--- 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 - [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:** - **Repository:** https://github.com/AI4Bharat/IndicLID - **Paper:** [Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages](https://arxiv.org/abs/2305.15814) - **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 ### 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 | 6883 | | Romanized | 512 | 4595 | 433 | 4785 | 4606 | 4848 | 450 | 0 | 444 | 439 | 4617 | 0 | 442 | 4603 | 423 | 512 | 4765 | 448 | 0 | 4881 | 4767 | 4741 | 4371 | ## Dataset Creation Information in the paper. [Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages](https://arxiv.org/abs/2305.15814) ### 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](https://arxiv.org/abs/2305.15814) #### 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](https://arxiv.org/abs/2305.15814) #### Who are the annotators? Information in the paper. [Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages](https://arxiv.org/abs/2305.15814) ## 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** CC0

- 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”)](http://creativecommons.org/publicdomain/zero/1.0). - 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} } ``` ### Contributions ---