Convert dataset to Parquet (#4)

- Convert dataset to Parquet (97fd57cc462e09e89e18d65cb004ddec70a69357)
- Delete loading script (1638c2b89ff2dbd6fe0c93eb3137f87c8a6edfcb)

README.md

@@ -248,16 +248,25 @@ dataset_info:
           '1': 'true'
   splits:
   - name: train
-    num_bytes: 10060799
+    num_bytes: 10060751
     num_examples: 21046
   - name: test
-    num_bytes: 1253810
+    num_bytes: 1253794
     num_examples: 2629
   - name: validation
-    num_bytes: 1266874
+    num_bytes: 1266858
     num_examples: 2573
-  download_size: 7201807
-  dataset_size: 12581483
+  download_size: 3635948
+  dataset_size: 12581403
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  - split: validation
+    path: data/validation-*
 ---
 
 # Dataset Card for Disaster Response Messages 

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae38ee653cfe31d712b8d8b36114f2106d064855ce76e577b1d2b768bb1a26e0
+size 379323

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4287f7b70f24b0026565fe1017be00694702188275c422e1fbcb92b93a0e3196
+size 2868843

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8a3c3f415cdb659113d9a2e956b1ce15cceaebd12d21b12a815d965a3810ea54
+size 387782

disaster_response_messages.py

@@ -1,204 +0,0 @@
-# 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),
-                }