गणित_डेटासेट

सन्दर्भ:

• कोड
• गले लगाना

बीजगणित__रैखिक_1d

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बीजगणित__रैखिक_1d_रचित

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बीजगणित__रैखिक_2d

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

algebra__linear_2d_composition

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बीजगणित__बहुपद_मूल

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बीजगणित__बहुपद_मूल_रचना

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बीजगणित_अनुक्रम_अगला_पद

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बीजगणित_क्रम_nवाँ_पद

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

अंकगणित__जोड़_या_उप

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

अंकगणित_जोड़ें_या_आधार_में_सब_करें

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

अंकगणित__जोड़ें_उप_एकाधिक

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

अंकगणित__div

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

अंकगणित__मिश्रित

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

अंकगणित_मूल

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

अंकगणित__mul_div_multiple

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

अंकगणित__निकटतम_पूर्णांक_मूल

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

अंकगणित__सरलीकरण_सरड

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

कलन_अंतर करें

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

कैलकुलस__डिफरेंशियेट_रचित

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

तुलना__निकटतम

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

तुलना__निकटतम_रचित

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

तुलना__kth_सबसे बड़ा

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

तुलना__kth_biggest_composed

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

तुलना__जोड़ी

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

तुलना__जोड़ी_रचना

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

तुलना__क्रम

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

तुलना__सॉर्ट_रचना

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

माप_रूपांतरण

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

माप__समय

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

संख्या__आधार_रूपांतरण

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

संख्याएँ__div_शेष

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

संख्याएँ__div_remainder_composed

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

नंबर__जीसीडी

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

नंबर__gcd_composed

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

संख्या_कारक_है

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

संख्याएँ_कारक_संयोजित होती हैं

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

संख्याएँ_अभाज्य हैं

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

नंबर_प्राइम_रचित_है

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

नंबर__एलसीएम

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

नंबर__एलसीएम_रचित

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

संख्याएँ__सूची_प्रधान_कारक

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

संख्या__सूची_प्रधान_कारक_संयुक्त

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

संख्याएँ__स्थान_मूल्य

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

संख्याएँ__स्थान_मान_संकलित

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

नंबर__राउंड_नंबर

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

नंबर__राउंड_नंबर_रचित

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बहुपद_जोड़ें

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बहुपद_गुणांक_नामांकित

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बहुपद_संग्रह

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बहुपद_रचना

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बहुपद_मूल्यांकन करें

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बहुपद_मूल्यांकन_रचित

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बहुपद_विस्तार

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

बहुपद_सरलीकरण_शक्ति

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

प्रायिकता__swr_p_level_set

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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

प्रायिकता__swr_p_अनुक्रम

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

• विवरण :

Mathematics database.

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

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

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

• लाइसेंस : कोई ज्ञात लाइसेंस नहीं
• संस्करण : 1.0.0
• विभाजन :

• विशेषताएँ :

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