granite embedding 30m sparse EU AI Act Compliance Profile

Granite-Embedding-30m-Sparse

Model Summary: Granite-Embedding-30m-Sparse is a 30M parameter sparse biencoder embedding model from the Granite Experimental suite that can be used to generate high quality text embeddings. This model produces variable length bag-of-word like dictionary, containing expansions of sentence tokens and their corresponding weights and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and 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 for improved performance.

• Developers: Granite Embedding Team, IBM
• GitHub Repository: ibm-granite/granite-embedding-models
• Paper: Techincal Report
• Release Date: February 26th, 2025
• License: Apache 2.0

Supported Languages: English.

Intended use: The model is designed to produce variable length bag-of-word like dictionary, containing expansions of sentence tokens and their corresponding weights, for a given text, which can be used for text similarity, retrieval, and search applications.

Usage with Milvus: The model is compatible with Milvus Vector DB and is very easy to use:

First, install the pymilvus library

pip install pymilvus[model]

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

from pymilvus import model
from pymilvus import MilvusClient, DataType

client = MilvusClient("./milvus_demo.db")

client.drop_collection(collection_name="my_sparse_collection")

schema = client.create_schema(
    auto_id=True,
    enable_dynamic_fields=True,
)

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