Update README.md

README.md

@@ -47,15 +47,14 @@ This was later utilized to prompt the Transformer's Masked Language Modeling(MLM
 Additionally, Data Engineers that work with patient electronic records consistently spend an excessive amount of time parsing through the unstructured discharge notes format to accomplish their tasks.
 The solution will be instrumental for agents who are not directly facing patients but hold back-end roles that are also of immense importance.
 
-Data Engineer?
 #### Use Cases
 This model allows for the efficient summarization of complexly documented doctor notes. It provides instant access to insight with proper semantic cues in place.
 
-##### Limitations & Future Aspirations
-With an increased amount of data, more deliberate results might be achieved through more training. Also, further improvements on the model's summarization capabilities have been considered.
+#### Limitations & Future Aspirations
+With an increased amount of data, more deliberate results might be achieved through more training. Synthetic transcriptions could be created with GPT models to in turn train on. Also, further improvements on the model's summarization capabilities have been considered.
 One of which is implementing summarization based on clustered titles within the discharge notes. The feature would allow for easier traversal through partitioned summarization and result in better structure.
 
-##### Training and evaluation data
+#### Training and evaluation data
 The generated summaries were assigned to the original transcription and after splitting the data into the train and test sets, the table was converted into a json file.
 The structure allowed us to effectively train the model on the premise of transcription to summarization prompts. After all the metrics were evaluated, a number of medical transcriptions were generated through
 generative transformers to summarize and upon testing the model performed well.