Datasets:
metadata
annotations_creators:
- LM-generated
language:
- eng
license: apache-2.0
multilinguality: monolingual
source_datasets:
- thebajajra/ERESSReranking
task_categories:
- text-ranking
task_ids: []
dataset_info:
- config_name: corpus
features:
- name: id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 66595737
num_examples: 67459
download_size: 40997352
dataset_size: 66595737
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 3506097
num_examples: 72078
download_size: 915617
dataset_size: 3506097
- config_name: queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 256660
num_examples: 4367
download_size: 145612
dataset_size: 256660
- config_name: top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: test
num_bytes: 1685047
num_examples: 4367
download_size: 864437
dataset_size: 1685047
configs:
- config_name: corpus
data_files:
- split: test
path: corpus/test-*
- config_name: qrels
data_files:
- split: test
path: qrels/test-*
- config_name: queries
data_files:
- split: test
path: queries/test-*
- config_name: top_ranked
data_files:
- split: test
path: top_ranked/test-*
tags:
- mteb
- text
E-commerce query-product relevance reranking dataset with graded relevance labels
| Task category | t2t |
| Domains | Web, E-commerce |
| Reference | https://huggingface.co/datasets/thebajajra/ERESSReranking |
Source datasets:
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("ERESSReranking")
evaluator = mteb.MTEB([task])
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run models on mteb task check out the GitHub repository.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@article{Bajaj2026RexRerankers,
title = {{RexRerankers}: {SOTA} Rankers for Product Discovery and {AI} Assistants},
author = {Bajaj, Rahul and Garg, Anuj and Nupur, Jaya},
journal = {Hugging Face Blog (Community Article)},
year = {2026},
month = jan,
url = {https://huggingface.co/blog/thebajajra/rexrerankers},
urldate = {2026-01-24}
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
Dataset Statistics
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("ERESSReranking")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 71826,
"number_of_characters": 69062368,
"documents_text_statistics": {
"total_text_length": 68905069,
"min_text_length": 61,
"average_text_length": 1021.4362649905869,
"max_text_length": 29847,
"unique_texts": 67459
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 157299,
"min_text_length": 14,
"average_text_length": 36.019922143347834,
"max_text_length": 62,
"unique_texts": 4367
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 72078,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 16.505152278452027,
"max_relevant_docs_per_query": 107,
"unique_relevant_docs": 67459
},
"top_ranked_statistics": {
"num_top_ranked": 72078,
"min_top_ranked_per_query": 1,
"average_top_ranked_per_query": 16.505152278452027,
"max_top_ranked_per_query": 107
}
}
}
This dataset card was automatically generated using MTEB