The smallest typo fix in the guide

docs/source/en/conceptual_guides/setfit.mdx

@@ -11,7 +11,7 @@ The first phase has one primary goal: finetune a sentence transformer embedding
 
 However, models that are good at Semantic Textual Similarity (STS) are not necessarily immediately good at *our* classification task. For example, according to an embedding model, the sentence of 1) `"He biked to work."` will be much more similar to 2) `"He drove his car to work."` than to 3) `"Peter decided to take the bicycle to the beach party!"`. But if our classification task involves classifying texts into transportation modes, then we want our embedding model to place sentences 1 and 3 closely together, and 2 further away.
 
-To do so, we can finetune the chosen sentence transformer embedding model. The goal here is to nudge the model to use its pretrained knowledge in a different way that better aligns with our classification task, rather than making the completely forget what it has learned. 
+To do so, we can finetune the chosen sentence transformer embedding model. The goal here is to nudge the model to use its pretrained knowledge in a different way that better aligns with our classification task, rather than making it completely forget what it has learned. 
 
 For finetuning, SetFit uses **contrastive learning**. This training approach involves creating **positive and negative pairs** of sentences. A sentence pair will be positive if both of the sentences are of the same class, and negative otherwise. For example, in the case of binary "positive"-"negative" sentiment analysis, `("The movie was awesome", "I loved it")` is a positive pair, and `("The movie was awesome", "It was quite disappointing")` is a negative pair.
 
@@ -25,4 +25,4 @@ Once the sentence transformer embedding model has been finetuned for our task at
 
 Unlike with the first phase, training the classifier is done from scratch and using the labeled samples directly, rather than using pairs. By default, the classifier is a simple **logistic regression** classifier from scikit-learn. First, all training sentences are fed through the now-finetuned sentence transformer embedding model, and then the sentence embeddings and labels are used to fit the logistic regression classifier. The result is a strong and efficient classifier. 
 
-Using these two parts, SetFit models are efficient, performant and easy to train, even on CPU-only devices.
+Using these two parts, SetFit models are efficient, performant and easy to train, even on CPU-only devices.