File size: 3,012 Bytes
f7b6133 | 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 | from __future__ import annotations
import csv
import json
import os
import tempfile
import zipfile
from dataclasses import dataclass
from pathlib import Path
@dataclass
class BEIRDataset:
corpus_ids: list[str]
corpus_texts: list[str]
queries: dict[str, str]
qrels: dict[str, dict[str, float]]
name: str = "dataset"
def _find(root: Path, basename: str) -> Path:
hits = list(root.rglob(basename))
if not hits:
raise FileNotFoundError(f"Could not find {basename!r} under {root}")
if len(hits) > 1:
# Prefer the shallowest path, which is normally the dataset root.
hits.sort(key=lambda p: len(p.parts))
return hits[0]
def load_beir_directory(path: str | os.PathLike, split: str = "test") -> BEIRDataset:
root = Path(path)
corpus_path = _find(root, "corpus.jsonl")
queries_path = _find(root, "queries.jsonl")
qrels_hits = list(root.rglob(f"qrels/{split}.tsv"))
if not qrels_hits:
# Some archives flatten qrels paths.
qrels_hits = [p for p in root.rglob(f"{split}.tsv") if p.parent.name == "qrels"]
if not qrels_hits:
raise FileNotFoundError(f"Could not find qrels/{split}.tsv under {root}")
qrels_path = qrels_hits[0]
corpus_ids, corpus_texts = [], []
with corpus_path.open("r", encoding="utf-8") as f:
for line in f:
if not line.strip():
continue
obj = json.loads(line)
did = str(obj.get("_id", obj.get("id")))
title = obj.get("title", "") or ""
text = obj.get("text", "") or ""
merged = (title + " " + text).strip()
corpus_ids.append(did)
corpus_texts.append(merged)
queries = {}
with queries_path.open("r", encoding="utf-8") as f:
for line in f:
if not line.strip():
continue
obj = json.loads(line)
qid = str(obj.get("_id", obj.get("id")))
queries[qid] = obj.get("text", "") or ""
qrels: dict[str, dict[str, float]] = {}
with qrels_path.open("r", encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t")
for row in reader:
# BEIR normally uses query-id, corpus-id, score.
qid = str(row.get("query-id", row.get("query_id", row.get("qid"))))
did = str(row.get("corpus-id", row.get("corpus_id", row.get("docid"))))
score = float(row.get("score", row.get("relevance", row.get("rel", 0))))
qrels.setdefault(qid, {})[did] = score
name = corpus_path.parent.name
return BEIRDataset(corpus_ids, corpus_texts, queries, qrels, name=name)
def load_beir_zip(path: str | os.PathLike, split: str = "test") -> BEIRDataset:
"""Load a standard BEIR zip without requiring internet access."""
with tempfile.TemporaryDirectory(prefix="geomretrieval_beir_") as td:
with zipfile.ZipFile(path, "r") as zf:
zf.extractall(td)
return load_beir_directory(td, split=split)
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