| import os | |
| from pathlib import Path | |
| from typing import Dict, List, Tuple | |
| import datasets | |
| from nusacrowd.nusa_datasets.id_short_answer_grading.utils.id_short_answer_grading_utils import \ | |
| create_saintek_and_soshum_dataset | |
| from nusacrowd.utils import schemas | |
| from nusacrowd.utils.configs import NusantaraConfig | |
| from nusacrowd.utils.constants import Tasks | |
| _CITATION = """\ | |
| @article{ | |
| JLK, | |
| author = {Muh Haidir and Ayu Purwarianti}, | |
| title = { Short Answer Grading Using Contextual Word Embedding and Linear Regression}, | |
| journal = {Jurnal Linguistik Komputasional}, | |
| volume = {3}, | |
| number = {2}, | |
| year = {2020}, | |
| keywords = {}, | |
| abstract = {Abstract—One of the obstacles in an efficient MOOC is the evaluation of student answers, including the short answer grading which requires large effort from instructors to conduct it manually. | |
| Thus, NLP research in short answer grading has been conducted in order to support the automation, using several techniques such as rule | |
| and machine learning based. Here, we’ve conducted experiments on deep learning based short answer grading to compare the answer | |
| representation and answer assessment method. In the answer representation, we compared word embedding and sentence embedding models | |
| such as BERT, and its modification. In the answer assessment method, we use linear regression. There are 2 datasets that we used, available | |
| English short answer grading dataset with 80 questions and 2442 to get the best configuration for model and Indonesian short answer grading | |
| dataset with 36 questions and 9165 short answers as testing data. Here, we’ve collected Indonesian short answers for Biology and Geography | |
| subjects from 534 respondents where the answer grading was done by 7 experts. The best root mean squared error for both dataset was achieved | |
| by using BERT pretrained, 0.880 for English dataset dan 1.893 for Indonesian dataset.}, | |
| issn = {2621-9336}, pages = {54--61}, doi = {10.26418/jlk.v3i2.38}, | |
| url = {https://inacl.id/journal/index.php/jlk/article/view/38} | |
| }\ | |
| """ | |
| _DATASETNAME = "id_short_answer_grading" | |
| _DESCRIPTION = """\ | |
| Indonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts.\ | |
| """ | |
| _HOMEPAGE = "https://github.com/AgeMagi/tugas-akhir" | |
| _LOCAL = False | |
| _LANGUAGES = ["ind"] | |
| _LICENSE = "Unknown" | |
| _URLS = { | |
| "saintek": { | |
| "train": { | |
| "question": "https://raw.githubusercontent.com/AgeMagi/tugas-akhir/master/data/question-saintek.csv", | |
| "score": "https://raw.githubusercontent.com/AgeMagi/tugas-akhir/master/data/score-saintek.csv", | |
| }, | |
| "test": { | |
| "question": "https://raw.githubusercontent.com/AgeMagi/tugas-akhir/master/data/question-saintek-test.csv", | |
| "score": "https://raw.githubusercontent.com/AgeMagi/tugas-akhir/master/data/score-saintek-test.csv", | |
| }, | |
| }, | |
| "soshum": { | |
| "train": { | |
| "question": "https://raw.githubusercontent.com/AgeMagi/tugas-akhir/master/data/question-soshum.csv", | |
| "score": "https://raw.githubusercontent.com/AgeMagi/tugas-akhir/master/data/score-soshum.csv", | |
| }, | |
| "test": { | |
| "question": "https://raw.githubusercontent.com/AgeMagi/tugas-akhir/master/data/question-soshum-test.csv", | |
| "score": "https://raw.githubusercontent.com/AgeMagi/tugas-akhir/master/data/score-soshum-test.csv", | |
| }, | |
| }, | |
| } | |
| _SUPPORTED_TASKS = [Tasks.SHORT_ANSWER_GRADING] | |
| _SOURCE_VERSION = "1.0.0" | |
| _NUSANTARA_VERSION = "1.0.0" | |
| class IdShortAnswerGrading(datasets.GeneratorBasedBuilder): | |
| """Indonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts.""" | |
| SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) | |
| NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION) | |
| BUILDER_CONFIGS = [ | |
| NusantaraConfig( | |
| name="id_short_answer_grading_source", | |
| version=SOURCE_VERSION, | |
| description="id_short_answer_grading source schema", | |
| schema="source", | |
| subset_id="id_short_answer_grading", | |
| ), | |
| NusantaraConfig( | |
| name="id_short_answer_grading_nusantara_pairs_score", | |
| version=NUSANTARA_VERSION, | |
| description="id_short_answer_grading Nusantara schema", | |
| schema="nusantara_pairs_score", | |
| subset_id="id_short_answer_grading", | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = "id_short_answer_grading_source" | |
| def _info(self) -> datasets.DatasetInfo: | |
| if self.config.schema == "source": | |
| features = datasets.Features( | |
| { | |
| "index": datasets.Value("int64"), | |
| "type-problem": datasets.Value("int64"), | |
| "pertanyaan": datasets.Value("string"), | |
| "kunci-jawaban": datasets.Value("string"), | |
| "jawaban": datasets.Value("string"), | |
| "score": datasets.Value("int64"), | |
| } | |
| ) | |
| elif self.config.schema == "nusantara_pairs_score": | |
| features = schemas.pairs_features([0, 1, 2, 3, 4, 5]) | |
| 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.""" | |
| saintek_question = Path(dl_manager.download_and_extract(_URLS["saintek"]["train"]["question"])) | |
| saintek_score = Path(dl_manager.download_and_extract(_URLS["saintek"]["train"]["score"])) | |
| saintek_question_test = Path(dl_manager.download_and_extract(_URLS["saintek"]["test"]["question"])) | |
| saintek_score_test = Path(dl_manager.download_and_extract(_URLS["saintek"]["test"]["score"])) | |
| soshum_question = Path(dl_manager.download_and_extract(_URLS["soshum"]["train"]["question"])) | |
| soshum_score = Path(dl_manager.download_and_extract(_URLS["soshum"]["train"]["score"])) | |
| soshum_question_test = Path(dl_manager.download_and_extract(_URLS["soshum"]["test"]["question"])) | |
| soshum_score_test = Path(dl_manager.download_and_extract(_URLS["soshum"]["test"]["score"])) | |
| data_files = { | |
| "saintek_question": saintek_question, | |
| "saintek_score": saintek_score, | |
| "saintek_question_test": saintek_question_test, | |
| "saintek_score_test": saintek_score_test, | |
| "soshum_question": soshum_question, | |
| "soshum_score": soshum_score, | |
| "soshum_question_test": soshum_question_test, | |
| "soshum_score_test": soshum_score_test, | |
| } | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "saintek_question": os.path.join(data_files["saintek_question"]), | |
| "soshum_question": os.path.join(data_files["soshum_question"]), | |
| "saintek_score": os.path.join(data_files["saintek_score"]), | |
| "soshum_score": os.path.join(data_files["soshum_score"]), | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "saintek_question": os.path.join(data_files["saintek_question_test"]), | |
| "soshum_question": os.path.join(data_files["soshum_question_test"]), | |
| "saintek_score": os.path.join(data_files["saintek_score_test"]), | |
| "soshum_score": os.path.join(data_files["soshum_score_test"]), | |
| "split": "test", | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, saintek_question: Path, soshum_question: Path, saintek_score: Path, soshum_score: Path, split: str) -> Tuple[int, Dict]: | |
| """Yields examples as (key, example) tuples.""" | |
| df = create_saintek_and_soshum_dataset(saintek_question, soshum_question, saintek_score, soshum_score) | |
| if self.config.schema == "source": | |
| for row in df.itertuples(): | |
| entry = { | |
| "index": row.index, | |
| "type-problem": row.type_problem, | |
| "pertanyaan": row.pertanyaan, | |
| "kunci-jawaban": row.kunci_jawaban, | |
| "jawaban": row.jawaban, | |
| "score": row.score, | |
| } | |
| yield row.index, entry | |
| elif self.config.schema == "nusantara_pairs_score": | |
| for row in df.itertuples(): | |
| entry = { | |
| "id": str(row.index), | |
| "text_1": row.pertanyaan, | |
| "text_2": row.jawaban, | |
| "label": row.score, | |
| } | |
| yield row.index, entry | |