Datasets:
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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()]
} |