urchade / token-classification updated 7 months ago

gliner_multi-v2.1

Name: gliner_multi-v2.1
Author: urchade

GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.

Params

290 M

Context

—

Downloads 30d

235 K

Likes

168

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

Download history

tracking started — chart appears after 7 days of snapshots (5 recorded)

235 K downloads in the last 30 days

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	1.2 GB	1.8 GB	✅ Runs comfortably

Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.

Specifications

Parameters: 290 M
Tensor type: F32
Licence: apache-2.0
First seen on the Hub: 2024-04-09
Training datasets: urchade/pile-mistral-v0.1
Added to our catalog: 2026-08-02

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 bert-large-cased-finetuned-conll03-english 699 K ner-english-fast 543 K

Appears in

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