diff --git a/data/README.txt b/data/README.txt new file mode 100644 index 0000000000000000000000000000000000000000..3494e2df0b418d3cca1a2f9aab666cbb8ae75a72 --- /dev/null +++ b/data/README.txt @@ -0,0 +1,22 @@ +This file contains the dev, val, and test data for our multitask test. +The dev dataset is for few-shot learning to prime the model, and the test set the source of evaluation questions. +The auxiliary_training data could be used for fine-tuning, something important for models without few-shot capabilities. This auxiliary training data comes from other NLP multiple choice datasets such as MCTest (Richardson et al., 2013), RACE (Lai et al., 2017), ARC (Clark et al., 2018, 2016), and OBQA (Mihaylov et al., 2018). +Unless otherwise specified, the questions are in reference to human knowledge as of January 1st, 2020. In the far future, it may be useful to add to the prompt that the question is written for 2020 audiences. + +-- + +If you find this useful in your research, please consider citing the test and also the ETHICS dataset it draws from: + +@article{hendryckstest2021, + title={Measuring Massive Multitask Language Understanding}, + author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, + journal={Proceedings of the International Conference on Learning Representations (ICLR)}, + year={2021} +} + +@article{hendrycks2021ethics, + title={Aligning AI With Shared Human Values}, + author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, + journal={Proceedings of the International Conference on Learning Representations (ICLR)}, + year={2021} +} diff --git a/data/auxiliary_train/arc_easy.csv b/data/auxiliary_train/arc_easy.csv new file mode 100755 index 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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 os + +import datasets + +from pathlib import Path + + +# 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} +} +""" + +# TODO: Add description of the dataset here +# You can copy an official description +_DESCRIPTION = """\ +This new dataset is designed to solve this great NLP task and is crafted with a lot of care. +""" + +# 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 = "MIT License" + +ROOT = Path("data") +_URLS = { + "validation": list((ROOT / "val").glob("*.csv")), + "dev": list((ROOT / "dev").glob("*.csv")), + "test": list((ROOT / "test").glob("*.csv")), +} +CONFIG_NAMES = [str(task.name).replace("_test.csv", "") for task in _URLS["test"]] + + +# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case +class NewDataset(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("1.1.0"), + description=f"Task {task}" + ) + for task in CONFIG_NAMES + ] + + DEFAULT_CONFIG_NAME = CONFIG_NAMES[0] + + def _info(self): + features = datasets.Features( + { + "question": datasets.Value("string"), + "option1": datasets.Value("string"), + "option2": datasets.Value("string"), + "option3": datasets.Value("string"), + "option4": datasets.Value("string"), + "answer": datasets.Value("string") + # 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, # 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): + split_generators = [] + return [ + datasets.SplitGenerator( + name="dev", + gen_kwargs={ + "filename": ROOT / f"dev/{self.config.name}_dev.csv", + } + ), + datasets.SplitGenerator( + name=datasets.Split.VALIDATION, + gen_kwargs={ + "filename": ROOT / f"val/{self.config.name}_val.csv", + } + ), + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filename": ROOT / f"test/{self.config.name}_test.csv", + } + ) + ] + # if "validation" in self.config.splits: + # split_name = "validation" + # print(_URLs[split_name]) + # # data_dir = dl_manager.download(_URLs[split_name]) + # split_generators.append( + # datasets.SplitGenerator( + # name=datasets.Split.TRAIN, + # gen_kwargs={ + # "filename": ROOT / f"{split_name}/{self.config.name}_{split_name}.csv", + # } + # ) + # ) + # if "dev" in self.config.splits: + # split_name = "dev" + # print(_URLs[split_name]) + # # data_dir = dl_manager.download(_URLS[split_name]) + # split_generators.append( + # datasets.SplitGenerator( + # name=datasets.Split.VALIDATION, + # gen_kwargs={ + # "filename": ROOT / f"{split_name}/{self.config.name}_{split_name}.csv", + # } + # ) + # ) + # if "test" in self.config.splits: + # split_name = "test" + # print(_URLs[split_name]) + # # data_dir = dl_manager.download(_URLS[split_name]) + # split_generators.append( + # datasets.SplitGenerator( + # name=datasets.Split.TEST, + # gen_kwargs={ + # "filename": ROOT / f"{split_name}/{self.config.name}_{split_name}.csv", + # } + # ) + # ) + # return split_generators + + # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` + def _generate_examples(self, filename): + # read in the csv file + print(filename) + with open(filename, encoding="utf-8") as f: + csv_reader = csv.reader(f, delimiter=",") + # next(csv_reader) + for id_, row in enumerate(csv_reader): + print(row) + # row format: question, option1, option2, option3, option4, answer + yield id_, { + "question": str(row[0]), + "option1": str(row[1]), + "option2": str(row[2]), + "option3": str(row[3]), + "option4": str(row[4]), + "answer": str(row[5]), + } \ No newline at end of file