model_card.md

metadata

language: ar
license: mit
library_name: transformers
pipeline_tag: text-classification
datasets:
  - custom
tags:
  - arabic
  - text-classification
  - iraqi-dialect
  - msa
  - message-classification
  - xlm-roberta
widget:
  - text: السلام عليكم ورحمة الله وبركاته
    example_title: Arabic Greeting
  - text: شلونك اليوم؟
    example_title: Iraqi Question
  - text: عندي مشكلة بالانترنت
    example_title: Iraqi Complaint
  - text: أحب القراءة كثيراً
    example_title: General Statement
model-index:
  - name: Arabic_MI_Classifier
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          type: custom
          name: Arabic Messages Dataset
        metrics:
          - type: accuracy
            value: 0.95
            name: Accuracy

Arabic Message Classification Model

This model fine-tunes XLM-RoBERTa for Arabic message classification, supporting both Modern Standard Arabic (MSA) and Iraqi dialect.

Model Description

• Developed by: Ahmed Majid
• Model type: XLM-RoBERTa for Sequence Classification
• Language(s): Arabic (MSA and Iraqi dialect)
• License: MIT
• Finetuned from model: morit/arabic_xlm_xnli

Intended Uses

Direct Use

This model can be used for:

• Classifying Arabic messages in customer service systems
• Organizing Arabic text messages by intent
• Building chatbots for Arabic-speaking users
• Content moderation for Arabic forums and social media

Downstream Use

The model can be further fine-tuned for:

• Other Arabic dialects
• Domain-specific message classification
• Multi-label classification tasks

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_name = "ahmedmajid92/Arabic_MI_Classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = classifier("السلام عليكم")
print(result)

Training Details

Training Data

The model was trained on a custom dataset of 5,000 Arabic messages:

• 50% Modern Standard Arabic (MSA)
• 50% Iraqi dialect
• 4 classes: greeting, question, complaint, general
• Balanced distribution: 1,250 examples per class

Training Procedure

Preprocessing

• Tokenization using XLM-RoBERTa tokenizer
• Maximum sequence length: 128 tokens
• Padding and truncation applied

Training Hyperparameters

• Training regime: fp32
• Epochs: 20
• Batch size: 8 (training), 16 (evaluation)
• Learning rate: Default AdamW
• Warmup steps: Not specified
• Weight decay: Default
• Optimizer: AdamW

Speeds, Sizes, Times

• Model size: ~280M parameters
• Training time: Approximately 2-3 hours on GPU
• Inference time: ~50ms per message on GPU

Evaluation

Testing Data, Factors & Metrics

Testing Data

• 10% of the custom dataset (500 examples)
• Balanced across all 4 classes
• Mix of MSA and Iraqi dialect

Factors

• Dialects: MSA vs Iraqi dialect
• Message length: Short to medium messages
• Domain: General conversational messages

Metrics

• Accuracy: Primary metric
• Per-class performance: Evaluated for each label

Results

The model achieves good performance across all classes with particular strength in:

• Greeting detection (both MSA and Iraqi)
• Question identification
• Complaint classification
• General statement recognition

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator.

Technical Specifications

Model Architecture and Objective

• Architecture: XLM-RoBERTa with classification head
• Objective: Multi-class text classification
• Base model: morit/arabic_xlm_xnli
• Classification head: 4 output classes

Compute Infrastructure

Hardware

• GPU: NVIDIA GPU (recommended)
• Memory: 8GB+ GPU memory recommended

Software

• Framework: PyTorch
• Libraries: Transformers, Datasets
• Python version: 3.8+

Bias, Risks, and Limitations

Bias

• The model may exhibit biases present in the training data
• Performance may vary between different Arabic dialects
• Regional variations in Iraqi dialect may not be fully captured

Risks

• Misclassification of ambiguous messages
• Potential cultural bias in greeting/complaint detection
• Limited generalization to formal/informal register variations

Limitations

• Only supports 4 predefined classes
• Optimized for MSA and Iraqi dialect specifically
• Maximum input length of 128 tokens
• May not generalize well to other Arabic dialects

Additional Information

Author

Ahmed Majid

Licensing Information

This model is released under the MIT License.

Citation Information

@misc{arabic-mi-classifier,
  title={Arabic Message Classification Model},
  author={Ahmed Majid},
  year={2025},
  howpublished={Hugging Face Model Hub},
  url={https://huggingface.co/ahmedmajid92/Arabic_MI_Classifier}
}

Acknowledgments

• Based on the XLM-RoBERTa model by Facebook AI
• Fine-tuned from morit/arabic_xlm_xnli
• Trained on custom Arabic message dataset