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import json |
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import tarfile |
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from datasets import DatasetInfo, DatasetBuilder, DownloadManager, BuilderConfig, SplitGenerator, Split, Version |
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import datasets |
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import os |
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import requests |
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import re |
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from tqdm import tqdm |
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SUPPORTED_YEARS = ["1774"] |
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SUPPORTED_YEARS = SUPPORTED_YEARS + [str(year) for year in range(1798, 1964)] |
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def make_year_file_splits(): |
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""" |
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Collects a list of available files for each year. |
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Returns: |
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dict: A dictionary mapping each year to its corresponding file URL. |
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list: A list of years. |
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""" |
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base_url = "https://huggingface.co/datasets/dell-research-harvard/AmericanStories/resolve/main/" |
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year_list = [str(year) for year in range(1774, 1960)] |
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data_files = [f"faro_{year}.tar.gz" for year in year_list] |
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url_list = [base_url + file for file in data_files] |
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splits = {year: file for year, file in zip(year_list, url_list)} |
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years = year_list |
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return splits, years |
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_CITATION = """\ |
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Coming Soon |
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""" |
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_DESCRIPTION = """\ |
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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. |
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""" |
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_FILE_DICT, _YEARS = make_year_file_splits() |
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class CustomBuilderConfig(datasets.BuilderConfig): |
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"""BuilderConfig for AmericanStories dataset with different configurations.""" |
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def __init__(self, year_list=None,features=["article_id", "newspaper_name", "edition", "date", "page", "headline", "byline", "article"], **kwargs): |
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""" |
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BuilderConfig for AmericanStories dataset. |
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Args: |
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year_list (list): A list of years to include in the dataset. |
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**kwargs: Additional keyword arguments forwarded to the superclass. |
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""" |
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super(CustomBuilderConfig, self).__init__(**kwargs) |
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self.year_list = year_list |
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self.features = features |
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class AmericanStories(datasets.GeneratorBasedBuilder): |
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"""Dataset builder class for AmericanStories dataset.""" |
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VERSION = datasets.Version("0.1.0") |
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BUILDER_CONFIGS = [ |
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CustomBuilderConfig( |
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name="all_years", |
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version=VERSION, |
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description="All years in the dataset" |
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), |
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CustomBuilderConfig( |
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name="subset_years", |
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version=VERSION, |
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description="Subset of years in the dataset", |
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year_list=["1774", "1804"] |
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), |
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CustomBuilderConfig( |
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name="all_years_content_regions", |
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version=VERSION, |
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description="All years in the dataset", |
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), |
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CustomBuilderConfig( |
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name="subset_years_content_regions", |
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version=VERSION, |
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description="Subset of years in the dataset", |
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year_list=["1774", "1804"], |
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) |
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] |
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DEFAULT_CONFIG_NAME = "subset_years" |
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def _info(self): |
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""" |
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Specifies the DatasetInfo object for the AmericanStories dataset. |
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Returns: |
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datasets.DatasetInfo: The DatasetInfo object. |
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""" |
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if not self.config.name.endswith("content_regions"): |
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features = datasets.Features( |
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{ |
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"article_id": datasets.Value("string"), |
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"newspaper_name": datasets.Value("string"), |
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"edition": datasets.Value("string"), |
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"date": datasets.Value("string"), |
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"page": datasets.Value("string"), |
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"headline": datasets.Value("string"), |
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"byline": datasets.Value("string"), |
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"article": datasets.Value("string"), |
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} |
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) |
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else: |
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features = datasets.Features( |
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{ |
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"raw_data_string": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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""" |
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Downloads and extracts the data, and defines the dataset splits. |
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Args: |
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dl_manager (datasets.DownloadManager): The DownloadManager instance. |
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Returns: |
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list: A list of SplitGenerator objects. |
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""" |
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if self.config.name == "subset_years": |
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print("Only taking a subset of years. Change name to 'all_years' to use all years in the dataset.") |
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if not self.config.year_list: |
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raise ValueError("Please provide a valid year_list") |
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elif not set(self.config.year_list).issubset(set(SUPPORTED_YEARS)): |
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raise ValueError(f"Only {SUPPORTED_YEARS} are supported. Please provide a valid year_list") |
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urls = _FILE_DICT |
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year_list = _YEARS |
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if self.config.year_list: |
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urls = {year: urls[year] for year in self.config.year_list} |
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year_list = self.config.year_list |
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data_dir = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=year, |
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gen_kwargs={ |
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"year_dir": os.path.join(data_dir[year], "mnt", "122a7683-fa4b-45dd-9f13-b18cc4f4a187", "ca_rule_based_fa_clean", "faro_" + year), |
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"split": year, |
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"associated": True if not self.config.name.endswith("content_regions") else False, |
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}, |
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) for year in year_list |
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] |
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def _generate_examples(self, year_dir,split, associated): |
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""" |
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Generates examples for the specified year and split. |
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Args: |
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year_dir (str): The directory path for the year. |
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associated (bool): Whether or not the output should be contents associated into an "article" or raw contents. |
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Yields: |
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tuple: The key-value pair containing the example ID and the example data. |
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""" |
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print("Associated: " + str(associated)) |
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if associated: |
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for filepath in os.listdir(year_dir): |
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with open(os.path.join(year_dir, filepath), encoding="utf-8") as f: |
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try : |
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data = json.load(f) |
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except: |
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print("Error loading file: " + filepath) |
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continue |
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if "lccn" in data.keys(): |
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scan_id = filepath.split('.')[0] |
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scan_date = filepath.split("_")[0] |
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scan_page = filepath.split("_")[1] |
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scan_edition = filepath.split("_")[-2][8:] |
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newspaper_name = data["lccn"]["title"] |
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full_articles_in_data = data["full articles"] |
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for article in full_articles_in_data: |
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article_id = str(article["full_article_id"]) + "_" + scan_id |
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yield article_id, { |
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"article_id": article_id, |
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"newspaper_name": newspaper_name, |
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"edition": scan_edition, |
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"date": scan_date, |
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"page": scan_page, |
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"headline": article["headline"], |
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"byline": article["byline"], |
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"article": article["article"], |
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} |
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else: |
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print("Returning a json as a string, feel free to parse it yourself!") |
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for filepath in os.listdir(year_dir): |
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with open(os.path.join(year_dir, filepath), encoding="utf-8") as f: |
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try : |
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data = json.load(f) |
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except: |
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continue |
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data=json.dumps(data) |
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print((data)) |
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print(type(data)) |
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scan_id=filepath.split('.')[0] |
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yield scan_id, { |
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"raw_data_string": str(data) |
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} |
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