indice

Références:

ynat

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:klue/ynat')
  • Descriptif :
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.
  • Licence : CC-BY-SA-4.0
  • Version : 1.0.0
  • Fractionnements :
Diviser Exemples
'train' 45678
'validation' 9107
  • Caractéristiques :
{
    "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"
    }
}

mailles

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:klue/sts')
  • Descriptif :
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.
  • Licence : CC-BY-SA-4.0
  • Version : 1.0.0
  • Fractionnements :
Diviser Exemples
'train' 11668
'validation' 519
  • Caractéristiques :
{
    "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

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:klue/nli')
  • Descriptif :
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.
  • Licence : CC-BY-SA-4.0
  • Version : 1.0.0
  • Fractionnements :
Diviser Exemples
'train' 24998
'validation' 3000
  • Caractéristiques :
{
    "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

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:klue/ner')
  • Descriptif :
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.
  • Licence : CC-BY-SA-4.0
  • Version : 1.0.0
  • Fractionnements :
Diviser Exemples
'train' 21008
'validation' 5000
  • Caractéristiques :
{
    "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"
    }
}

concernant

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:klue/re')
  • Descriptif :
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.
  • Licence : CC-BY-SA-4.0
  • Version : 1.0.0
  • Fractionnements :
Diviser Exemples
'train' 32470
'validation' 7765
  • Caractéristiques :
{
    "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"
    }
}

dp

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:klue/dp')
  • Descriptif :
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.
  • Licence : CC-BY-SA-4.0
  • Version : 1.0.0
  • Fractionnements :
Diviser Exemples
'train' 10000
'validation' 2000
  • Caractéristiques :
{
    "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

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:klue/mrc')
  • Descriptif :
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.
  • Licence : CC-BY-SA-4.0
  • Version : 1.0.0
  • Fractionnements :
Diviser Exemples
'train' 17554
'validation' 5841
  • Caractéristiques :
{
    "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"
    }
}

wos

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:klue/wos')
  • Descriptif :
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.
  • Licence : CC-BY-SA-4.0
  • Version : 1.0.0
  • Fractionnements :
Diviser Exemples
'train' 8000
'validation' 1000
  • Caractéristiques :
{
    "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"
                }
            ]
        }
    ]
}