File size: 4,402 Bytes
80a0d7a 7f1176d 80a0d7a 7f1176d 80a0d7a 7f1176d 80a0d7a 7f1176d 80a0d7a 7f1176d e3925a3 80a0d7a 7f1176d 80a0d7a 7f1176d 80a0d7a 7fd10e2 7f1176d 80a0d7a 7f1176d 7fd10e2 5c25b73 7fd10e2 7f1176d 80a0d7a 7fd10e2 80a0d7a 7f1176d 80a0d7a e0ee352 f6e03bc 7fd10e2 e0ee352 7f1176d 7fd10e2 80a0d7a 08cb6c7 7fd10e2 80a0d7a 7f1176d 553ebeb 7a12b1c 7f1176d 7a12b1c bf92d2d 7a12b1c 7fd10e2 7f1176d 553ebeb 7fd10e2 7a12b1c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
# 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_all.json"}
# 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"),
"context": datasets.Value("string"),
"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_title": 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):
print("split generators called")
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,
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."""
print("enter generate")
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_title = docket["title"]
docket_context = docket["context"]
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,
"title": docket_title,
"context": docket_context,
"purpose": docket_purpose,
"keywords": docket_keywords,
"comments": comments
}
key += 1 |