readme fixed

app.py

@@ -322,6 +322,7 @@ The full results of the tool are given below in <i>Table 1</i> below.
 | Precision  | 0.91                        | 0.93        | 0.28                 |
 | Recall     | 0.94                        | 0.16        | 0.49                 |
 | F1 Score   | 0.93                        | 0.28        | 0.36                 |
+
 <small><i>Table 1: Summary of Model Performance Metrics</i></small>
 
 ### Strengths
@@ -329,12 +330,12 @@ The full results of the tool are given below in <i>Table 1</i> below.
 
 - [The Stanford De-Identifier Base Model](https://huggingface.co/StanfordAIMI/stanford-deidentifier-base)[1] is 99% accurate on our test set of radiology reports and achieves an F1 score of 93% on our challenging open-source benchmark. The others models are really to demonstrate the potential of Pteredactyl to deploy any transfomer model.
 
-- We have submitted the code to [OHDSI](https://www.ohdsi.org/) as an abstract and aim strongly to incorporate this into a wider open-source effort to solve intractable clinical informatics problems. 
+- We have submitted the code to [OHDSI](https://www.ohdsi.org/) as an abstract and aim strongly to incorporate this into a wider open-source effort to solve intractable clinical informatics problems.
 
 ### Limitations
 - The tool was not designed initially to redact clinic letters as it was developed primarily on radiology reports in the US. We have made some augmentations to cover elements like postcodes using checksums but these might not always work. The same is true of NHS numbers as illustrated above.
 
-- It may overly aggressively redact text because it was built as a research tool where precision is prized > recall. However, in our experience this is uncommon enough that it is still very useful. 
+- It may overly aggressively redact text because it was built as a research tool where precision is prized > recall. However, in our experience this is uncommon enough that it is still very useful.
 
 - This is very much a research tool and should not be relied upon as a catch-all in any production-type capacity. The app makes the limitations very transparently obvious via the attached confusion matrix.