| # 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 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]), | |
| } |