Update README.md

README.md

@@ -57,7 +57,7 @@ And now the hypothesis in French and the premise in English (cross-language cont
 # Zero-shot Classification
 The primary interest of training such models lies in their zero-shot classification performance. This means that the model is able to classify any text with any label
 without a specific training. What sets the Bloomz-3b-NLI LLMs apart in this domain is their ability to model and extract information from significantly more complex
-and lengthy test structures compared to models like BERT, RoBERTa, or CamemBERT.
+and lengthy text structures compared to models like BERT, RoBERTa, or CamemBERT.
 
 The zero-shot classification task can be summarized by:
 $$P(hypothesis=i\in\mathcal{C}|premise)=\frac{e^{P(premise=entailment\vert hypothesis=i)}}{\sum_{j\in\mathcal{C}}e^{P(premise=entailment\vert hypothesis=j)}}$$