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nllb / nllb.py

Guillaume Wenzek

add ccmatrix bitext

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	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""No Language Left Behind (NLLB)

	The "No Language Left Behind" paper is a dataset with translation examples across 200 languages.
	This paper reuses prior work, and for some language pairs is just reusing CC-Matrix published on statmt.org.
	Depending on the language pair chosen, this script will fetch either the original
	version from statmt.org, or the new one from AllenAI.
	"""

	import datasets
	import typing as tp

	_CITATION = (
	"@article{team2022NoLL,"
	"title={No Language Left Behind: Scaling Human-Centered Machine Translation},"
	"author={Nllb team and Marta Ruiz Costa-juss{\`a} and James Cross and Onur Celebi and Maha Elbayad and Kenneth Heafield and Kevin Heffernan and Elahe Kalbassi and Janice Lam and Daniel Licht and Jean Maillard and Anna Sun and Skyler Wang and Guillaume Wenzek and Alison Youngblood and Bapi Akula and Lo{\"i}c Barrault and Gabriel Mejia Gonzalez and Prangthip Hansanti and John Hoffman and Semarley Jarrett and Kaushik Ram Sadagopan and Dirk Rowe and Shannon L. Spruit and C. Tran and Pierre Andrews and Necip Fazil Ayan and Shruti Bhosale and Sergey Edunov and Angela Fan and Cynthia Gao and Vedanuj Goswami and Francisco Guzm'an and Philipp Koehn and Alexandre Mourachko and Christophe Ropers and Safiyyah Saleem and Holger Schwenk and Jeff Wang},"
	"journal={ArXiv},"
	"year={2022},"
	"volume={abs/2207.04672}"
	"}"
	)


	_DESCRIPTION = "" # TODO

	_HOMEPAGE = "" # TODO

	_LICENSE = "https://opendatacommons.org/licenses/by/1-0/"

	from .nllb_lang_pairs import LANG_PAIRS as _LANGUAGE_PAIRS
	from .ccmatrix_lang_pairs import LANG_PAIRS as _CCMATRIX_LANGUAGE_PAIRS

	_URL_BASE = "https://storage.googleapis.com/allennlp-data-bucket/nllb/"

	_URLs = {
	f"{src_lg}-{trg_lg}": f"{_URL_BASE}{src_lg}-{trg_lg}.gz"
	for src_lg, trg_lg in _LANGUAGE_PAIRS
	}

	_STATMT_URL = "http://data.statmt.org/cc-matrix/"
	_URLs.update(
	{
	f"{src_lg}-{trg_lg}": f"{_STATMT_URL}{src_lg}-{trg_lg}.bitextf.tsv.gz"
	for src_lg, trg_lg in _CCMATRIX_LANGUAGE_PAIRS
	}
	)


	class NLLBTaskConfig(datasets.BuilderConfig):
	"""BuilderConfig for No Language Left Behind Dataset."""

	def __init__(self, src_lg, tgt_lg, source, **kwargs):
	super(NLLBTaskConfig, self).__init__(**kwargs)
	self.src_lg = src_lg
	self.tgt_lg = tgt_lg
	url = _URLs.get(f"{src_lg}-{tgt_lg}", "")
	self.source = source


	def _builder_configs() -> tp.List[NLLBTaskConfig]:
	configs = []
	for (src_lg, tgt_lg) in _LANGUAGE_PAIRS:
	configs.append(
	NLLBTaskConfig(
	name=f"{src_lg}-{tgt_lg}",
	version=datasets.Version("1.0.0"),
	description=f"No Language Left Behind (NLLB): {src_lg} - {tgt_lg}",
	src_lg=src_lg,
	tgt_lg=tgt_lg,
	source="allenai",
	)
	)

	for (src_lg, tgt_lg) in _CCMATRIX_LANGUAGE_PAIRS:
	configs.append(
	NLLBTaskConfig(
	name=f"{src_lg}-{tgt_lg}",
	version=datasets.Version("1.0.0"),
	description=f"No Language Left Behind (NLLB): {src_lg} - {tgt_lg}",
	src_lg=src_lg,
	tgt_lg=tgt_lg,
	source="mtstats",
	)
	)
	return configs


	class NLLB(datasets.GeneratorBasedBuilder):
	"""No Language Left Behind Dataset."""

	BUILDER_CONFIGS = _builder_configs()
	BUILDER_CONFIG_CLASS = NLLBTaskConfig

	def _info(self):
	# define feature types
	features = datasets.Features(
	{
	"translation": datasets.Translation(
	languages=(self.config.src_lg, self.config.tgt_lg)
	),
	"laser_score": datasets.Value("float32"),
	"source_sentence_lid": datasets.Value("float32"),
	"target_sentence_lid": datasets.Value("float32"),
	"source_sentence_source": datasets.Value("string"),
	"source_sentence_url": datasets.Value("string"),
	"target_sentence_source": datasets.Value("string"),
	"target_sentence_url": datasets.Value("string"),
	}
	)
	if self.config.source == "mtstats":
	# MT stats didn't published all the metadata
	features = datasets.Features(
	{
	"translation": datasets.Translation(
	languages=(self.config.src_lg, self.config.tgt_lg)
	),
	"laser_score": datasets.Value("float32"),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	pair = f"{self.config.src_lg}-{self.config.tgt_lg}" # string identifier for language pair
	url = _URLs[pair] # url for download of pair-specific file
	data_file = dl_manager.download_and_extract(
	url
	) # extract downloaded data and store path in data_file

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": data_file,
	"source_lg": self.config.src_lg,
	"target_lg": self.config.tgt_lg,
	},
	)
	]

	def _generate_examples(self, filepath, source_lg, target_lg):
	if self.config.source == "mtstats":
	# MT stats didn't published all the metadata
	return self._generate_minimal_examples(filepath, source_lg, target_lg)

	return self._generate_full_examples(filepath, source_lg, target_lg)

	def _generate_full_examples(self, filepath, source_lg, target_lg):
	with open(filepath, encoding="utf-8") as f:
	# reader = csv.reader(f, delimiter="\t")
	for id_, example in enumerate(f):
	try:
	datarow = example.split("\t")
	row = {}
	row["translation"] = {
	source_lg: datarow[0],
	target_lg: datarow[1],
	} # create translation json
	row["laser_score"] = float(datarow[2])
	row["source_sentence_lid"] = float(datarow[3])
	row["target_sentence_lid"] = float(datarow[4])
	row["source_sentence_source"] = datarow[5]
	row["source_sentence_url"] = datarow[6]
	row["target_sentence_source"] = datarow[7]
	row["target_sentence_url"] = datarow[8]
	row = {
	k: None if not v else v for k, v in row.items()
	} # replace empty values
	except:
	print(datarow)
	raise
	yield id_, row

	def _generate_minimal_examples(self, filepath, source_lg, target_lg):
	with open(filepath, encoding="utf-8") as f:
	for i, example in enumerate(f):
	try:
	(score, src, tgt) = example.rstrip("\n").split("\t")
	row = {
	"translation": {
	source_lg: src,
	target_lg: tgt,
	},
	"laser_score": score,
	}
	except:
	print(example)
	raise
	yield i, row


	# to test the script, go to the root folder of the repo (nllb) and run:
	# datasets-cli test nllb --save_infos --all_configs