Update README.md

README.md

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   dataset_size: 89132091867
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-# Dataset Card for "s2orc_full_parsed"
+# Dataset Card for "ArtifactAI/arxiv_s2orc_parsed"
 
-[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_s2orc_parsed
+
+
+### Dataset Summary
+
+ArtifactAI/arxiv_s2orc_parsed is a subset of the [AllenAI S2ORC dataset](https://github.com/allenai/s2orc), a general-purpose corpus for NLP and text mining research over scientific papers, 
+The dataset is filtered strictly for ArXiv papers, including the full text for each paper. Github links have been extracted from each paper to aid in the development of [ArtifactAI/arxiv_python_research_code](https://huggingface.co/datasets/ArtifactAI/arxiv_python_research_code)
+
+### How to use it
+```python
+from datasets import load_dataset
+
+ds = load_dataset("ArtifactAI/arxiv_s2orc_parsed", split="train")
+
+# dataset streaming (will only download the data as needed)
+ds = load_dataset("ArtifactAI/arxiv_s2orc_parsed", streaming=True, split="train")
+```
+
+## Dataset Structure
+### Data Instances
+Each data instance corresponds to one file. The content of the file is in the `text` feature, and other features provide some metadata.
+### Data Fields
+- `title` (sequence): list of titles.
+- `author` (sequence): list of authors.
+- `authoraffiliation` (sequence): list of institution affiliations for each author.
+- `venue`: (integer): paper publication venue.
+- `doi`: (float): paper doi.
+- `pdfurls`: (integer): url link to the paper.
+- `corpusid`: (int): corpus ID as defined by s2orc.
+- `arxivid`: (int): arxiv paper id.
+- `pdfsha`: (string): unique pdf hash.
+- `text`: (string): full text of the arxiv paper.
+- github_urls: (sequence): list of github urls referenced within the text
+
+### Data Splits
+
+The dataset has no splits and all data is loaded as train split by default.
+
+## Additional Information
+
+### Dataset Curators
+Matthew Kenney, Artifact AI, [email protected]
+
+### Citation Information
+```
+@misc{arxiv_python_research_code,
+    title={arxiv_python_research_code},
+    author={Matthew Kenney},
+    year={2023}
+}
+```