Description

Cohere introduces an extensive archive of embedding vectors derived from millions of Wikipedia articles in various languages using their Multilingual model. The dataset is organized into passages with corresponding embeddings, making it accessible on Hugging Face Datasets for download. Developers can utilize these embeddings to build neural search systems and vector databases capable of advanced retrieval functions. By embedding user queries and calculating similarity through dot product multiplication, relevant knowledge can be retrieved from Wikipedia's vast store of information. Additionally, a subset of this dataset is hosted by Weaviate for direct querying without downloading or processing. Cross-lingual properties enable using multiple languages in applications, with the potential to search specific sections of Wikipedia and employ these embeddings for various use cases.