File size: 2,314 Bytes
3a9b8fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
# 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.
|