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--- |
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license: apache-2.0 |
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task_categories: |
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- reinforcement-learning |
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- tabular-classification |
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- tabular-regression |
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tags: |
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- math |
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- linear-algebra |
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- matrix-calculus |
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- mathematics |
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- tabular |
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- text |
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- matrix-multiplication |
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- eigenvalues |
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- determinants |
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- inverse-matrix |
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- numerical-computing |
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--- |
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[](https://webxos.netlify.app) |
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[](https://github.com/webxos/webxos) |
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[](https://huggingface.co/webxos) |
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[](https://x.com/webxos) |
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<div style=" |
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background: #00FF00; |
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border-left: 4px solid #00FF00; |
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padding: 1.5rem; |
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margin: 2rem 0; |
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font-family: 'Fira Code', 'Courier New', monospace; |
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color: #00FF00; |
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border-radius: 0 8px 8px 0; |
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"> |
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<pre style=" |
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font-size: 8px; |
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line-height: 1.2; |
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margin: 0; |
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overflow-x: auto; |
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color: #00FF00; |
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"> |
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</div> |
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# matrix_operations |
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*UNDER DEVELOPMENT* |
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*This dataset was generated using MATRIXMA by webXOS in the /generator/ folder of the repo* |
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A Synthetic matrix operations dataset for ML training. The webxos/matrix_operations dataset is a collection of structured data designed |
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for training and evaluating machine learning models on computational mathematics and linear algebra tasks. It includes various matrix |
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pairs and their corresponding operational results,such as addition, multiplication, inversion, and determinant calculations. This |
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dataset is intended to help researchers develop robust systems for symbolic reasoning, automated theorem proving, or neural networks |
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capable of performing complex numerical computations with high precision. |
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## Dataset Details |
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- **Generated:** 2026-01-05T22:20:06.264Z |
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- **Total Samples:** 500 |
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- **Splits:** Train (400), Test (100) |
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- **Matrix Size:** 8×8 |
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- **Operations:** matmul, add |
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- **Backend:** WEBGL |
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- **Format:** jsonl |
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## Usage |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("matrix_operations") |
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# Access train and test splits |
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train_dataset = dataset["train"] |
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test_dataset = dataset["test"] |
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``` |
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## Example |
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```python |
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import datasets |
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# Load dataset |
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ds = datasets.load_dataset("matrix_operations") |
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# Get first example |
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example = ds["train"][0] |
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print(f"ID: {example['id']}") |
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print(f"Matrix Size: {example['matrix_size']}") |
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print(f"Operations: {len(example['operations'])}") |
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``` |
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## Citation |
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If you use this dataset in research, please cite: |
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```bibtex |
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@dataset{matrix_operations_2026, |
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title = {matrix_operations}, |
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author = {webXOS}, |
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year = {2026}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/webXOS} |
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} |
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``` |
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## License |
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apache-2.0 |
