
# 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.

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:**
- **Repository:** https://github.com/armancohan/long-summarization
- **Paper:** [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://arxiv.org/abs/1804.05685)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)


### Dataset Summary

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


### Supported Tasks and Leaderboards

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## 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

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Licensing Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 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](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
task_categories:summarization annotations_creators:found language_creators:found multilinguality:monolingual size_categories:100K<n<1M source_datasets:scientific_papers language:en license:unknown abstractive-summarization arxiv:1804.05685 region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["scientific_papers"], "task_categories": ["summarization"], "task_ids": [], "pretty_name": "ScientificPapers", "tags": ["abstractive-summarization"], "dataset_info": {"features": [{"name": "article", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "embeddings", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 8367611540, "num_examples": 203037}, {"name": "validation", "num_bytes": 256178362, "num_examples": 6440}, {"name": "test", "num_bytes": 255771184, "num_examples": 6436}], "download_size": 4718720913, "dataset_size": 8879561086}}
TAGS
#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-scientific_papers #language-English #license-unknown #abstractive-summarization #arxiv-1804.05685 #region-us 

Dataset Card for "scientific\_papers"
=====================================


This dataset is derived from URL with additional creation of embeddings via URL for Natural Questions trained Base Model.
This dataset is created for purpose of Retrieval Augmented Generation examples and experiments.


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage:
* Repository: URL
* Paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
* Point of Contact:


### Dataset Summary


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


### Supported Tasks and Leaderboards


### Languages


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.


### 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



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


### Contributions


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



### Dataset Summary


Scientific papers datasets contains one sets of long and structured documents.
The datasets are obtained from ArXiv repositories. ### Supported Tasks and Leaderboards ### Languages


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. ### 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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @thomwolf, @jplu, @lewtun, @patrickvonplaten for adding this dataset.
TAGS
#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-scientific_papers #language-English #license-unknown #abstractive-summarization #arxiv-1804.05685 #region-us 
 ### Dataset Summary


Scientific papers datasets contains one sets of long and structured documents.
The datasets are obtained from ArXiv repositories. ### Supported Tasks and Leaderboards ### Languages


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. ### 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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @thomwolf, @jplu, @lewtun, @patrickvonplaten for adding this dataset.
passage: TAGS
#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-scientific_papers #language-English #license-unknown #abstractive-summarization #arxiv-1804.05685 #region-us 
### Dataset Summary


Scientific papers datasets contains one sets of long and structured documents.
The datasets are obtained from ArXiv repositories.### Supported Tasks and Leaderboards### Languages


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.### 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



Dataset Creation
----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations


Additional Information
----------------------### Dataset Curators### Licensing Information### Contributions


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