---
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}
}
```