Upload id_google_play_review.py with huggingface_hub

id_google_play_review.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@misc{
+   research, 
+   title={Jakartaresearch/google-play-review · datasets at hugging face}, 
+   url={https://huggingface.co/datasets/jakartaresearch/google-play-review},
+   author={Research, Jakarta AI}
+} 
+"""
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+
+_DATASETNAME = "id_google_play_review"
+_DESCRIPTION = """\
+Indonesian Google Play Review, dataset scrapped from e-commerce app on Google Play for sentiment analysis.
+Total number of data: 10041 (train: 7028, validation: 3012). Provided by Jakarta AI Research.
+"""
+
+_HOMEPAGE = "https://github.com/jakartaresearch/hf-datasets/tree/main/google-play-review/google-play-review"
+_LICENSE = "CC-BY 4.0"
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://media.githubusercontent.com/media/jakartaresearch/hf-datasets/main/google-play-review/google-play-review/train.csv",
+        "valid": "https://media.githubusercontent.com/media/jakartaresearch/hf-datasets/main/google-play-review/google-play-review/validation.csv",
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class IDGooglePlayReview(datasets.GeneratorBasedBuilder):
+    """
+    Indonesian Google Play Review is a dataset containing reviews from Google Play Indonesia, used for sentiment
+    analysis.
+    The language content is mainly Indonesian, however beware of context-switching (some sentences are partly or
+    entirely in English).
+    The available labels:
+        label: ['pos', 'neg'] for source and seacrowd_text scheme
+        stars: [1, 2, 3, 4, 5] for source
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="id_google_play_review_source",
+            version=SOURCE_VERSION,
+            description="id_google_play_review source schema",
+            schema="source",
+            subset_id="id_google_play_review",
+        ),
+        SEACrowdConfig(
+            name="id_google_play_review_posneg_source",
+            version=SOURCE_VERSION,
+            description="id_google_play_review source schema",
+            schema="source",
+            subset_id="id_google_play_review_posneg",
+        ),
+        SEACrowdConfig(
+            name="id_google_play_review_seacrowd_text",
+            version=SEACROWD_VERSION,
+            description="id_google_play_review Nusantara schema, 1-5 stars rating only (for pos/neg labels, please use the subset_id \"id_google_play_review_posneg\")",
+            schema="seacrowd_text",
+            subset_id="id_google_play_review",
+        ),
+        SEACrowdConfig(
+            name="id_google_play_review_posneg_seacrowd_text",
+            version=SEACROWD_VERSION,
+            description="id_google_play_review Nusantara schema, pos/neg label only",
+            schema="seacrowd_text",
+            subset_id="id_google_play_review_posneg",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "id_google_play_review_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        # Create the source schema; this schema will keep all keys/information/labels as close to the original dataset
+        # as possible.
+
+        # You can arbitrarily nest lists and dictionaries.
+        # For iterables, use lists over tuples or `datasets.Sequence`
+
+        if self.config.schema == "source":
+            features = datasets.Features({
+                "text": datasets.Value("string"),
+                "label": datasets.Value("string"),
+                "stars": datasets.Value("int8")
+            })
+        elif self.config.schema == "seacrowd_text":
+            if self.config.subset_id == "id_google_play_review_posneg":
+                features = schemas.text_features(["pos", "neg"])
+            elif self.config.subset_id == "id_google_play_review":
+                features = schemas.text_features(["1", "2", "3", "4", "5"])
+            else:
+                raise ValueError(f"Invalid config: {self.config.name}")
+
+        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]
+        train_data_path = Path(dl_manager.download(urls["train"]))
+        valid_data_path = Path(dl_manager.download(urls["valid"]))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": train_data_path, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": valid_data_path, "split": "valid"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        df = pd.read_csv(filepath, sep=",").reset_index()
+        for row in df.itertuples(index=False):
+            if self.config.schema == "source":
+                example = {"text": row.text, "label": row.label, "stars": row.stars}
+                yield row.index, example
+            elif self.config.schema == "seacrowd_text":
+                if self.config.subset_id == "id_google_play_review_posneg":
+                    example = {"id": row.index, "text": row.text, "label": row.label}
+                elif self.config.subset_id == "id_google_play_review":
+                    example = {"id": row.index, "text": row.text, "label": str(row.stars)}
+                else:
+                    raise ValueError(f"Invalid config: {self.config.name}")
+                yield row.index, example