# 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) # This gets all folders within the directory named `data` DATA_DIRS = next(os.walk('data'))[1] _URLs = {s: {'train': f"data/{s}/train.csv", 'test': f"data/{s}/test_unlabeled.csv"} for s in DATA_DIRS} 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') # TODO: Load task jsons tasks = {} for sd in DATA_DIRS: with open(os.path.join('data', sd, 'task.json')) as f: task_data = json.load(f) tasks[sd] = task_data BUILDER_CONFIGS = [] for key in tasks: td = tasks[key] name = td['name'] description = td['description'] BUILDER_CONFIGS.append(datasets.BuilderConfig(name=name, version=VERSION, description=description)) DEFAULT_CONFIG_NAME = "tai_safety_research" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): DEFAULT_LABEL_NAME = "Unlabeled" task = Raft.tasks[self.config.name] # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset data_columns = {col_name: datasets.Value("string") for col_name in task['data_columns']} label_columns = {} for label_name in task['label_columns']: labels = ["Unlabeled"] + task['label_columns'][label_name] label_columns[label_name] = datasets.ClassLabel(len(labels), labels) # Merge dicts features = datasets.Features(**data_columns, **label_columns) 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. task = Raft.tasks[self.config.name] column_names = task['data_columns'] + list(task['label_columns']) num_labels = len(task['label_columns']) 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 split == "test": row += ["Unlabeled"] * num_labels yield id_, {name: value for name, value in zip(column_names, row)}