GoktugD/DUSUNEN-Rota-270M-v2

DUSUNEN Rota 270M v2

A 268.1M-parameter Turkish dense retriever continued from DUSUNEN Rota v1 on 50,000 model-mined difficult negatives. The release is designed as a transparent hard-negative experiment: it publishes positive, neutral and negative evidence.

Five-task measured results

Model	Params	Dim	TurHist	XQuAD	WebFAQ	MKQA	Belebele	Macro
multilingual E5 base	278.0M	768	0.49726	0.95335	0.65032	0.07213	0.92503	0.619618
DUSUNEN Rota 270M v2	268.1M	640	0.42198	0.86393	0.56886	0.10331	0.88493	0.568602
DUSUNEN Rota 270M v1	268.1M	640	0.42196	0.85832	0.56402	0.10296	0.88222	0.565896
DUSUNEN Pusula 118M v0	117.7M	384	0.25299	0.81123	0.46307	0.04855	0.82451	0.480070

v2 improves v1 on all five tasks, with a macro change of +0.002706 points (about +0.48% relative). The hard-negative triplet validation score itself was unchanged at 0.8935. The appropriate claim is a small, consistent held-out gain—not a major jump. E5 remains the overall suite leader; DUSUNEN Rota v2 exceeds it only on MKQA in this matrix.

Use

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("GoktugD/DUSUNEN-Rota-270M-v2")
task = "Given a Turkish web search query, retrieve relevant passages that answer the query"
query = f"Instruct: {task}\nQuery: Hard negative neden önemlidir?"
query_vector = model.encode(query, normalize_embeddings=True)
document_vectors = model.encode(
    ["Zor negatifler karar sınırını güçlendirir.", "Ankara Türkiye'nin başkentidir."],
    normalize_embeddings=True,
)
print(document_vectors @ query_vector)

Queries require the instruction format shown above. Documents are plain text.

Mining and training

• Base: GoktugD/DUSUNEN-Rota-270M-v1
• Mined data: 50,000 train / 2,000 validation triplets
• Candidate pool: 70,172 source-labeled negatives; search depth: 32
• Successful mined rows: 50,000; fallbacks: 0; mean cosine: 0.550376
• Exact normalized TurHistQuad overlap: 0
• Objective: Cached Multiple Negatives Ranking Loss
• Sequence length: 256; effective batch: 64; learning rate: 8e-6
• One epoch, BF16, seed 3407, one local RTX 5060 Laptop GPU
• Training time: 2,528 seconds

Limitations

• The measured improvement is small and may not transfer to a target corpus.
• A source-labeled negative can still be semantically relevant to a query.
• The upstream corpus is machine translated.
• Similarity is not a probability or a factuality score.
• Five retrieval tasks do not cover every Turkish domain, dialect or intent.

The repository ships raw per-task MTEB objects, checksums, training state, environment metadata, the mining audit and the exact evaluation code.