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livedoor-news-corpus.py

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+import random
+from pathlib import Path
+from typing import Generator
+
+from datasets import (
+    BuilderConfig,
+    DatasetInfo,
+    DownloadManager,
+    Features,
+    GeneratorBasedBuilder,
+    Split,
+    SplitGenerator,
+    Value,
+    Version,
+)
+
+_CITATION = ""
+_DESCRIPTION = "This is a dataset of livedoor news articles."
+_HOMEPAGE = "https://www.rondhuit.com/download.html#news%20corpus"
+_LICENSE = "This work is license under CC BY-ND 2.1 JP"
+_URL = "https://www.rondhuit.com/download/ldcc-20140209.tar.gz"
+
+
+class LivedoorNewsCorpus(GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        BuilderConfig(
+            name="livedoor-news-corpus",
+            version=Version("1.1.0"),
+            description=_DESCRIPTION,
+        ),
+    ]
+
+    def _info(self) -> DatasetInfo:
+        return DatasetInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            features=Features(
+                {
+                    "url": Value("string"),
+                    "date": Value("string"),
+                    "title": Value("string"),
+                    "content": Value("string"),
+                    "category": Value("string"),
+                }
+            ),
+        )
+
+    def _split_generators(
+        self, dl_manager: DownloadManager
+    ) -> list[SplitGenerator]:
+        dataset_dir = Path(dl_manager.download_and_extract(_URL))
+
+        data = []
+        for file_name in dataset_dir.glob("*/*/*"):
+            if "LICENSE.txt" in str(file_name):
+                continue
+            with open(file_name, "r") as f:
+                d = [line.strip() for line in f]
+                data.append(
+                    {
+                        "url": d[0],
+                        "date": d[1],
+                        "title": d[2],
+                        "content": " ".join(d[3:]),
+                        "category": file_name.parent.name,
+                    }
+                )
+
+        random.seed(42)
+        random.shuffle(data)
+
+        num_data = len(data)
+        num_train_data = int(num_data * 0.8)
+        num_validation_data = (num_data - num_train_data) // 2
+        train_data = data[:num_train_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}
+            ),
+            SplitGenerator(name=Split.TEST, gen_kwargs={"data": test_data}),
+        ]
+
+    def _generate_examples(self, data: list[dict[str, str]]) -> Generator:
+        for i, d in enumerate(data):
+            yield i, d