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

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.