README.md

metadata

license: apache-2.0
language:
  - uk
  - en
size_categories:
  - 10K<n<100K

Pretrain Dataset for Ukrainian Reranker/Embedder (80k EN→UK)

English retrieval dataset with pre-mined hard negatives, designed for translation to Ukrainian and use as a pretraining stage before fine-tuning on competition/domain-specific data.

Purpose

Stage 1 (pretrain) of a 3-stage training pipeline:

1. Pretrain on this dataset (translated to Ukrainian) — teaches general retrieval mechanism
2. Finetune on UA-SQuAD — adapts to Ukrainian QA patterns
3. Finetune on competition train set — adapts to target domain (UNLP 2026)

Dataset Summary

Source	Rows	Avg Positive Length	Negatives/Row	Domain
Natural Questions	30,000	611 chars	3	Wikipedia (broad coverage)
HotpotQA	20,000	438 chars	3	Wikipedia (multi-hop reasoning)
MS MARCO	20,000	348 chars	1–3	Web search (diverse queries)
GooAQ	10,000	252 chars	3	Google answer boxes (lay explanations)
Total	80,000	median 372 chars

Design Rationale

• Passage length diversity: 50-token Google snippets → 300-token multi-paragraph → 2000+ token Wikipedia sections. Matches variable chunking strategies (512-token chunks to full-page chunks).
• Hard negatives only: All sources have pre-mined hard negatives from dense retrievers or BM25 — no random/easy negatives.
• Domain coverage: Broad encyclopedic (NQ, HotpotQA), web search (MS MARCO), and informal Q&A (GooAQ) to generalize across unseen domains.

Format

Parquet file with columns:

Column	Type	Description
query	str	Question or search query (English)
positive	str	Relevant passage (English)
negative_1	str	Hard negative passage #1
negative_2	str	Hard negative passage #2 (may be empty for some MS MARCO rows)
negative_3	str	Hard negative passage #3 (may be empty for some MS MARCO rows)
source	str	Dataset origin: nq, hotpotqa, msmarco, gooaq

Statistics

Total rows: 80,000

Per source:
  gooaq       :  10000 rows | avg positive length: 252 chars | has neg_1: 100%
  hotpotqa    :  20000 rows | avg positive length: 438 chars | has neg_1: 100%
  msmarco     :  20000 rows | avg positive length: 348 chars | has neg_1: 100%
  nq          :  30000 rows | avg positive length: 611 chars | has neg_1: 100%

Query length:    min=5, median=45, max=630
Positive length: min=10, median=372, max=9437

Translation

This dataset is in English. For Ukrainian pretraining, translate using:

• TranslateGemma-27B (self-hosted, best quality for EN→UK)
• Lapa LLM v0.1.2 (Ukrainian-optimized, efficient tokenizer)
• Claude Haiku 4.5 / GPT-5.4-mini Batch API (commercial alternative)

Translated columns (query_uk, positive_uk, negative_1_uk, etc.) will be added after translation.

Intended Use

Training bi-encoder (embedding) and cross-encoder (reranker) models for Ukrainian document retrieval, specifically for the UNLP 2026 Shared Task on Multi-Domain Document Understanding.

Compatible with:

• FlagEmbedding training format (convert to {query, pos, neg} JSONL)
• Sentence Transformers triplet training
• Any contrastive learning framework

License

This dataset aggregates data from multiple sources. Please refer to the original dataset licenses:

• Natural Questions: Apache 2.0
• HotpotQA: CC BY-SA 4.0
• MS MARCO: MIT
• GooAQ: Apache 2.0