Toxic Comment Classification


Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments.

The Conversation AI team, a research initiative founded by Jigsaw and Google (both a part of Alphabet) is working on tools to help improve the online conversation. One area of focus is the study of negative online behaviors, like toxic comments (i.e. comments that are rude, disrespectful, or otherwise likely to make someone leave a discussion). So far they’ve built a range of publicly available models served through the Perspective API, including toxicity. But the current models still make errors, and they don’t allow users to select which types of toxicity they’re interested in finding (e.g. some platforms may be fine with profanity, but not with other types of toxic content).

Automatically detect identity hate, insult, obscene, severe toxic, threat, or toxic content in SM comments using our out-of-the-box Spark NLP Multiclassifier DL.

Predicted Entities

toxic, severe_toxic, identity_hate, insult, obscene, threat

Live Demo Open in Colab Download

How to use

document = DocumentAssembler()\

use = UniversalSentenceEncoder.pretrained() \

docClassifier = MultiClassifierDLModel.pretrained("multiclassifierdl_use_toxic") \

pipeline = Pipeline(
    stages = [
val documentAssembler = new DocumentAssembler()

val use = UniversalSentenceEncoder.pretrained()

val docClassifier = MultiClassifierDLModel.pretrained("multiclassifierdl_use_toxic")

val pipeline = new Pipeline()

Model Information

Model Name: multiclassifierdl_use_toxic
Compatibility: Spark NLP 2.7.1+
License: Open Source
Edition: Official
Input Labels: [use_embeddings]
Output Labels: [category]
Language: en

Data Source


               precision    recall  f1-score   support

           0       0.56      0.30      0.39       127
           1       0.71      0.70      0.70       761
           2       0.76      0.72      0.74       824
           3       0.55      0.21      0.31       147
           4       0.79      0.38      0.51        50
           5       0.94      1.00      0.97      1504

   micro avg       0.83      0.80      0.81      3413
   macro avg       0.72      0.55      0.60      3413
weighted avg       0.81      0.80      0.80      3413
 samples avg       0.84      0.83      0.80      3413

F1 micro averaging: 0.8113432835820896