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bm_pt3.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 for 15 years old Malaysia student, it is about reading comprehension and general knowledge for malay language.
+"""
+from pathlib import Path
+from typing import Dict, List, Tuple
+import pandas as pd
+import re
+
+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 = "bm_pt3"
+
+_DESCRIPTION = """\
+This test is for 15 years old Malaysia student, it is about reading comprehension and general knowledge for malay language.
+"""
+
+_HOMEPAGE = "https://github.com/mesolitica/malaysian-dataset/tree/master/llm-benchmark/BM-pt3"
+
+_LANGUAGES = ["zlm"]
+
+_LICENSE = Licenses.UNLICENSE.value
+
+_LOCAL = False
+
+_URLS = {
+    "A": "https://raw.githubusercontent.com/mesolitica/malaysian-dataset/master/llm-benchmark/BM-pt3/BM-A-pt3",
+    "B": "https://raw.githubusercontent.com/mesolitica/malaysian-dataset/master/llm-benchmark/BM-pt3/BM-B-pt3"
+}
+
+_SUPPORTED_TASKS = [Tasks.COMMONSENSE_REASONING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class BMPT3Dataset(datasets.GeneratorBasedBuilder):
+    """This test is for 15 years old Malaysia student, it is about reading comprehension and general knowledge for malay language."""
+
+
+    SUBSETS = ["A", "B"]
+    SEACROWD_SCHEMA = TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema for {subset} subset",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        ) 
+        for subset in SUBSETS
+    ] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_seacrowd_qa",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema for {subset} subset",
+            schema=f"seacrowd_qa",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in SUBSETS
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "num": datasets.Value("string"),
+                    "objective": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "choices": datasets.Sequence(datasets.Value("string")),
+                    "answer": datasets.Value("string"),
+                    "source": {
+                        "title": datasets.Value("string"),
+                        "num": datasets.Value("string"),
+                        "url": datasets.Value("string"),
+                    }
+                }
+            )
+
+        elif self.config.schema == "seacrowd_qa":
+            features = schemas.qa_features
+            features["meta"] = {
+                "source": {
+                    "title": datasets.Value("string"),
+                    "num": datasets.Value("string"),
+                    "url": 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]:
+
+        if "A" in self.config.subset_id:
+            subset_type = "A"
+            data_dir = dl_manager.download_and_extract(_URLS["A"])
+        elif "B" in self.config.subset_id:
+            subset_type = "B"
+            data_dir = dl_manager.download_and_extract(_URLS["B"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "subset_type": subset_type
+                },
+            ),
+        ]
+
+
+    def _generate_examples(self, filepath: Path, subset_type: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        with open(filepath, "r", encoding="utf-8") as f:
+            data = self._extract_data(f.read(), subset_type)
+        
+        if self.config.schema == "source":
+            for i, entry in enumerate(data):
+                yield i, entry
+
+        elif self.config.schema == "seacrowd_qa":
+            for i, entry in enumerate(data):
+                yield i, {
+                    "id": str(i),
+                    "question_id": entry["num"],
+                    "document_id": None,
+                    "question": entry["question"],
+                    "type": "multiple_choice" if entry["choices"] else "open_ended",
+                    "choices": entry["choices"],
+                    "context": entry["objective"],
+                    "answer": [entry["answer"]] if entry["answer"] else [],
+                    "meta": {
+                        "source": entry["source"]
+                    }
+                }
+    
+    def _extract_data(self, doc: str, subset_type: str) -> List[Dict]:
+        """Extracts data from the source schema"""
+
+        # RegEx pattern
+        pattern_num = re.compile(r"(no:\s*\d+)")
+        pattern_objective = re.compile(r"objektif:\s*(.*)")
+        pattern_question = re.compile(r'soalan:\s*(.*?)(?=\njawapan:|asal soalan:)', re.DOTALL)
+        pattern_choices = re.compile(r'([A-D]\.\s+.+?)(?=\n[A-D]\.|\Z)', re.DOTALL)
+        if subset_type == "A":
+            pattern_answer = re.compile(r'jawapan:\s*([A-D])[,\s]', re.DOTALL)
+        elif subset_type == "B":
+            pattern_answer = re.compile(r'jawapan:\s*(.*?)\s*asal soalan:', re.DOTALL)
+        pattern_asal_soalan = re.compile(r'asal soalan:\s*(.*?),\s*no\s*(\d+),\s*(.*?)\n', re.DOTALL)
+
+        res = []
+        doc_split = re.sub(pattern_num, "<NUMBER>", doc).split("<NUMBER>")[1:]
+
+        for i, entry in enumerate(doc_split):
+            # Objektif
+            objective = re.findall(pattern_objective, entry)
+            objective = objective[0] if objective else None
+            
+            # Soalan
+            _question = re.findall(pattern_question, entry)
+            question = re.sub(pattern_choices, '', _question[0]).strip("\n") if _question else None
+            
+            # Choices Soalan
+            choices = {}
+            if _question and subset_type == "A":
+                _choices = re.findall(pattern_choices, _question[0])
+                for _c in _choices:
+                    alpha, txt = _c.split(". ")[0], ' '.join(_c.split(". ")[1:])
+                    choices[alpha] = txt
+            
+            # Answer
+            if subset_type == "A":
+                _answer = re.findall(pattern_answer, entry)
+                answer = choices[_answer[0]] if (_answer and choices) else None
+            elif subset_type == "B":
+                answer = re.findall(pattern_answer, entry)
+                answer = answer[0] if answer else None
+            
+            # Asal soalan
+            source = re.findall(pattern_asal_soalan, entry)
+            source = source[0] if source else [None,None,None]
+
+            res.append({
+                "num": str(i+1),
+                "objective": objective,
+                "question": question,
+                "choices": list(choices.values()) if choices else [],
+                "answer": answer,
+                "source": {
+                    "title": source[0],
+                    "num": source[1],
+                    "url": source[2]
+                }
+            })
+
+        return res