Update README.md

README.md

@@ -48,17 +48,13 @@ print(result)
 
 ## Model description
 
-This model, based on the RoBERTa architecture (roberta-base), is fine-tuned for a sentiment classification task specific to the finance sector. It is designed to  
-classify auditor reports into three sentiment categories: "negative", "neutral", and "positive". This capability can be crucial for financial analysis,  
-investment decision-making, and trend analysis in financial reports.
+This model, based on the RoBERTa architecture (roberta-base), is fine-tuned for a sentiment classification task specific to the finance sector. It is designed to classify auditor reports into three sentiment categories: "negative", "neutral", and "positive". This capability can be crucial for financial analysis, investment decision-making, and trend analysis in financial reports.
 
 ## Intended uses & limitations
 
 ### Intended Uses
 
-This model is intended for professionals and researchers working in the finance industry who require an automated tool to assess the sentiment conveyed in textual  
-data, specifically auditor reports. It can be integrated into financial analysis systems to provide quick insights into the sentiment trends, which can aid in  
-decision-making processes.
+This model is intended for professionals and researchers working in the finance industry who require an automated tool to assess the sentiment conveyed in textual data, specifically auditor reports. It can be integrated into financial analysis systems to provide quick insights into the sentiment trends, which can aid in decision-making processes.
 
 ### Limitations
 
@@ -70,29 +66,23 @@ decision-making processes.
 
 ### Training Data
 
-The model was trained on a proprietary dataset FinanceInc/auditor_sentiment sourced from Hugging Face datasets, which consists of labeled examples of auditor reports.  
-Each report is annotated with one of three sentiment labels: negative, neutral, and positive.
+The model was trained on a proprietary dataset FinanceInc/auditor_sentiment sourced from Hugging Face datasets, which consists of labeled examples of auditor reports. Each report is annotated with one of three sentiment labels: negative, neutral, and positive.
 
 ### Evaluation Data
 
-The evaluation was conducted using a split of the same dataset. The data was divided into training and validation sets with a sharding method to ensure a diverse  
-representation of samples in each set.
+The evaluation was conducted using a split of the same dataset. The data was divided into training and validation sets with a sharding method to ensure a diverse representation of samples in each set.
 
 ## Training Procedure
 
-The model was fine-tuned for 5 epochs with a batch size of 8 for both training and evaluation. An initial learning rate of 5e-5 was used with a warm-up step of 500  
-to prevent overfitting at the early stages of training. The best model was selected based on its performance on the validation set, and only the top two performing  
-models were saved to conserve disk space.
+The model was fine-tuned for 5 epochs with a batch size of 8 for both training and evaluation. An initial learning rate of 5e-5 was used with a warm-up step of 500 to prevent overfitting at the early stages of training. The best model was selected based on its performance on the validation set, and only the top two performing models were saved to conserve disk space.
 
 ## Evaluation Metrics
 
-Evaluation metrics included accuracy, macro precision, macro recall, and macro F1-score, calculated after each epoch. These metrics helped monitor the model's  
-performance and ensure it generalized well beyond the training data.
+Evaluation metrics included accuracy, macro precision, macro recall, and macro F1-score, calculated after each epoch. These metrics helped monitor the model's performance and ensure it generalized well beyond the training data.
 
 ## Model Performance
 
-The final model's performance on the test set will be reported in terms of accuracy, precision, recall, and F1-score to provide a comprehensive overview  
-of its predictive capabilities.
+The final model's performance on the test set will be reported in terms of accuracy, precision, recall, and F1-score to provide a comprehensive overview of its predictive capabilities.
 
 ## Model Status