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
406 K
Likes
8
Download history
daily snapshots · 54 days
▲ 46 K in the last 30 days (10.3%)
451 K406 K
Aug 23Sep 2Sep 12Sep 21
457 K406 K
Jul 30Aug 17Sep 4Sep 21
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