References:
algebra__linear_1d
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__linear_1d_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__linear_2d
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__linear_2d_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__polynomial_roots
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__polynomial_roots_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__sequence_next_term
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_next_term')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__sequence_nth_term
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_nth_term')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__add_or_sub
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__add_or_sub_in_base
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub_in_base')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__add_sub_multiple
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_sub_multiple')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__div
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__div')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__mixed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mixed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__mul
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__mul_div_multiple
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul_div_multiple')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__nearest_integer_root
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__nearest_integer_root')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__simplify_surd
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__simplify_surd')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
calculus__differentiate
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
calculus__differentiate_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparison__closest
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__closest')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparison__closest_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__closest_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparison__kth_biggest
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparison__kth_biggest_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparison__pair
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__pair')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparison__pair_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__pair_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparison__sort
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__sort')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparison__sort_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__sort_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
measurement__conversion
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/measurement__conversion')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
measurement__time
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/measurement__time')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__base_conversion
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__base_conversion')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__div_remainder
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__div_remainder_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__gcd
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__gcd')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__gcd_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__gcd_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__is_factor
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__is_factor_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__is_prime
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__is_prime_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__lcm
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__lcm')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__lcm_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__lcm_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__list_prime_factors
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__list_prime_factors_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__place_value
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__place_value')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__place_value_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__place_value_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__round_number
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__round_number')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__round_number_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__round_number_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polynomials__add
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__add')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polynomials__coefficient_named
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__coefficient_named')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polynomials__collect
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__collect')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polynomials__compose
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__compose')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polynomials__evaluate
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polynomials__evaluate_composed
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate_composed')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polynomials__expand
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__expand')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polynomials__simplify_power
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__simplify_power')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
probability__swr_p_level_set
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_level_set')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
probability__swr_p_sequence
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_sequence')
- Description:
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)
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
10000 |
'train' |
1999998 |
- Features:
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}