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 # Model Description
-BioMobileBERT is the result of training the [MobileBERT-uncased](https://huggingface.co/google/mobilebert-uncased) model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset. 
+BioMobileBERT is the result of training the [MobileBERT-uncased](https://huggingface.co/google/mobilebert-uncased) model in a continual learning scenario for 200k training steps using a total batch size of 192 on the PubMed dataset. 
 
 # Initialisation
-We initialise our model with the pre-trained checkpoints of the [MobileBERT-uncased](https://huggingface.co/google/mobilebert-uncased) model available on the Huggingface.
+We initialise our model with the pre-trained checkpoints of the [MobileBERT-uncased](https://huggingface.co/google/mobilebert-uncased) model available on Huggingface.
 
 # Architecture
 MobileBERT uses a 128-dimensional embedding layer followed by 1D convolutions to up-project its output to the desired hidden dimension expected by the transformer blocks. For each of these blocks, MobileBERT uses linear down-projection at the beginning of the transformer block and up-projection at its end, followed by a residual connection originating from the input of the block before down-projection. Because of these linear projections, MobileBERT can reduce the hidden size and hence the computational cost of multi-head attention and feed-forward blocks. This model additionally incorporates up to four feed-forward blocks in order to enhance its representation learning capabilities. Thanks to the strategically placed linear projections, a 24-layer MobileBERT (which is used in this work) has around 25M parameters.