hda_nli_হিন্দি

তথ্যসূত্র:

HDA হিন্দি nli

TFDS এ এই ডেটাসেট লোড করতে নিম্নলিখিত কমান্ডটি ব্যবহার করুন:

ds = tfds.load('huggingface:hda_nli_hindi/HDA hindi nli')
  • বর্ণনা :
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.
  • লাইসেন্স : এমআইটি লাইসেন্স
  • সংস্করণ : 1.1.0
  • বিভাজন :
বিভক্ত উদাহরণ
'test' 9970
'train' 31892
'validation' 9460
  • বৈশিষ্ট্য :
{
    "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 হিন্দি

TFDS এ এই ডেটাসেট লোড করতে নিম্নলিখিত কমান্ডটি ব্যবহার করুন:

ds = tfds.load('huggingface:hda_nli_hindi/hda nli hindi')
  • বর্ণনা :
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.
  • লাইসেন্স : এমআইটি লাইসেন্স
  • সংস্করণ : 1.1.0
  • বিভাজন :
বিভক্ত উদাহরণ
'test' 9970
'train' 31892
'validation' 9460
  • বৈশিষ্ট্য :
{
    "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"
    }
}