| --- |
| 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} |
| } |
| ``` |
