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Dataset Card for "IndicQuestionGeneration"

Dataset Summary

IndicQuestionGeneration is the question generation dataset released as part of IndicNLG Suite. Each example has five fields: id, squad_id, answer, context and question. We create this dataset in eleven languages, including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. This is translated data. The examples in each language are exactly similar but in different languages. The number of examples in each language is 98,027.

Supported Tasks and Leaderboards

Tasks: Question Generation

Leaderboards: Currently there is no Leaderboard for this dataset.


  • Assamese (as)
  • Bengali (bn)
  • Gujarati (gu)
  • Kannada (kn)
  • Hindi (hi)
  • Malayalam (ml)
  • Marathi (mr)
  • Oriya (or)
  • Punjabi (pa)
  • Tamil (ta)
  • Telugu (te)

Dataset Structure

Data Instances

One random example from the hi dataset is given below in JSON format.

"id": 8, 
"squad_id": "56be8e613aeaaa14008c90d3", 
"answer": "अमेरिकी फुटबॉल सम्मेलन", 
"context": "अमेरिकी फुटबॉल सम्मेलन (एएफसी) के चैंपियन डेनवर ब्रोंकोस ने नेशनल फुटबॉल कांफ्रेंस (एनएफसी) की चैंपियन कैरोलिना पैंथर्स को 24-10 से हराकर अपना तीसरा सुपर बाउल खिताब जीता।", 
"question": "एएफसी का मतलब क्या है?"

Data Fields

  • id (string): Unique identifier.
  • squad_id (string): Unique identifier in Squad dataset.
  • answer (strings): Answer as one of the two inputs.
  • context (string): Context, the other input.
  • question (string): Question, the output.

Data Splits

Here is the number of samples in each split for all the languages.

Language ISO 639-1 Code Train Dev Test
Assamese as 69,979 17,495 10,553
Bengali bn 69,979 17,495 10,553
Gujarati gu 69,979 17,495 10,553
Hindi hi 69,979 17,495 10,553
Kannada kn 69,979 17,495 10,553
Malayalam ml 69,979 17,495 10,553
Marathi mr 69,979 17,495 10,553
Oriya or 69,979 17,495 10,553
Punjabi pa 69,979 17,495 10,553
Tamil ta 69,979 17,495 10,553
Telugu te 69,979 17,495 10,553

Dataset Creation

Curation Rationale

Detailed in the paper

Source Data

Squad Dataset(

Initial Data Collection and Normalization

Detailed in the paper

Who are the source language producers?

Detailed in the paper


[More information needed]

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

Contents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Copyright of the dataset contents belongs to the original copyright holders.

Citation Information

If you use any of the datasets, models or code modules, please cite the following paper:

  title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
  author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
  url = "",     


Detailed in the paper

Models trained or fine-tuned on ai4bharat/IndicQuestionGeneration