reichenbach/arxiv_ppr_embeds

Dataset Card for "scientific_papers"

This dataset is derived from https://huggingface.co/datasets/scientific_papers with additional creation of embeddings via https://huggingface.co/docs/transformers/model_doc/rag for Natural Questions trained Base Model. This dataset is created for purpose of Retrieval Augmented Generation examples and experiments.

Dataset Summary

Scientific papers datasets contains one sets of long and structured documents. The datasets are obtained from ArXiv repositories.

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

Data Instances

arxiv

• Size of downloaded dataset files: 4.50 GB
• Size of the generated dataset: 7.58 GB
• Total amount of disk used: 12.09 GB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "abstract": "\" we have studied the leptonic decay @xmath0 , via the decay channel @xmath1 , using a sample of tagged @xmath2 decays collected...",
    "article": "\"the leptonic decays of a charged pseudoscalar meson @xmath7 are processes of the type @xmath8 , where @xmath9 , @xmath10 , or @...",
    "section_names": "[sec:introduction]introduction\n[sec:detector]data and the cleo- detector\n[sec:analysys]analysis method\n[sec:conclusion]summary"
}

Data Fields

The data fields are the same among all splits.

arxiv

• article: a string feature.
• abstract: a string feature.
• section_names: a string feature.
• embeddings: a float 768 dimensional vector

Data Splits

name	train	validation	test
arxiv	203037	6436	6440

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@article{Cohan_2018,
   title={A Discourse-Aware Attention Model for Abstractive Summarization of
            Long Documents},
   url={http://dx.doi.org/10.18653/v1/n18-2097},
   DOI={10.18653/v1/n18-2097},
   journal={Proceedings of the 2018 Conference of the North American Chapter of
          the Association for Computational Linguistics: Human Language
          Technologies, Volume 2 (Short Papers)},
   publisher={Association for Computational Linguistics},
   author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},
   year={2018}
}

Contributions

Thanks to @thomwolf, @jplu, @lewtun, @patrickvonplaten for adding this dataset.