diffgram VS doccano

As a few examples of how much depth we have considered, here's a detailed comparison with sagemaker. Part of an integration with scale. Part of code for labelbox integration, datasaur (scroll to trusted by for our logo) etc. To the best of my knowledge I am trying to track every firm that is in this direct space.

(Direct repo link https://github.com/diffgram/diffgram)

We list some benefits here but if I had to pick one thing, it's that it's a complete system. You can be up and running in 2 minutes on docker. And scale to "big tech co" level on multiple k8s clusters.

Huh?

I wrote my own system for classifying a stream of texts in Python, I might Open Source it one of these days but I have to get it to the point where it is modular enough that I can customize it to do the particular things I want without subjecting people to my whims... I use it every day and I'm not afraid to demo it because it is rock solid.

My understanding is that my system would not be hard to adapt to work on images for certain kinds of tasks.

Pytorch is open source, Huggingface is open source. CUDA isn't. This is

https://labelstud.io/

and for annotating text spans there are so many open source tools

https://github.com/doccano/doccano

I worked for a company a few years back that built annotation tools for projects we sold to customers but never quite got to a polished general purpose annotator. Today there are an overwhelming number of companies in this space and products I never heard of, many of which are cloud based or paid. Looks like a gold rush to me.

I followed the guide to have doccano https://github.com/doccano/doccano setup in a notebook server in Kubeflow. It is running and the django connection is established, but it fails to connect with my localmachine so when I try to open the link, it does not respond. Is there a way to connect apps running on remove notebook servers to local in Kubeflow?

Why not just use the docker instructions on the GitHub page? https://github.com/doccano/doccano

doccano is built on a Django backend though I believe it uses extensive front end code for the annotation UI.

I worked at a place where we developed a system for doing this kind of tagging but it was for making training sets and there was no expectation that you could export the document from the system for normal use.

Quite a few NLP annotation systems are out there

https://prodi.gy/

https://brat.nlplab.org/

https://doccano.herokuapp.com/