granite embedding 278m multilingual EU AI Act Compliance Profile

Granite-Embedding-278m-multilingual

Model Summary: Granite-Embedding-278M-Multilingual is a 278M parameter model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 768 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-278M-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

pip install sentence_transformers

The model can then be used to encode pairs of text and find the similarity between their representations

from sentence_transformers import SentenceTransformer, util

model_path = "ibm-granite/granite-embedding-278m-multilingual"
# Load the Sentence Transformer model
model = SentenceTransformer(model_path)

input_queries = [
    ' Who made the song My achy breaky heart? ',
    'summit define'

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