Weaviate 1.2 release – transformer models

Weaviate v1.2 introduced support for transformers (DistilBERT, BERT, RoBERTa, Sentence-BERT, etc) to vectorize and semantically search through your data

Bob van Luijt
Bob van Luijt
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Table of Contents


Intro

In the v1.0 release of Weaviate (docs — Github) we introduced the concept of modules. Weaviate modules are used to extend the vector search engine with vectorizers or functionality that can be used to query your dataset. With the release of Weaviate v1.2, we have introduced the use of transformers (DistilBERT, BERT, RoBERTa, Sentence-BERT, etc) to vectorize and semantically search through your data.

Weaviate v1.2 introduction video

What are transformers?

A transformer (e.g., BERT) is a deep learning model that is used for NLP-tasks. Within Weaviate the transformer module can be used to vectorize and query your data.

Getting started with out-of-the-box transformers in Weaviate

By selecting the text-module in the Weaviate configuration tool, you can run Weaviate with transformers in one command. You can learn more about the Weaviate transformer module here.

Weaviate configurator — selecting the Transformers module Weaviate configurator — selecting the Transformers module

Custom transformer models

You can also use custom transformer models that are compatible with Huggingface’s AutoModel and AutoTokenzier. Learn more about using custom models in Weaviate here.

Q&A style questions on your own dataset answered in milliseconds

Weaviate now allows you to get to sub-50ms results by using transformers on your own data, you can learn more about Weaviate’s speed in combination with transformers in this article.

What next

Check out the Getting Started with Weaviate and begin building amazing apps with Weaviate.

You can reach out to us on Slack or Twitter.

Weaviate is open source, you can see the follow the project on GitHub. Don’t forget to give us a ⭐️ while you are there.