bert-base-turkish-cased-mean-nli-stsb-tr
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The model was trained on Turkish machine translated versions of NLI and STS-b datasets, using example training scripts from sentence-transformers GitHub repository.
Params
110 M
Context
512
Downloads 30d
523 K
Likes
53
Download history
daily snapshots · 106 days
▲ 133 K in the last 30 days (34.0%)
525 K375 K
Oct 12Oct 22Nov 1Nov 10
525 K375 K
Aug 13Sep 12Oct 12Nov 10
525 K375 K
Jul 28Sep 1Oct 6Nov 10
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 0.4 GB | 1.0 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- BertModel
- Parameters
- 110 M
- Tensor type
- F32
- Context length
- 512
- Vocabulary
- 32,000
- Layers / heads
- 12 / 12
- Licence
- apache-2.0
- First seen on the Hub
- 2022-03-02
- Training datasets
- nli_tr, emrecan/stsb-mt-turkish
- Added to our catalog
- 2026-07-28
Compare with any sentence-similarity model