Elastic Learned Sparse Encoder is an AI model for high relevance semantic search across domains. As a sparse vector model, it expands the query with terms that don't exist in the query itself, delivering superior relevance without domain adaptation.

Searching for meaning, not just words

Introducing Elastic Learned Sparse Encoder, a new text expansion model for semantic search

Superior semantic search out of the box

Why choose Elastic’s Learned Sparse Encoder?

Let’s speak to performance and Elasticsearch as a vector database

The future: The most powerful hybrid search in the market out of the box

Get started with Elastic’s AI-powered search today

Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search

This long-form article explains how generative AI works, from the ground all the way up to generative transformer architectures with a focus on intuitions. 

The semantic stage of GenAI: Understanding natural language

1. Understanding embeddings, vector similarity and language models

1.1 Learning dense vectors

2. Context: The bloodstream of GenAI

3. The transformer-encoder architecture

3.1 Self-attention

3.2 Multi-headed self-attention

3.3 The feed-forward neural network

4. Training and fine tuning language models for AI search and NLP

4.1 Training patterns and language model transfer learning

4.2 The AI barrier

5. Elastic Learned Sparse Encoder for AI search out of the box

Conclusion

References

Generative AI architectures with transformers explained from the ground up

Author

Aris Papadopoulos

Articles

Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search

Generative AI architectures with transformers explained from the ground up