feat(dataset-loader): added dataset loader script

pale.py

@@ -0,0 +1,83 @@
+from pandas import read_csv
+
+from datasets import GeneratorBasedBuilder, Value, Version, BuilderConfig, Features, DatasetInfo, SplitGenerator, Split
+
+_DESCRIPTION = '''
+This dataset contains transcribed sounds emitted by characters of the League of Legends game.
+The data can be useful for building text classification models, fine-tuning language generation models, speech synthesis and speech recognition models.
+The underlying web dump for the dataset construction has been last refreshed **20.10.2023**
+'''
+
+_HOMEPAGE = 'https://huggingface.co/datasets/zeio/pale'
+
+_LICENSE = 'Apache License Version 2.0'
+
+_URLS = {
+    'full': 'https://huggingface.co/datasets/zeio/pale/resolve/main/pale.tsv',
+    'quotes': 'https://huggingface.co/datasets/zeio/pale/resolve/main/quotes.tsv',
+    'annotated': 'https://huggingface.co/datasets/zeio/pale/resolve/main/annotated.tsv'
+}
+
+
+class Pale(GeneratorBasedBuilder):
+
+    VERSION = Version('27.10.2023')
+
+    BUILDER_CONFIGS = [
+        BuilderConfig(name = 'quotes', version = VERSION, description = 'Truncated version of the corpus, which does\'t contain sound effects'),
+        BuilderConfig(name = 'annotated', version = VERSION, description = 'An extended version of the full configuration with a couple of additional columns with labels'),
+        BuilderConfig(name = 'vanilla', version = VERSION, description = 'All data pulled from the website without significant modifications apart from the web page structure parsing')
+    ]
+
+    DEFAULT_CONFIG_NAME = 'quotes'
+
+    def _info(self):
+        if self.config.name == 'vanilla':
+            features = Features({
+                'header': Value('string'),
+                'subheader': Value('string'),
+                'text': Value('string'),
+                'source': Value('string'),
+                'champion': Value('string')
+            })
+        elif self.config.name == 'annotated':
+            features = Features({
+                'header': Value('string'),
+                'subheader': Value('string'),
+                'text': Value('string'),
+                'source': Value('string'),
+                'champion': Value('string'),
+                'quote': Value('bool')
+            })
+        elif self.config.name == 'quotes':
+            features = Features({
+                'header': Value('string'),
+                'subheader': Value('string'),
+                'text': Value('string'),
+                'champion': Value('string')
+            })
+
+        return DatasetInfo(
+            description=_DESCRIPTION,
+            features = features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE
+        )
+
+    def _split_generators(self, dl_manager):
+        name = self.config.name
+
+        url = _URLS[name]
+
+        return [
+            SplitGenerator(
+                name = Split.TRAIN,
+                gen_kwargs = {
+                    "path": dl_manager.download_and_extract(url)
+                }
+            )
+        ]
+
+    def _generate_examples(self, path: str):
+        for i, row in read_csv(path, sep = '\t').iterrows():
+            yield i, dict(row)