README.md

metadata

language: ar
tags:
  - text-classification
  - sentiment-analysis
  - levantine
  - arabic
  - marbert
  - ordinal-classification
metrics:
  - accuracy
  - f1_macro
model-index:
  - name: Levantine Sentiment Classifier (Ordinal)
    results:
      - task:
          type: text-classification
          name: Sentiment Analysis
        dataset:
          name: ArSenTD-LEV
          type: amitca71/marabert2-levantine-sentiment-model-dataset
        metrics:
          - type: accuracy
            value: 79.25%
          - type: f1_macro
            value: 0.7635

🎭 Levantine Arabic Sentiment Classifier (Ordinal MARBERTv2)

This model is a fine-tuned version of MARBERTv2, designed to predict the sentiment of Levantine Arabic tweets (Jordanian, Lebanese, Palestinian, Syrian).

Technical Highlight: This model was trained using an Ordinal Loss Function (Mean Squared Error combined with Cross-Entropy). This makes the model "distance-aware," meaning it heavily penalizes extreme mistakes (like confusing a highly positive tweet for a highly negative one). This makes its predictions far more reliable in edge cases!

📊 Performance

Metric	Score	Description
Accuracy	79.25%	Overall correctness on the test set.
F1 (Macro)	0.7635	The balanced F1 score across all 3 classes.

📖 Labels

ID	Label	Meaning
0	Negative 😠	Anger, complaints, sadness, or frustration.
1	Neutral 😐	Objective facts, mixed emotions, or ambiguous statements.
2	Positive 😃	Joy, praise, excitement, or satisfaction.

🚀 How to Use (Python)

Because this is a standard 3-class model, you can easily load it using Hugging Face's built-in pipeline.

from transformers import pipeline

# 1. Load Pipeline
model_id = "amitca71/marabert2-levantine-sentiment"
classifier = pipeline("text-classification", model=model_id)

def predict_sentiment(text):
    # Get the top prediction
    result = classifier(text)[0]

    # Format the output cleanly
    return {"text": text, "label": result['label'], "confidence": round(result['score'], 4)}

# 2. Test Examples
print(predict_sentiment("الجو اليوم بيعقد! طالعين مشوار"))            # Should be Positive
print(predict_sentiment("والله طقت روحي من هالزحمة، شي بيقرف"))        # Should be Negative
print(predict_sentiment("وصلت عالبيت من شوي."))                       # Should be Neutral

⚠️ Limitations

• Dialect Focus: Optimized heavily for Levantine Twitter. It may underperform or misunderstand idioms in Egyptian, Gulf, or Maghrebi dialects.
• The "Neutral" Bottleneck: Like most sentiment models, detecting true "Neutral" text is the most challenging, as human annotators often mix objective facts with subtle sarcasm in this category.
• Arabizi: While MARBERTv2 has some exposure to Arabizi (Arabic written in English/Latin letters), this model performs best on native Arabic script.