Update README.md

README.md

@@ -45,19 +45,19 @@ It achieves the following results on the evaluation set:
 - Task: Sentiment Classification (Positive/Negative)
 
 ## Training Procedure
-1. The model was fine-tuned using the transformers library and the Trainer class from Hugging Face. The training process involved the following steps:
+The model was fine-tuned using the transformers library and the Trainer class from Hugging Face. The training process involved the following steps:
 
-2. Tokenization: The input Urdu text was tokenized using the RobertaTokenizerFast from the "urduhack/roberta-urdu-small" pre-trained model. The texts were padded and truncated to a maximum length of 256 tokens.
+1. Tokenization: The input Urdu text was tokenized using the RobertaTokenizerFast from the "urduhack/roberta-urdu-small" pre-trained model. The texts were padded and truncated to a maximum length of 256 tokens.
 
-3. Model Architecture: The "urduhack/roberta-urdu-small" pre-trained model was loaded as the base model for sequence classification using the RobertaForSequenceClassification class.
+2. Model Architecture: The "urduhack/roberta-urdu-small" pre-trained model was loaded as the base model for sequence classification using the RobertaForSequenceClassification class.
 
-4. Training Arguments: The training arguments were set, including the number of training epochs, batch size, learning rate, evaluation strategy, logging strategy, and more.
+3. Training Arguments: The training arguments were set, including the number of training epochs, batch size, learning rate, evaluation strategy, logging strategy, and more.
 
-5. Training: The model was trained on the training dataset using the Trainer class. The training process was performed with gradient-based optimization techniques to minimize the cross-entropy loss between predicted and actual sentiment labels.
+4. Training: The model was trained on the training dataset using the Trainer class. The training process was performed with gradient-based optimization techniques to minimize the cross-entropy loss between predicted and actual sentiment labels.
 
-6. Evaluation: After each epoch, the model was evaluated on the validation dataset to monitor its performance. The evaluation results, including training loss and validation loss, were logged for analysis.
+5. Evaluation: After each epoch, the model was evaluated on the validation dataset to monitor its performance. The evaluation results, including training loss and validation loss, were logged for analysis.
 
-7. Fine-Tuning: The model parameters were fine-tuned during the training process to optimize its performance on the IMDb Urdu movie reviews sentiment analysis task.
+6. Fine-Tuning: The model parameters were fine-tuned during the training process to optimize its performance on the IMDb Urdu movie reviews sentiment analysis task.
 
 ### Training hyperparameters