数学データセット

参考文献:

• コード
• ハグフェイス

代数__linear_1d

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/algebra__linear_1d')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

代数__linear_1d_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/algebra__linear_1d_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

代数__linear_2d

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/algebra__linear_2d')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

代数__linear_2d_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/algebra__linear_2d_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

代数__多項式根

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

代数__多項式根_合成

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

algebra__sequence_next_term

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/algebra__sequence_next_term')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

algebra__sequence_nth_term

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/algebra__sequence_nth_term')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

算術__add_or_sub

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

arithmetic__add_or_sub_in_base

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub_in_base')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

算術__add_sub_multiple

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/arithmetic__add_sub_multiple')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

算術__div

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/arithmetic__div')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

算術__混合

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/arithmetic__mixed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

算術__mul

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/arithmetic__mul')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

算術__mul_div_multiple

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/arithmetic__mul_div_multiple')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

arithmetic__nearest_integer_root

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/arithmetic__nearest_integer_root')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

arithmetic__simplify_surd

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/arithmetic__simplify_surd')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

微積分__微分

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/calculus__differentiate')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

微積分__微分_合成

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/calculus__differentiate_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

比較__最も近い

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/comparison__closest')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

比較__最も近い_構成

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/comparison__closest_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

比較__kth_biggest

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

比較__kth_biggest_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

比較__ペア

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/comparison__pair')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

比較__ペア_構成

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/comparison__pair_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

比較__ソート

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/comparison__sort')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

比較__sort_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/comparison__sort_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

測定__変換

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/measurement__conversion')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

測定__時間

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/measurement__time')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__base_conversion

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__base_conversion')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__div_remainder

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__div_remainder')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__div_remainder_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__div_remainder_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__gcd

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__gcd')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__gcd_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__gcd_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__is_factor

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__is_factor')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__is_factor_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__is_factor_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__is_prime

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__is_prime')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__is_prime_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__is_prime_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__lcm

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__lcm')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__lcm_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__lcm_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__list_prime_factors

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__list_prime_factors_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__場所_値

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__place_value')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値_場所_値_構成

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__place_value_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数値__round_number

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__round_number')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

数字__round_number_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/numbers__round_number_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

多項式__add

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/polynomials__add')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

多項式__coefficient_named

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/polynomials__coefficient_named')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

多項式__collect

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/polynomials__collect')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

多項式__compose

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/polynomials__compose')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

多項式__評価

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/polynomials__evaluate')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

多項式__evaluate_comped

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/polynomials__evaluate_composed')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

多項式__expand

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/polynomials__expand')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

多項式__simplify_power

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/polynomials__simplify_power')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

確率__swr_p_level_set

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/probability__swr_p_level_set')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

確率__swr_p_sequence

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:math_dataset/probability__swr_p_sequence')

• 説明：

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

• ライセンス: 既知のライセンスはありません
• バージョン: 1.0.0
• 分割:

• 特徴：

{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}