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import csv |
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import os |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Licenses, Tasks |
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_CITATION = "" |
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_DATASETNAME = "wongnai_reviews" |
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_DESCRIPTION = """ |
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Wongnai features over 200,000 restaurants, beauty salons, and spas across Thailand on its platform, with detailed |
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information about each merchant and user reviews. Its over two million registered users can search for what’s top rated |
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in Bangkok, follow their friends, upload photos, and do quick write-ups about the places they visit. Each write-up |
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(review) also comes with a rating score ranging from 1-5 stars. The task here is to create a rating prediction model |
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using only textual information. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/wongnai_reviews" |
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_LANGUAGES = ["tha"] |
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_LICENSE = Licenses.LGPL_3_0.value |
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_LOCAL = False |
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_URLS = {_DATASETNAME: "https://archive.org/download/wongnai_reviews/wongnai_reviews_withtest.zip"} |
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_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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_CLASSES = ["1", "2", "3", "4", "5"] |
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class WongnaiReviewsDataset(datasets.GeneratorBasedBuilder): |
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"""WongnaiReviews consists reviews for over 200,000 restaurants, beauty salons, and spas across Thailand.""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_source", |
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version=SOURCE_VERSION, |
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description=f"{_DATASETNAME} source schema", |
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schema="source", |
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subset_id=_DATASETNAME, |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_seacrowd_text", |
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version=SEACROWD_VERSION, |
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description=f"{_DATASETNAME} SEACrowd schema", |
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schema="seacrowd_text", |
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subset_id=_DATASETNAME, |
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), |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"review_body": datasets.Value("string"), |
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"star_rating": datasets.ClassLabel(names=_CLASSES), |
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} |
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) |
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elif self.config.schema == "seacrowd_text": |
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features = schemas.text_features(label_names=_CLASSES) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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urls = _URLS[_DATASETNAME] |
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data_dir = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": os.path.join(data_dir, "w_review_train.csv"), "split": "train"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": os.path.join(data_dir, "w_review_test.csv"), "split": "test"}, |
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), |
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] |
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: |
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if self.config.schema == "source": |
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with open(filepath, encoding="utf-8") as f: |
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spamreader = csv.reader(f, delimiter=";", quotechar='"') |
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for i, row in enumerate(spamreader): |
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yield i, {"review_body": row[0], "star_rating": row[1]} |
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elif self.config.schema == "seacrowd_text": |
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with open(filepath, encoding="utf-8") as f: |
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spamreader = csv.reader(f, delimiter=";", quotechar='"') |
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for i, row in enumerate(spamreader): |
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yield i, {"id": str(i), "text": row[0], "label": _CLASSES[int(row[1].strip()) - 1]} |
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