Update README.md

README.md

@@ -18,18 +18,16 @@ pipeline_tag: text-classification
 
 ## 📌 Model Description
 
-This model is a fine-tuned version** of `RoBERTa-base-bne`, specifically trained to classify the toxicity level of **Spanish-language user comments on news articles**. It distinguishes between tow categories:
+This model is a fine-tuned version** of `RoBERTa-base-bne`, specifically trained to classify the toxicity level of **Spanish-language user comments on news articles**. It distinguishes between two categories:
 
 - **Non-toxic**
 - **Toxic**
 
-The model follows instruction-based prompts and returns a single classification label in response.
-
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 ## 📂 Training Data
 
-The model was fine-tuned on the **[SocialTOX dataset](https://huggingface.co/datasets/gplsi/SocialTOX)**, a collection of Spanish-language comments annotated for varying levels of toxicity. These comments come from news platforms and represent real-world scenarios of online discourse. In this case, a Binary classifier was develop, where the classes \textit{Slightly toxic} and \textit{Toxic} were merged into a single \textit{Toxic} category.  
+The model was fine-tuned on the **[SocialTOX dataset](https://huggingface.co/datasets/gplsi/SocialTOX)**, a collection of Spanish-language comments annotated for varying levels of toxicity. These comments come from news platforms and represent real-world scenarios of online discourse. In this case, a Binary classifier was developed, where the classes \textit{Slightly toxic} and \textit{Toxic} were merged into a single \textit{Toxic} category.  
 
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