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parallel_id_nyo.py

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+from pathlib import Path
+from typing import List
+
+import datasets
+import pandas as pd
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import (
+    DEFAULT_NUSANTARA_VIEW_NAME,
+    DEFAULT_SOURCE_VIEW_NAME,
+    Tasks,
+)
+
+_DATASETNAME = "parallel_id_nyo"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
+
+_LOCAL = False
+_LANGUAGES = ["ind", "abl"]
+
+_CITATION = """\
+@article{Abidin_2021,
+doi = {10.1088/1742-6596/1751/1/012036},
+url = {https://dx.doi.org/10.1088/1742-6596/1751/1/012036},
+year = {2021},
+month = {jan},
+publisher = {IOP Publishing},
+volume = {1751},
+number = {1},
+pages = {012036},
+author = {Z Abidin and  Permata and I Ahmad and  Rusliyawati},
+title = {Effect of mono corpus quantity on statistical machine translation
+Indonesian - Lampung dialect of nyo},
+journal = {Journal of Physics: Conference Series},
+abstract = {Lampung Province is located on the island of Sumatera. For the
+immigrants in Lampung, they have difficulty in
+communicating with the indigenous people of Lampung. As an alternative, both
+immigrants and the indigenous people of Lampung speak Indonesian.
+This research aims to build a language model from Indonesian language and a
+ translation model from the Lampung language dialect of nyo, both models will
+ be combined in a Moses decoder.
+This research focuses on observing the effect of adding mono corpus to the
+ experimental statistical machine translation of
+ Indonesian - Lampung dialect of nyo.
+This research uses 3000 pair parallel corpus in Indonesia language and
+Lampung language dialect of nyo as source language
+and uses 3000 mono corpus sentences in Lampung language
+dialect of nyo as target language. The results showed that the accuracy
+value in bilingual evalution under-study score when using 1000 sentences,
+2000 sentences, 3000 sentences mono corpus
+show the accuracy value of the bilingual evaluation under-study,
+respectively, namely 40.97 %, 41.80 % and 45.26 %.}
+}
+"""
+
+_DESCRIPTION = """\
+Dataset that contains Indonesian - Lampung language pairs.
+
+The original data should contains 3000 rows, unfortunately,
+not all of the instances in the original data is aligned perfectly.
+Thus, this data only have the aligned ones, which only contain 1727 pairs.
+"""
+
+_HOMEPAGE = "https://drive.google.com/drive/folders/1oNpybrq5OJ_4Ne0HS5w9eHqnZlZASpmC?usp=sharing"
+
+_LICENSE = "Unknown"
+
+# WARNING: Incomplete data!
+_URLs = {
+    "train": "https://raw.githubusercontent.com/haryoa/IndoData/main/data_ind_lampung_1729_line.csv"
+}
+
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+COL_INDONESIA = "indo"
+COL_LAMPUNG = "lampung"
+
+
+class ParallelIdNyo(datasets.GeneratorBasedBuilder):
+    """Dataset that contains Indonesian - Lampung language pairs."""
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="parallel_id_nyo_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="Parallel Id-Nyo source schema",
+            schema="source",
+            subset_id="parallel_id_nyo",
+        ),
+        NusantaraConfig(
+            name="parallel_id_nyo_nusantara_t2t",
+            version=datasets.Version(_NUSANTARA_VERSION),
+            description="Parallel Id-Nyo Nusantara schema",
+            schema="nusantara_t2t",
+            subset_id="parallel_id_nyo",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "ted_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]:
+        path = Path(dl_manager.download_and_extract(_URLs["train"]))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": path},
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path):
+
+        df = pd.read_csv(filepath).reset_index()
+
+        if self.config.schema == "source":
+            for idx, row in df.iterrows():
+                ex = {
+                    "id": str(idx),
+                    "text": str(row[COL_INDONESIA]).rstrip(),
+                    "label": str(row[COL_LAMPUNG]).rstrip(),
+                }
+                yield idx, ex
+        elif self.config.schema == "nusantara_t2t":
+            for idx, row in df.iterrows():
+                ex = {
+                    "id": str(idx),
+                    "text_1": str(row[COL_INDONESIA]).rstrip(),
+                    "text_2": str(row[COL_LAMPUNG]).rstrip(),
+                    "text_1_name": "ind",
+                    "text_2_name": "abl",  # code name for lampung Nyo
+                }
+                yield idx, ex
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+
+if __name__ == "__main__":
+    datasets.load_dataset(__file__)