
# Dataset Card for "trec"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [https://cogcomp.seas.upenn.edu/Data/QA/QC/](https://cogcomp.seas.upenn.edu/Data/QA/QC/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 0.36 MB
- **Size of the generated dataset:** 0.41 MB
- **Total amount of disk used:** 0.78 MB

### 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 coarse class labels and 50 fine class 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. These questions were manually labeled.

### Supported Tasks and Leaderboards

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

The language in this dataset is English (`en`).

## Dataset Structure

### Data Instances

- **Size of downloaded dataset files:** 0.36 MB
- **Size of the generated dataset:** 0.41 MB
- **Total amount of disk used:** 0.78 MB

An example of 'train' looks as follows.
```
{
  'text': 'How did serfdom develop in and then leave Russia ?',
  'coarse_label': 2,
  'fine_label': 26
}
```

### Data Fields

The data fields are the same among all splits.

- `text` (`str`): Text of the question.
- `coarse_label` (`ClassLabel`): Coarse class label. Possible values are:
  - 'ABBR' (0): Abbreviation.
  - 'ENTY' (1): Entity.
  - 'DESC' (2): Description and abstract concept.
  - 'HUM' (3): Human being.
  - 'LOC' (4): Location.
  - 'NUM' (5): Numeric value.
- `fine_label` (`ClassLabel`): Fine class label. Possible values are:
  - ABBREVIATION:
    - 'ABBR:abb' (0): Abbreviation.
    - 'ABBR:exp' (1): Expression abbreviated.
  - ENTITY:
    - 'ENTY:animal' (2): Animal.
    - 'ENTY:body' (3): Organ of body.
    - 'ENTY:color' (4): Color.
    - 'ENTY:cremat' (5): Invention, book and other creative piece.
    - 'ENTY:currency' (6): Currency name.
    - 'ENTY:dismed' (7): Disease and medicine.
    - 'ENTY:event' (8): Event.
    - 'ENTY:food' (9): Food.
    - 'ENTY:instru' (10): Musical instrument.
    - 'ENTY:lang' (11): Language.
    - 'ENTY:letter' (12): Letter like a-z.
    - 'ENTY:other' (13): Other entity.
    - 'ENTY:plant' (14): Plant.
    - 'ENTY:product' (15): Product.
    - 'ENTY:religion' (16): Religion.
    - 'ENTY:sport' (17): Sport.
    - 'ENTY:substance' (18): Element and substance.
    - 'ENTY:symbol' (19): Symbols and sign.
    - 'ENTY:techmeth' (20): Techniques and method.
    - 'ENTY:termeq' (21): Equivalent term.
    - 'ENTY:veh' (22): Vehicle.
    - 'ENTY:word' (23): Word with a special property.
  - DESCRIPTION:
    - 'DESC:def' (24): Definition of something.
    - 'DESC:desc' (25): Description of something.
    - 'DESC:manner' (26): Manner of an action.
    - 'DESC:reason' (27): Reason.
  - HUMAN:
    - 'HUM:gr' (28): Group or organization of persons
    - 'HUM:ind' (29): Individual.
    - 'HUM:title' (30): Title of a person.
    - 'HUM:desc' (31): Description of a person.
  - LOCATION:
    - 'LOC:city' (32): City.
    - 'LOC:country' (33): Country.
    - 'LOC:mount' (34): Mountain.
    - 'LOC:other' (35): Other location.
    - 'LOC:state' (36): State.
  - NUMERIC:
    - 'NUM:code' (37): Postcode or other code.
    - 'NUM:count' (38): Number of something.
    - 'NUM:date' (39): Date.
    - 'NUM:dist' (40): Distance, linear measure.
    - 'NUM:money' (41): Price.
    - 'NUM:ord' (42): Order, rank.
    - 'NUM:other' (43): Other number.
    - 'NUM:period' (44): Lasting time of something
    - 'NUM:perc' (45): Percent, fraction.
    - 'NUM:speed' (46): Speed.
    - 'NUM:temp' (47): Temperature.
    - 'NUM:volsize' (48): Size, area and volume.
    - 'NUM:weight' (49): Weight.


### Data Splits

| name    | train | test |
|---------|------:|-----:|
| default |  5452 |  500 |

## Dataset Creation

### Curation Rationale

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Licensing Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Citation Information

```
@inproceedings{li-roth-2002-learning,
    title = "Learning Question Classifiers",
    author = "Li, Xin  and
      Roth, Dan",
    booktitle = "{COLING} 2002: The 19th International Conference on Computational Linguistics",
    year = "2002",
    url = "https://www.aclweb.org/anthology/C02-1150",
}
@inproceedings{hovy-etal-2001-toward,
    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 = "https://www.aclweb.org/anthology/H01-1069",
}
```


### Contributions

Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
task_categories:text-classification task_ids:multi-class-classification annotations_creators:expert-generated language_creators:expert-generated multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:en license:unknown region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "paperswithcode_id": "trecqa", "pretty_name": "Text Retrieval Conference Question Answering", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "coarse_label", "dtype": {"class_label": {"names": {"0": "ABBR", "1": "ENTY", "2": "DESC", "3": "HUM", "4": "LOC", "5": "NUM"}}}}, {"name": "fine_label", "dtype": {"class_label": {"names": {"0": "ABBR:abb", "1": "ABBR:exp", "2": "ENTY:animal", "3": "ENTY:body", "4": "ENTY:color", "5": "ENTY:cremat", "6": "ENTY:currency", "7": "ENTY:dismed", "8": "ENTY:event", "9": "ENTY:food", "10": "ENTY:instru", "11": "ENTY:lang", "12": "ENTY:letter", "13": "ENTY:other", "14": "ENTY:plant", "15": "ENTY:product", "16": "ENTY:religion", "17": "ENTY:sport", "18": "ENTY:substance", "19": "ENTY:symbol", "20": "ENTY:techmeth", "21": "ENTY:termeq", "22": "ENTY:veh", "23": "ENTY:word", "24": "DESC:def", "25": "DESC:desc", "26": "DESC:manner", "27": "DESC:reason", "28": "HUM:gr", "29": "HUM:ind", "30": "HUM:title", "31": "HUM:desc", "32": "LOC:city", "33": "LOC:country", "34": "LOC:mount", "35": "LOC:other", "36": "LOC:state", "37": "NUM:code", "38": "NUM:count", "39": "NUM:date", "40": "NUM:dist", "41": "NUM:money", "42": "NUM:ord", "43": "NUM:other", "44": "NUM:period", "45": "NUM:perc", "46": "NUM:speed", "47": "NUM:temp", "48": "NUM:volsize", "49": "NUM:weight"}}}}], "splits": [{"name": "train", "num_bytes": 385090, "num_examples": 5452}, {"name": "test", "num_bytes": 27983, "num_examples": 500}], "download_size": 359212, "dataset_size": 413073}}
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-unknown #region-us 

Dataset Card for "trec"
=======================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* Repository:
* Paper:
* Point of Contact:
* Size of downloaded dataset files: 0.36 MB
* Size of the generated dataset: 0.41 MB
* Total amount of disk used: 0.78 MB


### 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 coarse class labels and 50 fine class 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. These questions were manually labeled.


### Supported Tasks and Leaderboards


### Languages


The language in this dataset is English ('en').


Dataset Structure
-----------------


### Data Instances


* Size of downloaded dataset files: 0.36 MB
* Size of the generated dataset: 0.41 MB
* Total amount of disk used: 0.78 MB


An example of 'train' looks as follows.


### Data Fields


The data fields are the same among all splits.


* 'text' ('str'): Text of the question.
* 'coarse\_label' ('ClassLabel'): Coarse class label. Possible values are:
	+ 'ABBR' (0): Abbreviation.
	+ 'ENTY' (1): Entity.
	+ 'DESC' (2): Description and abstract concept.
	+ 'HUM' (3): Human being.
	+ 'LOC' (4): Location.
	+ 'NUM' (5): Numeric value.
* 'fine\_label' ('ClassLabel'): Fine class label. Possible values are:
	+ ABBREVIATION:
		- 'ABBR:abb' (0): Abbreviation.
		- 'ABBR:exp' (1): Expression abbreviated.
	+ ENTITY:
		- 'ENTY:animal' (2): Animal.
		- 'ENTY:body' (3): Organ of body.
		- 'ENTY:color' (4): Color.
		- 'ENTY:cremat' (5): Invention, book and other creative piece.
		- 'ENTY:currency' (6): Currency name.
		- 'ENTY:dismed' (7): Disease and medicine.
		- 'ENTY:event' (8): Event.
		- 'ENTY:food' (9): Food.
		- 'ENTY:instru' (10): Musical instrument.
		- 'ENTY:lang' (11): Language.
		- 'ENTY:letter' (12): Letter like a-z.
		- 'ENTY:other' (13): Other entity.
		- 'ENTY:plant' (14): Plant.
		- 'ENTY:product' (15): Product.
		- 'ENTY:religion' (16): Religion.
		- 'ENTY:sport' (17): Sport.
		- 'ENTY:substance' (18): Element and substance.
		- 'ENTY:symbol' (19): Symbols and sign.
		- 'ENTY:techmeth' (20): Techniques and method.
		- 'ENTY:termeq' (21): Equivalent term.
		- 'ENTY:veh' (22): Vehicle.
		- 'ENTY:word' (23): Word with a special property.
	+ DESCRIPTION:
		- 'DESC:def' (24): Definition of something.
		- 'DESC:desc' (25): Description of something.
		- 'DESC:manner' (26): Manner of an action.
		- 'DESC:reason' (27): Reason.
	+ HUMAN:
		- 'HUM:gr' (28): Group or organization of persons
		- 'HUM:ind' (29): Individual.
		- 'HUM:title' (30): Title of a person.
		- 'HUM:desc' (31): Description of a person.
	+ LOCATION:
		- 'LOC:city' (32): City.
		- 'LOC:country' (33): Country.
		- 'LOC:mount' (34): Mountain.
		- 'LOC:other' (35): Other location.
		- 'LOC:state' (36): State.
	+ NUMERIC:
		- 'NUM:code' (37): Postcode or other code.
		- 'NUM:count' (38): Number of something.
		- 'NUM:date' (39): Date.
		- 'NUM:dist' (40): Distance, linear measure.
		- 'NUM:money' (41): Price.
		- 'NUM:ord' (42): Order, rank.
		- 'NUM:other' (43): Other number.
		- 'NUM:period' (44): Lasting time of something
		- 'NUM:perc' (45): Percent, fraction.
		- 'NUM:speed' (46): Speed.
		- 'NUM:temp' (47): Temperature.
		- 'NUM:volsize' (48): Size, area and volume.
		- 'NUM:weight' (49): Weight.


### Data Splits



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


### Contributions


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



### 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 coarse class labels and 50 fine class 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. These questions were manually labeled. ### Supported Tasks and Leaderboards ### Languages


The language in this dataset is English ('en').


Dataset Structure
----------------- ### Data Instances


* Size of downloaded dataset files: 0.36 MB
* Size of the generated dataset: 0.41 MB
* Total amount of disk used: 0.78 MB


An example of 'train' looks as follows. ### Data Fields


The data fields are the same among all splits.


* 'text' ('str'): Text of the question.
* 'coarse\_label' ('ClassLabel'): Coarse class label. Possible values are:
	+ 'ABBR' (0): Abbreviation.
	+ 'ENTY' (1): Entity.
	+ 'DESC' (2): Description and abstract concept.
	+ 'HUM' (3): Human being.
	+ 'LOC' (4): Location.
	+ 'NUM' (5): Numeric value.
* 'fine\_label' ('ClassLabel'): Fine class label. Possible values are:
	+ ABBREVIATION:
		- 'ABBR:abb' (0): Abbreviation.
		- 'ABBR:exp' (1): Expression abbreviated.
	+ ENTITY:
		- 'ENTY:animal' (2): Animal.
		- 'ENTY:body' (3): Organ of body.
		- 'ENTY:color' (4): Color.
		- 'ENTY:cremat' (5): Invention, book and other creative piece.
		- 'ENTY:currency' (6): Currency name.
		- 'ENTY:dismed' (7): Disease and medicine.
		- 'ENTY:event' (8): Event.
		- 'ENTY:food' (9): Food.
		- 'ENTY:instru' (10): Musical instrument.
		- 'ENTY:lang' (11): Language.
		- 'ENTY:letter' (12): Letter like a-z.
		- 'ENTY:other' (13): Other entity.
		- 'ENTY:plant' (14): Plant.
		- 'ENTY:product' (15): Product.
		- 'ENTY:religion' (16): Religion.
		- 'ENTY:sport' (17): Sport.
		- 'ENTY:substance' (18): Element and substance.
		- 'ENTY:symbol' (19): Symbols and sign.
		- 'ENTY:techmeth' (20): Techniques and method.
		- 'ENTY:termeq' (21): Equivalent term.
		- 'ENTY:veh' (22): Vehicle.
		- 'ENTY:word' (23): Word with a special property.
	+ DESCRIPTION:
		- 'DESC:def' (24): Definition of something.
		- 'DESC:desc' (25): Description of something.
		- 'DESC:manner' (26): Manner of an action.
		- 'DESC:reason' (27): Reason.
	+ HUMAN:
		- 'HUM:gr' (28): Group or organization of persons
		- 'HUM:ind' (29): Individual.
		- 'HUM:title' (30): Title of a person.
		- 'HUM:desc' (31): Description of a person.
	+ LOCATION:
		- 'LOC:city' (32): City.
		- 'LOC:country' (33): Country.
		- 'LOC:mount' (34): Mountain.
		- 'LOC:other' (35): Other location.
		- 'LOC:state' (36): State.
	+ NUMERIC:
		- 'NUM:code' (37): Postcode or other code.
		- 'NUM:count' (38): Number of something.
		- 'NUM:date' (39): Date.
		- 'NUM:dist' (40): Distance, linear measure.
		- 'NUM:money' (41): Price.
		- 'NUM:ord' (42): Order, rank.
		- 'NUM:other' (43): Other number.
		- 'NUM:period' (44): Lasting time of something
		- 'NUM:perc' (45): Percent, fraction.
		- 'NUM:speed' (46): Speed.
		- 'NUM:temp' (47): Temperature.
		- 'NUM:volsize' (48): Size, area and volume.
		- 'NUM:weight' (49): Weight. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @lhoestq, @thomwolf for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-unknown #region-us 
 ### 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 coarse class labels and 50 fine class 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. These questions were manually labeled. ### Supported Tasks and Leaderboards ### Languages


The language in this dataset is English ('en').


Dataset Structure
----------------- ### Data Instances


* Size of downloaded dataset files: 0.36 MB
* Size of the generated dataset: 0.41 MB
* Total amount of disk used: 0.78 MB


An example of 'train' looks as follows. ### Data Fields


The data fields are the same among all splits.


* 'text' ('str'): Text of the question.
* 'coarse\_label' ('ClassLabel'): Coarse class label. Possible values are:
	+ 'ABBR' (0): Abbreviation.
	+ 'ENTY' (1): Entity.
	+ 'DESC' (2): Description and abstract concept.
	+ 'HUM' (3): Human being.
	+ 'LOC' (4): Location.
	+ 'NUM' (5): Numeric value.
* 'fine\_label' ('ClassLabel'): Fine class label. Possible values are:
	+ ABBREVIATION:
		- 'ABBR:abb' (0): Abbreviation.
		- 'ABBR:exp' (1): Expression abbreviated.
	+ ENTITY:
		- 'ENTY:animal' (2): Animal.
		- 'ENTY:body' (3): Organ of body.
		- 'ENTY:color' (4): Color.
		- 'ENTY:cremat' (5): Invention, book and other creative piece.
		- 'ENTY:currency' (6): Currency name.
		- 'ENTY:dismed' (7): Disease and medicine.
		- 'ENTY:event' (8): Event.
		- 'ENTY:food' (9): Food.
		- 'ENTY:instru' (10): Musical instrument.
		- 'ENTY:lang' (11): Language.
		- 'ENTY:letter' (12): Letter like a-z.
		- 'ENTY:other' (13): Other entity.
		- 'ENTY:plant' (14): Plant.
		- 'ENTY:product' (15): Product.
		- 'ENTY:religion' (16): Religion.
		- 'ENTY:sport' (17): Sport.
		- 'ENTY:substance' (18): Element and substance.
		- 'ENTY:symbol' (19): Symbols and sign.
		- 'ENTY:techmeth' (20): Techniques and method.
		- 'ENTY:termeq' (21): Equivalent term.
		- 'ENTY:veh' (22): Vehicle.
		- 'ENTY:word' (23): Word with a special property.
	+ DESCRIPTION:
		- 'DESC:def' (24): Definition of something.
		- 'DESC:desc' (25): Description of something.
		- 'DESC:manner' (26): Manner of an action.
		- 'DESC:reason' (27): Reason.
	+ HUMAN:
		- 'HUM:gr' (28): Group or organization of persons
		- 'HUM:ind' (29): Individual.
		- 'HUM:title' (30): Title of a person.
		- 'HUM:desc' (31): Description of a person.
	+ LOCATION:
		- 'LOC:city' (32): City.
		- 'LOC:country' (33): Country.
		- 'LOC:mount' (34): Mountain.
		- 'LOC:other' (35): Other location.
		- 'LOC:state' (36): State.
	+ NUMERIC:
		- 'NUM:code' (37): Postcode or other code.
		- 'NUM:count' (38): Number of something.
		- 'NUM:date' (39): Date.
		- 'NUM:dist' (40): Distance, linear measure.
		- 'NUM:money' (41): Price.
		- 'NUM:ord' (42): Order, rank.
		- 'NUM:other' (43): Other number.
		- 'NUM:period' (44): Lasting time of something
		- 'NUM:perc' (45): Percent, fraction.
		- 'NUM:speed' (46): Speed.
		- 'NUM:temp' (47): Temperature.
		- 'NUM:volsize' (48): Size, area and volume.
		- 'NUM:weight' (49): Weight. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @lhoestq, @thomwolf for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-unknown #region-us 
### 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 coarse class labels and 50 fine class 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. These questions were manually labeled.### Supported Tasks and Leaderboards### Languages


The language in this dataset is English ('en').


Dataset Structure
-----------------### Data Instances


* Size of downloaded dataset files: 0.36 MB
* Size of the generated dataset: 0.41 MB
* Total amount of disk used: 0.78 MB


An example of 'train' looks as follows.