metadata

license: cc0-1.0
annotations_creators: []
language_creators:
  - crowdsourced
  - expert-generated
  - machine-generated
  - found
  - other
language:
  - asm
  - ben
  - brx
  - guj
  - hin
  - kan
  - kas
  - kok
  - mai
  - mal
  - mar
  - mni
  - nep
  - ori
  - pan
  - san
  - sat
  - sid
  - snd
  - tam
  - tel
  - urd
multilinguality:
  - multilingual
pretty_name: Bhasha-Abhijnaanam
size_categories: []
source_datasets:
  - original
task_categories:
  - text-generation
task_ids: []

Dataset Card for Aksharantar

Table of Contents
Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage:
Repository: https://github.com/AI4Bharat/IndicLID
Paper: paper name
Leaderboard:
Point of Contact:

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