Upload jd21.py

jd21.py

@@ -0,0 +1,102 @@
+
+from datasets import Value, ClassLabel,Sequence
+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= r"D:\Personal\CodeBase\Continual Learning\continual-learning-framework-for-NLP\datasets\jd21\\",
+                    data_url = "",
+                    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_dir}data\test\{self.config.name}.txt'
+        dev_file = rf'{self.config.data_dir}data\dev\{self.config.name}.txt'
+        train_file = rf'{self.config.data_dir}data\train\{self.config.name}.txt'
+        return [datasets.SplitGenerator(name=datasets.Split.TEST,
+                                        gen_kwargs={
+                                            "data_file": test_file,
+                                            "split": "test",
+                                        },), 
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION,
+                                        gen_kwargs={
+                                            "data_file": dev_file,
+                                            "split": "dev",
+                                        },), 
+                datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                                        gen_kwargs={
+                                            "data_file": 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]}