set_dati_matematici

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__sequence_next_term

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"
    }
}