# Dataset Card for matrix_operations

## Dataset Description

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### 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.