Datasets:
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pretty_name: ArXiv Deep Learning Python Research Code
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: repo
dtype: string
- name: file
dtype: string
- name: code
dtype: string
- name: file_length
dtype: int64
- name: avg_line_length
dtype: float64
- name: max_line_length
dtype: int64
- name: extension_type
dtype: string
splits:
- name: train
num_bytes: 3590067176.125193
num_examples: 391496
download_size: 1490724325
dataset_size: 3590067176.125193
language:
- en
license: other
size_categories:
- 100K<n<1M
tags:
- code
- deep-learning
- arxiv
- research
- python
task_categories:
- text-generation
---
# ArXiv Deep Learning Python Research Code
A curated corpus of Python source code files extracted from GitHub repositories referenced in ArXiv papers. Contains 391,496 files (1.49 GB) filtered to deep learning frameworks, designed for training and evaluating Code LLMs on research-grade code.
## Dataset Summary
| Statistic | Value |
|-----------|-------|
| Total files | 391,496 |
| Total size | 1.49 GB |
| Source repos | 34,099 |
| Time span | ArXiv inception through July 2023 |
## Dataset Structure
| Field | Type | Description |
|-------|------|-------------|
| `repo` | string | GitHub repository name |
| `file` | string | File path in the repository |
| `code` | string | File contents |
| `file_length` | int64 | Number of characters in the file |
| `avg_line_length` | float64 | Average line length |
| `max_line_length` | int64 | Maximum line length |
| `extension_type` | string | File extension |
## Usage
```python
from datasets import load_dataset
# full dataset
ds = load_dataset("AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code", split="train")
# streaming
ds = load_dataset("AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code", streaming=True, split="train")
for sample in ds:
print(sample["repo"], sample["file"])
break
```
## Data Collection
34,099 active GitHub repository names were extracted from [ArXiv](https://arxiv.org/) papers from its inception through July 21st, 2023, totaling 773 GB of compressed GitHub repositories.
These repositories were filtered to files mentioning any of the following frameworks: `torch`, `jax`, `flax`, `stax`, `haiku`, `keras`, `fastai`, `xgboost`, `caffe`, `mxnet`, yielding 1.4 million files which were further filtered to the final 391k.
## Sensitive Information
The dataset may contain emails, IP addresses, and API/SSH keys that were previously published in public GitHub repositories.
## Related Resources
- [ArXiv DL Instruct](https://huggingface.co/datasets/AlgorithmicResearchGroup/ArXivDLInstruct) - Instruction-tuning dataset derived from this code
- [Algorithmic Research Group - Open Source](https://algorithmicresearchgroup.com/opensource.html)
## Citation
```bibtex
@misc{arxiv_deep_learning_python_research_code,
title={ArXiv Deep Learning Python Research Code},
author={Matthew Kenney},
year={2023},
publisher={Hugging Face},
url={https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code}
}
```
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