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# 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}")
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