v1 of dataset loading script

BabyLM.py

@@ -0,0 +1,99 @@
+import datasets
+
+_DESCRIPTION = """\
+Dataset for the shared baby language modeling task.
+The goal is to train a language model from scratch on this data which represents
+roughly the amount of text and speech data a young child observes.  
+"""
+
+_HOMEPAGE = "https://babylm.github.io"
+
+class BabyLM(datasets.GeneratorBasedBuilder):
+    
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="strict_small",
+            description="Small version of the dataset with 10M words",
+            version="1.0.0",
+            data_dir="10M",
+            features=["text"]
+        ),
+        datasets.BuilderConfig(
+            name="strict",
+            description="Full version of the dataset with 100M words",
+            version="1.0.0",
+            data_dir="100M",
+            features=["text"]
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "strict_small"
+
+    def _info(self):
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                }
+            )
+            return datasets.DatasetInfo(
+                # This is the description that will appear on the datasets page.
+                description=_DESCRIPTION,
+                features=features,  # Here we define them above because they are different between the two configurations
+                homepage=_HOMEPAGE,
+            )
+
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """ 
+        Returns data for different splits 
+        """
+    
+        if self.config.name == "strict_small":
+            train_data_dir = "10M"
+        else: 
+            train_data_dir = "100M"
+
+        urls_to_download = {
+            "train": f"{train_data_dir}/*.txt",
+            "dev": "dev/*.txt",
+            "test": "test/*.txt"
+        } 
+
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "split": "train",
+                    "filepaths": downloaded_files["train"]}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "split": "dev",
+                    "filepaths": downloaded_files["dev"]}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "split": "test",
+                    "filepaths": downloaded_files["test"]
+                }
+            ),
+        ]
+
+     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, split, filepaths):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+
+        # the filepaths should be a list of filepaths 
+        if isinstance(filepaths, str):
+            filepaths = [filepaths]
+        
+        global_idx = 0 
+
+        for filepath in filepaths:
+            with open(filepath, encoding="utf-8") as f:
+                for row in f:
+                    yield global_idx, {"text": row}