Dehwali Bhili Krishi Darshini

About Dataset

Bhili is an indigenous Indo-Aryan language spoken by over 10 million members of the Bhil tribal community across western and central India. Through Project Astitva, Karya collaborated with 400+ native Bhili speakers in partnership with the district administration of Nandurbar, Maharashtra, to create high-quality digital language resources for Dehwali Bhili. These efforts enabled the development of India's first tribal language model in Bhili, which has subsequently been integrated into MahaVISTAAR AI with support from the Maharashtra state government and Bhashini.The Dehwali Bhili Krishi Darshani Data and Translation Pipeline is a comprehensive dataset designed to support agricultural development in the Dehwali Bhili region. The dataset provides translations of sentences related to agriculture, facilitating communication between farmers and agricultural experts. This data can be used by researchers, policymakers, and agricultural experts to better understand the needs and challenges of farmers in the region. The dataset can also be utilized to develop targeted interventions and policies to improve agricultural productivity and sustainability. Additionally, this data can be leveraged by language learning platforms to create educational content for Dehwali Bhili speakers. Furthermore, the dataset can be used by businesses to develop marketing strategies and products tailored to the needs of farmers in the region. The dataset's primary objective is to promote agricultural development and improve the livelihoods of farmers in the Dehwali Bhili region. This dataset is part of a translation pipeline for agricultural-related content from Dehwali Bhili to Marathi, containing translations of sentences related to agricultural practices, along with their validated translations. The dataset is primarily aimed at supporting agricultural development in the region by providing accurate translations of critical information. The target audience includes agricultural experts, researchers, and policymakers. This dataset can be used to improve the accessibility of agricultural information, enhance knowledge sharing, and inform policy decisions. It can also be used to develop machine translation models, improve language understanding, and support language preservation efforts.