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dd990bf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | """Run: python examples/use.py (network needed for first model download)."""
import json
from evidence_graph_reranker import EvidenceGraphReranker, rank_with_scores
query = "Who founded the company that released the Atlas device?"
with open("examples/passages.json", encoding="utf-8") as f:
passages = json.load(f)
# Full pretrained MS MARCO -> graph reranking
reranker = EvidenceGraphReranker(backend="cross-encoder", device="cpu")
for passage in reranker.rank(query, passages, k=3):
print(passage["index"], passage["title"], round(passage["step_score"], 4))
# If you already have neural relevance scores, the selector needs no checkpoint:
print([r["index"] for r in rank_with_scores(query, passages, [.91, .45, .20, .15], k=3)])
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