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sentence-similarity -- Bi-encoder embedding model producing similarity scores for case-to-case matchingvoyage-ai/voyage-law-2 -- Purpose-built legal embedding model, deployed via AWS SageMaker (Voyage Law-2 marketplace subscription active)| Source | Records | Description |
|---|---|---|
| CourtListener Paired Cases | ~2M pairs | Cases citing each other as analogous or distinguishable |
| Legal Brief Citations | ~500K pairs | Brief-to-cited-case pairs showing attorney judgment of relevance |
| Expert Fact Pattern Matches | ~100K pairs | Attorney-curated "similar cases" datasets |
| Negative Pairs | ~1M | Same practice area but factually dissimilar cases |
| Metric | Target | Description |
|---|---|---|
| Recall@20 | >= 90% | Relevant precedents in top-20 results |
| MAP | >= 0.85 | Mean average precision across test queries |
| Factual Similarity Correlation | >= 0.87 | Agreement with expert fact-pattern ratings |
| Cross-jurisdiction accuracy | >= 82% | Finding persuasive authority across jurisdictions |
| Embedding throughput | >= 500 docs/sec | Batch encoding speed |
STAGE 2 (Research) --> precedent-matcher retrieves analogous cases from bedrock-legal cache
STAGE 3 (WDC #1) --> matched precedents validate legal theory strength
STAGE 5 (WDC #2) --> verify cited precedents are truly analogous (not just topical)legal-research-ranker.| Version | Date | Notes |
|---|---|---|
| v0.1 | 2026-04-10 | Initial model card, repo created |