Upload news_en_id.py with huggingface_hub

news_en_id.py

@@ -0,0 +1,127 @@
+from pathlib import Path
+from typing import List
+
+import datasets
+import json
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks, DEFAULT_SOURCE_VIEW_NAME, DEFAULT_NUSANTARA_VIEW_NAME
+
+_DATASETNAME = "news_en_id"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
+
+_LANGUAGES = ["ind", "eng"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+_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",
+}
+"""
+
+_DESCRIPTION = """\
+News En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the news. The news dataset is collected from multiple sources: Pan Asia Networking Localization (PANL), Bilingual BBC news articles, Berita Jakarta, and GlobalVoices. We split the dataset and use 75% as the training set, 10% as the validation set, and 15% as the test set. Each of the datasets is evaluated in both directions, i.e., English to Indonesian (En → Id) and Indonesian to English (Id → En) translations.
+"""
+
+_HOMEPAGE = "https://github.com/gunnxx/indonesian-mt-data"
+
+_LICENSE = "Creative Commons Attribution Share-Alike 4.0 International"
+
+_URLs = {"indonlg": "https://storage.googleapis.com/babert-pretraining/IndoNLG_finals/downstream_task/downstream_task_datasets.zip"}
+
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class NewsEnId(datasets.GeneratorBasedBuilder):
+    """Bible Su-Id is a machine translation dataset containing Indonesian-Sundanese parallel sentences collected from the bible.."""
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="news_en_id_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="News En-Id source schema",
+            schema="source",
+            subset_id="news_en_id",
+        ),
+        NusantaraConfig(
+            name="news_en_id_nusantara_t2t",
+            version=datasets.Version(_NUSANTARA_VERSION),
+            description="News En-Id Nusantara schema",
+            schema="nusantara_t2t",
+            subset_id="news_en_id",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "news_en_id_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features({"id": datasets.Value("string"), "text": datasets.Value("string"), "label": 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]:
+        base_path = Path(dl_manager.download_and_extract(_URLs["indonlg"])) / "IndoNLG_downstream_tasks" / "MT_IMD_NEWS"
+        data_files = {
+            "train": base_path / "train_preprocess.json",
+            "validation": base_path / "valid_preprocess.json",
+            "test": base_path / "test_preprocess.json",
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_files["validation"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_files["test"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path):
+        data = json.load(open(filepath, "r"))
+        if self.config.schema == "source":
+            for row in data:
+                ex = {"id": row["id"], "text": row["text"], "label": row["label"]}
+                yield row["id"], ex
+        elif self.config.schema == "nusantara_t2t":
+            for row in data:
+                ex = {
+                    "id": row["id"],
+                    "text_1": row["text"],
+                    "text_2": row["label"],
+                    "text_1_name": "eng",
+                    "text_2_name": "ind",
+                }
+                yield row["id"], ex
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")