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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.
# TODO: Address all TODOs and remove all explanatory comments
"""NLP4SGPAPERS dataset: a scientific dataset with three associated tasks that can help identify NLP4SG papers"""
import csv
import json
import os
import datasets
_CITATION = """
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
NLP4SGPAPERS dataset: a scientific dataset with three associated tasks that can help identify NLP4SG papers
"""
_HOMEPAGE = ""
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"train": "https://huggingface.co/datasets/feradauto/NLP4SGPapers/resolve/main/data/train.json",
"test": "https://huggingface.co/datasets/feradauto/NLP4SGPapers/resolve/main/data/test.json",
"validation": "https://huggingface.co/datasets/feradauto/NLP4SGPapers/resolve/main/data/validation.json",
}
class NLP4SGPapers(datasets.GeneratorBasedBuilder):
"""A scientific dataset with three associated tasks that can help identify NLP4SG papers"""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="data", version=VERSION, description="data")]
DEFAULT_CONFIG_NAME = "data" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
features = datasets.Features(
{
"ID": datasets.Value("string"),
"url": datasets.Value("string"),
"title": datasets.Value("string"),
"abstract": datasets.Value("string"),
"label_nlp4sg": datasets.Value("bool"),
"task": datasets.Sequence(feature=datasets.Value("string")),
"method": datasets.Sequence(feature=datasets.Value("string")),
"goal1": datasets.Value("string"),
"goal2": datasets.Value("string"),
"goal3": datasets.Value("string"),
"acknowledgments": datasets.Value("string"),
"year": datasets.Value("string"),
"sdg1": datasets.Value("bool"),
"sdg2": datasets.Value("bool"),
"sdg3": datasets.Value("bool"),
"sdg4": datasets.Value("bool"),
"sdg5": datasets.Value("bool"),
"sdg6": datasets.Value("bool"),
"sdg7": datasets.Value("bool"),
"sdg8": datasets.Value("bool"),
"sdg9": datasets.Value("bool"),
"sdg10": datasets.Value("bool"),
"sdg11": datasets.Value("bool"),
"sdg12": datasets.Value("bool"),
"sdg13": datasets.Value("bool"),
"sdg14": datasets.Value("bool"),
"sdg15": datasets.Value("bool"),
"sdg16": datasets.Value("bool"),
"sdg17": datasets.Value("bool"),
# These are the features of your dataset like images, labels ...
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features,
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
yield key, {
"ID": data["ID"],
"url": data["url"],
"title": data["title"],
"abstract": data["abstract"],
"label_nlp4sg": data["label_nlp4sg"],
"task": data["task"],
"method": data["method"],
"goal1": data["goal1"],
"goal2": data["goal2"],
"goal3": data["goal3"],
"acknowledgments": data["acknowledgments"],
"year": data["year"],
"sdg1": data["sdg1"],
"sdg2": data["sdg2"],
"sdg3": data["sdg3"],
"sdg4": data["sdg4"],
"sdg5": data["sdg5"],
"sdg6": data["sdg6"],
"sdg7": data["sdg7"],
"sdg8": data["sdg8"],
"sdg9": data["sdg9"],
"sdg10": data["sdg10"],
"sdg11": data["sdg11"],
"sdg12": data["sdg12"],
"sdg13": data["sdg13"],
"sdg14": data["sdg14"],
"sdg15": data["sdg15"],
"sdg16": data["sdg16"],
"sdg17": data["sdg17"],
}
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