Datasets:
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---
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
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">LocBenchRR</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
Software Issue Localization.
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Programming, Written |
| Reference | https://arxiv.org/abs/2503.09089 |
Source datasets:
- [tarsur909/mteb-loc-bench-reranking](https://huggingface.co/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:
```python
import mteb
task = mteb.get_task("LocBenchRR")
evaluator = mteb.MTEB([task])
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repository](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@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
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("LocBenchRR")
desc_stats = task.metadata.descriptive_stats
```
```json
{}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |