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ntrex_128.py

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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}")