Dataset:
indic_glue

Dataset Card for "indic_glue"

Dataset Summary

IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.

The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, we construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. We call converted dataset WNLI (Winograd NLI). This dataset is translated and publicly released for 3 Indian languages by AI4Bharat.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

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

Data Instances

actsa-sc.te

  • Size of downloaded dataset files: 0.36 MB
  • Size of the generated dataset: 1.63 MB
  • Total amount of disk used: 1.99 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "label": 0,
    "text": "\"ప్రయాణాల్లో ఉన్నవారికోసం బస్ స్టేషన్లు, రైల్వే స్టేషన్లలో పల్స్పోలియో బూతులను ఏర్పాటు చేసి చిన్నారులకు పోలియో చుక్కలు వేసేలా ఏర..."
}

bbca.hi

  • Size of downloaded dataset files: 5.50 MB
  • Size of the generated dataset: 26.35 MB
  • Total amount of disk used: 31.85 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "label": "pakistan",
    "text": "\"नेटिजन यानि इंटरनेट पर सक्रिय नागरिक अब ट्विटर पर सरकार द्वारा लगाए प्रतिबंधों के समर्थन या विरोध में अपने विचार व्यक्त करते है..."
}

copa.en

  • Size of downloaded dataset files: 0.72 MB
  • Size of the generated dataset: 0.11 MB
  • Total amount of disk used: 0.83 MB

An example of 'validation' looks as follows.

{
    "choice1": "I swept the floor in the unoccupied room.",
    "choice2": "I shut off the light in the unoccupied room.",
    "label": 1,
    "premise": "I wanted to conserve energy.",
    "question": "effect"
}

copa.gu

  • Size of downloaded dataset files: 0.72 MB
  • Size of the generated dataset: 0.22 MB
  • Total amount of disk used: 0.94 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "choice1": "\"સ્ત્રી જાણતી હતી કે તેનો મિત્ર મુશ્કેલ સમયમાંથી પસાર થઈ રહ્યો છે.\"...",
    "choice2": "\"મહિલાને લાગ્યું કે તેના મિત્રએ તેની દયાળુ લાભ લીધો છે.\"...",
    "label": 0,
    "premise": "મહિલાએ તેના મિત્રની મુશ્કેલ વર્તન સહન કરી.",
    "question": "cause"
}

copa.hi

  • Size of downloaded dataset files: 0.72 MB
  • Size of the generated dataset: 0.22 MB
  • Total amount of disk used: 0.94 MB

An example of 'validation' looks as follows.

{
    "choice1": "मैंने उसका प्रस्ताव ठुकरा दिया।",
    "choice2": "उन्होंने मुझे उत्पाद खरीदने के लिए राजी किया।",
    "label": 0,
    "premise": "मैंने सेल्समैन की पिच पर शक किया।",
    "question": "effect"
}

Data Fields

The data fields are the same among all splits.

actsa-sc.te

  • text: a string feature.
  • label: a classification label, with possible values including positive (0), negative (1).

bbca.hi

  • label: a string feature.
  • text: a string feature.

copa.en

  • premise: a string feature.
  • choice1: a string feature.
  • choice2: a string feature.
  • question: a string feature.
  • label: a int32 feature.

copa.gu

  • premise: a string feature.
  • choice1: a string feature.
  • choice2: a string feature.
  • question: a string feature.
  • label: a int32 feature.

copa.hi

  • premise: a string feature.
  • choice1: a string feature.
  • choice2: a string feature.
  • question: a string feature.
  • label: a int32 feature.

Data Splits

actsa-sc.te

train validation test
actsa-sc.te 4328 541 541

bbca.hi

train test
bbca.hi 3467 866

copa.en

train validation test
copa.en 400 100 500

copa.gu

train validation test
copa.gu 362 88 448

copa.hi

train validation test
copa.hi 362 88 449

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

More Information Needed

Annotations

Annotation process

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Who are the annotators?

More Information Needed

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

    @inproceedings{kakwani2020indicnlpsuite,
    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
    year={2020},
    booktitle={Findings of EMNLP},
}

@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
@inproceedings{Levesque2011TheWS,
title={The Winograd Schema Challenge},
author={H. Levesque and E. Davis and L. Morgenstern},
booktitle={KR},
year={2011}
}

Contributions

Thanks to @sumanthd17 for adding this dataset.

Models trained or fine-tuned on indic_glue

None yet