added Indic config

bernice-pretrain-data.py

@@ -52,16 +52,37 @@ _LICENSE = ""
 # you can just pass the relative paths to the files instead of URLs.
 # Only train data, validation split not provided
 _URLS = {
-    "train": "data"
+    "all": [f"data/{f}" for f in os.listdir("data")],
+    "indic": ["data/indic_tweet_ids.txt.gz"]
 }
 
 
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
 class BernicePretrainData(datasets.GeneratorBasedBuilder):
     """Tweet IDs for the 2.5 billion multilingual tweets used to train Bernice, a Twitter encoder."""
-    VERSION = datasets.Version("1.1.0")
+    VERSION = datasets.Version("1.0.0")
 
     def _info(self):
+        # This is an example of a dataset with multiple configurations.
+        # If you don't want/need to define several sub-sets in your dataset,
+        # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+        # If you need to make complex sub-parts in the datasets with configurable options
+        # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+        # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+        # You will be able to load one or the other configurations in the following list with
+        # data = datasets.load_dataset('my_dataset', 'first_domain')
+        # data = datasets.load_dataset('my_dataset', 'second_domain')
+        BUILDER_CONFIGS = [
+            datasets.BuilderConfig(name="all", version=VERSION,
+                                   description="Includes all tweets"),
+            datasets.BuilderConfig(name="indic", version=VERSION,
+                                   description="Only the Indic languages, plus `undefined'"),
+        ]
+
+        DEFAULT_CONFIG_NAME = "all"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
@@ -92,8 +113,7 @@ class BernicePretrainData(datasets.GeneratorBasedBuilder):
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        dir_url = self._URLS["train"]
-        urls_to_download = [f"{dir_url}/{f}" for f in os.listdir(dir_url)]
+        urls_to_download = self._URLS[self.config.name]
         downloaded_files = dl_manager.download_and_extract(urls_to_download)
         return [
             datasets.SplitGenerator(