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README.md

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+---
+language:
+- uk
+license: cc-by-nc-sa-4.0
+task_categories:
+- text-retrieval
+tags:
+- legal
+- court-decisions
+- citation-graph
+- statute-retrieval
+- ukrainian
+- benchmark
+pretty_name: UA-StatuteRetrieval
+size_categories:
+- 1K<n<10K
+---
+
+# UA-StatuteRetrieval: Legal Article Prediction Benchmark
+
+A large-scale benchmark for evaluating legal statute retrieval methods on Ukrainian court decisions. Ground truth is derived exhaustively from 37M+ codex citations extracted from the Ukrainian Unified State Register of Court Decisions (EDRSR).
+
+## Dataset Description
+
+Instead of manual annotations, this benchmark exploits **implicit relevance judgments**: when a judge cites Article 625 of the Civil Code, that constitutes a relevance judgment for that article given the case context.
+
+### Key Statistics (2024)
+- **37.5M** codex citations extracted
+- **2.25M** court decisions
+- **3,671** statutory articles evaluated
+- **1.8M** retrieval predictions (leave-one-out protocol)
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `article_retrieval_performance.parquet` | Per-article metrics: degree, mean rank, Hit@5, MRR for 3,667 articles (year 2024) |
+| `temporal_metrics.parquet` | Retrieval metrics by year (2007-2026) showing temporal degradation |
+| `temporal_metrics.csv` | Same as above in CSV format |
+| `difficulty_stratification.csv` | Performance by article frequency bin (hub/high/mid/low/rare) |
+
+## Evaluation Protocol
+
+**Leave-one-out on the full bipartite citation graph:**
+
+For each court decision with cited articles {a1, ..., an}:
+1. Mask each article ai in turn
+2. Score all candidate articles using remaining citations as seed
+3. Compute rank of masked article among non-seed candidates
+
+### Baselines
+
+| Method | Hit@10 | MRR |
+|--------|:------:|:---:|
+| Adamic-Adar | 0.545 | 0.272 |
+| Common Neighbors | 0.534 | 0.266 |
+| Degree Baseline | 0.111 | 0.059 |
+
+### Difficulty Stratification
+
+| Bin | Articles | Hit@10 (AA) | MRR (AA) |
+|-----|:--------:|:-----------:|:--------:|
+| Hub (>100K cites) | 21 | 0.890 | 0.536 |
+| High (10K-100K) | 354 | 0.601 | 0.274 |
+| Mid (1K-10K) | 864 | 0.138 | 0.074 |
+| Low (100-1K) | 1,689 | 0.041 | 0.020 |
+| Rare (<100) | 739 | 0.012 | 0.010 |
+
+### Temporal Degradation
+
+MRR degrades from 0.42-0.56 (2008-2012) to 0.27-0.28 (2024-2026), demonstrating that co-citation patterns become less predictive over time as legal practice evolves.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("overthelex/ua-statute-retrieval")
+
+# Per-article performance
+articles = ds["train"].to_pandas()
+print(articles[["target_article", "degree", "mrr_aa", "hit5_aa"]].head(20))
+```
+
+## Citation
+
+```bibtex
+@article{keshchenko2025statute,
+  title={UA-StatuteRetrieval: A Full-Graph Benchmark for Legal Article Prediction from 37M Citation Records},
+  author={Keshchenko, Volodymyr},
+  year={2025}
+}
+```
+
+## Source Data
+
+Derived from the Ukrainian Unified State Register of Court Decisions (EDRSR). Raw citation data (502M records) is not published.
+
+## Related Datasets
+
+- [overthelex/ua-court-citation-graph](https://huggingface.co/datasets/overthelex/ua-court-citation-graph) - Co-citation graph
+- [overthelex/ukrainian-court-decisions](https://huggingface.co/datasets/overthelex/ukrainian-court-decisions) - Court decision metadata
+- [overthelex/ua-case-outcome](https://huggingface.co/datasets/overthelex/ua-case-outcome) - Case outcome prediction

article_retrieval_performance.parquet

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+oid sha256:06f544bb07da66d5313825c63754057d962a5693653d7c545f1135e5217cddbd
+size 225154

difficulty_stratification.csv

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+degree_bin,n_predictions,n_articles,hit1_cn,hit5_cn,hit10_cn,mrr_cn,hit1_aa,hit5_aa,hit10_aa,mrr_aa,hit1_dg,hit5_dg,mrr_dg
+hub(>100K),298925,21,0.363398845864347,0.79773187254328,0.898024588107385,0.543821656383038,0.355192774107218,0.788294722756544,0.890056034122272,0.535525984923118,0.181329764991219,0.381881742912102,0.301099276199947
+high(10K-100K),1127489,354,0.124254870779227,0.408409305988795,0.59020797542149,0.266446459130413,0.131266025655239,0.418518495524125,0.600500758765717,0.274310921368596,0,0,0.0141067798050333
+mid(1K-10K),316972,864,0.0228726827606224,0.073321933798569,0.113347551203261,0.0637024796806887,0.028901606451043,0.089257095263935,0.138037429173555,0.0744873734279912,0,0,0.00173933988002968
+low(100-1K),68807,1689,0.00283401398113564,0.0114523231647943,0.0182975569346142,0.0140961569097905,0.00472335663522607,0.0182684901245513,0.0409842021887308,0.0202480131879962,0,0,0.000597797580154083
+rare(<100),5338,739,0.00337204945672537,0.00824278756088423,0.00974147620831772,0.0079957420334313,0.00505807418508805,0.0104908205320345,0.011989509179468,0.0104316586511722,0,0,0.00031162620737795

temporal_metrics.csv

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