metadata

language:
  - tr
paperswithcode_id: winogrande
pretty_name: WinoGrande
dataset_info:
  - config_name: winogrande_xs
    features:
      - name: sentence
        dtype: string
      - name: option1
        dtype: string
      - name: option2
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 20704
        num_examples: 160
      - name: test
        num_bytes: 227649
        num_examples: 1767
      - name: validation
        num_bytes: 164199
        num_examples: 1267
    download_size: 3395492
    dataset_size: 412552
  - config_name: winogrande_s
    features:
      - name: sentence
        dtype: string
      - name: option1
        dtype: string
      - name: option2
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 82308
        num_examples: 640
      - name: test
        num_bytes: 227649
        num_examples: 1767
      - name: validation
        num_bytes: 164199
        num_examples: 1267
    download_size: 3395492
    dataset_size: 474156
  - config_name: winogrande_m
    features:
      - name: sentence
        dtype: string
      - name: option1
        dtype: string
      - name: option2
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 329001
        num_examples: 2558
      - name: test
        num_bytes: 227649
        num_examples: 1767
      - name: validation
        num_bytes: 164199
        num_examples: 1267
    download_size: 3395492
    dataset_size: 720849
  - config_name: winogrande_l
    features:
      - name: sentence
        dtype: string
      - name: option1
        dtype: string
      - name: option2
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 1319576
        num_examples: 10234
      - name: test
        num_bytes: 227649
        num_examples: 1767
      - name: validation
        num_bytes: 164199
        num_examples: 1267
    download_size: 3395492
    dataset_size: 1711424
  - config_name: winogrande_xl
    features:
      - name: sentence
        dtype: string
      - name: option1
        dtype: string
      - name: option2
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 5185832
        num_examples: 40398
      - name: test
        num_bytes: 227649
        num_examples: 1767
      - name: validation
        num_bytes: 164199
        num_examples: 1267
    download_size: 3395492
    dataset_size: 5577680
  - config_name: winogrande_debiased
    features:
      - name: sentence
        dtype: string
      - name: option1
        dtype: string
      - name: option2
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 1203420
        num_examples: 9248
      - name: test
        num_bytes: 227649
        num_examples: 1767
      - name: validation
        num_bytes: 164199
        num_examples: 1267
    download_size: 3395492
    dataset_size: 1595268
license: apache-2.0

Dataset Card for "winogrande"

This Dataset is part of a series of datasets aimed at advancing Turkish LLM Developments by establishing rigid Turkish benchmarks to evaluate the performance of LLM's Produced in the Turkish Language. malhajar/winogrande-tr is a translated version of winogrande aimed specifically to be used in the OpenLLMTurkishLeaderboard

Translated by: Mohamad Alhajar

Dataset Summary

WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires commonsense reasoning.

Supported Tasks and Leaderboards

aimed specifically to be used in the OpenLLMTurkishLeaderboard

Languages

Turkish

Dataset Structure

Data Instances

winogrande_debiased

Size of downloaded dataset files: 3.40 MB
Size of the generated dataset: 1.59 MB
Total amount of disk used: 4.99 MB

An example of 'train' looks as follows.

winogrande_l

Size of downloaded dataset files: 3.40 MB
Size of the generated dataset: 1.71 MB
Total amount of disk used: 5.11 MB

An example of 'validation' looks as follows.

winogrande_m

Size of downloaded dataset files: 3.40 MB
Size of the generated dataset: 0.72 MB
Total amount of disk used: 4.12 MB

An example of 'validation' looks as follows.

winogrande_s

Size of downloaded dataset files: 3.40 MB
Size of the generated dataset: 0.47 MB
Total amount of disk used: 3.87 MB

An example of 'validation' looks as follows.

winogrande_xl

Size of downloaded dataset files: 3.40 MB
Size of the generated dataset: 5.58 MB
Total amount of disk used: 8.98 MB

An example of 'train' looks as follows.

Data Fields

The data fields are the same among all splits.

winogrande_debiased

sentence: a string feature.
option1: a string feature.
option2: a string feature.
answer: a string feature.

winogrande_l

sentence: a string feature.
option1: a string feature.
option2: a string feature.
answer: a string feature.

winogrande_m

sentence: a string feature.
option1: a string feature.
option2: a string feature.
answer: a string feature.

winogrande_s

sentence: a string feature.
option1: a string feature.
option2: a string feature.
answer: a string feature.

winogrande_xl

sentence: a string feature.
option1: a string feature.
option2: a string feature.
answer: a string feature.

Data Splits

name	train	validation	test
winogrande_debiased	9248	1267	1767
winogrande_l	10234	1267	1767
winogrande_m	2558	1267	1767
winogrande_s	640	1267	1767
winogrande_xl	40398	1267	1767
winogrande_xs	160	1267	1767

Citation Information

@InProceedings{ai2:winogrande,
title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi
},
year={2019}
}

`