  The availability of text or topic classification datasets in the low-resource
Marathi language is limited, typically consisting of fewer than 4 target
labels, with some achieving nearly perfect accuracy. In this work, we introduce
L3Cube-MahaNews, a Marathi text classification corpus that focuses on News
headlines and articles. This corpus stands out as the largest supervised
Marathi Corpus, containing over 1.05L records classified into a diverse range
of 12 categories. To accommodate different document lengths, MahaNews comprises
three supervised datasets specifically designed for short text, long documents,
and medium paragraphs. The consistent labeling across these datasets
facilitates document length-based analysis. We provide detailed data statistics
and baseline results on these datasets using state-of-the-art pre-trained BERT
models. We conduct a comparative analysis between monolingual and multilingual
BERT models, including MahaBERT, IndicBERT, and MuRIL. The monolingual MahaBERT
model outperforms all others on every dataset. These resources also serve as
Marathi topic classification datasets or models and are publicly available at
https://github.com/l3cube-pune/MarathiNLP .
