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senti_bahasa_rojak.py

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+import csv
+import os
+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{romadhona2022brcc,
+  title={BRCC and SentiBahasaRojak: The First Bahasa Rojak Corpus for Pretraining and Sentiment Analysis Dataset},
+  author={Romadhona, Nanda Putri and Lu, Sin-En and Lu, Bo-Han and Tsai, Richard Tzong-Han},
+  booktitle={Proceedings of the 29th International Conference on Computational Linguistics},
+  pages={4418--4428},
+  year={2022},
+  organization={International Committee on Computational Linguistics},
+  address={Taiwan},
+  email={[email protected], {alznn, lu110522028, thtsai}@g.ncu.edu.tw}
+}
+"""
+
+_DATASETNAME = "senti_bahasa_rojak"
+
+_DESCRIPTION = """\
+This dataset contains reviews for products, movies, and stocks in the Bahasa Rojak dialect,
+a popular dialect in Malaysia that consists of English, Malay, and Chinese.
+Each review is manually annotated as positive (bullish for stocks) or negative (bearish for stocks).
+Reviews are generated through data augmentation using English and Malay sentiment analysis datasets.
+"""
+
+_HOMEPAGE = "https://data.depositar.io/dataset/brcc_and_sentibahasarojak/resource/8a558f64-98ff-4922-a751-0ce2ce8447bd"
+
+_LANGUAGES = ["zlm", "eng", "cmn"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://data.depositar.io/dataset/304d1572-27d6-4549-8292-b1c8f5e9c086/resource/8a558f64-98ff-4922-a751-0ce2ce8447bd/download/BahasaRojak_Datasets.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class SentiBahasaRojakDataset(datasets.GeneratorBasedBuilder):
+    """The BRCC (Bahasa Rojak Crawled Corpus) is a novel dataset designed for the study of Bahasa Rojak,
+    a code-mixed dialect combining English, Malay, and Chinese, prevalent in Malaysia.
+    This corpus is intended for pre-training language models and sentiment analysis,
+    addressing the unique challenges of processing code-mixed languages."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    subsets = ["movie", "product", "stock"]
+
+    BUILDER_CONFIGS = [SEACrowdConfig(name=f"{_DATASETNAME}.{sub}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME}.{sub} source schema", schema="source", subset_id=f"{_DATASETNAME}.{sub}",) for sub in subsets] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}.{sub}_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME}.{sub} SEACrowd schema",
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}.{sub}",
+        )
+        for sub in subsets
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}.movie_source"
+    LABELS = ["positive", "negative"]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=self.LABELS),
+                }
+            )
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(self.LABELS)
+
+        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 = dl_manager.download_and_extract(urls)
+        data_dir = os.path.join(data_dir, "BahasaRojak Datasets", "SentiBahasaRojak")
+
+        subset = self.config.name.split(".")[-1].split("_")[0]
+        subset_dir = os.path.join(data_dir, f"SentiBahasaRojak-{subset.capitalize()}")
+        filepath = {}
+        if subset == "stock":
+            for split in ["train", "valid", "test"]:
+                filepath[split] = os.path.join(subset_dir, f"{split}_labeled.tsv")
+        else:
+            for split in ["train", "valid", "test"]:
+                filepath[split] = os.path.join(subset_dir, f"mix.{split}")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": filepath["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": filepath["test"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": filepath["valid"],
+                    "split": "valid",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        if filepath.endswith(".tsv"):
+            with open(filepath, encoding="utf-8") as file:
+                reader = csv.reader(file, delimiter="\t", quoting=csv.QUOTE_NONE)
+                for row_idx, row in enumerate(reader):
+                    if self.config.schema == "source":
+                        yield row_idx, {
+                            "text": row[0],
+                            "label": "positive" if row[1] == 1 else "negative",
+                        }
+                    elif self.config.schema == "seacrowd_text":
+                        yield row_idx, {
+                            "id": row_idx,
+                            "text": row[0],
+                            "label": "positive" if row[1] == 1 else "negative",
+                        }
+        else:
+            labelpath = filepath + ".label"
+            with open(filepath, encoding="utf-8") as file, open(labelpath, encoding="utf-8") as label_file:
+                for row_idx, (text, label) in enumerate(zip(file, label_file)):
+                    if self.config.schema == "source":
+                        yield row_idx, {
+                            "text": text.strip(),
+                            "label": label.strip(),
+                        }
+                    elif self.config.schema == "seacrowd_text":
+                        yield row_idx, {
+                            "id": row_idx,
+                            "text": text.strip(),
+                            "label": label.strip(),
+                        }