granite-embedding-multilingual

About

What granite-embedding-multilingual is

Granite-Embedding-107m-multilingual Model Summary: Granite-Embedding-107M-Multilingual is a 107M parameter dense biencoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. This model is developed using contrastive finetuning, knowledge distillation and model merging for improved performance. - Developers: Granite Embedding Team, IBM - GitHub Repository: ibm-granite/granite-embedding-models - Website: Granite Docs - Paper: Technical Report - Release Date: December 18th, 2024 - License: Apache 2.0 Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite-Embedding-107M-Multilingual for languages beyond these 12 languages. Intended use: The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications. Usage with Sentence Transformers: The model is compatible with SentenceTransformer library and is very easy to use: First, install the sentence transformers library The model can then be used to encode pairs of text and find the similarity between their representations Usage with Huggingface Transformers: This is a simple example of how to use the Granite-Embedding-107m-Multilingual model with the Transformers library and PyTorch. First, install the required libraries The model can then be used to encode pairs of text Evaluation: The average performance of the Granite-Embedding-107M-Multilingual on Multilingual Miracl (across 18 langauges), Mintaka Retrieval (across 8 languages) and MTEB Retrieval for English (across 15 tasks), German (across 4 tasks), Spanish (across 2 tasks), Frenc (across 5 tasks), Japanese (across 2 tasks), Arabic (1 task), Korean (1 task) and Chinese (across 8 tasks) is reported below. Granite-Embedding-107M-Multilingual is twice as fast as other models with similar embedding dimensions. M

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

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

Explore

More embedding models