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Dataset: common_gen 🏷
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from datasets import load_dataset dataset = load_dataset("common_gen")


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Table of Contents

Dataset Description

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

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances


  • Size of downloaded dataset files: 1.76 MB
  • Size of the generated dataset: 6.88 MB
  • Total amount of disk used: 8.64 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.


  • concept_set_idx: a int32 feature.
  • concepts: a list of string features.
  • target: a string feature.

Data Splits Sample Size

name train validation test
default 67389 4018 1497

Dataset Creation

Curation Rationale

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Source Data

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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

     author = {Bill Yuchen Lin and Ming Shen and Wangchunshu Zhou and Pei Zhou and Chandra Bhagavatula and Yejin Choi and Xiang Ren},
     title = {CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning},
     journal = {CoRR},
     volume = {abs/1911.03705},
     year = {2019}