
# Dataset Card for "common_gen"

## 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://inklab.usc.edu/CommonGen/index.html](https://inklab.usc.edu/CommonGen/index.html)
- **Repository:** https://github.com/INK-USC/CommonGen
- **Paper:** [CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning](https://arxiv.org/abs/1911.03705)
- **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:** 1.85 MB
- **Size of the generated dataset:** 7.21 MB
- **Total amount of disk used:** 9.06 MB

### Dataset Summary

CommonGen is a constrained text generation task, associated with a benchmark dataset,
to explicitly test machines for the ability of generative commonsense reasoning. Given
a set of common concepts; the task is to generate a coherent sentence describing an
everyday scenario using these concepts.

CommonGen is challenging because it inherently requires 1) relational reasoning using
background commonsense knowledge, and 2) compositional generalization ability to work
on unseen concept combinations. Our dataset, constructed through a combination of
crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and
50k sentences in total.

### 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:** 1.85 MB
- **Size of the generated dataset:** 7.21 MB
- **Total amount of disk used:** 9.06 MB

An example of 'train' looks as follows.
```
{
    "concept_set_idx": 0,
    "concepts": ["ski", "mountain", "skier"],
    "target": "Three skiers are skiing on a snowy mountain."
}
```

### Data Fields

The data fields are the same among all splits.

#### default
- `concept_set_idx`: a `int32` feature.
- `concepts`: a `list` of `string` features.
- `target`: a `string` feature.

### Data Splits

| name  |train|validation|test|
|-------|----:|---------:|---:|
|default|67389|      4018|1497|

## 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 dataset is licensed under [MIT License](https://github.com/INK-USC/CommonGen/blob/master/LICENSE).

### Citation Information

```bib
@inproceedings{lin-etal-2020-commongen,
    title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning",
    author = "Lin, Bill Yuchen  and
      Zhou, Wangchunshu  and
      Shen, Ming  and
      Zhou, Pei  and
      Bhagavatula, Chandra  and
      Choi, Yejin  and
      Ren, Xiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165",
    doi = "10.18653/v1/2020.findings-emnlp.165",
    pages = "1823--1840"
}
```


### Contributions

Thanks to [@JetRunner](https://github.com/JetRunner), [@yuchenlin](https://github.com/yuchenlin), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
task_categories:text2text-generation annotations_creators:crowdsourced language_creators:found language_creators:crowdsourced multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:mit concepts-to-text arxiv:1911.03705 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["found", "crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "commongen", "pretty_name": "CommonGen", "tags": ["concepts-to-text"], "dataset_info": {"features": [{"name": "concept_set_idx", "dtype": "int32"}, {"name": "concepts", "sequence": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6724166, "num_examples": 67389}, {"name": "validation", "num_bytes": 408740, "num_examples": 4018}, {"name": "test", "num_bytes": 77518, "num_examples": 1497}], "download_size": 3434865, "dataset_size": 7210424}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
TAGS
#task_categories-text2text-generation #annotations_creators-crowdsourced #language_creators-found #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #concepts-to-text #arxiv-1911.03705 #region-us 

Dataset Card for "common\_gen"
==============================


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: URL
* Paper: CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
* Point of Contact:
* Size of downloaded dataset files: 1.85 MB
* Size of the generated dataset: 7.21 MB
* Total amount of disk used: 9.06 MB


### Dataset Summary


CommonGen is a constrained text generation task, associated with a benchmark dataset,
to explicitly test machines for the ability of generative commonsense reasoning. Given
a set of common concepts; the task is to generate a coherent sentence describing an
everyday scenario using these concepts.


CommonGen is challenging because it inherently requires 1) relational reasoning using
background commonsense knowledge, and 2) compositional generalization ability to work
on unseen concept combinations. Our dataset, constructed through a combination of
crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and
50k sentences in total.


### Supported Tasks and Leaderboards


### Languages


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


### Data Instances


#### default


* Size of downloaded dataset files: 1.85 MB
* Size of the generated dataset: 7.21 MB
* Total amount of disk used: 9.06 MB


An example of 'train' looks as follows.


### Data Fields


The data fields are the same among all splits.


#### default


* 'concept\_set\_idx': a 'int32' feature.
* 'concepts': a 'list' of 'string' features.
* 'target': a 'string' 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 dataset is licensed under MIT License.


### Contributions


Thanks to @JetRunner, @yuchenlin, @thomwolf, @lhoestq for adding this dataset.



### Dataset Summary


CommonGen is a constrained text generation task, associated with a benchmark dataset,
to explicitly test machines for the ability of generative commonsense reasoning. Given
a set of common concepts; the task is to generate a coherent sentence describing an
everyday scenario using these concepts.


CommonGen is challenging because it inherently requires 1) relational reasoning using
background commonsense knowledge, and 2) compositional generalization ability to work
on unseen concept combinations. Our dataset, constructed through a combination of
crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and
50k sentences in total. ### Supported Tasks and Leaderboards ### Languages


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


* Size of downloaded dataset files: 1.85 MB
* Size of the generated dataset: 7.21 MB
* Total amount of disk used: 9.06 MB


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


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


* 'concept\_set\_idx': a 'int32' feature.
* 'concepts': a 'list' of 'string' features.
* 'target': a 'string' 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 dataset is licensed under MIT License. ### Contributions


Thanks to @JetRunner, @yuchenlin, @thomwolf, @lhoestq for adding this dataset.
TAGS
#task_categories-text2text-generation #annotations_creators-crowdsourced #language_creators-found #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #concepts-to-text #arxiv-1911.03705 #region-us 
 ### Dataset Summary


CommonGen is a constrained text generation task, associated with a benchmark dataset,
to explicitly test machines for the ability of generative commonsense reasoning. Given
a set of common concepts; the task is to generate a coherent sentence describing an
everyday scenario using these concepts.


CommonGen is challenging because it inherently requires 1) relational reasoning using
background commonsense knowledge, and 2) compositional generalization ability to work
on unseen concept combinations. Our dataset, constructed through a combination of
crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and
50k sentences in total. ### Supported Tasks and Leaderboards ### Languages


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


* Size of downloaded dataset files: 1.85 MB
* Size of the generated dataset: 7.21 MB
* Total amount of disk used: 9.06 MB


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


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


* 'concept\_set\_idx': a 'int32' feature.
* 'concepts': a 'list' of 'string' features.
* 'target': a 'string' 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 dataset is licensed under MIT License. ### Contributions


Thanks to @JetRunner, @yuchenlin, @thomwolf, @lhoestq for adding this dataset.
passage: TAGS
#task_categories-text2text-generation #annotations_creators-crowdsourced #language_creators-found #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #concepts-to-text #arxiv-1911.03705 #region-us 
### Dataset Summary


CommonGen is a constrained text generation task, associated with a benchmark dataset,
to explicitly test machines for the ability of generative commonsense reasoning. Given
a set of common concepts; the task is to generate a coherent sentence describing an
everyday scenario using these concepts.


CommonGen is challenging because it inherently requires 1) relational reasoning using
background commonsense knowledge, and 2) compositional generalization ability to work
on unseen concept combinations. Our dataset, constructed through a combination of
crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and
50k sentences in total.### Supported Tasks and Leaderboards### Languages


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


* Size of downloaded dataset files: 1.85 MB
* Size of the generated dataset: 7.21 MB
* Total amount of disk used: 9.06 MB


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


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


* 'concept\_set\_idx': a 'int32' feature.
* 'concepts': a 'list' of 'string' features.
* 'target': a 'string' 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