pista

Referencias:

ynat

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:klue/ynat')
  • Descripción :
KLUE (Korean Language Understanding Evaluation)
Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language
understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible
to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain
unambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics
for every task, thus establishing fair comparison across Korean language models.
  • Licencia : CC-BY-SA-4.0
  • Versión : 1.0.0
  • Divisiones :
Separar Ejemplos
'train' 45678
'validation' 9107
  • Características :
{
    "guid": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 7,
        "names": [
            "IT\uacfc\ud559",
            "\uacbd\uc81c",
            "\uc0ac\ud68c",
            "\uc0dd\ud65c\ubb38\ud654",
            "\uc138\uacc4",
            "\uc2a4\ud3ec\uce20",
            "\uc815\uce58"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "url": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

pts

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:klue/sts')
  • Descripción :
KLUE (Korean Language Understanding Evaluation)
Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language
understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible
to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain
unambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics
for every task, thus establishing fair comparison across Korean language models.
  • Licencia : CC-BY-SA-4.0
  • Versión : 1.0.0
  • Divisiones :
Separar Ejemplos
'train' 11668
'validation' 519
  • Características :
{
    "guid": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "source": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "sentence1": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "sentence2": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "label": {
            "dtype": "float64",
            "id": null,
            "_type": "Value"
        },
        "real-label": {
            "dtype": "float64",
            "id": null,
            "_type": "Value"
        },
        "binary-label": {
            "num_classes": 2,
            "names": [
                "negative",
                "positive"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        }
    }
}

nli

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:klue/nli')
  • Descripción :
KLUE (Korean Language Understanding Evaluation)
Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language
understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible
to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain
unambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics
for every task, thus establishing fair comparison across Korean language models.
  • Licencia : CC-BY-SA-4.0
  • Versión : 1.0.0
  • Divisiones :
Separar Ejemplos
'train' 24998
'validation' 3000
  • Características :
{
    "guid": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "source": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

ner

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:klue/ner')
  • Descripción :
KLUE (Korean Language Understanding Evaluation)
Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language
understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible
to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain
unambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics
for every task, thus establishing fair comparison across Korean language models.
  • Licencia : CC-BY-SA-4.0
  • Versión : 1.0.0
  • Divisiones :
Separar Ejemplos
'train' 21008
'validation' 5000
  • Características :
{
    "sentence": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "tokens": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "ner_tags": {
        "feature": {
            "num_classes": 13,
            "names": [
                "B-DT",
                "I-DT",
                "B-LC",
                "I-LC",
                "B-OG",
                "I-OG",
                "B-PS",
                "I-PS",
                "B-QT",
                "I-QT",
                "B-TI",
                "I-TI",
                "O"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

re

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:klue/re')
  • Descripción :
KLUE (Korean Language Understanding Evaluation)
Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language
understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible
to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain
unambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics
for every task, thus establishing fair comparison across Korean language models.
  • Licencia : CC-BY-SA-4.0
  • Versión : 1.0.0
  • Divisiones :
Separar Ejemplos
'train' 32470
'validation' 7765
  • Características :
{
    "guid": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "sentence": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "subject_entity": {
        "word": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "start_idx": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "end_idx": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "type": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    },
    "object_entity": {
        "word": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "start_idx": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "end_idx": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "type": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    },
    "label": {
        "num_classes": 30,
        "names": [
            "no_relation",
            "org:dissolved",
            "org:founded",
            "org:place_of_headquarters",
            "org:alternate_names",
            "org:member_of",
            "org:members",
            "org:political/religious_affiliation",
            "org:product",
            "org:founded_by",
            "org:top_members/employees",
            "org:number_of_employees/members",
            "per:date_of_birth",
            "per:date_of_death",
            "per:place_of_birth",
            "per:place_of_death",
            "per:place_of_residence",
            "per:origin",
            "per:employee_of",
            "per:schools_attended",
            "per:alternate_names",
            "per:parents",
            "per:children",
            "per:siblings",
            "per:spouse",
            "per:other_family",
            "per:colleagues",
            "per:product",
            "per:religion",
            "per:title"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "source": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

doble penetración

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:klue/dp')
  • Descripción :
KLUE (Korean Language Understanding Evaluation)
Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language
understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible
to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain
unambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics
for every task, thus establishing fair comparison across Korean language models.
  • Licencia : CC-BY-SA-4.0
  • Versión : 1.0.0
  • Divisiones :
Separar Ejemplos
'train' 10000
'validation' 2000
  • Características :
{
    "sentence": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "index": [
        {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        }
    ],
    "word_form": [
        {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    ],
    "lemma": [
        {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    ],
    "pos": [
        {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    ],
    "head": [
        {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        }
    ],
    "deprel": [
        {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    ]
}

mrc

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:klue/mrc')
  • Descripción :
KLUE (Korean Language Understanding Evaluation)
Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language
understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible
to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain
unambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics
for every task, thus establishing fair comparison across Korean language models.
  • Licencia : CC-BY-SA-4.0
  • Versión : 1.0.0
  • Divisiones :
Separar Ejemplos
'train' 17554
'validation' 5841
  • Características :
{
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "context": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "news_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "source": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "guid": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "is_impossible": {
        "dtype": "bool",
        "id": null,
        "_type": "Value"
    },
    "question_type": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answers": {
        "feature": {
            "answer_start": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

ay

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:klue/wos')
  • Descripción :
KLUE (Korean Language Understanding Evaluation)
Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language
understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible
to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain
unambiguous annotations for all datasets. Futhermore, we build an evaluation system and carefully choose evaluations metrics
for every task, thus establishing fair comparison across Korean language models.
  • Licencia : CC-BY-SA-4.0
  • Versión : 1.0.0
  • Divisiones :
Separar Ejemplos
'train' 8000
'validation' 1000
  • Características :
{
    "guid": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "domains": [
        {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    ],
    "dialogue": [
        {
            "role": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "state": [
                {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                }
            ]
        }
    ]
}