Update README.md

README.md

@@ -28,17 +28,29 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Arabic-Book-Review-Sentiment-Assessment
 
-This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on an unknown dataset.
+This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on [labr](https://huggingface.co/datasets/labr) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 1.5290
 
 ## Model description
 
-More information needed
+The purpose of this model is to analyze Arabic review texts and predict the appropriate rating for them, based on the sentiment and content of the review. 
+This can be particularly useful in tasks such as sentiment analysis, customer feedback analysis, or any application where understanding the sentiment conveyed in an Arabic textual review is important.
+
+The labels associated with the ratings are `LABEL_0`, `LABEL_1`, `LABEL_2`, `LABEL_3`, and `LABEL_4`. These labels can be interpreted as follows:
+
+- `LABEL_0`: Poor
+- `LABEL_1`: Fair
+- `LABEL_2`: Good
+- `LABEL_3`: Very Good
+- `LABEL_4`: Excellent
+
 
 ## Intended uses & limitations
 
-More information needed
+While the model performs well with formal Arabic text (Examples 1, 3, and 4), it may struggle with slang or informal language, occasionally assigning higher ratings than expected (Example 2). 
+Additionally, the model is not capable of interpreting verbally given ratings (Example 5). 
+Users should be aware of these limitations and provide context-appropriate input for optimal performance.
 
 ## Training and evaluation data
 
@@ -46,6 +58,7 @@ More information needed
 
 ## Training procedure
 
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: