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