# 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"], }