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| # 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://opus.nlpl.eu/download.php?f=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 | |