compose VS refinery

Compose Compose targets labeling raw data, allowing you to set labeling functions for your data in Python in order to make the labeling process easier.

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

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.

Shout out to both Kern.AI (an excellent open-source NLP labelling tool) https://github.com/code-kern-ai/refinery and Voxel51 (an excellent open-source Computer Vision analysis tool) https://github.com/voxel51/fiftyone for being early adopters of the technology in their platforms, but I don't believe either have yet made use of all of the value it can provide.

Show HN: If VS Code had a data-centric IDE sibling, what would that look like?\ (23 comments)

Hi Ruben,

you can take a look at our architecture overview here: https://github.com/code-kern-ai/refinery#-architecture

A bit below it, you find a table with the links to all repositories. All of them are open-source. But thanks for the feedback, I'll try to make it a bit easier to understand! I appreciate that! :)

Hi Tom! Thanks, happy to hear that :)

We've focused on JSON as the user-specified data model. So you can upload anything fitting into a JSON. We're using pandas to process the uploaded data, so spreadsheets or CSV-ish also work.

We've got a public roadmap (https://github.com/code-kern-ai/refinery/projects/1), and we're looking forward to also integrate e.g. native PDF labeling sometime soon.