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  datasets:
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  - burgerbee/wikipedia-en-20240320
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  ---
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- # Wikipedia txtai embeddings index (aka Embeddings is all you need)
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- This is a [txtai](https://github.com/neuml/txtai) embeddings index (36h GPU crunch gave us 5GB embeddings + 25GB documents) for the [english edition of Wikipedia](https://en.wikipedia.org/).
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  Embeddings is the engine that delivers semantic search. Data is transformed into embeddings vectors where similar concepts will produce similar vectors.
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  An embeddings index generated by txtai is a fully encapsulated index format. It dosen't require a database server.
 
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  datasets:
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  - burgerbee/wikipedia-en-20240320
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  ---
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+ # Wikipedia txtai embeddings index
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+ This is a [txtai](https://github.com/neuml/txtai) embeddings index (5GB embeddings + 25GB documents) for the [english edition of Wikipedia](https://en.wikipedia.org/).
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  Embeddings is the engine that delivers semantic search. Data is transformed into embeddings vectors where similar concepts will produce similar vectors.
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  An embeddings index generated by txtai is a fully encapsulated index format. It dosen't require a database server.