Datasets:

mteb
/

LocBenchRR

annotations_creators:
  - derived
language:
  - code
  - eng
license: mit
multilinguality: multilingual
source_datasets:
  - tarsur909/mteb-loc-bench-reranking
task_categories:
  - text-ranking
task_ids: []
dataset_info:
  - config_name: corpus
    features:
      - name: title
        dtype: string
      - name: text
        dtype: string
      - name: id
        dtype: string
    splits:
      - name: train
        num_bytes: 3568433274
        num_examples: 3630051
    download_size: 1130646855
    dataset_size: 3568433274
  - config_name: qrels
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
      - name: score
        dtype: int64
    splits:
      - name: train
        num_bytes: 213007950
        num_examples: 3630051
    download_size: 25606242
    dataset_size: 213007950
  - config_name: queries
    features:
      - name: id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: train
        num_bytes: 1656784
        num_examples: 660
    download_size: 718545
    dataset_size: 1656784
  - config_name: top_ranked
    features:
      - name: query-id
        dtype: string
      - name: corpus-ids
        sequence: string
    splits:
      - name: train
        num_bytes: 119224332
        num_examples: 660
    download_size: 18167896
    dataset_size: 119224332
configs:
  - config_name: corpus
    data_files:
      - split: train
        path: corpus/train-*
  - config_name: qrels
    data_files:
      - split: train
        path: qrels/train-*
  - config_name: queries
    data_files:
      - split: train
        path: queries/train-*
  - config_name: top_ranked
    data_files:
      - split: train
        path: top_ranked/train-*
tags:
  - mteb
  - text

LocBenchRR

An MTEB dataset

Massive Text Embedding Benchmark

Software Issue Localization.


Task category	t2t
Domains	Programming, Written
Reference	https://arxiv.org/abs/2503.09089

Source datasets:

tarsur909/mteb-loc-bench-reranking

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("LocBenchRR")
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.


@misc{chen2025locagentgraphguidedllmagents,
  archiveprefix = {arXiv},
  author = {Zhaoling Chen and Xiangru Tang and Gangda Deng and Fang Wu and Jialong Wu and Zhiwei Jiang and Viktor Prasanna and Arman Cohan and Xingyao Wang},
  eprint = {2503.09089},
  primaryclass = {cs.SE},
  title = {LocAgent: Graph-Guided LLM Agents for Code Localization},
  url = {https://arxiv.org/abs/2503.09089},
  year = {2025},
}


@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

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("LocBenchRR")

desc_stats = task.metadata.descriptive_stats

{}

This dataset card was automatically generated using MTEB