TFDS는 이제 Croissant 🥐 형식을 지원합니다! 자세한 내용은 설명서를 읽어보세요.

이 페이지는 Cloud Translation API를 통해 번역되었습니다.

보호자_저작자

참고자료:

cross_topic_1

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_1')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 1.0.0
분할 :

나뉘다	예
`'test'`	207
`'train'`	112
`'validation'`	62

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_genre_1

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_genre_1')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 13.0.0
분할 :

나뉘다	예
`'test'`	269
`'train'`	63
`'validation'`	112

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_topic_2

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_2')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 2.0.0
분할 :

나뉘다	예
`'test'`	179
`'train'`	112
`'validation'`	90

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_topic_3

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_3')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 3.0.0
분할 :

나뉘다	예
`'test'`	152
`'train'`	112
`'validation'`	117

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_topic_4

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_4')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 4.0.0
분할 :

나뉘다	예
`'test'`	207
`'train'`	62
`'validation'`	112

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_topic_5

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_5')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 5.0.0
분할 :

나뉘다	예
`'test'`	229
`'train'`	62
`'validation'`	90

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_topic_6

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_6')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 6.0.0
분할 :

나뉘다	예
`'test'`	202
`'train'`	62
`'validation'`	117

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_topic_7

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_7')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 7.0.0
분할 :

나뉘다	예
`'test'`	179
`'train'`	90
`'validation'`	112

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_topic_8

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_8')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 8.0.0
분할 :

나뉘다	예
`'test'`	229
`'train'`	90
`'validation'`	62

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_topic_9

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_9')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 9.0.0
분할 :

나뉘다	예
`'test'`	174
`'train'`	90
`'validation'`	117

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_topic_10

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_10')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 10.0.0
분할 :

나뉘다	예
`'test'`	152
`'train'`	117
`'validation'`	112

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_topic_11

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_11')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 11.0.0
분할 :

나뉘다	예
`'test'`	202
`'train'`	117
`'validation'`	62

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_topic_12

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_topic_12')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 12.0.0
분할 :

나뉘다	예
`'test'`	174
`'train'`	117
`'validation'`	90

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_genre_2

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_genre_2')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 14.0.0
분할 :

나뉘다	예
`'test'`	319
`'train'`	63
`'validation'`	62

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_genre_3

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_genre_3')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 15.0.0
분할 :

나뉘다	예
`'test'`	291
`'train'`	63
`'validation'`	90

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

cross_genre_4

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:guardian_authorship/cross_genre_4')

설명 :

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. 
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", 
                        split='train[-40%:]+validation[-40%:]+test[-40%:]')            

Important: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced

* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

라이센스 : 알려진 라이센스 없음
버전 : 16.0.0
분할 :

나뉘다	예
`'test'`	264
`'train'`	63
`'validation'`	117

특징 :

{
    "author": {
        "num_classes": 13,
        "names": [
            "catherinebennett",
            "georgemonbiot",
            "hugoyoung",
            "jonathanfreedland",
            "martinkettle",
            "maryriddell",
            "nickcohen",
            "peterpreston",
            "pollytoynbee",
            "royhattersley",
            "simonhoggart",
            "willhutton",
            "zoewilliams"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Politics",
            "Society",
            "UK",
            "World",
            "Books"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}