import json import os import datasets # Define the dataset class M3Retrieve(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") # Define available subfolders (datasets) SUBFOLDERS = [ "Anatomy and Physiology", "Cardiology", "Dermatology", "Endocrinology_and_Diabetes", "Gastroenterology", "Hematology", "Microbiology_and_Cell_Biology", "Miscellaneous", "Neurology_and_Neuroscience", "Ophthalmology_and_Sensory_Systems", "Orthopedics_and_Musculoskeletal", "Pharmacology", "Psychiatry_and_Mental_Health", "Pubmed", "Radiology_and_Imaging", "Reproductive_System", "Respiratory_and_Pulmonology", "Surgical_Specialties", ] # Define Builder Configs for each subfolder BUILDER_CONFIGS = [ datasets.BuilderConfig( name=subfolder, version=datasets.Version("1.0.0"), description=f"Dataset for {subfolder.replace('_', ' ')}" ) for subfolder in SUBFOLDERS ] # def _info(self): # return datasets.DatasetInfo( # description="M3Retrieve: Benchmarking Multimodal Retrieval for Medicine", # features={ # "queries": { # "_id": datasets.Value("string"), # "caption": datasets.Value("string"), # "image_path": datasets.Value("string"), # }, # "corpus": { # "_id": datasets.Value("string"), # "text": datasets.Value("string"), # }, # "qrels": { # "query-id": datasets.Value("string"), # "corpus-id": datasets.Value("string"), # "score": datasets.Value("float32"), # }, # }, # supervised_keys=None, # ) def _info(self): return datasets.DatasetInfo( description="M3Retrieve: Benchmarking Multimodal Retrieval for Medicine", features=datasets.Features( { "_id": datasets.Value("string"), "caption": datasets.Value("string"), "image_path": datasets.Value("string"), "text": datasets.Value("string"), "query-id": datasets.Value("string"), "corpus-id": datasets.Value("string"), "score": datasets.Value("float32"), } ), supervised_keys=None, ) def _split_generators(self, dl_manager): """Returns SplitGenerators for the selected subfolder""" data_dir = os.path.join(dl_manager.download_and_extract(self.config.data_dir), self.config.name) return [ datasets.SplitGenerator( name="queries", gen_kwargs={"filepath": os.path.join(data_dir, "queries.jsonl"), "key": "queries"}, ), datasets.SplitGenerator( name="corpus", gen_kwargs={"filepath": os.path.join(data_dir, "corpus.jsonl"), "key": "corpus"}, ), datasets.SplitGenerator( name="qrels", gen_kwargs={"filepath": os.path.join(data_dir, "qrels/test.tsv"), "key": "qrels"}, ), ] def _generate_examples(self, filepath, key): """Yields examples as (key, example) tuples.""" if key in ["queries", "corpus"]: with open(filepath, "r", encoding="utf-8") as f: for i, line in enumerate(f): data = json.loads(line) yield i, data elif key == "qrels": with open(filepath, "r", encoding="utf-8") as f: for i, line in enumerate(f): query_id, corpus_id, score = line.strip().split("\t") yield i, {"query-id": query_id, "corpus-id": corpus_id, "score": float(score)}