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"""TIS Daten aus Hamburg"""
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{lif-15,
title = "LIF 15 LI Hamburg",
journal = "Data",
volume = "2",
number = "2",
year = "2023",
url = "https://li-hamburg.de",
pages = "313--330",
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
Daten von LIF 15 zum TIS System für Fortbildungen
"""
_HOMEPAGE = "https://li-hamburg.de"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "LDC User Agreement for Non-Members"
# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "alexkueck/tis"
_TRAINING_FILE = "tis.train.txt"
#_DEV_FILE = "tis.valid.txt"
_TEST_FILE = "tis.test.txt"
class TISConfig(datasets.BuilderConfig):
"""BuilderConfig for PtbTextOnly"""
def __init__(self, **kwargs):
"""BuilderConfig PtbTextOnly.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(TISConfig, self).__init__(**kwargs)
class TIS(datasets.GeneratorBasedBuilder):
"""Load the Penn Treebank dataset."""
VERSION = datasets.Version("1.1.0")
# 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 = [
TISConfig(
name="tis",
version=VERSION,
description="Load TIS dataset",
),
]
def _info(self):
features = datasets.Features({"sentence": 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,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# 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):
"""Returns SplitGenerators."""
# 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
my_urls = {
"train": f"{_URL}{_TRAINING_FILE}",
#"dev": f"{_URL}{_DEV_FILE}",
"test": f"{_URL}{_TEST_FILE}",
}
data_dir = dl_manager.download_and_extract(my_urls)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir["train"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir["test"]}),
#datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir["dev"]}),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
# It is in charge of opening the given file and yielding (key, example) tuples from the dataset
# The key is not important, it's more here for legacy reason (legacy from tfds)
with open(filepath, encoding="utf-8") as f:
for id_, line in enumerate(f):
line = line.strip()
yield id_, {"sentence": line} |