neenax/finetuneMistral-explanationMathData
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Dataset Card for "math_dataset"
Dataset Summary
Mathematics database.
This dataset code generates mathematical question and answer pairs, from a range of question types at roughly school-level difficulty. This is designed to test the mathematical learning and algebraic reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models (Saxton, Grefenstette, Hill, Kohli).
Example usage: train_examples, val_examples = datasets.load_dataset( 'math_dataset/arithmetic__mul', split=['train', 'test'], as_supervised=True)
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
algebra__linear_1d
- Size of downloaded dataset files: 2.33 GB
- Size of the generated dataset: 92.60 MB
- Total amount of disk used: 2.43 GB
An example of 'train' looks as follows.
algebra__linear_1d_composed
- Size of downloaded dataset files: 2.33 GB
- Size of the generated dataset: 200.58 MB
- Total amount of disk used: 2.53 GB
An example of 'train' looks as follows.
algebra__linear_2d
- Size of downloaded dataset files: 2.33 GB
- Size of the generated dataset: 127.41 MB
- Total amount of disk used: 2.46 GB
An example of 'train' looks as follows.
algebra__linear_2d_composed
- Size of downloaded dataset files: 2.33 GB
- Size of the generated dataset: 235.59 MB
- Total amount of disk used: 2.57 GB
An example of 'train' looks as follows.
algebra__polynomial_roots
- Size of downloaded dataset files: 2.33 GB
- Size of the generated dataset: 164.01 MB
- Total amount of disk used: 2.50 GB
An example of 'train' looks as follows.
Data Fields
The data fields are the same among all splits.
algebra__linear_1d
question: astringfeature.answer: astringfeature.
algebra__linear_1d_composed
question: astringfeature.answer: astringfeature.
algebra__linear_2d
question: astringfeature.answer: astringfeature.
algebra__linear_2d_composed
question: astringfeature.answer: astringfeature.
algebra__polynomial_roots
question: astringfeature.answer: astringfeature.
Data Splits
| name | train | test |
|---|---|---|
| algebra__linear_1d | 1999998 | 10000 |
| algebra__linear_1d_composed | 1999998 | 10000 |
| algebra__linear_2d | 1999998 | 10000 |
| algebra__linear_2d_composed | 1999998 | 10000 |
| algebra__polynomial_roots | 1999998 | 10000 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@article{2019arXiv,
author = {Saxton, Grefenstette, Hill, Kohli},
title = {Analysing Mathematical Reasoning Abilities of Neural Models},
year = {2019},
journal = {arXiv:1904.01557}
}
Contributions
Thanks to @patrickvonplaten, @lewtun, @thomwolf for adding this dataset.
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Models trained or fine-tuned on deepmind/math_dataset