Datasets:

Hyukkyu
/

train-gerlayqa

pretty_name: Training · GerLayQA
license: apache-2.0
language:
  - de
multilinguality:
  - monolingual
task_categories:
  - text-retrieval
task_ids:
  - document-retrieval
tags:
  - train
  - retrieval
  - legal
configs:
  - config_name: corpus
    data_files:
      - split: train
        path: corpus/train-*.parquet
  - config_name: hard-negatives
    data_files:
      - split: train
        path: hard-negatives/train-*.parquet
  - config_name: qrels
    data_files:
      - split: train
        path: qrels/train-*.parquet
  - config_name: queries
    data_files:
      - split: train
        path: queries/train-*.parquet
  - config_name: teacher-scores
    data_files:
      - split: train
        path: teacher-scores/train-*.parquet

GerLayQA — Training, unified schema

A seeded sample of ViolaCamille/GerLayQA, 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	`ViolaCamille/GerLayQA` @ `7b7b14c5adab`
Task	German lay legal question → lawyer's answer
Domain · languages	legal (German) · deu
Queries / documents / qrels	23,923 / 23,923 / 23,923
Qrels per query	min 1 · mean 1.0 · max 1
Score values	2 ×23,923 (2: the first positive, 1: any other)
Layout	`queries` · `corpus` · `qrels` · `hard-negatives` · `teacher-scores`, split `train`
Splits	`corpus`: train · `hard-negatives`: train · `qrels`: train · `queries`: train · `teacher-scores`: train
Hard negatives	sources: `dense` · 2,370,498 rows
Teacher scores	none yet (0 rows): `jinaai/jina-reranker-v3.5` scores come next
Ids	`sha1(text)[:20]`; identical texts collapse to one document / query
License	`apache-2.0`

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

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 25,000 pairs
reshaped: the layperson's question (at most 2,000 characters) is the query, the lawyer's answer (at least 40 characters) the document; configs german_laymen_bgb_qa, _stgb_qa, _zpo_qa
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]: 0 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 collection's annotation pipeline (annotation=jina35). Interim: the candidates are final, the teacher scores are still to come.

Candidates: dense retrieval with jinaai/jina-embeddings-v5-text-small over this 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.
Teacher scores: none yet. teacher-scores holds 0 rows until the jinaai/jina-reranker-v3.5 scores (listwise, as in the other repositories) are filled in; datasets cannot return a 0-example split, so read that file with pyarrow / pandas meanwhile. The candidates stay.

configs	queries	hard negatives	teacher scores
`hard-negatives` · `teacher-scores`	23,923 (all)	2,370,498 (2,370,498 dense)	0

Load it

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

License and attribution

The data is redistributed under the source's terms — apache-2.0. All credit belongs to the original authors; see the source repository (https://huggingface.co/datasets/ViolaCamille/GerLayQA). This repository is an independent repackaging.