Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
document-retrieval
Languages:
Slovak
Size:
1K - 10K
ArXiv:
License:
File size: 3,502 Bytes
06a0b78 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | """Build script for mteb/SMESumRetrieval.
This dataset is the MTEB-ready (retrieval) version of the SMESum dataset, used by
the `SMESumRetrieval` task in https://github.com/embeddings-benchmark/mteb.
Source: NaiveNeuron/SMESum (columns: title, introduction, document)
https://huggingface.co/datasets/NaiveNeuron/SMESum
Transform: reformulate summarization into retrieval -- take the first `n_sample`
rows of the "test" split, use `introduction` as the query, `title`+`document` as
the corpus document, and pair each query 1:1 with its source document as the
qrel. Produces the three-config (corpus/queries/qrels) layout MTEB's
AbsTaskRetrieval expects.
If the source dataset publishes a new revision, regenerate this dataset with:
python build_dataset.py --source-revision <new-sha> --token $HF_TOKEN
then update the `SMESumRetrieval` task's `dataset.path`/`revision` in mteb to the
new revision this script prints on push.
"""
from __future__ import annotations
import argparse
from datasets import Dataset, DatasetDict, load_dataset
from huggingface_hub import HfApi
SOURCE_REPO = "NaiveNeuron/SMESum"
SOURCE_REVISION = "c5a6521a4ddce3450fb04ba218623681a9189c6d"
TARGET_REPO = "mteb/SMESumRetrieval"
N_SAMPLE = 600
def build(
source_revision: str = SOURCE_REVISION, n_sample: int = N_SAMPLE
) -> dict[str, DatasetDict]:
corpus, queries, qrels = {}, {}, {}
for split in ["test"]:
split_ds = load_dataset(
SOURCE_REPO, revision=source_revision, split=f"{split}[:{n_sample}]"
)
ids = [f"d{e + 1}" for e in range(len(split_ds))]
query_ids = [f"q{e + 1}" for e in range(len(split_ds))]
queries[split] = Dataset.from_dict(
{"id": query_ids, "text": split_ds["introduction"]}
)
corpus[split] = Dataset.from_dict(
{"id": ids, "title": split_ds["title"], "text": split_ds["document"]}
)
qrels[split] = Dataset.from_dict(
{"query-id": query_ids, "corpus-id": ids, "score": [1] * len(split_ds)}
)
return {
"corpus": DatasetDict(corpus),
"queries": DatasetDict(queries),
"qrels": DatasetDict(qrels),
}
def main() -> None:
p = argparse.ArgumentParser(description=__doc__)
p.add_argument("--source-revision", default=SOURCE_REVISION)
p.add_argument("--n-sample", type=int, default=N_SAMPLE)
p.add_argument("--repo", default=TARGET_REPO)
p.add_argument("--token", default=None, help="HF token (or use HF_TOKEN env / `huggingface-cli login`).")
p.add_argument("--private", action="store_true")
p.add_argument("--dry-run", action="store_true")
args = p.parse_args()
result = build(args.source_revision, args.n_sample)
for config_name, dataset_dict in result.items():
for split, d in dataset_dict.items():
print(f" config={config_name} split={split} rows={len(d)} columns={d.column_names}")
if args.dry_run:
print(f"[dry-run] would push configs {list(result)} to {args.repo}")
return
for config_name, dataset_dict in result.items():
dataset_dict.push_to_hub(
args.repo,
config_name,
commit_message=f"Rebuild {config_name} from source revision",
token=args.token,
private=args.private,
)
revision = HfApi(token=args.token).dataset_info(args.repo).sha
print(f"-> pushed to {args.repo}, revision={revision}")
if __name__ == "__main__":
main()
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