Update megawika.py

megawika.py

@@ -35,12 +35,24 @@ _HOMEPAGE = "https://huggingface.co/datasets/DataProvenanceInitiative/Megawika_s
 _LICENSE = "cc-by-sa-4.0"
 _URL = "https://huggingface.co/datasets/DataProvenanceInitiative/Megawika_subset"
 
-# Load language-specific file paths
 def load_file_paths():
+    """Load and parse the files.yml containing dataset file paths.
+    
+    Expected YAML structure:
+    en:
+        - https://huggingface.co/datasets/DataProvenanceInitiative/Megawika_subset/resolve/main/en/en-00000-of-06154.jsonl
+    fr:
+        - https://huggingface.co/datasets/DataProvenanceInitiative/Megawika_subset/resolve/main/fr/fr-00000-of-00123.jsonl
+    ...
+    
+    Returns:
+        dict: Dictionary mapping language codes to lists of file URLs
+    """
     file_list_url = "https://huggingface.co/datasets/DataProvenanceInitiative/Megawika_subset/raw/main/files.yml"
     try:
         with urllib.request.urlopen(file_list_url) as f:
-            return yaml.safe_load(f)['fnames']
+            # Direct YAML parsing - the structure is already in the correct format
+            return yaml.safe_load(f)
     except (yaml.YAMLError, urllib.error.URLError) as exc:
         print(f"Error loading dataset file paths: {exc}")
         return {}
@@ -63,11 +75,9 @@ class MegaWika(datasets.GeneratorBasedBuilder):
     
     VERSION = datasets.Version("1.0.0")
     
-    # Load available languages
-    _DATA_URL = load_file_paths()
-    LANGUAGES = list(_DATA_URL.keys())
+    # Load available languages directly from YAML structure
+    LANGUAGES = list(load_file_paths().keys())
     
-    # Create configs for each language and an 'all' config
     BUILDER_CONFIGS = ([
         MegaWikaConfig(
             name="all",
@@ -87,79 +97,31 @@ class MegaWika(datasets.GeneratorBasedBuilder):
     
     DEFAULT_CONFIG_NAME = "all"
 
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "article_title": datasets.Value("string"),
-                    "article_text": datasets.Value("string"),
-                    "entries": datasets.features.Sequence(
-                        {
-                            "id": datasets.Value("string"),
-                            "passage": {
-                                "text": [datasets.Value("string")],
-                                "parse": datasets.Value("string"),
-                                "en_tokens": [datasets.Value("string")],
-                                "lang_tokens": [datasets.Value("string")],
-                                "en_lang_token_map": [[datasets.Value("int32")]]
-                            },
-                            "mt": {
-                                "original": datasets.Value("string"),
-                                "original_sents": [datasets.Value("string")],
-                                "translation": datasets.Value("string"),
-                                "translation_sents": [datasets.Value("string")],
-                                "translation_probs": [[datasets.Value("float32")]],
-                                "repetitious_translation": datasets.Value("bool")
-                            },
-                            "source_lang": datasets.Value("string"),
-                            "source_url": datasets.Value("string"),
-                            "source_text": datasets.Value("string"),
-                            "qa_pairs": datasets.Sequence(
-                                {
-                                    "question": datasets.Value("string"),
-                                    "en_answer": datasets.Value("string"),
-                                    "lang_answer": datasets.Value("string"),
-                                    "frames": datasets.Sequence(
-                                        {
-                                            "frame": datasets.Value("string"),
-                                            "argument": datasets.Value("string")
-                                        }
-                                    ),
-                                    "en_matches_in_source": [[datasets.Value("int32")]],
-                                    "en_match_in_passage": [datasets.Value("int32")],
-                                    "lang_matches_in_source": [[datasets.Value("int32")]],
-                                    "lang_match_in_passage": [datasets.Value("int32")],
-                                    "passage": [datasets.Value("string")],
-                                    "en_answer_tokens": [datasets.Value("string")],
-                                    "match_disambiguated_question": datasets.Value("string"),
-                                }
-                            )
-                        }
-                    )
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
+        # Load the file paths afresh to ensure we have the latest data
+        data_sources = load_file_paths()
+        
         if self.config.name == "all":
-            data_sources = self._DATA_URL
+            # Process all languages
+            selected_sources = data_sources
         else:
-            if self.config.name not in self._DATA_URL:
-                raise ValueError(f"Language {self.config.name} not found in available languages: {list(self._DATA_URL.keys())}")
-            data_sources = {self.config.name: self._DATA_URL[self.config.name]}
+            # Process single language
+            if self.config.name not in data_sources:
+                raise ValueError(
+                    f"Language '{self.config.name}' not found in available languages: {list(data_sources.keys())}"
+                )
+            selected_sources = {self.config.name: data_sources[self.config.name]}
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"filepaths": dl_manager.download(data_sources[lang]), "language": lang}
+                gen_kwargs={
+                    "filepaths": dl_manager.download(urls),
+                    "language": lang
+                }
             )
-            for lang in data_sources
+            for lang, urls in selected_sources.items()
         ]
 
     def _get_qa_pair_list_features(self, qa_pair, feature_name):