Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
expert-generated
Annotations Creators:
no-annotation
Source Datasets:
original
License:
File size: 6,375 Bytes
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# 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.
"""
A dataset containing every speech in the House of Commons from May 1979-July 2020.
"""
import json
import os
import time
import pandas as pd
from datetime import datetime
import datasets
_CITATION = """@misc{odell, evan_2021,
title={Hansard Speeches 1979-2021: Version 3.1.0},
DOI={10.5281/zenodo.4843485},
abstractNote={<p>Full details are available at <a href="https://evanodell.com/projects/datasets/hansard-data">https://evanodell.com/projects/datasets/hansard-data</a></p> <p><strong>Version 3.1.0 contains the following changes:</strong></p> <p>- Coverage up to the end of April 2021</p>},
note={This release is an update of previously released datasets. See full documentation for details.},
publisher={Zenodo},
author={Odell, Evan},
year={2021},
month={May} }
"""
_DESCRIPTION = """
A dataset containing every speech in the House of Commons from May 1979-July 2020.
"""
_HOMEPAGE = "https://evanodell.com/projects/datasets/hansard-data/"
_LICENSE = "Creative Commons Attribution 4.0 International License"
_URLS = {
"csv": "https://zenodo.org/record/4843485/files/hansard-speeches-v310.csv.zip?download=1",
"json": "https://zenodo.org/record/4843485/files/parliamentary_posts.json?download=1",
}
fields = [
"id",
"speech",
"display_as",
"party",
"constituency",
"mnis_id",
"date",
"time",
"colnum",
"speech_class",
"major_heading",
"minor_heading",
"oral_heading",
"year",
"hansard_membership_id",
"speakerid",
"person_id",
"speakername",
"url",
"parliamentary_posts",
"opposition_posts",
"government_posts",
]
logger = datasets.utils.logging.get_logger(__name__)
class HansardSpeech(datasets.GeneratorBasedBuilder):
"""A dataset containing every speech in the House of Commons from May 1979-July 2020."""
VERSION = datasets.Version("3.1.0")
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"speech": datasets.Value("string"),
"display_as": datasets.Value("string"),
"party": datasets.Value("string"),
"constituency": datasets.Value("string"),
"mnis_id": datasets.Value("string"),
"date": datasets.Value("string"),
"time": datasets.Value("string"),
"colnum": datasets.Value("string"),
"speech_class": datasets.Value("string"),
"major_heading": datasets.Value("string"),
"minor_heading": datasets.Value("string"),
"oral_heading": datasets.Value("string"),
"year": datasets.Value("string"),
"hansard_membership_id": datasets.Value("string"),
"speakerid": datasets.Value("string"),
"person_id": datasets.Value("string"),
"speakername": datasets.Value("string"),
"url": datasets.Value("string"),
"government_posts": datasets.Sequence(datasets.Value("string")),
"opposition_posts": datasets.Sequence(datasets.Value("string")),
"parliamentary_posts": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
temp_dir = dl_manager.download_and_extract(_URLS["csv"])
csv_file = os.path.join(temp_dir, "hansard-speeches-v310.csv")
json_file = dl_manager.download(_URLS["json"])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepaths": [csv_file, json_file], "split": "train",},
),
]
def _generate_examples(self, filepaths, split):
logger.warn("\nThis is a large dataset. Please be patient")
json_data = pd.read_json(filepaths[1])
csv_data_chunks = pd.read_csv(filepaths[0], chunksize=50000, dtype="object")
for data_chunk in csv_data_chunks:
data_chunk.fillna("", inplace=True)
for _, row in data_chunk.iterrows():
data_point = {}
for field in fields[:-3]:
data_point[field] = str(row[field]) if row[field] else ""
parl_post_list = []
if data_point["mnis_id"] and data_point["date"]:
speech_dt = data_point["date"] + " 00:00:00"
try:
parl_posts = json_data[
(json_data["mnis_id"] == int(data_point["mnis_id"]))
& (json_data["date"] == speech_dt)
]["parliamentary_posts"]
if len(parl_posts) > 0:
parl_posts = parl_posts.iloc[0]
for item in parl_posts:
parl_post_list.append(item["parl_post_name"])
except Exception as e:
logger.warn(
f"Data could not be fetched for mnis_id: {data_point['mnis_id']}, date: {data_point['date']}\nError: {repr(e)}"
)
opp_post = []
gov_post = []
data_point["government_posts"] = gov_post
data_point["opposition_posts"] = opp_post
data_point["parliamentary_posts"] = parl_post_list
yield data_point["id"], data_point
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