Building pre-train LLM Dataset for the INDIC Languages: a case study on Hindi

TL;DR

This paper presents a large Hindi dataset of 1.28 billion tokens for pre-training LLMs, addressing data scarcity in Indic languages and providing a pipeline that can be extended to other low-resource languages.

Contribution

It introduces a comprehensive Hindi dataset and a scalable pipeline for data collection and processing, facilitating LLM development in Indic languages.

Findings

Dataset contains 1.28 billion Hindi tokens.

Pipeline is adaptable for other Indic and low-resource languages.

Dataset and pipeline are freely available for research.

Abstract

Large language models (LLMs) demonstrated transformative capabilities in many applications that require automatically generating responses based on human instruction. However, the major challenge for building LLMs, particularly in Indic languages, is the availability of high-quality data for building foundation LLMs. In this paper, we are proposing a large pre-train dataset in Hindi useful for the Indic language Hindi. We have collected the data span across several domains including major dialects in Hindi. The dataset contains 1.28 billion Hindi tokens. We have explained our pipeline including data collection, pre-processing, and availability for LLM pre-training. The proposed approach can be easily extended to other Indic and low-resource languages and will be available freely for LLM pre-training and LLM research purposes.