Upload bookcoref.py with huggingface_hub

bookcoref.py

@@ -145,13 +145,12 @@ class BookCoref(datasets.GeneratorBasedBuilder):
         self,
     ) -> dict[Literal["train", "validation", "test"], dict]:
         # Custom method to load local data files
-        match self.config.name:
-            case "full":
-                data_dir = self.LOCAL_DATA_DIR / "full"
-            case "splitted":
-                data_dir = self.LOCAL_DATA_DIR / "splitted"
-            case _:
-                raise ValueError(f"Unknown config name: {self.config.name}")
+        if self.config.name == "full":
+            data_dir = self.LOCAL_DATA_DIR / "full"
+        elif self.config.name == "splitted":
+            data_dir = self.LOCAL_DATA_DIR / "splitted"
+        else:
+            raise ValueError(f"Unknown config name: {self.config.name}")
 
         data = {}
         for split_name in ["train", "validation", "test"]:
@@ -228,11 +227,12 @@ class BookCoref(datasets.GeneratorBasedBuilder):
         all_split_docs = {}
         for doc_key, sentences in complete_sentences.items():
             split_name = keys_to_split[doc_key]
-            match split_name:
-                case "train" | "validation":
-                    length = 1350
-                case "test":
-                    length = 1500
+            if split_name == "train" or split_name == "validation":
+                length = 1350
+            elif split_name == "test":
+                length = 1500
+            else:
+                raise ValueError(f"Unknown split name: {split_name}")
 
             eos_to_sent = {}
             tokens = []
@@ -315,29 +315,27 @@ class BookCoref(datasets.GeneratorBasedBuilder):
                 complete_sentences[key].append(sentences[current_pos : current_pos + length])
                 current_pos += length
 
-        match self.config.name:
-            case "full":
-                # If the config is "full", we simply add the sentences to each sample
-                for split, split_data in gutenberg_data.items():
-                    for key, sample in split_data.items():
-                        if "animal_farm" not in sample["doc_key"]:
-                            sample["sentences"] = complete_sentences[key]
-            case "splitted":
-                # If the config is "splitted", we split the sentences into chunks
-                # We also need a mapping from data split (train, validation, test) to gutenberg keys
-                split_keys: dict[str, set[str]] = {
-                    split: set(sample["gutenberg_key"].split("_")[0] for sample in split_data)
-                    for split, split_data in local_data.items()
-                }
-                split_complete_sentences = self._cut_into_split(complete_sentences, split_keys)
-                # Then we add the chunks to each sample
-                for split, split_data in gutenberg_data.items():
-                    for key, sample in split_data.items():
-                        if "animal_farm" not in sample["doc_key"]:
-                            sample["sentences"] = split_complete_sentences[key]
-
-            case _:
-                raise ValueError(f"Unknown config name: {self.config.name}")
+        if self.config.name == "full":
+            # If the config is "full", we simply add the sentences to each sample
+            for split, split_data in gutenberg_data.items():
+                for key, sample in split_data.items():
+                    if "animal_farm" not in sample["doc_key"]:
+                        sample["sentences"] = complete_sentences[key]
+        elif self.config.name == "splitted":
+            # If the config is "splitted", we split the sentences into chunks
+            # We also need a mapping from data split (train, validation, test) to gutenberg keys
+            split_keys: dict[str, set[str]] = {
+                split: set(sample["gutenberg_key"].split("_")[0] for sample in split_data)
+                for split, split_data in local_data.items()
+            }
+            split_complete_sentences = self._cut_into_split(complete_sentences, split_keys)
+            # Then we add the chunks to each sample
+            for split, split_data in gutenberg_data.items():
+                for key, sample in split_data.items():
+                    if "animal_farm" not in sample["doc_key"]:
+                        sample["sentences"] = split_complete_sentences[key]
+        else:
+            raise ValueError(f"Unknown config name: {self.config.name}")
 
         return [
             datasets.SplitGenerator(