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import os |
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import re |
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import warnings |
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import datasets |
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import requests |
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_DESCRIPTION = """\ |
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CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB |
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We show that margin-based bitext mining in LASER's multilingual sentence space can be applied to |
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monolingual corpora of billions of sentences to produce high quality aligned translation data. |
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We use thirty-two snapshots of a curated common crawl corpus [1] totaling 69 billion unique sentences. |
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Using one unified approach for 80 languages, we were able to mine 10.8 billion parallel sentences, |
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out of which only 2.9 billion are aligned with English. |
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IMPORTANT: Please cite reference [2][3] if you use this data. |
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[1] Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Jouli |
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and Edouard Grave, CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data |
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[2] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin, |
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CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB |
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[3] Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, |
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Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, |
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Sergey Edunov, Edouard Grave, Michael Auli, and Armand Joulin. |
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Beyond English-Centric Multilingual Machine Translation |
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90 languages, 1,197 bitexts |
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total number of files: 90 |
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total number of tokens: 112.14G |
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total number of sentence fragments: 7.37G |
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""" |
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_HOMEPAGE_URL = "https://opus.nlpl.eu/CCMatrix.php" |
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_CITATION = """\ |
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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 |
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""" |
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_VERSION = "1.0.0" |
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_FILE = "CCMatrix.{}.{}" |
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_URL = "https://opus.nlpl.eu/CCMatrix.php" |
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_DOWNLOAD_URL = "https://object.pouta.csc.fi/OPUS-CCMatrix/v1/moses/{}.txt.zip" |
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def get_language_pairs(): |
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try: |
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response = requests.get(_URL) |
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except requests.exceptions.RequestException: |
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warnings.warn( |
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"Unable to download language pairs from '{}'. Using cached version".format( |
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_URL |
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) |
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) |
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from language_pairs_cache import language_pairs |
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return language_pairs |
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html = response.text |
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ccmatrix_hrefs = [ |
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href |
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for href in re.findall(r'href=[\'"]?([^\'" >]+)', html) |
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if href.startswith("CCMatrix/") |
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] |
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language_pairs = [] |
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for href in ccmatrix_hrefs: |
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match = re.search(r"CCMatrix/v1/(\w+-\w+)_sample.html", href) |
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if match: |
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language1, language2 = match.group(1).split("-") |
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language_pairs.append((language1, language2)) |
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language_pairs.append((language2, language1)) |
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language_pairs.sort() |
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return language_pairs |
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_CONFIGS = get_language_pairs() |
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class CCMatrixConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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lang1, lang2 = kwargs["name"].split("-") |
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self.lang1 = lang1 |
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self.lang2 = lang2 |
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x, y = (lang1, lang2) if lang1 < lang2 else (lang2, lang1) |
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self.download_pair = f"{x}-{y}" |
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class CCMatrix(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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CCMatrixConfig( |
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name=f"{lang1}-{lang2}", |
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description=f"Translating {lang1} to {lang2} or vice versa", |
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version=datasets.Version(_VERSION), |
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) |
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for lang1, lang2 in _CONFIGS |
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] |
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BUILDER_CONFIG_CLASS = CCMatrixConfig |
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def __init__(self, *args, **kwargs): |
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if "max_train_samples" in kwargs and kwargs.get("cache_dir", None) is None: |
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kwargs["cache_dir"] = os.path.join( |
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str(datasets.config.HF_DATASETS_CACHE), |
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"trainsamples_{}".format(kwargs["max_train_samples"]), |
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) |
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self.max_samples = { |
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"train": kwargs.get("max_train_samples", 2**64), |
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} |
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kwargs = { |
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k: v |
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for k, v in kwargs.items() |
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if k not in ["max_train_samples", "id_filter"] |
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} |
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super().__init__(*args, **kwargs) |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("int32"), |
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"score": datasets.Value("float"), |
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"translation": datasets.Translation( |
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languages=(self.config.lang1, self.config.lang2) |
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), |
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}, |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE_URL, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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download_url = _DOWNLOAD_URL.format(self.config.download_pair) |
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path = dl_manager.download_and_extract(download_url) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"datapath": path, "max_samples": self.max_samples["train"]}, |
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) |
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] |
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def _generate_examples(self, datapath, max_samples): |
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l1_path = os.path.join( |
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datapath, _FILE.format(self.config.download_pair, self.config.lang1) |
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) |
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l2_path = os.path.join( |
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datapath, _FILE.format(self.config.download_pair, self.config.lang2) |
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) |
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scores_path = os.path.join( |
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datapath, _FILE.format(self.config.download_pair, "scores") |
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) |
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with open(l1_path, encoding="utf-8") as f1, open( |
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l2_path, encoding="utf-8" |
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) as f2, open(scores_path, encoding="utf-8") as f3: |
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for sentence_counter, (x, y, score) in enumerate(zip(f1, f2, f3)): |
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if sentence_counter == max_samples: |
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return |
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result = ( |
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sentence_counter, |
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{ |
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"id": sentence_counter, |
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"score": score, |
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"translation": { |
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self.config.lang1: x.strip(), |
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self.config.lang2: y.strip(), |
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}, |
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}, |
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) |
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yield result |
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