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Pretrained Transformer Language Models for Search - part 2

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Pretrained Transformer Language Models for Search - part 2

This is the second blog post in a series of posts where we introduce using pretrained Transformer models for search and document ranking with Vespa.ai.

Vespa Product Updates, May 2021

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Vespa Product Updates, May 2021

Advances in features and performance include new int8 and bfloat16 tensor cell types, compact tensor feed format, Approximate Nearest Neighbor using Hamming distance, hash-based attribute dictionaries and case-sensitive attribute search...

Build a News recommendation app from python with Vespa: Part 3

Part 3 - Efficient use of click-through rate via parent-child relationship.

Pretrained Transformer Language Models for Search - part 1

Photo by Jamie Street on Unsplash

Pretrained Transformer Language Models for Search - part 1

This is the first blog post in a series of posts where we introduce using pretrained Transformer models for search and document ranking with Vespa.ai.

Build sentence/paragraph level QA application from python with Vespa

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Build sentence/paragraph level QA application from python with Vespa

Retrieve paragraph and sentence level information with sparse and dense ranking features.

Vespa Product Updates, March 2021

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Vespa Product Updates, March 2021

Advances in Vespa features and performance last month include mass update/delete in /document/v1/, improved memory usage, OR-to-WeakAnd and better full node protection.

Build a News recommendation app from python with Vespa: Part 2

Part 2 - From news search to news recommendation with embeddings.

How to use Postman with Vespa APIs

Interfacing with secure Vespa APIs is easy with Postman. Use Postman collections to template read and write operations to Vespa.

Run search engine experiments in Vespa from python

Three ways to get started with pyvespa.

How I learned Vespa by thinking in Solr

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How I learned Vespa by thinking in Solr

Learning Vespa in terms of Solr analogies can help flatten the learning curve.

Parent-child joins and tensors for content recommendation

A real-world application of parent-child joins and tensor functions to model topic popularity for content recommendation.