Datasets:
ArXiv:
License:
| # 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. | |
| """ | |
| 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(), | |
| } | |