ml5-library VS nlp_compromise

Important APIs - ml5 for in-browser detection, face-api that uses tensorflow-node to accelerate on-server detection. VueUse for a bunch of useful component tools like the QR Code generator. Yahoo's Gifshot for creating gif files in-browser etc.

> Have we seen any creator of a deep learning library, take a similar position if not stopping any support for anyone using it for mass surveillance?

ml5.js license:

> This license gives everyone as much permission to work with this software as possible as long as they comply with the ml5.js Code of Conduct [...]

ml5.js code of conduct:

> Do not: [...] Use ml5.js to build tools of mass surveillance and prediction to repress the rights of people

https://github.com/ml5js/ml5-library/blob/main/LICENSE.md

Not sure how enforcable this is but it exists.

See also: https://ml5js.org/

"The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies."

(5) ml5.js

ML.js

I used ml5js.org , p5js.org and https://teachablemachine.withgoogle.com to train the Banana images. When you create a new image project on Teachable Machine, you can output the p5js and basically use it right out of the box - I customized js, css, and html from there.

Going forward: I'll be 100% into JavaScript. You can use JavaScript in so many fields nowadays. Websites React, Mobile Apps React Native, Machine Learning TensorFlow & ML5, Desktop Applications Electron, and of course the backend Node as well. It's kind of a no-brainer. Of course, they all have specific languages that are better, but for now, JavaScript is a bit of a catch-all.

Yeah they made ml5.js for this reason: https://ml5js.org/

I do feel like Google could do better communicating all of their different tools though. Their ecosystem is large and pretty confusing - they've got so many projects going on at once that it always seems like everyone gets fed up with them before they take a second pass and make them more friendly to newcomers.

Facebook seems to have taken a much more focused approach as you can see with PyTorch Live

From other comments, a lot of JavaScript developers who want to use TensorFlow had never heard of TensorFlow.js or ml5.js!

To go around this problem, I leveraged the Natural Language Processing library we are using in the project (Compromise) to make sure that each chunk was less than a specified character length, and the chunks ended with a full sentence (as long as the sentence itself wasn't longer than the limit).

It's a part of the underpinnings of LUIS, Microsoft's Azure service for language understanding, and indeed part of building things like chatbots.

An interesting comparison is that Microsoft.Recognizers.Text is a cross-platform cousin to JS library compromise/one: https://compromise.cool/

I’m not sure how much my suggestions answer your original question, but in the pursuit of proper grammatical structure when trying to rewrite speech, compromise [0] might be of use to you. I’ve only played around with it for a few hours at most, but from my limited experience it is a very effective (if verbose and/or a little bloated in it’s API) tool for language analysis. With some tomfoolery I’m confident you could combine it with the GP’s recommendation of alexjs to replace strings slightly-less-naively.

https://github.com/spencermountain/compromise

(7) Compromise

yeah, i do know 2 libraries that do this so i'm gonna try them: sherlock.js & compromise dates

compromise is a great library for natural language processing which will run in React Native (it's pure javascript), though I believe it only understands English because it was populated using English vocabulary.

I discovered an excellent little natural-language-processing library for javascript called compromise.js. Integrating it into twine was not hard at all -- I just used the same techniques described in u/HiEv's Sample Code for integrating jQuery UI. Others have used this same library to develop parser game mechanics -- I am really excited about the possibility of a finished product where the player can execute commands by typing them in plain english.

Named Entity Extraction identifies entities like names, locations, or phone numbers inside a given text. Compromise is a JavaScript package that we can use that allows us to not only extract entities in a text but also identify what types of entities they are. Here is a sample program that allows you to enter a text file into the input field, and it would extract and identify any recognizable entities in that text.

I use a natural language processing library called compromise and a tiiiiny bit of RegEx here and there.