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copal.py

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+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@article{wibowo2023copal,
+  title={COPAL-ID: Indonesian Language Reasoning with Local Culture and Nuances},
+  author={Wibowo, Haryo Akbarianto and Fuadi, Erland Hilman and Nityasya, Made Nindyatama and Prasojo, Radityo Eko and Aji, Alham Fikri},
+  journal={arXiv preprint arXiv:2311.01012},
+  year={2023}
+}
+"""
+_DATASETNAME = "copal"
+
+_DESCRIPTION = """\
+COPAL is a novel Indonesian language common sense reasoning dataset. Unlike the previous Indonesian COPA dataset (XCOPA-ID), COPAL-ID incorporates Indonesian local and cultural nuances,
+providing a more natural portrayal of day-to-day causal reasoning within the Indonesian cultural sphere.
+Professionally written by natives from scratch, COPAL-ID is more fluent and free from awkward phrases, unlike the translated XCOPA-ID.
+Additionally, COPAL-ID is presented in both standard Indonesian and Jakartan Indonesian–a commonly used dialect.
+It consists of premise, choice1, choice2, question, and label, similar to the COPA dataset.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/haryoaw/COPAL"
+
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+
+_URLS = {"test": "https://huggingface.co/datasets/haryoaw/COPAL/resolve/main/test_copal.csv?download=true", "test_colloquial": "https://huggingface.co/datasets/haryoaw/COPAL/resolve/main/test_copal_colloquial.csv?download=true"}
+
+_SUPPORTED_TASKS = [Tasks.COMMONSENSE_REASONING]
+
+_LOCAL = False
+_LANGUAGES = ["ind"]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class COPAL(datasets.GeneratorBasedBuilder):
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description="COPAL test source schema",
+            schema="source",
+            subset_id="copal",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_colloquial_source",
+            version=SOURCE_VERSION,
+            description="COPAL test colloquial source schema",
+            schema="source",
+            subset_id="copal",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_qa",
+            version=SEACROWD_VERSION,
+            description="COPAL test seacrowd schema",
+            schema="seacrowd_qa",
+            subset_id="copal",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_colloquial_seacrowd_qa",
+            version=SEACROWD_VERSION,
+            description="COPAL test colloquial seacrowd schema",
+            schema="seacrowd_qa",
+            subset_id="copal",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "copal_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "premise": datasets.Value("string"),
+                    "choice1": datasets.Value("string"),
+                    "choice2": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "idx": datasets.Value("int64"),
+                    "label": datasets.Value("int64"),
+                    "terminology": datasets.Value("int64"),
+                    "culture": datasets.Value("int64"),
+                    "language": datasets.Value("int64"),
+                }
+            )
+        elif self.config.schema == "seacrowd_qa":
+            features = schemas.qa_features
+            features["meta"] = {"terminology": datasets.Value("int64"), "culture": datasets.Value("int64"), "language": datasets.Value("int64")}
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URLS)
+        if "colloquial" in self.config.name:
+            data_url = data_dir["test_colloquial"]
+        else:
+            data_url = data_dir["test"]
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_url},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        df = pd.read_csv(filepath, sep=",", header="infer").reset_index()
+        if self.config.schema == "source":
+            for row in df.itertuples():
+                entry = {
+                    "premise": row.premise,
+                    "choice1": row.choice1,
+                    "choice2": row.choice2,
+                    "question": row.question,
+                    "idx": row.idx,
+                    "label": row.label,
+                    "terminology": row.Terminology,
+                    "culture": row.Culture,
+                    "language": row.Language,
+                }
+                yield row.index, entry
+
+        elif self.config.schema == "seacrowd_qa":
+            for row in df.itertuples():
+                entry = {
+                    "id": row.idx,
+                    "question_id": str(row.idx),
+                    "document_id": str(row.idx),
+                    "question": row.question,
+                    "type": "multiple_choice",
+                    "choices": [row.choice1, row.choice2],
+                    "context": row.premise,
+                    "answer": [row.choice1 if row.label == 0 else row.choice2],
+                    "meta": {"terminology": row.Terminology, "culture": row.Culture, "language": row.Language},
+                }
+                yield row.index, entry
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")