Upload tatoeba-challenge.py

tatoeba-challenge.py

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+import gzip
+from datasets import (
+    BuilderConfig,
+    GeneratorBasedBuilder,
+    DownloadManager,
+    StreamingDownloadManager,
+    Version,
+    SplitGenerator,
+    Split,
+    DatasetInfo,
+    Features,
+    Value,
+)
+from typing import Union
+from pathlib import Path
+
+
+class TatoebaChallengeConfig(BuilderConfig):
+    """Builder config for Tatoeba challenge dataset."""
+
+    def __init__(self, name: str, version: str, **kwargs):
+        assert version == "2023-09-26", "Only v2023-09-26 is supported"
+        super().__init__(
+            name=name, version=Version(version.replace("-", ".")), **kwargs
+        )
+        self.version_str = version
+        self.data_url = (
+            f"https://object.pouta.csc.fi/Tatoeba-Challenge-v{version}/{name}.tar"
+        )
+
+
+class TatoebaChallenge(GeneratorBasedBuilder):
+    """Tatoeba challenge dataset."""
+
+    BUILDER_CONFIG_CLASS = TatoebaChallengeConfig
+    BUILDER_CONFIGS = [
+        TatoebaChallengeConfig(name="chv-eng", version="2023-09-26"),
+        TatoebaChallengeConfig(name="chv-rus", version="2023-09-26"),
+    ]
+
+    def _info(self) -> DatasetInfo:
+        src, trg = self.config.name.split("-")
+        return DatasetInfo(
+            description="""
+               The Tatoeba Translation Challenge.
+               You can find more about the data here: https://github.com/Helsinki-NLP/Tatoeba-Challenge
+
+                Here we have only Chuvash-English subset.
+               We do not use official dataset https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt
+               here because chv-eng of the dataset contains only test split.
+               """,
+            features=Features(
+                {
+                    "id": Value("string"),
+                    src: Value("string"),
+                    trg: Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/Helsinki-NLP/Tatoeba-Challenge",
+            citation="""
+           @inproceedings{tiedemann-2020-tatoeba,
+               title = "The {T}atoeba {T}ranslation {C}hallenge {--} {R}ealistic Data Sets for Low Resource and Multilingual {MT}",
+               author = {Tiedemann, J{\"o}rg},
+               booktitle = "Proceedings of the Fifth Conference on Machine Translation",
+               month = nov,
+               year = "2020",
+               address = "Online",
+               publisher = "Association for Computational Linguistics",
+               url = "https://www.aclweb.org/anthology/2020.wmt-1.139",
+               pages = "1174--1182"
+           }
+           """,
+        )
+
+    def _split_generators(
+        self, dl_manager: Union[DownloadManager, StreamingDownloadManager]
+    ):
+        dl_dir = dl_manager.download_and_extract(self.config.data_url)
+        assert isinstance(dl_dir, str)
+
+        path_to_folder = (
+            Path(dl_dir)
+            / "data"
+            / "release"
+            / f"v{self.config.version_str}"
+            / self.config.name
+        )
+        return [
+            SplitGenerator(
+                name=Split.TRAIN._name,
+                gen_kwargs={
+                    "langs": path_to_folder / "train.id.gz",
+                    "src": path_to_folder / "train.src.gz",
+                    "trg": path_to_folder / "train.trg.gz",
+                },
+            ),
+            SplitGenerator(
+                name=Split.TEST._name,
+                gen_kwargs={
+                    "langs": path_to_folder / "test.id",
+                    "src": path_to_folder / "test.src",
+                    "trg": path_to_folder / "test.trg",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, langs: Path, src: Path, trg: Path):
+        if langs.suffix == ".gz":
+            assert src.suffix == trg.suffix
+            assert trg.suffix == langs.suffix
+
+            opener = gzip.open
+        else:
+            opener = open
+
+        with opener(langs, "rb") as langs_src:
+            with opener(src, "rb") as src_src:
+                with opener(trg, "rb") as trg_src:
+                    for id_, (langs_line, src_line, trg_line) in enumerate(
+                        zip(langs_src, src_src, trg_src)
+                    ):
+                        langs_row = langs_line.decode("utf8").strip().split("\t")
+                        # train contains 3 symbols
+                        if len(langs_row) == 3:
+                            _, src_lang, trg_lang = langs_row
+                        else:
+                            src_lang, trg_lang = langs_row
+
+                        yield (
+                            id_,
+                            {
+                                "id": id_,
+                                src_lang: src_line.decode("utf8").strip(),
+                                trg_lang: trg_line.decode("utf8").strip(),
+                            },
+                        )