Upload indo_general_mt_en_id.py with huggingface_hub

indo_general_mt_en_id.py

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+from pathlib import Path
+from typing import List
+
+import datasets
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@inproceedings{guntara-etal-2020-benchmarking,
+    title = "Benchmarking Multidomain {E}nglish-{I}ndonesian Machine Translation",
+    author = "Guntara, Tri Wahyu  and
+      Aji, Alham Fikri  and
+      Prasojo, Radityo Eko",
+    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2020.bucc-1.6",
+    pages = "35--43",
+    language = "English",
+    ISBN = "979-10-95546-42-9",
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_DATASETNAME = "indo_general_mt_en_id"
+_DESCRIPTION = """\
+"In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,
+and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic.
+In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and
+conversation,to train and benchmark some variants of transformer-based NMT models across the domains.
+We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models,
+and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on https://github.com/gunnxx/indonesian-mt-data."
+"""
+
+_HOMEPAGE = "https://github.com/gunnxx/indonesian-mt-data"
+
+_LICENSE = "Creative Commons Attribution Share-Alike 4.0 International"
+
+_URLS = {
+    _DATASETNAME: "https://github.com/gunnxx/indonesian-mt-data/archive/refs/heads/master.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+# Dataset does not have versioning
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class IndoGeneralMTEnId(datasets.GeneratorBasedBuilder):
+    """Indonesian General Domain MT En-Id is a machine translation dataset containing English-Indonesian parallel sentences collected from the general manuscripts."""
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="indo_general_mt_en_id_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="Indonesian General Domain MT En-Id source schema",
+            schema="source",
+            subset_id="indo_general_mt_en_id",
+        ),
+        NusantaraConfig(
+            name="indo_general_mt_en_id_nusantara_t2t",
+            version=datasets.Version(_NUSANTARA_VERSION),
+            description="Indonesian General Domain MT Nusantara schema",
+            schema="nusantara_t2t",
+            subset_id="indo_general_mt_en_id",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "indo_general_mt_en_id_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "src": datasets.Value("string"),
+                    "tgt": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "nusantara_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]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = Path(dl_manager.download_and_extract(urls)) / "indonesian-mt-data-master" / "general"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": {
+                        "en": [data_dir / "train.en.0", data_dir / "train.en.1", data_dir / "train.en.2", data_dir / "train.en.3"],
+                        "id": [data_dir / "train.id.0", data_dir / "train.id.1", data_dir / "train.id.2", data_dir / "train.id.3"],
+                    }
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": {
+                        "en": [data_dir / "test.en"],
+                        "id": [data_dir / "test.id"],
+                    }
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": {
+                        "en": [data_dir / "valid.en"],
+                        "id": [data_dir / "valid.id"],
+                    }
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: dict):
+
+        data_en = None
+        for file in filepath["en"]:
+            if data_en is None:
+                data_en = open(file, "r").readlines()
+            else:
+                data_en += open(file, "r").readlines()
+
+        data_id = None
+        for file in filepath["id"]:
+            if data_id is None:
+                data_id = open(file, "r").readlines()
+            else:
+                data_id += open(file, "r").readlines()
+
+        data_en = list(map(str.strip, data_en))
+        data_id = list(map(str.strip, data_id))
+
+        if self.config.schema == "source":
+            for id, (src, tgt) in enumerate(zip(data_en, data_id)):
+                row = {
+                    "id": str(id),
+                    "src": src,
+                    "tgt": tgt,
+                }
+                yield id, row
+        elif self.config.schema == "nusantara_t2t":
+            for id, (src, tgt) in enumerate(zip(data_en, data_id)):
+                row = {
+                    "id": str(id),
+                    "text_1": src,
+                    "text_2": tgt,
+                    "text_1_name": "eng",
+                    "text_2_name": "ind",
+                }
+                yield id, row
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")