dougtrajano/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

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.