Update README.md

README.md

@@ -29,18 +29,67 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model is a fine-tuned version of
+[aubmindlab/bert-base-arabertv02](aubmindlab/bert-base-arabertv02)
+,
+adapted for Arabic Sentiment Analysis.
+
+The model is trained to classify Arabic text into binary sentiment classes (Positive / Negative).
+It is suitable for analyzing opinions expressed in Modern Standard Arabic (MSA) as well as dialectal Arabic, commonly found in social media posts, product reviews, and user feedback.
+
+The model benefits from AraBERT’s strong contextual understanding of Arabic morphology and syntax, resulting in high classification accuracy.
 
 ## Intended uses & limitations
 
-More information needed
+This model can be used for:
+
+Arabic sentiment analysis
+
+Social media opinion mining
+
+Customer feedback analysis
+
+Academic research and NLP experiments
+
+Graduation and portfolio projects
+
+It is designed for inference on short to medium-length Arabic texts.
+
+Limitations
+
+The model performs binary sentiment classification only (no neutral class).
+
+Performance may degrade on very long documents.
 
 ## Training and evaluation data
 
-More information needed
+Training and Evaluation Data
+
+The model was trained and evaluated using the [ramybaly/arsentd_lev dataset](ramybaly/arsentd_lev) dataset, which consists of Arabic text labeled for sentiment polarity.
+
+Dataset Characteristics
+
+Language: Arabic
+
+Labels: Positive, Negative
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+Text Type: Short Arabic opinions and statements
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+Domains: General opinionated text
+
+The dataset was split into training, evaluation, and test sets following standard supervised learning practices.
+
 
 ## Training procedure
 
+Preprocessing
+
+Arabic text normalization handled by AraBERT tokenizer
+
+Tokenization using the AraBERT v02 tokenizer
+
+Padding and truncation applied to ensure fixed input length
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: