README.md

metadata

language:
  - ro
license: cc-by-4.0
task_categories:
  - text-retrieval
  - sentence-similarity
tags:
  - romanian
  - retrieval
  - triplets
  - hard-negatives
  - bm25
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/triplets_27b_bm25_hf_train.parquet
      - split: eval
        path: data/triplets_27b_bm25_hf_eval.parquet
      - split: test
        path: data/triplets_27b_bm25_hf_test.parquet

Romanian retrieval triplets (BM25 hard negatives)

(anchor, positive, negative) triplets for training a Romanian retriever, generated with Gemma 3 27B over a merged Romanian corpus and mined for hard negatives with BM25.

A late-interaction variant of the same triplets is at PaulBurca2005/ro-retrieval-triplets-late-interaction. The two are close to disjoint - over the queries both contain, mean Jaccard overlap between their negative sets is 0.053 and 73% share no negative at all - so training on the union exposes a model to both failure modes.

The column layout follows alina0195/ro-msmarco-divided, with one column added.

column
anchor	the query
positive	the document the query was generated from
negative	one hard negative, mined by BM25
query_source	the upstream dataset or outlet the query comes from

One row per (query, positive, negative) pair, so a query with four negatives is four rows repeating the anchor and the positive.

How it was built

The positive is not retrieved, it is known: every query was generated from a document, so that document is the positive by construction. Retrieval is used only to mine negatives.

Candidates come from the BM25 top-100 within the positive's own document type. A candidate becomes a negative only if it scores in a band relative to the positive's own score for that query, shares enough of the query's idf mass, contains one of the query's rarest terms, and is not a near-copy of the positive or of a negative already picked.

The corpus is deduplicated by a near-duplicate key first, boilerplate is dropped, and self-referential queries ("what does the article say…") are removed - identically in both variants, so the two are comparable.

Sources

source	rows	share
realitatea	8 098	10.1%
readerbench/ro-stories	7 206	9.0%
protv	7 141	8.9%
readerbench/ro-text-summarization (alephnews)	6 982	8.7%
readerbench/ro-text-summarization (digi24)	6 530	8.1%
zf	5 994	7.5%
evz	5 885	7.3%
libertatea	5 616	7.0%
mediafax	5 543	6.9%
digi24	5 479	6.8%
aleph	4 799	6.0%
cotidianul	3 953	4.9%
BlackKakapo/recipes-ro	3 819	4.8%
adevarul	3 087	3.8%
readerbench/ro-text-summarization (mediafax)	231	0.3%

Known limitations

Read these before training on it.

1. Residual false negatives. The guards remove candidates that answer the query better than the positive does, not every candidate that happens to answer it. A second article about the same event, worded differently, can still appear as a negative. The builder also records candidates it judged to be the positive's own story — that judgement was measured at roughly 70% precision, because lexical overlap sees shared topic, not shared event: it cannot separate two different earthquakes, or two different articles about the same person. Those are recorded for a reranker to re-judge, not treated as verified positives.
2. Source imbalance. The corpus is a merge of news outlets, folk tales, recipes and summarisation corpora with very different sizes and registers. query_source exists so this can be weighted or filtered; the distribution above is not uniform.
3. Machine-generated queries. The queries are Gemma 3 27B output over the source documents. They were filtered but not human-verified.
4. No stemming. The BM25 tokenizer folds diacritics but does not stem, and Romanian is heavily inflected, so lexical matching is weaker than it could be.

Splits

eval, test, train.

Where splits exist they are grouped by the positive's duplicate group, so a query and a re-published copy of its positive never land on opposite sides.