Datasets:
metadata
annotations_creators:
- expert-annotated
language:
- code
- eng
license: bsd-3-clause
multilinguality: multilingual
source_datasets:
- embedding-benchmark/WikiSQL_mteb
task_categories:
- text-retrieval
task_ids: []
dataset_info:
- config_name: corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 170575
num_examples: 2048
download_size: 69134
dataset_size: 170575
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 61440
num_examples: 2048
download_size: 26300
dataset_size: 61440
- config_name: queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 3337763
num_examples: 2048
download_size: 653307
dataset_size: 3337763
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-*
tags:
- mteb
- text
A code retrieval task based on WikiSQL dataset with natural language questions and corresponding SQL queries. Each query is a natural language question (e.g., 'What is the name of the team that has scored the most goals?'), and the corpus contains SQL query implementations. The task is to retrieve the correct SQL query that answers the natural language question. Queries are natural language questions while the corpus contains SQL SELECT statements with proper syntax and logic for querying database tables.
| Task category | t2t |
| Domains | Programming |
| Reference | https://huggingface.co/datasets/embedding-benchmark/WikiSQL_mteb |
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("WikiSQLRetrieval")
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{zhong2017seq2sql,
archiveprefix = {arXiv},
author = {Zhong, Victor and Xiong, Caiming and Socher, Richard},
eprint = {1709.00103},
primaryclass = {cs.CL},
title = {Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning},
year = {2017},
}
@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("WikiSQLRetrieval")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 4096,
"number_of_characters": 3416755,
"documents_text_statistics": {
"total_text_length": 127326,
"min_text_length": 24,
"average_text_length": 62.1708984375,
"max_text_length": 211,
"unique_texts": 2022
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 3289429,
"min_text_length": 540,
"average_text_length": 1606.16650390625,
"max_text_length": 5833,
"unique_texts": 2043
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 2048,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 2048
},
"top_ranked_statistics": null
}
}
This dataset card was automatically generated using MTEB