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| """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)]) | |