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+ # coding=utf-8
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+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """RAFT AI papers, test set."""
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+
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+
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+ import csv
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+ import json
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+ import os
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+
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+ import datasets
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+
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+
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+ # TODO: Add BibTeX citation
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+ # Find for instance the citation on arxiv or on the dataset repo/website
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+ _CITATION = """\
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+ @InProceedings{huggingface:dataset,
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+ title = {A great new dataset},
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+ author={huggingface, Inc.
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+ },
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+ year={2020}
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+ }
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+ """
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+
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+ # You can copy an official description
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+ _DESCRIPTION = """\
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+ This dataset contains a corpus of AI papers. The first task is to determine\
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+ whether or not a datapoint is an AI safety paper. The second task is to\
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+ determine what type of paper it is.
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+ """
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+
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+ # TODO: Add a link to an official homepage for the dataset here
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+ _HOMEPAGE = ""
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+
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+ # TODO: Add the licence for the dataset here if you can find it
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+ _LICENSE = ""
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+
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+ # TODO: Add link to the official dataset URLs here
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+ # The HuggingFace dataset library don't host the datasets but only point to the original files
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+ # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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+ _URLs = {
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+ 'train': "https://raw.githubusercontent.com/neel-alex/raft/master/AISafety_train.csv",
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+ 'test': "https://raw.githubusercontent.com/neel-alex/raft/master/AISafety_test_unlabeled.csv"
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+ }
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+
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+
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+ class RaftAisafetyTest(datasets.GeneratorBasedBuilder):
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+ """Dataset of AI papers for safety-related classification."""
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+
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+ VERSION = datasets.Version("1.1.0")
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+
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+ # This is an example of a dataset with multiple configurations.
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+ # If you don't want/need to define several sub-sets in your dataset,
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+ # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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+
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+ # If you need to make complex sub-parts in the datasets with configurable options
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+ # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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+ # BUILDER_CONFIG_CLASS = MyBuilderConfig
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+
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+ # You will be able to load one or the other configurations in the following list with
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+ # data = datasets.load_dataset('my_dataset', 'first_domain')
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+ # data = datasets.load_dataset('my_dataset', 'second_domain')
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(name="safety_or_not", version=VERSION,
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+ description="Decide whether the papers focus on AI safety methods."),
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+ datasets.BuilderConfig(name="safety_type", version=VERSION,
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+ description="If a paper has AI safety methods, determine if it is meta"
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+ "safety or technical safety."),
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+ ]
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+
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+ DEFAULT_CONFIG_NAME = "safety_or_not" # It's not mandatory to have a default configuration. Just use one if it make sense.
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+
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+ def _info(self):
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+ # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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+ if self.config.name == "safety_or_not": # This is the name of the configuration selected in BUILDER_CONFIGS above
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+ features = datasets.Features(
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+ {
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+ "title": datasets.Value("string"),
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+ "publication": datasets.Value("string"),
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+ "abstract": datasets.Value("string"),
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+ "answer": datasets.Value("string"),
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+ }
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+ )
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+ else: # This is an example to show how to have different features for "first_domain" and "second_domain"
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+ features = datasets.Features(
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+ {
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+ "title": datasets.Value("string"),
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+ "publication": datasets.Value("string"),
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+ "abstract": datasets.Value("string"),
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+ "answer": datasets.Value("string"),
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+ }
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+ )
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+ return datasets.DatasetInfo(
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+ # This is the description that will appear on the datasets page.
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+ description=_DESCRIPTION,
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+ # This defines the different columns of the dataset and their types
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+ features=features, # Here we define them above because they are different between the two configurations
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+ # If there's a common (input, target) tuple from the features,
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+ # specify them here. They'll be used if as_supervised=True in
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+ # builder.as_dataset.
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+ supervised_keys=None,
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+ # Homepage of the dataset for documentation
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+ homepage=_HOMEPAGE,
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+ # License for the dataset if available
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+ license=_LICENSE,
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+ # Citation for the dataset
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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+ # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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+
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+ # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
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+ # 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.
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+ # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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+ data_dir = dl_manager.download_and_extract(_URLs)
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN,
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+ gen_kwargs={"filepath": data_dir['train'],
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+ "split": "train"}),
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+ datasets.SplitGenerator(name=datasets.Split.TEST,
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+ gen_kwargs={"filepath": data_dir['test'],
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+ "split": "test"})
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+ ]
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+
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+ def _generate_examples(
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+ self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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+ ):
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+ """ Yields examples as (key, example) tuples. """
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+ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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+ # The `key` is here for legacy reason (tfds) and is not important in itself.
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+
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+ with open(filepath, encoding="utf-8") as f:
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+ csv_reader = csv.reader(
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+ f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
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+ )
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+ for id_, row in enumerate(csv_reader):
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+ if id_ == 0: # First row is column names
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+ continue
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+ if split == "train":
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+ title, publication, abstract, category, binary = row
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+ answer = category if self.config.name == "safety_type" else binary
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+ elif split == "test":
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+ title, publication, abstract = row
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+ answer = ""
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+ yield id_, {"title": title,
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+ "publication": publication,
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+ "abstract": abstract,
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+ "answer": answer}