--- 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](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## 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 (kok) | ## 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 | asm-en | ben-en | brx-en | guj-en | hin-en | kan-en | kas-en | kok-en | mai-en | mal-en | mni-en | mar-en | nep-en | ori-en | pan-en | san-en | sid-en | tam-en | tel-en | urd-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 CC0 CC-BY

This data is released under the following licensing scheme: - We do not own any of the text from which this data has been extracted. - We license the actual packaging of the mined data under the [Creative Commons CC0 license (“no rights reserved”)](http://creativecommons.org/publicdomain/zero/1.0), and the Aksharantar benchmark and all manually transliterated data under the [Creative Commons CC-BY license (“no rights reserved”)](https://creativecommons.org/licenses/by/4.0/). - To the extent possible under law, AI4Bharat has waived all copyright and related or neighboring rights to Aksharantar. - This work is published from: India. [Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/). ### Citation Information ``` ``` ### Contributions