pubmedbert-base-embeddings
This is a PubMedBERT-base model fined-tuned using sentence-transformers. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The training dataset was generated using a random sample of PubMed title-abstract pairs along with similar title pairs.
Params
110 M
Context
512
Downloads 30d
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Likes
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Download history
daily snapshots · 55 days
▲ 111 K in the last 30 days (13.3%)
951 K764 K
Aug 22Sep 1Sep 11Sep 20
951 K764 K
Jul 28Aug 15Sep 2Sep 20
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
- apache-2.0
- First seen on the Hub
- 2023-10-18
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-28
Compare with any sentence-similarity model