[update]add main

main.py

@@ -0,0 +1,16 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "music_comment.py",
+    name="music_comment",
+    split="train",
+)
+
+for sample in dataset:
+    print(sample)
+
+
+if __name__ == '__main__':
+    pass

music_comment.py

@@ -0,0 +1,103 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+from glob import glob
+import json
+import os
+from pathlib import Path
+
+import datasets
+
+
+_URLS = {
+    "music_comment": "data/music.jsonl",
+}
+
+
+_CITATION = """\
+@dataset{music_comment,
+  author       = {Xing Tian},
+  title        = {music_comment},
+  month        = sep,
+  year         = 2023,
+  publisher    = {Xing Tian},
+  version      = {1.0},
+}
+"""
+
+
+class MusicComment(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    configs = list()
+    for name in _URLS.keys():
+        config = datasets.BuilderConfig(name=name, version=VERSION, description=name)
+        configs.append(config)
+
+    BUILDER_CONFIGS = [
+        *configs
+    ]
+
+    def _info(self):
+        features = datasets.Features({
+            "singer_name": datasets.Sequence(datasets.Value("string")),
+            "song_name": datasets.Value("string"),
+            "subtitle": datasets.Value("string"),
+            "album_name": datasets.Value("string"),
+            "singer_id": datasets.Sequence(datasets.Value("int32")),
+            "singer_mid": datasets.Sequence(datasets.Value("string")),
+            "song_time_public": datasets.Value("string"),
+            "song_type": datasets.Value("int32"),
+            "language": datasets.Value("int32"),
+            "song_id": datasets.Value("int32"),
+            "song_mid": datasets.Value("string"),
+            "song_url": datasets.Value("string"),
+            "hot_comments": datasets.Sequence(feature=datasets.Features({
+                "comment_name": datasets.Value("string"),
+                "comment_text": datasets.Value("string"),
+            })),
+            "lyric": datasets.Value("string"),
+
+        })
+        return datasets.DatasetInfo(
+            features=features,
+            supervised_keys=None,
+            homepage="",
+            license="",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        url = _URLS[self.config.name]
+        dl_path = dl_manager.download(url)
+        archive_path = dl_path
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"archive_path": archive_path, "split": "train"},
+            ),
+        ]
+
+    def _generate_examples(self, archive_path, split):
+        """Yields examples."""
+        archive_path = Path(archive_path)
+
+        idx = 0
+
+        with open(archive_path, "r", encoding="utf-8") as f:
+            for row in f:
+                sample = json.loads(row)
+
+                hot_comments = sample["hot_comments"]
+                if isinstance(hot_comments, str):
+                    continue
+
+                yield idx, {
+                    k: sample.get(k, None) for k in self._info().features.keys()
+                }
+                idx += 1
+
+
+if __name__ == '__main__':
+    pass