ArtifactAI
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README.md
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download_size: 35993359504
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dataset_size: 89132091867
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---
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# Dataset Card for "
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download_size: 35993359504
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dataset_size: 89132091867
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---
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# Dataset Card for "ArtifactAI/arxiv_s2orc_parsed"
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## Dataset Description
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https://huggingface.co/datasets/ArtifactAI/arxiv_s2orc_parsed
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### Dataset Summary
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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,
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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)
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### How to use it
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```python
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from datasets import load_dataset
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ds = load_dataset("ArtifactAI/arxiv_s2orc_parsed", split="train")
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# dataset streaming (will only download the data as needed)
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ds = load_dataset("ArtifactAI/arxiv_s2orc_parsed", streaming=True, split="train")
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```
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## Dataset Structure
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### Data Instances
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Each data instance corresponds to one file. The content of the file is in the `text` feature, and other features provide some metadata.
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### Data Fields
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- `title` (sequence): list of titles.
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- `author` (sequence): list of authors.
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- `authoraffiliation` (sequence): list of institution affiliations for each author.
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- `venue`: (integer): paper publication venue.
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- `doi`: (float): paper doi.
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- `pdfurls`: (integer): url link to the paper.
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- `corpusid`: (int): corpus ID as defined by s2orc.
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- `arxivid`: (int): arxiv paper id.
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- `pdfsha`: (string): unique pdf hash.
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- `text`: (string): full text of the arxiv paper.
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- github_urls: (sequence): list of github urls referenced within the text
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### Data Splits
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The dataset has no splits and all data is loaded as train split by default.
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## Additional Information
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### Dataset Curators
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Matthew Kenney, Artifact AI, matt@artifactai.com
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### Citation Information
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```
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@misc{arxiv_python_research_code,
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title={arxiv_python_research_code},
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author={Matthew Kenney},
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year={2023}
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}
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```
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