Bringing More Humans into the AI Lifecycle



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2– After spending some time with the annotation, we are ready to use rb.load and prepare our dataset for fine-tuning the model. Normally, we would split it into train, test, and validation, but let’s keep the example as straightforward as possible:

3– From this point on, it’s just regular fine-tuning with the Trainer API as described in the guide. Please note we are using distilbert-base-uncased instead of bert-base-cased:

If this got you interested, you can:

3. Embrace integration with other tools, libraries, and frameworks.

We’re living in exciting times with new libraries and tools being released every month. People feel comfortable with many tools for different things, such as data annotation, or model experiment tracking, and we want to embrace this diversity.

Rubrix is not an all-or-nothing tool or a platform, our goal is to enable users to build novel workflows and combine Rubrix with their tools of choice. That’s why, besides the Python library and the web app, we’ve designed an open REST API for developers to build on.

Also, to spur your imagination and creativity, check out Rubrix’s docs:

4. Provide a minimal feature set and let the community guide what comes next.

As you might have noticed, I’ve been talking about AI projects but we’ve only seen examples of natural language processing. The reason is simple: natural language processing and knowledge graph use cases have been the original driver for Rubrix’s development at Recognai. However, Rubrix’s data model and architecture are designed to easily add new use cases and applications. Rubrix covers a wide range of NLP and knowledge graph applications with only two supported tasks, Text Classification, and Token Classification.

For knowledge graph use cases, feel free to check the node classification tutorial using the amazing kglab and PyTorch Geometric libraries with Rubrix.

Immediate use cases we envision are text2text, which will cover many other NLP applications such as text summarization or machine translation; computer vision tasks such as Image Classification; and speech recognition tasks such as speech2text. But before this, we want to hear your voice.

This is why we are excited to announce Rubrix so you can be part of an open and friendly community and drive what comes next.

If you want to join the Rubrix community or talk about your immediate and envisioned applications, drop us a message on Rubrix’s Github’s Discussion forum.

“I call this number
For a data date
I don’t know what to do
I need a rendez-vous”

«Computer love» by Kraftwerk, 1981

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