Datasets:

davidstap
/

ted_talks

	import datasets


	_DESCRIPTION = """\
	Train, validation and test splits for TED talks as in http://phontron.com/data/ted_talks.tar.gz (detokenized)
	"""

	_CITATION = """\
	@inproceedings{Ye2018WordEmbeddings,
	author = {Ye, Qi and Devendra, Sachan and Matthieu, Felix and Sarguna, Padmanabhan and Graham, Neubig},
	title = {When and Why are pre-trained word embeddings useful for Neural Machine Translation},
	booktitle = {HLT-NAACL},
	year = {2018},
	}
	"""

	_DATA_URL = "data/TED.tar"

	_LANGUAGES = ["ar", "az", "be", "bg", "bn", "bs", "cs", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fr-ca", "gl", "he", "hi", "hr", "hu", "hy", "id", "it", "ja", "ka", "kk", "ko", "ku", "lt", "mk", "mn", "mr", "ms", "my", "nb", "nl", "pl", "pt", "pt-br", "ro", "ru", "sk", "sl", "sq", "sr", "sv", "ta", "th", "tr", "uk", "ur", "vi", "zh", "zh-cn", "zh-tw"]


	class TedTalksConfig(datasets.BuilderConfig):
	"""BuilderConfig for TED talk dataset."""

	def __init__(self, language_pair=(None, None), **kwargs):
	self.language_pair = language_pair
	self.source, self.target = self.language_pair[0], self.language_pair[1]

	name = f"{self.source}_{self.target}"
	description = f"Parallel sentences in `{self.source}` and `{self.target}`."
	super(TedTalksConfig, self).__init__(name=name, description=description, **kwargs)


	class TedTalks(datasets.GeneratorBasedBuilder):
	"""TED talk data from http://phontron.com/data/ted_talks.tar.gz."""

	unique_pairs = [
	"_".join([l1, l2])
	for l1 in _LANGUAGES
	for l2 in _LANGUAGES
	if l1 != l2
	]

	BUILDER_CONFIGS = [
	TedTalksConfig(
	language_pair=(pair.split("_")[0], pair.split("_")[1]),
	version=datasets.Version("1.0.0", ""),
	)
	for pair in unique_pairs
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	self.config.source: datasets.features.Value("string"),
	self.config.target: datasets.features.Value("string"),
	}
	),
	homepage="https://github.com/neulab/word-embeddings-for-nmt",
	citation=_CITATION,
	)

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

	def _get_overlap(source_file, target_file):
	for path, f in dl_manager.iter_archive(archive):
	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")

	return len([
	(src, tgt)
	for src, tgt
	in zip(source_sentences, target_sentences)
	if src != "" and tgt != ""
	])

	split2tedsplit = {"train": "train", "validation": "dev", "test": "test"}

	overlap = {
	split: _get_overlap(
	f"{split}/ted.{split2tedsplit[split]}.{self.config.source}",
	f"{split}/ted.{split2tedsplit[split]}.{self.config.target}"
	) for split in ["train", "validation", "test"]
	}

	generators = []
	if overlap["train"] > 0:
	generators.append(
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"source_file": f"train/ted.train.{self.config.source}",
	"target_file": f"train/ted.train.{self.config.target}",
	"files": dl_manager.iter_archive(archive),
	},
	),
	)
	if overlap["validation"] > 0:
	generators.append(
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"source_file": f"validation/ted.dev.{self.config.source}",
	"target_file": f"validation/ted.dev.{self.config.target}",
	"files": dl_manager.iter_archive(archive),
	},
	),
	)
	if overlap["test"] > 0:
	generators.append(
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"source_file": f"test/ted.test.{self.config.source}",
	"target_file": f"test/ted.test.{self.config.target}",
	"files": dl_manager.iter_archive(archive),
	},
	),
	)

	return generators


	def _generate_examples(self, source_file, target_file, files):
	"""Returns examples as raw text."""

	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")

	assert len(target_sentences) == len(source_sentences), (
	f"Sizes do not match: {len(source_sentences)} vs {len(target_sentences)}."
	)

	# ignore empty
	source_target_pairs = [
	(src, tgt)
	for src, tgt
	in zip(source_sentences, target_sentences)
	if src != "" and tgt != ""
	]

	if len(source_target_pairs) > 0:
	source_sentences, target_sentences = zip(*source_target_pairs)
	for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
	yield idx, {self.config.source: l1, self.config.target: l2}