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Dataset Card for "trec"

Dataset Summary

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.

Supported Tasks and Leaderboards

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances


  • Size of downloaded dataset files: 0.34 MB
  • Size of the generated dataset: 0.39 MB
  • Total amount of disk used: 0.74 MB

An example of 'train' looks as follows.

    "label-coarse": 1,
    "label-fine": 2,
    "text": "What fowl grabs the spotlight after the Chinese Year of the Monkey ?"

Data Fields

The data fields are the same among all splits.


  • label-coarse: a classification label, with possible values including DESC (0), ENTY (1), ABBR (2), HUM (3), NUM (4).
  • label-fine: a classification label, with possible values including manner (0), cremat (1), animal (2), exp (3), ind (4).
  • text: a string feature.

Data Splits

name train test
default 5452 500

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

    title = "Learning Question Classifiers",
    author = "Li, Xin  and
      Roth, Dan",
    booktitle = "{COLING} 2002: The 19th International Conference on Computational Linguistics",
    year = "2002",
    url = "",
    title = "Toward Semantics-Based Answer Pinpointing",
    author = "Hovy, Eduard  and
      Gerber, Laurie  and
      Hermjakob, Ulf  and
      Lin, Chin-Yew  and
      Ravichandran, Deepak",
    booktitle = "Proceedings of the First International Conference on Human Language Technology Research",
    year = "2001",
    url = "",


Thanks to @lhoestq, @thomwolf for adding this dataset.

Models trained or fine-tuned on trec