tomaarsen / token-classification updated 2 years ago

span-marker-bert-base-uncased-acronyms

Name: span-marker-bert-base-uncased-acronyms
Author: tomaarsen

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

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Downloads 30d

252 K

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Commercial use: allowed apache-2.0 Not gated SAFETENSORS 1 languages View on Hugging Face ↗

Download history

daily snapshots · 10 days

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Jul 28Jul 31Aug 3Aug 6

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

indonesian-roberta-base-posp-tagger 3.1 M stanford-deidentifier-base 1.9 M bert-base-NER 1.3 M punctuate-all 708 K ner-english-fast 543 K bert-portuguese-ner 452 K

Appears in

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