Datasets:

GroNLP
/

ik-nlp-22_pestyle

	import os

	import datasets
	import pandas as pd

	_CITATION = """No citation information available."""

	_DESCRIPTION = """\
	This dataset contains a sample of sentences taken from the FLORES-101 dataset that were either translated
	from scratch or post-edited from an existing automatic translation by three human translators.
	Translation were performed for the English-Italian language pair, and translators' behavioral data
	(keystrokes, pauses, editing times) were collected using the PET platform.
	"""

	_HOMEPAGE = "https://www.rug.nl/masters/information-science/?lang=en"

	_LICENSE = "Sharing and publishing of the data is not allowed at the moment."

	_PATHS = {
	"full": os.path.join("IK_NLP_22_PESTYLE", "train.tsv"),
	"mask_subject": os.path.join("IK_NLP_22_PESTYLE", "test.tsv"),
	"mask_modality": os.path.join("IK_NLP_22_PESTYLE", "test.tsv"),
	"mask_time": os.path.join("IK_NLP_22_PESTYLE", "test.tsv")
	}

	_ALL_FIELDS = [
	"item_id", "subject_id", "modality",
	"src_text", "mt_text", "tgt_text",
	"edit_time", "k_total", "k_letter", "k_digit", "k_white", "k_symbol", "k_nav", "k_erase",
	"k_copy", "k_cut", "k_paste", "n_pause_geq_300", "len_pause_geq_300",
	"n_pause_geq_1000", "len_pause_geq_1000", "num_annotations",
	"n_insert", "n_delete", "n_substitute", "n_shift", "bleu", "chrf", "ter", "aligned_edit"
	]

	_FIELDS_MASK_SUBJECT = [f for f in _ALL_FIELDS if f not in ["subject_id"]]
	_FIELDS_MASK_MODALITY = [f for f in _ALL_FIELDS if f not in [
	"modality", "mt_text", "n_insert", "n_delete", "n_substitute",
	"n_shift", "ter", "bleu", "chrf", "aligned_edit"
	]]
	_FIELDS_MASK_TIME = [f for f in _ALL_FIELDS if f not in [
	"edit_time", "n_pause_geq_300", "len_pause_geq_300",
	"n_pause_geq_1000", "len_pause_geq_1000"
	]]

	_DICT_FIELDS = {
	"full": _ALL_FIELDS,
	"mask_subject": _FIELDS_MASK_SUBJECT,
	"mask_modality": _FIELDS_MASK_MODALITY,
	"mask_time": _FIELDS_MASK_TIME
	}

	class IkNlp22PEStyleConfig(datasets.BuilderConfig):
	"""BuilderConfig for the IK NLP '22 Post-editing Stylometry Dataset."""

	def __init__(
	self,
	features,
	**kwargs,
	):
	"""
	Args:
	features: `list[string]`, list of the features that will appear in the
	feature dict. Should not include "label".
	**kwargs: keyword arguments forwarded to super.
	"""
	super().__init__(version=datasets.Version("1.0.0"), **kwargs)
	self.features = features


	class IkNlp22PEStyle(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	IkNlp22PEStyleConfig(
	name=name,
	features=fields,
	)
	for name, fields in _DICT_FIELDS.items()
	]

	DEFAULT_CONFIG_NAME = "full"

	@property

	def manual_download_instructions(self):
	return (
	"The access to the data is restricted to students of the IK MSc NLP 2022 course working on a related project."
	"To load the data using this dataset, download and extract the IK_NLP_22_PESTYLE folder you were provided upon selecting the final project."
	"After extracting it, the folder (referred to as root) must contain a IK_NLP_22_PESTYLE subfolder, containing train.tsv and test.tsv files."
	f"Then, load the dataset with: `datasets.load_dataset('GroNLP/ik-nlp-22_pestyle', '{self.config.name}', data_dir='path/to/root/folder')`"
	)

	def _info(self):
	features = {feature: datasets.Value("int32") for feature in self.config.features}
	for field in ["subject_id", "modality", "src_text", "mt_text", "tgt_text", "aligned_edit"]:
	if field in self.config.features:
	features[field] = datasets.Value("string")
	for field in ["edit_time", "bleu", "chrf", "ter", "n_insert", "n_delete", "n_substitute", "n_shift"]:
	if field in self.config.features:
	features[field] = datasets.Value("float32")
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(features),
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
	if not os.path.exists(data_dir):
	raise FileNotFoundError(
	"{} does not exist. Make sure you insert the unzipped IK_NLP_22_PESTYLE dir via "
	"`datasets.load_dataset('GroNLP/ik-nlp-22_pestyle', data_dir=...)`"
	"Manual download instructions: {}".format(
	data_dir, self.manual_download_instructions
	)
	)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN if self.config.name == "full" else datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(data_dir, _PATHS[self.config.name]),
	"features": self.config.features,
	},
	)
	]

	def _generate_examples(self, filepath: str, features):
	"""Yields examples as (key, example) tuples."""
	data = pd.read_csv(filepath, sep="\t")
	data = data[features]
	for id_, row in data.iterrows():
	yield id_, row.to_dict()