Agrega script de carga con dos configuraciones: valencia y arousal

emotional-dataset-chile.py

@@ -0,0 +1,69 @@
+import json
+import datasets
+
+_CITATION = """\
+@misc{quintero2024emotionaldatasetchile,
+  author       = {cypher-256},
+  title        = {Emotional Dataset Chile},
+  year         = 2025,
+  url          = {https://huggingface.co/datasets/cypher-256/emotional-dataset-chile}
+}
+"""
+
+_DESCRIPTION = """\
+Este dataset contiene ejemplos en español chileno etiquetados con valencia o arousal emocional \
+en formato de regresión continua (-1.0 a 1.0). Incluye dos configuraciones independientes: 'valencia' y 'arousal'.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/cypher-256/emotional-dataset-chile"
+
+_LICENSE = "MIT"
+
+_DATA_FILES = {
+    "valencia": "valencia_dataset.jsonl",
+    "arousal": "arousal_dataset.jsonl",
+}
+
+class EmotionalDatasetChileConfig(datasets.BuilderConfig):
+    def __init__(self, name, **kwargs):
+        super().__init__(name=name, **kwargs)
+
+class EmotionalDatasetChile(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        EmotionalDatasetChileConfig(
+            name="valencia",
+            version=datasets.Version("1.0.0"),
+            description="Text samples labeled with emotional valence",
+        ),
+        EmotionalDatasetChileConfig(
+            name="arousal",
+            version=datasets.Version("1.0.0"),
+            description="Text samples labeled with emotional arousal",
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "texto": datasets.Value("string"),
+                self.config.name: datasets.Value("float32"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_file = _DATA_FILES[self.config.name]
+        data_path = dl_manager.download_and_extract(data_file)
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_path})]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                data = json.loads(line)
+                yield idx, data