| # Dataset Card for matrix_operations | |
| ## Dataset Description | |
| - **Homepage:** [Add homepage URL if available] | |
| - **Repository:** [Add repository URL] | |
| - **Point of Contact:** [Add contact name/email] | |
| ### Dataset Summary | |
| Synthetic matrix operations dataset for ML training | |
| This dataset was automatically generated using the HF Dataset Generator v2.2 with TensorFlow.js backend (webgl). | |
| ### Supported Tasks | |
| - Matrix operation prediction | |
| - Computational performance benchmarking | |
| - Synthetic data for ML training | |
| - Algorithm validation and testing | |
| ### Languages | |
| English | |
| ## Dataset Structure | |
| ### Data Instances | |
| Each instance contains: | |
| - Unique sample ID | |
| - Generation timestamp | |
| - Matrix size (n×n) | |
| - List of operations performed with: | |
| - Operation type | |
| - Execution time in milliseconds | |
| - Input matrices | |
| - Result matrices | |
| - Error messages (if any) | |
| ### Data Fields | |
| - `id`: Unique identifier (string) | |
| - `timestamp`: Generation timestamp (string) | |
| - `matrix_size`: Dimension of matrices (int32) | |
| - `operations`: List of operations performed (list of dicts) | |
| ### Data Splits | |
| - **Train:** 400 samples | |
| - **Test:** 100 samples | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| This dataset was created to provide synthetic matrix operation data for machine learning research, benchmarking computational kernels, and testing numerical algorithms. | |
| ### Source Data | |
| Synthetically generated using TensorFlow.js matrix operations with random normal distributions. | |
| ### Annotations | |
| No human annotations. | |
| ### Personal and Sensitive Information | |
| None. All data is synthetically generated. | |
| ## Considerations for Using the Data | |
| ### Social Impact | |
| This dataset enables research in computational mathematics, machine learning optimization, and numerical analysis education. | |
| ### Discussion of Biases | |
| Matrices are randomly generated from normal distributions (mean=0, std=1). Real-world matrices may have different distributions. | |
| ### Other Known Limitations | |
| 1. Matrix inverse may fail for singular matrices | |
| 2. Performance timing varies by hardware (webgl backend) | |
| 3. Limited to square matrices | |
| ## Additional Information | |
| ### Dataset Curators | |
| Generated automatically by HF Dataset Generator v2.2 | |
| ### Licensing Information | |
| apache-2.0 License | |
| ### Contributions | |
| Thanks to TensorFlow.js and Hugging Face communities. | |