Update README.md

README.md

@@ -18,19 +18,28 @@ This model is a fine-tuned version of [urduhack/roberta-urdu-small](https://hugg
 It achieves the following results on the evaluation set:
 - Loss: 0.4703
 
-## Model description
+## Model Details
 
-More information needed
+- Model Name: Urdu Sentiment Classification
+- Model Architecture: RobertaForSequenceClassification
+- Base Model: urduhack/roberta-urdu-small
+- Dataset: IMDB Urdu Reviews
+- Task: Sentiment Classification (Positive/Negative)
 
-## Intended uses & limitations
+## 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:
 
-More information needed
+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.
 
-## Training and evaluation data
+3. Model Architecture: The "urduhack/roberta-urdu-small" pre-trained model was loaded as the base model for sequence classification using the RobertaForSequenceClassification class.
 
-More information needed
+4. Training Arguments: The training arguments were set, including the number of training epochs, batch size, learning rate, evaluation strategy, logging strategy, and more.
 
-## Training procedure
+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.
+
+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.
+
+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.
 
 ### Training hyperparameters