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introduction.md

@@ -39,7 +39,7 @@ To get competitive results we followed three strategies:
 
 For those interested, we have a :comet: [Comet](https://www.comet.ml/g8a9/clip-italian/reports/clip-italian-training-metrics) report 
 that shows a **subset** of the experiments we run. Different hyper-parameters played a role in reducing the validation 
-loss. The optimizer gave us great performance and huge conversion speed, more data and augmentations helped a lot in generalizing,
+loss. The optimizer we used gave us great performance and huge conversion speed, more data and augmentations helped a lot in generalizing,
 working on the training and on the loss gave us the final increase that you can see in the results.
 
 ## More and Better Data