Aranda-Reranker-v1

Cross-encoder reranker specialized for Malaysian text, designed to work as Stage 2 after Aranda-v1 dense retrieval.

Pipeline

Query → Aranda-v1 (retrieve top-25) → Aranda-Reranker-v1 (rerank) → top-5 results

Aranda-v1 is fast but encodes query and documents separately. Aranda-Reranker-v1 processes query+document together with cross-attention, catching subtle mismatches the bi-encoder misses.

Evaluation (4,149 queries, BM + Manglish + English + Cross-lingual)

Overall Pipeline vs Aranda-v1 Alone

Metric	Aranda-v1 alone	+ Aranda-Reranker-v1	Improvement
Recall@1	0.8891	0.9311	+4.2
Recall@5	0.9961	0.9867	-0.9
Recall@10	0.9998	0.9971	-0.3
MRR	0.9364	0.9563	+2.0

Per-Language Recall@1

Language	Aranda-v1 alone	+ Aranda-Reranker-v1	Improvement
BM	0.8431	0.8874	+4.4
Cross-lingual	0.8500	0.9833	+13.3
English	0.8792	0.8940	+1.5
Manglish	0.9290	0.9656	+3.7

The reranker improves Recall@1 on all four languages, with a dramatic +13.3 point gain on cross-lingual (BM↔English) retrieval.

Training

Fine-tuned from BAAI/bge-reranker-v2-m3 on 30,925 Malaysian hard-negative triplets:

• 20K social media (Lowyat, Twitter, Facebook)
• 5.6K formal BM QA (mesolitica common-crawl-qa)
• 3.5K English + cross-lingual anchors (up-sampled)
• 1.8K holdout + negation pairs

Only top 4 of 24 transformer layers were fine-tuned (9.1% of parameters). Contrastive ranking loss. LR=2e-5, bf16.

Usage

from sentence_transformers import SentenceTransformer, CrossEncoder

# Stage 1: Dense retrieval with Aranda-v1
retriever = SentenceTransformer("rekabytes/Aranda-v1")
query_emb = retriever.encode([query], normalize_embeddings=True)
doc_embs = retrieaver.encode(documents, normalize_embeddings=True)
scores = query_emb @ doc_embs.T
top_25 = scores.argsort()[0][-25:][::-1]

# Stage 2: Rerank with Aranda-Reranker-v1
reranker = CrossEncoder("rekabytes/Aranda-Reranker-v1")
candidates = [documents[i] for i in top_25]
pairs = [[query, doc] for doc in candidates]
rerank_scores = reranker.predict(pairs)
final_order = rerank_scores.argsort()[::-1]
top_5 = [candidates[i] for i in final_order[:5]]

Model Details

• Architecture: XLM-RoBERTa (24 layers, 1024 hidden) with sequence classification head
• Base model: BAAI/bge-reranker-v2-m3
• Max sequence length: 512 tokens (query + document)
• Input: [CLS] query [SEP] document [SEP]
• Output: Single relevance score (higher = more relevant)
• Latency: ~2ms per (query, document) pair on GPU