# coding=utf-8 # 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. """RAFT AI papers, test set.""" import csv import json import os import datasets # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2020} } """ # You can copy an official description _DESCRIPTION = """\ This dataset contains a corpus of AI papers. The first task is to determine\ whether or not a datapoint is an AI safety paper. The second task is to\ determine what type of paper it is. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: Add link to the official dataset URLs here # The HuggingFace dataset library don't host the datasets but only point to the original files # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLs = { 'TAISafety': { 'train': "./data/TAISafety/train.csv", 'test': "./data/TAISafety/test.csv" }, 'AIInitiatives': { 'train': "./data/AIInitiatives/train.csv", 'test': "./data/AIInitiatives/test.csv" } } # TODO: Generate these automatically. class Raft(datasets.GeneratorBasedBuilder): """RAFT Dataset.""" 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="TAISafety-binary", version=VERSION, description="Decide whether the papers focus on AI safety methods."), datasets.BuilderConfig(name="TAISafety-multiclass", version=VERSION, description="If a paper has AI safety methods, determine if it is meta" "safety or technical safety."), datasets.BuilderConfig(name="AIInitiatives-multilabel", version=VERSION, description="For each initiative, decide which (if any) of Ethics, " "Governance, and Social Good apply to the initiative's AI goals") ] DEFAULT_CONFIG_NAME = "TAISafety/binary" # 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 if self.config.name.startswith("TAISafety"): features = datasets.Features( { "title": datasets.Value("string"), "publication": datasets.Value("string"), "abstract": datasets.Value("string"), "answer": datasets.Value("string"), } ) elif self.config.name.startswith("AIInitiatives"): features = datasets.Features( { "name": datasets.Value("string"), "organization": datasets.Value("string"), "description": datasets.Value("string"), "sector": datasets.Value("string"), "scope": datasets.Value("string"), "audience": datasets.Value("string"), "stage": datasets.Value("string"), "date": datasets.Value("string"), "country": datasets.Value("string"), "notes": datasets.Value("string"), "answer_ethics": datasets.Value("string"), "answer_governance": datasets.Value("string"), "answer_socialgood": datasets.Value("string"), } ) 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, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # 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 data_dir = dl_manager.download_and_extract(_URLs) dataset = self.config.name.split("-")[0] return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir[dataset]['train'], "split": "train"}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir[dataset]['test'], "split": "test"}) ] def _generate_examples( self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` ): """ Yields examples as (key, example) tuples. """ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is here for legacy reason (tfds) and is not important in itself. dataset, config = self.config.name.split("-") with open(filepath, encoding="utf-8") as f: csv_reader = csv.reader( f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True ) for id_, row in enumerate(csv_reader): if id_ == 0: # First row is column names continue if dataset == "TAISafety": if split == "train": title, publication, abstract, category, binary = row answer = category if config == "multiclass" else binary elif split == "test": title, publication, abstract = row answer = "" yield id_, {"title": title, "publication": publication, "abstract": abstract, "answer": answer} if dataset == "AIInitiatives": name, organization, description, sector, scope, audience, \ stage, date, country, notes, answer_ethics, \ answer_governance, answer_socialgood = row if split == "test": answer = "" yield id_, {"name": name, "organization": organization, "description": description, "sector": sector, "scope": scope, "audience": audience, "stage": stage, "date": date, "country": country, "notes": notes, "answer_ethics": answer_ethics, "answer_governance": answer_governance, "answer_socialgood": answer_socialgood}