BAAI / text-classification updated 2 years ago

bge-reranker-v2-m3

Name: bge-reranker-v2-m3
Author: BAAI

Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function.

Params

570 M

Context

8,192

Downloads 30d

19.2 M

Likes

1,125

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

Download history

daily snapshots · 10 days

19.3 M18.9 M

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	2.3 GB	3.1 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: XLMRobertaForSequenceClassification
Parameters: 570 M
Tensor type: F32
Context length: 8,192
Vocabulary: 250,002
Layers / heads: 24 / 16
Licence: apache-2.0
First seen on the Hub: 2024-03-15
Training datasets: undisclosed
Added to our catalog: 2026-07-28

Compare with

finbert 5.6 M bge-reranker-base 4.6 M Prompt-Guard-86M 4.5 M twitter-roberta-base-sentiment-latest 2.6 M bertweet-base-sentiment-analysis 1.5 M RADAR-Vicuna-7B 1.0 M

Appears in

Best open coding models Commercial-use text models Models that fit in 12 GB