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muse.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.
+
+"""
+Contains 110 large-scale ground-truth bilingual dictionaries created and released by Meta using an internal translation tool.
+The dictionaries account for polysemy. The data comprises of a train and test split of 5000 and 1500 unique source words, as well as a larger set of up to 100k pairs.
+It comprises of Europeans languages in every direction, and SEA languages to and from English.
+"""
+
+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 Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{lample2018word,
+  title={Word translation without parallel data},
+  author={Lample, Guillaume and Conneau, Alexis and Ranzato, Marc'Aurelio and Denoyer, Ludovic and J{\'e}gou, Herv{\'e}},
+  booktitle={International Conference on Learning Representations},
+  year={2018}}
+}
+"""
+
+_DATASETNAME = "muse"
+
+_DESCRIPTION = """\
+Contains 110 large-scale ground-truth bilingual dictionaries created and released by Meta using an internal translation tool.
+The dictionaries account for polysemy. The data comprises of a train and test split of 5000 and 1500 unique source words, as well as a larger set of up to 100k pairs.
+It comprises of Europeans languages in every direction, and SEA languages to and from English.
+"""
+
+_HOMEPAGE = "https://github.com/facebookresearch/MUSE#ground-truth-bilingual-dictionaries"
+
+_LANGUAGES = ["tgl", "ind", "zlm", "tha", "vie"]
+
+_LICENSE = Licenses.CC_BY_NC_ND_4_0.value
+
+_LOCAL = False
+
+_TRAIN_URL_TEMPLATE = "https://dl.fbaipublicfiles.com/arrival/dictionaries/{src}-{tgt}.0-5000.txt"
+_TEST_URL_TEMPLATE = "https://dl.fbaipublicfiles.com/arrival/dictionaries/{src}-{tgt}.5000-6500.txt"
+
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+configs = {
+    "tgl": ["eng"],
+    "ind": ["eng"],
+    "zlm": ["eng"],
+    "tha": ["eng"],
+    "vie": ["eng"],
+    "eng": ["tha", "vie", "tgl", "zlm", "ind"],
+}
+
+langid_dict = {
+    "eng": "en",
+    "tgl": "tl",
+    "ind": "id",
+    "zlm": "ms",
+    "tha": "th",
+    "vie": "vi",
+}
+
+
+class MUSEDataset(datasets.GeneratorBasedBuilder):
+    """Large-scale ground-truth bilingual dictionaries"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = (
+        [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_source",
+                version=SOURCE_VERSION,
+                description=f"{_DATASETNAME} source schema",
+                schema="source",
+                subset_id=f"{_DATASETNAME}_tgl_eng",
+            ),
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_seacrowd_t2t",
+                version=SEACROWD_VERSION,
+                description=f"{_DATASETNAME} SEACrowd schema",
+                schema="seacrowd_t2t",
+                subset_id=f"{_DATASETNAME}_tgl_eng",
+            ),
+        ]
+        + [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{src_lang}_{tgt_lang}_source",
+                version=datasets.Version(_SOURCE_VERSION),
+                description=f"{_DATASETNAME} source schema",
+                schema="source",
+                subset_id=f"{_DATASETNAME}_{src_lang}_{tgt_lang}",
+            )
+            for src_lang in configs
+            for tgt_lang in configs[src_lang]
+        ]
+        + [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{src_lang}_{tgt_lang}_seacrowd_t2t",
+                version=datasets.Version(_SOURCE_VERSION),
+                description=f"{_DATASETNAME} SEACrowd schema",
+                schema="seacrowd_t2t",
+                subset_id=f"{_DATASETNAME}_{src_lang}_{tgt_lang}",
+            )
+            for src_lang in configs
+            for tgt_lang in configs[src_lang]
+        ]
+    )
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "src_text": datasets.Value("string"),
+                    "tgt_text": 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]:
+
+        _, src_lang, tgt_lang = self.config.subset_id.split("_")
+        train_url = _TRAIN_URL_TEMPLATE.format(src=langid_dict[src_lang], tgt=langid_dict[tgt_lang])
+        test_url = _TEST_URL_TEMPLATE.format(src=langid_dict[src_lang], tgt=langid_dict[tgt_lang])
+
+        train_file = dl_manager.download_and_extract(train_url)
+        test_file = dl_manager.download_and_extract(test_url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "src_lang": src_lang,
+                    "tgt_lang": tgt_lang,
+                    "filepath": train_file,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "src_lang": src_lang,
+                    "tgt_lang": tgt_lang,
+                    "filepath": test_file,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, src_lang: str, tgt_lang: str, filepath: Path) -> Tuple[int, Dict]:
+        if self.config.schema == "source":
+            for row_id, line in enumerate(open(filepath)):
+                src_text, tgt_text = line.strip().split("\t")
+                yield row_id, {"id": row_id, "src_text": src_text, "tgt_text": tgt_text}
+
+        elif self.config.schema == "seacrowd_t2t":
+            for row_id, line in enumerate(open(filepath)):
+                src_text, tgt_text = line.strip().split("\t")
+                yield row_id, {
+                    "id": row_id,
+                    "text_1": src_text,
+                    "text_2": tgt_text,
+                    "text_1_name": src_lang,
+                    "text_2_name": tgt_lang,
+                }