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Browse files- europa_eac_tm.py +0 -226
europa_eac_tm.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""European commission Joint Reasearch Center's Education And Culture Translation Memory dataset"""
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import os
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from itertools import repeat
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from xml.etree import ElementTree
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import datasets
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_CITATION = """\
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@Article{Steinberger2014,
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author={Steinberger, Ralf
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and Ebrahim, Mohamed
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and Poulis, Alexandros
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and Carrasco-Benitez, Manuel
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and Schl{\"u}ter, Patrick
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and Przybyszewski, Marek
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and Gilbro, Signe},
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title={An overview of the European Union's highly multilingual parallel corpora},
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journal={Language Resources and Evaluation},
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year={2014},
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month={Dec},
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day={01},
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volume={48},
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number={4},
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pages={679-707},
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issn={1574-0218},
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doi={10.1007/s10579-014-9277-0},
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url={https://doi.org/10.1007/s10579-014-9277-0}
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}
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"""
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_DESCRIPTION = """\
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In October 2012, the European Union's (EU) Directorate General for Education and Culture ( DG EAC) released a \
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translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in \
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twenty-six languages. This resource bears the name EAC Translation Memory, short EAC-TM.
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EAC-TM covers up to 26 languages: 22 official languages of the EU (all except Irish) plus Icelandic, Croatian, \
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Norwegian and Turkish. EAC-TM thus contains translations from English into the following 25 languages: Bulgarian, \
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Czech, Danish, Dutch, Estonian, German, Greek, Finnish, French, Croatian, Hungarian, Icelandic, Italian, Latvian, \
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Lithuanian, Maltese, Norwegian, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish and Turkish.
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All documents and sentences were originally written in English (source language is English) and then translated into \
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the other languages. The texts were translated by staff of the National Agencies of the Lifelong Learning and Youth in \
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Action programmes. They are typically professionals in the field of education/youth and EU programmes. They are thus not \
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professional translators, but they are normally native speakers of the target language.
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"""
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_HOMEPAGE = "https://ec.europa.eu/jrc/en/language-technologies/eac-translation-memory"
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_LICENSE = "\
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Creative Commons Attribution 4.0 International(CC BY 4.0) licence \
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© European Union, 1995-2020"
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_VERSION = "1.0.0"
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_DATA_URL = "https://wt-public.emm4u.eu/Resources/EAC-TM/EAC-TM-all.zip"
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_AVAILABLE_LANGUAGES = (
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"bg",
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"cs",
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"da",
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"de",
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"el",
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"en",
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"es",
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"et",
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"fi",
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"fr",
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"hu",
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"is",
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"it",
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"lt",
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"lv",
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"mt",
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"nb",
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"nl",
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"pl",
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"pt",
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"ro",
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"sk",
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"sl",
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"sv",
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"tr",
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)
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def _find_sentence(translation, language):
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"""Util that returns the sentence in the given language from translation, or None if it is not found
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Args:
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translation: `xml.etree.ElementTree.Element`, xml tree element extracted from the translation memory files.
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language: `str`, language of interest e.g. 'en'
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Returns: `str` or `None`, can be `None` if the language of interest is not found in the translation
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"""
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# Retrieve the first <tuv> children of translation having xml:lang tag equal to language
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namespaces = {"xml": "http://www.w3.org/XML/1998/namespace"}
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seg_tag = translation.find(path=f".//tuv[@xml:lang='{language}']/seg", namespaces=namespaces)
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if seg_tag is not None:
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return seg_tag.text
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return None
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class EuropaEacTMConfig(datasets.BuilderConfig):
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"""BuilderConfig for EuropaEacTM"""
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def __init__(self, *args, language_pair=(None, None), **kwargs):
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"""BuilderConfig for EuropaEacTM
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Args:
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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: ("ro", "en").
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**kwargs: keyword arguments forwarded to super.
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"""
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name = f"{language_pair[0]}2{language_pair[1]}"
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description = f"Translation dataset from {language_pair[0]} to {language_pair[1]}"
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super(EuropaEacTMConfig, self).__init__(
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*args,
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name=name,
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description=description,
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**kwargs,
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)
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source, target = language_pair
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assert source != target, "Source and target languages must be different}"
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assert (source in _AVAILABLE_LANGUAGES) and (
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target in _AVAILABLE_LANGUAGES
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), f"Either source language {source} or target language {target} is not supported. Both must be one of : {_AVAILABLE_LANGUAGES}"
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self.language_pair = language_pair
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# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
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class EuropaEacTM(datasets.GeneratorBasedBuilder):
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"""European Commission Joint Research Center's EAC Translation Memory"""
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FORM_SENTENCE_TYPE = "form_data"
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REFERENCE_SENTENCE_TYPE = "sentence_data"
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# Only a few language pairs are listed here. You can use config to generate all language pairs !
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BUILDER_CONFIGS = [
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EuropaEacTMConfig(language_pair=("en", target), version=_VERSION) for target in ["bg", "es", "fr"]
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]
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BUILDER_CONFIG_CLASS = EuropaEacTMConfig
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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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{
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"translation": datasets.features.Translation(languages=self.config.language_pair),
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"sentence_type": datasets.features.ClassLabel(
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names=[self.FORM_SENTENCE_TYPE, self.REFERENCE_SENTENCE_TYPE]
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),
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}
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),
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supervised_keys=(source, target),
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homepage=_HOMEPAGE,
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license=_LICENSE,
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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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form_data_file = os.path.join(dl_dir, "EAC_FORMS.tmx")
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reference_data_file = os.path.join(dl_dir, "EAC_REFRENCE_DATA.tmx")
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source, target = self.config.language_pair
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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={
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"form_data_file": form_data_file,
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"reference_data_file": reference_data_file,
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"source_language": source,
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"target_language": target,
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},
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),
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]
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def _generate_examples(
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self,
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form_data_file,
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reference_data_file,
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source_language,
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target_language,
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):
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_id = 0
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for (sentence_type, filepath) in [
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(self.FORM_SENTENCE_TYPE, form_data_file),
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(self.REFERENCE_SENTENCE_TYPE, reference_data_file),
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]:
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# Retrieve <tu></tu> tags in the tmx file
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xml_element_tree = ElementTree.parse(filepath)
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xml_body_tag = xml_element_tree.getroot().find("body")
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assert xml_body_tag is not None, f"Invalid data: <body></body> tag not found in {filepath}"
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translation_units = xml_body_tag.iter("tu")
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# Pair sentence_type and translation_units
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for sentence_type, translation in zip(repeat(sentence_type), translation_units):
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source_sentence = _find_sentence(translation=translation, language=source_language)
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target_sentence = _find_sentence(translation=translation, language=target_language)
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if source_sentence is None or target_sentence is None:
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continue
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_id += 1
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sentence_label = 0 if sentence_type == self.FORM_SENTENCE_TYPE else 1
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yield _id, {
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"translation": {source_language: source_sentence, target_language: target_sentence},
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"sentence_type": sentence_label,
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}
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