Riferimenti:
algebra__lineare_1d
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__lineare_1d_composta
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__lineare_2d
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__lineare_2d_composta
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
radici_algebra__polinomiali
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__radici_polinomiali_composte
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__sequenza_prossimo_termine
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_next_term')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
algebra__sequenza_nesimo_termine
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_nth_term')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmetica__aggiungi_o_sub
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__add_or_sub_in_base
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub_in_base')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmetica__add_sub_multiple
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_sub_multiple')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmetica__div
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__div')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmetica__mista
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mixed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmetica__mul
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__mul_div_multiple
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul_div_multiple')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__nearest_integer_root
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__nearest_integer_root')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__simplify_surd
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__simplify_surd')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
calcolo__differenziare
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
calcolo__differenziare_composto
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
confronto__più vicino
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__closest')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
confronto__più_vicino_composto
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__closest_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
confronto__kth_più grande
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
confronto__kth_biggest_composed
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
confronto__coppia
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__pair')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
confronto__coppia_composto
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__pair_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
confronto__ordinamento
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__sort')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
confronto__ordina_composto
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__sort_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
misura__conversione
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/measurement__conversion')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
misurazione__tempo
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/measurement__time')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__conversione_base
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__base_conversion')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__div_resto
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__div_resto_composto
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__gcd
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__gcd')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__gcd_composti
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__gcd_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__è_fattore
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__è_fattore_composto
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__è_primo
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__è_primo_composto
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__lcm
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__lcm')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__lcm_composti
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__lcm_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__list_prime_factors
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__list_prime_factors_composed
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__luogo_valore
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__place_value')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__luogo_valore_composto
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__place_value_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__numero_tondo
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__round_number')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numeri__numero_tondo_composto
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__round_number_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomi__add
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__add')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomi__coefficiente_nominati
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__coefficient_named')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomi__raccogli
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__collect')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomi__componi
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__compose')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomi__valutare
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomi__valuta_composti
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate_composed')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomi__espandi
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__expand')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomi__semplifica_potenza
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__simplify_power')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
probabilità__swr_p_level_set
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_level_set')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
probabilità__swr_p_sequenza
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_sequence')
- Descrizione :
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)
- Licenza : nessuna licenza conosciuta
- Versione : 1.0.0
- Divide :
Diviso | Esempi |
---|---|
'test' | 10000 |
'train' | 1999998 |
- Caratteristiche :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}