Datasets:
flores

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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	# Lint as: python3
	"""Facebook Low Resource (FLoRes) machine translation benchmark dataset."""


	import collections

	import datasets


	_DESCRIPTION = """\
	Evaluation datasets for low-resource machine translation: Nepali-English and Sinhala-English.
	"""

	_CITATION = """\
	@misc{guzmn2019new,
	title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English},
	author={Francisco Guzman and Peng-Jen Chen and Myle Ott and Juan Pino and Guillaume Lample and Philipp Koehn and Vishrav Chaudhary and Marc'Aurelio Ranzato},
	year={2019},
	eprint={1902.01382},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DATA_URL = "https://github.com/facebookresearch/flores/raw/main/floresv1/data/wikipedia_en_ne_si_test_sets.tgz"

	# Tuple that describes a single pair of files with matching translations.
	# language_to_file is the map from language (2 letter string: example 'en')
	# to the file path in the extracted directory.
	TranslateData = collections.namedtuple("TranslateData", ["url", "language_to_file"])


	class FloresConfig(datasets.BuilderConfig):
	"""BuilderConfig for FLoRes."""

	def __init__(self, language_pair=(None, None), **kwargs):
	"""BuilderConfig for FLoRes.

	Args:
	for the `datasets.features.text.TextEncoder` used for the features feature.
	language_pair: pair of languages that will be used for translation. Should
	contain 2-letter coded strings. First will be used at source and second
	as target in supervised mode. For example: ("se", "en").
	**kwargs: keyword arguments forwarded to super.
	"""
	name = "%s%s" % (language_pair[0], language_pair[1])

	description = ("Translation dataset from %s to %s") % (language_pair[0], language_pair[1])
	super(FloresConfig, self).__init__(
	name=name,
	description=description,
	version=datasets.Version("1.1.0", ""),
	**kwargs,
	)

	# Validate language pair.
	assert "en" in language_pair, ("Config language pair must contain `en`, got: %s", language_pair)
	source, target = language_pair
	non_en = source if target == "en" else target
	assert non_en in ["ne", "si"], ("Invalid non-en language in pair: %s", non_en)

	self.language_pair = language_pair


	class Flores(datasets.GeneratorBasedBuilder):
	"""FLoRes machine translation dataset."""

	BUILDER_CONFIGS = [
	FloresConfig(
	language_pair=("ne", "en"),
	),
	FloresConfig(
	language_pair=("si", "en"),
	),
	]

	def _info(self):
	source, target = self.config.language_pair
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{"translation": datasets.features.Translation(languages=self.config.language_pair)}
	),
	supervised_keys=(source, target),
	homepage="https://github.com/facebookresearch/flores/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	archive = dl_manager.download(_DATA_URL)

	source, target = self.config.language_pair
	non_en = source if target == "en" else target
	path_tmpl = "wikipedia_en_ne_si_test_sets/wikipedia.{split}.{non_en}-en." "{lang}"

	files = {}
	for split in ("dev", "devtest"):
	files[split] = {
	"source_file": path_tmpl.format(split=split, non_en=non_en, lang=source),
	"target_file": path_tmpl.format(split=split, non_en=non_en, lang=target),
	"files": dl_manager.iter_archive(archive),
	}

	return [
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=files["dev"]),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=files["devtest"]),
	]

	def _generate_examples(self, files, source_file, target_file):
	"""This function returns the examples in the raw (text) form."""
	source_sentences, target_sentences = None, None
	for path, f in files:
	if path == source_file:
	source_sentences = f.read().decode("utf-8").split("\n")
	elif path == target_file:
	target_sentences = f.read().decode("utf-8").split("\n")
	if source_sentences is not None and target_sentences is not None:
	break

	assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % (
	len(source_sentences),
	len(target_sentences),
	source_file,
	target_file,
	)

	source, target = self.config.language_pair
	for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
	result = {"translation": {source: l1, target: l2}}
	# Make sure that both translations are non-empty.
	if all(result.values()):
	yield idx, result