doc: cleanup documentation of dataloader

conversational_weather.py

@@ -12,7 +12,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""TODO: Add a description here."""
 
 
 import csv
@@ -52,7 +51,7 @@ _LICENSE = "CC-BY-NC-4.0"
 # The HuggingFace dataset library don't host the datasets but only point to the original files
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLs = {
-    'weather': {
+    'default': {
         'train': 'https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/train.tsv',
         'validation': 'https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/val.tsv',
         'test': 'https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/test.tsv'
@@ -60,51 +59,31 @@ _URLs = {
 }
 
 
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
 class ConversationalWeather(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
+    """The Conversational Weather dataset is designed for generation of responses to weather queries 
+    based on a structured input data. The input allows specifying data attributes such as dates, times, 
+    locations, weather conditions, and errors, and also offers control over structure of response through 
+    discourse relations such as join, contrast, and justification."""
 
     VERSION = datasets.Version("1.1.0")
 
-    # 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="weather", version=VERSION, description="This part of my dataset covers a first domain"),
-        #datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "weather"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "weather":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "gem_id": datasets.Value("string"),
-                    "data_id": datasets.Value("string"),
-                    "user_query": datasets.Value("string"),
-                    "tree_str_mr": datasets.Value("string"),
-                    "response": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features({})
+        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features(
+            {
+                "gem_id": datasets.Value("string"),
+                "data_id": datasets.Value("string"),
+                "user_query": datasets.Value("string"),
+                "tree_str_mr": datasets.Value("string"),
+                "response": datasets.Value("string"),
+            }
+        )
 
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
             # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
+            features=features,  
             # If there's a common (input, target) tuple from the features,
             # specify them here. They'll be used if as_supervised=True in
             # builder.as_dataset.
@@ -119,16 +98,14 @@ class ConversationalWeather(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
 
         # 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
+
         my_urls = _URLs[self.config.name]
-        print(my_urls)
         data_dir = dl_manager.download_and_extract(my_urls)
-        print(data_dir)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -159,18 +136,18 @@ class ConversationalWeather(datasets.GeneratorBasedBuilder):
     def _generate_examples(
         self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     ):
+        print(filepath)
         """ Yields examples as (key, example) tuples. """
         # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is here for legacy reason (tfds) and is not important in itself.
         with open(filepath, encoding="utf-8") as f:
             csv_reader = csv.reader(f, delimiter='\t')
             for id_, row in enumerate(csv_reader):
-                if self.config.name == "weather":
-                    assert len(row) == 4
-                    yield id_, {
-                        "gem_id": f"{self.config.name}-{split}-{id_}",
-                        "data_id": row[0],
-                        "user_query": row[1],
-                        "tree_str_mr": row[2],
-                        "response": row[3],
-                    }
+                assert len(row) == 4
+                yield id_, {
+                    "gem_id": f"{self.config.name}-{split}-{id_}",
+                    "data_id": row[0],
+                    "user_query": row[1],
+                    "tree_str_mr": row[2],
+                    "response": row[3],
+                }