Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
Chinese
Size:
100K - 1M
Tags:
jd
License:
from datasets import Value, ClassLabel | |
import datasets | |
_JD21_CITATION = """\ | |
""" | |
_JD21_DESCRIPTION = """\ | |
GLUE, the General Language Understanding Evaluation benchmark | |
(https://gluebenchmark.com/) is a collection of resources for training, | |
evaluating, and analyzing natural language understanding systems. | |
""" | |
class JD21Config(datasets.BuilderConfig): | |
def __init__( | |
self, | |
text_features, | |
label_column, | |
data_url, | |
data_dir, | |
citation, | |
url, | |
label_classes=None, | |
process_label=lambda x: x, | |
**kwargs, | |
): | |
super(JD21Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) | |
self.text_features = text_features | |
self.label_column = label_column | |
self.label_classes = label_classes | |
self.data_url = data_url | |
self.data_dir = data_dir | |
self.citation = citation | |
self.url = url | |
self.process_label = process_label | |
class JD21(datasets.GeneratorBasedBuilder): | |
domain_list = ['褪黑素', '维生素', '无线耳机', '蛋白粉', '游戏机', '电视', 'MacBook', '洗面奶', '智能手表', '吹风机', '小米手机', '红米手机', '护肤品', | |
'电动牙刷', 'iPhone', '海鲜', '酒', '平板电脑', '修复霜', '运动鞋', '智能手环'] | |
BUILDER_CONFIGS = [ | |
JD21Config(name=domain_name, | |
description= f'comments of JD {domain_name}.', | |
text_features={'sentence':'sentence', 'domain':'domain'}, | |
label_classes=['POS','NEG'], | |
label_column='label', | |
citation="", | |
data_dir= "", | |
data_url = r"https://huggingface.co/datasets/kuroneko5943/jd21/resolve/main/", | |
url='https://github.com/ws719547997/LNB-DA') | |
for domain_name in domain_list | |
] | |
def _info(self): | |
features = {'id':Value(dtype='int32', id=None), | |
'domain':Value(dtype='string', id=None), | |
'label':ClassLabel(num_classes=2, names=['POS', 'NEG'], names_file=None, id=None), | |
'rank':Value(dtype='int32', id=None), | |
'sentence':Value(dtype='string', id=None)} | |
return datasets.DatasetInfo( | |
description=_JD21_DESCRIPTION, | |
features=datasets.Features(features), | |
homepage=self.config.url, | |
citation=self.config.citation + "\n" + _JD21_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
downloaded_file = dl_manager.download_and_extract(self.config.data_url) | |
print(downloaded_file) | |
test_file = rf'{downloaded_file}test\{self.config.name}.txt' | |
dev_file = rf'{downloaded_file}dev\{self.config.name}.txt' | |
train_file = rf'{downloaded_file}train\{self.config.name}.txt' | |
return [datasets.SplitGenerator(name=datasets.Split.TEST, | |
gen_kwargs={ | |
"data_file": dl_manager.download(test_file), | |
"split": "test", | |
},), | |
datasets.SplitGenerator(name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"data_file": dl_manager.download(dev_file), | |
"split": "dev", | |
},), | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"data_file": dl_manager.download(train_file), | |
"split": "train", | |
},)] | |
def _generate_examples(self, data_file, split): | |
with open(data_file, 'r', encoding='utf-8') as f: | |
for line in f: | |
lin = line.strip() | |
if not lin: | |
continue | |
lin_sp = lin.split('\t') | |
if len(lin_sp) < 5: | |
continue | |
# id, {example} | |
yield lin_sp[0], {'sentence':lin_sp[4],'domain':lin_sp[1], 'label':lin_sp[2], 'id':lin_sp[0], 'rank':lin_sp[3]} | |