This study proposes a language-agnostic transformer-based POS tagging framework designed for low-resource languages, using Bangla and Hindi as case studies. With only three lines of framework-specific code, the model was adapted from Bangla to Hindi, demonstrating effective portability with minimal modification. The framework achieves 96.85 percent and 97 percent token-level accuracy across POS categories in Bangla and Hindi while sustaining strong F1 scores despite dataset imbalance and linguistic overlap. A performance discrepancy in a specific POS category underscores ongoing challenges in dataset curation. The strong results stem from the underlying transformer architecture, which can be replaced with limited code adjustments. Its modular and open-source design enables rapid cross-lingual adaptation while reducing model design and tuning overhead, allowing researchers to focus on linguistic preprocessing and dataset refinement, which are essential for advancing NLP in underrepresented languages.

翻译：本研究提出了一种基于Transformer的语言无关词性标注框架，专为低资源语言设计，并以孟加拉语和印地语作为案例研究。仅需三行框架专用代码，模型即从孟加拉语适配至印地语，展现了通过最小修改实现的有效可移植性。该框架在孟加拉语和印地语的词性类别中分别实现了96.85%和97%的词元级准确率，同时在数据集不平衡和语言特征重叠的情况下保持了较高的F1分数。特定词性类别中的性能差异凸显了数据集构建中持续存在的挑战。优异性能源于底层Transformer架构，该架构可通过有限的代码调整进行替换。其模块化与开源设计支持快速跨语言适配，同时减少了模型设计与调优开销，使研究者能够专注于语言预处理和数据集优化——这些对于推进欠表征语言的自然语言处理研究至关重要。