| --- |
| configs: |
| - config_name: indiccorp_v2 |
| data_files: |
| - split: asm_Beng |
| path: "data/as.txt" |
| - split: ben_Beng |
| path: "data/bn.txt" |
| - split: brx_Deva |
| path: "data/bd.txt" |
| - split: doi_Deva |
| path: "data/dg.txt" |
| - split: gom_Deva |
| path: "data/gom.txt" |
| - split: guj_Gujr |
| path: "data/gu.txt" |
| - split: hin_Deva |
| path: "data/hi-*.txt" |
| - split: kan_Knda |
| path: "data/kn.txt" |
| - split: kas_Arab |
| path: "data/ks.txt" |
| - split: mai_Deva |
| path: "data/mai.txt" |
| - split: mal_Mlym |
| path: "data/ml.txt" |
| - split: mar_Deva |
| path: "data/mr.txt" |
| - split: mni_Mtei |
| path: "data/mni.txt" |
| - split: npi_Deva |
| path: "data/ne.txt" |
| - split: ory_Orya |
| path: "data/or.txt" |
| - split: pan_Guru |
| path: "data/pa.txt" |
| - split: san_Deva |
| path: "data/sa.txt" |
| - split: snd_Deva |
| path: "data/sd.txt" |
| - split: tam_Taml |
| path: "data/ta.txt" |
| - split: tel_Telu |
| path: "data/te.txt" |
| - split: urd_Arab |
| path: "data/ur.txt" |
| - split: khasi |
| path: "data/kha.txt" |
| - split: santhali |
| path: "data/sat.txt" |
| --- |
| # IndicCorp v2 Dataset |
|
|
| ## Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages |
| > This repository contains the pretraining data for the paper published at ACL 2023. |
|
|
| # Example Usage |
| ```python |
| from datasets import load_dataset |
| |
| # Load the Telugu subset of the dataset |
| dataset = load_dataset("ai4bharat/IndicCorpV2", "indiccorp_v2", data_dir="data/tel_Telu") |
| ``` |
|
|
|
|
| # License |
| All the datasets created as part of this work will be released under a [CC-0](https://creativecommons.org/publicdomain/zero/1.0) license and all models & code will be release under an [MIT license](https://github.com/ai4bharat/IndicBERT/blob/main/LICENSE) |
|
|
|
|
| # Citation |
| ```bibtex |
| @inproceedings{doddapaneni-etal-2023-towards, |
| title = "Towards Leaving No {I}ndic Language Behind: Building Monolingual Corpora, Benchmark and Models for {I}ndic Languages", |
| author = "Doddapaneni, Sumanth and |
| Aralikatte, Rahul and |
| Ramesh, Gowtham and |
| Goyal, Shreya and |
| Khapra, Mitesh M. and |
| Kunchukuttan, Anoop and |
| Kumar, Pratyush", |
| editor = "Rogers, Anna and |
| Boyd-Graber, Jordan and |
| Okazaki, Naoaki", |
| booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
| month = jul, |
| year = "2023", |
| address = "Toronto, Canada", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2023.acl-long.693", |
| doi = "10.18653/v1/2023.acl-long.693", |
| pages = "12402--12426", |
| abstract = "Building Natural Language Understanding (NLU) capabilities for Indic languages, which have a collective speaker base of more than one billion speakers is absolutely crucial. In this work, we aim to improve the NLU capabilities of Indic languages by making contributions along 3 important axes (i) monolingual corpora (ii) NLU testsets (iii) multilingual LLMs focusing on Indic languages. Specifically, we curate the largest monolingual corpora, IndicCorp, with 20.9B tokens covering 24 languages from 4 language families - a 2.3x increase over prior work, while supporting 12 additional languages. Next, we create a human-supervised benchmark, IndicXTREME, consisting of nine diverse NLU tasks covering 20 languages. Across languages and tasks, IndicXTREME contains a total of 105 evaluation sets, of which 52 are new contributions to the literature. To the best of our knowledge, this is the first effort towards creating a standard benchmark for Indic languages that aims to test the multilingual zero-shot capabilities of pretrained language models. Finally, we train IndicBERT v2, a state-of-the-art model supporting all the languages. Averaged across languages and tasks, the model achieves an absolute improvement of 2 points over a strong baseline. The data and models are available at \url{https://github.com/AI4Bharat/IndicBERT}.", |
| } |
| ``` |
