granite-embedding-english

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What granite-embedding-english is

Granite-Embedding-125m-English News: Granite Embedding R2 models with 8192 context length released. - granite-embedding-english-r2 (149M parameters): with an output embedding size of 768, replacing granite-embedding-125m-english. - granite-embedding-small-english-r2 (47M parameters): A first-of-its-kind reduced-size model, with fewer layers and a smaller output embedding size (384), replacing granite-embedding-30m-english. Model Summary: Granite-Embedding-125m-English is a 125M 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 768. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissive, enterprise-friendly license, plus IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pretraining, contrastive finetuning and knowledge distillation. - 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. 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-125m-English 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 performance of the Granite-Embedding-125M-English model on MTEB Retrieval (i.e., BEIR) and code retrieval (CoIR) benchmarks is report

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

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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