Hyukkyu/train-clerc

CLERC — Training, unified schema

A seeded sample of jhu-clsp/CLERC, made into retrieval training pairs and reshaped into the strict schema shared by every dataset in this collection. One of the 15 domain sources (code, medical, science, finance, legal) added to the collection's general sources.

Source	jhu-clsp/CLERC @ ef042f8ab436
Task	case paragraph → cited case
Domain · languages	legal · eng
Queries / documents / qrels	50,000 / 611,660 / 50,000
Qrels per query	min 1 · mean 1.0 · max 1
Score values	2 ×50,000 (2: the first positive, 1: any other)
Layout	queries · corpus · qrels · hard-negatives · teacher-scores, split train
Splits	corpus: train · hard-negatives: train · judgments: train · qrels: train · queries: train · teacher-scores: train
Hard negatives	sources: dataset, dense · 5,321,535 rows
Teacher scores	jinaai/jina-reranker-v3.5 · 5,316,965 rows (positives included)
Judgments	judgments: typesafe/jev-1.13.0 · 1,977,690 rows
Ids	sha1(text)[:20]; identical texts collapse to one document / query
License	unspecified

Schema

config	columns	rules
queries	id: string, text: string	ids unique and non-empty; every query has ≥ 1 qrel
corpus	id: string, title: string, text: string	title is always present ("" when the source has none)
qrels	query-id: string, corpus-id: string, score: int32	referential integrity to both tables; no duplicate pairs; no floats
hard-negatives	query-id: string, corpus-id: string, rank: int32, source: string	one row per negative; (query-id, corpus-id, source) unique; never a labelled positive of the same query
teacher-scores	query-id: string, corpus-id: string, teacher: string, score: float32	one row per scored pair (positives included); a row means scored — never a placeholder
judgments	query-id: string, corpus-id: string, judge: string, role: string, p_yes: float64, round: int32	one row per judged pair; role is positive (the training positive) or candidate (a mined candidate, never a labelled positive or a labelled negative); p_yes in [0, 1]; round 0 the first request, 1.. the top-ups

Files are Parquet, sorted by id, zstd-compressed, sharded at 500 MB. Every rule above is checked before publishing; provenance.json records the source revision, what changed, and the output file hashes.

What changed from the source

• sampled: a seeded random sample (seed 1) of up to 50,000 pairs, streamed through a shuffle buffer of 20,000
• reshaped: the case paragraph (query, REDACTED replaced by a space) is the query, the passages of the case it cites the documents (with their titles)
• negatives the source provides: up to 15 of the row's negative passages (hard-negatives source = dataset)
• decontaminated (exact): a pair was dropped when its normalised query equals any evaluation query, or a positive equals a document of a test or dev corpus; a repeated query keeps its first pair
• decontaminated (near-duplicates): 0 passages and 0 queries that nearly copy a text of an evaluation set (word 13-grams for passages, 8-grams for queries; at least half shared with one text of the 23 test corpora (BEIR, RTEB, LitSearch) and the 3 dev corpora) were removed, and with them 0 queries in total
• text: leading and trailing whitespace stripped; otherwise as converted above
• ids re-keyed to sha1(text)[:20]: 6 documents and 0 queries collapsed into identical texts
• added a title column filled with "" (the source has none)

Hard negatives and teacher scores

Filled by the owner's annotation pipeline (annotation=jina35-u2) for the train split of the query set(s) below; queries without a labelled positive are left out.

• Candidates: dense retrieval with jinaai/jina-embeddings-v5-text-small over the full corpus to depth 1,000; 100 candidates per query drawn from the rank windows 1–30 (30), 31–100 (30), 101–300 (20), 301–1000 (20), the query's labelled positives excluded. rank is the dense rank; source is dense for a mined row and dataset for a negative the source labels itself (those are kept for every query of the split, sampled or not).
• Teacher: jinaai/jina-reranker-v3.5, listwise: a query's positive and all of its candidates are scored together in one context of up to 32,768 tokens. score is the raw cosine score, one row per (query, positive) and per (query, candidate); a labelled negative that was also mined is scored once. Every document was cut to its first 1,024 reranker tokens before scoring (max_doc_tokens=1024). No filtering is applied to the tables.

configs	queries	hard negatives	teacher scores
hard-negatives · teacher-scores	50,000 (all)	5,321,535 (342,420 dataset, 4,979,115 dense)	5,316,965

from datasets import load_dataset
negatives = load_dataset("Hyukkyu/train-clerc", "hard-negatives", split="train")
scores    = load_dataset("Hyukkyu/train-clerc", "teacher-scores", split="train")

Jev judgments

judgments holds, for every query of the training sample (the queries with teacher scores), whether TypeSafe's Jev (jev-1.13.0) judged its training positive and its mined candidates relevant: p_yes is Jev's P(yes) for the source's question (e.g. does the passage answer the query?). They locate the false negatives among the mined candidates and the mislabelled positives.

• Requests. One request per query (round 0): its training positive, its candidates whose teacher score taken as (cos + 1) / 2 is at least 0.85 × the positive's (at most 12, the highest scores) and 4 random candidates below that, shuffled under neutral ids, one yes/no question per passage. Queries left with fewer than 10 candidates under their source's cutoff got their next hardest unjudged candidates in rounds 1–8 (8 per request), those still under 10 in rounds 9–10 (24 per request). The dataset's own labelled negatives were never sent. Texts were cut to 512 (query) and 512 (passage) tokens of the jina-embeddings-v5 small tokenizer. Jev answers a request's passages in one context, so P(yes) is calibrated to these groups: the thresholds below apply to this table, not to single-pair calls.
• Accuracy (an audit of 1237 pairs from the pilot's first requests (100 queries per source; the five long-query sources re-piloted at 512-token queries), labelled blind by an LLM (Claude), at the pilot's fixed thresholds 0.35 and 0.15): a candidate at P(yes) ≥ 0.35 was relevant 67% of the time inside the band (n = 350) and 45% below it (n = 87); one under 0.35 was relevant 6% (band, n = 387) and 1% (below the band, n = 210) of the time. A positive under 0.15 was mislabelled 100% of the time (n = 20) in the sources that keep the check; in dom-casehold, dom-clerc, dom-cornstack-py, dom-finqa10k, dom-gerlayqa, dom-investopedia, dom-lawse, dom-magicoder, dom-medmcqa, dom-pubmedqa, dom-s2orc, dom-tatqa, Jev's flags were right less often (0%–57% in this audit), under the 70% the check needs, so their positives are not checked.
• Use (the SPARSE loader, annotation.filter.judge): a candidate at P(yes) ≥ its source's cutoff (below; fitted on 1,521 labelled pairs) is never a negative; a positive under 0.15 is replaced by the candidate Jev scores highest if that is ≥ 0.8, else the query is dropped; no candidate becomes an extra positive here (promotion is off for this source). Compare p_yes as a float64 (it is stored as one).

config	queries	rows	candidates per query	top-up rows	candidate cutoff	positive check
judgments	50,000	1,977,690	38.6	1,148,296	0.30	skipped

Load it

from datasets import load_dataset
queries   = load_dataset("Hyukkyu/train-clerc", "queries", split="train")
corpus    = load_dataset("Hyukkyu/train-clerc", "corpus", split="train")
qrels     = load_dataset("Hyukkyu/train-clerc", "qrels", split="train")
negatives = load_dataset("Hyukkyu/train-clerc", "hard-negatives", split="train")
scores    = load_dataset("Hyukkyu/train-clerc", "teacher-scores", split="train")
judgments = load_dataset("Hyukkyu/train-clerc", "judgments", split="train")

License and attribution

The data is redistributed under the source's terms — unspecified. All credit belongs to the original authors; see the source repository (https://huggingface.co/datasets/jhu-clsp/CLERC). This repository is an independent repackaging.

No license declared upstream. The source repository states no redistribution terms. This copy is published for the reproducibility of our training data only; check with the original authors before any other use.