@tensorflow-models/toxicity

Toxicity classifier

The toxicity model detects whether text contains toxic content such as threatening language, insults, obscenities, identity-based hate, or sexually explicit language. The model was trained on the civil comments dataset: https://figshare.com/articles/data_json/7376747 which contains ~2 million comments labeled for toxicity. The model is built on top of the Universal Sentence Encoder (Cer et al., 2018).

More information about how the toxicity labels were calibrated can be found here.

Check out our demo, which uses the toxicity model to predict the toxicity of several sentences taken from this Kaggle dataset. Users can also input their own text for classification.

Installation

Using yarn:

$ yarn add @tensorflow/tfjs @tensorflow-models/toxicity

Using npm:

$ npm install @tensorflow/tfjs @tensorflow-models/toxicity

Usage

To import in npm:

import * as toxicity from '@tensorflow-models/toxicity';

or as a standalone script tag:

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/toxicity"></script>

Then:

// The minimum prediction confidence.
const threshold = 0.9;

// Load the model. Users optionally pass in a threshold and an array of
// labels to include.
toxicity.load(threshold).then(model => {
  const sentences = ['you suck'];

  model.classify(sentences).then(predictions => {
    // `predictions` is an array of objects, one for each prediction head,
    // that contains the raw probabilities for each input along with the
    // final prediction in `match` (either `true` or `false`).
    // If neither prediction exceeds the threshold, `match` is `null`.

    console.log(predictions);
    /*
    prints:
    {
      "label": "identity_attack",
      "results": [{
        "probabilities": [0.9659664034843445, 0.03403361141681671],
        "match": false
      }]
    },
    {
      "label": "insult",
      "results": [{
        "probabilities": [0.08124706149101257, 0.9187529683113098],
        "match": true
      }]
    },
    ...
     */
  });
});