Learn about the Elastic Learned Sparse Encoder (ELSER), an AI model for high relevance semantic search across domains.

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

The 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.

What is the Elastic Learned Sparse Encoder?

ELSER: Introducing Elastic Learned Sparse Encoder

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

Here's how generative AI works from the ground up, including embeddings, transformer-encoder architecture, training/fine-tuning models & more.

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

BERT is the most prominent encoder architecture. It was introduced in 2018 and revolutionized NLP by outperforming most benchmarks for natural language understanding and search. Encoders like BERT are the basis for modern AI: translation, AI search, GenAI and other NLP applications.

What is BERT (Bidirectional Encoder Representation from Transformers)?

Vectors are the fundamental construct with which AI understands language. Vectors represent words and are simply long arrays of numerical values. Technically, vectors may also represent sequences of words or sub-word parts.

What are vectors in generative AI?

Understanding generative AI architectures with transformers

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