|
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 |
