NLP4SGPapers.py

# 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("int32"),
                    "test": datasets.ClassLabel(num_classes=3, names=[0,1,2]),
                    "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"],
                    "label_nlp4sg": data["label_nlp4sg"],
                    "task": data["task"],
                    "method": data["method"],
                    "goal1": data["goal1"],
                    "goal2": data["goal2"],
                    "goal3": data["goal3"],
                    "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"],
                }