Update README.md

Fixed appearance of benchmark metrics. Fixed training description.

README.md

@@ -34,9 +34,10 @@ tags:
 
 This is a Keras 3.x model trained specifically to detect malicious SQLs. It is able to detect various SQL injection vectors such as Error-based, Union-based, Blind, Boolean-based
 ,Time-based, Out-of-band, Stacked queries. This was trained on ~167K SQLs containing an almost even distribution of malicious and benign SQLs. Its training involved preprocessing specifically for
-SQL with special masking tokens. 28 additional numeric features were also generated and top 10 among them were selected for training using Recursive Feature Elimination. The training consisted of a warm-up period with a smaller, sinusoidally decaying learning rate 
-followed by a higher learning rate with cosine decay. A special callback was used to monitor for and protect against gradient
-explosions. Weight and kernel constraints are applied to help prevent overfitting and achieve better generalization.
+SQL with special masking tokens. 28 additional numeric features were also generated and the top 10 among them were selected for training using Recursive Feature Elimination. The training consisted of a warm-up period with a smaller, sinusoidally decaying learning rate 
+followed by a higher learning rate with cosine decay. A special callback was used to monitor for and protect against gradient explosions and 
+automatically adjust the learning rate and model weights based on the scale of the explosion.
+Weight and kernel constraints are applied to help prevent overfitting and achieve better generalization.
 For faster model loading and inference, [mixed precision](https://www.tensorflow.org/guide/mixed_precision) has been used.
 
 The best checkpoint has been saved and made available for use.
@@ -57,12 +58,12 @@ available [here](dataset/benchmark.csv). The data was curated from the following
 
 #### Benchmark Results
 
-Total SQLs: 30919
-Total Negatives: 11382
-Total Positives: 19537
-Total hits: 30844/30919 with accuracy of 99.76%.
-False Negatives: 69(0.61%)       
-False Positives: 6(0.03%)
+**Total SQLs:** 30919  
+**Total Negatives:** 11382  
+**Total Positives:** 19537  
+**Total hits:** 30844/30919 with accuracy of **99.76%**.  
+**False Negatives:** 69 - **0.61%**  
+**False Positives:** 6  - **0.03%**
 
 #### Training Data