Hugging Face launches IBM Granite Embeddings Multilingual R2 - 32K Context
Hugging Face and IBM have launched Granite Embeddings Multilingual R2, a new series of multilingual embedding models under the Apache 2.0 licence, featuring 32K context support and a focus on high retrieval quality.
What happened?
Hugging Face, in collaboration with IBM, has released Granite Embeddings Multilingual R2. These embedding models are available under the Apache 2.0 licence and target multilingual applications. The models have a context length of 32,000 tokens and are optimised to deliver high retrieval quality, particularly within the sub-100M parameter range. The launch includes four variants: L, M, S, and XS, tailored for various performance needs and size constraints.
Key facts
| Modellnamn | Granite Embeddings Multilingual R2 |
|---|---|
| Lanseringsdatum | 29 maj 2024 |
| Utvecklare | Hugging Face, IBM |
| Licens | Apache 2.0 |
| Kontextlängd | 32 000 tokens |
| Antal varianter | 4 (L, M, S, XS) |
”Granite Embeddings Multilingual R2 introduce open Apache 2.0 licensed models with up to 32K context and best-in-class retrieval quality among sub-100M models.”
Why it matters
The launch of Granite Embeddings Multilingual R2 is significant as it offers advanced multilingual embeddings as open source. With an Apache 2.0 licence, broad use and customisation are possible for developers and enterprises. The extended context length of 32,000 tokens improves the ability to handle long documents and complex queries, which is crucial for applications in information retrieval and AI.
Who is affected?
Developers, researchers, and companies working with AI applications based on text understanding and information retrieval are directly affected. Particularly those building solutions with multilingual support and large datasets can benefit from the models' performance and open-source licence. Users of AI services based on these models can expect improved precision and relevance in search results and AI-generated responses.
What else you should know
Granite Embeddings Multilingual R2 is designed to outperform similar models in the segment for embeddings under 100 million parameters. They have been trained on a large and diverse multilingual dataset to ensure robust performance across different languages and domains.
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