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import os |
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import json |
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import datasets |
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import logging |
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_CITATION = """Your citation here""" |
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_DESCRIPTION = """Description of your medical QA dataset""" |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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class MedQaMazeConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super().__init__(version=datasets.Version("1.0.0"), **kwargs) |
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class MedQaMaze(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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MedQaMazeConfig(name="default", description="A default config"), |
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MedQaMazeConfig(name="advance", description="Advanced-level test data"), |
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MedQaMazeConfig(name="all", description="Full dataset with train and test"), |
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MedQaMazeConfig(name="basic", description="Basic-level test data"), |
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MedQaMazeConfig(name="challenge", description="Challenge-level test data"), |
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] |
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DEFAULT_CONFIG_NAME = "all" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features({ |
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"context": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"prerequisit": datasets.Value("string"), |
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"groundtruth_zoo": datasets.Sequence(datasets.Value("string")), |
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"answer": datasets.Value("string"), |
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}), |
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supervised_keys=None, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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base_path = os.path.dirname(os.path.abspath(__file__)) |
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logger.info(f"Base path: {base_path}") |
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config_name = self.config.name |
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logger.info(f"Using config: {config_name}") |
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if config_name == "advance": |
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filepath = os.path.join(base_path, "advance", "test.jsonl") |
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logger.info(f"Looking for advance test file at: {filepath}") |
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if not os.path.exists(filepath): |
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raise ValueError(f"File not found: {filepath}") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": filepath} |
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) |
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] |
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elif config_name == "all": |
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train_path = os.path.join(base_path, "all", "train.jsonl") |
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test_path = os.path.join(base_path, "all", "test.jsonl") |
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logger.info(f"Looking for train file at: {train_path}") |
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logger.info(f"Looking for test file at: {test_path}") |
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if not os.path.exists(train_path): |
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raise ValueError(f"Train file not found: {train_path}") |
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if not os.path.exists(test_path): |
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raise ValueError(f"Test file not found: {test_path}") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": train_path} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": test_path} |
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) |
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] |
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def _generate_examples(self, filepath): |
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"""Yields examples.""" |
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logger.info(f"Processing file: {filepath}") |
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try: |
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with open(filepath, "r", encoding="utf-8") as f: |
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for idx, line in enumerate(f): |
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try: |
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data = json.loads(line.strip()) |
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example = { |
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"context": data.get("context", ""), |
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"question": data.get("question", ""), |
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"prerequisit": data.get("prerequisit", ""), |
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"groundtruth_zoo": data.get("groundtruth_zoo", []), |
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"answer": data.get("answer", ""), |
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} |
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yield idx, example |
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except json.JSONDecodeError as e: |
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logger.error(f"Error parsing JSON at line {idx} in {filepath}: {e}") |
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continue |
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except Exception as e: |
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logger.error(f"Error reading file {filepath}: {e}") |
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raise |
