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stringlengths 30
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| timestamp
stringdate 2026-01-05 22:19:45
2026-01-05 22:19:59
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sample_1767651585614_ktuao3pw6
|
2026-01-05T22:19:45.615Z
| 8
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sample_1767651585709_lgp7sp6de
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2026-01-05T22:19:45.709Z
| 8
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sample_1767651585742_el18xrj7i
|
2026-01-05T22:19:45.742Z
| 8
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sample_1767651585791_b3dourefo
|
2026-01-05T22:19:45.791Z
| 8
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sample_1767651585836_0wjavykc9
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2026-01-05T22:19:45.836Z
| 8
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sample_1767651585876_bebltlr7x
|
2026-01-05T22:19:45.876Z
| 8
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sample_1767651585902_ms3jpt6ug
|
2026-01-05T22:19:45.902Z
| 8
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sample_1767651585925_c91vdoa49
|
2026-01-05T22:19:45.925Z
| 8
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sample_1767651585950_mcoxevxj5
|
2026-01-05T22:19:45.950Z
| 8
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sample_1767651585973_gcurjq8rs
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2026-01-05T22:19:45.973Z
| 8
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sample_1767651585998_ywyqwkc1z
|
2026-01-05T22:19:45.998Z
| 8
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sample_1767651586025_71500uniq
|
2026-01-05T22:19:46.025Z
| 8
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sample_1767651586057_shb6196x1
|
2026-01-05T22:19:46.057Z
| 8
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sample_1767651586090_l855iorx1
|
2026-01-05T22:19:46.090Z
| 8
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sample_1767651586122_opu4b1xt5
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2026-01-05T22:19:46.122Z
| 8
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sample_1767651586165_0f2r6vhay
|
2026-01-05T22:19:46.165Z
| 8
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sample_1767651586197_qjsot68gr
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2026-01-05T22:19:46.197Z
| 8
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sample_1767651586240_2nq91z64v
|
2026-01-05T22:19:46.240Z
| 8
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sample_1767651586279_n8unqmkw2
|
2026-01-05T22:19:46.279Z
| 8
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sample_1767651586329_zc56tn1p8
|
2026-01-05T22:19:46.330Z
| 8
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sample_1767651586358_zds5r1uq5
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2026-01-05T22:19:46.358Z
| 8
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sample_1767651586398_pi9q9a84i
|
2026-01-05T22:19:46.398Z
| 8
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sample_1767651586428_qzxanxcjw
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2026-01-05T22:19:46.428Z
| 8
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sample_1767651586460_qwx1b3zu5
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2026-01-05T22:19:46.460Z
| 8
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sample_1767651586490_cbop2lh33
|
2026-01-05T22:19:46.490Z
| 8
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0.32100141048431396
]
}
] |
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matrix_operations
UNDER DEVELOPMENT
This dataset was generated using MATRIXMA by webXOS in 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_2026,
title = {matrix_operations},
author = {webXOS},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/webXOS}
}
License
apache-2.0
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