Delete regulatory_comments.py

regulatory_comments.py

@@ -1,122 +0,0 @@
-# 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