Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
Chinese
Size:
100K - 1M
Tags:
jd
License:
File size: 4,207 Bytes
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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):
test_file = rf'{self.config.data_url}test/{self.config.name}.txt'
dev_file = rf'{self.config.data_url}dev/{self.config.name}.txt'
train_file = rf'{self.config.data_url}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]}
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