hda_nli_hindi

مراجع:

HDA هندی nli

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:hda_nli_hindi/HDA hindi nli')
  • توضیحات :
This dataset is a recasted version of the Hindi Discourse Analysis Dataset used to train models for Natural Language Inference Tasks in Low-Resource Languages like Hindi.
  • مجوز : مجوز MIT
  • نسخه : 1.1.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 9970
'train' 31892
'validation' 9460
  • ویژگی ها :
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 2,
        "names": [
            "not-entailment",
            "entailment"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Argumentative",
            "Descriptive",
            "Dialogic",
            "Informative",
            "Narrative"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

hda nli هندی

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:hda_nli_hindi/hda nli hindi')
  • توضیحات :
This dataset is a recasted version of the Hindi Discourse Analysis Dataset used to train models for Natural Language Inference Tasks in Low-Resource Languages like Hindi.
  • مجوز : مجوز MIT
  • نسخه : 1.1.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 9970
'train' 31892
'validation' 9460
  • ویژگی ها :
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 2,
        "names": [
            "not-entailment",
            "entailment"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "topic": {
        "num_classes": 5,
        "names": [
            "Argumentative",
            "Descriptive",
            "Dialogic",
            "Informative",
            "Narrative"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}