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trec

References:

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:trec')
  • Description:
The Text REtrieval Conference (TREC) Question Classification dataset contains 5500 labeled questions in training set and another 500 for test set. The dataset has 6 labels, 47 level-2 labels. Average length of each sentence is 10, vocabulary size of 8700.

Data are collected from four sources: 4,500 English questions published by USC (Hovy et al., 2001), about 500 manually constructed questions for a few rare classes, 894 TREC 8 and TREC 9 questions, and also 500 questions from TREC 10 which serves as the test set.
  • License: No known license
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 500
'train' 5452
  • Features:
{
    "label-coarse": {
        "num_classes": 6,
        "names": [
            "DESC",
            "ENTY",
            "ABBR",
            "HUM",
            "NUM",
            "LOC"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "label-fine": {
        "num_classes": 47,
        "names": [
            "manner",
            "cremat",
            "animal",
            "exp",
            "ind",
            "gr",
            "title",
            "def",
            "date",
            "reason",
            "event",
            "state",
            "desc",
            "count",
            "other",
            "letter",
            "religion",
            "food",
            "country",
            "color",
            "termeq",
            "city",
            "body",
            "dismed",
            "mount",
            "money",
            "product",
            "period",
            "substance",
            "sport",
            "plant",
            "techmeth",
            "volsize",
            "instru",
            "abb",
            "speed",
            "word",
            "lang",
            "perc",
            "code",
            "dist",
            "temp",
            "symbol",
            "ord",
            "veh",
            "weight",
            "currency"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}