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Recent advancements in language models based on recurrent neural networks andtransformers architecture have achieved state-of-the-art results on a widerange of natural language processing tasks such as pos tagging, named entityrecognition, and text classification. However, most of these language modelsare pre-trained in high resource languages like English, German, Spanish.Multi-lingual language models include Indian languages like Hindi, Telugu,Bengali in their training corpus, but they often fail to represent thelinguistic features of these languages as they are not the primary language ofthe study. We introduce HinFlair, which is a language representation model(contextual string embeddings) pre-trained on a large monolingual Hindi corpus.Experiments were conducted on 6 text classification datasets and a Hindidependency treebank to analyze the performance of these contextualized stringembeddings for the Hindi language. Results show that HinFlair outperformsprevious state-of-the-art publicly available pre-trained embeddings fordownstream tasks like text classification and pos tagging. Also, HinFlair whencombined with FastText embeddings outperforms many transformers-based languagemodels trained particularly for the Hindi language.

HinFlair: pre-trained contextual string embeddings for pos tagging and text classification in the Hindi language