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---
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annotations_creators: []
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language_creators:
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- crowdsourced
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- expert-generated
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- machine-generated
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- found
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- other
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languages:
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- asm-IN
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- ben-IN
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- brx-IN
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- guj-IN
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- hin-IN
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- kan-IN
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- kas-IN
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- kok-IN
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- mai-IN
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- mal-IN
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- mar-IN
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- mni-IN
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- nep-IN
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- ori-IN
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- pan-IN
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- san-IN
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- sid-IN
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- tam-IN
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- tel-IN
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- urd-IN
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licenses:
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- cc-by-nc-4.0
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multilinguality:
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- multilingual
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pretty_name: Aksharantar
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size_categories: []
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source_datasets:
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- original
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task_categories:
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- text-generation
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task_ids: []
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---
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# Dataset Card for Aksharantar
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## Table of Contents
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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+
- [Discussion of Biases](#discussion-of-biases)
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+
- [Other Known Limitations](#other-known-limitations)
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+
- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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+
- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** https://indicnlp.ai4bharat.org/indic-xlit/
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- **Repository:** https://github.com/AI4Bharat/IndicXlit/
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- **Paper:** []()
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- **Leaderboard:**
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- **Point of Contact:**
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### Dataset Summary
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Aksharantar is the largest publicly available transliteration dataset for 20 Indic languages. The corpus has 26M Indic language-English transliteration pairs.
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### Supported Tasks and Leaderboards
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[More Information Needed]
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### Languages
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| <!-- --> | <!-- --> | <!-- --> | <!-- --> | <!-- --> | <!-- --> |
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| -------------- | -------------- | -------------- | --------------- | -------------- | ------------- |
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| Assamese (asm) | Hindi (hin) | Maithili (mai) | Marathi (mar) | Punjabi (pan) | Tamil (tam) |
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| Bengali (ben) | Kannada (kan) | Malayalam (mal)| Nepali (nep) | Sanskrit (san) | Telugu (tel) |
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| Bodo(brx) | Kashmiri (kas) | Manipuri (mni) | Oriya (ori) | Sindhi (snd) | Urdu (urd) |
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| Gujarati (guj) | Konkani (gom) |
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## Dataset Structure
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### Data Instances
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```
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A random sample from Hindi (hin) Train dataset.
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{
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'unique_identifier': 'hin1241393',
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'native word': 'स्वाभिमानिक',
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'english word': 'swabhimanik',
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'source': 'IndicCorp',
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'score': -0.1028788579
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}
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```
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### Data Fields
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- `unique_identifier` (string): 3-letter language code followed by a unique number in each set (Train, Test, Val).
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- `native word` (string): A word in Indic language.
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- `english word` (string): Transliteration of native word in English (Romanised word).
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- `source` (string): Source of the data.
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- `score` (num): Average threshold of the pair (0.35)
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For created data sources, depending on the destination/sampling method of a pair in a language, it will be one of:
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- Dakshina Dataset
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- IndicCorp
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- Samanantar
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- Wikidata
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- Existing sources
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- Named Entities Indian (AK-NEI)
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- Named Entities Foreign (AK-NEF)
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- Data from Uniform Sampling method. (Ak-Uni)
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- Data from Most Frequent words sampling method. (Ak-Freq)
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### Data Splits
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| 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 |
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|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
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| Training | 179K | 1231K | 36K | 1143K | 1299K | 2907K | 47K | 613K | 283K | 4101K | 10K | 1453K | 2397K | 346K | 515K | 1813K | 60K | 3231K | 2430K | 699K |
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| Validation | 4K | 11K | 3K | 12K | 6K | 7K | 4K | 4K | 4K | 8K | 3K | 8K | 3K | 3K | 9K | 3K | 8K | 9K | 8K | 12K |
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| Test | 5531 | 5009 | 4136 | 7768 | 5693 | 6396 | 7707 | 5093 | 5512 | 6911 | 4925 | 6573 | 4133 | 4256 | 4316 | 5334 | - | 4682 | 4567 | 4463 |
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## Dataset Creation
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### Curation Rationale
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[More Information Needed]
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+
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### Source Data
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+
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#### Initial Data Collection and Normalization
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[More Information Needed]
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#### Who are the source language producers?
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[More Information Needed]
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### Annotations
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163 |
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#### Annotation process
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[More Information Needed]
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167 |
+
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#### Who are the annotators?
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169 |
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[More Information Needed]
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+
|
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### Personal and Sensitive Information
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173 |
+
|
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[More Information Needed]
|
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+
|
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## Considerations for Using the Data
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177 |
+
|
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### Social Impact of Dataset
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179 |
+
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[More Information Needed]
|
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+
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### Discussion of Biases
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+
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[More Information Needed]
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### Other Known Limitations
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[More Information Needed]
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## Additional Information
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### Dataset Curators
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[More Information Needed]
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### Licensing Information
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[Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/).
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### Citation Information
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```
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```
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### Contributions
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