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Improve throughput and concurrency with HTTP/2
The Vespa HTTP container now accepts HTTP/2 with TLS enabled. Learn how this improves HTTP throughput and efficiency, and how to get started using HTTP/2.
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Pretrained Transformer Language Models for Search - part 4
This is the fourth blog post in a series of posts where we introduce using pretrained Transformer models for search and document ranking with Vespa.ai.
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Build a basic text search application from python with Vespa: Part 2
Evaluate search engine experiments from python.
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Pretrained Transformer Language Models for Search - part 3
This is the third blog post in a series of posts where we introduce using pretrained Transformer models for search and document ranking with Vespa.ai.
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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.
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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...
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Build a News recommendation app from python with Vespa: Part 3
Part 3 - Efficient use of click-through rate via parent-child relationship.
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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
Retrieve paragraph and sentence level information with sparse and dense ranking features.
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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.
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Build a News recommendation app from python with Vespa: Part 2
Part 2 - From news search to news recommendation with embeddings.
Photo by Filip Mishevski on Unsplash
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