In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
<fluency> In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanksto deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ [ [ [ [ [ . But these models are humongous in size.