Update README.md

README.md

@@ -33,13 +33,13 @@ tags:
 ## Overview
 
 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. SQiD's training involved preprocessing specifically for
-SQL with special masking tokens. The training consisted of a warm-up period with a smaller, sinusoidally decaying learning rate 
+,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.
 For faster model loading and inference, [mixed precision](https://www.tensorflow.org/guide/mixed_precision) has been used.
 
-The best checkpoint is saved and made available for use.
+The best checkpoint has been saved and made available for use.
 
 **CONTEXT WINDOW:** 1200 tokens  
 **PARAMETERS:**  30.7M *(**Trainable:** 7.7M, **Frozen:** 2K, **Optimizer:** 23M)*