  Recent advancements in language models based on recurrent neural networks and
transformers architecture have achieved state-of-the-art results on a wide
range of natural language processing tasks such as pos tagging, named entity
recognition, and text classification. However, most of these language models
are 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 the
linguistic features of these languages as they are not the primary language of
the 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 Hindi
dependency treebank to analyze the performance of these contextualized string
embeddings for the Hindi language. Results show that HinFlair outperforms
previous state-of-the-art publicly available pre-trained embeddings for
downstream tasks like text classification and pos tagging. Also, HinFlair when
combined with FastText embeddings outperforms many transformers-based language
models trained particularly for the Hindi language.
