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matrix_operations

UNDER DEVELOPMENT

This dataset was generated using MATRIXMA by webXOS, download it to create your own datasets from the /generator/ folder of the repo

A Synthetic matrix operations dataset for ML training. The webxos/matrix_operations dataset is a collection of structured data designed for training and evaluating machine learning models on computational mathematics and linear algebra tasks. It includes various matrix pairs and their corresponding operational results,such as addition, multiplication, inversion, and determinant calculations. This dataset is intended to help researchers develop robust systems for symbolic reasoning, automated theorem proving, or neural networks capable of performing complex numerical computations with high precision.

Dataset Details

• Generated: 2026-01-05T22:20:06.264Z
• Total Samples: 500
• Splits: Train (400), Test (100)
• Matrix Size: 8×8
• Operations: matmul, add
• Backend: WEBGL
• Format: jsonl

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("matrix_operations")

# Access train and test splits
train_dataset = dataset["train"]
test_dataset = dataset["test"]

Example

import datasets

# Load dataset
ds = datasets.load_dataset("matrix_operations")

# Get first example
example = ds["train"][0]
print(f"ID: {example['id']}")
print(f"Matrix Size: {example['matrix_size']}")
print(f"Operations: {len(example['operations'])}")

Citation

If you use this dataset in research, please cite:

@dataset{matrix_operations,
  title = {matrix_operations},
  author = {webXOS},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/webXOS}
}

License

apache-2.0