cruciverb-it/crosswordspacepp-reranker

CrosswordSpace++: Italian BERT cross-encoder reranker

The second-stage reranker of CrosswordSpace++. It is a sentence-transformers CrossEncoder that scores (clue, answer) pairs for Italian crosswords.

It reranks the top-100 length-filtered candidates retrieved by the bi-encoder cruciverb-it/crosswordspacepp-dualencoder. The final score is a convex blend of the two models' per-clue softmax-normalized scores, α · CE + (1 − α) · BE with α = 0.17, where α was tuned on the validation set.

Code: https://github.com/snizio/crosswordspacepp

Model Description

• Model type: Cross-encoder (BertForSequenceClassification, 1 output label, sigmoid activation)
• Base model: nickprock/sentence-bert-base-italian-xxl-uncased
• Max sequence length: 84 tokens
• Training data: EVALITA 2026 CruciverbIT Task 1 train split. Each of the 374,766 clues is paired with its gold answer and 9 negatives mined from the bi-encoder candidates (3 hard, 3 medium, 3 easy), for 3,747,628 pairs in total. Gold answers, their synonyms and stem-matches are excluded from the negatives.
• Training objective: binary cross-entropy (pos_weight = 9), lr 2e-5, batch size 256, 10 epochs, bf16; the best checkpoint by validation reranking MRR@10 is released

Usage

from sentence_transformers import CrossEncoder

model = CrossEncoder("cruciverb-it/crosswordspacepp-reranker")

clue = "Giorni di metà mese nell'antica Roma"
candidates = ["idi", "net", "ier", "die", "vii"]

scores = model.predict([(clue, c) for c in candidates])
print(scores)
# [9.98e-01 9.45e-01 2.05e-03 3.80e-04 2.22e-04]

print(model.rank(clue, candidates))

Evaluation

Results on the EVALITA 2026 CruciverbIT Task 1 test set (20,821 clues). All systems rank the top-100 length-filtered candidates from the bi-encoder.

System	Acc@1	Acc@10	MRR@10
Bi-encoder only	57.9	80.9	65.8
Cross-encoder only (this model)	59.9	83.1	67.9
Blend (α = 0.17)	68.2	85.5	74.5

Citation

TBD

License

CC BY 4.0