changed citation

regulatory_comments.py

@@ -0,0 +1,122 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+import json
+import datasets
+
+# Description of the dataset
+_DESCRIPTION = """\
+United States governmental agencies often make proposed regulations open to the public for comment. 
+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. 
+
+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/"
+
+# URL to download the dataset
+_URLS = {"url": "https://huggingface.co/datasets/ro-h/regulatory_comments/raw/main/docket_comments_v4.json"}
+_CITATION = """@misc{ro_huang_regulatory_2023-1,
+	author = {{Ro Huang}},
+	date = {2023-03-19},
+	publisher = {Hugging Face},
+	title = {Regulatory Comments},
+	url = {https://huggingface.co/datasets/ro-h/regulatory_comments},
+	version = {1.1.4},
+	bdsk-url-1 = {https://huggingface.co/datasets/ro-h/regulatory_comments}}
+"""
+
+# Class definition for handling the dataset
+class RegComments(datasets.GeneratorBasedBuilder):
+
+    # Version of the dataset
+    VERSION = datasets.Version("1.1.4")
+    
+    # Method to define the structure of the dataset
+    def _info(self):
+        # Defining the structure of the dataset
+        features = datasets.Features({
+            "id": datasets.Value("string"),
+            "agency": datasets.Value("string"), #Added In
+            "title": datasets.Value("string"),
+            "update_date": datasets.Value("string"), #Added In
+            "update_time": datasets.Value("string"), #Added In
+            "purpose": datasets.Value("string"),
+            "keywords": datasets.Sequence(datasets.Value("string")),
+            "comments": datasets.Sequence({
+                "text": datasets.Value("string"),
+                "comment_id": datasets.Value("string"),
+                "comment_url": datasets.Value("string"),
+                "comment_date": datasets.Value("string"),
+                "comment_time": datasets.Value("string"),
+                "commenter_fname": datasets.Value("string"),
+                "commenter_lname": datasets.Value("string"),
+                "comment_length": datasets.Value("int32")
+            })
+        })
+
+        # 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):
+        urls = _URLS["url"]
+        data_dir = dl_manager.download_and_extract(urls)
+        # Defining the split (here, only train split is defined)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                },
+            ),
+        ]
+    
+    # Method to generate examples from the dataset
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        key = 0
+        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_agency = docket["agency"]
+                docket_title = docket["title"]
+                docket_update_date = docket["update_date"]
+                docket_update_time = docket["update_time"]
+                docket_purpose = docket.get("purpose", "unspecified")
+                docket_keywords = docket.get("keywords", [])
+                comments = docket["comments"]
+
+                # Yielding each docket with its information
+                yield key, {
+                    "id": docket_id,
+                    "agency": docket_agency,
+                    "title": docket_title,
+                    "update_date": docket_update_date,
+                    "update_time": docket_update_time,
+                    "purpose": docket_purpose, 
+                    "keywords": docket_keywords, 
+                    "comments": comments
+                }
+                key += 1