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tatabahasa.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This test is a general test for Malay grammar. Contains 349 questions that may be reinforced with instructions.
+"""
+import json
+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 Tasks, Licenses, TASK_TO_SCHEMA
+
+_CITATION = None
+
+_DATASETNAME = "tatabahasa"
+
+_DESCRIPTION = """\
+This test is a general test for Malay grammar. Contains 349 questions.
+"""
+
+_HOMEPAGE = "https://github.com/mesolitica/malaysian-dataset/tree/master/llm-benchmark/tatabahasabm.tripod.com"
+
+_LANGUAGES = ["zlm"]  
+
+_LICENSE = Licenses.UNLICENSE.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://raw.githubusercontent.com/mesolitica/malaysian-dataset/master/llm-benchmark/tatabahasabm.tripod.com/quiz-tatabahasa.jsonl",
+}
+
+_SUPPORTED_TASKS = [Tasks.COMMONSENSE_REASONING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class TatabahasaDataset(datasets.GeneratorBasedBuilder):
+    """This test is a general test for Malay grammar. Contains 349 questions."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA = TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA}",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            multi_choice = {"text" : datasets.Value("string"), "answer": datasets.Value("bool")}
+            features = datasets.Features({
+                "question" : datasets.Value("string"),
+                "instruction": datasets.Value("string"),
+                "choices": {
+                    "A": multi_choice,
+                    "B": multi_choice,
+                    "C": multi_choice,
+                    "D": multi_choice,
+                },
+                "website": datasets.Value("string")
+            })
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA}":
+            features = schemas.qa_features
+            features["meta"] = {"website": datasets.Value("string")}
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        with open(filepath ,'r') as f:
+            data = [json.loads(line) for line in f]
+
+        if self.config.schema == "source":
+            for i in range(len(data)):
+                out = {
+                    "question": data[i]["question"],
+                    "instruction": data[i]["instruction"],
+                    "choices": data[i]["choices"],
+                    "website": data[i]["website"]
+                }
+                yield i, out
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA}":
+            for i in range(len(data)):
+                out = {
+                    "id": i + 1,
+                    "question_id": None,
+                    "document_id": None,
+                    "question": data[i]["question"],
+                    "type": "multiple_choice",
+                    "choices": [
+                        data[i]["choices"]["A"]["text"],
+                        data[i]["choices"]["B"]["text"],
+                        data[i]["choices"]["C"]["text"],
+                        data[i]["choices"]["D"]["text"],
+                    ],
+                    "context": data[i]["instruction"],
+                    "answer": [choice["text"] for choice in data[i]["choices"].values() if choice["answer"]],
+                    "meta": {"website": data[i]["website"]},
+                }
+                yield i, out