AraBert-Arabic-Sentiment-Analysis
| Property | Value |
|---|---|
| Parameter Count | 135M |
| License | Apache 2.0 |
| Tensor Type | F32 |
| Accuracy | 80.03% |
| Macro F1 | 0.6543 |
What is AraBert-Arabic-Sentiment-Analysis?
AraBert-Arabic-Sentiment-Analysis is a fine-tuned version of AraBERT specifically designed for sentiment analysis tasks in Arabic text. This model represents a significant advancement in Arabic natural language processing, leveraging the power of transformer architecture while being optimized for sentiment-specific tasks.
Implementation Details
The model was trained using carefully selected hyperparameters, including a learning rate of 2e-05 and a total train batch size of 32. Training was conducted over 2 epochs using the Adam optimizer with betas=(0.9,0.999) and epsilon=1e-08. The model employs a linear learning rate scheduler and demonstrates stable training progression with validation loss improving from 0.5337 to 0.5327.
- Gradient accumulation steps: 2
- Training batch size: 16
- Evaluation batch size: 128
- Seed value: 25
Core Capabilities
- Arabic text sentiment analysis with 80.03% accuracy
- Robust performance with 0.6543 macro F1 score
- Efficient processing with F32 tensor type support
- Integration with Transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on Arabic sentiment analysis, achieving impressive accuracy while maintaining computational efficiency with 135M parameters. It's built on the proven AraBERT architecture and offers a practical balance between performance and resource utilization.
Q: What are the recommended use cases?
The model is particularly suited for Arabic text sentiment analysis tasks in production environments. It's ideal for applications requiring sentiment analysis of Arabic social media content, customer feedback, and general text classification tasks where sentiment understanding is crucial.
