Datasets:

xezpeleta
/

ccmatrix

	# coding=utf-8
	# Copyright 2022 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.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# Lint as: python3
	import os
	import re
	import warnings

	import datasets
	import requests

	_DESCRIPTION = """\
	CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB

	We show that margin-based bitext mining in LASER's multilingual sentence space can be applied to
	monolingual corpora of billions of sentences to produce high quality aligned translation data.
	We use thirty-two snapshots of a curated common crawl corpus [1] totaling 69 billion unique sentences.
	Using one unified approach for 80 languages, we were able to mine 10.8 billion parallel sentences,
	out of which only 2.9 billion are aligned with English.

	IMPORTANT: Please cite reference [2][3] if you use this data.

	[1] Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Jouli
	and Edouard Grave, CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data

	[2] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin,
	CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB

	[3] Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines,
	Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky,
	Sergey Edunov, Edouard Grave, Michael Auli, and Armand Joulin.
	Beyond English-Centric Multilingual Machine Translation

	90 languages, 1,197 bitexts
	total number of files: 90
	total number of tokens: 112.14G
	total number of sentence fragments: 7.37G
	"""
	_HOMEPAGE_URL = "https://opus.nlpl.eu/CCMatrix.php"
	_CITATION = """\
	Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Jouli and Edouard Grave, CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data
	"""

	_VERSION = "1.0.0"
	_FILE = "CCMatrix.{}.{}" # E.g. CCMatrix.en-nl.nl
	_URL = "https://opus.nlpl.eu/CCMatrix.php"
	_DOWNLOAD_URL = "https://object.pouta.csc.fi/OPUS-CCMatrix/v1/moses/{}.txt.zip"


	def get_language_pairs():
	try:
	response = requests.get(_URL)
	except requests.exceptions.RequestException:
	warnings.warn(
	"Unable to download language pairs from '{}'. Using cached version".format(
	_URL
	)
	)
	from language_pairs_cache import language_pairs

	return language_pairs

	html = response.text

	ccmatrix_hrefs = [
	href
	for href in re.findall(r'href=[\'"]?([^\'" >]+)', html)
	if href.startswith("CCMatrix/")
	]

	language_pairs = []
	for href in ccmatrix_hrefs:
	match = re.search(r"CCMatrix/v1/(\w+-\w+)_sample.html", href)
	if match:
	language1, language2 = match.group(1).split("-")
	language_pairs.append((language1, language2))
	language_pairs.append((language2, language1))
	language_pairs.sort()
	return language_pairs


	_CONFIGS = get_language_pairs()


	class CCMatrixConfig(datasets.BuilderConfig):
	def __init__(self, **kwargs):
	super().__init__(**kwargs)
	lang1, lang2 = kwargs["name"].split("-")
	self.lang1 = lang1
	self.lang2 = lang2
	x, y = (lang1, lang2) if lang1 < lang2 else (lang2, lang1)
	self.download_pair = f"{x}-{y}"


	class CCMatrix(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	CCMatrixConfig(
	name=f"{lang1}-{lang2}",
	description=f"Translating {lang1} to {lang2} or vice versa",
	version=datasets.Version(_VERSION),
	)
	for lang1, lang2 in _CONFIGS
	]
	BUILDER_CONFIG_CLASS = CCMatrixConfig

	def __init__(self, args, *kwargs):
	if "max_train_samples" in kwargs and kwargs.get("cache_dir", None) is None:
	kwargs["cache_dir"] = os.path.join(
	str(datasets.config.HF_DATASETS_CACHE),
	"trainsamples_{}".format(kwargs["max_train_samples"]),
	)
	self.max_samples = {
	"train": kwargs.get("max_train_samples", 2**64),
	}
	kwargs = {
	k: v
	for k, v in kwargs.items()
	if k not in ["max_train_samples", "id_filter"]
	}
	super().__init__(args, *kwargs)

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("int32"),
	"score": datasets.Value("float"),
	"translation": datasets.Translation(
	languages=(self.config.lang1, self.config.lang2)
	),
	},
	),
	supervised_keys=None,
	homepage=_HOMEPAGE_URL,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	download_url = _DOWNLOAD_URL.format(self.config.download_pair)
	path = dl_manager.download_and_extract(download_url)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"datapath": path, "max_samples": self.max_samples["train"]},
	)
	]

	def _generate_examples(self, datapath, max_samples):
	l1_path = os.path.join(
	datapath, _FILE.format(self.config.download_pair, self.config.lang1)
	)
	l2_path = os.path.join(
	datapath, _FILE.format(self.config.download_pair, self.config.lang2)
	)
	scores_path = os.path.join(
	datapath, _FILE.format(self.config.download_pair, "scores")
	)
	with open(l1_path, encoding="utf-8") as f1, open(
	l2_path, encoding="utf-8"
	) as f2, open(scores_path, encoding="utf-8") as f3:
	for sentence_counter, (x, y, score) in enumerate(zip(f1, f2, f3)):
	if sentence_counter == max_samples:
	return
	result = (
	sentence_counter,
	{
	"id": sentence_counter,
	"score": score,
	"translation": {
	self.config.lang1: x.strip(),
	self.config.lang2: y.strip(),
	},
	},
	)
	yield result