Update README.md

README.md

@@ -27,6 +27,70 @@ dataset_info:
   download_size: 1490724325
   dataset_size: 3590067176.125193
 ---
-# Dataset Card for "arxiv_dl_research_code"
+# Dataset Card for "ArtifactAI/arxiv_python_research_code"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## 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 mentiones ["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, [email protected]
+
+### Citation Information
+```
+@misc{arxiv_deep_learning_python_research_code,
+    title={arxiv_deep_learning_python_research_code},
+    author={Matthew Kenney},
+    year={2023}
+}
+```