modify how to import

livedoor-news-corpus.py

@@ -4,10 +4,7 @@ import random
 from pathlib import Path
 from typing import Generator
 
-from datasets import (BuilderConfig, DatasetInfo, DownloadManager, Features,
-                      GeneratorBasedBuilder, Split, SplitGenerator, Value,
-                      Version)
-from datasets.data_files import DataFilesDict
+import datasets
 
 _CITATION = ""
 _DESCRIPTION = "This is a dataset of livedoor news articles."
@@ -16,14 +13,14 @@ _LICENSE = "This work is license under CC BY-ND 2.1 JP"
 _URL = "https://www.rondhuit.com/download/ldcc-20140209.tar.gz"
 
 
-class LivedoorNewsCorpusConfig(BuilderConfig):
+class LivedoorNewsCorpusConfig(datasets.BuilderConfig):
     def __init__(
         self,
         name: str = "default",
-        version: Version | str | None = Version("0.0.0"),
+        version: datasets.Version | str | None = datasets.Version("0.0.0"),
         data_dir: str | None = None,
-        data_files: DataFilesDict | None = None,
-        description: str | None = None,
+        data_files: datasets.data_files.DataFilesDict | None = None,
+        description: str | None = _DESCRIPTION,
         shuffle: bool = True,
         seed: int = 42,
         train_ratio: float = 0.8,
@@ -42,37 +39,29 @@ class LivedoorNewsCorpusConfig(BuilderConfig):
         self.validation_ratio = validation_ratio
 
 
-class LivedoorNewsCorpus(GeneratorBasedBuilder):
+class LivedoorNewsCorpus(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIG_CLASS = LivedoorNewsCorpusConfig
 
-    BUILDER_CONFIGS = [
-        LivedoorNewsCorpusConfig(
-            name="livedoor-news-corpus",
-            version=Version("1.1.0"),
-            description=_DESCRIPTION,
-        ),
-    ]
-
-    def _info(self) -> DatasetInfo:
-        return DatasetInfo(
+    def _info(self) -> datasets.DatasetInfo:
+        return datasets.DatasetInfo(
             description=_DESCRIPTION,
             citation=_CITATION,
             homepage=_HOMEPAGE,
             license=_LICENSE,
-            features=Features(
+            features=datasets.Features(
                 {
-                    "url": Value("string"),
-                    "date": Value("string"),
-                    "title": Value("string"),
-                    "content": Value("string"),
-                    "category": Value("string"),
+                    "url": datasets.Value("string"),
+                    "date": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "content": datasets.Value("string"),
+                    "category": datasets.Value("string"),
                 }
             ),
         )
 
     def _split_generators(
-        self, dl_manager: DownloadManager
-    ) -> list[SplitGenerator]:
+        self, dl_manager: datasets.DownloadManager
+    ) -> list[datasets.SplitGenerator]:
         dataset_dir = Path(dl_manager.download_and_extract(_URL))
 
         data = []
@@ -91,7 +80,7 @@ class LivedoorNewsCorpus(GeneratorBasedBuilder):
                     }
                 )
 
-        if self.config.shuffle == True:
+        if self.config.shuffle:
             random.seed(self.config.seed)
             random.shuffle(data)
 
@@ -99,16 +88,18 @@ class LivedoorNewsCorpus(GeneratorBasedBuilder):
         num_train_data = int(num_data * self.config.train_ratio)
         num_validation_data = int(num_data * self.config.validation_ratio)
         train_data = data[:num_train_data]
-        validation_data = data[
-            num_train_data : num_train_data + num_validation_data
-        ]
+        validation_data = data[num_train_data : num_train_data + num_validation_data]
         test_data = data[num_train_data + num_validation_data :]
         return [
-            SplitGenerator(name=Split.TRAIN, gen_kwargs={"data": train_data}),
-            SplitGenerator(
-                name=Split.VALIDATION, gen_kwargs={"data": validation_data}
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"data": train_data}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"data": validation_data}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"data": test_data}
             ),
-            SplitGenerator(name=Split.TEST, gen_kwargs={"data": test_data}),
         ]
 
     def _generate_examples(self, data: list[dict[str, str]]) -> Generator: