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README.md

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 # Model Description
-BioDistilBERT-cased is the result of training the [DistilBERT-cased](https://huggingface.co/distilbert-base-cased?text=The+goal+of+life+is+%5BMASK%5D.) model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset. 
+BioDistilBERT-cased was developed by training the [DistilBERT-cased](https://huggingface.co/distilbert-base-cased?text=The+goal+of+life+is+%5BMASK%5D.) model in a continual learning fashion 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 [DistilBERT-cased](https://huggingface.co/distilbert-base-cased?text=The+goal+of+life+is+%5BMASK%5D.) model available on the Huggingface.
+We initialise our model with the pre-trained checkpoints of the [DistilBERT-cased](https://huggingface.co/distilbert-base-cased?text=The+goal+of+life+is+%5BMASK%5D.) model available on Huggingface.
 
 # Architecture
-In this model, the size of the hidden dimension and the embedding layer are both set to 768. The vocabulary size is 28996. The number of transformer layers is 6 and the expansion rate of the feed-forward layer is 4. Overall this model has around 65 million parameters.
+In this model, the size of the hidden dimension and the embedding layer are both set to 768. The vocabulary size is 28996. The number of transformer layers is 6 and the expansion rate of the feed-forward layer is 4. Overall, this model has around 65 million parameters.
 
 # Citation
 If you use this model, please consider citing the following paper: