from datasets import load_dataset load_dataset("path/to/my_dataset") datasets.Features( { "id": datasets.Value("string"), "title": datasets.Value("string"), "context": datasets.Value("string"), "question": datasets.Value("string"), "answers": datasets.Sequence( { "text": datasets.Value("string"), "answer_start": datasets.Value("int32"), } ), } ) def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "title": datasets.Value("string"), "context": datasets.Value("string"), "question": datasets.Value("string"), "answers": datasets.features.Sequence( {"text": datasets.Value("string"), "answer_start": datasets.Value("int32"),} ), } ), # No default supervised_keys (as we have to pass both question # and context as input). supervised_keys=None, homepage="https://rajpurkar.github.io/SQuAD-explorer/", citation=_CITATION, ) class SuperGlueConfig(datasets.BuilderConfig): """BuilderConfig for SuperGLUE.""" def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs): """BuilderConfig for SuperGLUE. Args: features: *list[string]*, list of the features that will appear in the feature dict. Should not include "label". data_url: *string*, url to download the zip file from. citation: *string*, citation for the data set. url: *string*, url for information about the data set. label_classes: *list[string]*, the list of classes for the label if the label is present as a string. Non-string labels will be cast to either 'False' or 'True'. **kwargs: keyword arguments forwarded to super. """ # Version history: # 1.0.2: Fixed non-nondeterminism in ReCoRD. # 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to # the full release (v2.0). # 1.0.0: S3 (new shuffling, sharding and slicing mechanism). # 0.0.2: Initial version. super(SuperGlueConfig, self).__init__(version=datasets.Version("1.0.2"), **kwargs) self.features = features self.label_classes = label_classes self.data_url = data_url self.citation = citation self.url = url class SuperGlue(datasets.GeneratorBasedBuilder): """The SuperGLUE benchmark.""" BUILDER_CONFIGS = [ SuperGlueConfig( name="boolq", description=_BOOLQ_DESCRIPTION, features=["question", "passage"], data_url="https://dl.fbaipublicfiles.com/glue/superglue/data/v2/BoolQ.zip", citation=_BOOLQ_CITATION, url="https://github.com/google-research-datasets/boolean-questions", ), ... ... SuperGlueConfig( name="axg", description=_AXG_DESCRIPTION, features=["premise", "hypothesis"], label_classes=["entailment", "not_entailment"], data_url="https://dl.fbaipublicfiles.com/glue/superglue/data/v2/AX-g.zip", citation=_AXG_CITATION, url="https://github.com/rudinger/winogender-schemas", ), from datasets import load_dataset dataset = load_dataset('super_glue', 'boolq') class NewDataset(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"), datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"), ] DEFAULT_CONFIG_NAME = "first_domain" _URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" _URLS = { "train": _URL + "train-v1.1.json", "dev": _URL + "dev-v1.1.json", } def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: urls_to_download = self._URLS downloaded_files = dl_manager.download_and_extract(urls_to_download) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), ] def _generate_examples(self, filepath): """This function returns the examples in the raw (text) form.""" logger.info("generating examples from = %s", filepath) with open(filepath) as f: squad = json.load(f) for article in squad["data"]: title = article.get("title", "").strip() for paragraph in article["paragraphs"]: context = paragraph["context"].strip() for qa in paragraph["qas"]: question = qa["question"].strip() id_ = qa["id"] answer_starts = [answer["answer_start"] for answer in qa["answers"]] answers = [answer["text"].strip() for answer in qa["answers"]] # Features currently used are "context", "question", and "answers". # Others are extracted here for the ease of future expansions. yield id_, { "title": title, "context": context, "question": question, "id": id_, "answers": {"answer_start": answer_starts, "text": answers,}, } datasets-cli test datasets/ --save_infos --all_configs datasets-cli dummy_data datasets/ --auto_generate datasets-cli dummy_data datasets/ ==============================DUMMY DATA INSTRUCTIONS============================== - In order to create the dummy data for my-dataset, please go into the folder './datasets/my-dataset/dummy/1.1.0' with *cd ./datasets/my-dataset/dummy/1.1.0* . - Please create the following dummy data files 'dummy_data/TREC_10.label, dummy_data/train_5500.label' from the folder './datasets/my-dataset/dummy/1.1.0' - For each of the splits 'train, test', make sure that one or more of the dummy data files provide at least one example - If the method *_generate_examples(...)* includes multiple *open()* statements, you might have to create other files in addition to 'dummy_data/TREC_10.label, dummy_data/train_5500.label'. In this case please refer to the *_generate_examples(...)* method - After all dummy data files are created, they should be zipped recursively to 'dummy_data.zip' with the command *zip -r dummy_data.zip dummy_data/* - You can now delete the folder 'dummy_data' with the command *rm -r dummy_data* - To get the folder 'dummy_data' back for further changes to the dummy data, simply unzip dummy_data.zip with the command *unzip dummy_data.zip* - Make sure you have created the file 'dummy_data.zip' in './datasets/my-dataset/dummy/1.1.0' =================================================================================== RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_ RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_