mining-negatives/fiqa_pl

FIQA PL/EN Reranked Hard Negatives

This dataset contains two aligned JSONL training files for embedding-model fine-tuning:

• train_pl.jsonl: Polish texts.
• train_en.jsonl: English texts.

Both files contain the same 5,500 training records in the same order. The text fields (query, pos, neg) are language-specific, while all metadata fields are shared across the aligned Polish and English examples. This means that the same query-document pair has the same IDs, reranker scores, retrieval scores, and selection metadata in both language versions.

The Polish subset is configured as the default Dataset Viewer subset.

Dataset Statistics

Metric	Value
Queries per language	5,500
Total positives per language	14,850
Negatives per query	10
Queries with mined synthetic positives	684
Mined synthetic positives	684
Positives with pos_scores_stronger_reranker > 23.50	3,190
Queries with at least one positive above 23.50	2,086
Queries with no positive above 23.50	3,414
Mean neg_scores	-3.5121
Mean neg_scores for queries with at least one positive above 23.50	-3.4766

Text Length Statistics

Character lengths are measured in characters. Token lengths include model special tokens and were computed without padding or truncation.

Character Lengths

Language	Texts	Count	Mean	Median	p90	p95	p97	p99
EN	Queries	5,500	61.5	59	92	103	109	124
EN	Positive passages	14,850	1,019.7	779	2,050	2,707	3,157	4,389
EN	Negative passages	55,000	640.2	409	1,339	2,041	2,598	3,678
PL	Queries	5,500	72.4	69	111	124	134	153
PL	Positive passages	14,850	1,086.5	840	2,158	2,846	3,319	4,508
PL	Negative passages	55,000	683.8	436.5	1,406	2,252	2,798	3,708

Token Lengths

Tokenizer	Language	Texts	Count	Mean	Median	p90	p95	p97	p99
sdadas/mmlw-retrieval-roberta-base	EN	Queries	5,500	23.5	23	35	39	41	47
sdadas/mmlw-retrieval-roberta-base	EN	Positive passages	14,850	352.5	270	704	929	1,084	1,515
sdadas/mmlw-retrieval-roberta-base	EN	Negative passages	55,000	226.1	144	474	725	912	1,277
sdadas/mmlw-retrieval-roberta-base	PL	Queries	5,500	16.2	15	24	27	30	34
sdadas/mmlw-retrieval-roberta-base	PL	Positive passages	14,850	221.2	171	438	574	671	934
sdadas/mmlw-retrieval-roberta-base	PL	Negative passages	55,000	147.2	94	302	469	575	852
sdadas/mmlw-retrieval-roberta-large-v2	EN	Queries	5,500	21.1	20	31	34	36	41
sdadas/mmlw-retrieval-roberta-large-v2	EN	Positive passages	14,850	310.0	237	618	819	952	1,329
sdadas/mmlw-retrieval-roberta-large-v2	EN	Negative passages	55,000	199.5	128	421	642	800	1,142
sdadas/mmlw-retrieval-roberta-large-v2	PL	Queries	5,500	15.5	15	23	26	28	31
sdadas/mmlw-retrieval-roberta-large-v2	PL	Positive passages	14,850	207.1	161	409	536	623	866
sdadas/mmlw-retrieval-roberta-large-v2	PL	Negative passages	55,000	137.1	88	281	438	536	784

Data Construction

This dataset was prepared with the help of lightonai/embeddings-fine-tuning. In the LightOnAI dataset, the authors computed 2,048 document candidates for each query using Alibaba-NLP/gte-modernbert-base. The similarity scores produced by LightOnAI are stored in original_pos_scores and original_neg_scores.

For this prepared FIQA export, candidate query-document pairs were reranked with mixedbread-ai/mxbai-rerank-base-v2. The resulting scores are stored in pos_scores and neg_scores. Reranking was performed until 10 negatives satisfying the selection rules were found for each query. During the same process, if candidate documents met the positive thresholds, they were added as mined positives.

At the end of the pipeline, positives were also scored with BAAI/bge-reranker-v2.5-gemma2-lightweight. These scores are stored in pos_scores_stronger_reranker. This stronger reranker score is intended for quality filtering before training: original datasets can contain query-positive pairs where the positive passage does not actually answer the query, so positives with too-low pos_scores_stronger_reranker values should be removed before model training.

Dataset Structure

Each JSONL row has the following structure:

{
  "query_id": "448",
  "query": "...",
  "pos": ["..."],
  "neg": ["..."],
  "pos_scores": [5.75],
  "neg_scores": [-2.5, -2.5],
  "prompt": "",
  "type": "retrieval"
}

The actual records contain all fields documented below.

Text Fields

Field	Description
query	Query text in the language of the subset.
pos	List of positive passages/documents in the language of the subset.
neg	List of negative passages/documents in the language of the subset.

Metadata Fields

Field	Description
query_id	Query/record identifier used to align the Polish and English versions.
pos_scores	Scores for query + positive pairs computed with mixedbread-ai/mxbai-rerank-base-v2.
neg_scores	Scores for query + negative pairs computed with mixedbread-ai/mxbai-rerank-base-v2.
prompt	Training prompt field; empty in these files.
type	Task type; retrieval in these files.
pos_id	Document IDs for positives.
neg_id	Document IDs for negatives.
pos_is_synthetic	Boolean flags indicating whether each positive was mined by the pipeline. false means the positive came from the original FIQA dataset; true means it was mined and should be treated as synthetic.
neg_selection_tier	Negative-selection label for each negative. Negatives selected from the 2,048 LightOnAI candidates keep their tier label, such as strict in this export. If the 2,048 candidates did not provide enough valid negatives, additional documents were sampled from the full corpus and the highest-scoring valid ones were added with relaxed_backfill.
original_pos_scores	LightOnAI similarity scores for positives, computed with Alibaba-NLP/gte-modernbert-base.
original_neg_scores	LightOnAI similarity scores for negatives, computed with Alibaba-NLP/gte-modernbert-base.
pos_scores_stronger_reranker	Positive scores computed at the final stage with BAAI/bge-reranker-v2.5-gemma2-lightweight; intended for filtering low-quality positives before training.
mean_neg_score	Mean value of neg_scores for the record.
mean_pos_score	Mean value of pos_scores for the record.
max_neg_score	Maximum value of neg_scores for the record.
difference_between_mean_scores	Difference between mean_pos_score and mean_neg_score.
difference_between_max_scores	Difference between mean_pos_score and max_neg_score.

Intended Use

The dataset is intended for training and evaluating retrieval or embedding models with query-positive-negative examples. The aligned Polish and English subsets can be used for monolingual training, bilingual comparisons, or multilingual fine-tuning setups.

Before training, consider filtering positives using pos_scores_stronger_reranker to remove weak query-positive pairs.