Upload minangnlp_mt.py with huggingface_hub

minangnlp_mt.py

@@ -0,0 +1,159 @@
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@inproceedings{koto-koto-2020-towards,
+    title = "Towards Computational Linguistics in {M}inangkabau Language: Studies on Sentiment Analysis and Machine Translation",
+    author = "Koto, Fajri  and
+      Koto, Ikhwan",
+    booktitle = "Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation",
+    month = oct,
+    year = "2020",
+    address = "Hanoi, Vietnam",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.paclic-1.17",
+    pages = "138--148",
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["min", "ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_DATASETNAME = "minangnlp_mt"
+_DESCRIPTION = """\
+In this work, we create Minangkabau–Indonesian (MIN-ID) parallel corpus by using Wikipedia. We obtain 224,180 Minangkabau and
+510,258 Indonesian articles, and align documents through title matching, resulting in 111,430 MINID document pairs.
+After that, we do sentence segmentation based on simple punctuation heuristics and obtain 4,323,315 Minangkabau sentences. We
+then use the bilingual dictionary to translate Minangkabau article (MIN) into Indonesian language (ID'). Sentence alignment is conducted using
+ROUGE-1 (F1) score (unigram overlap) (Lin, 2004) between ID’ and ID, and we pair each MIN sentencewith an ID sentence based on the highest ROUGE1.
+We then discard sentence pairs with a score of less than 0.5 to result in 345,146 MIN-ID parallel sentences.
+We observe that the sentence pattern in the collection is highly repetitive (e.g. 100k sentences are about biological term definition). Therefore,
+we conduct final filtering based on top-1000 trigram by iteratively discarding sentences until the frequency of each trigram equals to 100. Finally, we
+obtain 16,371 MIN-ID parallel sentences and conducted manual evaluation by asking two native Minangkabau speakers to assess the adequacy and
+fluency (Koehn and Monz, 2006). The human judgement is based on scale 1–5 (1 means poor quality and 5 otherwise) and conducted against 100 random
+samples. We average the weights of two annotators before computing the overall score, and we achieve 4.98 and 4.87 for adequacy and fluency respectively.
+This indicates that the resulting corpus is high-quality for machine translation training.
+"""
+
+_HOMEPAGE = "https://github.com/fajri91/minangNLP"
+_LICENSE = "MIT"
+_URLS = {
+    _DATASETNAME: "https://github.com/fajri91/minangNLP/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 MinangNLPmt(datasets.GeneratorBasedBuilder):
+    """16,371-size parallel Minangkabau-Indonesian sentence pairs."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="minangnlp_mt_source",
+            version=SOURCE_VERSION,
+            description="MinangNLP Machine Translation source schema",
+            schema="source",
+            subset_id="minangnlp_mt",
+        ),
+        NusantaraConfig(
+            name="minangnlp_mt_nusantara_t2t",
+            version=NUSANTARA_VERSION,
+            description="MinangNLP Machine Translation Nusantara schema",
+            schema="nusantara_t2t",
+            subset_id="minangnlp_mt",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "minangnlp_mt_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        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]:
+        """Returns SplitGenerators."""
+        urls = _URLS[_DATASETNAME]
+        data_dir = Path(dl_manager.download_and_extract(urls)) / "minangNLP-master" / "translation" / "wiki_data"
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "src_filepath": data_dir / "src_train.txt",
+                    "tgt_filepath": data_dir / "tgt_train.txt",
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "src_filepath": data_dir / "src_test.txt",
+                    "tgt_filepath": data_dir / "tgt_test.txt",
+                    "split": "test",
+                },
+            ),
+            # Dataset has a secondary test split
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     gen_kwargs={
+            #         "src_filepath": data_dir / "src_test_sent.txt",
+            #         "tgt_filepath": data_dir / "tgt_test_sent.txt",
+            #         "split": "test_sent",
+            #     },
+            # ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "src_filepath": data_dir / "src_dev.txt",
+                    "tgt_filepath": data_dir / "tgt_dev.txt",
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, src_filepath: Path, tgt_filepath: Path, split: str) -> Tuple[int, Dict]:
+        with open(src_filepath, encoding="utf-8") as fsrc, open(tgt_filepath, encoding="utf-8") as ftgt:
+            for idx, pair in enumerate(zip(fsrc, ftgt)):
+                src, tgt = pair
+                if self.config.schema == "source":
+                    row = {
+                        "id": str(idx),
+                        "src": src,
+                        "tgt": tgt,
+                    }
+                    yield idx, row
+
+                elif self.config.schema == "nusantara_t2t":
+                    row = {
+                        "id": str(idx),
+                        "text_1": src,
+                        "text_2": tgt,
+                        "text_1_name": "min",
+                        "text_2_name": "id",
+                    }
+                    yield idx, row