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--- |
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license: apache-2.0 |
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language: |
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- ar |
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pipeline_tag: text-generation |
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tags: |
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- 'arabic ' |
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- text-generation |
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widget: |
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- text: "أعلنت وزارة الحج في المملكة العربية السعودية" |
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example_title: "مثال ١" |
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- text: "يبدو اليوم جميلا، سأقوم بتحضير" |
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example_title: "مثال ٢" |
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- text: "إن التقنيات الحديثة" |
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example_title: "مثال ٣" |
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--- |
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# ArabianGPT Model Overview |
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## Disclaimer for the Use of Large Language Models (LLMs) for Text Generation |
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<p style="color: red;">We disclaim all responsibility for any harm, inaccuracies, or inappropriate content generated by ArabianGPT-0.1B, and users engage with and apply the model's outputs at their own risk.</p> |
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> **Important Note:** Currently, we offer a raw pre-trained model. Our team is actively working on releasing instruction-based LLMs that are fine-tuned and augmented with LRHF. The first set of pre-trained models has been made available for community exploration. While we do have models fine-tuned for specific tasks such as summarization and sentiment analysis, they are still in the development phase. |
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## How you can use this Pre-Trained? |
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You are invited to utilize this pre-trained, native Arabic language model as an experimental tool to assess its capabilities, aid in its fine-tuning, and evaluate its performance across a variety of downstream tasks. We encourage you to review our technical report for a comprehensive understanding of the model's performance metrics and the specific downstream tasks it has been tested on. This will provide valuable insights into its applicability and effectiveness in diverse applications. |
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## Introduction |
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ArabianGPT-0.1B, developed under the ArabianLLM initiatives, is a specialized GPT-2 model optimized for Arabic language modeling. |
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It's a product of the collaborative efforts at Prince Sultan University's Robotics and Internet of Things Lab, focusing on enhancing natural language modeling and generation in Arabic. |
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This model represents a significant stride in LLM research, specifically addressing the linguistic complexities and nuances of the Arabic language. |
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## Key Features |
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- **Architecture**: GPT-2 |
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- **Model Size**: 134 million parameters |
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- **Layers**: 12 |
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- **Model Attention Layers (MAL)**: 12 |
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- **Context Window Size**: 768 tokens |
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## Training |
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- **Dataset**: Scraped Arabic newspaper articles |
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- **Data Size**: 15.5 GB |
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- **Words**: 237.8 million |
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- **Tokenizer**: Aranizer 64K |
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- **Tokens**: Over 1.75 billion |
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- **Hardware**: 2 NDIVIA A100 GPUs |
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- **Training Scale**: 7.5 million examples |
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- **Training Duration**: 3 days |
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- **Performance**: Final loss of 3.97 |
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## Role in ArabianLLM Initiatives |
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ArabianGPT-0.1B (Base Model) is crucial for advancing Arabic language processing, addressing challenges unique to Arabic morphology and dialects. |
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## Usage |
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Suitable for Arabic text generation tasks. Example usage with Transformers Pipeline: |
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```python |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="riotu-lab/ArabianGPT-01B", max_new_tokens=512) |
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text = '' |
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pipe.predict(text) |
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``` |
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## Limitations and Ethical Considerations |
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- The model may have context understanding or text generation limitations in certain scenarios. |
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- Emphasis on ethical use to prevent misinformation or harmful content propagation. |
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## Acknowledgments |
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Special thanks to Prince Sultan University, particularly the Robotics and Internet of Things Lab. |
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## Contact Information |
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For inquiries: [[email protected]](mailto:[email protected]). |
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## Disclaimer for the Use of Large Language Models (LLMs) for Text Generation |
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<p style="color: red;">We disclaim all responsibility for any harm, inaccuracies, or inappropriate content generated by ArabianGPT-0.1B, and users engage with and apply the model's outputs at their own risk.</p> |
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