| # 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 | |
| """TODO: Add a description here.""" | |
| import csv | |
| import json | |
| import os | |
| import datasets | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _CITATION = """\ | |
| @article{talmor2020olmpics, | |
| title={oLMpics-on what language model pre-training captures}, | |
| author={Talmor, Alon and Elazar, Yanai and Goldberg, Yoav and Berant, Jonathan}, | |
| journal={Transactions of the Association for Computational Linguistics}, | |
| volume={8}, | |
| pages={743--758}, | |
| year={2020}, | |
| publisher={MIT Press} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| This is a set a eight datasets from the paper "oLMpics - On what Language Model Pre-training Captures" | |
| by Alon Talmor et al. | |
| """ | |
| _HOMEPAGE = "https://github.com/alontalmor/oLMpics" | |
| _LICENSE = "Apache 2.0" | |
| # 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) | |
| _URL = "https://huggingface.co/datasets/KevinZ/oLMpics/resolve/main/" | |
| TASKS = ["Age_Comparison", "Always_Never", "Antonym_Negation", "Encyclopedic_Composition", | |
| "Multihop_Composition", "Object_Comparison", "Property_Conjunction", "Taxonomy_Conjunction"] | |
| _URLS = {task: [f"{_URL}{task}/train.jsonl", f"{_URL}{task}/test.jsonl"] for task in TASKS} | |
| class OLMpicsDataset(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| # 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=task, version=datasets.Version("1.0.0"), description=f"{task} description") for task in TASKS | |
| ] | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "stem": datasets.Value("string"), | |
| "choices": datasets.Sequence(datasets.Value("string")), | |
| "answerKey": datasets.ClassLabel(names=["A", "B", "C", "D", "E"]) # oLMpics has at most 5 answer choices | |
| } | |
| ) | |
| 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, 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 for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| # 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 | |
| urls = _URLS[self.config.name] | |
| data_dir = dl_manager.download_and_extract(urls) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": data_dir[0], | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": data_dir[1], | |
| "split": "test" | |
| }, | |
| ), | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, filepath, split): | |
| # 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, { | |
| "stem": data["question"]["stem"], | |
| "choices": [choice["text"] for choice in data["question"]["choices"]], | |
| "answerKey": data["answerKey"], | |
| } | |