cape-dataframes VS orange

- How can we mitigate hallucinations and bias so that we have higher trust in AI generated text?

The features of the Cape API are designed to help solve these problems for developers, and we have a number of early customers using the API in production already.

To get started, checkout our docs: https://docs.capeprivacy.com/

View the API reference: https://api.capeprivacy.com/redoc

Join the discussion on our Discord: https://discord.gg/nQW7YxUYjh

And of course try the CapeChat playground at https://chat.capeprivacy.com/

There are three essential components that enable this: cape encrypt, cape deploy, and cape run. The command cape encrypt encrypts inputs that can be sent into the Cape enclave for processing, cape deploy performs all needed actions for deploying a function into the enclave, and finally cape run invokes the deployed function with an input that was previously encrypted with cape encrypt. Learn more on the Cape docs.

Well, many of the features in this project are simply wrappers around other libraries like this one. Therefore, the value proposition of this project would either have to be the automation aspect or the idea that you can shield the user from the details of how the implemented techniques work. I think both approaches are risky in this setting.

I was wondering what other helpful and easy of use libraries are there for data anonymization like faker and cape-python ?

I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.

Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.

https://orangedatamining.com/

https://orange3.readthedocs.io/projects/orange-visual-progra...

For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.

[1] https://orangedatamining.com/

[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.

First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.

Both tools are ripoffs of a data mining framework named Orange 3

You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.

You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.