refinery VS hub-feedback

You definitely forgot https://www.kern.ai/ :)

We will build the web scraper in Kern AI workflow, labeled our news articles in refinery, and then enrich the data with gates AI. After that, we will use workflow again to send out the predictions and the enriched data via a webhook to Slack. If you'd like to follow along or explore these tools on your own, you can join our waitlist here: https://www.kern.ai/

A platform has been developed by the German startup Kern AI for NLP developers and data scientists to not only control the labeling process but also automate and orchestrate tangential tasks and enable them to address low-quality data that comes their way. Several companies exist substantively to power this labeling process.

Fast forward to July ‘22 (after many further product iterations and a full redesign), we open-sourced our product under a new name: Kern AI refinery (the origin of the name is very simple: we want to improve, i.e., refine, the foundation for building models).

Get it for free here: https://github.com/code-kern-ai/refinery

Containers are amazing for building applications. Because they allow you to pack up a programm together with all it's dependencies and execute it wherever you like. That is why our application consists of 20+ individual containers, forming our data-centric IDE for NLP, which you can check out here: https://github.com/code-kern-ai/refinery.

Today we launched bricks, an open-source library which provides enrichments for your natural language processing projects. Our main goal with bricks is to shorten the amount of time that you need from idea to implementation. Bricks also seamlessly integrates into our main tool, the Kern AI refinery.

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.

We've built our open-source IDE for data-centric NLP with the belief that data scientists and engineers know best what kind of framework they want to use for their model building. Today, we'll show you three new adapters for the SDK.

image: this key specifies the image this container is based on to be created. It can be a local image or an image from the Docker hub.

Finally, we can upload our application to Docker Hub so that other people can use the image we created. To do this, follow the steps below:

Pull the official Docker distribution of pgAdmin 4 from the Docker Hub repository with the following command:

To use Docker Scout, you'll first have to have a Docker Hub account. Follow the installation instructions for Docker Scout on GitHub. Once Docker Scout is installed, you can sign in to Docker Hub on the command line with the docker login command.

Go to Docker Hub, sign up, and log in to your account's overview page.

Login to Docker [Create an account with https://hub.docker.com/]

Docker Hub

Dockerhub account

There are a lot of docker-compatible registries almost every cloud provider has its registry but for this article, we will use the docker registry called docker hub. Go to the website create a new account and sign in then you can push or pull images.