|
|
|
"""QC question classification dataset.""" |
|
|
|
|
|
import csv |
|
|
|
import datasets |
|
from datasets.tasks import TextClassification |
|
|
|
|
|
_DESCRIPTION = """\ |
|
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]. |
|
""" |
|
|
|
_CITATION = """""" |
|
|
|
_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): |
|
"""AG News topic classification dataset.""" |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features( |
|
{ |
|
"text": datasets.Value("string"), |
|
"label": datasets.features.ClassLabel(names=list(CATEGORY_MAPPING.keys())), |
|
} |
|
), |
|
homepage="https://cogcomp.seas.upenn.edu/Data/QA/QC/", |
|
citation=_CITATION, |
|
task_templates=[TextClassification(text_column="text", label_column="label")], |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL) |
|
test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL) |
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}), |
|
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}), |
|
] |
|
|
|
def _generate_examples(self, filepath): |
|
"""Generate QC News examples.""" |
|
with open(filepath, encoding="utf-8") as csv_file: |
|
csv_reader = csv.reader( |
|
csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True |
|
) |
|
_ = next(csv_reader) |
|
for id_, row in enumerate(csv_reader): |
|
text, label = row |
|
label = CATEGORY_MAPPING[label] |
|
yield id_, {"text": text, "label": label} |
