DUSUNEN Atlas 278M v1
One Turkish retrieval model, six useful embedding sizes. This model adapts
intfloat/multilingual-e5-base with Matryoshka Representation Learning on
50,000 curated Turkish hard-negative triplets. Applications can select 768,
512, 384, 256, 128, or 64 dimensions at inference time without training or
storing a second model.
The practical target is a smaller vector index: 128-dimensional float32 vectors use 83.3% less storage than 768-dimensional vectors, while improving held-out hard-negative triplet accuracy from 92.65% to 94.75% over the truncated base model.
Measured dimension sweep
All rows use the same 2,000-example held-out validation split and cosine similarity. The base and fine-tuned model were evaluated with identical E5 query/passage prefixes.
| Dimensions | Base accuracy | Atlas accuracy | Gain | Float32 bytes/vector | Index reduction |
|---|---|---|---|---|---|
| 768 | 95.25% | 95.50% | +0.25 pp | 3,072 | 0% |
| 512 | 95.30% | 95.65% | +0.35 pp | 2,048 | 33.3% |
| 384 | 95.05% | 95.35% | +0.30 pp | 1,536 | 50.0% |
| 256 | 94.15% | 95.00% | +0.85 pp | 1,024 | 66.7% |
| 128 | 92.65% | 94.75% | +2.10 pp | 512 | 83.3% |
| 64 | 88.75% | 92.50% | +3.75 pp | 256 | 91.7% |
These are held-out in-domain triplet results, not a claim of state of the art.
Broader Turkish MTEB evaluation is planned. Machine-readable results are in
dimension-sweep.json.
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(
"GoktugD/DUSUNEN-Atlas-278M-v1",
truncate_dim=128,
)
queries = ["query: Türkiye'nin başkenti neresidir?"]
passages = [
"passage: Türkiye'nin başkenti Ankara'dır.",
"passage: İstanbul Türkiye'nin en kalabalık şehridir.",
]
query_embeddings = model.encode(queries, normalize_embeddings=True)
passage_embeddings = model.encode(passages, normalize_embeddings=True)
scores = query_embeddings @ passage_embeddings.T
print(scores)
Use the query: prefix for queries and passage: for documents. Apply the
same truncate_dim to both sides. Recommended operating points:
- 128 dimensions: best storage/quality balance for large indexes.
- 256 dimensions: conservative production default.
- 768 dimensions: maximum measured held-out accuracy.
- 64 dimensions: extremely compact indexes and edge experiments.
Training
- Base revision:
d128750597153bb5987e10b1c3493a34e5a4502a - Data: 50,000 training and 2,000 held-out Turkish hard-negative triplets
- Loss: cached multiple-negatives ranking loss wrapped by Matryoshka loss
- Dimensions: 768, 512, 384, 256, 128, 64
- Effective batch size: 64
- Sequence length: 256
- Epochs: 1
- Precision: bfloat16
- Seed: 3407
- Hardware: one NVIDIA GeForce RTX 3090 24 GB
- Training time: 2,039 seconds
- Peak memory allocated by the training process: 2.475 GiB
Exact hyperparameters are available in training_config.yaml; the recorded
environment is in training_environment.json.
Limitations
- Evaluation here is limited to an in-domain held-out hard-negative split.
- Training data is Turkish retrieval data; quality on other languages may differ from the multilingual base model.
- Prefix-free encoding was not evaluated.
- Similarity thresholds should be calibrated for each application.
Reproducibility and integrity
The published model.safetensors SHA-256 is:
66c65d4109a646d3f8a6dc6c19b20efda4d9c5ed9cd5054386812da0d6b263dd
Developed and released by Göktuğ Düşünen.