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README.md

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+---
+language: 
+  - en
+task_categories:
+  - matrix-computation
+  - synthetic-data-generation
+tags:
+  - matrix-operations
+  - synthetic-data
+  - machine-learning
+  - mathematics
+license: apache-2.0
+dataset_info:
+  features:
+    - name: id
+      dtype: string
+    - name: timestamp
+      dtype: string
+    - name: matrix_size
+      dtype: int32
+    - name: operations
+      list:
+        - name: type
+          dtype: string
+        - name: time_ms
+          dtype: float32
+        - name: matrix_a
+          sequence: float32
+        - name: matrix_b
+          sequence: float32
+        - name: matrix
+          sequence: float32
+        - name: result
+          sequence: float32
+        - name: error
+          dtype: string
+  splits:
+    - name: train
+      num_bytes: 3097616
+      num_examples: 400
+    - name: test
+      num_bytes: 774404
+      num_examples: 100
+  download_size: 3872020
+  dataset_size: 3872020
+pretty_name: "matrix_operations"
+size_categories:
+  - n<1K
+---
+
+# matrix_operations
+
+Synthetic matrix operations dataset for ML training
+
+## 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
+
+```python
+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
+
+```python
+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:
+
+```bibtex
+@dataset{matrix_operations_2026,
+  title = {matrix_operations},
+  author = {Generated by HF Dataset Generator v2.2},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/your-username/matrix_operations}
+}
+```
+
+## License
+
+apache-2.0

dataset_card.md

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+# 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.

load_dataset.py

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+#!/usr/bin/env python3
+"""
+Script to load and verify the matrix_operations dataset
+"""
+
+import json
+from pathlib import Path
+
+def load_and_verify_dataset():
+    dataset_path = Path(".")
+    
+    print(f"Loading {config.name} dataset...")
+    
+    # Load train split
+    train_data = []
+    if (dataset_path / "train.jsonl").exists():
+        with open(dataset_path / "train.jsonl", "r") as f:
+            for line in f:
+                train_data.append(json.loads(line))
+        print(f"Loaded {len(train_data)} train samples")
+    
+    # Load test split
+    test_data = []
+    if (dataset_path / "test.jsonl").exists():
+        with open(dataset_path / "test.jsonl", "r") as f:
+            for line in f:
+                test_data.append(json.loads(line))
+        print(f"Loaded {len(test_data)} test samples")
+    
+    # Basic validation
+    print("\nDataset Validation:")
+    print(f"Total samples: {len(train_data) + len(test_data)}")
+    
+    if train_data:
+        sample = train_data[0]
+        print(f"Sample keys: {list(sample.keys())}")
+        print(f"Matrix size: {sample.get('matrix_size')}")
+        print(f"Operations count: {len(sample.get('operations', []))}")
+    
+    print("\nDataset ready for use!")
+    print("To upload to Hugging Face Hub:")
+    print(f"  git push https://huggingface.co/datasets/your-username/{config.name}")
+
+if __name__ == "__main__":
+    load_and_verify_dataset()

metadata.json

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+{
+  "name": "matrix_operations",
+  "description": "Synthetic matrix operations dataset for ML training",
+  "license": "apache-2.0",
+  "format": "jsonl",
+  "generated_at": "2026-01-05T22:19:45.610Z",
+  "operations": [
+    "matmul",
+    "add"
+  ],
+  "matrix_size": 8,
+  "backend": "webgl",
+  "splits": {
+    "train": 400,
+    "test": 100
+  },
+  "samples": 500,
+  "train_samples": 400,
+  "test_samples": 100,
+  "size_kb": "3781.27"
+}