Upload shopee_reviews_tagalog.py with huggingface_hub

shopee_reviews_tagalog.py

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+import csv
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+from datasets.download.download_manager import DownloadManager
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """
+@article{riegoenhancement,
+  title={Enhancement to Low-Resource Text Classification via Sequential Transfer Learning},
+  author={Riego, Neil Christian R. and Villarba, Danny Bell and Sison, Ariel Antwaun Rolando C. and Pineda, Fernandez C. and Lagunzad, Herminiño C.}
+  journal={United International Journal for Research & Technology},
+  volume={04},
+  issue={08},
+  pages={72--82}
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["fil", "tgl"]
+_DATASETNAME = "shopee_reviews_tagalog"
+_DESCRIPTION = """\
+The Shopee reviews dataset is constructed by randomly taking 2100 training
+samples and 450 samples for testing and validation for each review star from 1
+to 5. In total, there are 10500 training samples and 2250 each in validation and
+testing samples.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/scaredmeow/shopee-reviews-tl-stars"
+_LICENSE = Licenses.MPL_2_0.value
+_URLS = {
+    "train": "https://huggingface.co/datasets/scaredmeow/shopee-reviews-tl-stars/resolve/main/train.csv",
+    "validation": "https://huggingface.co/datasets/scaredmeow/shopee-reviews-tl-stars/resolve/main/validation.csv",
+    "test": "https://huggingface.co/datasets/scaredmeow/shopee-reviews-tl-stars/resolve/main/test.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class ShopeeReviewsTagalogDataset(datasets.GeneratorBasedBuilder):
+    """Shopee Reviews Tagalog dataset from https://huggingface.co/datasets/scaredmeow/shopee-reviews-tl-stars"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "text"
+    # "N" means N+1 star(s) review, e.g. "2" means 3 stars review
+    LABEL_CLASSES = ["0", "1", "2", "3", "4"]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        # The SEACrowd schema and the source schema is the same
+        features = schemas.text_features(self.LABEL_CLASSES)
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
+        data_files = {
+            "train": Path(dl_manager.download_and_extract(_URLS["train"])),
+            "validation": Path(dl_manager.download_and_extract(_URLS["validation"])),
+            "test": Path(dl_manager.download_and_extract(_URLS["test"])),
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_files["train"], "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_files["validation"], "split": "validation"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_files["test"], "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yield (idx, example) tuples"""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file,
+                quotechar='"',
+                delimiter=",",
+                quoting=csv.QUOTE_ALL,
+                skipinitialspace=True,
+            )
+
+            next(csv_reader)
+            for idx, row in enumerate(csv_reader):
+                text, label = row
+                example = {"id": idx, "text": text, "label": label}
+                yield idx, example