
# Dataset Card for "asnq"

## 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:** [https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq](https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://arxiv.org/abs/1911.04118)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.56 GB
- **Size of the generated dataset:** 3.82 GB
- **Total amount of disk used:** 7.39 GB

### Dataset Summary

ASNQ is a dataset for answer sentence selection derived from
Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).

Each example contains a question, candidate sentence, label indicating whether or not
the sentence answers the question, and two additional features --
sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the
candidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.

For more details please see
https://arxiv.org/abs/1911.04118

and

https://research.google/pubs/pub47761/

### 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

#### default

- **Size of downloaded dataset files:** 3.56 GB
- **Size of the generated dataset:** 3.82 GB
- **Total amount of disk used:** 7.39 GB

An example of 'validation' looks as follows.
```
{
    "label": 0,
    "question": "when did somewhere over the rainbow come out",
    "sentence": "In films and TV shows ( edit ) In the film Third Finger , Left Hand ( 1940 ) with Myrna Loy , Melvyn Douglas , and Raymond Walburn , the tune played throughout the film in short sequences .",
    "sentence_in_long_answer": false,
    "short_answer_in_sentence": false
}
```

### Data Fields

The data fields are the same among all splits.

#### default
- `question`: a `string` feature.
- `sentence`: a `string` feature.
- `label`: a classification label, with possible values including `neg` (0), `pos` (1).
- `sentence_in_long_answer`: a `bool` feature.
- `short_answer_in_sentence`: a `bool` feature.

### Data Splits

| name  | train  |validation|
|-------|-------:|---------:|
|default|20377568|    930062|

## 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

The data is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License:
https://github.com/alexa/wqa_tanda/blob/master/LICENSE

### Citation Information

```
@article{Garg_2020,
   title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
   volume={34},
   ISSN={2159-5399},
   url={http://dx.doi.org/10.1609/AAAI.V34I05.6282},
   DOI={10.1609/aaai.v34i05.6282},
   number={05},
   journal={Proceedings of the AAAI Conference on Artificial Intelligence},
   publisher={Association for the Advancement of Artificial Intelligence (AAAI)},
   author={Garg, Siddhant and Vu, Thuy and Moschitti, Alessandro},
   year={2020},
   month={Apr},
   pages={7780–7788}
}
```

### Contributions

Thanks to [@mkserge](https://github.com/mkserge) for adding this dataset.
task_categories:multiple-choice task_ids:multiple-choice-qa annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:10M<n<100M source_datasets:extended|natural_questions language:en license:cc-by-nc-sa-3.0 arxiv:1911.04118 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-nc-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|natural_questions"], "task_categories": ["multiple-choice"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "asnq", "pretty_name": "Answer Sentence Natural Questions (ASNQ)", "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "sentence_in_long_answer", "dtype": "bool"}, {"name": "short_answer_in_sentence", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 3656865072, "num_examples": 20377568}, {"name": "validation", "num_bytes": 168004403, "num_examples": 930062}], "download_size": 2496835395, "dataset_size": 3824869475}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}]}
TAGS
#task_categories-multiple-choice #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-extended|natural_questions #language-English #license-cc-by-nc-sa-3.0 #arxiv-1911.04118 #region-us 

Dataset Card for "asnq"
=======================


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: URL
* Repository:
* Paper: TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
* Point of Contact:
* Size of downloaded dataset files: 3.56 GB
* Size of the generated dataset: 3.82 GB
* Total amount of disk used: 7.39 GB


### Dataset Summary


ASNQ is a dataset for answer sentence selection derived from
Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).


Each example contains a question, candidate sentence, label indicating whether or not
the sentence answers the question, and two additional features --
sentence\_in\_long\_answer and short\_answer\_in\_sentence indicating whether ot not the
candidate sentence is contained in the long\_answer and if the short\_answer is in the candidate sentence.


For more details please see
URL


and


URL


### Supported Tasks and Leaderboards


### Languages


Dataset Structure
-----------------


### Data Instances


#### default


* Size of downloaded dataset files: 3.56 GB
* Size of the generated dataset: 3.82 GB
* Total amount of disk used: 7.39 GB


An example of 'validation' looks as follows.


### Data Fields


The data fields are the same among all splits.


#### default


* 'question': a 'string' feature.
* 'sentence': a 'string' feature.
* 'label': a classification label, with possible values including 'neg' (0), 'pos' (1).
* 'sentence\_in\_long\_answer': a 'bool' feature.
* 'short\_answer\_in\_sentence': a 'bool' feature.


### 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


The data is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License:
URL


### Contributions


Thanks to @mkserge for adding this dataset.



### Dataset Summary


ASNQ is a dataset for answer sentence selection derived from
Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).


Each example contains a question, candidate sentence, label indicating whether or not
the sentence answers the question, and two additional features --
sentence\_in\_long\_answer and short\_answer\_in\_sentence indicating whether ot not the
candidate sentence is contained in the long\_answer and if the short\_answer is in the candidate sentence.


For more details please see
URL


and


URL ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### default


* Size of downloaded dataset files: 3.56 GB
* Size of the generated dataset: 3.82 GB
* Total amount of disk used: 7.39 GB


An example of 'validation' looks as follows. ### Data Fields


The data fields are the same among all splits. #### default


* 'question': a 'string' feature.
* 'sentence': a 'string' feature.
* 'label': a classification label, with possible values including 'neg' (0), 'pos' (1).
* 'sentence\_in\_long\_answer': a 'bool' feature.
* 'short\_answer\_in\_sentence': a 'bool' feature. ### 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


The data is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License:
URL ### Contributions


Thanks to @mkserge for adding this dataset.
TAGS
#task_categories-multiple-choice #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-extended|natural_questions #language-English #license-cc-by-nc-sa-3.0 #arxiv-1911.04118 #region-us 
 ### Dataset Summary


ASNQ is a dataset for answer sentence selection derived from
Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).


Each example contains a question, candidate sentence, label indicating whether or not
the sentence answers the question, and two additional features --
sentence\_in\_long\_answer and short\_answer\_in\_sentence indicating whether ot not the
candidate sentence is contained in the long\_answer and if the short\_answer is in the candidate sentence.


For more details please see
URL


and


URL ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### default


* Size of downloaded dataset files: 3.56 GB
* Size of the generated dataset: 3.82 GB
* Total amount of disk used: 7.39 GB


An example of 'validation' looks as follows. ### Data Fields


The data fields are the same among all splits. #### default


* 'question': a 'string' feature.
* 'sentence': a 'string' feature.
* 'label': a classification label, with possible values including 'neg' (0), 'pos' (1).
* 'sentence\_in\_long\_answer': a 'bool' feature.
* 'short\_answer\_in\_sentence': a 'bool' feature. ### 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


The data is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License:
URL ### Contributions


Thanks to @mkserge for adding this dataset.