Datasets:
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[{"unique_identifier":"as_2","native sentence":"অৱশ্যে লাচিত সংঘৰ স(...TRUNCATED) |
Dataset Card for Aksharantar
Dataset Summary
Bhasha-Abhijnaanam is a language identification test set for native-script as well as Romanized text which spans 22 Indic languages.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
Assamese (asm) | Hindi (hin) | Maithili (mai) | Nepali (nep) | Sanskrit (san) | Tamil (tam) |
Bengali (ben) | Kannada (kan) | Malayalam (mal) | Oriya (ori) | Santali (sat) | Telugu (tel) |
Bodo(brx) | Kashmiri (kas) | Manipuri (mni) | Punjabi (pan) | Sindhi (snd) | Urdu (urd) |
Gujarati (guj) | Konkani (kok) | Marathi (mar) |
Dataset Structure
Data Instances
A random sample from Hindi (hin) Test dataset.
{
"unique_identifier": "hin1",
"native sentence": "",
"romanized sentence": "",
"language": "Hindi",
"script": "Devanagari",
"source": "Dakshina",
}
Data Fields
unique_identifier
(string): 3-letter language code followed by a unique number in Test set.native sentence
(string): A sentence in Indic language.romanized sentence
(string): Transliteration of native sentence in English (Romanized sentence).language
(string): Language of native sentence.script
(string): Script in which native sentence is written.source
(string): Source of the data.For created data sources, depending on the destination/sampling method of a pair in a language, it will be one of:
- Dakshina Dataset
- Flores-200
- Manually Romanized
- Manually generated
Data Splits
Subset | asm | ben | brx | guj | hin | kan | kas (Perso-Arabic) | kas (Devanagari) | kok | mai | mal | mni (Bengali) | mni (Meetei Mayek) | mar | nep | ori | pan | san | sid | tam | tel | urd |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Native | 1012 | 5606 | 1500 | 5797 | 5617 | 5859 | 2511 | 1012 | 1500 | 2512 | 5628 | 1012 | 1500 | 5611 | 2512 | 1012 | 5776 | 2510 | 2512 | 5893 | 5779 | 5751 |
Romanized | 512 | 4595 | 433 | 4785 | 4606 | 4848 | 450 | 0 | 444 | 439 | 4617 | 0 | 442 | 4603 | 423 | 512 | 4765 | 448 | 0 | 4881 | 4767 | 4741 |
Dataset Creation
Information in the paper. Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
Information in the paper. Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages
Who are the source language producers?
[More Information Needed]
Annotations
Information in the paper. Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages
Who are the annotators?
Information in the paper. Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages
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
This data is released under the following licensing scheme:
- Manually collected data: Released under CC0 license.
CC0 License Statement
- We do not own any of the text from which this data has been extracted.
- We license the actual packaging of manually collected data under the Creative Commons CC0 license (“no rights reserved”).
- To the extent possible under law, AI4Bharat has waived all copyright and related or neighboring rights to Aksharantar manually collected data and existing sources.
- This work is published from: India.
Citation Information
@misc{madhani2023bhashaabhijnaanam,
title={Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages},
author={Yash Madhani and Mitesh M. Khapra and Anoop Kunchukuttan},
year={2023},
eprint={2305.15814},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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