| """Inspec benchmark dataset for keyphrase extraction an generation.""" | |
| import csv | |
| import json | |
| import os | |
| import datasets | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| PubMed benchmark dataset for keyphrase extraction and generation. | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "[schutz-2008]: https://github.com/snkim/AutomaticKeyphraseExtraction/blob/master/Schutz2008.tar.gz" | |
| _LICENSE = "" | |
| _CITATION = """@MasterThesis{Schutz:2008, | |
| author = {Alexander Thorsten Schutz}, | |
| title = {Keyphrase Extraction from Single Documents in the Open Domain Exploiting Linguistic and Statistical Methods}, | |
| booktitle = {National University of Ireland}, | |
| year = {2008} | |
| }""" | |
| # 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) | |
| _URLS = { | |
| "test": "data.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("1.0.0") | |
| # 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="raw", version=VERSION, description="This part of my dataset covers the raw data."), | |
| ] | |
| DEFAULT_CONFIG_NAME = "raw" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
| if self.config.name == "raw": # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| features = datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "title": datasets.Value("string"), | |
| "text": datasets.Value("string"), | |
| "keyphrases": datasets.features.Sequence(datasets.Value("string")), | |
| "prmu": 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, # 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): | |
| # TODO: 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 | |
| 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": os.path.join(data_dir["test"]), | |
| "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) | |
| # Yields examples as (key, example) tuples | |
| yield key, { | |
| "id": data["id"], | |
| "title": data["title"], | |
| "text": data["text"], | |
| "keyphrases": data["keyphrases"], | |
| "prmu": data["prmu"] | |
| } | |