metadata

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
inference: true

🍽️ 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.

🚀 Try the Model Now!

Run inference directly from the Hugging Face Space:

📥 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
Eval Loss	`0.354245`
Accuracy	`87.40%`
Precision	`85.28%`
Recall	`86.33%`
F1-score	`85.81%`

⚙️ 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,
)

💡 Usage

1️⃣ Quick Inference using `pipeline()`

from transformers import pipeline

model_name = "Abduuu/ArabReview-Sentiment"
sentiment_pipeline = pipeline("text-classification", model=model_name)

review = "الطعام كان رائعًا والخدمة ممتازة!"
result = sentiment_pipeline(review)
print(result)

✅ Example Output:

[{'label': 'Positive', 'score': 0.91551274061203}]

📉 Training & Validation Loss Curve

The model was trained for 4 epochs, and the best model was selected at Epoch 2 (lowest validation loss).

📊 Dataset Class Distribution

For questions or collaborations, feel free to reach out! 🚀