Introducing deepset-mxbai-embed-de-large-v1: A Revolutionary German/English Embedding Model
Deepset and Mixedbread have collaborated to launch an innovative open-source German/English embedding model, deepset-mxbai-embed-de-large-v1, aiming to address the dominance of English in AI. This model, built on intfloat/multilingual-e5-large, has been fine-tuned on over 30 million pairs of German data to excel in natural language processing (NLP) tasks, particularly for retrieval purposes.
Superior Performance and Benchmark
deepset-mxbai-embed-de-large-v1 has achieved an impressive NDCG@10 metric score of 51.7, surpassing other models like multilingual-e5-large and jina-embeddings-v2-base-de. Its remarkable performance makes it a reliable and effective tool for developers and researchers working with the German language.
Innovative Efficiency Techniques
The developers have prioritized efficiency with Matryoshka Representation Learning (MRL) and Binary Quantization techniques to optimize storage and inference efficiency. MRL reduces output dimensions without sacrificing accuracy, while Binary Quantization conserves memory and disk space while maintaining high performance during inference.
Seamless Integration and Utilization
Users can effortlessly integrate deepset-mxbai-embed-de-large-v1 with the Haystack framework using various components provided by Mixedbread. Furthermore, Mixedbread offers streamlined integration through MixedbreadDocumentEmbedder and MixedbreadTextEmbedder.
Empowering the German-Speaking AI Community
Deepset and Mixedbread aim to empower the German-speaking AI community with this state-of-the-art embedding model, enabling the development of innovative products, especially in retrieval-augmented generation (RAG) and beyond.
Learn more about the details and model on our website and connect with us on social media to stay updated with our latest developments.
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