S-PubMedBert-MedQuAD
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.
Params
110 M
Context
512
Downloads 30d
439 K
Likes
8
Download history
daily snapshots · 104 days
▲ 45 K in the last 30 days (11.4%)
453 K397 K
Oct 12Oct 22Nov 1Nov 10
455 K391 K
Aug 13Sep 12Oct 12Nov 10
457 K391 K
Jul 30Sep 2Oct 7Nov 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
- Licence
- mit
- First seen on the Hub
- 2022-06-09
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-30
Compare with any sentence-similarity model