bge-multilingual-gemma2

About

What bge-multilingual-gemma2 is

For more details please refer to our Github: FlagEmbedding. BGE-Multilingual-Gemma2 is a LLM-based multilingual embedding model. It is trained on a diverse range of languages and tasks based on google/gemma-2-9b. BGE-Multilingual-Gemma2 primarily demonstrates the following advancements: - Diverse training data: The model's training data spans a broad range of languages, including English, Chinese, Japanese, Korean, French, and more.Additionally, the data covers a variety of task types, such as retrieval, classification, and clustering. - Outstanding performance: The model exhibits state-of-the-art (SOTA) results on multilingual benchmarks like MIRACL, MTEB-pl, and MTEB-fr. It also achieves excellent performance on other major evaluations, including MTEB, C-MTEB and AIR-Bench. 📑 Open-source Plan - [x] Checkpoint - [x] Training Data The training data of BGE-Multilingual-Gemma2 is available at this link. Usage Using FlagEmbedding By default, FlagLLMModel will use all available GPUs when encoding. Please set os.environ["CUDAVISIBLEDEVICES"] to select specific GPUs. You also can set os.environ["CUDAVISIBLEDEVICES"]="" to make all GPUs unavailable. Using Sentence Transformers Using HuggingFace Transformers Evaluation bge-multilingual-gemma2 exhibits state-of-the-art (SOTA) results on benchmarks like MIRACL, MTEB-pl, and MTEB-fr. It also achieves excellent performance on other major evaluations, including MTEB, C-MTEB and AIR-Bench. - MIRACL nDCG@10: Recall@100: - MTEB-fr/pl - MTEB - BEIR - C-MTEB - AIR-Bench Long-Doc (en, Recall@10): QA (en&zh, nDCG@10): Model List bge is short for BAAI general embedding. Citation If you find this repository useful, please consider giving a star :star: and citation

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.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys bge-multilingual-gemma2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (bge-multilingual-gemma2 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-multilingual-gemma2","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Open, with conditions

Sources

Weights & code

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