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---
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
---
# Dataset Card for "ArtifactAI/arxiv_python_research_code"
## Dataset Description
https://huggingface.co/datasets/ArtifactAI/arxiv_deep_learning_python_research_code
### Dataset Summary
ArtifactAI/arxiv_deep_learning_python_research_code contains over 1.49B of source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs.
### How to use it
```python
from datasets import load_dataset
# full dataset (1.49GB of data)
ds = load_dataset("ArtifactAI/arxiv_deep_learning_python_research_code", split="train")
# dataset streaming (will only download the data as needed)
ds = load_dataset("ArtifactAI/arxiv_deep_learning_python_research_code", streaming=True, split="train")
for sample in iter(ds): print(sample["code"])
```
## Dataset Structure
### Data Instances
Each data instance corresponds to one file. The content of the file is in the `code` feature, and other features (`repo`, `file`, etc.) provide some metadata.
### Data Fields
- `repo` (string): code repository name.
- `file` (string): file path in the repository.
- `code` (string): code within the file.
- `file_length`: (integer): number of characters in the file.
- `avg_line_length`: (float): the average line-length of the file.
- `max_line_length`: (integer): the maximum line-length of the file.
- `extension_type`: (string): file extension.
### Data Splits
The dataset has no splits and all data is loaded as train split by default.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
34,099 active GitHub repository names were extracted from [ArXiv](https://arxiv.org/) papers from its inception through July 21st, 2023 totaling 773G of compressed github repositories.
These repositories were then filtered, and the code from each file that mentions ["torch", "jax", "flax", "stax", "haiku", "keras", "fastai", "xgboost", "caffe", "mxnet"] was extracted into 1.4 million files.
#### Who are the source language producers?
The source (code) language producers are users of GitHub that created unique repository
### Personal and Sensitive Information
The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub.
## Additional Information
### Dataset Curators
Matthew Kenney, Artifact AI, matt@artifactai.com
### Citation Information
```
@misc{arxiv_deep_learning_python_research_code,
title={arxiv_deep_learning_python_research_code},
author={Matthew Kenney},
year={2023}
}
``` |