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| license: mit |
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| CoreXDataSet/README.md |
| ```md |
| # CoreXDataSet |
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| ## Overview |
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| CoreXDataSet is a comprehensive multi-modal dataset curated specifically for training and evaluating the OmniCoreX AI model — the ultimate AI brain designed for integrating infinite knowledge streams with adaptive reasoning and real-time decision-making capabilities. |
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| This dataset includes diverse data modalities such as text, images, sensor readings, audio, and more, enabling OmniCoreX to learn cross-modal representations and perform advanced multi-stream reasoning across varied real-world scenarios. |
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| ## Contents |
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| - **Text**: Rich corpora including encyclopedic knowledge, technical documents, and conversational data. |
| - **Images**: High-resolution images covering a wide variety of domains such as nature, urban scenes, and technology. |
| - **Sensor Data**: Time-series sensor recordings from IoT devices, robotics, and mobile platforms. |
| - **Audio**: Speech and environmental audio clips for audio pattern understanding and integration. |
| - **Labels/Annotations**: Metadata and annotations required for supervised learning tasks. |
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| ## Dataset Structure |
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| ``` |
| CoreXDataSet/ |
| ├── metadata.json # Descriptions and references for dataset samples |
| ├── text/ # Directory containing text files or JSON documents |
| ├── images/ # Directory containing images in jpeg/png format |
| ├── sensors/ # CSV or binary files for sensor data sequences |
| ├── audio/ # Audio clips in WAV/MP3 format |
| └── annotations/ # Optional annotations for supervised tasks |
| ``` |
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| ## License |
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| CoreXDataSet is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Please review the LICENSE file for more details. |
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| ## Usage |
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| ### Accessing the Data |
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| Download and extract the CoreXDataSet archive. Use the provided metadata file to index and load samples efficiently using the OmniCoreX data loader utilities. |
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| ### Integration |
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| CoreXDataSet is designed for seamless integration with the OmniCoreX training pipelines and model architectures. Utilize the dataset modules and data loaders included within the OmniCoreX repository. |
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| ## Citation |
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| If you use CoreXDataSet in your research, please cite it as: |
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| ``` |
| @dataset{corexdataset2024, |
| title={CoreXDataSet: Multi-Modal Dataset for OmniCoreX AI}, |
| author={Kosasih, Team}, |
| year={2024}, |
| publisher={OmniCoreX Initiative}, |
| url={https://github.com/KOSASIH/CoreXDataSet} |
| } |
| ``` |
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| ## Contribution |
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| We welcome contributions to enhance CoreXDataSet with new modalities, expanded annotations, and improved quality. Please see the CONTRIBUTING.md file in the dataset repository for guidelines. |
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| ## Contact |
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| For inquiries, questions, or support related to CoreXDataSet: |
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| - Email: support@omnicorex.ai |
| - GitHub: https://github.com/KOSASIH/CoreXDataSet |
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| Empower your AI research with the rich and diverse CoreXDataSet — training the next generation AI brain. |
| ``` |
