- Updated the
multilingualmodel 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 theunbiasedmodel. 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
originalmodel to match theunbiasedclasses).
- Updated the
unbiasedmodel 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).
- Our opinion piece "Can AI identify toxic online content?" is now live on Scientific American
- Added smaller models trained with Albert for the
originalandunbiasedmodels! Can access these in the same way with detoxify usingoriginal-smallandunbiased-smallas inputs. Theoriginal-smallachieved a mean AUC score of 98.28 (98.64 before) and theunbiased-smallachieved a final score of 93.36 (93.64 before).
Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintendedย 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:
- ๐ค Transformers
- โก 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 thatโs 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.
| Language Subgroup | Subgroup size | Subgroup AUC Score % |
|---|---|---|
| ๐ฎ๐น it | 8494 | 89.18 |
| ๐ซ๐ท fr | 10920 | 89.61 |
| ๐ท๐บ ru | 10948 | 89.81 |
| ๐ต๐น pt | 11012 | 91.00 |
| ๐ช๐ธ es | 8438 | 92.74 |
| ๐น๐ท tr | 14000 | 97.19 |
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
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
from detoxify import Detoxify
# each model takes in either a string or a list of strings
results = 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','รถrnek metin','ะฟัะธะผะตั ัะตะบััะฐ'])
# to specify the device the model will be allocated on (defaults to cpu), accepts any torch.device input
model = Detoxify('original', device='cuda')
# optional to display results nicely (will need to pip install pandas)
import pandas as pd
print(pd.DataFrame(results, index=input_text).round(5))For more details check the Prediction section.
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.
This challenge includes the following labels:
toxicsevere_toxicobscenethreatinsultidentity_hate
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.
toxicitysevere_toxicityobscenethreatinsultidentity_attacksexual_explicit
Identity labels used:
malefemalehomosexual_gay_or_lesbianchristianjewishmuslimblackwhitepsychiatric_or_mental_illness
A complete list of all the identity labels available can be found here.
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
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
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_bertunbiased_toxic_robertamultilingual_toxic_xlm_r
model = torch.hub.load('unitaryai/detoxify','toxic_bert')Importing detoxify in python:
from detoxify import Detoxify
results = 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','รถrnek metin','ะฟัะธะผะตั ัะตะบััะฐ'])
# to display results nicely
import pandas as pd
print(pd.DataFrame(results,index=input_text).round(5))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
kaggle competitions download -c jigsaw-unintended-bias-in-toxicity-classification
kaggle competitions download -c jigsaw-multilingual-toxic-comment-classification
# 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.jsonpython train.py --config configs/Unintended_bias_toxic_comment_classification_RoBERTa_combined.json
The translated data (source 1 source 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
tensorboard --logdir=./saved
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
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
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
@misc{Detoxify,
title={Detoxify},
author={Hanu, Laura and {Unitary team}},
howpublished={Github. https://github.com/unitaryai/detoxify},
year={2020}
}
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