# coding=utf-8 # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. # # 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. """ NTREX-128, a data set for machine translation (MT) evaluation, includes 123 documents \ (1,997 sentences, 42k words) translated from English into 128 target languages. \ 9 languages are natively spoken in Southeast Asia, i.e., Burmese, Filipino, \ Hmong, Indonesian, Khmer, Lao, Malay, Thai, and Vietnamese. """ from pathlib import Path from typing import Dict, List, Tuple import datasets from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _CITATION = """\ @inproceedings{federmann-etal-2022-ntrex, title = "{NTREX}-128 {--} News Test References for {MT} Evaluation of 128 Languages", author = "Federmann, Christian and Kocmi, Tom and Xin, Ying", editor = "Ahuja, Kabir and Anastasopoulos, Antonios and Patra, Barun and Neubig, Graham and Choudhury, Monojit and Dandapat, Sandipan and Sitaram, Sunayana and Chaudhary, Vishrav", booktitle = "Proceedings of the First Workshop on Scaling Up Multilingual Evaluation", month = nov, year = "2022", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.sumeval-1.4", pages = "21--24", } """ _DATASETNAME = "ntrex_128" _DESCRIPTION = """\ NTREX-128, a data set for machine translation (MT) evaluation, includes 123 documents \ (1,997 sentences, 42k words) translated from English into 128 target languages. \ 9 languages are natively spoken in Southeast Asia, i.e., Burmese, Filipino, \ Hmong, Indonesian, Khmer, Lao, Malay, Thai, and Vietnamese. """ _HOMEPAGE = "https://github.com/MicrosoftTranslator/NTREX" _LANGUAGES = ["mya", "fil", "ind", "khm", "lao", "zlm", "tha", "vie", "hmv", "eng"] _LICENSE = Licenses.CC_BY_SA_4_0.value _LOCAL = False # _MAPPING = {"mya": "mya", "fil": "fil", "ind": "ind", "khm": "khm", "lao": "lao", "zlm": "msa", "tha": "tha", "vie": "vie", "hmv": "hmn"} _MAPPING = { "afr": "afr", "amh": "amh", "arb": "arb", "aze-Latn": "aze-Latn", "bak": "bak", "bel": "bel", "bem": "bem", "ben": "ben", "bod": "bod", "bos": "bos", "bul": "bul", "cat": "cat", "ces": "ces", "ckb-Arab": "ckb-Arab", "cym": "cym", "dan": "dan", "deu": "deu", "div": "div", "dzo": "dzo", "ell": "ell", "eng-GB": "eng-GB", "eng-IN": "eng-IN", "eng-US": "eng-US", "est": "est", "eus": "eus", "ewe": "ewe", "fao": "fao", "fas": "fas", "fij": "fij", "fil": "fil", "fin": "fin", "fra": "fra", "fra-CA": "fra-CA", "fuc": "fuc", "gle": "gle", "glg": "glg", "guj": "guj", "hau": "hau", "heb": "heb", "hin": "hin", "hmv": "hmn", "hrv": "hrv", "hun": "hun", "hye": "hye", "ibo": "ibo", "ind": "ind", "isl": "isl", "ita": "ita", "jpn": "jpn", "kan": "kan", "kat": "kat", "kaz": "kaz", "khm": "khm", "kin": "kin", "kir": "kir", "kmr": "kmr", "kor": "kor", "lao": "lao", "lav": "lav", "lit": "lit", "ltz": "ltz", "mal": "mal", "mar": "mar", "mey": "mey", "mkd": "mkd", "mlg": "mlg", "mlt": "mlt", "mon": "mon", "mri": "mri", "zlm": "msa", "mya": "mya", "nde": "nde", "nep": "nep", "nld": "nld", "nno": "nno", "nob": "nob", "nso": "nso", "nya": "nya", "orm": "orm", "pan": "pan", "pol": "pol", "por": "por", "por-BR": "por-BR", "prs": "prs", "pus": "pus", "ron": "ron", "rus": "rus", "shi": "shi", "sin": "sin", "slk": "slk", "slv": "slv", "smo": "smo", "sna-Latn": "sna-Latn", "snd-Arab": "snd-Arab", "som": "som", "spa": "spa", "spa-MX": "spa-MX", "sqi": "sqi", "srp-Cyrl": "srp-Cyrl", "srp-Latn": "srp-Latn", "ssw": "ssw", "swa": "swa", "swe": "swe", "tah": "tah", "tam": "tam", "tat": "tat", "tel": "tel", "tgk-Cyrl": "tgk-Cyrl", "tha": "tha", "tir": "tir", "ton": "ton", "tsn": "tsn", "tuk": "tuk", "tur": "tur", "uig": "uig", "ukr": "ukr", "urd": "urd", "uzb": "uzb", "ven": "ven", "vie": "vie", "wol": "wol", "xho": "xho", "yor": "yor", "yue": "yue", "zho-CN": "zho-CN", "zho-TW": "zho-TW", "zul": "zul", } _URLS = { _DATASETNAME: "https://raw.githubusercontent.com/MicrosoftTranslator/NTREX/main/NTREX-128/newstest2019-ref.{lang}.txt", } _ALL_LANG = [ "afr", "amh", "arb", "aze-Latn", "bak", "bel", "bem", "ben", "bod", "bos", "bul", "cat", "ces", "ckb-Arab", "cym", "dan", "deu", "div", "dzo", "ell", "eng-GB", "eng-IN", "eng-US", "est", "eus", "ewe", "fao", "fas", "fij", "fil", "fin", "fra", "fra-CA", "fuc", "gle", "glg", "guj", "hau", "heb", "hin", "hmv", "hrv", "hun", "hye", "ibo", "ind", "isl", "ita", "jpn", "kan", "kat", "kaz", "khm", "kin", "kir", "kmr", "kor", "lao", "lav", "lit", "ltz", "mal", "mar", "mey", "mkd", "mlg", "mlt", "mon", "mri", "zlm", "mya", "nde", "nep", "nld", "nno", "nob", "nso", "nya", "orm", "pan", "pol", "por", "por-BR", "prs", "pus", "ron", "rus", "shi", "sin", "slk", "slv", "smo", "sna-Latn", "snd-Arab", "som", "spa", "spa-MX", "sqi", "srp-Cyrl", "srp-Latn", "ssw", "swa", "swe", "tah", "tam", "tat", "tel", "tgk-Cyrl", "tha", "tir", "ton", "tsn", "tuk", "tur", "uig", "ukr", "urd", "uzb", "ven", "vie", "wol", "xho", "yor", "yue", "zho-CN", "zho-TW", "zul", ] # aze-Latn: Azerbaijani (Latin) # ckb-Arab: Central Kurdish (Sorani) # eng-GB: English (British), eng-IN: English (India), eng-US: English (US) # fra: French, fra-CA: French (Canada) # mya: Myanmar # por: Portuguese, por-BR: Portuguese (Brazil) # shi: Shilha # sna-Latn: Shona (Latin) # snd-Arab: Sindhi (Arabic) # spa: Spanish, spa-MX: Spanish (Mexico) # srp-Cyrl: Serbian (Cyrillic), srp-Latn: Serbian (Latin) # tgk-Cyrl: Tajik (Cyrillic) # yue: Cantonese # zho-CN: Chinese (Simplified), zho-TW: Chinese (Traditional) _SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] _SOURCE_VERSION = "11.24.2022" _SEACROWD_VERSION = "2024.06.20" class Ntrex128Dataset(datasets.GeneratorBasedBuilder): """NTREX-128, a data set for machine translation (MT) evaluation, includes 123 documents \ (1,997 sentences, 42k words) translated from English into 128 target languages. \ 9 languages are natively spoken in Southeast Asia, i.e., Burmese, Filipino, \ Hmong, Indonesian, Khmer, Lao, Malay, Thai, and Vietnamese.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_{subset1}_{subset2}_source", version=datasets.Version(_SEACROWD_VERSION), description=f"{_DATASETNAME} {subset1}2{subset2} source schema", schema="source", subset_id=f"{_DATASETNAME}_{subset1}_{subset2}", ) for subset2 in _ALL_LANG for subset1 in _ALL_LANG if subset1 != subset2 and (subset1 in _LANGUAGES or subset2 in _LANGUAGES) ] + [ SEACrowdConfig( name=f"{_DATASETNAME}_{subset1}_{subset2}_seacrowd_t2t", version=datasets.Version(_SEACROWD_VERSION), description=f"{_DATASETNAME} {subset1}2{subset2} SEACrowd schema", schema="seacrowd_t2t", subset_id=f"{_DATASETNAME}_{subset1}_{subset2}", ) for subset2 in _ALL_LANG for subset1 in _ALL_LANG if subset1 != subset2 and (subset1 in _LANGUAGES or subset2 in _LANGUAGES) ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_mya_fil_source" def _info(self): # The format of the source is just texts in different .txt files (each file corresponds to one language). # Decided make source schema the same as the seacrowd_t2t schema. if self.config.schema == "source" or self.config.schema == "seacrowd_t2t": features = schemas.text2text_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" lang1 = self.config.name.split("_")[2] lang2 = self.config.name.split("_")[3] lang1_txt_path = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME].format(lang=_MAPPING[lang1]))) lang2_txt_path = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME].format(lang=_MAPPING[lang2]))) return [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": [lang1_txt_path, lang2_txt_path]}, ), ] def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" lang1 = self.config.name.split("_")[2] lang2 = self.config.name.split("_")[3] texts1 = [] texts2 = [] texts1 = open(filepath[0], "r").readlines() texts2 = open(filepath[1], "r").readlines() if self.config.schema == "source" or self.config.schema == "seacrowd_t2t": idx = 0 for line1, line2 in zip(texts1, texts2): ex = { "id": str(idx), "text_1": line1, "text_2": line2, "text_1_name": lang1, "text_2_name": lang2, } yield idx, ex idx += 1 else: raise ValueError(f"Invalid config: {self.config.name}")