Update README.md

README.md

@@ -21,67 +21,6 @@ configs:
   data_files: labels.jsonl
 ---
 
-# Deka retrieval
-
-A legal case retrieval (LCR) benchmark on Thai Supreme Court decisions (คำพิพากษาฎีกา, *deka*):
-given a case, retrieve the prior cases that bear on it out of the full 9,998-case pool. Thailand
-is a civil-law jurisdiction, so relevance is defined over case components — charge, facts, issue,
-outcome — rather than citation links between judgments, and that criterion is what yields the four
-label quadrants below. Candidates were mined with a dense retriever and adjudicated by an LLM
-judge, and every anchor carries three query registers. Anonymous release accompanying a paper
-under review.
-
-## Files
-
-| file | rows | schema |
-|---|---|---|
-| `corpus.jsonl` | 9,998 | `_id`, `deka_code`, `long_text`, `metadata{deka_no, year, fact, reasoning, laws, crimes, case_type, case_type_group, issue, decision}` |
-| `queries.jsonl` | 1,381 | `_id`, `long_text`, `lay_query`, `lawyer_query`, `metadata{…, split}` |
-| `labels.jsonl` | 1,381 | `query_id`, `deka`, `split`, `mined`, `easy_pos[]`, `hard_pos[]`, `hard_neg[]`, `easy_neg[]`, `anomaly[]` |
-
-Every entry in a label list is `{candidate_id, deka, score, why, mining_query}` — `candidate_id`
-joins to `corpus._id`, `mining_query` records which query text surfaced that candidate.
-
-## Query registers
-
-Each anchor carries three usable query forms, which retrieve very differently:
-
-- `lay_query` — how a layperson would describe the situation (short, non-technical)
-- `lawyer_query` — how a lawyer would phrase it (statutory language)
-- `metadata.fact` — the case's own fact section, i.e. the symmetric case-to-case setting
-
-## Splits
-
-`labels.jsonl` is authoritative. Anchors are split `train` 1,176 / `eval` 100 / `test` 105.
-
-**Excluded:** 191 anchors marked `keep_test` are not part of this release. In `queries.jsonl`,
-100 rows whose `metadata.split` disagreed with `labels.jsonl` have been corrected to the label
-value, so the two files now agree row for row.
-
-The 9,998-case corpus is untouched and shared by every split.
-
-## Heuristic case criteria
-
-- `easy_pos` — same charge, same decision outcome
-- `hard_pos` — same charge, different outcome
-- `hard_neg` — retrieved as similar, judged not relevant
-- `easy_neg` — clearly unrelated
-- `anomaly` — candidates the judge could not place cleanly; not relevant by default
-
-Roughly one candidate per list per anchor (1,401 entries per list across 1,381 anchors);
-175 anchors have no `anomaly` entry.
-
-## Caveats
-
-- Positives are **mined, not exhaustively annotated**. A case absent from `easy_pos`/`hard_pos`
-  is not certified irrelevant, so recall computed on this data is a lower bound.
-- Judgements come from an LLM judge over retrieved candidates; they inherit both the retriever's
-  blind spots and the judge's.
-- `issue[]` and `decision` were extracted from `long_text` by rule, not by hand.
-- Decisions are published court documents, but they name parties and describe offences — treat
-  the text as sensitive and do not use it to profile individuals.
-- Absolute retrieval quality is low (best dense R@10 ≈ 31 on fact queries); this is a research
-  bed, not a solved task.
 
 ## Loading
 
@@ -91,6 +30,4 @@ from datasets import load_dataset
 corpus  = load_dataset("anonymousresearch123/deka-retrieval", "corpus",  split="train")
 queries = load_dataset("anonymousresearch123/deka-retrieval", "queries", split="train")
 labels  = load_dataset("anonymousresearch123/deka-retrieval", "labels",  split="train")
-```
-
-Each config is a single file; filter `metadata.split` / `split` for train / eval / test.
+``