Datasets:
Multilinguality:
multilingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
# | |
# 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. | |
# Lint as: python3 | |
"""Multilingual Disaster Response Messages dataset.""" | |
import csv | |
import datasets | |
_DESCRIPTION = """\ | |
This dataset contains 30,000 messages drawn from events including an earthquake in Haiti in 2010, an earthquake in Chile in 2010, floods in Pakistan in 2010, super-storm Sandy in the U.S.A. in 2012, and news articles spanning a large number of years and 100s of different disasters. | |
The data has been encoded with 36 different categories related to disaster response and has been stripped of messages with sensitive information in their entirety. | |
Upon release, this is the featured dataset of a new Udacity course on Data Science and the AI4ALL summer school and is especially utile for text analytics and natural language processing (NLP) tasks and models. | |
The input data in this job contains thousands of untranslated disaster-related messages and their English translations. | |
""" | |
_CITATION = """\ | |
@inproceedings{title={Multilingual Disaster Response Messages} | |
} | |
""" | |
_TRAIN_DOWNLOAD_URL = "https://s3.amazonaws.com/datasets.huggingface.co/disaster_response_messages_training.csv" | |
_TEST_DOWNLOAD_URL = "https://s3.amazonaws.com/datasets.huggingface.co/disaster_response_messages_test.csv" | |
_VALID_DOWNLOAD_URL = "https://s3.amazonaws.com/datasets.huggingface.co/disaster_response_messages_validation.csv" | |
class DisasterResponseMessages(datasets.GeneratorBasedBuilder): | |
"""Multilingual Disaster Response Messages dataset.""" | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"split": datasets.Value("string"), | |
"message": datasets.Value("string"), | |
"original": datasets.Value("string"), | |
"genre": datasets.Value("string"), | |
"related": datasets.ClassLabel(names=["false", "true", "maybe"]), | |
"PII": datasets.Value("int8"), | |
"request": datasets.ClassLabel(names=["false", "true"]), | |
"offer": datasets.Value("int8"), | |
"aid_related": datasets.ClassLabel(names=["false", "true"]), | |
"medical_help": datasets.ClassLabel(names=["false", "true"]), | |
"medical_products": datasets.ClassLabel(names=["false", "true"]), | |
"search_and_rescue": datasets.ClassLabel(names=["false", "true"]), | |
"security": datasets.ClassLabel(names=["false", "true"]), | |
"military": datasets.ClassLabel(names=["false", "true"]), | |
"child_alone": datasets.Value("int8"), | |
"water": datasets.ClassLabel(names=["false", "true"]), | |
"food": datasets.ClassLabel(names=["false", "true"]), | |
"shelter": datasets.ClassLabel(names=["false", "true"]), | |
"clothing": datasets.ClassLabel(names=["false", "true"]), | |
"money": datasets.ClassLabel(names=["false", "true"]), | |
"missing_people": datasets.ClassLabel(names=["false", "true"]), | |
"refugees": datasets.ClassLabel(names=["false", "true"]), | |
"death": datasets.ClassLabel(names=["false", "true"]), | |
"other_aid": datasets.ClassLabel(names=["false", "true"]), | |
"infrastructure_related": datasets.ClassLabel(names=["false", "true"]), | |
"transport": datasets.ClassLabel(names=["false", "true"]), | |
"buildings": datasets.ClassLabel(names=["false", "true"]), | |
"electricity": datasets.ClassLabel(names=["false", "true"]), | |
"tools": datasets.ClassLabel(names=["false", "true"]), | |
"hospitals": datasets.ClassLabel(names=["false", "true"]), | |
"shops": datasets.ClassLabel(names=["false", "true"]), | |
"aid_centers": datasets.ClassLabel(names=["false", "true"]), | |
"other_infrastructure": datasets.ClassLabel(names=["false", "true"]), | |
"weather_related": datasets.ClassLabel(names=["false", "true"]), | |
"floods": datasets.ClassLabel(names=["false", "true"]), | |
"storm": datasets.ClassLabel(names=["false", "true"]), | |
"fire": datasets.ClassLabel(names=["false", "true"]), | |
"earthquake": datasets.ClassLabel(names=["false", "true"]), | |
"cold": datasets.ClassLabel(names=["false", "true"]), | |
"other_weather": datasets.ClassLabel(names=["false", "true"]), | |
"direct_report": datasets.ClassLabel(names=["false", "true"]), | |
} | |
), | |
homepage="https://appen.com/datasets/combined-disaster-response-data/", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
train_path, test_path, valid_path = dl_manager.download_and_extract( | |
[_TRAIN_DOWNLOAD_URL, _TEST_DOWNLOAD_URL, _VALID_DOWNLOAD_URL] | |
) | |
return [ | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}), | |
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}), | |
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}), | |
] | |
def _generate_examples(self, filepath): | |
"""Generate Distaster Response Messages examples.""" | |
with open(filepath, encoding="utf-8") as csv_file: | |
csv_reader = csv.reader( | |
csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True | |
) | |
next(csv_reader, None) | |
for id_, row in enumerate(csv_reader): | |
row = row[1:] | |
( | |
split, | |
message, | |
original, | |
genre, | |
related, | |
PII, | |
request, | |
offer, | |
aid_related, | |
medical_help, | |
medical_products, | |
search_and_rescue, | |
security, | |
military, | |
child_alone, | |
water, | |
food, | |
shelter, | |
clothing, | |
money, | |
missing_people, | |
refugees, | |
death, | |
other_aid, | |
infrastructure_related, | |
transport, | |
buildings, | |
electricity, | |
tools, | |
hospitals, | |
shops, | |
aid_centers, | |
other_infrastructure, | |
weather_related, | |
floods, | |
storm, | |
fire, | |
earthquake, | |
cold, | |
other_weather, | |
direct_report, | |
) = row | |
yield id_, { | |
"split": (split), | |
"message": (message), | |
"original": (original), | |
"genre": (genre), | |
"related": int(related), | |
"PII": int(PII), | |
"request": int(request), | |
"offer": int(offer), | |
"aid_related": int(aid_related), | |
"medical_help": int(medical_help), | |
"medical_products": int(medical_products), | |
"search_and_rescue": int(search_and_rescue), | |
"security": int(security), | |
"military": int(military), | |
"child_alone": int(child_alone), | |
"water": int(water), | |
"food": int(food), | |
"shelter": int(shelter), | |
"clothing": int(clothing), | |
"money": int(money), | |
"missing_people": int(missing_people), | |
"refugees": int(refugees), | |
"death": int(death), | |
"other_aid": int(other_aid), | |
"infrastructure_related": int(infrastructure_related), | |
"transport": int(transport), | |
"buildings": int(buildings), | |
"electricity": int(electricity), | |
"tools": int(tools), | |
"hospitals": int(hospitals), | |
"shops": int(shops), | |
"aid_centers": int(aid_centers), | |
"other_infrastructure": int(other_infrastructure), | |
"weather_related": int(weather_related), | |
"floods": int(floods), | |
"storm": int(storm), | |
"fire": int(fire), | |
"earthquake": int(earthquake), | |
"cold": int(cold), | |
"other_weather": int(other_weather), | |
"direct_report": int(direct_report), | |
} | |