How to fine-tune your embeddings for better similarity search

This page summarizes the projects mentioned and recommended in the original post on dev.to

Our great sponsors
  • InfluxDB - Access the most powerful time series database as a service
  • SaaSHub - Software Alternatives and Reviews
  • refinery

    The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

    This blog post will share our experience with fine-tuning sentence embeddings on a commonly available dataset using similarity learning. We additionally explore how this could benefit the labeling workflow in the Kern AI refinery. To understand this post, you should know what embeddings are and how they are generated. A rough idea of what fine-tuning is also helps. All the code and data referenced in this post is available on GitHub.

  • refinery-sample-projects

    Containing examples of projects you can use to test refinery. Please select the use case from the branches.

    This blog post will share our experience with fine-tuning sentence embeddings on a commonly available dataset using similarity learning. We additionally explore how this could benefit the labeling workflow in the Kern AI refinery. To understand this post, you should know what embeddings are and how they are generated. A rough idea of what fine-tuning is also helps. All the code and data referenced in this post is available on GitHub.

  • InfluxDB

    Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

Suggest a related project

Related posts