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Toxicity Type Detection

Toxicity Type Detection is a model that predicts the type(s) of toxicity(s) in a given text.

Toxicity Labels: health, ideology, insult, lgbtqphobia, other_lifestyle, physical_aspects, profanity_obscene, racism, sexism, xenophobia

This BERT model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the OLID-BR dataset.

Overview#

Input: Text in Brazilian Portuguese

Output: Multilabel classification (toxicity types)

Usage#

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from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("dougtrajano/toxicity-type-detection")

model = AutoModelForSequenceClassification.from_pretrained("dougtrajano/toxicity-type-detection")

Limitations and bias#

The following factors may degrade the model’s performance.

Text Language: The model was trained on Brazilian Portuguese texts, so it may not work well with Portuguese dialects.

Text Origin: The model was trained on texts from social media and a few texts from other sources, so it may not work well on other types of texts.

Trade-offs#

Sometimes models exhibit performance issues under particular circumstances. In this section, we'll discuss situations in which you might discover that the model performs less than optimally, and should plan accordingly.

Text Length: The model was fine-tuned on texts with a word count between 1 and 178 words (average of 18 words). It may give poor results on texts with a word count outside this range.

Performance#

The model was evaluated on the test set of the OLID-BR dataset.

Accuracy: 0.4214

Precision: 0.8180

Recall: 0.7230

F1-Score: 0.7645

Label Precision Recall F1-Score Support
health 0.3182 0.1795 0.2295 39
ideology 0.6820 0.6842 0.6831 304
insult 0.9689 0.8068 0.8805 1351
lgbtqphobia 0.8182 0.5870 0.6835 92
other_lifestyle 0.4242 0.4118 0.4179 34
physical_aspects 0.4324 0.5783 0.4948 83
profanity_obscene 0.7482 0.7509 0.7496 562
racism 0.4737 0.3913 0.4286 23
sexism 0.5132 0.3391 0.4084 115
xenophobia 0.3333 0.4375 0.3784 32

Training procedure#

Training hyperparameters#

The following hyperparameters were used during training:

  • learning_rate: 7.044186985160909e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 1993
  • optimizer: Adam with betas=(0.9339215524915885,0.9916979096990963) and epsilon=3.4435900142455904e-07
  • lr_scheduler_type: linear
  • num_epochs: 30

Framework versions#

  • Transformers 4.26.0
  • Pytorch 1.10.2+cu113
  • Datasets 2.9.0
  • Tokenizers 0.13.2

Provide Feedback#

If you have any feedback on this model, please open an issue on GitHub.

Last update: February 20, 2023