| import os |
| import json |
| import datasets |
| import logging |
|
|
| logger = logging.getLogger(__name__) |
|
|
| _CITATION = "" |
| _DESCRIPTION = "" |
|
|
| _BASE_URL = "https://huggingface.co/datasets/JesseLiu/MedQA_Maze/resolve/main" |
|
|
| class MedQaMazeConfig(datasets.BuilderConfig): |
| def __init__(self, **kwargs): |
| super().__init__(version=datasets.Version("1.0.0"), **kwargs) |
|
|
| class MedQaMaze(datasets.GeneratorBasedBuilder): |
| BUILDER_CONFIGS = [ |
| MedQaMazeConfig(name="default", description="A default config"), |
| MedQaMazeConfig(name="advance", description="Advanced-level test data"), |
| MedQaMazeConfig(name="all", description="Full dataset with train and test"), |
| MedQaMazeConfig(name="basic", description="Basic-level test data"), |
| MedQaMazeConfig(name="challenge", description="Challenge-level test data"), |
| ] |
|
|
| DEFAULT_CONFIG_NAME = "all" |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features({ |
| "context": datasets.Value("string"), |
| "question": datasets.Value("string"), |
| "prerequisit": datasets.Value("string"), |
| "groundtruth_zoo": datasets.Sequence(datasets.Value("string")), |
| "answer": datasets.Value("string"), |
| }), |
| supervised_keys=None, |
| homepage="https://huggingface.co/datasets/JesseLiu/MedQA_Maze", |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators.""" |
| config_name = self.config.name if self.config.name != "default" else "all" |
|
|
| urls = {} |
| if config_name == "all": |
| urls = { |
| "train": f"{_BASE_URL}/all/train.jsonl", |
| "test": f"{_BASE_URL}/all/test.jsonl" |
| } |
| elif config_name in ["basic", "advance", "challenge"]: |
| urls = { |
| "test": f"{_BASE_URL}/{config_name}/test.jsonl" |
| } |
| else: |
| raise ValueError(f"Unsupported config: {config_name}") |
|
|
| try: |
| data_files = dl_manager.download(urls) |
| logger.info(f"Downloaded files: {data_files}") |
| except Exception as e: |
| raise ValueError(f"Failed to download files: {e}") |
|
|
| splits = [] |
| if "train" in data_files: |
| splits.append( |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={"filepath": data_files["train"]} |
| ) |
| ) |
| if "test" in data_files: |
| splits.append( |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={"filepath": data_files["test"]} |
| ) |
| ) |
|
|
| if not splits: |
| raise ValueError(f"No valid splits found for config {config_name}") |
|
|
| return splits |
|
|
| def _generate_examples(self, filepath): |
| """Yields examples.""" |
| logger.info(f"Generating examples from {filepath}") |
| |
| if not os.path.exists(filepath): |
| raise ValueError(f"File not found: {filepath}") |
| |
| with open(filepath, 'r', encoding='utf-8') as f: |
| content = f.read().strip() |
| |
| lines = [line.strip() for line in content.split('\n') if line.strip()] |
| |
| for idx, line in enumerate(lines): |
| try: |
| data = json.loads(line) |
| example = { |
| "context": str(data.get("context", "")), |
| "question": str(data.get("question", "")), |
| "prerequisit": str(data.get("prerequisit", "")), |
| "groundtruth_zoo": [str(x) for x in data.get("groundtruth_zoo", [])], |
| "answer": str(data.get("answer", "")), |
| } |
| yield idx, example |
| except json.JSONDecodeError as e: |
| logger.error(f"Error parsing JSON at line {idx} in {filepath}: {e}\nLine content: {line[:100]}...") |
| continue |
| except Exception as e: |
| logger.error(f"Unexpected error processing line {idx} in {filepath}: {e}") |
| continue |
