Datasets:

SEACrowd
/

wikimatrix

+from pathlib import Path
+from typing import List
+import datasets
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
+                                      DEFAULT_SOURCE_VIEW_NAME, Licenses,
+                                      Tasks)
+_DATASETNAME = "wikimatrix"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
+# ilo min sun are actually not available
+_LANGUAGES = ["ilo", "min", "jav", "sun", "ceb", "ind", "tgl", "vie"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+_CITATION = """\
+@inproceedings{schwenk-etal-2021-wikimatrix,
+    title = "{W}iki{M}atrix: Mining 135{M} Parallel Sentences in 1620 Language Pairs from {W}ikipedia",
+    author = "Schwenk, Holger  and
+      Chaudhary, Vishrav  and
+      Sun, Shuo  and
+      Gong, Hongyu  and
+      Guzm{\'a}n, Francisco",
+    editor = "Merlo, Paola  and
+      Tiedemann, Jorg  and
+      Tsarfaty, Reut",
+    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
+    month = apr,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.eacl-main.115",
+    doi = "10.18653/v1/2021.eacl-main.115",
+    pages = "1351--1361",
+    abstract = "We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content
+    of Wikipedia articles in 96 languages, including several dialects or low-resource languages. We do not limit the extraction process to
+    alignments with English, but we systematically consider all possible language pairs. In total, we are able to extract 135M parallel sentences
+    for 16720 different language pairs, out of which only 34M are aligned with English. This corpus is freely available. To get an indication
+    on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate
+    them on the TED corpus, achieving strong BLEU scores for many language pairs. The WikiMatrix bitexts seem to be particularly interesting
+    to train MT systems between distant languages without the need to pivot through English.",
+}
+"""
+_DESCRIPTION = """\
+WikiMatrix is automatically extracted parallel sentences from the content of Wikipedia articles in 96 languages, including several dialects
+or low-resource languages. 8 languages among them are spoken in Southeast Asia region. In total, there are 135M parallel sentences from 1620
+different language pairs.
+"""
+_HOMEPAGE = "https://github.com/facebookresearch/LASER/tree/main/tasks/WikiMatrix"
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+_URLs = "https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.{lang1}-{lang2}.tsv.gz"
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+config = {
+    "jv": ["en", "es", "fr", "id", "it", "pt"],
+    "ceb": ["bg", "ar", "ca", "cs", "de", "en", "es", "fi", "fr", "hu", "it", "ja", "nl", "no", "pl", "pt", "ro", "ru", "sv", "uk"],
+    "id": [
+        "jv",
+        "is",
+        "it",
+        "ja",
+        "ko",
+        "lt",
+        "mk",
+        "ml",
+        "mr",
+        "ne",
+        "nl",
+        "no",
+        "pl",
+        "pt",
+        "ro",
+        "ru",
+        "sh",
+        "si",
+        "sk",
+        "sl",
+        "sq",
+        "sr",
+        "sv",
+        "sw",
+        "ta",
+        "te",
+        "tl",
+        "tr",
+        "tt",
+        "uk",
+        "vi",
+        "zh",
+        "ar",
+        "az",
+        "ba",
+        "bg",
+        "bn",
+        "bs",
+        "ca",
+        "cs",
+        "da",
+        "de",
+        "el",
+        "en",
+        "eo",
+        "es",
+        "et",
+        "eu",
+        "fa",
+        "fi",
+        "fr",
+        "gl",
+        "he",
+        "hi",
+        "hr",
+        "hu",
+    ],
+    "tl": ["ar", "bg", "bs", "ca", "cs", "da", "de", "el", "en", "eo", "es", "et", "fi", "fr", "gl", "he", "hr", "hu", "id", "it", "ja", "lt", "mk", "nl", "no", "pl", "pt", "ro", "ru", "sh", "sk", "sl", "sq", "sr", "sv", "tr", "uk", "vi", "zh"],
+    "vi": [
+        "ar",
+        "az",
+        "bg",
+        "bn",
+        "bs",
+        "ca",
+        "cs",
+        "da",
+        "de",
+        "el",
+        "en",
+        "eo",
+        "es",
+        "et",
+        "eu",
+        "fa",
+        "fi",
+        "fr",
+        "gl",
+        "he",
+        "hi",
+        "hr",
+        "hu",
+        "id",
+        "is",
+        "it",
+        "ja",
+        "ko",
+        "lt",
+        "mk",
+        "ml",
+        "mr",
+        "nl",
+        "no",
+        "pl",
+        "pt",
+        "ro",
+        "ru",
+        "sh",
+        "si",
+        "sk",
+        "sl",
+        "sq",
+        "sr",
+        "sv",
+        "sw",
+        "ta",
+        "te",
+        "tl",
+        "tr",
+        "uk",
+        "zh",
+    ],
+}
+_SUBSETS = set()
+for lang, pairs in config.items():
+    for pair in pairs:
+        _SUBSETS.add("{}-{}".format(lang, pair) if lang < pair else "{}-{}".format(pair, lang))
+_SUBSETS = list(_SUBSETS)
+class WikiMatrixDataset(datasets.GeneratorBasedBuilder):
+    """WikiMatrix is automatically extracted parallel sentences from the content of Wikipedia articles in 96 languages, including several dialects
+    or low-resource languages."""
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"wikimatrix_{subset.replace('-', '_')}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="WikiMatrix source schema",
+            schema="source",
+            subset_id=f"wikimatrix_{subset.replace('-', '_')}",
+        )
+        for subset in _SUBSETS
+    ] + [
+        SEACrowdConfig(
+            name=f"wikimatrix_{subset.replace('-', '_')}_seacrowd_t2t",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description="WikiMatrix Nusantara schema",
+            schema="seacrowd_t2t",
+            subset_id=f"wikimatrix_{subset.replace('-', '_')}",
+        )
+        for subset in _SUBSETS
+    ]
+    DEFAULT_CONFIG_NAME = "wikimatrix_en_id_source"
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text_1": datasets.Value("string"),
+                    "text_2": datasets.Value("string"),
+                    "text_1_name": datasets.Value("string"),
+                    "text_2_name": datasets.Value("string"),
+                }
+            )
+        elif 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]:
+        lang1, lang2 = self.config.name.split("_")[1], self.config.name.split("_")[2]
+        filepath = Path(dl_manager.download_and_extract(_URLs.format(lang1=lang1, lang2=lang2)))
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": filepath},
+            ),
+        ]
+    def _generate_examples(self, filepath: Path):
+        with open(filepath, "r") as f:
+            data = f.readlines()
+        lang1, lang2 = self.config.name.split("_")[1], self.config.name.split("_")[2]
+        if self.config.schema == "source":
+            for _id, line in enumerate(data):
+                line = line.strip().split("\t")
+                ex = {
+                    "id": str(_id),
+                    "text_1": line[1],
+                    "text_2": line[2],
+                    "text_1_name": lang1,
+                    "text_2_name": lang2,
+                }
+                yield _id, ex
+        elif self.config.schema == "seacrowd_t2t":
+            for _id, line in enumerate(data):
+                line = line.strip().split("\t")
+                ex = {
+                    "id": str(_id),
+                    "text_1": line[1],
+                    "text_2": line[2],
+                    "text_1_name": lang1,
+                    "text_2_name": lang2,
+                }
+                yield _id, ex
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")