@safekids-ai/nlp-js-react-native

safekids.ai

Machine Learning Models to Detect Hate Speech and Adult/Weapon Images and User Intent

These are local models (12MB/5MB) that detect hate speech and more and vision models that detect adult images and images of weapons.

The models can be run on node or on the browser. SDK's are available for Python, JAVA, Node and in-the-browser that can be leverage by a chrome extension.

Install

npm install

Build/Test

npx nx run-many -t test,build

Release Management

npm run release

Publish

npx nx run-many --target=publish --projects=nlp-js-common,nlp-js-node,nlp-js-react-native,vision-js-common,vision-js-node,vision-js-web,ml-demo --parallel=false

a) make sure you have the following in your ~/.npmrc //registry.npmjs.org/:_authToken=${NPM_TOKEN}
b) define an environment variable NPM_TOKEN with the value of the token

NLP and Vision Classification

NLP Classification	Vision Classification
bullying_hate	porn
porn	weapons
proxy	clean ß
self_harm
weapons
clean

Model Accuracy

Label	Training Data Count	Test Data Count	f1 score	precision	recall
bullying_hate	96,523	7,500	0.97	0.991	0.949
clean	1,351,563	20,000	0.98	0.99	0.9702
porn	300,082	6,500	0.97	0.993	0.948
proxy	8,038	200	0.94	0.988	0.896
self_harm	180,826	5,000	0.96	0.984	0.937
weapons	74,802	4,000	0.96	0.989	0.932

Glossary of Terms

Precision
number of true positives / total positive predictions. - indicates the confidence of a model. i.e: if precision for class X is 0.99, 99% chance that if model predicts class X for an input, 99% chance that correct label is also X

Recall
number of true positives/ total positive labels in test set. --- indicates how many of the total inputs belonging to a class in test set are correctly caught by the model

F1 score
harmonic mean of Precision and Recall. - the general accuracy measure for classification that balances out precision and recall

API Reference

Run the text classification model in node

//initialize the model
import {NLPNode} from '@safekids-ai/nlp-js-node'

nlp = new NLPNode("nlp.onnx");
await nlp.init();

//run the hate classifier
expect(await nlp.findHate("I love samosa. Mike is an asshole. Safekids is awesome!"))
    .toEqual({flag: true, label: 'hate_bullying', flaggedText: 'Mike is an asshole.'});

//text classification
expect(await nlp.classifyText("Darrell Brooks' mother wants to 'curl up and die' after verdict | FOX6 News Milwaukee"))
    .toEqual("clean");

expect(await nlp.classifyText("I want to kill myself | Samaritans"))
    .toEqual("self_harm");

expect(await nlp.classifyText("Milf Porn Videos: Mature Mom Sex Videos - RedTube.com"))
    .toEqual("porn");

Run the image classification model in node

import {VisionNode} from '@safekids-ai/vision-js-node'
vision = new VisionNode("vision.onnx");
await vision.init();
const buffer: ImageData = getSync(qa_path + "gun1.jpg");
const pred = await vision.classifyImageData(buffer);
expect(pred).toEqual("weapons");

Run the text classification directly in the browser!

import {NLPWeb} from '@safekids-ai/nlp-js-react-native'
//initialize the model
nlp = new NLPWeb("nlp.onnx");
await nlp.init();

Run the image classification model directly in the browser or used in chrome extension

import {VisionWeb} from '@safekids-ai/vision-js-web'
vision = new VisionWeb("vision.onnx");
await vision.init();

Run the text and image classification model in Python

from safekids import SafeText
safe_text_classifier = SafeText()
safe_text_classifier.classify("text to classify")

from safekids import SafeImage
safe_image_classifier = SafeImage
safe_image_classifier.classify("path_to_image")

License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en