Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Languages:
code
Size:
10K - 100K
ArXiv:
License:
metadata
annotations_creators:
- derived
language:
- code
license: mit
multilinguality: monolingual
source_datasets:
- code-search-net/code_search_net
task_categories:
- text-retrieval
task_ids: []
dataset_info:
- config_name: go-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 687923
num_examples: 1000
download_size: 305126
dataset_size: 687923
- config_name: go-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 131084
num_examples: 1000
download_size: 30642
dataset_size: 131084
- config_name: go-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 136105
num_examples: 1000
download_size: 82017
dataset_size: 136105
- config_name: java-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 591398
num_examples: 1000
download_size: 186606
dataset_size: 591398
- config_name: java-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 176341
num_examples: 1000
download_size: 34953
dataset_size: 176341
- config_name: java-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
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num_examples: 1000
download_size: 255486
dataset_size: 701958
- config_name: javascript-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
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num_examples: 1000
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dataset_size: 1551805
- config_name: javascript-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 141801
num_examples: 1000
download_size: 39930
dataset_size: 141801
- config_name: javascript-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
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num_examples: 1000
download_size: 112208
dataset_size: 202696
- config_name: php-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 856887
num_examples: 1000
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- config_name: php-qrels
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- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
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num_examples: 1000
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features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
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num_examples: 1000
download_size: 88243
dataset_size: 173189
- config_name: python-corpus
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- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
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- config_name: python-qrels
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dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
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num_examples: 1000
download_size: 32945
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- config_name: python-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
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num_examples: 1000
download_size: 232579
dataset_size: 477722
- config_name: ruby-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
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num_examples: 1000
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- config_name: ruby-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
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num_examples: 1000
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- config_name: ruby-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 324866
num_examples: 1000
download_size: 169887
dataset_size: 324866
configs:
- config_name: go-corpus
data_files:
- split: test
path: go-corpus/test-*
- config_name: go-qrels
data_files:
- split: test
path: go-qrels/test-*
- config_name: go-queries
data_files:
- split: test
path: go-queries/test-*
- config_name: java-corpus
data_files:
- split: test
path: java-corpus/test-*
- config_name: java-qrels
data_files:
- split: test
path: java-qrels/test-*
- config_name: java-queries
data_files:
- split: test
path: java-queries/test-*
- config_name: javascript-corpus
data_files:
- split: test
path: javascript-corpus/test-*
- config_name: javascript-qrels
data_files:
- split: test
path: javascript-qrels/test-*
- config_name: javascript-queries
data_files:
- split: test
path: javascript-queries/test-*
- config_name: php-corpus
data_files:
- split: test
path: php-corpus/test-*
- config_name: php-qrels
data_files:
- split: test
path: php-qrels/test-*
- config_name: php-queries
data_files:
- split: test
path: php-queries/test-*
- config_name: python-corpus
data_files:
- split: test
path: python-corpus/test-*
- config_name: python-qrels
data_files:
- split: test
path: python-qrels/test-*
- config_name: python-queries
data_files:
- split: test
path: python-queries/test-*
- config_name: ruby-corpus
data_files:
- split: test
path: ruby-corpus/test-*
- config_name: ruby-qrels
data_files:
- split: test
path: ruby-qrels/test-*
- config_name: ruby-queries
data_files:
- split: test
path: ruby-queries/test-*
tags:
- mteb
- text
The dataset is a collection of code snippets and their corresponding natural language queries. The task is to retrieve the most relevant code snippet for a given query.
| Task category | t2t |
| Domains | Programming, Written |
| Reference | https://huggingface.co/datasets/code_search_net/ |
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("CodeSearchNetRetrieval")
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{husain2019codesearchnet,
author = {Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal = {arXiv preprint arXiv:1909.09436},
title = {{CodeSearchNet} challenge: Evaluating the state of semantic code search},
year = {2019},
}
@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("CodeSearchNetRetrieval")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 12000,
"number_of_characters": 6496327,
"documents_text_statistics": {
"total_text_length": 4552253,
"min_text_length": 69,
"average_text_length": 758.7088333333334,
"max_text_length": 334374,
"unique_texts": 6000
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 1944074,
"min_text_length": 2,
"average_text_length": 324.01233333333334,
"max_text_length": 17533,
"unique_texts": 5765
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 6000,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 6000
},
"top_ranked_statistics": null,
"hf_subset_descriptive_stats": {
"python": {
"num_samples": 2000,
"number_of_characters": 1329388,
"documents_text_statistics": {
"total_text_length": 862842,
"min_text_length": 91,
"average_text_length": 862.842,
"max_text_length": 10914,
"unique_texts": 1000
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 466546,
"min_text_length": 8,
"average_text_length": 466.546,
"max_text_length": 8636,
"unique_texts": 982
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 1000,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 1000
},
"top_ranked_statistics": null
},
"javascript": {
"num_samples": 2000,
"number_of_characters": 1601650,
"documents_text_statistics": {
"total_text_length": 1415632,
"min_text_length": 95,
"average_text_length": 1415.632,
"max_text_length": 334374,
"unique_texts": 1000
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 186018,
"min_text_length": 2,
"average_text_length": 186.018,
"max_text_length": 7657,
"unique_texts": 951
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 1000,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 1000
},
"top_ranked_statistics": null
},
"go": {
"num_samples": 2000,
"number_of_characters": 688942,
"documents_text_statistics": {
"total_text_length": 563729,
"min_text_length": 69,
"average_text_length": 563.729,
"max_text_length": 15904,
"unique_texts": 1000
},
"documents_image_statistics": null,
"queries_text_statistics": {
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"min_text_length": 14,
"average_text_length": 125.213,
"max_text_length": 1501,
"unique_texts": 988
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 1000,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 1000
},
"top_ranked_statistics": null
},
"ruby": {
"num_samples": 2000,
"number_of_characters": 891452,
"documents_text_statistics": {
"total_text_length": 577634,
"min_text_length": 79,
"average_text_length": 577.634,
"max_text_length": 8171,
"unique_texts": 1000
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 313818,
"min_text_length": 5,
"average_text_length": 313.818,
"max_text_length": 17533,
"unique_texts": 978
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 1000,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 1000
},
"top_ranked_statistics": null
},
"java": {
"num_samples": 2000,
"number_of_characters": 1110647,
"documents_text_statistics": {
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"min_text_length": 106,
"average_text_length": 420.287,
"max_text_length": 9142,
"unique_texts": 1000
},
"documents_image_statistics": null,
"queries_text_statistics": {
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"min_text_length": 2,
"average_text_length": 690.36,
"max_text_length": 6473,
"unique_texts": 956
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 1000,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 1000
},
"top_ranked_statistics": null
},
"php": {
"num_samples": 2000,
"number_of_characters": 874248,
"documents_text_statistics": {
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"min_text_length": 108,
"average_text_length": 712.129,
"max_text_length": 15584,
"unique_texts": 1000
},
"documents_image_statistics": null,
"queries_text_statistics": {
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"min_text_length": 5,
"average_text_length": 162.119,
"max_text_length": 1240,
"unique_texts": 911
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 1000,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 1000
},
"top_ranked_statistics": null
}
}
}
}
This dataset card was automatically generated using MTEB