make suggested typo changes

implementation.typ

@@ -145,7 +145,7 @@ FGVCx Fungi, VGG Flower, Traffic Signs and MSCOCO~@pmfpaper]
  of diverse domains  by the authors of the original paper.~@pmfpaper
 
 Finally, this model is finetuned with the support set of every test iteration.
-Everytime the support set changes we need to finetune the model again.
+Every time the support set changes, we need to finetune the model again.
 In a real world scenario this should not be the case because the support set is fixed and only the query set changes.
 
 === Results
@@ -188,7 +188,7 @@ This brings the limitation that it can only process default few-shot learning ta
 Since it expects the input sequence to be distributed with the same number of shots per class.
 This is the reason why for this method the two imbalanced test cases couldn't be conducted.
 
-As a feture extractor a ViT-B/16 model was used, which is a Vision Transformer with a patch size of 16.
+As a feature extractor a ViT-B/16 model was used, which is a Vision Transformer with a patch size of 16.
 This feature extractor was already pretrained when used by the authors of the original paper.
 In this case for the non-causal sequence model a transformer model was used.
 It consists of 24 Layers with 16 Attention-heads and a hidden dimension of 1024 and output MLP size of 4096.
@@ -201,7 +201,7 @@ The model was trained on a large number of general purpose images and is not fin
 Moreover, it was not fine-tuned on the support set similar to the P>M>F method, which could have a huge impact on performance.
 It might also not handle very similar images well.
 
-Compared the the other two methods CAML performed poorly in almost all experiments.
+Compared the the other two methods, CAML performed poorly in almost all experiments.
 The normal few-shot classification reached only 40% accuracy in @camlperfa at best.
 The only test it did surprisingly well was the detection of the anomaly class for the cable class in @camlperfb were it reached almost 60% accuracy.