msmarco-bert-base-dot-v5
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 500K (query, answer) pairs from the MS MARCO dataset. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search
Params
110 M
Context
512
Downloads 30d
528 K
Likes
21
Download history
daily snapshots · 106 days
▲ 23 K in the last 30 days (4.5%)
529 K496 K
Oct 12Oct 22Nov 1Nov 10
529 K413 K
Aug 13Sep 12Oct 12Nov 10
607 K413 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
- 30,522
- Layers / heads
- 12 / 12
- First seen on the Hub
- 2022-03-02
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-28
Compare with any sentence-similarity model