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