We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Neural Search - Let's talk about quality

Formal Metadata

Title
Neural Search - Let's talk about quality
Title of Series
Number of Parts
56
Author
Contributors
License
CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
Identifiers
Publisher
Release Date
Language

Content Metadata

Subject Area
Genre
Abstract
In the past year the interest in Neural Search and vector search engines increased heavily. They promise to solve multi modal, cross modal and semantic search problems with ease. However, when quickly trying Neural search with off-the-shelf pre-trained models the results are quite disillusioning. They lacking knowledge about the data at hand. In order to explicitly solve model finetuning for search problems we implemented an open-source finetuner. It is directly usable with several vector databases due to the underlying data structure. In our talk we present our methodology and performance on an example dataset. Afterwards, we show how well the approach transfers to other datasets, such as deepfashion, geolocation geoguessr and more. It will give hands-on guidance on how you can finetune a model in order to make your data better searchable.