Upload wongnai_reviews.py with huggingface_hub

wongnai_reviews.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
+
+# no BibTeX citation
+_CITATION = ""
+
+_DATASETNAME = "wongnai_reviews"
+
+_DESCRIPTION = """
+Wongnai features over 200,000 restaurants, beauty salons, and spas across Thailand on its platform, with detailed
+information about each merchant and user reviews. Its over two million registered users can search for what’s top rated
+in Bangkok, follow their friends, upload photos, and do quick write-ups about the places they visit. Each write-up
+(review) also comes with a rating score ranging from 1-5 stars. The task here is to create a rating prediction model
+using only textual information.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/wongnai_reviews"
+
+_LANGUAGES = ["tha"]
+
+_LICENSE = Licenses.LGPL_3_0.value
+
+_LOCAL = False
+
+_URLS = {_DATASETNAME: "https://archive.org/download/wongnai_reviews/wongnai_reviews_withtest.zip"}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+_CLASSES = ["1", "2", "3", "4", "5"]
+
+
+class WongnaiReviewsDataset(datasets.GeneratorBasedBuilder):
+    """WongnaiReviews consists reviews for over 200,000 restaurants, beauty salons, and spas across Thailand."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    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_text",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_text",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "review_body": datasets.Value("string"),
+                    "star_rating": datasets.ClassLabel(names=_CLASSES),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(label_names=_CLASSES)
+
+        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)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": os.path.join(data_dir, "w_review_train.csv"), "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, "w_review_test.csv"), "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        if self.config.schema == "source":
+            with open(filepath, encoding="utf-8") as f:
+                spamreader = csv.reader(f, delimiter=";", quotechar='"')
+                for i, row in enumerate(spamreader):
+                    yield i, {"review_body": row[0], "star_rating": row[1]}
+
+        elif self.config.schema == "seacrowd_text":
+            with open(filepath, encoding="utf-8") as f:
+                spamreader = csv.reader(f, delimiter=";", quotechar='"')
+                for i, row in enumerate(spamreader):
+                    yield i, {"id": str(i), "text": row[0], "label": _CLASSES[int(row[1].strip()) - 1]}