Language models based on the Transformer architecture [1] have achieved state-ofthe-art performance on a wide range of natural language processing (NLP) tasks such as text classification, question-answering, and token classification. However, this performance is usually tested and reported on high-resource languages, like English, French, Spanish, and German. Indian languages, on the other hand, are underrepresented in such benchmarks. Despite some Indian languages being included in training multilingual Transformer models, they have not been the primary focus of such work. In order to evaluate the performance on Indian languages specifically, we analyze these language models through extensive experiments on multiple downstream tasks in Hindi, Bengali, and Telugu language. Here, we compare the efficacy of fine-tuning model parameters of pre-trained models against that of training a language model from scratch. Moreover, we empirically argue against the strict dependency between the dataset size and model performance, but rather encourage task-specific model and method selection. We achieve state-of-the-art performance on Hindi and Bengali languages for text classification task. Finally, we present effective strategies for handling the modeling of Indian languages and we release our model checkpoints for the community : https://huggingface.co/neuralspace-reverie. * Equal contribution Preprint. Under review.

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Using high-performance LC (E7) filled microfabricated refractive Fresnel chambers, we experimentally demonstrate a thin low-profile adaptive optical system with high analog tunability (2.1 D) that can be integrated with an adaptive contact-lens system.

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