README.md

metadata

language:
  - en
paperswithcode_id: hellaswag
pretty_name: HellaSwag
dataset_info:
  features:
    - name: ind
      dtype: int32
    - name: activity_label
      dtype: string
    - name: ctx_a
      dtype: string
    - name: ctx_b
      dtype: string
    - name: ctx
      dtype: string
    - name: endings
      sequence: string
    - name: source_id
      dtype: string
    - name: split
      dtype: string
    - name: split_type
      dtype: string
    - name: label
      dtype: string
  splits:
    - name: train
      num_bytes: 43232624
      num_examples: 39905
    - name: test
      num_bytes: 10791853
      num_examples: 10003
    - name: validation
      num_bytes: 11175717
      num_examples: 10042
  download_size: 71494896
  dataset_size: 65200194

Dataset Card for "hellaswag"

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: https://rowanzellers.com/hellaswag/
• Repository: https://github.com/rowanz/hellaswag/
• Paper: HellaSwag: Can a Machine Really Finish Your Sentence?
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 71.49 MB
• Size of the generated dataset: 65.32 MB
• Total amount of disk used: 136.81 MB

Dataset Summary

HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

default

• Size of downloaded dataset files: 71.49 MB
• Size of the generated dataset: 65.32 MB
• Total amount of disk used: 136.81 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "activity_label": "Removing ice from car",
    "ctx": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles. then",
    "ctx_a": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles.",
    "ctx_b": "then",
    "endings": "[\", the man adds wax to the windshield and cuts it.\", \", a person board a ski lift, while two men supporting the head of the per...",
    "ind": 4,
    "label": "3",
    "source_id": "activitynet~v_-1IBHYS3L-Y",
    "split": "train",
    "split_type": "indomain"
}

Data Fields

The data fields are the same among all splits.

default

• ind: a int32 feature.
• activity_label: a string feature.
• ctx_a: a string feature.
• ctx_b: a string feature.
• ctx: a string feature.
• endings: a list of string features.
• source_id: a string feature.
• split: a string feature.
• split_type: a string feature.
• label: a string feature.

Data Splits

name	train	validation	test
default	39905	10042	10003

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

MIT https://github.com/rowanz/hellaswag/blob/master/LICENSE

Citation Information

@inproceedings{zellers2019hellaswag,
    title={HellaSwag: Can a Machine Really Finish Your Sentence?},
    author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
    booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
    year={2019}
}

Contributions

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