|
--- |
|
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" |
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|
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## Dataset Description |
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|
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https://huggingface.co/datasets/ArtifactAI/arxiv_deep_learning_python_research_code |
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|
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|
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### Dataset Summary |
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|
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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. |
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|
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### How to use it |
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```python |
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from datasets import load_dataset |
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|
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# full dataset (1.49GB of data) |
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ds = load_dataset("ArtifactAI/arxiv_deep_learning_python_research_code", split="train") |
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|
|
# dataset streaming (will only download the data as needed) |
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ds = load_dataset("ArtifactAI/arxiv_deep_learning_python_research_code", streaming=True, split="train") |
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for sample in iter(ds): print(sample["code"]) |
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``` |
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|
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## Dataset Structure |
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### Data Instances |
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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. |
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### Data Fields |
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- `repo` (string): code repository name. |
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- `file` (string): file path in the repository. |
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- `code` (string): code within the file. |
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- `file_length`: (integer): number of characters in the file. |
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- `avg_line_length`: (float): the average line-length of the file. |
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- `max_line_length`: (integer): the maximum line-length of the file. |
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- `extension_type`: (string): file extension. |
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|
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### Data Splits |
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|
|
The dataset has no splits and all data is loaded as train split by default. |
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|
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## Dataset Creation |
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|
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### Source Data |
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#### Initial Data Collection and Normalization |
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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. |
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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. |
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#### Who are the source language producers? |
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The source (code) language producers are users of GitHub that created unique repository |
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|
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### Personal and Sensitive Information |
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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. |
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|
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## Additional Information |
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### Dataset Curators |
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Matthew Kenney, Artifact AI, matt@artifactai.com |
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### Citation Information |
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``` |
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@misc{arxiv_deep_learning_python_research_code, |
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title={arxiv_deep_learning_python_research_code}, |
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author={Matthew Kenney}, |
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year={2023} |
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} |
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``` |
