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Toxicity Target Type Identification

Toxicity Target Type Identification is a model that classifies the type (individual, group, or other) of a given targeted text.

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: Multiclass classification (individual, group, or other)

Usage#

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from transformers import AutoTokenizer, AutoModelForSequenceClassification

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

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

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: 0.7505

Precision: 0.7812

Recall: 0.7505

F1-Score: 0.7603

Class Precision Recall F1-Score Support
INDIVIDUAL 0.8850 0.7964 0.8384 609
GROUP 0.6766 0.6385 0.6570 213
OTHER 0.4518 0.7177 0.5545 124

Training procedure#

Training hyperparameters#

The following hyperparameters were used during training:

  • learning_rate: 3.952388499692274e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 1993
  • optimizer: Adam with betas=(0.9944095815441554,0.8750000522553327) and epsilon=1.8526084265228802e-07
  • lr_scheduler_type: linear
  • num_epochs: 30

Framework versions#

  • Transformers 4.26.1
  • 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