مراجع:
جبر__خطی_1d
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
جبر__خطی_1d_composed
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
جبر__خطی_2d
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
جبر__خطی_2d_composed
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
جبر__ریشه_چند جمله ای
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
جبر__ریشه_چند جمله ای_ترکیب شده
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
جبر__دنباله_ترم_بعدی
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_next_term')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
جبر__دنباله_نهمین_ترم
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_nth_term')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
حسابی__افزودن_یا_فرعی
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__add_or_sub_in_base
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub_in_base')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__add_sub_multiple
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_sub_multiple')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
arithmetic__div
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/arithmetic__div')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
حسابی__مخلوط
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/arithmetic__mixed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
حسابی__مول
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
حسابی__mul_div_multiple
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul_div_multiple')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
حسابی__نزدیکترین_ریشه_صحیح
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/arithmetic__nearest_integer_root')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
حسابی__ساده_کردن
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/arithmetic__simplify_surd')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
calculus__متمایز کردن
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
calculus__differentiate_composed
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
مقایسه__نزدیکترین
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/comparison__closest')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
مقایسه__نزدیک_ترکیب شده
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/comparison__closest_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
مقایسه__kth_بزرگترین
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
مقایسه__کث_بزرگترین_ساخته شده
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
مقایسه__جفت
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/comparison__pair')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
مقایسه__جفت_تشکیل شده
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/comparison__pair_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
مقایسه__مرتب سازی
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/comparison__sort')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
مقایسه__مرتب_سازی شده
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/comparison__sort_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اندازه گیری__تبدیل
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/measurement__conversion')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اندازه گیری__زمان
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/measurement__time')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__تبدیل_پایه
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__base_conversion')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__div_remainder
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__div_remainder_composed
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__gcd
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__gcd')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__gcd_composed
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__gcd_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__فاکتور است
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__فاکتور_ترکیب شده است
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__اول است
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__اصلی_ترکیب شده است
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__lcm
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__lcm')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__lcm_composed
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__lcm_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__list_prime_factors
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
numbers__list_prime_factors_composed
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__مقدار_مکان
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__place_value')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__مکان_ارزش_تشکیل شده
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__place_value_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__عدد_گرد
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__round_number')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
اعداد__عدد_گرد_تشکیل شده
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/numbers__round_number_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
چند جمله ای__افزودن
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/polynomials__add')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
چند جمله ای__ضریب_نامگذاری شده
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/polynomials__coefficient_named')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
چند جمله ای__جمع آوری
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/polynomials__collect')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
چند جمله ای__ترکیب
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/polynomials__compose')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
چند جمله ای__ارزیابی
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
چند جمله ای__ارزش_ساز_شده
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate_composed')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیم ها :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
چند جمله ای__گسترش
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/polynomials__expand')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
چند جمله ای__ساده_توان
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/polynomials__simplify_power')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
احتمال__swr_p_level_set
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_level_set')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
probability__swr_p_sequence
برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_sequence')
- توضیحات :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- مجوز : مجوز شناخته شده ای وجود ندارد
- نسخه : 1.0.0
- تقسیمات :
| تقسیم کنید | نمونه ها |
|---|---|
'test' | 10000 |
'train' | 1999998 |
- ویژگی ها :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}