Referencias:
álgebra__lineal_1d
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
álgebra__lineal_1d_compuesta
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
álgebra__lineal_2d
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
álgebra__lineal_2d_compuesta
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
álgebra__raíces_polinómicas
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
álgebra__raíces_polinómicas_compuestas
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
álgebra__sequence_next_term
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_next_term')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
álgebra__secuencia_nth_term
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_nth_term')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmética__add_or_sub
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmética__add_or_sub_in_base
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub_in_base')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmética__add_sub_multiple
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_sub_multiple')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmética__div
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__div')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmética__mixta
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mixed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmética__mul
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmética__mul_div_multiple
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul_div_multiple')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmética__raíz_entera_más cercana
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__nearest_integer_root')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmética__simplificar_surd
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__simplify_surd')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
cálculo__diferenciar
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
cálculo__diferenciar_compuesto
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparación__más cercana
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__closest')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparación__más cercano_compuesto
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__closest_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparación__kth_biggest
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparación__kth_biggest_composed
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparación__par
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__pair')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparación__par_compuesto
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__pair_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparación__ordenar
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__sort')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
comparación__sort_composed
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__sort_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
medición__conversión
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/measurement__conversion')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
medición_tiempo
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/measurement__time')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__base_conversión
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__base_conversion')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__div_resto
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__div_remainder_composed
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__gcd
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__gcd')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__gcd_compuestos
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__gcd_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeros_es_factor
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__is_factor_composed
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
los números_es_primos
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__is_prime_compuestos
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeros__lcm
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__lcm')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__lcm_compuestos
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__lcm_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__list_prime_factors
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__list_prime_factors_composed
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__valor_posicional
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__place_value')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__valor_posicional_compuesto
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__place_value_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__número_redondo
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__round_number')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
números__número_redondo_compuesto
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__round_number_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomios__sumar
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__add')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomios__coeficiente_nombrado
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__coefficient_named')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomios__recopilar
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__collect')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomios__componer
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__compose')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomios__evaluar
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomios__evaluar_compuestos
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate_composed')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomios__expandir
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__expand')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomios__simplificar_potencia
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__simplify_power')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
probabilidad__swr_p_level_set
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_level_set')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
probabilidad__swr_p_sequence
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_sequence')
- Descripción :
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)
- Licencia : Ninguna licencia conocida
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Características :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}