# coding=utf-8 # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. 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{van2018uit, title={UIT-VSFC: Vietnamese students’ feedback corpus for sentiment analysis}, author={Van Nguyen, Kiet and Nguyen, Vu Duc and Nguyen, Phu XV and Truong, Tham TH and Nguyen, Ngan Luu-Thuy}, booktitle={2018 10th international conference on knowledge and systems engineering (KSE)}, pages={19--24}, year={2018}, organization={IEEE} } """ _DATASETNAME = "uit_vsfc" _DESCRIPTION = """\ This corpus consists of student feedback obtained from end-of-semester surveys at a Vietnamese university. Feedback is classified into four possible topics: lecturer, curriculum, facility or others. Feedback is also labeled as one of three sentiment polarities: positive, negative or neutral. """ _HOMEPAGE = "https://drive.google.com/drive/folders/1HooABJyrddVGzll7fgkJ6VzkG_XuWfRu" _LANGUAGES = ["vie"] _LICENSE = Licenses.UNKNOWN.value _LOCAL = False _URLS = { "train": { "sentences": "https://drive.google.com/uc?id=1nzak5OkrheRV1ltOGCXkT671bmjODLhP&export=download", "sentiments": "https://drive.google.com/uc?id=1ye-gOZIBqXdKOoi_YxvpT6FeRNmViPPv&export=download", "topics": "https://drive.google.com/uc?id=14MuDtwMnNOcr4z_8KdpxprjbwaQ7lJ_C&export=download", }, "validation": { "sentences": "https://drive.google.com/uc?id=1sMJSR3oRfPc3fe1gK-V3W5F24tov_517&export=download", "sentiments": "https://drive.google.com/uc?id=1GiY1AOp41dLXIIkgES4422AuDwmbUseL&export=download", "topics": "https://drive.google.com/uc?id=1DwLgDEaFWQe8mOd7EpF-xqMEbDLfdT-W&export=download", }, "test": { "sentences": "https://drive.google.com/uc?id=1aNMOeZZbNwSRkjyCWAGtNCMa3YrshR-n&export=download", "sentiments": "https://drive.google.com/uc?id=1vkQS5gI0is4ACU58-AbWusnemw7KZNfO&export=download", "topics": "https://drive.google.com/uc?id=1_ArMpDguVsbUGl-xSMkTF_p5KpZrmpSB&export=download", }, } _SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS, Tasks.TOPIC_MODELING] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class UITVSFCDataset(datasets.GeneratorBasedBuilder): """This corpus consists of student feedback obtained from end-of-semester surveys at a Vietnamese university. Feedback is classified into four possible topics: lecturer, curriculum, facility or others. Feedback is also labeled as one of three sentiment polarities: positive, negative or neutral.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) SENTIMENT_LABEL_CLASSES = ["positive", "negative", "neutral"] TOPIC_LABEL_CLASSES = ["lecturer", "training_program", "others", "facility"] SEACROWD_SCHEMA_NAME = "text" BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_sentiment_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=_DATASETNAME, ), SEACrowdConfig( name=f"{_DATASETNAME}_topic_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=_DATASETNAME, ), SEACrowdConfig( name=f"{_DATASETNAME}_sentiment_seacrowd_{SEACROWD_SCHEMA_NAME}", version=SEACROWD_VERSION, description=f"{_DATASETNAME} SEACrowd schema", schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}", subset_id=_DATASETNAME, ), SEACrowdConfig( name=f"{_DATASETNAME}_topic_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: if self.config.schema == "source": features = datasets.Features( { "sentence": datasets.Value("string"), "sentiment": datasets.ClassLabel(names=self.SENTIMENT_LABEL_CLASSES), "topic": datasets.ClassLabel(names=self.TOPIC_LABEL_CLASSES), } ) elif self.config.name == f"{_DATASETNAME}_sentiment_seacrowd_{self.SEACROWD_SCHEMA_NAME}": features = schemas.text_features(self.SENTIMENT_LABEL_CLASSES) elif self.config.name == f"{_DATASETNAME}_topic_seacrowd_{self.SEACROWD_SCHEMA_NAME}": features = schemas.text_features(self.TOPIC_LABEL_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]: data_dir = dl_manager.download(_URLS) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "sentences_path": data_dir["train"]["sentences"], "sentiments_path": data_dir["train"]["sentiments"], "topics_path": data_dir["train"]["topics"], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "sentences_path": data_dir["test"]["sentences"], "sentiments_path": data_dir["test"]["sentiments"], "topics_path": data_dir["test"]["topics"], "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "sentences_path": data_dir["validation"]["sentences"], "sentiments_path": data_dir["validation"]["sentiments"], "topics_path": data_dir["validation"]["topics"], "split": "dev", }, ), ] def _generate_examples(self, sentences_path: Path, sentiments_path: Path, topics_path: Path, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" if self.config.schema == "source": with open(sentences_path, encoding="utf-8") as sentences, open(sentiments_path, encoding="utf-8") as sentiments, open(topics_path, encoding="utf-8") as topics: for key, (sentence, sentiment, topic) in enumerate(zip(sentences, sentiments, topics)): yield key, { "sentence": sentence.strip(), "sentiment": int(sentiment.strip()), "topic": int(topic.strip()), } elif self.config.name == f"{_DATASETNAME}_sentiment_seacrowd_{self.SEACROWD_SCHEMA_NAME}": with open(sentences_path, encoding="utf-8") as sentences, open(sentiments_path, encoding="utf-8") as sentiments: for key, (sentence, sentiment) in enumerate(zip(sentences, sentiments)): yield key, {"id": str(key), "text": sentence.strip(), "label": int(sentiment.strip())} elif self.config.name == f"{_DATASETNAME}_topic_seacrowd_{self.SEACROWD_SCHEMA_NAME}": with open(sentences_path, encoding="utf-8") as sentences, open(topics_path, encoding="utf-8") as topics: for key, (sentence, topic) in enumerate(zip(sentences, topics)): yield key, { "id": str(key), "text": sentence.strip(), "label": int(topic.strip()), }