import json import tarfile from datasets import DatasetInfo, DatasetBuilder, DownloadManager,BuilderConfig, SplitGenerator, Split, Version import datasets import os import requests import re ####data dir DATA_DIR="." def make_year_file_splits(data_dir): ###Get list of files data_files=os.listdir(data_dir) ###Get only files containing faro_ data_files=[file for file in data_files if file.startswith('faro_')] ###Get only files for 17__ years # data_files=[file for file in data_files if file.split('_')[1].startswith('17')] ###Arrange into splits by year - files follow the format faro_YYYY.tar.gz splits={} years=[] for file in data_files: year=file.split('_')[1].split('.')[0] if year not in splits: splits[year]=[] splits[year].append(file) years.append(year) return splits, years def make_year_file_splits(data_dir): base_url="https://huggingface.co/datasets/dell-research-harvard/AmericanStories/resolve/main/" year_list=["1774","1804","1807"] data_files=[f"faro_{year}.tar.gz" for year in year_list] url_list=[base_url+file for file in data_files] splits={year:file for year,file in zip(year_list,url_list)} years=year_list return splits, years _CITATION = """\ Coming Soon """ _DESCRIPTION = """\ American Stories offers high-quality structured data from historical newspapers suitable for pre-training large language models to enhance the understanding of historical English and world knowledge. It can also be integrated into external databases of retrieval-augmented language models, enabling broader access to historical information, including interpretations of political events and intricate details about people's ancestors. Additionally, the structured article texts facilitate the application of transformer-based methods for popular tasks like detecting reproduced content, significantly improving accuracy compared to traditional OCR methods. American Stories serves as a substantial and valuable dataset for advancing multimodal layout analysis models and other multimodal applications. """ _FILE_DICT,_YEARS=make_year_file_splits(DATA_DIR) ###Make a class of builderconfig that supports an year_list attribute class MyBuilderConfig(datasets.BuilderConfig): """BuilderConfig for MyDataset for different configurations.""" def __init__(self, year_list=None, **kwargs): """BuilderConfig for MyDataset. Args: **kwargs: keyword arguments forwarded to super. """ super(MyBuilderConfig, self).__init__(**kwargs) self.year_list = year_list class AmericanStories(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("0.0.1") # 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') ##Now use the custom builder config class BUILDER_CONFIGS = [ MyBuilderConfig( name="all_years", version=VERSION, description="All years in the dataset", ), MyBuilderConfig( name="subset_years", version=VERSION, description="Subset of years in the dataset", year_list=["1774","1804"], )] DEFAULT_CONFIG_NAME = "subset_years" # 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 features = datasets.Features( { "newspaper_name": datasets.Value("string"), "edition": datasets.Value("string"), "date": datasets.Value("string"), "page": datasets.Value("string"), "headline": datasets.Value("string"), "byline": datasets.Value("string"), "article": 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 # License for the dataset if available # 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 = _FILE_DICT year_list=_YEARS ##Subset _FILE_DICT and year_list to only years in the config.year_list if self.config.year_list: urls={year:urls[year] for year in self.config.year_list} year_list=self.config.year_list data_dir = dl_manager.download_and_extract(urls) ###REturn a list of splits - but each split is for a year! return [ datasets.SplitGenerator( name=year, # These kwargs will be passed to _generate_examples gen_kwargs={ "year_dir": os.path.join(data_dir[year][0], "mnt/122a7683-fa4b-45dd-9f13-b18cc4f4a187/ca_rule_based_fa_clean/faro_"+year), "split": year, }, ) for year in year_list ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, year_dir, 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. for filepath in os.listdir(year_dir): with open(os.path.join(year_dir,filepath), encoding="utf-8") as f: data = json.load(f) scan_id=filepath.split('.')[0] scan_date=filepath.split("_")[0] scan_page=filepath.split("_")[1] scan_edition=filepath.split("_")[-2][8:] newspaper_name=data["lccn"]["title"] full_articles_in_data=data["full articles"] for article in full_articles_in_data: article_id=str(article["full_article_id"]) +"_" +scan_id yield article_id, { "newspaper_name": newspaper_name, "edition": scan_edition, "date": scan_date, "page": scan_page, "headline": article["headline"], "byline": article["byline"], "article": article["article"] }