Datasets:

BeIR
/

beir

	import json
	import csv
	import os
	import datasets

	logger = datasets.logging.get_logger(__name__)


	_DESCRIPTION = "BEIR Benchmark"
	_DATASETS = ["fiqa", "trec-covid", ""]

	URL = ""
	_URLs = {
	dataset: {
	"queries": URL + f"{dataset}/queries.jsonl",
	"qrels": {
	"train": URL + f"{dataset}/qrels/train.tsv",
	"dev": URL + f"{dataset}/qrels/dev.tsv",
	"test": URL + f"{dataset}/qrels/test.tsv"
	}} for dataset in _DATASETS}


	class BEIR(datasets.GeneratorBasedBuilder):
	"""BEIR BenchmarkDataset."""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name=dataset,
	description=f"This is the {dataset} dataset in BEIR Benchmark.",
	) for dataset in _DATASETS
	]


	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({
	"query": datasets.Value("string"),
	"relevant": [{
	"_id": datasets.Value("string"),
	"score": datasets.Value("int32"),
	}],
	}),
	supervised_keys=None,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	my_urls = _URLs[self.config.name]

	# All train, dev and test splits available for these datasets
	if self.config.name in ["msmarco", "nfcorpus", "hotpotqa", "fiqa", "fever"]:
	data_dir = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"query_path": data_dir["queries"],
	"qrels_path": data_dir["qrels"]["train"]}
	),
	datasets.SplitGenerator(
	name="dev",
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"query_path": data_dir["queries"],
	"qrels_path": data_dir["qrels"]["dev"]}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"query_path": data_dir["queries"],
	"qrels_path": data_dir["qrels"]["test"]}
	),
	]

	# Only train and test splits available for these datasets
	elif self.config.name in ["nq", "scifact"]:
	my_urls["qrels"].pop("dev", None)
	data_dir = dl_manager.download_and_extract(my_urls)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"query_path": data_dir["queries"],
	"qrels_path": data_dir["qrels"]["train"]}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"query_path": data_dir["queries"],
	"qrels_path": data_dir["qrels"]["test"]}
	),
	]

	# Only dev and test splits available for these datasets
	elif self.config.name in ["dbpedia", "quora"]:
	my_urls["qrels"].pop("train", None)
	data_dir = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name="dev",
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"query_path": data_dir["queries"],
	"qrels_path": data_dir["qrels"]["dev"]}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"query_path": data_dir["queries"],
	"qrels_path": data_dir["qrels"]["test"]}
	),
	]

	# Only test split available for these datasets
	else:
	for split in ["train", "dev"]:
	my_urls["qrels"].pop(split, None)
	data_dir = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"query_path": data_dir["queries"],
	"qrels_path": data_dir["qrels"]["test"]}
	),
	]


	def _generate_examples(self, query_path, qrels_path):
	"""Yields examples."""

	queries, qrels = {}, {}

	with open(query_path, encoding="utf-8") as fIn:
	text = fIn.readlines()

	for line in text:
	line = json.loads(line)
	queries[line.get("_id")] = line.get("text", "")

	reader = csv.reader(open(qrels_path, encoding="utf-8"),
	delimiter="\t", quoting=csv.QUOTE_MINIMAL)

	next(reader)

	for id, row in enumerate(reader):
	query_id, corpus_id, score = row[0], row[1], int(row[2])
	if query_id not in qrels:
	qrels[query_id] = {corpus_id: score}
	else:
	qrels[query_id][corpus_id] = score

	for i, query_id in enumerate(qrels):
	yield i, {
	"query": queries[query_id],
	"relevant": [{"_id": doc_id, "score": score
	} for doc_id, score in qrels[query_id].items()]
	}