These are local models (12MB/5MB) that detect toxic 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.
You can try out our API using docs at:
https://api.safekids.ai/api
curl -X 'POST' \
'https://api.safekids.ai/v1/ml/classify-toxic' \
-H 'accept: */*' \
-H 'Content-Type: application/json' \
-d '{
"message": "you'\''re a disgusting person"
}'RESPONSE:
{
"flag": true,
"label": "bullying_hate",
"flaggedText": "you're a disgusting person"
}
curl -X 'POST' \
'https://api.safekids.ai/v1/ml/classify-text' \
-H 'accept: */*' \
-H 'Content-Type: application/json' \
-d '{
"message": "find adult sex links videos"
}'RESPONSE:
porn
curl "https://api.safekids.ai/v1/ml/classify-image-url?url=https://cdn.britannica.com/96/176196-050-EFC5E6A6/Glock-pistol.jpg"RESPONSE:
weapons
| NLP Classification | Vision Classification |
|---|---|
| bullying_hate | porn |
| porn | weapons |
| proxy | clean |
| self_harm | |
| weapons | |
| clean |
| 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 |
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
Model Files ONNX for NLP and Vision
//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");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");import {NLPWeb} from '@safekids-ai/nlp-js-web'
//initialize the model
nlp = new NLPWeb("nlp.onnx");
await nlp.init();import {VisionWeb} from '@safekids-ai/vision-js-web'
vision = new VisionWeb("vision.onnx");
await vision.init();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")Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
