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license: apache-2.0 datasets: - hadyelsahar/ar_res_reviews language: - ar metrics: - accuracy - precision - recall - f1 base_model: - aubmindlab/bert-base-arabertv02 pipeline_tag: text-classification tags: - arabic - sentiment-analysis - transformers - huggingface - bert - restaurants - fine-tuning - nlp

🍽️ Arabic Restaurant Review Sentiment Analysis 🚀

📌 Overview

This fine-tuned AraBERT model classifies Arabic restaurant reviews as Positive or Negative.
It is based on aubmindlab/bert-base-arabertv2 and fine-tuned using Hugging Face Transformers.

🔥 Why This Model?

✅ Trained on Real Restaurant Reviews from the Hugging Face Dataset.
✅ Fine-tuned with Full Training (not LoRA or Adapters).
✅ Balanced Dataset (2418 Positive vs. 2418 Negative Reviews).
✅ High Accuracy & Performance for Sentiment Analysis in Arabic.

📥 Dataset & Preprocessing

Dataset Source: hadyelsahar/ar_res_reviews
Text Cleaning:
- Removed non-Arabic text, special characters, and extra spaces.
- Normalized Arabic characters (إ, أ, آ → ا, ة → ه).
- Balanced Positive & Negative sentiment distribution.
Tokenization:
- Used AraBERT tokenizer (aubmindlab/bert-base-arabertv2).
Train-Test Split:
- 80% Training | 20% Testing.

🏋️ Training & Performance

The model was fine-tuned using Hugging Face Transformers with the following hyperparameters:

📊 Final Model Results

Metric	Score
Train Loss	`0.470`
Eval Loss	`0.373`
Accuracy	`86.41%`
Precision	`87.01%`
Recall	`86.49%`
F1-score	`86.75%`

⚙️ Training Configuration

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    save_strategy="epoch",
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=4,
    weight_decay=1,
    learning_rate=1e-5,
    lr_scheduler_type="cosine",
    warmup_ratio=0.1,
    fp16=True,
    save_total_limit=2,
    gradient_accumulation_steps=2,
    load_best_model_at_end=True,
    max_grad_norm=1.0,
    metric_for_best_model="eval_loss",
    greater_is_better=False,
)