M3Retrieve / load.py
ArkaAcharya's picture
Rename M3Retrieve.py to load.py
3ac24f4 verified
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)}