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""" WMT16 English-Romanian Translation Data with further preprocessing """ |
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from __future__ import absolute_import, division, print_function |
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import csv |
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import json |
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import os |
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import datasets |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {WMT14 English-German Translation Data with further preprocessing}, |
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authors={}, |
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year={2016} |
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} |
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""" |
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_DESCRIPTION = "WMT14 English-German Translation Data with further preprocessing" |
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_HOMEPAGE = "http://www.statmt.org/wmt16/" |
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_LICENSE = "" |
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_DATA_URL = "https://cdn-datasets.huggingface.co/translation/wmt_en_de.tgz" |
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class Wmt14EnDePreProcessedConfig(datasets.BuilderConfig): |
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"""BuilderConfig for wmt16.""" |
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def __init__(self, language_pair=(None, None), **kwargs): |
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"""BuilderConfig for wmt16 |
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Args: |
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for the `datasets.features.text.TextEncoder` used for the features feature. |
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language_pair: pair of languages that will be used for translation. Should |
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contain 2-letter coded strings. First will be used at source and second |
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as target in supervised mode. For example: ("se", "en"). |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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name = "%s%s" % (language_pair[0], language_pair[1]) |
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description = ("Translation dataset from %s to %s") % (language_pair[0], language_pair[1]) |
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super(Wmt14EnDePreProcessedConfig, self).__init__( |
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name=name, |
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description=description, |
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version=datasets.Version("1.1.0", ""), |
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**kwargs, |
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) |
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assert "en" in language_pair, ("Config language pair must contain `en`, got: %s", language_pair) |
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source, target = language_pair |
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non_en = source if target == "en" else target |
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assert non_en in ["de"], ("Invalid non-en language in pair: %s", non_en) |
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self.language_pair = language_pair |
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class Wmt14EnDePreProcessed(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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Wmt14EnDePreProcessedConfig( |
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language_pair=("en", "de"), |
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), |
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] |
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def _info(self): |
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source, target = self.config.language_pair |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{"translation": datasets.features.Translation(languages=self.config.language_pair)} |
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), |
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supervised_keys=(source, target), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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dl_dir = dl_manager.download_and_extract(_DATA_URL) |
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source, target = self.config.language_pair |
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non_en = source if target == "en" else target |
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path_tmpl = "{dl_dir}/wmt_en_de/{split}.{type}" |
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files = {} |
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for split in ("train", "val", "test"): |
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files[split] = { |
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"source_file": path_tmpl.format(dl_dir=dl_dir, split=split, type="source"), |
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"target_file": path_tmpl.format(dl_dir=dl_dir, split=split, type="target"), |
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} |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=files["train"]), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=files["val"]), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=files["test"]), |
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] |
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def _generate_examples(self, source_file, target_file): |
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"""This function returns the examples in the raw (text) form.""" |
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with open(source_file, mode="rb") as f: |
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source_sentences = f.read().decode("utf8").split("\n") |
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with open(target_file, mode="rb") as f: |
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target_sentences = f.read().decode("utf8").split("\n") |
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assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % ( |
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len(source_sentences), |
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len(target_sentences), |
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source_file, |
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target_file, |
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) |
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source, target = self.config.language_pair |
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for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)): |
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result = {"translation": {source: l1, target: l2}} |
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if all(result.values()): |
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yield idx, result |
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