We introduce EELBERT, an approach for compression of transformer-based models (for example, BERT), with minimal impact on the accuracy of downstream tasks. This is achieved by replacing the input embedding layer of the model with dynamic, for example, on-the-fly, embedding computations. Since the input embedding layer accounts for a significant fraction of the model size, especially for the smaller BERT variants, replacing this layer with an embedding computation function helps us reduce the model size significantly. Empirical evaluation on the GLUE benchmark shows that our BERT variants (EELBERT) suffer minimal regression compared to the traditional BERT models. Through this approach, we are able to develop our smallest model, UNO-EELBERT, which achieves a GLUE score within 4% of fully trained BERT-tiny while being 15x smaller (1.2 MB) in size.