Upload indo_story_cloze.py with huggingface_hub

indo_story_cloze.py

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+import csv
+import random
+import string
+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{koto-etal-2022-cloze,
+    title = "Cloze Evaluation for Deeper Understanding of Commonsense Stories in {I}ndonesian",
+    author = "Koto, Fajri  and
+      Baldwin, Timothy  and
+      Lau, Jey Han",
+    editor = "Bosselut, Antoine  and
+      Li, Xiang  and
+      Lin, Bill Yuchen  and
+      Shwartz, Vered  and
+      Majumder, Bodhisattwa Prasad  and
+      Lal, Yash Kumar  and
+      Rudinger, Rachel  and
+      Ren, Xiang  and
+      Tandon, Niket  and
+      Zouhar, Vil{\'e}m",
+    booktitle = "Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)",
+    month = may,
+    year = "2022",
+    address = "Dublin, Ireland",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.csrr-1.2",
+    doi = "10.18653/v1/2022.csrr-1.2",
+    pages = "8--16",
+}
+"""
+
+_DATASETNAME = "indo_story_cloze"
+
+_DESCRIPTION = """
+A Story Cloze Test framework in Indonesian. A story in our dataset consists of four-sentence premise, one-sentence
+correct ending, and one-sentence incorrect ending. In total, we have created 2,325 Indonesian stories with the
+train/dev/test split 1,000/200/1,135.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/indolem/indo_story_cloze"
+
+_LANGUAGES = ["ind"]
+
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://huggingface.co/datasets/indolem/indo_story_cloze/resolve/main/train.csv",
+        "dev": "https://huggingface.co/datasets/indolem/indo_story_cloze/resolve/main/dev.csv",
+        "test": "https://huggingface.co/datasets/indolem/indo_story_cloze/resolve/main/test.csv",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.COMMONSENSE_REASONING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class IndoStoryClozeDataset(datasets.GeneratorBasedBuilder):
+    """IndoStoryCloze is a Story Cloze dataset in Indonesian."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_qa",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_qa",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "sentence-1": datasets.Value("string"),
+                    "sentence-2": datasets.Value("string"),
+                    "sentence-3": datasets.Value("string"),
+                    "sentence-4": datasets.Value("string"),
+                    "correct_ending": datasets.Value("string"),
+                    "incorrect_ending": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_qa":
+            features = schemas.qa_features
+
+        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."""
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_dir, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_dir, "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_dir, "split": "dev"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        if self.config.schema == "source":
+            data = csv.DictReader(open(filepath[split], newline="", encoding="utf-8"))
+            for i, row in enumerate(data):
+                yield i, {
+                    "sentence-1": row["Kalimat-1"],
+                    "sentence-2": row["Kalimat-2"],
+                    "sentence-3": row["Kalimat-3"],
+                    "sentence-4": row["Kalimat-4"],
+                    "correct_ending": row["Correct Ending"],
+                    "incorrect_ending": row["Incorrect Ending"],
+                }
+
+        elif self.config.schema == "seacrowd_qa":
+            data = csv.DictReader(open(filepath[split], newline="", encoding="utf-8"))
+
+            def build_question(line):
+                # Concatenate the 4 sentences, this can either be the question of the context. Set is as question for
+                # now. Some sentences do not have punctuation, hence adding . before concatenation.
+                sentences = []
+                for k in ["Kalimat-1", "Kalimat-2", "Kalimat-3", "Kalimat-4"]:
+                    if line[k].strip()[-1] not in string.punctuation:
+                        sentences.append(line[k] + ".")
+                    else:
+                        sentences.append(line[k])
+                return " ".join(sentences)
+
+            for i, row in enumerate(data):
+                yield i, {
+                    "id": str(i),
+                    "question_id": str(i),
+                    "document_id": str(i),
+                    "question": build_question(row),
+                    "type": "multiple_choice",
+                    # Reorder choices based on the randomly generated labels, avoiding correct answer at the same order.
+                    "choices": [row["Correct Ending"], row["Incorrect Ending"]] if random.randint(0, 1) == 0 else [row["Incorrect Ending"], row["Correct Ending"]],
+                    "context": "",
+                    "answer": [row["Correct Ending"]],
+                    "meta": {},
+                }