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Dataset Card for FreshStack (Corpus)
Homepage | Repository | Paper
FreshStack is a holistic framework to construct challenging IR/RAG evaluation datasets that focuses on search across niche and recent topics.
This dataset (October 2024) contains the query, nuggets, answers and nugget-level relevance judgments of 5 niche topics focused on software engineering and machine learning.
The queries and answers (accepted) are taken from Stack Overflow, GPT-4o generates the nuggets and labels the relevance between each nugget and a given document list.
This repository contains the corpus of GitHub chunked documents of five niche topics in freshstack. The queries, answers and nuggets can be found here.
Dataset Structure
To access the data using HuggingFace datasets:
topic='langchain' # or any of the 5 topics
freshstack = datasets.load_dataset('freshstack/corpus-oct-2024', topic)
# train set
for data in freshstack['train']:
doc_id = data['_id']
doc_text = data['text']
Dataset Statistics
The following table contains the number of documents (#D) and the number of GitHub repositories used (#G) in the FreshStack collection.
| Topic | Versions | Domain | Train | |
|---|---|---|---|---|
| #D | #G | |||
| langchain | - | Machine Learning | 49,514 | 10 |
| yolo | v7 & v8 | Computer Vision | 27,207 | 5 |
| laravel | 10 & 11 | Back-end Development | 52,351 | 9 |
| angular | 16, 17 & 18 | Front-end Development | 117,288 | 4 |
| godot | 4 | Game Development | 25,482 | 6 |
The following table contains the list of original GitHub repositories used to construct the following corpus for each topic.
License
The FreshStack datasets are provided under the CC-BY-SA 4.0 license.
The original GitHub repositories used for constructing the corpus may contain non-permissive licenses; we advise the reader to check the licenses for each repository carefully.
Citation
@misc{thakur2025freshstack,
title={FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents},
author={Nandan Thakur and Jimmy Lin and Sam Havens and Michael Carbin and Omar Khattab and Andrew Drozdov},
year={2025},
eprint={2504.13128},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2504.13128},
}
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