task_categories:text-classification size_categories:1K<n<10K language:en license:mit region:us
This data collection contains all the data used in our learning question classification experiments(see [1]), which has question class definitions, the training and testing question sets, examples of preprocessing the questions, feature definition scripts and examples of semantically related word features. 
This work has been done by Xin Li and Dan Roth and supported by [2].
"""

_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/QC/raw/main/train.csv"
_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/QC/raw/main/test.csv"


CATEGORY_MAPPING = {'ENTY_cremat': 0,
 'DESC_manner': 1,
 'ENTY_animal': 2,
 'ABBR_exp': 3,
 'HUM_ind': 4,
 'HUM_gr': 5,
 'HUM_title': 6,
 'DESC_def': 7,
 'NUM_date': 8,
 'DESC_reason': 9,
 'ENTY_event': 10,
 'LOC_state': 11,
 'DESC_desc': 12,
 'NUM_count': 13,
 'ENTY_other': 14,
 'ENTY_letter': 15,
 'LOC_other': 16,
 'ENTY_religion': 17,
 'ENTY_food': 18,
 'LOC_country': 19,
 'ENTY_color': 20,
 'ENTY_termeq': 21,
 'LOC_city': 22,
 'ENTY_body': 23,
 'ENTY_dismed': 24,
 'LOC_mount': 25,
 'NUM_money': 26,
 'ENTY_product': 27,
 'NUM_period': 28,
 'ENTY_substance': 29,
 'ENTY_sport': 30,
 'ENTY_plant': 31,
 'ENTY_techmeth': 32,
 'NUM_volsize': 33,
 'HUM_desc': 34,
 'ENTY_instru': 35,
 'ABBR_abb': 36,
 'NUM_other': 37,
 'NUM_speed': 38,
 'ENTY_word': 39,
 'ENTY_lang': 40,
 'NUM_perc': 41,
 'NUM_code': 42,
 'NUM_dist': 43,
 'NUM_temp': 44,
 'ENTY_symbol': 45,
 'NUM_ord': 46,
 'ENTY_veh': 47,
 'NUM_weight': 48,
 'ENTY_currency': 49}

class AGNews(datasets.GeneratorBasedBuilder):
---
license: mit
task_categories:
- text-classification
language:
- en
size_categories:
- 1K<n<10K
pretty_name: uiuc-qc
---

# Question Classification dataset

**Fixed version** (added some examples to test in order to have the same labels in train and test)

This data collection contains all the data used in our learning question classification experiments(see [1]), which has question class definitions, the training and testing question sets, examples of preprocessing the questions, feature definition scripts and examples of semantically related word features. This work has been done by Xin Li and Dan Roth 

Source: https://cogcomp.seas.upenn.edu/Data/QA/QC/