🙊 Detoxify

Toxic Comment Classification with ⚡ Pytorch Lightning and 🤗 Transformers

News & Updates

22-10-2021: New improved multilingual model & standardised class names

• Updated the multilingual model weights used by Detoxify with a model trained on the translated data from the 2nd Jigsaw challenge (as well as the 1st). This model has also been trained to minimise bias and now returns the same categories as the unbiased model. New best AUC score on the test set: 92.11 (89.71 before).
• All detoxify models now return consistent class names (e.g. "identity_attack" replaces "identity_hate" in the original model to match the unbiased classes).

03-09-2021: New improved unbiased model

• Updated the unbiased model weights used by Detoxify with a model trained on both datasets from the first 2 Jigsaw challenges. New best score on the test set: 93.74 (93.64 before).

15-02-2021: Detoxify featured in Scientific American!

• Our opinion piece "Can AI identify toxic online content?" is now live on Scientific American

14-01-2021: Lightweight models

• Added smaller models trained with Albert for the original and unbiased models! Can access these in the same way with detoxify using original-small and unbiased-small as inputs. The original-small achieved a mean AUC score of 98.28 (98.64 before) and the unbiased-small achieved a final score of 93.36 (93.64 before).

Description

Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification.

Built by Laura Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content in context.

Dependencies:

• For inference:
  • 🤗 Transformers
  • ⚡ Pytorch lightning
• For training will also need:
  • Kaggle API (to download data)

Challenge	Year	Goal	Original Data Source	Detoxify Model Name	Top Kaggle Leaderboard Score %	Detoxify Score %
Toxic Comment Classification Challenge	2018	build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate.	Wikipedia Comments	original	98.86	98.64
Jigsaw Unintended Bias in Toxicity Classification	2019	build a model that recognizes toxicity and minimizes this type of unintended bias with respect to mentions of identities. You'll be using a dataset labeled for identity mentions and optimizing a metric designed to measure unintended bias.	Civil Comments	unbiased	94.73	93.74
Jigsaw Multilingual Toxic Comment Classification	2020	build effective multilingual models	Wikipedia Comments + Civil Comments	multilingual	95.36	92.11

It is also noteworthy to mention that the top leadearboard scores have been achieved using model ensembles. The purpose of this library was to build something user-friendly and straightforward to use.

Multilingual model language breakdown

Language Subgroup	Subgroup size	Subgroup AUC Score %
🇮🇹 it	8494	89.18
🇫🇷 fr	10920	89.61
🇷🇺 ru	10948	89.81
🇵🇹 pt	11012	91.00
🇪🇸 es	8438	92.74
🇹🇷 tr	14000	97.19

Limitations and ethical considerations

If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups.

The intended use of this library is for research purposes, fine-tuning on carefully constructed datasets that reflect real world demographics and/or to aid content moderators in flagging out harmful content quicker.

Some useful resources about the risk of different biases in toxicity or hate speech detection are:

• The Risk of Racial Bias in Hate Speech Detection
• Automated Hate Speech Detection and the Problem of Offensive Language
• Racial Bias in Hate Speech and Abusive Language Detection Datasets

Quick prediction

The multilingual model has been trained on 7 different languages so it should only be tested on: english, french, spanish, italian, portuguese, turkish or russian.

# install detoxify
pip install detoxify

fromdetoxifyimportDetoxify# each model takes in either a string or a list of stringsresults=Detoxify('original').predict('example text')
results=Detoxify('unbiased').predict(['example text 1','example text 2'])
results=Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста'])
# to specify the device the model will be allocated on (defaults to cpu), accepts any torch.device inputmodel=Detoxify('original', device='cuda')
# optional to display results nicely (will need to pip install pandas)importpandasaspdprint(pd.DataFrame(results, index=input_text).round(5))

For more details check the Prediction section.

Labels

All challenges have a toxicity label. The toxicity labels represent the aggregate ratings of up to 10 annotators according the following schema:

• Very Toxic (a very hateful, aggressive, or disrespectful comment that is very likely to make you leave a discussion or give up on sharing your perspective)
• Toxic (a rude, disrespectful, or unreasonable comment that is somewhat likely to make you leave a discussion or give up on sharing your perspective)
• Hard to Say
• Not Toxic

More information about the labelling schema can be found here.

Toxic Comment Classification Challenge

This challenge includes the following labels:

• toxic
• severe_toxic
• obscene
• threat
• insult
• identity_hate

Jigsaw Unintended Bias in Toxicity Classification

This challenge has 2 types of labels: the main toxicity labels and some additional identity labels that represent the identities mentioned in the comments.

Only identities with more than 500 examples in the test set (combined public and private) are included during training as additional labels and in the evaluation calculation.

• toxicity
• severe_toxicity
• obscene
• threat
• insult
• identity_attack
• sexual_explicit

Identity labels used:

• male
• female
• homosexual_gay_or_lesbian
• christian
• jewish
• muslim
• black
• white
• psychiatric_or_mental_illness

A complete list of all the identity labels available can be found here.

Jigsaw Multilingual Toxic Comment Classification

Since this challenge combines the data from the previous 2 challenges, it includes all labels from above, however the final evaluation is only on:

• toxicity

How to run

First, install dependencies

# clone project
git clone https://github.com/unitaryai/detoxify
# create virtual env
python3 -m venv toxic-env
source toxic-env/bin/activate
# install project
pip install -e detoxify
# or for training
pip install -e 'detoxify[dev]'cd detoxify

Prediction

Trained models summary:

Model name	Transformer type	Data from
original	bert-base-uncased	Toxic Comment Classification Challenge
unbiased	roberta-base	Unintended Bias in Toxicity Classification
multilingual	xlm-roberta-base	Multilingual Toxic Comment Classification

For a quick prediction can run the example script on a comment directly or from a txt containing a list of comments.

# load model via torch.hub
python run_prediction.py --input 'example' --model_name original
# load model from from checkpoint path
python run_prediction.py --input 'example' --from_ckpt_path model_path
# save results to a .csv file
python run_prediction.py --input test_set.txt --model_name original --save_to results.csv
# to see usage
python run_prediction.py --help

Checkpoints can be downloaded from the latest release or via the Pytorch hub API with the following names:

• toxic_bert
• unbiased_toxic_roberta
• multilingual_toxic_xlm_r

model = torch.hub.load('unitaryai/detoxify','toxic_bert')

Importing detoxify in python:

fromdetoxifyimportDetoxifyresults=Detoxify('original').predict('some text')
results=Detoxify('unbiased').predict(['example text 1','example text 2'])
results=Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста'])
# to display results nicelyimportpandasaspdprint(pd.DataFrame(results,index=input_text).round(5))

Training

If you do not already have a Kaggle account:

• you need to create one to be able to download the data

• go to My Account and click on Create New API Token - this will download a kaggle.json file

• make sure this file is located in ~/.kaggle

# create data directory
mkdir jigsaw_data
cd jigsaw_data
# download data
kaggle competitions download -c jigsaw-toxic-comment-classification-challenge
unzip jigsaw-toxic-comment-classification-challenge.zip -d jigsaw-toxic-comment-classification-challenge
find jigsaw-toxic-comment-classification-challenge -name '*.csv.zip'| xargs -n1 unzip -d jigsaw-toxic-comment-classification-challenge
kaggle competitions download -c jigsaw-unintended-bias-in-toxicity-classification
unzip jigsaw-unintended-bias-in-toxicity-classification.zip -d jigsaw-unintended-bias-in-toxicity-classification
kaggle competitions download -c jigsaw-multilingual-toxic-comment-classification
unzip jigsaw-multilingual-toxic-comment-classification.zip -d jigsaw-multilingual-toxic-comment-classification

Start Training

Toxic Comment Classification Challenge

# combine test.csv and test_labels.csv
python preprocessing_utils.py --test_csv jigsaw_data/jigsaw-toxic-comment-classification-challenge/test.csv --update_test
python train.py --config configs/Toxic_comment_classification_BERT.json

Unintended Bias in Toxicicity Challenge

python train.py --config configs/Unintended_bias_toxic_comment_classification_RoBERTa_combined.json

Multilingual Toxic Comment Classification

The translated data (source 1source 2) can be downloaded from Kaggle in french, spanish, italian, portuguese, turkish, and russian (the languages available in the test set).

# combine test.csv and test_labels.csv
python preprocessing_utils.py --test_csv jigsaw_data/jigsaw-multilingual-toxic-comment-classification/test.csv --update_test
python train.py --config configs/Multilingual_toxic_comment_classification_XLMR.json

Monitor progress with tensorboard

tensorboard --logdir=./saved

Model Evaluation

Toxic Comment Classification Challenge

This challenge is evaluated on the mean AUC score of all the labels.

python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv

Unintended Bias in Toxicicity Challenge

This challenge is evaluated on a novel bias metric that combines different AUC scores to balance overall performance. More information on this metric here.

python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
# to get the final bias metric
python model_eval/compute_bias_metric.py

Multilingual Toxic Comment Classification

This challenge is evaluated on the AUC score of the main toxic label.

python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv

Citation

@misc{Detoxify,
title={Detoxify},
author={Hanu, Laura and {Unitary team}},
howpublished={Github. https://github.com/unitaryai/detoxify},
year={2020}
}