Datasets:
metadata
annotations_creators:
- derived
language:
- code
- eng
license: mit
multilinguality: multilingual
source_datasets:
- embedding-benchmark/HumanEval
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: 32129
num_examples: 158
download_size: 17088
dataset_size: 32129
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 4740
num_examples: 158
download_size: 3465
dataset_size: 4740
- config_name: queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 48142
num_examples: 158
download_size: 26704
dataset_size: 48142
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 164 Python programming problems from HumanEval. Each query is a natural language description of a programming task (e.g., 'Check if in given list of numbers, are any two numbers closer to each other than given threshold'), and the corpus contains Python code implementations. The task is to retrieve the correct code snippet that solves the described problem. Queries are problem descriptions while the corpus contains Python function implementations with proper indentation and logic.
| Task category | t2t |
| Domains | Programming |
| Reference | https://huggingface.co/datasets/embedding-benchmark/HumanEval |
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("HumanEvalRetrieval")
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{chen2021evaluating,
archiveprefix = {arXiv},
author = {Chen, Mark and Tworek, Jerry and Jun, Heewoo and Yuan, Qiming and Pinto, Henrique Ponde de Oliveira and Kaplan, Jared and Edwards, Harri and Burda, Yuri and Joseph, Nicholas and Brockman, Greg and Ray, Alex and Puri, Raul and Krueger, Gretchen and Petrov, Michael and Khlaaf, Heidy and Sastry, Girish and Mishkin, Pamela and Chan, Brooke and Gray, Scott and Ryder, Nick and Pavlov, Mikhail and Power, Alethea and Kaiser, Lukasz and Bavarian, Mohammad and Winter, Clemens and Tillet, Philippe and Such, Felipe Petroski and Cummings, Dave and Plappert, Matthias and Chantzis, Fotios and Barnes, Elizabeth and Herbert-Voss, Ariel and Guss, William Hebgen and Nichol, Alex and Paino, Alex and Tezak, Nikolas and Tang, Jie and Babuschkin, Igor and Balaji, Suchir and Jain, Shantanu and Saunders, William and Hesse, Christopher and Carr, Andrew N. and Leike, Jan and Achiam, Joshua and Misra, Vedant and Morikawa, Evan and Radford, Alec and Knight, Matthew and Brundage, Miles and Murati, Mira and Mayer, Katie and Welinder, Peter and McGrew, Bob and Amodei, Dario and McCandlish, Sam and Sutskever, Ilya and Zaremba, Wojciech},
eprint = {2107.03374},
primaryclass = {cs.LG},
title = {Evaluating Large Language Models Trained on Code},
year = {2021},
}
@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("HumanEvalRetrieval")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 316,
"number_of_characters": 73983,
"documents_text_statistics": {
"total_text_length": 27965,
"min_text_length": 11,
"average_text_length": 176.99367088607596,
"max_text_length": 854,
"unique_texts": 158
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 46018,
"min_text_length": 23,
"average_text_length": 291.253164556962,
"max_text_length": 1200,
"unique_texts": 158
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 158,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 158
},
"top_ranked_statistics": null
}
}
This dataset card was automatically generated using MTEB