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gnome.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.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import requests
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import SCHEMA_TO_FEATURES, TASK_TO_SCHEMA, Licenses, Tasks
+
+_CITATION = r"""\
+@inproceedings{tiedemann-2012-parallel,
+    title = "Parallel Data, Tools and Interfaces in {OPUS}",
+    author = {Tiedemann, J{\"o}rg},
+    editor = "Calzolari, Nicoletta  and
+        Choukri, Khalid  and
+        Declerck, Thierry  and
+        Do{\u{g}}an, Mehmet U{\u{g}}ur  and
+        Maegaard, Bente  and
+        Mariani, Joseph  and
+        Moreno, Asuncion  and
+        Odijk, Jan  and
+        Piperidis, Stelios",
+    booktitle = "Proceedings of the Eighth International Conference on Language
+    Resources and Evaluation ({LREC}'12)",
+    month = may,
+    year = "2012",
+    address = "Istanbul, Turkey",
+    publisher = "European Language Resources Association (ELRA)",
+    url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf",
+    pages = "2214--2218",
+    abstract = "This paper presents the current status of OPUS, a growing
+    language resource of parallel corpora and related tools. The focus in OPUS
+    is to provide freely available data sets in various formats together with
+    basic annotation to be useful for applications in computational linguistics,
+    translation studies and cross-linguistic corpus studies. In this paper, we
+    report about new data sets and their features, additional annotation tools
+    and models provided from the website and essential interfaces and on-line
+    services included in the project.",
+}
+"""
+
+_DATASETNAME = "gnome"
+
+_DESCRIPTION = """\
+A parallel corpus of GNOME localization files, which contains the interface text
+in the GNU Network Object Model Environment (GNOME) and published by GNOME
+translation teams. Text in this dataset is relatively short and technical.
+"""
+
+_HOMEPAGE = "https://opus.nlpl.eu/GNOME/corpus/version/GNOME"
+
+_LANGUAGES = ["eng", "vie", "mya", "ind", "tha", "tgl", "zlm", "lao"]
+_SUBSETS = ["en", "vi", "my", "id", "th", "tl", "ms", "lo"]
+_SUBSET_PAIRS = [(src, tgt) for src in _SUBSETS for tgt in _SUBSETS if src != tgt]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    "api": "http://opus.nlpl.eu/opusapi/?source={src_lang}&target={tgt_lang}&corpus=GNOME&version=v1",
+    "data": "https://object.pouta.csc.fi/OPUS-GNOME/v1/moses/{lang_pair}.txt.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}"  # t2t
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class GnomeDataset(datasets.GeneratorBasedBuilder):
+    """A parallel corpus of GNOME localization files"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = []
+    for subset in _SUBSET_PAIRS:
+        lang_pair = f"{subset[0]}-{subset[1]}"
+        BUILDER_CONFIGS += [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{lang_pair}_source",
+                version=SOURCE_VERSION,
+                description=f"{_DATASETNAME} {lang_pair} source schema",
+                schema="source",
+                subset_id=lang_pair,
+            ),
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{lang_pair}_{_SEACROWD_SCHEMA}",
+                version=SEACROWD_VERSION,
+                description=f"{_DATASETNAME} {lang_pair} SEACrowd schema",
+                schema=_SEACROWD_SCHEMA,
+                subset_id=lang_pair,
+            ),
+        ]
+
+    DEFAULT_CONFIG_NAME = (
+        f"{_DATASETNAME}_{_SUBSET_PAIRS[0][0]}-{_SUBSET_PAIRS[0][1]}_source"
+    )
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "source": datasets.Value("string"),
+                    "target": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == _SEACROWD_SCHEMA:
+            features = SCHEMA_TO_FEATURES[
+                TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]
+            ]  # 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."""
+        src_lang, tgt_lang = self.config.subset_id.split("-")
+        api_url = _URLS["api"].format(src_lang=src_lang, tgt_lang=tgt_lang)
+        data_url = None
+
+        response = requests.get(api_url, timeout=10)
+        if response:
+            corpora = response.json()["corpora"]
+            for corpus in corpora:
+                if ".txt.zip" in corpus["url"]:
+                    data_url = corpus["url"]
+                    break
+        else:
+            raise requests.exceptions.HTTPError(
+                f"Non-success status code: {response.status_code}"
+            )
+
+        if not data_url:
+            raise ValueError(f"No suitable corpus found, check {api_url}")
+        else:
+            lang_pair = data_url.split("/")[-1].split(".")[0]
+            data_dir = Path(dl_manager.download_and_extract(data_url))
+            src_file = data_dir / f"GNOME.{lang_pair}.{src_lang}"
+            tgt_file = data_dir / f"GNOME.{lang_pair}.{tgt_lang}"
+
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "src_file": src_file,
+                        "tgt_file": tgt_file,
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, src_file: Path, tgt_file: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        with open(src_file, "r", encoding="utf-8") as src_f, open(
+            tgt_file, "r", encoding="utf-8"
+        ) as tgt_f:
+            for idx, (src_line, tgt_line) in enumerate(zip(src_f, tgt_f)):
+                if self.config.schema == "source":
+                    yield idx, {"source": src_line.strip(), "target": tgt_line.strip()}
+                elif self.config.schema == _SEACROWD_SCHEMA:
+                    yield idx, {
+                        "id": str(idx),
+                        "text_1": src_line.strip(),
+                        "text_2": tgt_line.strip(),
+                        "text_1_name": f"source ({src_file.name.split('.')[-1]})",
+                        "text_2_name": f"target ({tgt_file.name.split('.')[-1]})",
+                    }