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 .

翻译：低资源语言马拉地语的文本或主题分类数据集极为有限，通常仅有不到4个目标标签，部分数据集甚至已达到近乎完美的准确率。本文提出L3Cube-MahaNews——一个聚焦于新闻标题与文章的马拉地语文本分类语料库。该语料库是当前规模最大的有监督马拉地语语料库，包含超过10.5万条记录，涵盖12个多样化的类别。为适配不同文档长度，MahaNews包含三个专门针对短文本、长文档及中等长度段落的有监督数据集。这些数据集采用一致的标签体系，便于进行基于文档长度的分析。我们提供了详细的数据统计信息，并采用最先进的预训练BERT模型在这些数据集上建立了基线结果。我们对单语与多语BERT模型（包括MahaBERT、IndicBERT和MuRIL）进行了比较分析。实验表明，单语MahaBERT模型在所有数据集上的表现均优于其他模型。这些资源亦可作为马拉地语主题分类数据集或模型使用，并已公开于https://github.com/l3cube-pune/MarathiNLP。