refinery VS Prisma

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.

In the world of software development, there are two kinds of developers: those who have never had to complain about ORMs and those who have actually used them. Whether it’s Django ORM for Python, Active Record for Ruby, GORM for Golang, Doctrine for PHP, or Prisma for TypeScript, a common issue persists: writing simple queries is straightforward, but constructing complex or optimized queries can take hours, if not days.

Support for a Union type #2505

generator client { provider = "prisma-client-js" binaryTargets = ["native", "rhel-openssl-1.0.x"] } datasource db { provider = "postgresql" // NOTE: When using mysql or sqlserver, uncomment the @db.Text annotations in model Account below // Further reading: // https://next-auth.js.org/adapters/prisma#create-the-prisma-schema // https://www.prisma.io/docs/reference/api-reference/prisma-schema-reference#string url = env("DATABASE_URL") } model Post { id Int @id @default(autoincrement()) name String createdAt DateTime @default(now()) updatedAt DateTime @updatedAt createdBy User @relation(fields: [createdById], references: [id]) createdById String @@index([name]) } // ... rest of the schema

Unfortunately Prisma hasn’t supported polymorphism yet. As such, you can't use inheritance to model the entity in the same way as in your programming language, as depicted in the above class diagram. The good news is that we could intimate it using table inheritance to imitate it.

In this project I am manually declaring the data types. For better type-safety, use Prisma, which automatically generates types based on your database schema.

When you're creating a full-stack application, you'll also need to write logic to interact with your database. For relational databases like Postgres, you can do this with SQL, or an ORM like Prisma.

Prisma is a modern TypeScript-first ORM that allows you to manage database schemas easily, make queries and mutations with great flexibility, and ensure excellent type safety.

Prisma

There have been multiple feature requests to add native support for AsyncLocalStorage to Prisma, but they haven't been met with much enthusiasm from the maintainers. Some people solved it by extending and overriding the client (which is arguably prone to breaking with updates).