Ссылки:
алгебра__linear_1d
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
алгебра__linear_1d_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
алгебра__linear_2d
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
алгебра__linear_2d_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
алгебра__polynomial_roots
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
алгебра__polynomial_roots_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
алгебра__sequence_next_term
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_next_term')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
алгебра__sequence_nth_term
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_nth_term')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
арифметика__add_or_sub
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
арифметика__add_or_sub_in_base
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub_in_base')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__add_sub_multiple
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_sub_multiple')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
арифметика__div
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__div')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
арифметика__смешанная
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mixed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
арифметика__mul
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
арифметика__mul_div_multiple
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul_div_multiple')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__nearest_integer_root
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__nearest_integer_root')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__simplify_surd
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__simplify_surd')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
исчисление__дифференцировать
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
исчисление__дифференциат_состав
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
сравнение__ближайший
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__closest')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
сравнение__ближайший_составленный
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__closest_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
сравнение__kth_biggest
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
сравнение__kth_biggest_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
сравнение__пара
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__pair')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
сравнение__pair_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__pair_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
сравнение__сортировка
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__sort')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
сравнение__sort_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__sort_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
измерение__конверсия
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/measurement__conversion')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
измерение__время
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/measurement__time')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
числа__base_conversion
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__base_conversion')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
цифры__div_remainder
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Numbers__div_remainder_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
цифры__gcd
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__gcd')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Numbers__gcd_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__gcd_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
цифры__is_factor
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Numbers__is_factor_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
цифры__is_prime
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Numbers__is_prime_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
цифры__lcm
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__lcm')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
цифры__lcm_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__lcm_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Numbers__list_prime_factors
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Numbers__list_prime_factors_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
цифры__place_value
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__place_value')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
цифры__place_value_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__place_value_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
цифры__round_number
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__round_number')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
цифры__round_number_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__round_number_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
полиномы__add
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__add')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
полиномы__coefficient_named
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__coefficient_named')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
полиномы__collect
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__collect')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
полиномы__compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__compose')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
полиномы__оценить
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
полиномы__evaluate_compose
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate_composed')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
полиномы__развернуть
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__expand')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
полиномы__simplify_power
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__simplify_power')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
вероятность__swr_p_level_set
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_level_set')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
вероятность__swr_p_sequence
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_sequence')
- Описание :
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)
- Лицензия : Нет известной лицензии.
- Версия : 1.0.0
- Расколы :
Расколоть | Примеры |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Функции :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}