Datasets:

qanastek
/

WMT-16-PubMed

	# coding=utf-8
	# Source: https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py

	"""WMT'16 Biomedical Translation Task - PubMed parallel datasets"""

	import gzip
	import datasets
	import pandas as pd

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """
	@inproceedings{bojar-etal-2016-findings,
	title = Findings of the 2016 Conference on Machine Translation,
	author = {
	Bojar, Ondrej and
	Chatterjee, Rajen and
	Federmann, Christian and
	Graham, Yvette and
	Haddow, Barry and
	Huck, Matthias and
	Jimeno Yepes, Antonio and
	Koehn, Philipp and
	Logacheva, Varvara and
	Monz, Christof and
	Negri, Matteo and
	Neveol, Aurelie and
	Neves, Mariana and
	Popel, Martin and
	Post, Matt and
	Rubino, Raphael and
	Scarton, Carolina and
	Specia, Lucia and
	Turchi, Marco and
	Verspoor, Karin and
	Zampieri, Marcos
	},
	booktitle = Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers,
	month = aug,
	year = 2016,
	address = Berlin, Germany,
	publisher = Association for Computational Linguistics,
	url = https://aclanthology.org/W16-2301,
	doi = 10.18653/v1/W16-2301,
	pages = 131--198,
	}
	"""

	_LANGUAGE_PAIRS = ['en-pt', 'en-es', 'en-fr']
	_LANGUAGE_PAIRS_TUPLES = [('en','pt'), ('en','es'), ('en','fr')]

	_LICENSE = """
	This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Attribution 4.0 International (CC BY 4.0) License</a>.
	"""

	_DESCRIPTION = """
	WMT'16 Biomedical Translation Task - PubMed parallel datasets
	http://www.statmt.org/wmt16/biomedical-translation-task.html
	"""

	_URL = "https://huggingface.co/datasets/qanastek/WMT-16-PubMed/resolve/main/WMT16.csv.gz"


	class WMT_16_CONFIG(datasets.BuilderConfig):
	def __init__(self, args, lang1=None, lang2=None, *kwargs):
	super().__init__(
	*args,
	name=f"{lang1}-{lang2}",
	**kwargs,
	)
	self.name = f"{lang1}-{lang2}"
	self.lang1 = lang1
	self.lang2 = lang2

	class WMT_16_PubMed(datasets.GeneratorBasedBuilder):
	"""WMT-16-PubMed dataset."""

	DEFAULT_CONFIG_NAME = "en-fr"


	BUILDER_CONFIGS = [
	WMT_16_CONFIG(
	lang1=lang1,
	lang2=lang2,
	description=f"Translating {lang1} to {lang2} or vice versa",
	version=datasets.Version("16.0.0"),
	)
	for lang1, lang2 in _LANGUAGE_PAIRS_TUPLES
	]
	BUILDER_CONFIG_CLASS = WMT_16_CONFIG

	# BUILDER_CONFIGS = [
	# datasets.BuilderConfig(name=name, version=datasets.Version("16.0.0"), description=_DESCRIPTION) for name in _LANGUAGE_PAIRS
	# ]

	def _info(self):
	src, target = self.config.name.split("-")
	pair = (src, target)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{"translation": datasets.features.Translation(languages=pair)}
	),
	supervised_keys=(src, target),
	homepage="https://www.statmt.org/wmt16/biomedical-translation-task.html",
	citation=_CITATION,
	# license=_LICENSE,
	)

	def _split_generators(self, dl_manager):

	# Download the CSV
	data_dir = dl_manager.download(_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": data_dir,
	"split": "train",
	}
	),
	]

	def _generate_examples(self, filepath, split):

	logger.info("⏳ Generating examples from = %s", filepath)

	key_ = 0

	with open(filepath, 'rb') as fd:
	gzip_fd = gzip.GzipFile(fileobj=fd)
	df = pd.read_csv(gzip_fd)

	# df = pd.read_csv(filepath, compression='gzip', header=0, sep=',')

	for index, row in df.loc[df['lang'] == self.config.name].iterrows():

	# Get langue pair
	src, target = str(row['lang']).split("-")

	yield key_, {
	"translation": {
	src: str(row['source_text']).strip(),
	target: str(row['target_text']).strip(),
	},
	}

	key_ += 1