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