Adding in comments

regulatory_comments.py

@@ -11,36 +11,36 @@
 # 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 json
 import datasets
 
-
+# Description of the dataset
 _DESCRIPTION = """\
 United States governmental agencies often make proposed regulations open to the public for comment. 
-This project will use Regulation.gov public API to aggregate and clean public comments for dockets 
-related to Medication Assisted Treatment for Opioid Use Disorders. 
+Proposed regulations are organized into "dockets". This project will use Regulation.gov public API 
+to aggregate and clean public comments for dockets that mention opioid use. 
 
-The dataset will contain docket metadata, docket text-content, comment metadata, and comment text-content. 
+Each example will consist of one docket, and include metadata such as docket id, docket title, etc. 
+Each docket entry will also include information about the top 10 comments, including comment metadata
+and comment text. 
 """
 
+# Homepage URL of the dataset
 _HOMEPAGE = "https://www.regulations.gov/"
 
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+# URL to download the dataset
 _URLS = {"url": "https://huggingface.co/datasets/ro-h/regulatory_comments/raw/main/docket_comments_all.json"}
 
-
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+# Class definition for handling the dataset
 class RegComments(datasets.GeneratorBasedBuilder):
 
+    # Version of the dataset
     VERSION = datasets.Version("1.1.0")
     
+    # Method to define the structure of the dataset
     def _info(self):
+        # Defining the structure of the dataset
         features = datasets.Features({
             "id": datasets.Value("string"),
             "title": datasets.Value("string"),
@@ -59,18 +59,20 @@ class RegComments(datasets.GeneratorBasedBuilder):
             })
         })
 
+        # Returning the dataset structure
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=features,
             homepage=_HOMEPAGE
         )
 
+    # Method to handle dataset splitting (e.g., train/test)
     def _split_generators(self, dl_manager):
         print("split generators called")
-        # URLS should point to where your dataset is located
         urls = _URLS["url"]
         data_dir = dl_manager.download_and_extract(urls)
         print("urls accessed")
+        # Defining the split (here, only train split is defined)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -80,6 +82,7 @@ class RegComments(datasets.GeneratorBasedBuilder):
             ),
         ]
     
+    # Method to generate examples from the dataset
     def _generate_examples(self, filepath):
         """This function returns the examples in the raw (text) form."""
         print("enter generate")
@@ -87,6 +90,7 @@ class RegComments(datasets.GeneratorBasedBuilder):
         with open(filepath, 'r', encoding='utf-8') as f:
             data = json.load(f)
             for docket in data:
+                # Extracting data fields from each docket
                 docket_id = docket["id"]
                 docket_title = docket["title"]
                 docket_context = docket["context"]
@@ -94,6 +98,7 @@ class RegComments(datasets.GeneratorBasedBuilder):
                 docket_keywords = docket.get("keywords", [])
                 comments = docket["comments"]
 
+                # Yielding each docket with its information
                 yield key, {
                     "id": docket_id,
                     "title": docket_title,