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import json |
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import os |
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import datasets |
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class M3Retrieve(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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SUBFOLDERS = [ |
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"Anatomy and Physiology", |
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"Cardiology", |
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"Dermatology", |
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"Endocrinology_and_Diabetes", |
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"Gastroenterology", |
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"Hematology", |
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"Microbiology_and_Cell_Biology", |
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"Miscellaneous", |
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"Neurology_and_Neuroscience", |
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"Ophthalmology_and_Sensory_Systems", |
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"Orthopedics_and_Musculoskeletal", |
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"Pharmacology", |
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"Psychiatry_and_Mental_Health", |
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"Pubmed", |
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"Radiology_and_Imaging", |
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"Reproductive_System", |
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"Respiratory_and_Pulmonology", |
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"Surgical_Specialties", |
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] |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name=subfolder, |
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version=datasets.Version("1.0.0"), |
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description=f"Dataset for {subfolder.replace('_', ' ')}" |
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) |
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for subfolder in SUBFOLDERS |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description="M3Retrieve: Benchmarking Multimodal Retrieval for Medicine", |
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features=datasets.Features( |
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{ |
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"_id": datasets.Value("string"), |
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"caption": datasets.Value("string"), |
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"image_path": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"query-id": datasets.Value("string"), |
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"corpus-id": datasets.Value("string"), |
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"score": datasets.Value("float32"), |
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} |
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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 for the selected subfolder""" |
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data_dir = os.path.join(dl_manager.download_and_extract(self.config.data_dir), self.config.name) |
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return [ |
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datasets.SplitGenerator( |
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name="queries", |
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gen_kwargs={"filepath": os.path.join(data_dir, "queries.jsonl"), "key": "queries"}, |
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), |
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datasets.SplitGenerator( |
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name="corpus", |
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gen_kwargs={"filepath": os.path.join(data_dir, "corpus.jsonl"), "key": "corpus"}, |
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), |
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datasets.SplitGenerator( |
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name="qrels", |
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gen_kwargs={"filepath": os.path.join(data_dir, "qrels/test.tsv"), "key": "qrels"}, |
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), |
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] |
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def _generate_examples(self, filepath, key): |
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"""Yields examples as (key, example) tuples.""" |
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if key in ["queries", "corpus"]: |
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with open(filepath, "r", encoding="utf-8") as f: |
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for i, line in enumerate(f): |
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data = json.loads(line) |
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yield i, data |
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elif key == "qrels": |
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with open(filepath, "r", encoding="utf-8") as f: |
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for i, line in enumerate(f): |
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query_id, corpus_id, score = line.strip().split("\t") |
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yield i, {"query-id": query_id, "corpus-id": corpus_id, "score": float(score)} |
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