import json import datasets # _SPLIT = ['test'] _CITATION = """\ @inproceedings{Schutz2008KeyphraseEF, title={Keyphrase Extraction from Single Documents in the Open Domain Exploiting Linguistic and Statistical Methods}, author={Alexander Schutz}, year={2008} } """ _DESCRIPTION = """\ """ _HOMEPAGE = "" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: Add link to the official dataset URLs here _URLS = { "test": "test.jsonl" } # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class Pubmed(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("0.0.1") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="extraction", version=VERSION, description="This part of my dataset covers extraction"), datasets.BuilderConfig(name="generation", version=VERSION, description="This part of my dataset covers generation"), datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data"), ] DEFAULT_CONFIG_NAME = "extraction" def _info(self): if self.config.name == "extraction": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "id": datasets.Value("string"), "document": datasets.features.Sequence(datasets.Value("string")), "doc_bio_tags": datasets.features.Sequence(datasets.Value("string")) } ) elif self.config.name == "generation": features = datasets.Features( { "id": datasets.Value("string"), "document": datasets.features.Sequence(datasets.Value("string")), "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")), "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")) } ) else: features = datasets.Features( { "id": datasets.Value("string"), "document": datasets.features.Sequence(datasets.Value("string")), "doc_bio_tags": datasets.features.Sequence(datasets.Value("string")), "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")), "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")), "other_metadata": datasets.features.Sequence( { "text": datasets.features.Sequence(datasets.Value("string")), "bio_tags": datasets.features.Sequence(datasets.Value("string")) } ) } ) 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, homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): data_dir = dl_manager.download_and_extract(_URLS) return [ datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['test'], "split": "test" }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): with open(filepath, encoding="utf-8") as f: for key, row in enumerate(f): data = json.loads(row) if self.config.name == "extraction": # Yields examples as (key, example) tuples yield key, { "id": data['paper_id'], "document": data["document"], "doc_bio_tags": data.get("doc_bio_tags") } elif self.config.name == "generation": yield key, { "id": data['paper_id'], "document": data["document"], "extractive_keyphrases": data.get("extractive_keyphrases"), "abstractive_keyphrases": data.get("abstractive_keyphrases") } else: yield key, { "id": data['paper_id'], "document": data["document"], "doc_bio_tags": data.get("doc_bio_tags"), "extractive_keyphrases": data.get("extractive_keyphrases"), "abstractive_keyphrases": data.get("abstractive_keyphrases"), "other_metadata": data["other_metadata"] }