1-reranking
Dataset Card for "CMedQAv1-reranking"
More Information needed
Mmarco-reranking
Dataset Card for "Mmarco-reranking"
More Information needed
mteb-swe-bench-poly-rerankingmteb-fr-reranking-syntec-s2p
Description
This dataset was built upon Syntec information retrieval dataset, negative samples were created using BM25.
Please refer to our paper for more details.
Citation
If you use this dataset in your work, please consider citing:
@misc{ciancone2024extending,
title={Extending the Massive Text Embedding Benchmark to French},
author={Mathieu Ciancone and Imene Kerboua and Marion Schaeffer and Wissam Siblini},
year={2024},
eprint={2405.20468}… See the full description on the dataset page: https://huggingface.co/datasets/lyon-nlp/mteb-fr-reranking-syntec-s2p.mteb-swe-bench-multi-rerankingmiracl_ind_reranking
miracl_ind_reranking
Deduplicated copy of kornwtp/miracl_ind_reranking,
part of the SEA-BED data-quality work.
Source dataset: kornwtp/miracl_ind_reranking
Deduplicated on: 2026-09-04
Task type: reranking
Splits: dev
What changed
Kept in this dataset's ORIGINAL schema. Repeats within a row's positive or negative candidate list collapsed, and fully identical (query, positive pool, negative pool) rows collapsed. A document listed as BOTH positive and negative for… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/miracl_ind_reranking.code-rerankingmiracl_tha_reranking
miracl_tha_reranking
Deduplicated copy of kornwtp/miracl_tha_reranking,
part of the SEA-BED data-quality work.
Source dataset: kornwtp/miracl_tha_reranking
Deduplicated on: 2026-09-04
Task type: reranking
Splits: dev
What changed
Kept in this dataset's ORIGINAL schema. Repeats within a row's positive or negative candidate list collapsed, and fully identical (query, positive pool, negative pool) rows collapsed. A document listed as BOTH positive and negative for… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/miracl_tha_reranking.mteb-swe-bench-multilingual-rerankingnq-reranking-enromteb-grile-rerankingmteb-swe-bench-verified-rerankingRerankingsqa_reranking_eval
Dataset Details
Dataset to evaluate retrieval/reranking models or techniques for scientific QA.
The questions are sourced from:
Real researchers
Stack exchange communities from computing related domains - CS, stats, math, data science
Synthetic questions generated by prompting an LLM
Each question has passages text in markdown format and the paper Semantic Scholar id, along with a relevance label ranging from 0-3 (higher implies more relevant) obtained from GPT-4o.
The label… See the full description on the dataset page: https://huggingface.co/datasets/allenai/sqa_reranking_eval.miracl_tha_rerankingnq_reranking_dprnqmteb-swe-bench-lite-reranking