annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
- machine-generated
- found
- other
languages:
- asm-IN
- ben-IN
- brx-IN
- guj-IN
- hin-IN
- kan-IN
- kas-IN
- kok-IN
- mai-IN
- mal-IN
- mar-IN
- mni-IN
- nep-IN
- ori-IN
- pan-IN
- san-IN
- sid-IN
- tam-IN
- tel-IN
- urd-IN
licenses:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: Aksharantar
size_categories: []
source_datasets:
- original
task_categories:
- text-generation
task_ids: []
Dataset Card for Aksharantar
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://indicnlp.ai4bharat.org/indic-xlit/
- Repository: https://github.com/AI4Bharat/IndicXlit/
- Paper:
- Leaderboard:
- Point of Contact:
Dataset Summary
Aksharantar is the largest publicly available transliteration dataset for 20 Indic languages. The corpus has 26M Indic language-English transliteration pairs.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
| Assamese (asm) | Hindi (hin) | Maithili (mai) | Marathi (mar) | Punjabi (pan) | Tamil (tam) |
| Bengali (ben) | Kannada (kan) | Malayalam (mal) | Nepali (nep) | Sanskrit (san) | Telugu (tel) |
| Bodo(brx) | Kashmiri (kas) | Manipuri (mni) | Oriya (ori) | Sindhi (snd) | Urdu (urd) |
| Gujarati (guj) | Konkani (gom) |
Dataset Structure
Data Instances
A random sample from Hindi (hin) Train dataset.
{
'unique_identifier': 'hin1241393',
'native word': 'स्वाभिमानिक',
'english word': 'swabhimanik',
'source': 'IndicCorp',
'score': -0.1028788579
}
Data Fields
unique_identifier(string): 3-letter language code followed by a unique number in each set (Train, Test, Val).native word(string): A word in Indic language.english word(string): Transliteration of native word in English (Romanised word).source(string): Source of the data.score(num): Character level log probability of indic word given roman word by IndicXlit (model). Pairs with average threshold of the 0.35 are considered.For created data sources, depending on the destination/sampling method of a pair in a language, it will be one of:
- Dakshina Dataset
- IndicCorp
- Samanantar
- Wikidata
- Existing sources
- Named Entities Indian (AK-NEI)
- Named Entities Foreign (AK-NEF)
- Data from Uniform Sampling method. (Ak-Uni)
- Data from Most Frequent words sampling method. (Ak-Freq)
Data Splits
| Subset | as-en | bn-en | brx-en | gu-en | hi-en | kn-en | ks-en | kok-en | mai-en | ml-en | mni-en | mr-en | ne-en | or-en | pa-en | san-en | sd-en | ta-en | te-en | ur-en |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Training | 179K | 1231K | 36K | 1143K | 1299K | 2907K | 47K | 613K | 283K | 4101K | 10K | 1453K | 2397K | 346K | 515K | 1813K | 60K | 3231K | 2430K | 699K |
| Validation | 4K | 11K | 3K | 12K | 6K | 7K | 4K | 4K | 4K | 8K | 3K | 8K | 3K | 3K | 9K | 3K | 8K | 9K | 8K | 12K |
| Test | 5531 | 5009 | 4136 | 7768 | 5693 | 6396 | 7707 | 5093 | 5512 | 6911 | 4925 | 6573 | 4133 | 4256 | 4316 | 5334 | - | 4682 | 4567 | 4463 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
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
Creative Commons Attribution-NonCommercial 4.0 International.
Citation Information