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 ---
license: apache-2.0
language:
- en
- code
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splits:
- name: train
num_bytes: 18280019094
num_examples: 631161
download_size: 8540941243
dataset_size: 18280019094
configs:
- config_name: go
data_files:
- split: train
path: go/train-*
- config_name: java
data_files:
- split: train
path: java/train-*
- config_name: javascript
data_files:
- split: train
path: javascript/train-*
- config_name: php
data_files:
- split: train
path: php/train-*
- config_name: python
data_files:
- split: train
path: python/train-*
- config_name: ruby
data_files:
- split: train
path: ruby/train-*
---
This dataset is a copy of the [CoRNStack data](https://huggingface.co/collections/nomic-ai/cornstack) from Nomic. This is just a reformating of the data to make it easily usable in [PyLate](https://lightonai.github.io/pylate/) and [sentence-transformers](https://github.com/huggingface/sentence-transformers). The data has been created from the Stack v2 by using docstring as queries and corresponding functions as target documents. Other functions has been mined to provide hard negatives. For more information about the creation of the dataset, please refer to the [original paper](https://arxiv.org/abs/2412.01007) and please considering citing them if you use this data.
Notably, this dataset has been used to perform the pre-training of the state-of-the-art late interaction [LateOn-Code models](https://huggingface.co/collections/lightonai/lateon-code). The boilerplate used to train the model using this data can be found [here](https://github.com/lightonai/pylate/blob/main/examples/train/lateon_code/pre-training.py). For more information about the training and usage of the models, please refer to our [blogpost](https://huggingface.co/blog/lightonai/colgrep-lateon-code) (or model cards).
### Citation
#### CoRNStack
```bibtex
@inproceedings{DBLP:conf/iclr/SureshRXNMDJ25,
author = {Tarun Suresh and
Revanth Gangi Reddy and
Yifei Xu and
Zach Nussbaum and
Andriy Mulyar and
Brandon Duderstadt and
Heng Ji},
title = {CoRNStack: High-Quality Contrastive Data for Better Code Retrieval
and Reranking},
booktitle = {The Thirteenth International Conference on Learning Representations,
{ICLR} 2025, Singapore, April 24-28, 2025},
publisher = {OpenReview.net},
year = {2025},
url = {https://openreview.net/forum?id=iyJOUELYir},
timestamp = {Sun, 25 May 2025 21:25:19 +0200},
biburl = {https://dblp.org/rec/conf/iclr/SureshRXNMDJ25.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
#### PyLate
```bibtex
@inproceedings{DBLP:conf/cikm/ChaffinS25,
author = {Antoine Chaffin and
Rapha{"{e}}l Sourty},
editor = {Meeyoung Cha and
Chanyoung Park and
Noseong Park and
Carl Yang and
Senjuti Basu Roy and
Jessie Li and
Jaap Kamps and
Kijung Shin and
Bryan Hooi and
Lifang He},
title = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
booktitle = {Proceedings of the 34th {ACM} International Conference on Information
and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
10-14, 2025},
pages = {6334--6339},
publisher = {{ACM}},
year = {2025},
url = {https://github.com/lightonai/pylate},
doi = {10.1145/3746252.3761608},
}
```
#### LateOn-Code
```bibtex
@misc{LateOn-Code,
title = {LateOn-Code: a Family of State-Of-The-Art Late Interaction Code Retrieval Models},
author = {Chaffin, Antoine},
url = {https://huggingface.co/collections/lightonai/lateon-code},
year = {2026}
}
```