bge-reranker-m3-ko

About

What bge-reranker-m3-ko is

Reranker (Cross-Encoder) 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. Model Details - Base model : BAAI/bge-reranker-v2-m3 - The multilingual model has been optimized for Korean. Usage with Transformers Usage with SentenceTransformers First install the Sentence Transformers library: Usage with FlagEmbedding First install the FlagEmbedding library: Fine-tune Refer to https://github.com/FlagOpen/FlagEmbedding Evaluation Bi-encoder and Cross-encoder Bi-Encoders convert texts into fixed-size vectors and efficiently calculate similarities between them. They are fast and ideal for tasks like semantic search and classification, making them suitable for processing large datasets quickly. Cross-Encoders directly compare pairs of texts to compute similarity scores, providing more accurate results. While they are slower due to needing to process each pair, they excel in re-ranking top results and are important in Advanced RAG techniques for enhancing text generation. Korean Embedding Benchmark with AutoRAG (https://github.com/Marker-Inc-Korea/AutoRAG-example-korean-embedding-benchmark) This is a Korean embedding benchmark for the financial sector. Top-k 1 Bi-Encoder (Sentence Transformer) Cross-Encoder (Reranker) Top-k 3 Bi-Encoder (Sentence Transformer) Cross-Encoder (Reranker)

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys bge-reranker-m3-ko for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (bge-reranker-m3-ko below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"bge-reranker-m3-ko","input":"text to embed"}'

Details

Languages, data & research

Licence

Open weights

Sources

Weights & code

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