README.md

---
license: cc-by-sa-4.0
size_categories:
- 10K<n<100K
task_categories:
- question-answering
dataset_info:
- config_name: angular
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- config_name: langchain
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configs:
- config_name: angular
  data_files:
  - split: train
    path: angular/train-*
- config_name: godot
  data_files:
  - split: train
    path: godot/train-*
- config_name: langchain
  data_files:
  - split: train
    path: langchain/train-*
- config_name: laravel
  data_files:
  - split: train
    path: laravel/train-*
- config_name: yolo
  data_files:
  - split: train
    path: yolo/train-*
---

# Dataset Card for FreshStack (Corpus)

[Homepage](https://fresh-stack.github.io) | 
[Repository](https://github.com/fresh-stack/freshstack) |
[Paper](https://arxiv.org/abs/2504.13128)

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](https://huggingface.co/datasets/freshstack/queries-oct-2024).

## 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.

| Topic | GitHub Repositories |
|:----:|:-----|
|  langchain | [LangChain (Python)](https://github.com/langchain-ai/langchain), [LangChain (JS)](https://github.com/langchain-ai/langchainjs), [LangChain Next.js Template](https://github.com/langchain-ai/langchain-nextjs-template), [Chroma Vector DB](https://github.com/chroma-core/chroma), [OpenAI Cookbook](https://github.com/openai/openai-cookbook), [OpenAI Python Library](https://github.com/openai/openai-python), [LlamaIndex](https://github.com/run-llama/llama_index), [Azure OpenAI Samples](https://github.com/Azure-Samples/openai), [Azure Search OpenAI Demo](https://github.com/Azure-Samples/azure-search-openai-demo), [Hugging Face Transformers](https://github.com/huggingface/transformers) |
|  yolo      | [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), [YOLOv8 Documentation](https://github.com/ultralytics/docs), [PyTorch Framework](https://github.com/pytorch/pytorch), [YOLOv7 Implementation](https://github.com/WongKinYiu/yolov7), [OpenCV Library](https://github.com/opencv/opencv) |
|  laravel   | [Laravel Framework](https://github.com/laravel/framework), [Laravel Application Skeleton](https://github.com/laravel/laravel), [Laravel Official Website](https://github.com/laravel/laravel.com), [Laravel Documentation](https://github.com/laravel/docs), [Laravel Breeze (Auth Starter)](https://github.com/laravel/breeze), [Livewire (Full-stack Framework)](https://github.com/livewire/livewire), [PHP Language Source](https://github.com/php/php-src), [PHP Official Documentation](https://github.com/php/doc-en), [PHP Website Source](https://github.com/php/web-php) |
|  angular   | [Angular Framework](https://github.com/angular/angular), [Angular Components](https://github.com/angular/components), [Angular CLI](https://github.com/angular/angular-cli), [TypeScript Language](https://github.com/microsoft/TypeScript) |
|  godot     | [Godot Engine](https://github.com/godotengine/godot), [Godot Demo Projects](https://github.com/godotengine/godot-demo-projects), [Godot Documentation](https://github.com/godotengine/godot-docs), [Godot Official Website](https://github.com/godotengine/godot-website), [Learn GDScript with GDQuest](https://github.com/GDQuest/learn-gdscript), [C# Language](https://github.com/dotnet/csharplang) |

## 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}, 
}
```