Referensi:
aljabar__linear_1d
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aljabar__linear_1d_compose
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_1d_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aljabar__linear_2d
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aljabar__linear_2d_compose
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__linear_2d_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aljabar__polinomial_akar
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aljabar__polinomial_akar_tersusun
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__polynomial_roots_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aljabar__urutan_istilah_berikutnya
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_next_term')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aljabar__urutan_istilah_n
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/algebra__sequence_nth_term')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmatika__tambahkan_atau_sub
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmatika__tambah_atau_sub_dalam_basis
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_or_sub_in_base')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmatika__tambahkan_sub_multiple
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__add_sub_multiple')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmatika__div
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__div')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmatika__campuran
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mixed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmatika__mul
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmatika__mul_div_multiple
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__mul_div_multiple')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmatika__akar_bilangan_terdekat
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__nearest_integer_root')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
aritmatika__simplify_surd
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/arithmetic__simplify_surd')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
kalkulus__membedakan
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
kalkulus__membedakan_tersusun
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/calculus__differentiate_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
perbandingan__terdekat
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__closest')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
perbandingan__terdekat_terdiri
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__closest_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
perbandingan__kth_terbesar
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
perbandingan__kth_terbesar_terdiri
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__kth_biggest_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
perbandingan__pasangan
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__pair')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
perbandingan__pasangan_terdiri
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__pair_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
perbandingan__sort
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__sort')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
perbandingan__sort_compose
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/comparison__sort_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
pengukuran__konversi
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/measurement__conversion')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
pengukuran__waktu
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/measurement__time')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__basis_konversi
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__base_conversion')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__div_sisa
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__div_sisa_komposisi
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__div_remainder_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__gcd
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__gcd')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__gcd_compose
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__gcd_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__adalah_faktor
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__adalah_faktor_terdiri
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_factor_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__adalah_prime
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__adalah_prima_terdiri
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__is_prime_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__lcm
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__lcm')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__lcm_komposisi
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__lcm_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__daftar_faktor_prima
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__daftar_prime_factors_compose
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__list_prime_factors_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__nilai_tempat
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__place_value')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__tempat_nilai_terdiri
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__place_value_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__angka_bulatan
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__round_number')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
angka__bulatan_angka_terdiri
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/numbers__round_number_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomial__tambahkan
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__add')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomial__nama_koefisien
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__coefficient_named')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomial__kumpulkan
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__collect')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomial__komposisi
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__compose')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomial__evaluasi
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomial__evaluasi_terdiri
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__evaluate_composed')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomial__perluas
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__expand')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
polinomial__menyederhanakan_kekuatan
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/polynomials__simplify_power')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
probabilitas__swr_p_level_set
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_level_set')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
probabilitas__swr_p_urutan
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:math_dataset/probability__swr_p_sequence')
- Keterangan :
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
Membelah | Contoh |
---|---|
'test' | 10.000 |
'train' | 1999998 |
- Fitur :
{
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}