Update dataset card with improved documentation

README.md

@@ -1,4 +1,5 @@
 ---
+pretty_name: ArXiv Deep Learning Python Research Code
 configs:
 - config_name: default
   data_files:
@@ -26,71 +27,84 @@ dataset_info:
     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
 ---
-# Dataset Card for "AlgorithmicResearchGroup/arxiv_python_research_code"
 
-## Dataset Description
+# ArXiv Deep Learning Python Research Code
 
-https://huggingface.co/datasets/AlgorithmicResearchGroup/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
 
-### Dataset Summary
+| Statistic | Value |
+|-----------|-------|
+| Total files | 391,496 |
+| Total size | 1.49 GB |
+| Source repos | 34,099 |
+| Time span | ArXiv inception through July 2023 |
 
-AlgorithmicResearchGroup/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.
+## 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
 
-### 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")
+# full dataset
+ds = load_dataset("AlgorithmicResearchGroup/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"])
+# 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
 ```
 
-## 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
+## Data Collection
 
-The dataset has no splits and all data is loaded as train split by default.
+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.
 
-## Dataset Creation
+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.
 
-### 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.
+## Sensitive Information
 
-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.
+The dataset may contain emails, IP addresses, and API/SSH keys that were previously published in public GitHub repositories.
 
-#### Who are the source language producers?
+## Related Resources
 
-The source (code) language producers are users of GitHub that created unique repository
+- [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)
 
-### 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. 
+## Citation
 
-## Additional Information
-
-### Dataset Curators
-Matthew Kenney, AlgorithmicResearchGroup, matt@algorithmicresearchgroup.com
-
-### Citation Information
-```
+```bibtex
 @misc{arxiv_deep_learning_python_research_code,
-    title={arxiv_deep_learning_python_research_code},
+    title={ArXiv Deep Learning Python Research Code},
     author={Matthew Kenney},
-    year={2023}
+    year={2023},
+    publisher={Hugging Face},
+    url={https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code}
 }
-```
+```