Upload id_short_answer_grading.py with huggingface_hub

id_short_answer_grading.py

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+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