qed.py

# coding=utf-8
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"""
QED - The QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform.
It's developed by Qatar Computing Research Institute, Arabic Language Technologies Group. Along with English, it covers multiple SEA languages, such as vie (Vietnamese), mya (Burnmese), jav (Javanese), id (Indonesia), tha (Thai),
tl (Tagalog),ms (Malaysia).
"""
import os
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 TASK_TO_SCHEMA, Licenses, Tasks

_CITATION = """\
@inproceedings{abdelali-etal-2014-amara,
    title = "The {AMARA} Corpus: Building Parallel Language Resources for the Educational Domain",
    author = "Abdelali, Ahmed  and
      Guzman, Francisco  and
      Sajjad, Hassan  and
      Vogel, Stephan",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Loftsson, Hrafn  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
    month = may,
    year = "2014",
    address = "Reykjavik, Iceland",
    publisher = "European Language Resources Association (ELRA)",
    url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/877_Paper.pdf",
    pages = "1856--1862",
    abstract = "This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora.
    This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel,
    community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a document-level, jointly aligned development and test sets for 14 language pairs,
    designed for tuning and testing Machine Translation systems. We provide baseline results for these tasks, and highlight some of the challenges we face when building machine translation systems for educational content.",
}
"""

_DATASETNAME = "qed"

_DESCRIPTION = """\
QED - The QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform.
It's developed by Qatar Computing Research Institute, Arabic Language Technologies Group. Along with English, it covers multiple SEA languages, such as vie (Vietnamese), mya (Burnmese), jav (Javanese), id (Indonesia), tha (Thai), tl (Tagalog),
ms (Malaysia).
"""

_HOMEPAGE = "https://opus.nlpl.eu/QED/corpus/version/QED"

_LANGUAGES = ["eng", "vie", "tha", "mya", "jav", "ind", "tgl", "zlm", "ceb", "fil", "khm", "lao", "mad", "pam"]

_LICENSE = Licenses.OTHERS.value

_LOCAL = False

_FILE = "QED.{}.{}"  # E.g. QED.en-id.id

_PAIR_URL = "https://object.pouta.csc.fi/OPUS-QED/v2.0a/moses/{}.txt.zip"
_MONO_URL = "https://object.pouta.csc.fi/OPUS-QED/v2.0a/mono/{}.txt.gz"

_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION, Tasks.SELF_SUPERVISED_PRETRAINING]

_SOURCE_VERSION = "2.0.0"

_SEACROWD_VERSION = "2024.06.20"

_LANG_MAPPER = {
    "eng": "en",
    "vie": "vi",
    "tha": "th",
    "mya": "my",
    "jav": "jv",
    "ind": "id",
    "tgl": "tl",
    "zlm": "ms",
    "ceb": "ceb",
    "fil": "fil",
    "khm": "km",
    "lao": "lo",
    "mad": "mad",
    "pam": "pam",
}


class QEDDataset(datasets.GeneratorBasedBuilder):
    """QED - The QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform.
    It's developed by Qatar Computing Research Institute, Arabic Language Technologies Group. Along with English, it covers multiple SEA languages, such as vie (Vietnamese), mya (Burnmese), jav (Javanese), id (Indonesia), tha (Thai), tl (Tagalog),
    ms (Malaysia)."""

    SEACROWD_SCHEMA = TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()

    LANG_PAIRS = [
        ("eng", "vie"),
        ("eng", "tha"),
        ("eng", "mya"),
        ("eng", "jav"),
        ("eng", "ind"),
        ("eng", "tgl"),
        ("eng", "zlm"),
        ("eng", "fil"),
        ("eng", "khm"),
        ("eng", "lao"),
        ("eng", "mad"),
        ("eng", "pam"),
        ("fil", "vie"),
        ("khm", "vie"),
        ("lao", "vie"),
        ("pam", "vie"),
        ("fil", "tha"),
        ("khm", "tha"),
        ("lao", "tha"),
        ("pam", "tha"),
        ("fil", "mya"),
        ("khm", "mya"),
        ("lao", "mya"),
        ("fil", "jav"),
        ("jav", "lao"),
        ("fil", "ind"),
        ("ind", "khm"),
        ("ind", "lao"),
        ("fil", "tgl"),
        ("khm", "tgl"),
        ("lao", "tgl"),
        ("fil", "zlm"),
        ("khm", "zlm"),
        ("lao", "zlm"),
        ("tha", "vie"),
        ("tha", "mya"),
        ("tha", "jav"),
        ("tha", "tgl"),
        ("mya", "tgl"),
        ("mya", "vie"),
        ("jav", "vie"),
        ("jav", "mya"),
        ("jav", "tgl"),
        ("jav", "zlm"),
        ("ind", "jav"),
        ("ind", "tha"),
        ("ind", "vie"),
        ("ind", "mya"),
        ("ind", "tgl"),
        ("ind", "zlm"),
        ("tgl", "vie"),
        ("zlm", "tgl"),
        ("zlm", "tha"),
        ("zlm", "vie"),
        ("zlm", "mya"),
        ("ceb", "eng"),
        ("ceb", "vie"),
        ("ceb", "tha"),
        ("ceb", "mya"),
        ("ceb", "jav"),
        ("ceb", "ind"),
        ("ceb", "tgl"),
        ("ceb", "zlm"),
        ("ceb", "fil"),
        ("ceb", "khm"),
        ("ceb", "lao"),
        ("ceb", "pam"),
        ("fil", "khm"),
        ("fil", "lao"),
        ("khm", "lao"),
    ]

    MONO_LANGS = ["eng", "vie", "tha", "mya", "jav", "ind", "tgl", "zlm", "ceb", "fil", "khm", "lao", "mad", "pam"]

    BUILDER_CONFIGS = (
        [
            SEACrowdConfig(
                name=f"{_DATASETNAME}_{lang1}-{lang2}_source",
                version=datasets.Version(_SOURCE_VERSION),
                description=f"{_DATASETNAME} source schema for translation from {lang1} to {lang2}",
                schema="source",
                subset_id=f"{_DATASETNAME}_{lang1}-{lang2}",
            )
            for lang1, lang2 in LANG_PAIRS
        ]
        + [
            SEACrowdConfig(
                name=f"{_DATASETNAME}_{lang}_source",
                version=datasets.Version(_SEACROWD_VERSION),
                description=f"{_DATASETNAME} source {lang} schema for Self-supervised Pretraining task",
                schema="source",
                subset_id=f"{_DATASETNAME}_{lang}",
            )
            for lang in MONO_LANGS
        ]
        + [
            SEACrowdConfig(
                name=f"{_DATASETNAME}_{lang1}-{lang2}_seacrowd_t2t",
                version=datasets.Version(_SEACROWD_VERSION),
                description=f"{_DATASETNAME} SEACrowd schema for translation from {lang1} to {lang2} for Machine Translation task",
                schema="seacrowd_t2t",
                subset_id=f"{_DATASETNAME}_{lang1}-{lang2}",
            )
            for lang1, lang2 in LANG_PAIRS
        ]
        + [
            SEACrowdConfig(
                name=f"{_DATASETNAME}_{lang}_seacrowd_ssp",
                version=datasets.Version(_SEACROWD_VERSION),
                description=f"{_DATASETNAME} SEACrowd {lang} schema for Self-supervised Pretraining task",
                schema="seacrowd_ssp",
                subset_id=f"{_DATASETNAME}_{lang}",
            )
            for lang in MONO_LANGS
        ]
    )

    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_eng-ind_source"

    def _info(self) -> datasets.DatasetInfo:
        if self.config.schema == "source":
            if len(self.config.subset_id.split("_")[-1].split("-")) == 2:  # MT TASK
                lang1, lang2 = self.config.subset_id.split("_")[-1].split("-")
                features = datasets.Features(
                    {
                        "id": datasets.Value("int32"),
                        "translation": datasets.Translation(languages=(lang1, lang2)),
                    }
                )
            elif len(self.config.subset_id.split("_")[-1].split("-")) == 1:  # ssp task
                features = datasets.Features(
                    {
                        "id": datasets.Value("int32"),
                        "text": datasets.Value("string"),
                    }
                )

        elif self.config.schema == "seacrowd_t2t":
            features = schemas.text2text_features

        elif self.config.schema == "seacrowd_ssp":
            features = schemas.ssp_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."""
        lang_pair = self.config.subset_id.split("_")[-1]
        lang_info = "-".join([_LANG_MAPPER[lang] for lang in lang_pair.split("-")])

        if len(self.config.subset_id.split("_")[-1].split("-")) == 1:  # SSP Task
            url = _MONO_URL.format(lang_info)
        elif len(self.config.subset_id.split("_")[-1].split("-")) == 2:  # MT Task
            url = _PAIR_URL.format(lang_info)

        data_dir = dl_manager.download_and_extract(url)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_dir,
                },
            )
        ]

    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""

        if len(self.config.subset_id.split("_")[-1].split("-")) == 2:  # MT Task
            lang_pair = self.config.subset_id.split("_")[-1]
            lang1, lang2 = lang_pair.split("-")

            l1_path = os.path.join(filepath, _FILE.format("-".join([_LANG_MAPPER[lang1], _LANG_MAPPER[lang2]]), _LANG_MAPPER[lang1]))
            l2_path = os.path.join(filepath, _FILE.format("-".join([_LANG_MAPPER[lang1], _LANG_MAPPER[lang2]]), _LANG_MAPPER[lang2]))

            if self.config.schema == "source":
                with open(l1_path, encoding="utf-8") as f1, open(l2_path, encoding="utf-8") as f2:
                    for i, (x, y) in enumerate(zip(f1, f2)):
                        yield i, {
                            "id": i,
                            "translation": {
                                lang1: x.strip(),
                                lang2: y.strip(),
                            },
                        }

            elif self.config.schema == "seacrowd_t2t":
                with open(l1_path, encoding="utf-8") as f1, open(l2_path, encoding="utf-8") as f2:
                    for i, (x, y) in enumerate(zip(f1, f2)):
                        yield i, {
                            "id": str(i),
                            "text_1": x.strip(),
                            "text_2": y.strip(),
                            "text_1_name": lang1,
                            "text_2_name": lang2,
                        },

        elif len(self.config.subset_id.split("_")[-1].split("-")) == 1:  # SSP Task
            with open(filepath, "r", encoding="utf-8") as f:
                for i, x in enumerate(f.readlines()):
                    yield i, {
                        "id": str(i),
                        "text": x.strip(),
                    }