| --- |
| language: |
| - tr |
| license: mit |
| library_name: sentence-transformers |
| pipeline_tag: sentence-similarity |
| base_model: GoktugD/DUSUNEN-Rota-270M-v1 |
| datasets: |
| - GoktugD/DUSUNEN-HardNegatives-50K-v1 |
| tags: |
| - sentence-transformers |
| - semantic-search |
| - information-retrieval |
| - turkish |
| - hard-negatives |
| --- |
| |
| # 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 |
|
|
| ```python |
| 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. |
|
|
