span-marker-bert-base-uncased-acronyms
This is a SpanMarker model trained on the Acronym Identification dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-uncased as the underlying encoder. See train.py for the training script.
Params
110 M
Context
—
Downloads 30d
238 K
Likes
6
Download history
daily snapshots · 55 days
▲ 16 K in the last 30 days (6.2%)
254 K238 K
Aug 22Sep 1Sep 11Sep 20
256 K235 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
- SpanMarkerModel
- Parameters
- 110 M
- Tensor type
- F32
- Vocabulary
- 30,524
- Licence
- apache-2.0
- First seen on the Hub
- 2023-08-14
- Training datasets
- acronym_identification
- Acronym Identification (reported)
- 0.90626313577133
- Added to our catalog
- 2026-07-28
Compare with any token-classification model