The Embedding Archives: Millions of Wikipedia Article Embeddings in Many Languages

Cohere has launched a significant archive of embedding vectors derived from millions of Wikipedia articles in multiple languages, using their Multilingual embedding model. This resource supports developers by providing freely downloadable embedding vectors that power search systems, facilitating rapid application development with common datasets. The embeddings, available on Hugging Face Datasets, are structured as passages with accompanying metadata, and are particularly useful for creating neural search systems. The archive contains 94 million embedded passages across languages such as English, German, French, Spanish, and more, with the potential for cross-lingual applications due to the model's properties. Furthermore, a subset of 10 million vectors is hosted by Weaviate, allowing for direct querying without downloading, and the archive encourages innovation in specialized search applications by enabling searches within specific Wikipedia sections or topics.

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